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Remote Sens., Volume 10, Issue 3 (March 2018)

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Cover Story (view full-size image) Remote and proximal hyperspectral sensing are increasingly applied in agricultural research for [...] Read more.
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Open AccessArticle Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes
Remote Sens. 2018, 10(3), 191; doi:10.3390/rs10030191
Received: 15 January 2018 / Revised: 2 March 2018 / Accepted: 5 March 2018 / Published: 8 March 2018
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Abstract
The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton
[...] Read more.
The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs), in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD) high performance liquid chromatography (HPLC) database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS), artificial neural networks (ANN), support vector machine (SVM) and random forests (RF), and feature selection techniques, including genetic algorithm (GA), successive projection algorithm (SPA) and recursive feature elimination based on support vector machine (SVM-RFE), for inferring PSCs from remote sensing data. Results showed that: (1) SVM-RFE worked better in selecting sensitive features; (2) RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3) machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4) sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5) the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Open AccessArticle Climate Change and Anthropogenic Impacts on Wetland and Agriculture in the Songnen and Sanjiang Plain, Northeast China
Remote Sens. 2018, 10(3), 356; doi:10.3390/rs10030356
Received: 19 January 2018 / Revised: 21 February 2018 / Accepted: 22 February 2018 / Published: 25 February 2018
Cited by 1 | PDF Full-text (5813 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Influences of the increasing pressure of climate change and anthropogenic activities on wetlands ecosystems and agriculture are significant around the world. This paper assessed the spatiotemporal land use and land cover changes (LULCC), especially for conversion from marshland to other LULC types (e.g.,
[...] Read more.
Influences of the increasing pressure of climate change and anthropogenic activities on wetlands ecosystems and agriculture are significant around the world. This paper assessed the spatiotemporal land use and land cover changes (LULCC), especially for conversion from marshland to other LULC types (e.g., croplands) over the Songnen and Sanjiang Plain (SNP and SJP), northeast China, during the past 35 years (1980–2015). The relative role of human activities and climatic changes in terms of their impacts on wetlands and agriculture dynamics were quantitatively distinguished and evaluated in different periods based on a seven-stage LULC dataset. Our results indicated that human activities, such as population expansion and socioeconomic development, and institutional policies related to wetlands and agriculture were the main driving forces for LULCC of the SJP and SNP during the past decades, while increasing contributions of climatic changes were also found. Furthermore, as few studies have identified which geographic regions are most at risk, how the future climate changes will spatially and temporally impact wetlands and agriculture, i.e., the suitability of wetlands and agriculture distributions under different future climate change scenarios, were predicted and analyzed using a habitat distribution model (Maxent) at the pixel-scale. The present findings can provide valuable references for policy makers on regional sustainability for food security, water resource rational management, agricultural planning and wetland protection as well as restoration of the region. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle A Study of Rank Defect and Network Effect in Processing the CMONOC Network on Bernese
Remote Sens. 2018, 10(3), 357; doi:10.3390/rs10030357
Received: 4 February 2018 / Revised: 4 February 2018 / Accepted: 22 February 2018 / Published: 25 February 2018
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Abstract
High-precision GPS data processing on Bernese has been employed to routinely resolve daily position solutions of GPS stations in the Crustal Movement Observation Network of China (CMONOC). The rank-deficient problems of the normal equation (NEQ) system and the network effect on the frame
[...] Read more.
High-precision GPS data processing on Bernese has been employed to routinely resolve daily position solutions of GPS stations in the Crustal Movement Observation Network of China (CMONOC). The rank-deficient problems of the normal equation (NEQ) system and the network effect on the frame alignment of NEQs in the processing of CMONOC data on Bernese still present difficulties. In this study, we diagnose the rank-deficient problems of the original NEQ, review the efficiency of the controlled datum removal (CDR) method in filtering out the three frame-origin-related datum contents, investigate the reliabilities of the inherited frame orientation and scale information from the fixation of the GPS satellite orbits and the Earth rotation parameters in establishing the NEQ of the CMONOC network on Bernese, and analyze the impact of the network effect on the position time series of GPS stations. Our results confirm the nonsingularity of the original NEQ and the efficiency of the CDR filtering in resolving the rank-deficient problems; show that the frame origin parameters are weakly defined and should be stripped off, while the frame orientation and scale parameters should be retained due to their insufficient redefinition from the minimal constraint (MC) implementation through inhomogeneous and asymmetrical fiducial networks; and reveal the superiority of a globally distributed fiducial network for frame alignment of the reconstructed NEQs via No-Net-Translation (NNT) MC conditions. Finally, we attribute the two apparent discontinuities in the position time series to the terrestrial reference frame (TRF) conversions of the GPS satellite orbits, and identify it as the orbit TRF effect. Full article
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Open AccessEditor’s ChoiceArticle Impacts of Climate Change on Tibetan Lakes: Patterns and Processes
Remote Sens. 2018, 10(3), 358; doi:10.3390/rs10030358
Received: 16 December 2017 / Revised: 14 February 2018 / Accepted: 21 February 2018 / Published: 26 February 2018
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Abstract
High-altitude inland-drainage lakes on the Tibetan Plateau (TP), the earth’s third pole, are very sensitive to climate change. Tibetan lakes are important natural resources with important religious, historical, and cultural significance. However, the spatial patterns and processes controlling the impacts of climate and
[...] Read more.
High-altitude inland-drainage lakes on the Tibetan Plateau (TP), the earth’s third pole, are very sensitive to climate change. Tibetan lakes are important natural resources with important religious, historical, and cultural significance. However, the spatial patterns and processes controlling the impacts of climate and associated changes on Tibetan lakes are largely unknown. This study used long time series and multi-temporal Landsat imagery to map the patterns of Tibetan lakes and glaciers in 1977, 1990, 2000, and 2014, and further to assess the spatiotemporal changes of lakes and glaciers in 17 TP watersheds between 1977 and 2014. Spatially variable changes in lake and glacier area as well as climatic factors were analyzed. We identified four modes of lake change in response to climate and associated changes. Lake expansion was predominantly attributed to increased precipitation and glacier melting, whereas lake shrinkage was a main consequence of a drier climate or permafrost degradation. These findings shed new light on the impacts of recent environmental changes on Tibetan lakes. They suggest that protecting these high-altitude lakes in the face of further environmental change will require spatially variable policies and management measures. Full article
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Open AccessArticle Snow Wetness Retrieved from L-Band Radiometry
Remote Sens. 2018, 10(3), 359; doi:10.3390/rs10030359
Received: 13 December 2017 / Revised: 15 February 2018 / Accepted: 21 February 2018 / Published: 26 February 2018
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Abstract
The present study demonstrates the successful use of the high sensitivity of L-band brightness temperatures to snow liquid water in the retrieval of snow liquid water from multi-angular L-band brightness temperatures. The emission model employed was developed from parts of the “microwave emission
[...] Read more.
The present study demonstrates the successful use of the high sensitivity of L-band brightness temperatures to snow liquid water in the retrieval of snow liquid water from multi-angular L-band brightness temperatures. The emission model employed was developed from parts of the “microwave emission model of layered snowpacks” (MEMLS), coupled with components adopted from the “L-band microwave emission of the biosphere” (L-MEB) model. Two types of snow liquid water retrievals were performed based on L-band brightness temperatures measured over (i) areas with a metal reflector placed on the ground (“reflector area”— T B , R ), and (ii) natural snow-covered ground (“natural area”— T B , N ). The reliable representation of temporal variations of snow liquid water is demonstrated for both types of the aforementioned quasi-simultaneous retrievals. This is verified by the fact that both types of snow liquid water retrievals indicate a dry snowpack throughout the “cold winter period” with frozen ground and air temperatures well below freezing, and synchronously respond to snowpack moisture variations during the “early spring period”. The robust and reliable performance of snow liquid water retrieved from T B , R , together with their level of detail, suggest the use of these retrievals as “references” to assess the meaningfulness of the snow liquid water retrievals based on T B , N . It is noteworthy that the latter retrievals are achieved in a two-step retrieval procedure using exclusively L-band brightness temperatures, without the need for in-situ measurements, such as ground permittivity ε G and snow mass-density ρ S . The latter two are estimated in the first retrieval-step employing the well-established two-parameter ( ρ S , ε G ) retrieval scheme designed for dry snow conditions and explored in the companion paper that is included in this special issue in terms of its sensitivity with respect to disturbative melting effects. The two-step retrieval approach proposed and investigated here, opens up the possibility of using airborne or spaceborne L-band radiometry to estimate ( ρ S , ε G ) and additionally snow liquid water as a new passive L-band data product. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Open AccessArticle Assessing the Defoliation of Pine Forests in a Long Time-Series and Spatiotemporal Prediction of the Defoliation Using Landsat Data
Remote Sens. 2018, 10(3), 360; doi:10.3390/rs10030360
Received: 6 December 2017 / Revised: 4 February 2018 / Accepted: 20 February 2018 / Published: 26 February 2018
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Abstract
Pine forests (Pinus tabulaeformis) have been in danger of defoliation by a caterpillar in the west Liaoning province of China for more than thirty years. This paper aims to assess and predict the degree of damage to pine forests by using
[...] Read more.
Pine forests (Pinus tabulaeformis) have been in danger of defoliation by a caterpillar in the west Liaoning province of China for more than thirty years. This paper aims to assess and predict the degree of damage to pine forests by using remote sensing and ancillary data. Through regression analysis of the pine foliage remaining ratios of field plots with several vegetation indexes of Landsat data, a feasible inversion model was obtained to detect the degree of damage using the Normalized Difference Infrared Index of 5th band (NDII5). After comparing the inversion result of the degree of damage to the pine in 29 years and the historical damage record, quantized results of damage assessment in a long time-series were accurately obtained. Based on the correlation analysis between meteorological variables and the degree of damage from 1984 to 2015, the average degree of damage was predicted in temporal scale. By adding topographic and other variables, a linear prediction model in spatiotemporal scale was constructed. The spatiotemporal model was based on 5015 public pine points for 24 years and reached 0.6169 in the correlation coefficient. This paper provided a feasible and quantitative method in the spatiotemporal prediction of forest pest occurrence by remote sensing. Full article
(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)
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Open AccessArticle Directional 0 Sparse Modeling for Image Stripe Noise Removal
Remote Sens. 2018, 10(3), 361; doi:10.3390/rs10030361
Received: 29 December 2017 / Revised: 11 February 2018 / Accepted: 22 February 2018 / Published: 26 February 2018
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Abstract
Remote sensing images are often polluted by stripe noise, which leads to negative impact on visual performance. Thus, it is necessary to remove stripe noise for the subsequent applications, e.g., classification and target recognition. This paper commits to remove the stripe noise to
[...] Read more.
Remote sensing images are often polluted by stripe noise, which leads to negative impact on visual performance. Thus, it is necessary to remove stripe noise for the subsequent applications, e.g., classification and target recognition. This paper commits to remove the stripe noise to enhance the visual quality of images, while preserving image details of stripe-free regions. Instead of solving the underlying image by variety of algorithms, we first estimate the stripe noise from the degraded images, then compute the final destriping image by the difference of the known stripe image and the estimated stripe noise. In this paper, we propose a non-convex 0 sparse model for remote sensing image destriping by taking full consideration of the intrinsically directional and structural priors of stripe noise, and the locally continuous property of the underlying image as well. Moreover, the proposed non-convex model is solved by a proximal alternating direction method of multipliers (PADMM) based algorithm. In addition, we also give the corresponding theoretical analysis of the proposed algorithm. Extensive experimental results on simulated and real data demonstrate that the proposed method outperforms recent competitive destriping methods, both visually and quantitatively. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China
Remote Sens. 2018, 10(3), 362; doi:10.3390/rs10030362
Received: 30 November 2017 / Revised: 15 February 2018 / Accepted: 18 February 2018 / Published: 26 February 2018
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Abstract
Precipitation is the main component of global water cycle. At present, satellite quantitative precipitation estimates (QPEs) are widely applied in the scientific community. However, the evaluations of satellite QPEs have some limitations in terms of the deficiency in observation, evaluation methodology, the selection
[...] Read more.
Precipitation is the main component of global water cycle. At present, satellite quantitative precipitation estimates (QPEs) are widely applied in the scientific community. However, the evaluations of satellite QPEs have some limitations in terms of the deficiency in observation, evaluation methodology, the selection of time windows for evaluation and short periods for evaluation. The objective of this work is to make some improvements by evaluating the spatio-temporal pattern of the long-terms Climate Hazard Group InfraRed Precipitation Satellite’s (CHIRPS’s) QPEs over mainland China. In this study, we compared the daily precipitation estimates from CHIRPS with 2480 rain gauges across China and gridded observation using several statistical metrics in the long-term period of 1981–2014. The results show that there is significant difference between point evaluation and grid evaluation for CHIRPS. CHIRPS has better performance for a large amount of precipitation than it does for arid and semi-arid land. The change in good performance zones has strong relationship with monsoon’s movement. Therefore, CHIRPS performs better in river basins of southern China and exhibits poor performance in river basins in northwestern and northern China. Moreover, CHIRPS exhibits better in warm season than in Winter, owing to its limited ability to detect snowfall. Nevertheless, CHIRPS is moderately sensitive to the precipitation from typhoon weather systems. The limitations for CHIRPS result from the Tropical Rainfall Measuring Mission (TRMM) 3B42 estimates’ accuracy and valid spatial coverage. Full article
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Open AccessArticle Empirical Algorithm for Significant Wave Height Retrieval from Wave Mode Data Provided by the Chinese Satellite Gaofen-3
Remote Sens. 2018, 10(3), 363; doi:10.3390/rs10030363
Received: 17 January 2018 / Revised: 23 February 2018 / Accepted: 24 February 2018 / Published: 26 February 2018
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Abstract
Gaofen-3 (GF-3), the first Chinese civil C-band synthetic aperture radar (SAR), was successfully launched by the China Academy of Space Technology on 10 August 2016. Among its 12 imaging modes, wave mode is designed to monitor the ocean surface waves over the open
[...] Read more.
Gaofen-3 (GF-3), the first Chinese civil C-band synthetic aperture radar (SAR), was successfully launched by the China Academy of Space Technology on 10 August 2016. Among its 12 imaging modes, wave mode is designed to monitor the ocean surface waves over the open ocean. An empirical retrieval algorithm of significant wave height (SWH), termed Quad-Polarized C-band WAVE algorithm for GF-3 wave mode (QPCWAVE_GF3), is developed for quad-polarized SAR measurements from GF-3 in wave mode. QPCWAVE_GF3 model is built using six SAR image and spectrum related parameters. Based on a total of 2576 WaveWatch III (WW3) and GF-3 wave mode match-ups, 12 empirical coefficients of the model are determined for 6 incidence angle modes. The validation of the QPCWAVE_GF3 model is performed through comparisons against independent WW3 modelling hindcasts, and observations from altimeters and buoys from January to October in 2017. The assessment shows a good agreement with root mean square error from 0.5 m to 0.6 m, and scatter index around 20%. In particular, applications of the QPCWAVE_GF3 model in SWH estimation for two storm cases from GF-3 data in wave mode and Quad-Polarization Strip I mode are presented respectively. Results indicate that the proposed algorithm is suitable for SWH estimation from GF-3 wave mode and is promising for other similar data. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Vertical Deformation Monitoring of the Suspension Bridge Tower Using GNSS: A Case Study of the Forth Road Bridge in the UK
Remote Sens. 2018, 10(3), 364; doi:10.3390/rs10030364
Received: 20 January 2018 / Revised: 18 February 2018 / Accepted: 21 February 2018 / Published: 26 February 2018
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Abstract
The vertical deformation monitoring of a suspension bridge tower is of paramount importance to maintain the operational safety since nearly all forces are eventually transferred as the vertical stress on the tower. This paper analyses the components affecting the vertical deformation and attempts
[...] Read more.
The vertical deformation monitoring of a suspension bridge tower is of paramount importance to maintain the operational safety since nearly all forces are eventually transferred as the vertical stress on the tower. This paper analyses the components affecting the vertical deformation and attempts to reveal its deformation mechanism. Firstly, we designed a strategy for high-precision GNSS data processing aiming at facilitating deformation extraction and analysis. Then, 33 months of vertical deformation time series of the southern tower of the Forth Road Bridge (FRB) in the UK were processed, and the accurate subsidence and the parameters of seasonal signals were estimated based on a classic function model that has been widely studied to analyse GNSS coordinate time series. We found that the subsidence rate is about 4.7 mm/year, with 0.1 mm uncertainty. Meanwhile, a 15-month meteorological dataset was utilised with a thermal expansion model (TEM) to explain the effects of seasonal signals on tower deformation. The amplitude of the annual signals correlated quite well that obtained by the TEM, with the consistency reaching 98.9%, demonstrating that the thermal effect contributes significantly to the annual signals. The amplitude of daily signals displays poor consistency with the ambient temperature data. However, the phase variation tendencies between the daily signals of the vertical deformation and the ambient temperature are highly consistent after February 2016. Finally, the potential contribution of the North Atlantic Drift (NAD) to the characteristics of annual and daily signals is discussed because of the special geographical location of the FRB. Meanwhile, this paper emphasizes the importance of collecting more detailed meteorological and other loading data for the investigation of the vertical deformation mechanism of the bridge towers over time with the support of GNSS. Full article
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Open AccessArticle Regional Land Subsidence Analysis in Eastern Beijing Plain by InSAR Time Series and Wavelet Transforms
Remote Sens. 2018, 10(3), 365; doi:10.3390/rs10030365
Received: 25 December 2017 / Revised: 26 January 2018 / Accepted: 18 February 2018 / Published: 26 February 2018
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Abstract
Land subsidence is the disaster phenomenon of environmental geology with regionally surface altitude lowering caused by the natural or man-made factors. Beijing, the capital city of China, has suffered from land subsidence since the 1950s, and extreme groundwater extraction has led to subsidence
[...] Read more.
Land subsidence is the disaster phenomenon of environmental geology with regionally surface altitude lowering caused by the natural or man-made factors. Beijing, the capital city of China, has suffered from land subsidence since the 1950s, and extreme groundwater extraction has led to subsidence rates of more than 100 mm/year. In this study, we employ two SAR datasets acquired by Envisat and TerraSAR-X satellites to investigate the surface deformation in Beijing Plain from 2003 to 2013 based on the multi-temporal InSAR technique. Furthermore, we also use observation wells to provide in situ hydraulic head levels to perform the evolution of land subsidence and spatial-temporal changes of groundwater level. Then, we analyze the accumulated displacement and hydraulic head level time series using continuous wavelet transform to separate periodic signal components. Finally, cross wavelet transform (XWT) and wavelet transform coherence (WTC) are implemented to analyze the relationship between the accumulated displacement and hydraulic head level time series. The results show that the subsidence centers in the northern Beijing Plain is spatially consistent with the groundwater drop funnels. According to the analysis of well based results located in different areas, the long-term groundwater exploitation in the northern subsidence area has led to the continuous decline of the water level, resulting in the inelastic and permanent compaction, while for the monitoring wells located outside the subsidence area, the subsidence time series show obvious elastic deformation characteristics (seasonal characteristics) as the groundwater level changes. Moreover, according to the wavelet transformation, the land subsidence time series at monitoring well site lags several months behind the groundwater level change. Full article
(This article belongs to the Special Issue Remote Sensing of Land Subsidence)
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Open AccessArticle A New Approach for Monitoring the Terra Nova Bay Polynya through MODIS Ice Surface Temperature Imagery and Its Validation during 2010 and 2011 Winter Seasons
Remote Sens. 2018, 10(3), 366; doi:10.3390/rs10030366
Received: 30 January 2018 / Revised: 20 February 2018 / Accepted: 22 February 2018 / Published: 26 February 2018
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Abstract
Polynyas are dynamic stretches of open water surrounded by ice. They typically occur in remote regions of the Arctic and Antarctic, thus remote sensing is essential for monitoring their dynamics. On regional scales, daily passive microwave radiometers provide useful information about their extent
[...] Read more.
Polynyas are dynamic stretches of open water surrounded by ice. They typically occur in remote regions of the Arctic and Antarctic, thus remote sensing is essential for monitoring their dynamics. On regional scales, daily passive microwave radiometers provide useful information about their extent because of their independence from cloud coverage and daylight; nonetheless, their coarse resolution often does not allow an accurate discrimination between sea ice and open water. Despite its sensitivity to the presence of clouds, thermal infrared (TIR) Moderate Resolution Imaging Spectroradiometer (MODIS) provides higher-resolution information (typically 1 km) at large swath widths, several times per day, proving to be useful for the retrieval of the size of polynyas. In this study, we deal with Aqua satellite MODIS observations of a frequently occurring coastal polynya in the Terra Nova Bay (TNB), Ross Sea (Antarctica). The potential of a new methodology for estimating the variability of this polynya through MODIS TIR during the 2010 and 2011 freezing season (April to October) is presented and discussed. The polynya is observed in more than 1600 radiance scenes, after a preliminary filter evaluates and discards cloudy and fog-contaminated scenes. This reduces the useful MODIS swaths to about 50% of the available acquisitions, but a revisit time of less than 24 h is kept for about 90% of the study period. As expected, results show a high interannual variability with an opening/closing fluctuation clearly depending on the regime of the katabatic winds recorded by the automatic weather stations Rita and Eneide along the TNB coast. Retrievals are also validated through a comparison with a set of 196 co-located high-resolution ENVISAT ASAR images. Although our estimations slightly underestimate the ASAR derived extents, a good agreement is found, the linear correlation reaching 0.75 and the average relative error being about 6%. Finally, a sensitivity test on the applied thermal thresholds supports the effectiveness of our setting. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Progressive Sample Processing of Band Selection for Hyperspectral Image Transmission
Remote Sens. 2018, 10(3), 367; doi:10.3390/rs10030367
Received: 18 December 2017 / Revised: 14 February 2018 / Accepted: 16 February 2018 / Published: 26 February 2018
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Abstract
Band selection (BS) is one of the important topics in hyperspectral image (HSI) processing. Many types of BS algorithms were proposed in the last decade. However, most of them were designed for off-line use. They can only be used with pre-collected data, and
[...] Read more.
Band selection (BS) is one of the important topics in hyperspectral image (HSI) processing. Many types of BS algorithms were proposed in the last decade. However, most of them were designed for off-line use. They can only be used with pre-collected data, and are sometimes ineffective for applications that require timeliness, such as disaster prevention or target detection. This paper proposes an online BS method that allows us obtain instant BS results in a progressive manner during HSI data transmission, which is carried out under band-interleaved-by-sample/pixel (BIS/BIP) format. Such a revolutionary method is called progressive sample processing of band selection (PSP-BS). In PSP-BS, BS can be done recursively pixel by pixel, so that the instantaneous BS can be achieved without waiting for all the pixels of an image. To develop a PSP-BS algorithm, we proposed PSP-OMPBS, which adopted the recursive version of a self-sparse regression BS method (OMPBS) as a native algorithm. The experiments conducted on two real hyperspectral images demonstrate that PSP-OMPBS can progressively output the BS with extremely low computing time. In addition, the convergence of BS results during transmission can be further accelerated by using a pre-defined pixel transmission sequence. Such a significant advantage not only allows BS to be done in a real-time manner for the future satellite data downlink, but also determines the BS results in advance, without waiting to receive every pixel of an image. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Sparse Bayesian Learning Based Three-Dimensional Imaging Algorithm for Off-Grid Air Targets in MIMO Radar Array
Remote Sens. 2018, 10(3), 369; doi:10.3390/rs10030369
Received: 20 December 2017 / Revised: 24 February 2018 / Accepted: 26 February 2018 / Published: 27 February 2018
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Abstract
In recent years, the development of compressed sensing (CS) and array signal processing provides us with a broader perspective of 3D imaging. The CS-based imaging algorithms have a better performance than traditional methods. In addition, the sparse array can overcome the limitation of
[...] Read more.
In recent years, the development of compressed sensing (CS) and array signal processing provides us with a broader perspective of 3D imaging. The CS-based imaging algorithms have a better performance than traditional methods. In addition, the sparse array can overcome the limitation of aperture size and number of antennas. Since the signal to be reconstructed is sparse for air targets, many CS-based imaging algorithms using a sparse array are proposed. However, most of those algorithms assume that the scatterers are exactly located at the pre-discretized grids, which will not hold in real scene. Aiming at finding an accurate solution to off-grid target imaging, we propose an off-grid 3D imaging method based on improved sparse Bayesian learning (SBL). Besides, the Bayesian Cramér-Rao Bound (BCRB) for off-grid bias estimator is provided. Different from previous algorithms, the proposed algorithm adopts a three-stage hierarchical sparse prior to introduce more degrees of freedom. Then variational expectation maximization method is applied to solve the sparse recovery problem through iteration, during each iteration joint sparsity is used to improve efficiency. Experimental results not only validate that the proposed method outperforms the existing off-grid imaging methods in terms of accuracy and resolution, but have compared the root mean square error with corresponding BCRB, proving effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
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Open AccessArticle Burned Area Mapping of an Escaped Fire into Tropical Dry Forest in Western Madagascar Using Multi-Season Landsat OLI Data
Remote Sens. 2018, 10(3), 371; doi:10.3390/rs10030371
Received: 27 December 2017 / Revised: 12 February 2018 / Accepted: 20 February 2018 / Published: 27 February 2018
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Abstract
A human-induced fire cleared a large area of tropical dry forest near the Ankoatsifaka Research Station at Kirindy Mitea National Park in western Madagascar over several weeks in 2013. Fire is a major factor in the disturbance and loss of global tropical dry
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A human-induced fire cleared a large area of tropical dry forest near the Ankoatsifaka Research Station at Kirindy Mitea National Park in western Madagascar over several weeks in 2013. Fire is a major factor in the disturbance and loss of global tropical dry forests, yet remotely sensed mapping studies of fire-impacted tropical dry forests lag behind fire research of other forest types. Methods used to map burns in temperature forests may not perform as well in tropical dry forests where it can be difficult to distinguish between multiple-age burn scars and between bare soil and burns. In this study, the extent of forest lost to stand-replacing fire in Kirindy Mitea National Park was quantified using both spectral and textural information derived from multi-date satellite imagery. The total area of the burn was 18,034 ha. It is estimated that 6% (4761 ha) of the Park’s primary tropical dry forest burned over the period 23 June to 27 September. Half of the forest burned (2333 ha) was lost in the large conflagration adjacent to the Research Station. The best model for burn scar mapping in this highly-seasonal tropical forest and pastoral landscape included the differenced Normalized Burn Ratio (dNBR) and both uni- and multi-temporal measures of greenness. Lessons for burn mapping of tropical dry forest are much the same as for tropical dry forest mapping—highly seasonal vegetation combined with a mixture of background spectral information make for a complicated analysis and may require multi-temporal imagery and site specific techniques. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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Open AccessArticle Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
Remote Sens. 2018, 10(3), 372; doi:10.3390/rs10030372
Received: 16 January 2018 / Revised: 20 February 2018 / Accepted: 22 February 2018 / Published: 28 February 2018
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Abstract
Accurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too
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Accurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too coarse to capture cropland heterogeneity. The fusion of data from different sensors can provide enhanced information and overcome many of the limitations of individual sensors. In thitables study, we estimate annual crop yields for seven important crop types across Montana in the continental USA from 2008–2015, including alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat. We used a satellite data-driven light use efficiency (LUE) model to estimate gross primary productivity (GPP) over croplands at 30-m spatial resolution and eight-day time steps using a fused NDVI dataset constructed by blending Landsat (5 or 7) and Terra MODIS reflectance data. The fused 30-m NDVI record showed good consistency with the original Landsat and MODIS data, but provides better spatiotemporal delineations of cropland vegetation growth. Crop yields were estimated at 30-m resolution as the product of estimated GPP accumulated over the growing season and a crop-specific harvest index (HIGPP). The resulting GPP estimates capture characteristic cropland productivity patterns and seasonal variations, while the estimated annual crop production results correspond favorably with reported county-level crop production data (r = 0.96, relative RMSE = 37.0%, p < 0.05) from the U.S. Department of Agriculture (USDA). The performance of estimated crop yields at a finer (field) scale was generally lower, but still meaningful (r = 0.42, relative RMSE = 50.8%, p < 0.05). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, suggesting the potential of using global satellite observations to improve agricultural management, policy decisions and regional/global food security. Full article
(This article belongs to the Special Issue Google Earth Engine Applications)
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Open AccessArticle Structure Tensor-Based Algorithm for Hyperspectral and Panchromatic Images Fusion
Remote Sens. 2018, 10(3), 373; doi:10.3390/rs10030373
Received: 19 December 2017 / Revised: 21 February 2018 / Accepted: 24 February 2018 / Published: 1 March 2018
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Abstract
Restricted by technical and budget constraints, hyperspectral (HS) image which contains abundant spectral information generally has low spatial resolution. Fusion of hyperspectral and panchromatic (PAN) images can merge spectral information of the former and spatial information of the latter. In this paper, a
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Restricted by technical and budget constraints, hyperspectral (HS) image which contains abundant spectral information generally has low spatial resolution. Fusion of hyperspectral and panchromatic (PAN) images can merge spectral information of the former and spatial information of the latter. In this paper, a new hyperspectral image fusion algorithm using structure tensor is proposed. An image enhancement approach is utilized to sharpen the spatial information of the PAN image, and the spatial details of the HS image is obtained by an adaptive weighted method. Since structure tensor represents structure and spatial information, a structure tensor is introduced to extract spatial details of the enhanced PAN image. Seeing that the HS and PAN images contain different and complementary spatial information for a same scene, a weighted fusion method is presented to integrate the extracted spatial information of the two images. To avoid artifacts at the boundaries, a guided filter is applied to the integrated spatial information image. The injection matrix is finally constructed to reduce spectral and spatial distortion, and the fused image is generated by injecting the complete spatial information. Comparative analyses validate the proposed method outperforms the state-of-art fusion methods, and provides more spatial details while preserving the spectral information. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessArticle SAR Target Recognition via Incremental Nonnegative Matrix Factorization
Remote Sens. 2018, 10(3), 374; doi:10.3390/rs10030374
Received: 28 December 2017 / Revised: 19 February 2018 / Accepted: 26 February 2018 / Published: 1 March 2018
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Abstract
In synthetic aperture radar (SAR) target recognition, the amount of target data increases continuously, and thus SAR automatic target recognition (ATR) systems are required to provide updated feature models in real time. Most recent SAR feature extraction methods have to use both existing
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In synthetic aperture radar (SAR) target recognition, the amount of target data increases continuously, and thus SAR automatic target recognition (ATR) systems are required to provide updated feature models in real time. Most recent SAR feature extraction methods have to use both existing and new samples to retrain a new model every time new data is acquired. However, this repeated calculation of existing samples leads to an increased computing cost. In this paper, a dynamic feature learning method called incremental nonnegative matrix factorization with L p sparse constraints (L p -INMF) is proposed as a solution to that problem. In contrast to conventional nonnegative matrix factorization (NMF) whereby existing and new samples are computed to retrain a new model, incremental NMF (INMF) computes only the new samples to update the trained model incrementally, which can improve the computing efficiency. Considering the sparse characteristics of scattering centers in SAR images, we set the updating process under a generic sparse constraint (L p ) for matrix decomposition of INMF. Thus, L p -INMF can extract sparse characteristics in SAR images. Experimental results using Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data illustrate that the proposed L p -INMF method can not only update models with new samples more efficiently than conventional NMF, but also has a higher recognition rate than NMF and INMF. Full article
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Open AccessArticle An Observation Task Chain Representation Model for Disaster Process-Oriented Remote Sensing Satellite Sensor Planning: A Flood Water Monitoring Application
Remote Sens. 2018, 10(3), 375; doi:10.3390/rs10030375
Received: 8 February 2018 / Revised: 8 February 2018 / Accepted: 26 February 2018 / Published: 1 March 2018
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Abstract
An accurate and comprehensive representation of an observation task is a prerequisite in disaster monitoring to achieve reliable sensor observation planning. However, the extant disaster event or task information models do not fully satisfy the observation requirements for the accurate and efficient planning
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An accurate and comprehensive representation of an observation task is a prerequisite in disaster monitoring to achieve reliable sensor observation planning. However, the extant disaster event or task information models do not fully satisfy the observation requirements for the accurate and efficient planning of remote-sensing satellite sensors. By considering the modeling requirements for a disaster observation task, we propose an observation task chain (OTChain) representation model that includes four basic OTChain segments and eight-tuple observation task metadata description structures. A prototype system, namely OTChainManager, is implemented to provide functions for modeling, managing, querying, and visualizing observation tasks. In the case of flood water monitoring, we use a flood remote-sensing satellite sensor observation task for the experiment. The results show that the proposed OTChain representation model can be used in modeling process-owned flood disaster observation tasks. By querying and visualizing the flood observation task instances in the Jinsha River Basin, the proposed model can effectively express observation task processes, represent personalized observation constraints, and plan global remote-sensing satellite sensor observations. Compared with typical observation task information models or engines, the proposed OTChain representation model satisfies the information demands of the OTChain and its processes as well as impels the development of a long time-series sensor observation scheme. Full article
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Open AccessArticle An Exploration of Some Pitfalls of Thematic Map Assessment Using the New Map Tools Resource
Remote Sens. 2018, 10(3), 376; doi:10.3390/rs10030376
Received: 23 January 2018 / Revised: 13 February 2018 / Accepted: 18 February 2018 / Published: 1 March 2018
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Abstract
A variety of metrics are commonly employed by map producers and users to assess and compare thematic maps’ quality, but their use and interpretation is inconsistent. This problem is exacerbated by a shortage of tools to allow easy calculation and comparison of metrics
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A variety of metrics are commonly employed by map producers and users to assess and compare thematic maps’ quality, but their use and interpretation is inconsistent. This problem is exacerbated by a shortage of tools to allow easy calculation and comparison of metrics from different maps or as a map’s legend is changed. In this paper, we introduce a new website and a collection of R functions to facilitate map assessment. We apply these tools to illustrate some pitfalls of error metrics and point out existing and newly developed solutions to them. Some of these problems have been previously noted, but all of them are under-appreciated and persist in published literature. We show that binary and categorical metrics, including information about true-negative classifications, are inflated for rare categories, and more robust alternatives should be chosen. Most metrics are useful to compare maps only if their legends are identical. We also demonstrate that combining land-cover classes has the often-neglected consequence of apparent improvement, particularly if the combined classes are easily confused (e.g., different forest types). However, we show that the average mutual information (AMI) of a map is relatively robust to combining classes, and reflects the information that is lost in this process; we also introduce a modified AMI metric that credits only correct classifications. Finally, we introduce a method of evaluating statistical differences in the information content of competing maps, and show that this method is an improvement over other methods in more common use. We end with a series of recommendations for the meaningful use of accuracy metrics by map users and producers. Full article
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Open AccessArticle Optimal, Recursive and Sub-Optimal Linear Solutions to Attitude Determination from Vector Observations for GNSS/Accelerometer/Magnetometer Orientation Measurement
Remote Sens. 2018, 10(3), 377; doi:10.3390/rs10030377
Received: 11 December 2017 / Revised: 17 February 2018 / Accepted: 22 February 2018 / Published: 1 March 2018
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Abstract
The integration of the Accelerometer and Magnetometer (AM) provides continuous, stable and accurate attitude information for land-vehicle navigation without magnetic distortion and external acceleration. However, magnetic disturbance and linear acceleration strongly degrade the overall system performance. As an important complement, the Global Navigation
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The integration of the Accelerometer and Magnetometer (AM) provides continuous, stable and accurate attitude information for land-vehicle navigation without magnetic distortion and external acceleration. However, magnetic disturbance and linear acceleration strongly degrade the overall system performance. As an important complement, the Global Navigation Satellite System (GNSS) produces the heading estimates, thus it can potentially benefit the AM system. Such a GNSS/AM system for attitude estimation is mathematically converted to a multi-observation vector pairs matching problem in this paper. The optimal and sub-optimal attitude determination and their time-varying recursive variants are all comprehensively investigated and discussed. The developed methods are named as the Optimal Linear Estimator of Quaternion (OLEQ), Suboptimal-OLEQ (SOLEQ) and Recursive-OLEQ (ROLEQ) for different application scenarios. The theory is established based on our previous contributions, and the multi-vector matrix multiplications are decomposed with the eigenvalue factorization. Some analytical results are proven and given, which provides the reader with a brand new viewpoint of the attitude determination and its evolution. With the derivations of the two-vector case, the n-vector case is then naturally formed. Simulations are carried out showing the advantages of the accuracy, robustness and time consumption of the proposed OLEQs, compared with representative methods. The algorithms are then implemented using the C++ programming language on the designed hardware with a GNSS module, three-axis accelerometer and three-axis magnetometer, giving an effective validation of them in real-world applications. The designed schemes have proven their fast speed and good accuracy in these verification scenarios. Full article
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Open AccessArticle Depolarization Ratio Profiles Calibration and Observations of Aerosol and Cloud in the Tibetan Plateau Based on Polarization Raman Lidar
Remote Sens. 2018, 10(3), 378; doi:10.3390/rs10030378
Received: 4 January 2018 / Revised: 13 February 2018 / Accepted: 22 February 2018 / Published: 1 March 2018
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Abstract
A brief description of the Water vapor, Cloud and Aerosol Lidar (WACAL) system is provided. To calibrate the volume linear depolarization ratio, the concept of “Δ90°-calibration” is applied in this study. This effective and accurate calibration method is adjusted
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A brief description of the Water vapor, Cloud and Aerosol Lidar (WACAL) system is provided. To calibrate the volume linear depolarization ratio, the concept of “ Δ 90 ° -calibration” is applied in this study. This effective and accurate calibration method is adjusted according to the design of WACAL. Error calculations and analysis of the gain ratio, calibrated volume linear depolarization ratio and particle linear depolarization ratio are provided as well. In this method, the influences of the gain ratio, the rotation angle of the plane of polarization and the polarizing beam splitter are discussed in depth. Two groups of measurements with half wave plate (HWP) at angles of (0 ° , 45 ° ) and (22.5 ° , −22.5 ° ) are operated to calibrate the volume linear depolarization ratio. Then, the particle linear depolarization ratios measured by WACAL and CALIOP (the Cloud-Aerosol Lidar with Orthogonal Polarization) during the simultaneous observations were compared. Good agreements are found. The calibration method was applied in the third Tibetan Plateau Experiment of Atmospheric Sciences (TIPEX III) in 2013 and 2014 in China. Vertical profiles of the particle depolarization ratio of clouds and aerosol in the Tibetan Plateau were measured with WACAL in Litang (30.03° N, 100.28° E, 3949 m above sea level (a.s.l.)) in 2013 and Naqu (31.48° N, 92.06° E, 4508 m a.s.l.) in 2014. Then an analysis on the polarizing properties of the aerosol, clouds and cirrus over the Tibetan Plateau is provided. The particle depolarization ratio of cirrus clouds varies from 0.36 to 0.52, with a mean value of 0.44 ± 0.04. Cirrus clouds occurred between 5.2 and 12 km above ground level (a.g.l.). The cloud thickness ranges from 0.12 to 2.55 km with a mean thickness of 1.22 ± 0.70 km. It is found that the particle depolarization ratio of cirrus clouds become larger as the height increases. However, the increase rate of the particle depolarization ratio becomes smaller as the height increases. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Properties)
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Open AccessFeature PaperArticle Reconstructing the Roman Site “Aquis Querquennis” (Bande, Spain) from GPR, T-LiDAR and IRT Data Fusion
Remote Sens. 2018, 10(3), 379; doi:10.3390/rs10030379
Received: 7 February 2018 / Revised: 26 February 2018 / Accepted: 27 February 2018 / Published: 1 March 2018
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Abstract
This work presents the three-dimensional (3D) reconstruction of one of the most important archaeological sites in Galicia: “Aquis Querquennis” (Bande, Spain) using in-situ non-invasive ground-penetrating radar (GPR) and Terrestrial Light Detection and Ranging (T-LiDAR) techniques, complemented with infrared thermography. T-LiDAR is
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This work presents the three-dimensional (3D) reconstruction of one of the most important archaeological sites in Galicia: “Aquis Querquennis” (Bande, Spain) using in-situ non-invasive ground-penetrating radar (GPR) and Terrestrial Light Detection and Ranging (T-LiDAR) techniques, complemented with infrared thermography. T-LiDAR is used for the recording of the 3D surface of this particular case and provides high resolution 3D digital models. GPR data processing is performed through the novel software tool “toGPRi”, developed by the authors, which allows the creation of a 3D model of the sub-surface and the subsequent XY images or time-slices at different depths. All these products are georeferenced, in such a way that the GPR orthoimages can be combined with the orthoimages from the T-LiDAR for a complete interpretation of the site. In this way, the GPR technique allows for the detection of the structures of the barracks that are buried, and their distribution is completed with the structure measured by the T-LiDAR on the surface. In addition, the detection of buried elements made possible the identification and labelling of the structures of the surface and their uses. These structures are additionally inspected with infrared thermography (IRT) to determine their conservation condition and distinguish between original and subsequent constructions. Full article
(This article belongs to the Special Issue Recent Advances in GPR Imaging)
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Open AccessArticle Remote Sensing and Geo-Archaeological Data: Inland Water Studies for the Conservation of Underwater Cultural Heritage in the Ferrara District, Italy
Remote Sens. 2018, 10(3), 380; doi:10.3390/rs10030380
Received: 26 December 2017 / Revised: 25 January 2018 / Accepted: 10 February 2018 / Published: 1 March 2018
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Abstract
In the southern area of the Ferrara District, Italy, remote sensing investigations associated with geo-archaeological drilling in underwater archaeological studies, have helped to broad our understanding of the historical evolution and cultural heritage of inland waterways. In working on prototype sites, we have
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In the southern area of the Ferrara District, Italy, remote sensing investigations associated with geo-archaeological drilling in underwater archaeological studies, have helped to broad our understanding of the historical evolution and cultural heritage of inland waterways. In working on prototype sites, we have taken a multidisciplinary approach of surveillance and preventive archaeology, and have collaborated with archaeologists, geologists, hydro-biologists, and engineers. In this area of research, often lakes, lagoons, and rivers are characterized by low visibility. Some Quaternary events have deeply modified Ferrara’s landscape. Analysis of preserved samples from micro-drillings, underwater direct and indirect surveys, and the cataloguing of historical artefacts, are giving to the researchers a remarkable ancient chronology line. Recent studies confirmed anthropization sequences from the 1st Century B.C. to the 6th Century A.D. Waterscape archaeology, a multidisciplinary science devoted to the study of the human use of wetlands and anthropological connection with the water environment, testifies the ways in which people, in the past, constructed and used the water environment. In this article, we describe underwater cultural heritage research using 3D side scan sonar surveys and artifacts analysis, comparing data from direct diving investigations and stratigraphic data from micro-geological drillings on sites of Lago Tramonto, Gambulaga, Portomaggiore (Ferrara). Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Archaeological Heritage)
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Open AccessArticle A Relative Radiometric Calibration Method Based on the Histogram of Side-Slither Data for High-Resolution Optical Satellite Imagery
Remote Sens. 2018, 10(3), 381; doi:10.3390/rs10030381
Received: 4 January 2018 / Revised: 25 February 2018 / Accepted: 27 February 2018 / Published: 1 March 2018
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Abstract
Relative radiometric calibration, or flat fielding, is indispensable for obtaining high-quality optical satellite imagery for sensors that have more than one detector per band. High-resolution optical push-broom sensors with thousands of detectors per band are now common. Multiple techniques have been employed for
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Relative radiometric calibration, or flat fielding, is indispensable for obtaining high-quality optical satellite imagery for sensors that have more than one detector per band. High-resolution optical push-broom sensors with thousands of detectors per band are now common. Multiple techniques have been employed for relative radiometric calibration. One technique, often called side-slither, where the sensor axis is rotated 90° in yaw relative to normal acquisitions, has been gaining popularity, being applied to Landsat 8, QuickBird, RapidEye, and other satellites. Side-slither can be more time efficient than some of the traditional methods, as only one acquisition may be required. In addition, the side-slither does not require any onboard calibration hardware, only a satellite capability to yaw and maintain a stable yawed attitude. A relative radiometric calibration method based on histograms of side-slither data is developed. This method has three steps: pre-processing, extraction of key points, and calculation of coefficients. Histogram matching and Otsu’s method are used to extract key points. Three datasets from the Chinese GaoFen-9 satellite were used: one to obtain the relative radiometric coefficients, and the others to verify the coefficients. Root-mean-square deviations of the corrected imagery were better than 0.1%. The maximum streaking metrics was less than 1. This method produced significantly better relative radiometric calibration than the traditional method used for GaoFen-9. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle Precise Orbit Determination of FY-3C with Calibration of Orbit Biases in BeiDou GEO Satellites
Remote Sens. 2018, 10(3), 382; doi:10.3390/rs10030382
Received: 19 January 2018 / Revised: 20 February 2018 / Accepted: 26 February 2018 / Published: 1 March 2018
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Abstract
The emerging BeiDou navigation satellite system has contributed to global precise positioning and has recently moved toward space-borne applications. However, the contribution of BeiDou on LEO orbit determination applications is limited by the poor precision of the GEO satellite orbit and clock products.
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The emerging BeiDou navigation satellite system has contributed to global precise positioning and has recently moved toward space-borne applications. However, the contribution of BeiDou on LEO orbit determination applications is limited by the poor precision of the GEO satellite orbit and clock products. Current researches suggest that BeiDou GEO satellites should not be included in LEO precise orbit determination. Based on analyzing the characteristics of errors existing in BeiDou GEO orbit products, we propose a feasible method to mitigate the offsets in BeiDou GEO orbit errors by in-flight calibration of the systematic daily constant biases in the along-track and cross-track of BeiDou GEO satellites. The proposed method is investigated and validated using one entire month of onboard BDS data from the Chinese FY-3C satellite. The average daily RMS compared with the GPS-derived orbit indicates that our method achieves 6.2 cm three-dimensional precision. When compared to the solutions that disregard the GEO orbit errors scheme and roughly exclude the GEO scheme, the FY-3C orbit precision has been improved by 89.1% and 20.2%, respectively. The average daily RMS values of phase residuals are about one centimeter for solutions that exclude GEO and that estimate systematic biases in GEO orbits. The calibrated orbits of GEO with the decimeter level in along-track and cross-track can be reconstructed by correcting the orbit biases estimated in the FY-3C precise orbit determination. Statistics of the FY-3C orbit quality, observation residuals, and precision of the recovered GEO orbits demonstrate that calibration of daily orbit biases in GEO can improve the precision of LEO orbit determination and enhance the reliability of the solution. Full article
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Open AccessArticle Land Cover Classification Using Integrated Spectral, Temporal, and Spatial Features Derived from Remotely Sensed Images
Remote Sens. 2018, 10(3), 383; doi:10.3390/rs10030383
Received: 28 January 2018 / Revised: 25 February 2018 / Accepted: 27 February 2018 / Published: 1 March 2018
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Abstract
Obtaining accurate and timely land cover information is an important topic in many remote sensing applications. Using satellite image time series data should achieve high-accuracy land cover classification. However, most satellite image time-series classification methods do not fully exploit the available data for
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Obtaining accurate and timely land cover information is an important topic in many remote sensing applications. Using satellite image time series data should achieve high-accuracy land cover classification. However, most satellite image time-series classification methods do not fully exploit the available data for mining the effective features to identify different land cover types. Therefore, a classification method that can take full advantage of the rich information provided by time-series data to improve the accuracy of land cover classification is needed. In this paper, a novel method for time-series land cover classification using spectral, temporal, and spatial information at an annual scale was introduced. Based on all the available data from time-series remote sensing images, a refined nonlinear dimensionality reduction method was used to extract the spectral and temporal features, and a modified graph segmentation method was used to extract the spatial features. The proposed classification method was applied in three study areas with land cover complexity, including Illinois, South Dakota, and Texas. All the Landsat time series data in 2014 were used, and different study areas have different amounts of invalid data. A series of comparative experiments were conducted on the annual time-series images using training data generated from Cropland Data Layer. The results demonstrated higher overall and per-class classification accuracies and kappa index values using the proposed spectral-temporal-spatial method compared to spectral-temporal classification methods. We also discuss the implications of this study and possibilities for future applications and developments of the method. Full article
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Open AccessArticle Paddy Field Expansion and Aggregation Since the Mid-1950s in a Cold Region and Its Possible Causes
Remote Sens. 2018, 10(3), 384; doi:10.3390/rs10030384
Received: 20 December 2017 / Revised: 26 February 2018 / Accepted: 27 February 2018 / Published: 1 March 2018
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Abstract
Over the last six decades, paddy fields on the Sanjiang Plain have experienced rapid expansion and aggregation. In our study, land use and land cover changes related to paddy fields were studied based on information acquired from topographic maps and remote-sensing images. Paddy
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Over the last six decades, paddy fields on the Sanjiang Plain have experienced rapid expansion and aggregation. In our study, land use and land cover changes related to paddy fields were studied based on information acquired from topographic maps and remote-sensing images. Paddy field expansion and aggregation were investigated through landscape indices and trajectory codes. Furthermore, the possible causes of paddy field expansion and aggregation were explored. Results indicated that such fields have increased by approximately 42,704 ha·y−1 over the past six decades. Approximately 98% of paddy fields in 2015 were converted from other land use types. In general, the gravity center moved 254.51 km toward the northeast, at a rate of approximately 4.17 km·y−1. The cohesion index increased from 96.8208 in 1954 to 99.5656 in 2015, and the aggregation index grew from 91.3533 in 1954 to 93.4448 in 2015, indicating the apparent aggregation of paddy fields on the Sanjiang Plain. Trajectory analyses showed that the transformations from marsh as well as from grassland to dry farmland and then into paddy fields were predominant. Climate warming provided a favorable environment for rice planting. Meanwhile, population growth, technological progress, and government policies drove paddy field expansion and aggregation during the study period. Full article
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Open AccessFeature PaperArticle Drone-Borne Hyperspectral Monitoring of Acid Mine Drainage: An Example from the Sokolov Lignite District
Remote Sens. 2018, 10(3), 385; doi:10.3390/rs10030385
Received: 12 January 2018 / Revised: 7 February 2018 / Accepted: 27 February 2018 / Published: 2 March 2018
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Abstract
This contribution explores the potential of unmanned aerial systems (UAS) to monitor areas affected by acid mine drainage (AMD). AMD is an environmental phenomenon that usually develops in the vicinity of mining operations or in post-mining landscapes. The investigated area covers a re-cultivated
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This contribution explores the potential of unmanned aerial systems (UAS) to monitor areas affected by acid mine drainage (AMD). AMD is an environmental phenomenon that usually develops in the vicinity of mining operations or in post-mining landscapes. The investigated area covers a re-cultivated tailing in the Sokolov lignite district of the Czech Republic. A high abundance of AMD minerals occurs in a confined space of the selected test site and illustrates potential environmental issues. The mine waste material contains pyrite and its consecutive weathering products, mainly iron hydroxides and oxides. These affect the natural pH values of the Earth’s surface. Prior research done in this area relies on satellite and airborne data, and our approach focuses on lightweight drone systems that enables rapid deployment for field campaigns and consequently-repeated surveys. High spatial image resolutions and precise target determination are additional advantages. Four field and flight campaigns were conducted from April to September 2016. For validation, the waste heap was probed in situ for pH, X-ray fluorescence (XRF), and reflectance spectrometry. Ground truth was achieved by collecting samples that were characterized for pH, X-ray diffraction, and XRF in laboratory conditions. Hyperspectral data were processed and corrected for atmospheric, topographic, and illumination effects using accurate digital elevation models (DEMs). High-resolution point clouds and DEMs were built from drone-borne RGB data using structure-from-motion multi-view-stereo photogrammetry. The supervised classification of hyperspectral image (HSI) data suggests the presence of jarosite and goethite minerals associated with the acidic environmental conditions (pH range 2.3–2.8 in situ). We identified specific iron absorption bands in the UAS-HSI data. These features were confirmed by ground-truth spectroscopy. The distribution of in situ pH data validates the UAS-based mineral classification results. Evaluation of the applied methods demonstrates that drone surveying is a fast, non-invasive, inexpensive technique for multi-temporal environmental monitoring of post-mining landscapes. Full article
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Open AccessFeature PaperArticle The Role of NWP Filter for the Satellite Based Detection of Cumulonimbus Clouds
Remote Sens. 2018, 10(3), 386; doi:10.3390/rs10030386
Received: 28 November 2017 / Revised: 22 February 2018 / Accepted: 26 February 2018 / Published: 2 March 2018
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Abstract
This study is motivated by the great importance of Cbs for aviation safety. The study investigates the role of Numerical Weather Prediction (NWP) filtering for the remote sensing of Cumulonimbus Clouds (Cbs) by implementation of about 30 different experiments, covering Central Europe. These
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This study is motivated by the great importance of Cbs for aviation safety. The study investigates the role of Numerical Weather Prediction (NWP) filtering for the remote sensing of Cumulonimbus Clouds (Cbs) by implementation of about 30 different experiments, covering Central Europe. These experiments compile different stability filter settings as well as the use of different channels for the InfraRed (IR) brightness temperatures (BT). As stability filters, parameters from Numerical Weather Prediction (NWP) are used. The application of the stability filters restricts the detection of Cbs to regions with a labile atmosphere. Various NWP filter settings are investigated in the experiments. The brightness temperature information results from the infrared (IR) Spinning Enhanced Visible and InfraRed Image (SEVIRI) instrument on-board of the Meteosat Second Generation satellite and enables the detection of very cold and high clouds close to the tropopause. Various satellite channels and BT thresholds are applied in the different experiments. The satellite only approaches (no NWP filtering) result in the detection of Cbs with a relative high probability of detection, but unfortunately combined with a large False Alarm Rate (FAR), leading to a Critical Success Index (CSI) below 60% for the investigated summer period in 2016. The false alarms result from other types of very cold and high clouds. It is shown that the false alarms can be significantly decreased by application of an appropriate NWP stability filter, leading to the increase of CSI to about 70% for 2016. CSI is increased from about 70 to about 75% by application of NWP filtering for the other investigated summer period in 2017. A brief review and reflection of the literature clarify that the function of the NWP filter can not be replaced by MSG IR spectroscopy. Thus, NWP filtering is strongly recommended to increase the quality of satellite based Cb detection. Further, it has been shown that the well established convective available potential energy (CAPE) and the convection index (KO) work well as a stability filter. Full article
(This article belongs to the Special Issue Remote Sensing Methods and Applications for Aeronautical Meteorology)
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Open AccessArticle Assessment of the Structural Integrity of the Roman Bridge of Alcántara (Spain) Using TLS and GPR
Remote Sens. 2018, 10(3), 387; doi:10.3390/rs10030387
Received: 16 December 2017 / Revised: 10 February 2018 / Accepted: 27 February 2018 / Published: 2 March 2018
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Abstract
The Roman bridge of Alcántara is the largest in Spain. Its preservation is of the utmost importance and to this end different aspects must be studied. The most prominent is the assessment of its structure, and this is especially important as the bridge
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The Roman bridge of Alcántara is the largest in Spain. Its preservation is of the utmost importance and to this end different aspects must be studied. The most prominent is the assessment of its structure, and this is especially important as the bridge remains in use. This paper documents the way the assessment of structural safety was carried out. The assessment methodology of existing structures was applied. The preliminary assessment was based on bibliographic data and non-destructive techniques. The geometric data of the bridge were obtained by Terrestrial Laser Scanning (TLS), which made possible the analysis of its deformations and assessment of its structure. Ground-Penetrating Radar (GPR) was also used with different antennae to work at different depths and spatial resolutions with the aim of analysing structural elements. From the above information, the assessment of structural safety was made using the limit analysis method by applying the historical works carried out on it and those described in the regulation of obligatory compliance in Spain (IAP11), studying the sensitivity of safety to the most relevant parameters. The state of preservation and structural integrity of the bridge is discussed and conclusions are drawn on the areas of greatest risk and the bases for the following assessment phase of preservation of the bridge. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Archaeological Heritage)
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Open AccessArticle Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia
Remote Sens. 2018, 10(3), 388; doi:10.3390/rs10030388
Received: 26 December 2017 / Revised: 22 February 2018 / Accepted: 23 February 2018 / Published: 2 March 2018
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Abstract
The Tropical Rainfall Measuring Mission (TRMM) was the first Earth Science mission dedicated to studying tropical and subtropical rainfall. Up until now, there is still limited knowledge on the accuracy of the version 7 research product TRMM 3B42-V7 despite having the advantage of
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The Tropical Rainfall Measuring Mission (TRMM) was the first Earth Science mission dedicated to studying tropical and subtropical rainfall. Up until now, there is still limited knowledge on the accuracy of the version 7 research product TRMM 3B42-V7 despite having the advantage of a high temporal resolution and large spatial coverage over oceans and land. This is particularly the case in tropical regions in Asia. The objective of this study is therefore to analyze the performance of rainfall estimation from TRMM 3B42-V7 (henceforth TRMM) using rain gauge data in Malaysia, specifically from the Pahang river basin as a case study, and using a set of performance indicators/scores. The results suggest that the altitude of the region affects the performances of the scores. Root Mean Squared Error (RMSE) is lower mostly at a higher altitude and mid-altitude. The correlation coefficient (CC) generally shows a positive but weak relationship between the rain gauge measurements and TRMM (0 < CC < 0.4), while the Nash-Sutcliffe Efficiency (NSE) scores are low (NSE < 0.1). The Percent Bias (PBIAS) shows that TRMM tends to overestimate the rainfall measurement by 26.95% on average. The Probability of Detection (POD) and Threat Score (TS) demonstrate that more than half of the pixel-point pairs have values smaller than 0.7. However, the Probability of False Detection (POFD) and False Alarm Rate (FAR) show that most of the pixel-point gauges have values lower than 0.55. The seasonal analysis shows that TRMM overestimates during the wet season and underestimates during the dry season. The bias adjustment shows that Mean Bias Correction (MBC) improved the scores better than Double-Kernel Residual Smoothing (DS) and Residual Inverse Distance Weighting (RIDW). The large errors imply that TRMM may not be suitable for applications in environmental, water resources, and ecological studies without prior correction. Full article
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Open AccessArticle Evaluating Endmember and Band Selection Techniques for Multiple Endmember Spectral Mixture Analysis using Post-Fire Imaging Spectroscopy
Remote Sens. 2018, 10(3), 389; doi:10.3390/rs10030389
Received: 21 December 2017 / Revised: 13 February 2018 / Accepted: 27 February 2018 / Published: 2 March 2018
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Abstract
Fire impacts many vegetated ecosystems across the world. The severity of a fire is major component in determining post-fire effects, including soil erosion, trace gas emissions, and the trajectory of recovery. In this study, we used imaging spectroscopy data combined with Multiple Endmember
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Fire impacts many vegetated ecosystems across the world. The severity of a fire is major component in determining post-fire effects, including soil erosion, trace gas emissions, and the trajectory of recovery. In this study, we used imaging spectroscopy data combined with Multiple Endmember Spectral Mixture Analysis (MESMA), a form of spectral mixture analysis that accounts for endmember variability, to map fire severity of the 2013 Rim Fire. We evaluated four endmember selection approaches: Iterative Endmember Selection (IES), count-based within endmember class (In-CoB), Endmember Average Root Mean Squared Error (EAR), and Minimum Average Spectral Angle (MASA). To reduce the dimensionality of the imaging spectroscopy data we used uncorrelated Stable Zone Unmixing (uSZU). Fractional cover maps derived from MESMA were validated using two approaches: (1) manual interpretation of fine spatial resolution WorldView-2 imagery; and (2) ground plots measuring the Geo Composite Burn Index (GeoCBI) and the percentage of co-dominant and dominant trees with green, brown, and black needles. Comparison to reference data demonstrated fairly high correlation for green vegetation and char fractions (r2 values as high as 0.741 for the MESMA ash fractions compared to classified WorldView-2 imagery and as high as 0.841 for green vegetation fractions). The combination of uSZU band selection and In-CoB endmember selection had the best trade-off between accuracy and computational efficiency. This study demonstrated that detailed fire severity retrievals based on imaging spectroscopy can be optimized using techniques that would be viable also in a satellite-based imaging spectrometer. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessArticle Understanding Temporal and Spatial Distribution of Crop Residue Burning in China from 2003 to 2017 Using MODIS Data
Remote Sens. 2018, 10(3), 390; doi:10.3390/rs10030390
Received: 31 December 2017 / Revised: 15 February 2018 / Accepted: 2 March 2018 / Published: 2 March 2018
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Abstract
Crop residue burning, which is a convenient approach to process excessive crop straws, has a negative impact on local and regional air quality and soil structures. China, as a major agricultural country with a large population, should take more effective measures to control
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Crop residue burning, which is a convenient approach to process excessive crop straws, has a negative impact on local and regional air quality and soil structures. China, as a major agricultural country with a large population, should take more effective measures to control crop residue burning. In this case, a better understanding of long-term spatio-temporal variations of crop residue burning in China is required. The MODIS products MOD14A1/MYD14A1 were employed in this research. Meanwhile, due to the vast territory of China, we divided the study area into seven regions based on the national administrative divisions to examine crop residue burning in each region, respectively. The temporal analysis of crop residue burning in different regions demonstrates a fluctuated, but generally upward, trend from 2003 to 2017. For monthly variations of crop residue burning in different regions, detected fire spots in June mainly concentrated in Central China (CC), East China (EC), and North China (NC). A majority of detected fire spots in Northeast China (NEC) and Northwest China (NWC) appeared in April and October. For other months, a small number of fire spots were distributed in all regions in a scattered manner. Furthermore, from a spatio-temporal perspective, this research revealed that crop residue burning in NEC was the most active among all regions both in spring and autumn. For summer, EC holds a larger proportion of burning spots than other regions. For winter, the number of burning spots in most regions was close. This research conducts a comprehensive analysis of crop residue burning in China at both a national and regional scale. The methodology and results from this research provide useful reference for better monitoring and controlling crop residue burning in China. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Sparse Subspace Clustering-Based Feature Extraction for PolSAR Imagery Classification
Remote Sens. 2018, 10(3), 391; doi:10.3390/rs10030391
Received: 8 January 2018 / Revised: 21 February 2018 / Accepted: 27 February 2018 / Published: 2 March 2018
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Abstract
Features play an important role in the learning technologies and pattern recognition methods for polarimetric synthetic aperture (PolSAR) image interpretation. In this paper, based on the subspace clustering algorithms, we combine sparse representation, low-rank representation, and manifold graphs to investigate the intrinsic property
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Features play an important role in the learning technologies and pattern recognition methods for polarimetric synthetic aperture (PolSAR) image interpretation. In this paper, based on the subspace clustering algorithms, we combine sparse representation, low-rank representation, and manifold graphs to investigate the intrinsic property of PolSAR data. In this algorithm framework, the features are projected through the projection matrix with the sparse or/and the low rank characteristic in the low dimensional space. Meanwhile, different kinds of manifold graphs explore the geometry structure of PolSAR data to make the projected feature more discriminative. Those learned matrices, that are constrained by the sparsity and low rank terms can search for a few points from the samples and capture the global structure. The proposed algorithms aim at constructing a projection matrix from the subspace clustering algorithms to achieve the features benefiting for the subsequent PolSAR image classification. Experiments test the different combinations of those constraints. It demonstrates that the proposed algorithms outperform other state-of-art linear and nonlinear approaches with better quantization and visualization performance in PolSAR data from spaceborne and airborne platforms. Full article
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Open AccessArticle Validation of the SARAH-E Satellite-Based Surface Solar Radiation Estimates over India
Remote Sens. 2018, 10(3), 392; doi:10.3390/rs10030392
Received: 15 December 2017 / Revised: 28 February 2018 / Accepted: 2 March 2018 / Published: 3 March 2018
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Abstract
We evaluate the accuracy of the satellite-based surface solar radiation dataset called Surface Solar Radiation Data Set - Heliosat (SARAH-E) against in situ measurements over a variety of sites in India between 1999 and 2014. We primarily evaluate the daily means of surface
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We evaluate the accuracy of the satellite-based surface solar radiation dataset called Surface Solar Radiation Data Set - Heliosat (SARAH-E) against in situ measurements over a variety of sites in India between 1999 and 2014. We primarily evaluate the daily means of surface solar radiation. The results indicate that SARAH-E consistently overestimates surface solar radiation, with a mean bias of 21.9 W/m2. The results are complicated by the fact that the estimation bias is stable between 1999 and 2009 with a mean of 19.6 W/m2 but increases sharply thereafter as a result of rapidly decreasing (dimming) surface measurements of solar radiation. In addition, between 1999 and 2009, both in situ measurements and SARAH-E estimates described a statistically significant (at 95% confidence interval) trend of approximately −0.6 W/m2/year, but diverged strongly afterward. We investigated the cause of decreasing solar radiation at one site (Pune) by simulating clear-sky irradiance with local measurements of water vapor and aerosols as input to a radiative transfer model. The relationship between simulated and measured irradiance appeared to change post-2009, indicating that measured changes in the clear-sky aerosol loading are not sufficient to explain the rapid dimming in measured total irradiance. Besides instrumentation biases, possible explanations in the diverging measurements and retrievals of solar radiation may be found in the aerosol climatology used for SARAH-E generation. However, at present, we have insufficient data to conclusively identify the cause of the increasing retrieval bias. Users of the datasets are advised to be aware of the increasing bias when using the post-2009 data. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing)
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Open AccessArticle Impact of Sea Ice Drift Retrieval Errors, Discretization and Grid Type on Calculations of Ice Deformation
Remote Sens. 2018, 10(3), 393; doi:10.3390/rs10030393
Received: 6 February 2018 / Revised: 27 February 2018 / Accepted: 2 March 2018 / Published: 3 March 2018
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Abstract
We studied two issues to be considered in the calculation of parameters characterizing sea ice deformation: the effect of uncertainties in an automatically retrieved sea ice drift field, and the influence of the type of drift vector grid. Sea ice deformation changes the
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We studied two issues to be considered in the calculation of parameters characterizing sea ice deformation: the effect of uncertainties in an automatically retrieved sea ice drift field, and the influence of the type of drift vector grid. Sea ice deformation changes the local ice mass balance and the interaction between atmosphere, ice, and ocean, and constitutes a hazard to marine traffic and operations. Due to numerical effects, the results of deformation retrievals may predict, e.g., openings and closings of the ice cover that do not exist in reality. We focus specifically on fields of ice drift obtained from synthetic aperture radar (SAR) imagery and analyze the Propagated Drift Retrieval Error (PDRE) and the Boundary Definition Error (BDE). From the theory of error propagation, the PDRE for the calculated deformation parameters can be estimated. To quantify the BDE, we devise five different grid types and compare theoretical expectation and numerical results for different deformation parameters assuming three scenarios: pure divergence, pure shear, and a mixture of both. Our findings for both sources of error help to set up optimal deformation retrieval schemes and are also useful for other applications working with vector fields and scalar parameters derived therefrom. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field
Remote Sens. 2018, 10(3), 395; doi:10.3390/rs10030395
Received: 21 January 2018 / Revised: 27 February 2018 / Accepted: 1 March 2018 / Published: 4 March 2018
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Abstract
Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern
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Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern the disease area. The spectra of hyperspectral image pixels in a manually selected region of interest are preprocessed via mean removal to eliminate interference, due to the time interval and the environment. The generalization of the classification model is considered, and two improvements are made to the model framework. First, the pixel spectra data are reshaped into a two-dimensional data structure for the input layer of a Convolutional Neural Network (CNN). After training two types of CNNs, the assessment shows that a two-dimensional CNN model is more efficient than a one-dimensional CNN. Second, a hybrid neural network with a convolutional layer and bidirectional recurrent layer is reconstructed to improve the generalization of the model. When considering the characteristics of the dataset and models, the confusion matrices that are based on the testing dataset indicate that the classification model is effective for background and disease classification of hyperspectral image pixels. The results of the model show that the two-dimensional convolutional bidirectional gated recurrent unit neural network (2D-CNN-BidGRU) has an F1 score and accuracy of 0.75 and 0.743, respectively, for the total testing dataset. A comparison of all the models shows that the hybrid neural network of 2D-CNN-BidGRU is the best at preventing over-fitting and optimize the generalization. Our results illustrate that the hybrid structure deep neural network is an excellent classification algorithm for healthy and Fusarium head blight diseased classification in the field of hyperspectral imagery. Full article
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Open AccessArticle Hyperspectral Classification Based on Texture Feature Enhancement and Deep Belief Networks
Remote Sens. 2018, 10(3), 396; doi:10.3390/rs10030396
Received: 2 January 2018 / Revised: 14 February 2018 / Accepted: 2 March 2018 / Published: 4 March 2018
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Abstract
With success of Deep Belief Networks (DBNs) in computer vision, DBN has attracted great attention in hyperspectral classification. Many deep learning based algorithms have been focused on deep feature extraction for classification improvement. Multi-features, such as texture feature, are widely utilized in classification
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With success of Deep Belief Networks (DBNs) in computer vision, DBN has attracted great attention in hyperspectral classification. Many deep learning based algorithms have been focused on deep feature extraction for classification improvement. Multi-features, such as texture feature, are widely utilized in classification process to enhance classification accuracy greatly. In this paper, a novel hyperspectral classification framework based on an optimal DBN and a novel texture feature enhancement (TFE) is proposed. Through band grouping, sample band selection and guided filtering, the texture features of hyperspectral data are improved. After TFE, the optimal DBN is employed on the hyperspectral reconstructed data for feature extraction and classification. Experimental results demonstrate that the proposed classification framework outperforms some state-of-the-art classification algorithms, and it can achieve outstanding hyperspectral classification performance. Furthermore, our proposed TFE method can play a significant role in improving classification accuracy. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Open AccessArticle Remote Sensing of River Erosion on the Colville River, North Slope Alaska
Remote Sens. 2018, 10(3), 397; doi:10.3390/rs10030397
Received: 27 November 2017 / Revised: 24 February 2018 / Accepted: 2 March 2018 / Published: 5 March 2018
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Abstract
The Colville is an Arctic river in the Alaska North Slope. The residents of Nuiqsut rely heavily on the Colville for their subsistence needs. Increased erosion has been reported on the Colville, especially along bluffs, which shaped the goals of this study: to
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The Colville is an Arctic river in the Alaska North Slope. The residents of Nuiqsut rely heavily on the Colville for their subsistence needs. Increased erosion has been reported on the Colville, especially along bluffs, which shaped the goals of this study: to use remote sensing techniques to map and quantify erosion rates and the volume of land loss at selected bluff sites along the main channel of the Colville, and to assess the suitability of automated methods of regional erosion monitoring. We used orthomosaics from high resolution aerial photos acquired in 1955 and 1979/1982, as well as high resolution WorldView-2 images from 2015 to quantify long-term erosion rates and the cubic volume of erosion. We found that, at the selected sites, erosion rates averaged 1 to 3.5 m per year. The erosion rate remained the same at one site and increased from 1955 to 2015 at two of the four sites. We estimated the volume of land loss to be in the magnitude of 166,000 m3 to 2.5 million m3 at our largest site. We also found that estimates of erosion were comparable for manual hand-digitized and automated methods, suggesting our automated method was effective and can be extended to monitor erosion at other sites along river systems that are bordered by bluffs. Full article
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
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Open AccessArticle Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations
Remote Sens. 2018, 10(3), 398; doi:10.3390/rs10030398
Received: 22 December 2017 / Revised: 11 February 2018 / Accepted: 27 February 2018 / Published: 5 March 2018
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Abstract
Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show
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Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show how ground-truth data from rain gauge stations can improve the quality of satellite imagery. To this end, a simulation study is conducted wherein different sizes of outlier outbreaks are spread and randomly introduced in the normalized difference vegetation index (NDVI) and the day and night land surface temperature (LST) of composite images from Navarre (Spain) between 2011 and 2015. To remove outliers, a new method called thin-plate splines with covariates (TpsWc) is proposed. This method consists of smoothing the median anomalies with a thin-plate spline model, whereby transformed ground-truth data are the external covariates of the model. The performance of the proposed method is measured with the square root of the mean square error (RMSE), calculated as the root of the pixel-by-pixel mean square differences between the original data and the predicted data with the TpsWc model and with a state-space model with and without covariates. The study shows that the use of ground-truth data reduces the RMSE in both the TpsWc model and the state-space model used for comparison purposes. The new method successfully removes the abnormal data while preserving the phenology of the raw data. The RMSE reduction percentage varies according to the derived variables (NDVI or LST), but reductions of up to 20% are achieved with the new proposal. Full article
(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)
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Open AccessArticle Calibration of GLONASS Inter-Frequency Code Bias for PPP Ambiguity Resolution with Heterogeneous Rover Receivers
Remote Sens. 2018, 10(3), 399; doi:10.3390/rs10030399
Received: 9 January 2018 / Revised: 9 February 2018 / Accepted: 16 February 2018 / Published: 5 March 2018
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Abstract
Integer ambiguity resolution (IAR) is important for rapid initialization of precise point positioning (PPP). Whereas many studies have been limited to Global Positioning System (GPS) alone, there is a strong need to add Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) to the PPP-IAR solution. However,
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Integer ambiguity resolution (IAR) is important for rapid initialization of precise point positioning (PPP). Whereas many studies have been limited to Global Positioning System (GPS) alone, there is a strong need to add Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) to the PPP-IAR solution. However, the frequency-division multiplexing of GLONASS signals causes inter-frequency code bias (IFCB) in the receiving equipment. The IFCB causes GLONASS wide-lane uncalibrated phase delay (UPD) estimation with heterogeneous receiver types to fail, so GLONASS ambiguity is therefore traditionally estimated as float values in PPP. A two-step method of calibrating GLONASS IFCB is proposed in this paper, such that GLONASS PPP-IAR can be performed with heterogeneous receivers. Experimental results demonstrate that with the proposed method, GLONASS PPP ambiguity resolution can be achieved across a variety of receiver types. For kinematic PPP with mixed receiver types, the fixing percentage within 10 min is only 33.5% for GPS-only. Upon adding GLONASS, the percentage improves substantially, to 84.9%. Full article
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Open AccessArticle Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor
Remote Sens. 2018, 10(3), 400; doi:10.3390/rs10030400
Received: 8 January 2018 / Revised: 14 February 2018 / Accepted: 1 March 2018 / Published: 5 March 2018
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Abstract
Major challenges for automatic ship detection in optical remote sensing (ORS) images include cloud, wave, island, wake clutters, and even the high variability of targets. This paper presents a practical ship detection scheme to resolve these existing issues. The scheme contains two main
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Major challenges for automatic ship detection in optical remote sensing (ORS) images include cloud, wave, island, wake clutters, and even the high variability of targets. This paper presents a practical ship detection scheme to resolve these existing issues. The scheme contains two main coarse-to-fine stages: prescreening and discrimination. In the prescreening stage, we construct a novel visual saliency detection method according to the difference of statistical characteristics between highly non-uniform regions which allude to regions of interest (ROIs) and homogeneous backgrounds. It can serve as a guide for locating candidate regions. In this way, not only can the targets be precisely detected, but false alarms are also significantly reduced. In the discrimination stage, to get a better representation of the target, both shape and texture features characterizing the ship target are extracted and concatenated as a feature vector for subsequent classification. Moreover, the combined feature is invariant to the rotation. Finally, a trainable Gaussian support vector machine (SVM) classifier is performed to validate real ships out of ship candidates. We demonstrate the superior performance of the proposed hierarchical detection method with detailed comparisons to existing efforts. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle In-Flight Retrieval of SCIAMACHY Instrument Spectral Response Function
Remote Sens. 2018, 10(3), 401; doi:10.3390/rs10030401
Received: 21 December 2017 / Revised: 20 February 2018 / Accepted: 23 February 2018 / Published: 5 March 2018
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Abstract
The instrument Spectral Response Function (ISRF) has a strong impact on spectral calibration and the atmospheric trace gases retrievals. An accurate knowledge or a fine characterization of the ISRF shape and its FWHM (Full width at half maximum) as well as its temporal
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The instrument Spectral Response Function (ISRF) has a strong impact on spectral calibration and the atmospheric trace gases retrievals. An accurate knowledge or a fine characterization of the ISRF shape and its FWHM (Full width at half maximum) as well as its temporal behavior is therefore crucial. Designing a strategy for the characterization of the ISRF both on ground and in-flight is critical for future missions, such as the spectral imagers in the Copernicus program. We developed an algorithm to retrieve the instrument ISRF in-flight. Our method uses solar measurements taken in-flight by the instrument to fit a parameterized ISRF from on ground based calibration, and then retrieves the shape and FWHM of the actual in-flight ISRF. With such a strategy, one would be able to derive and monitor the ISRF during the commissioning and operation of spectrometer imager missions. We applied our method to retrieve the SCIAMACHY instrument ISRF in its different channels. We compared the retrieved ones with the on ground estimated ones. Besides some peculiarities found in SCIAMACHY channel 8, the ISRF results in other channels were relatively consistent and stable over time in most cases. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Hydrological Variability and Changes in the Arctic Circumpolar Tundra and the Three Largest Pan-Arctic River Basins from 2002 to 2016
Remote Sens. 2018, 10(3), 402; doi:10.3390/rs10030402
Received: 24 November 2017 / Revised: 19 January 2018 / Accepted: 26 January 2018 / Published: 6 March 2018
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Abstract
The Arctic freshwater budget is critical for understanding the climate in the northern regions. However, the hydrology of the Arctic circumpolar tundra region (ACTR) and the largest pan-Arctic rivers are still not well understood. In this paper, we analyze the spatiotemporal variations in
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The Arctic freshwater budget is critical for understanding the climate in the northern regions. However, the hydrology of the Arctic circumpolar tundra region (ACTR) and the largest pan-Arctic rivers are still not well understood. In this paper, we analyze the spatiotemporal variations in the terrestrial water storage (TWS) of the ACTR and three of the largest pan-Arctic river basins (Lena, Mackenzie, Yukon). To do this, we utilize monthly Gravity Recovery and Climate Experiment (GRACE) data from 2002 to 2016. Together with global land reanalysis, and river runoff data, we identify declining TWS trends throughout the ACTR that we attribute largely to increasing evapotranspiration driven by increasing summer air temperatures. In terms of regional changes, large and significant negative trends in TWS are observed mainly over the North American continent. At basin scale, we show that, in the Lena River basin, the autumnal TWS signal persists until the spring of the following year, while in the Mackenzie River basin, the TWS level in the autumn and winter has no significant impact on the following year. As expected global warming is expected to be particularly significant in the northern regions, our results are important for understanding future TWS trends, with possible further decline. Full article
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Open AccessArticle An Orthogonal Projection Algorithm to Suppress Interference in High-Frequency Surface Wave Radar
Remote Sens. 2018, 10(3), 403; doi:10.3390/rs10030403
Received: 2 February 2018 / Revised: 1 March 2018 / Accepted: 1 March 2018 / Published: 6 March 2018
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Abstract
High-frequency surface wave radar (HFSWR) has been widely applied in sea-state monitoring, and its performance is known to suffer from various unwanted interferences and clutters. Radio frequency interference (RFI) from other radiating sources and ionospheric clutter dominate the various types of unwanted signals
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High-frequency surface wave radar (HFSWR) has been widely applied in sea-state monitoring, and its performance is known to suffer from various unwanted interferences and clutters. Radio frequency interference (RFI) from other radiating sources and ionospheric clutter dominate the various types of unwanted signals because the HF band is congested with many users and the ionosphere propagates interference from distant sources. In this paper, various orthogonal projection schemes are summarized, and three new schemes are proposed for interference cancellation. Simulations and field data recorded by experimental multi-frequency HFSWR from Wuhan University are used to evaluate the cancellation performances of these schemes with respect to both RFI and ionospheric clutter. The processing results may provide a guideline for identifying the appropriate orthogonal projection cancellation schemes in various HFSWR applications. Full article
(This article belongs to the Special Issue Radar Remote Sensing of Oceans and Coastal Areas)
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Open AccessFeature PaperArticle A Spectral Mapping Signature for the Rapid Ohia Death (ROD) Pathogen in Hawaiian Forests
Remote Sens. 2018, 10(3), 404; doi:10.3390/rs10030404
Received: 2 February 2018 / Revised: 27 February 2018 / Accepted: 4 March 2018 / Published: 6 March 2018
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Abstract
Pathogenic invasions are a major source of change in both agricultural and natural ecosystems. In forests, fungal pathogens can kill habitat-generating plant species such as canopy trees, but methods for remote detection, mapping and monitoring of such outbreaks are poorly developed. Two novel
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Pathogenic invasions are a major source of change in both agricultural and natural ecosystems. In forests, fungal pathogens can kill habitat-generating plant species such as canopy trees, but methods for remote detection, mapping and monitoring of such outbreaks are poorly developed. Two novel species of the fungal genus Ceratocystis have spread rapidly across humid and mesic forests of Hawaiʻi Island, causing widespread mortality of the keystone endemic canopy tree species, Metrosideros polymorpha (common name: ʻōhiʻa). The process, known as Rapid Ohia Death (ROD), causes browning of canopy leaves in weeks to months following infection by the pathogen. An operational mapping approach is needed to track the spread of the disease. We combined field studies of leaf spectroscopy with laboratory chemical studies and airborne remote sensing to develop a spectral signature for ROD. We found that close to 80% of ROD-infected plants undergo marked decreases in foliar concentrations of chlorophyll, water and non-structural carbohydrates, which collectively result in strong consistent changes in leaf spectral reflectance in the visible (400–700 nm) and shortwave-infrared (1300–2500 nm) wavelength regions. Leaf-level results were replicated at the canopy level using airborne laser-guided imaging spectroscopy, with quantitative spectral separability of normal green-leaf canopies from suspected ROD-infected brown-leaf canopies in the visible and shortwave-infrared spectrum. Our results provide the spectral–chemical basis for detection, mapping and monitoring of the spread of ROD in native Hawaiian forests. Full article
(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)
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Open AccessArticle Modeling Wildfire-Induced Permafrost Deformation in an Alaskan Boreal Forest Using InSAR Observations
Remote Sens. 2018, 10(3), 405; doi:10.3390/rs10030405
Received: 22 December 2017 / Revised: 24 February 2018 / Accepted: 2 March 2018 / Published: 6 March 2018
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Abstract
The discontinuous permafrost zone is one of the world’s most sensitive areas to climate change. Alaskan boreal forest is underlain by discontinuous permafrost, and wildfires are one of the most influential agents negatively impacting the condition of permafrost in the arctic region. Using
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The discontinuous permafrost zone is one of the world’s most sensitive areas to climate change. Alaskan boreal forest is underlain by discontinuous permafrost, and wildfires are one of the most influential agents negatively impacting the condition of permafrost in the arctic region. Using interferometric synthetic aperture radar (InSAR) of Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, we mapped extensive permafrost degradation over interior Alaskan boreal forest in Yukon Flats, induced by the 2009 Big Creek wildfire. Our analyses showed that fire-induced permafrost degradation in the second post-fire thawing season contributed up to 20 cm of ground surface subsidence. We generated post-fire deformation time series and introduced a model that exploited the deformation time series to estimate fire-induced permafrost degradation and changes in active layer thickness. The model showed a wildfire-induced increase of up to 80 cm in active layer thickness in the second post-fire year due to pore-ice permafrost thawing. The model also showed up to 15 cm of permafrost degradation due to excess-ice thawing with little or no increase in active layer thickness. The uncertainties of the estimated change in active layer thickness and the thickness of thawed excess ice permafrost are 27.77 and 1.50 cm, respectively. Our results demonstrate that InSAR-derived deformation measurements along with physics models are capable of quantifying fire-induced permafrost degradation in Alaskan boreal forests underlain by discontinuous permafrost. Our results also have illustrated that fire-induced increase of active layer thickness and excess ice thawing contributed to ground surface subsidence. Full article
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
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Open AccessArticle Automatic Building Segmentation of Aerial Imagery Using Multi-Constraint Fully Convolutional Networks
Remote Sens. 2018, 10(3), 407; doi:10.3390/rs10030407
Received: 21 December 2017 / Revised: 21 February 2018 / Accepted: 3 March 2018 / Published: 6 March 2018
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Abstract
Automatic building segmentation from aerial imagery is an important and challenging task because of the variety of backgrounds, building textures and imaging conditions. Currently, research using variant types of fully convolutional networks (FCNs) has largely improved the performance of this task. However, pursuing
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Automatic building segmentation from aerial imagery is an important and challenging task because of the variety of backgrounds, building textures and imaging conditions. Currently, research using variant types of fully convolutional networks (FCNs) has largely improved the performance of this task. However, pursuing more accurate segmentation results is still critical for further applications such as automatic mapping. In this study, a multi-constraint fully convolutional network (MC–FCN) model is proposed to perform end-to-end building segmentation. Our MC–FCN model consists of a bottom-up/top-down fully convolutional architecture and multi-constraints that are computed between the binary cross entropy of prediction and the corresponding ground truth. Since more constraints are applied to optimize the parameters of the intermediate layers, the multi-scale feature representation of the model is further enhanced, and hence higher performance can be achieved. The experiments on a very-high-resolution aerial image dataset covering 18 km 2 and more than 17,000 buildings indicate that our method performs well in the building segmentation task. The proposed MC–FCN method significantly outperforms the classic FCN method and the adaptive boosting method using features extracted by the histogram of oriented gradients. Compared with the state-of-the-art U–Net model, MC–FCN gains 3.2% (0.833 vs. 0.807) and 2.2% (0.893 vs. 0.874) relative improvements of Jaccard index and kappa coefficient with the cost of only 1.8% increment of the model-training time. In addition, the sensitivity analysis demonstrates that constraints at different positions have inconsistent impact on the performance of the MC–FCN. Full article
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
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Open AccessEditor’s ChoiceArticle Analysis of Secular Ground Motions in Istanbul from a Long-Term InSAR Time-Series (1992–2017)
Remote Sens. 2018, 10(3), 408; doi:10.3390/rs10030408
Received: 31 January 2018 / Revised: 22 February 2018 / Accepted: 1 March 2018 / Published: 6 March 2018
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Abstract
The identification and measurement of ground deformations in urban areas is of great importance for determining the vulnerable parts of the cities that are prone to geohazards, which is a crucial element of both sustainable urban planning and hazard mitigation. Interferometric synthetic aperture
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The identification and measurement of ground deformations in urban areas is of great importance for determining the vulnerable parts of the cities that are prone to geohazards, which is a crucial element of both sustainable urban planning and hazard mitigation. Interferometric synthetic aperture radar (InSAR) time series analysis is a very powerful tool for the operational mapping of ground deformation related to urban subsidence and landslide phenomena. With an analysis spanning almost 25 years of satellite radar observations, we compute an InSAR time series of data from multiple satellites (European Remote Sensing satellites ERS-1 and ERS-2, Envisat, Sentinel-1A, and its twin sensor Sentinel-1B) in order to investigate the spatial extent and rate of ground deformation in the megacity of Istanbul. By combining the various multi-track InSAR datasets (291 images in total) and analysing persistent scatterers (PS-InSAR), we present mean velocity maps of ground surface displacement in selected areas of Istanbul. We identify several sites along the terrestrial and coastal regions of Istanbul that underwent vertical ground subsidence at varying rates, from 5 ± 1.2 mm/yr to 15 ± 2.1 mm/yr. The results reveal that the most distinctive subsidence patterns are associated with both anthropogenic factors and relatively weak lithologies along the Haramirede valley in particular, where the observed subsidence is up to 10 ± 2 mm/yr. We show that subsidence has been occurring along the Ayamama river stream at a rate of up to 10 ± 1.8 mm/yr since 1992, and has also been slowing down over time following the restoration of the river and stream system. We also identify subsidence at a rate of 8 ± 1.2 mm/yr along the coastal region of Istanbul, which we associate with land reclamation, as well as a very localised subsidence at a rate of 15 ± 2.3 mm/yr starting in 2016 around one of the highest skyscrapers of Istanbul, which was built in 2010. Full article
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Open AccessArticle Integration of PSI, MAI, and Intensity-Based Sub-Pixel Offset Tracking Results for Landslide Monitoring with X-Band Corner Reflectors—Italian Alps (Corvara)
Remote Sens. 2018, 10(3), 409; doi:10.3390/rs10030409
Received: 17 January 2018 / Revised: 23 February 2018 / Accepted: 1 March 2018 / Published: 6 March 2018
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Abstract
This paper presents an analysis of the integration between interferometric and intensity-offset tracking-based SAR remote sensing for landslide hazard mitigation in the Italian Alps. Despite the advantages of Synthetic Aperture Radar Interferometry (InSAR) methods for quantifying landslide deformation, some limitations remain. The temporal
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This paper presents an analysis of the integration between interferometric and intensity-offset tracking-based SAR remote sensing for landslide hazard mitigation in the Italian Alps. Despite the advantages of Synthetic Aperture Radar Interferometry (InSAR) methods for quantifying landslide deformation, some limitations remain. The temporal decorrelation, the 1-D Line Of Sight (LOS) observation restriction, the high velocity rate and the multi-directional movement properties make it difficult to monitor accurately complex landslides in areas covered by vegetation. Therefore, complementary and integrated approaches, such as offset tracking-based techniques, are needed to overcome these InSAR limitations for monitoring ground surface deformations. As sub-pixel offset tracking is highly sensitive to data spatial resolution, the latest generations of SAR sensors, such as TerraSAR-X and COSMO-SkyMed, open interesting perspective for a more accurate hazard assessment. In this paper, we consider high-resolution X-band data acquired by the COSMO-SkyMed (CSK) constellation for Permanent Scatterers Interferometry (PSI), Multi-Aperture Interferometry (MAI) and offset tracking processing. We analyze the offset tracking techniques considering area and feature-based matching algorithms to evaluate their applicability to CSK data by improving sub-pixel offset estimations. To this end, PSI and MAI are used for extracting LOS and azimuthal displacement components. Then, four well-known area-based and five feature-based matching algorithms (taken from computer vision) are applied to 16 X-band corner reflectors. Results show that offset estimation accuracy can be considerably improved up to less than 3% of the pixel size using the combination of the different feature-based detectors and descriptors. A sensitivity analysis of these techniques applied to CSK data to monitor complex landslides in the Italian Alps provides indications on advantages and disadvantages of each of them. Full article
(This article belongs to the Special Issue Radar Interferometry for Geohazards)
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Open AccessArticle Deep Salient Feature Based Anti-Noise Transfer Network for Scene Classification of Remote Sensing Imagery
Remote Sens. 2018, 10(3), 410; doi:10.3390/rs10030410
Received: 16 January 2018 / Revised: 28 February 2018 / Accepted: 1 March 2018 / Published: 6 March 2018
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Abstract
Remote sensing (RS) scene classification is important for RS imagery semantic interpretation. Although tremendous strides have been made in RS scene classification, one of the remaining open challenges is recognizing RS scenes in low quality variance (e.g., various scales and noises). This paper
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Remote sensing (RS) scene classification is important for RS imagery semantic interpretation. Although tremendous strides have been made in RS scene classification, one of the remaining open challenges is recognizing RS scenes in low quality variance (e.g., various scales and noises). This paper proposes a deep salient feature based anti-noise transfer network (DSFATN) method that effectively enhances and explores the high-level features for RS scene classification in different scales and noise conditions. In DSFATN, a novel discriminative deep salient feature (DSF) is introduced by saliency-guided DSF extraction, which conducts a patch-based visual saliency (PBVS) algorithm using “visual attention” mechanisms to guide pre-trained CNNs for producing the discriminative high-level features. Then, an anti-noise network is proposed to learn and enhance the robust and anti-noise structure information of RS scene by directly propagating the label information to fully-connected layers. A joint loss is used to minimize the anti-noise network by integrating anti-noise constraint and a softmax classification loss. The proposed network architecture can be easily trained with a limited amount of training data. The experiments conducted on three different scale RS scene datasets show that the DSFATN method has achieved excellent performance and great robustness in different scales and noise conditions. It obtains classification accuracy of 98.25%, 98.46%, and 98.80%, respectively, on the UC Merced Land Use Dataset (UCM), the Google image dataset of SIRI-WHU, and the SAT-6 dataset, advancing the state-of-the-art substantially. Full article
(This article belongs to the collection Learning to Understand Remote Sensing Images)
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Open AccessArticle A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data
Remote Sens. 2018, 10(3), 411; doi:10.3390/rs10030411
Received: 9 December 2017 / Revised: 8 February 2018 / Accepted: 16 February 2018 / Published: 7 March 2018
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Abstract
Incoming surface solar irradiance (SSI) is essential for calculating Earth’s surface radiation budget and is a key parameter for terrestrial ecological modeling and climate change research. Remote sensing images from geostationary and polar-orbiting satellites provide an opportunity for SSI estimation through directly retrieving
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Incoming surface solar irradiance (SSI) is essential for calculating Earth’s surface radiation budget and is a key parameter for terrestrial ecological modeling and climate change research. Remote sensing images from geostationary and polar-orbiting satellites provide an opportunity for SSI estimation through directly retrieving atmospheric and land-surface parameters. This paper presents a new scheme for estimating SSI from the visible and infrared channels of geostationary meteorological and polar-orbiting satellite data. Aerosol optical thickness and cloud microphysical parameters were retrieved from Geostationary Operational Environmental Satellite (GOES) system images by interpolating lookup tables of clear and cloudy skies, respectively. SSI was estimated using pre-calculated offline lookup tables with different atmospheric input data of clear and cloudy skies. The lookup tables were created via the comprehensive radiative transfer model, Santa Barbara Discrete Ordinate Radiative Transfer (SBDART), to balance computational efficiency and accuracy. The atmospheric attenuation effects considered in our approach were water vapor absorption and aerosol extinction for clear skies, while cloud parameters were the only atmospheric input for cloudy-sky SSI estimation. The approach was validated using one-year pyranometer measurements from seven stations in the SURFRAD (SURFace RADiation budget network). The results of the comparison for 2012 showed that the estimated SSI agreed with ground measurements with correlation coefficients of 0.94, 0.69, and 0.89 with a bias of 26.4 W/m2, −5.9 W/m2, and 14.9 W/m2 for clear-sky, cloudy-sky, and all-sky conditions, respectively. The overall root mean square error (RMSE) of instantaneous SSI was 80.0 W/m2 (16.8%), 127.6 W/m2 (55.1%), and 99.5 W/m2 (25.5%) for clear-sky, cloudy-sky (overcast sky and partly cloudy sky), and all-sky (clear-sky and cloudy-sky) conditions, respectively. A comparison with other state-of-the-art studies suggests that our proposed method can successfully estimate SSI with a maximum improvement of an RMSE of 24 W/m2. The clear-sky SSI retrieval was sensitive to aerosol optical thickness, which was largely dependent on the diurnal surface reflectance accuracy. Uncertainty in the pre-defined horizontal visibility for ‘clearest sky’ will eventually lead to considerable SSI retrieval error. Compared to cloud effective radius, the retrieval error of cloud optical thickness was a primary factor that determined the SSI estimation accuracy for cloudy skies. Our proposed method can be used to estimate SSI for clear and one-layer cloud sky, but is not suitable for multi-layer clouds overlap conditions as a lower-level cloud cannot be detected by the optical sensor when a higher-level cloud has a higher optical thickness. Full article
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Open AccessArticle Modification of Local Urban Aerosol Properties by Long-Range Transport of Biomass Burning Aerosol
Remote Sens. 2018, 10(3), 412; doi:10.3390/rs10030412
Received: 31 December 2017 / Revised: 6 February 2018 / Accepted: 2 March 2018 / Published: 7 March 2018
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Abstract
During August 2016, a quasi-stationary high-pressure system spreading over Central and North-Eastern Europe, caused weather conditions that allowed for 24/7 observations of aerosol optical properties by using a complex multi-wavelength PollyXT lidar system with Raman, polarization and water vapour capabilities, based at the
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During August 2016, a quasi-stationary high-pressure system spreading over Central and North-Eastern Europe, caused weather conditions that allowed for 24/7 observations of aerosol optical properties by using a complex multi-wavelength PollyXT lidar system with Raman, polarization and water vapour capabilities, based at the European Aerosol Research Lidar Network (EARLINET network) urban site in Warsaw, Poland. During 24–30 August 2016, the lidar-derived products (boundary layer height, aerosol optical depth, Ångström exponent, lidar ratio, depolarization ratio) were analysed in terms of air mass transport (HYSPLIT model), aerosol load (CAMS data) and type (NAAPS model) and confronted with active and passive remote sensing at the ground level (PolandAOD, AERONET, WIOS-AQ networks) and aboard satellites (SEVIRI, MODIS, CATS sensors). Optical properties for less than a day-old fresh biomass burning aerosol, advected into Warsaw’s boundary layer from over Ukraine, were compared with the properties of long-range transported 3–5 day-old aged biomass burning aerosol detected in the free troposphere over Warsaw. Analyses of temporal changes of aerosol properties within the boundary layer, revealed an increase of aerosol optical depth and Ångström exponent accompanied by an increase of surface PM10 and PM2.5. Intrusions of advected biomass burning particles into the urban boundary layer seem to affect not only the optical properties observed but also the top height of the boundary layer, by moderating its increase. Full article
(This article belongs to the Special Issue Aerosol Remote Sensing)
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Open AccessArticle Radiometric Evaluation of SNPP VIIRS Band M11 via Sub-Kilometer Intercomparison with Aqua MODIS Band 7 over Snowy Scenes
Remote Sens. 2018, 10(3), 413; doi:10.3390/rs10030413
Received: 6 January 2018 / Revised: 23 February 2018 / Accepted: 28 February 2018 / Published: 8 March 2018
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Abstract
A refined intersensor comparison study is carried out to evaluate the radiometric stability of the 2257 nm channel (M11) of the first Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. This study is initiated as part of
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A refined intersensor comparison study is carried out to evaluate the radiometric stability of the 2257 nm channel (M11) of the first Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. This study is initiated as part of the examination into the performance of key shortwave infrared (SWIR) bands for SNPP VIIRS ocean color data processing and applications, with Band M11 playing key role over turbid and inland waters. The evaluation utilizes simultaneous nadir overpasses (SNOs) to compare SNPP VIIRS Band M11 against Band 7 of the MODerate-resolution Imaging Spectroradiometer (MODIS) in the Aqua satellite over concurrently observed scenes. The standard result of the radiance comparison is a seemingly uncontrolled and inconsistent time series unsuitable for further analyses, in great contrast to other matching band-pairs whose radiometric comparisons are typically stable around 1.0 within 1% variation. The mismatching relative spectral response (RSR) between the two respective bands, with SNPP VIIRS M11 at 2225 to 2275 nm and Aqua MODIS B7 at 2125 to 2175 nm, is demonstrated to be the cause of the large variation because of the different dependence of the spectral responses of the two bands over identical scenes. A consistent radiometric comparison time series, however, can be extracted from SNO events that occur over snowy surfaces. A customized selection and analysis procedure successfully identifies the snowy scenes within the SNO events and builds a stable comparison time series. Particularly instrumental for the success of the comparison is the use of the half-kilometer spatial resolution data of Aqua MODIS B7 that significantly enhances the statistics. The final refined time series of Aqua MODIS B7 radiance over the SNPP VIIRS M11 radiance is stable at around 0.39 within 2.5% showing no evidence of drift. The radiometric ratio near 0.39 suggests the strong presence of medium-grained snow of a mixed-snow condition in those SNO scenes leading to successful comparison. The multi-year stability indicates the correctness of the on-orbit RSB calibration of SNPP VIIRS M11 whose result does not suffer from long-term drift. Full article
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Open AccessArticle Improvement of Moderate Resolution Land Use and Land Cover Classification by Introducing Adjacent Region Features
Remote Sens. 2018, 10(3), 414; doi:10.3390/rs10030414
Received: 22 December 2017 / Revised: 12 February 2018 / Accepted: 7 March 2018 / Published: 8 March 2018
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Abstract
Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (LULC) classification. Limited by coarser resolutions, most of the traditional LULC classifications that are based on moderate resolution remote sensing images focus on the spectral features of a
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Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (LULC) classification. Limited by coarser resolutions, most of the traditional LULC classifications that are based on moderate resolution remote sensing images focus on the spectral features of a single pixel. Inspired by the spatial evaluation methods in landscape ecology, this study proposed a new method to extract neighborhood characteristics around a pixel for moderate resolution images. 3 landscape-metric-like indexes, i.e., mean index, standard deviation index, and distance weighted value index, were defined as adjacent region features to include the surrounding environmental characteristics. The effects of the adjacent region features and the different feature set configurations on improving the LULC classification were evaluated by a series of well-controlled LULC classification experiments using K nearest neighbor (KNN) and support vector machine (SVM) classifiers on a Landsat 8 Operational Land Imager (OLI) image. When the adjacent region features were added, the overall accuracies of both the classifiers were higher than when only spectral features were used. For the KNN and SVM classifiers that used only spectral features, the overall accuracies of the LULC classification were 85.45% and 88.87%, respectively, and the accuracies were improved to 94.52% and 96.97%. The classification accuracies of all the LULC types improved. Highly heterogeneous LULC types that are easily misclassified achieved greater improvements. As comparisons, the grey-level co-occurrence matrix (GLCM) and convolutional neural network (CNN) approaches were also implemented on the same dataset. The results revealed that the new method outperformed GLCM and CNN approaches and can significantly improve the classification performance that is based on moderate resolution data. Full article
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
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Open AccessArticle Automatic Kernel Size Determination for Deep Neural Networks Based Hyperspectral Image Classification
Remote Sens. 2018, 10(3), 415; doi:10.3390/rs10030415
Received: 6 December 2017 / Revised: 28 February 2018 / Accepted: 6 March 2018 / Published: 8 March 2018
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Abstract
Considering kernels in Convolutional Neural Networks (CNNs) as detectors for local patterns, K-means neural network proposes to cluster local patches extracted from training images and then fixate those kernels as the representative patches in each cluster without further training. Thus the amount of
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Considering kernels in Convolutional Neural Networks (CNNs) as detectors for local patterns, K-means neural network proposes to cluster local patches extracted from training images and then fixate those kernels as the representative patches in each cluster without further training. Thus the amount of labeled samples necessitated for training can be greatly reduced. One key property of those kernels is their spatial size which determines their capacity in detecting local patterns and is expected to be task-specific. However, most of literatures determine the spatial size of those kernels in a heuristic way. To address this problem, we propose to automatically determine the kernel size in order to better adapt the K-means neural network for hyperspectral imagery classification. Specifically, a novel kernel-size determination scheme is developed by measuring the clustering performance of local patches with different sizes. With the kernel of determined size, more discriminative local patterns can be detected in the hyperspectral imagery, with which the classification performance of K-means neural network can be obviously improved. Experimental results on two datasets demonstrate the effectiveness of the proposed method. Full article
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Open AccessEditor’s ChoiceArticle Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies
Remote Sens. 2018, 10(3), 416; doi:10.3390/rs10030416
Received: 16 January 2018 / Revised: 16 February 2018 / Accepted: 6 March 2018 / Published: 8 March 2018
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Abstract
The intensive rice cultivation area in northwestern Italy hosts the largest surface of rice paddies in Europe, and it is valued as a substantial habitat for aquatic biodiversity, with the paddies acting as a surrogate for the lost natural wetlands. The extent of
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The intensive rice cultivation area in northwestern Italy hosts the largest surface of rice paddies in Europe, and it is valued as a substantial habitat for aquatic biodiversity, with the paddies acting as a surrogate for the lost natural wetlands. The extent of submerged paddies strictly depends on crop management practices: in this framework, the recent diffusion of rice seeding in dry conditions has led to a reduction of flooded surfaces during spring and could have contributed to the observed decline of the populations of some waterbird species that exploit rice fields as foraging habitat. In order to test the existence and magnitude of a decreasing trend in the extent of submerged rice paddies during the rice-sowing period, MODIS remotely-sensed data were used to estimate the extent of the average flooded surface and the proportion of flooded rice fields in the years 2000–2016 during the nesting period of waterbirds. A general reduction of flooded rice fields during the rice-sowing season was observed, averaging 0.86 ± 0.20 % per year (p-value < 0.01). Overall, the loss in submerged surface area during the sowing season reached 44 % of the original extent in 2016, with a peak of 78 % in the sub-districts to the east of the Ticino River. Results highlight the usefulness of remote sensing data and techniques to map and monitor water dynamics within rice cropping systems. These techniques could be of key importance to analyze the effects at the regional scale of the recent increase of dry-seeded rice cultivations on watershed recharge and water runoff and to interpret the decline of breeding waterbirds via a loss of foraging habitat. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)
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Open AccessArticle A Randomized Subspace Learning Based Anomaly Detector for Hyperspectral Imagery
Remote Sens. 2018, 10(3), 417; doi:10.3390/rs10030417
Received: 21 January 2018 / Revised: 28 February 2018 / Accepted: 5 March 2018 / Published: 8 March 2018
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Abstract
This paper proposes a randomized subspace learning based anomaly detector (RSLAD) for hyperspectral imagery (HSI). Improved from robust principal component analysis, the RSLAD assumes that the background matrix is low-rank, and the anomaly matrix is sparse with a small portion of nonzero columns
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This paper proposes a randomized subspace learning based anomaly detector (RSLAD) for hyperspectral imagery (HSI). Improved from robust principal component analysis, the RSLAD assumes that the background matrix is low-rank, and the anomaly matrix is sparse with a small portion of nonzero columns (i.e., column-wise). It also assumes the anomalies do not lie in the column subspace of the background and aims to find a randomized subspace of the background to detect the anomalies. First, random techniques including random sampling and random Hadamard projections are implemented to construct a coarse randomized columns subspace of the background with reduced computational cost. Second, anomaly columns are searched and removed from the coarse randomized column subspace by solving a series of least squares problems, resulting in a purified randomized column subspace. Third, the nonzero columns in the anomaly matrix are located by projecting all the pixels on the orthogonal subspace of the purified subspace, and the anomalies are finally detected based on the L2 norm of the columns in the anomaly matrix. The detection performance of RSLAD is compared with four state-of-the-art methods, including global Reed-Xiaoli (GRX), local RX (LRX), collaborative-representation based detector (CRD), and low-rank and sparse matrix decomposition base anomaly detector (LRaSMD). Experimental results show good detection performance of RSLAD with lower computational cost. Therefore, the proposed RSLAD offers an alternative option for hyperspectral anomaly detection. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Inherent Optical Properties of the Baltic Sea in Comparison to Other Seas and Oceans
Remote Sens. 2018, 10(3), 418; doi:10.3390/rs10030418
Received: 29 December 2017 / Revised: 2 March 2018 / Accepted: 4 March 2018 / Published: 8 March 2018
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Abstract
In order to retrieve geophysical satellite products in coastal waters with high coloured dissolved organic matter (CDOM), models and processors require parameterization with regional specific inherent optical properties (sIOPs). The sIOPs of the Baltic Sea were evaluated and compared to a global NOMAD/COLORS
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In order to retrieve geophysical satellite products in coastal waters with high coloured dissolved organic matter (CDOM), models and processors require parameterization with regional specific inherent optical properties (sIOPs). The sIOPs of the Baltic Sea were evaluated and compared to a global NOMAD/COLORS Reference Data Set (RDS), covering a wide range of optical provinces. Ternary plots of relative absorption at 442 nm showed CDOM dominance over phytoplankton and non-algal particle absorption (NAP). At 670 nm, the distribution of Baltic measurements was not different from case 1 waters and the retrieval of Chl a was shown to be improved by red-ratio algorithms. For correct retrieval of CDOM from Medium Resolution Imaging Spectrometer (MERIS) data, a different CDOM slope over the Baltic region is required. The CDOM absorption slope, SCDOM, was significantly higher in the northwestern Baltic Sea: 0.018 (±0.002) compared to 0.016 (±0.005) for the RDS. Chl a-specific absorption and ad [SPM]*(442) and its spectral slope did not differ significantly. The comparison to the MERIS Reference Model Document (RMD) showed that the SNAP slope was generally much higher (0.011 ± 0.003) than in the RMD (0.0072 ± 0.00108), and that the SPM scattering slope was also higher (0.547 ± 0.188) vs. 0.4. The SPM-specific scattering was much higher (1.016 ± 0.326 m2 g−1) vs. 0.578 m2 g−1 in RMD. SPM retrieval could be improved by applying the local specific scattering. A novel method was implemented to derive the phase function (PF) from AC9 and VSF-3 data. b ˜ was calculated fitting a Fournier–Forand PF to the normalized VSF data. b ˜ was similar to Petzold, but the PF differed in the backwards direction. Some of the sIOPs showed a bimodal distribution, indicating different water types—e.g., coastal vs. open sea. This seems to be partially caused by the distribution of inorganic particles that fall out relatively close to the coast. In order to improve remote sensing retrieval from Baltic Sea data, one should apply different parameterization to these distinct water types, i.e., inner coastal waters that are more influenced by scattering of inorganic particles vs. open sea waters that are optically dominated by CDOM absorption. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Open AccessArticle Shortwave Radiation Affected by Agricultural Practices
Remote Sens. 2018, 10(3), 419; doi:10.3390/rs10030419
Received: 28 November 2017 / Revised: 8 February 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
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Abstract
The albedo of bare soil depends on its organic matter, iron oxide, carbonate contents, and reflectance geometry, features considered stable over time, and also depends on salinity, moisture and roughness, which change dynamically due to agricultural practices. This paper deals with the quantitative
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The albedo of bare soil depends on its organic matter, iron oxide, carbonate contents, and reflectance geometry, features considered stable over time, and also depends on salinity, moisture and roughness, which change dynamically due to agricultural practices. This paper deals with the quantitative estimation of the amount of shortwave radiation that could be reflected by air-dried bare soils in clear-sky conditions within arable lands in Israel throughout the year, assuming that they were shaped by a plough, a disk harrow, or a smoothing harrow. An area of bare soils was extracted from Landsat 8 images, within the contours of arable lands. The radiation reflected from the bare soils was calculated by equations predicting variations in their half-diurnal albedo as the solar zenith angle function. Accordingly, laboratory reflectance data of Israeli soil samples were used. The results clearly showed annual variation in the amount of short-wave radiation reflected from all bare soils within arable lands. The minimum radiation occurred in the winter, between the 1st and 70th day of the year (DOY), and the maximum was identified in the summer between 200th and 250th DOY. This could reach about 3–5 PJ/day and 16–23 PJ/day, respectively. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Evaluation of Three Parametric Models for Estimating Directional Thermal Radiation from Simulation, Airborne, and Satellite Data
Remote Sens. 2018, 10(3), 420; doi:10.3390/rs10030420
Received: 12 January 2018 / Revised: 20 February 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
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Abstract
An appropriate model to correct thermal radiation anisotropy is important for the wide applications of land surface temperature (LST). This paper evaluated the performance of three published directional thermal radiation models—the Roujean–Lagouarde (RL) model, the Bidirectional Reflectance Distribution Function (BRDF) model, and the
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An appropriate model to correct thermal radiation anisotropy is important for the wide applications of land surface temperature (LST). This paper evaluated the performance of three published directional thermal radiation models—the Roujean–Lagouarde (RL) model, the Bidirectional Reflectance Distribution Function (BRDF) model, and the Vinnikov model—at canopy and pixel scale using simulation, airborne, and satellite data. The results at canopy scale showed that (1) the three models could describe directional anisotropy well and the Vinnikov model performed the best, especially for erectophile canopy or low leaf area index (LAI); (2) the three models reached the highest fitting accuracy when the LAI varied from 1 to 2; and (3) the capabilities of the three models were all restricted by the hotspot effect, plant height, plant spacing, and three-dimensional structure. The analysis at pixel scale indicated a consistent result that the three models presented a stable effect both on verification and validation, but the Vinnikov model had the best ability in the erectophile canopy (savannas and grassland) and low LAI (barren or sparsely vegetated) areas. Therefore, the Vinnikov model was calibrated for different land cover types to instruct the angular correction of LST. Validation with the Surface Radiation Budget Network (SURFRAD)-measured LST demonstrated that the root mean square (RMSE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product could be decreased by 0.89 K after angular correction. In addition, the corrected LST showed better spatial uniformity and higher angular correlation. Full article
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Open AccessArticle Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images
Remote Sens. 2018, 10(3), 421; doi:10.3390/rs10030421
Received: 5 December 2017 / Revised: 27 January 2018 / Accepted: 7 March 2018 / Published: 9 March 2018
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Abstract
It is important to detect floating oil slicks after spill accidents, and hyperspectral remote sensing technology is capable of achieving this task. Traditional methods mainly utilize the spectral indices of hydrocarbons to detect floating oil slicks, but are poor at distinguishing the thickness
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It is important to detect floating oil slicks after spill accidents, and hyperspectral remote sensing technology is capable of achieving this task. Traditional methods mainly utilize the spectral indices of hydrocarbons to detect floating oil slicks, but are poor at distinguishing the thickness of oil slicks and cannot detect sheens. Since the spectra of oil slicks should be affected by seawater as well as oil, this paper investigated the use of spectral indices of hydrocarbons and seawater to identify different thicknesses of oil slicks. In this research, a measurement, called index separability (IS), was proposed for quantitatively evaluating the identification ability of these spectral indices. Based on the evaluation results, experiments were conducted to validate the applicability of these spectral indices. The results show that the spectral indices of hydrocarbons are more suitable for detecting continuous true color oil slicks and emulsions and that spectral indices of seawater are more suitable for sheens and seawater. In addition, the spectral indices of hydrocarbons and seawater are complementary for detecting oil slicks. Finally, combining the spectral indices of hydrocarbons and seawater is conducive to achieving more accurate oil slick recognition results. Full article
(This article belongs to the Special Issue Oil Spill Remote Sensing)
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Open AccessArticle Estimation of Daily Average Downward Shortwave Radiation over Antarctica
Remote Sens. 2018, 10(3), 422; doi:10.3390/rs10030422
Received: 11 January 2018 / Revised: 19 February 2018 / Accepted: 19 February 2018 / Published: 9 March 2018
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Abstract
Surface shortwave (SW) irradiation is the primary driving force of energy exchange in the atmosphere and land interface. The global climate is profoundly influenced by irradiation changes due to the special climatic condition in Antarctica. Remote-sensing retrieval can offer only the instantaneous values
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Surface shortwave (SW) irradiation is the primary driving force of energy exchange in the atmosphere and land interface. The global climate is profoundly influenced by irradiation changes due to the special climatic condition in Antarctica. Remote-sensing retrieval can offer only the instantaneous values in an area, whilst daily cycle and average values are necessary for further studies and applications, including climate change, ecology, and land surface process. When considering the large values of and small diurnal changes of solar zenith angle and cloud coverage, we develop two methods for the temporal extension of remotely sensed downward SW irradiance over Antarctica. The first one is an improved sinusoidal method, and the second one is an interpolation method based on cloud fraction change. The instantaneous irradiance data and cloud products are used in both methods to extend the diurnal cycle, and obtain the daily average value. Data from South Pole and Georg von Neumayer stations are used to validate the estimated value. The coefficient of determination (R2) between the estimated daily averages and the measured values based on the first method is 0.93, and the root mean square error (RMSE) is 32.21 W/m2 (8.52%). As for the traditional sinusoidal method, the R2 and RMSE are 0.68 and 70.32 W/m2 (18.59%), respectively The R2 and RMSE of the second method are 0.96 and 25.27 W/m2 (6.98%), respectively. These values are better than those of the traditional linear interpolation (0.79 and 57.40 W/m2 (15.87%)). Full article
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Open AccessArticle UAV Capability to Detect and Interpret Solar Radiation as a Potential Replacement Method to Hemispherical Photography
Remote Sens. 2018, 10(3), 423; doi:10.3390/rs10030423
Received: 30 January 2018 / Revised: 28 February 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
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Abstract
Solar radiation is one of the most significant environmental factors that regulates the rate of photosynthesis, and consequently, growth. Light intensity in the forest can vary both spatially and temporally, so precise assessment of canopy and potential solar radiation can significantly influence the
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Solar radiation is one of the most significant environmental factors that regulates the rate of photosynthesis, and consequently, growth. Light intensity in the forest can vary both spatially and temporally, so precise assessment of canopy and potential solar radiation can significantly influence the success of forest management actions, for example, the establishment of natural regeneration. In this case study, we investigated the possibilities and perspectives of close-range photogrammetric approaches for modeling the amount of potential direct and diffuse solar radiation during the growing seasons (spring–summer), by comparing the performance of low-cost Unmanned Aerial Vehicle (UAV) RGB imagery vs. Hemispherical Photography (HP). Characterization of the solar environment based on hemispherical photography has already been widely used in botany and ecology for a few decades, while the UAV method is relatively new. Also, we compared the importance of several components of potential solar irradiation and their impact on the regeneration of Pinus sylvestris L. For this purpose, a circular fisheye objective was used to obtain hemispherical images to assess sky openness and direct/diffuse photosynthetically active flux density under canopy average for the growing season. Concerning the UAV, a Canopy Height Model (CHM) was constructed based on Structure from Motion (SfM) algorithms using Photoscan professional. Different layers such as potential direct and diffuse radiation, direct duration, etc., were extracted from CHM using ArcGIS 10.3.1 (Esri: California, CA, USA). A zonal statistics tool was used in order to extract the digital data in tree positions and, subsequently, the correlation between potential solar radiation layers and the number of seedlings was evaluated. The results of this study showed that there is a high relation between the two used approaches (HP and UAV) with R2 = 0.74. Finally, potential diffuse solar radiation derived from both methods had the highest significant relation (−8.06% bias) and highest impact in the modeling of pine regeneration. Full article
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Open AccessArticle Satellite Leaf Area Index: Global Scale Analysis of the Tendencies Per Vegetation Type Over the Last 17 Years
Remote Sens. 2018, 10(3), 424; doi:10.3390/rs10030424
Received: 23 January 2018 / Revised: 2 March 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
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Abstract
The main objective of this study is to detect and quantify changes in the vegetation dynamics of each vegetation type at the global scale over the last 17 years. With recent advances in remote sensing techniques, it is now possible to study the
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The main objective of this study is to detect and quantify changes in the vegetation dynamics of each vegetation type at the global scale over the last 17 years. With recent advances in remote sensing techniques, it is now possible to study the Leaf Area Index (LAI) seasonal and interannual variability at the global scale and in a consistent way over the last decades. However, the coarse spatial resolution of these satellite-derived products does not permit distinguishing vegetation types within mixed pixels. Considering only the dominant type per pixel has two main drawbacks: the LAI of the dominant vegetation type is contaminated by spurious signal from other vegetation types and at the global scale, significant areas of individual vegetation types are neglected. In this study, we first developed a Kalman Filtering (KF) approach to disaggregate the satellite-derived LAI from GEOV1 over nine main vegetation types, including grasslands and crops as well as evergreen, broadleaf and coniferous forests. The KF approach permits the separation of distinct LAI values for individual vegetation types that coexist within a pixel. The disaggregated LAI product, called LAI-MC (Multi-Cover), consists of world-wide LAI maps provided every 10 days for each vegetation type over the 1999–2015 period. A trend analysis of the original GEOV1 LAI product and of the disaggregated LAI time series was conducted using the Mann-Kendall test. Resulting trends of the GEOV1 LAI (which accounts for all vegetation types) compare well with previous regional or global studies, showing a greening over a large part of the globe. When considering each vegetation type individually, the largest global trend from LAI-MC is found for coniferous forests (0.0419 m 2 m 2 yr 1 ) followed by summer crops (0.0394 m 2 m 2 yr 1 ), while winter crops and grasslands show the smallest global trends (0.0261 m 2 m 2 yr 1 and 0.0279 m 2 m 2 yr 1 , respectively). The LAI-MC presents contrasting trends among the various vegetation types within the same pixel. For instance, coniferous and broadleaf forests experience a marked greening in the North-East of Europe while crops and grasslands show a browning. In addition, trends from LAI-MC can significantly differ (by up to 50%) from trends obtained with GEOV1 by considering only the dominant vegetation type over each pixel. These results demonstrate the usefulness of the disaggregation method compared to simple ones. LAI-MC may provide a new tool to monitor and quantify tendencies of LAI per vegetation type all over the globe. Full article
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