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Remote Sens., Volume 8, Issue 12 (December 2016)

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Cover Story (view full-size image) A framework is presented for conceptualizing and understanding forest health using remote sensing. [...] Read more.
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Open AccessArticle The Potential Impact of Vertical Sampling Uncertainty on ICESat-2/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems
Remote Sens. 2016, 8(12), 1039; https://doi.org/10.3390/rs8121039
Received: 13 September 2016 / Revised: 9 December 2016 / Accepted: 14 December 2016 / Published: 21 December 2016
Cited by 5 | PDF Full-text (3800 KB) | HTML Full-text | XML Full-text
Abstract
With a planned launch no later than September 2018, the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) will provide a global distribution of geodetic elevation measurements for both the terrain surface and relative canopy heights. The Advanced Topographic Laser Altimeter System (ATLAS) instrument
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With a planned launch no later than September 2018, the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) will provide a global distribution of geodetic elevation measurements for both the terrain surface and relative canopy heights. The Advanced Topographic Laser Altimeter System (ATLAS) instrument on-board ICESat-2 is a LiDAR system sensitive to the photon level. The photon-counting technology has many advantages for space-based altimetry, but also has challenges, particularly with delineating the signal from background noise. As such, a current unknown facing the ecosystem community is the performance of ICESat-2 for terrain and canopy height retrievals. This paper aims to provide the science user community of ICESat-2 land/vegetation data products with a realistic understanding of the performance characteristics and potential uncertainties related to the vertical sampling error, which includes the error in the perceived height value and the measurement precision. Terrain and canopy heights from simulated ICESat-2 data are evaluated against the airborne LiDAR ground truth values to provide a baseline performance uncertainty for multiple ecosystems. Simulation results for wooded savanna and boreal forest result in a mean bias error and error uncertainty (precision) for terrain height retrievals at 0.06 m (0.24 m RMSE) and −0.13 m (0.77 m RMSE). In contrast, results over ecosystems with dense vegetation show terrain errors of 1.93 m (1.66 m RMSE) and 2.52 m (3.18 m RMSE), indicating problems extracting terrain height due to diminished ground returns. Simulated top of canopy heights from ICESat-2 underestimated true top of canopy returns for all types analyzed with errors ranging from 0.28 m (1.39 m RMSE) to 1.25 m (2.63 m RMSE). These results comprise a first step in a comprehensive evaluation of ICESat-2 anticipated performance. Future steps will include solar noise impact analysis and investigation into performance discrepancy between visible and near-infrared wavelengths. Full article
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Open AccessArticle Elevation Change Rates of Glaciers in the Lahaul-Spiti (Western Himalaya, India) during 2000–2012 and 2012–2013
Remote Sens. 2016, 8(12), 1038; https://doi.org/10.3390/rs8121038
Received: 13 September 2016 / Revised: 12 December 2016 / Accepted: 14 December 2016 / Published: 21 December 2016
Cited by 13 | PDF Full-text (14087 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Previous studies have shown contrasting glacier elevation and mass changes in the sub-regions of high-mountain Asia. However, the elevation changes on an individual catchment scale can be potentially influenced by supraglacial debris, ponds, lakes and ice cliffs besides regionally driven factors. Here, we
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Previous studies have shown contrasting glacier elevation and mass changes in the sub-regions of high-mountain Asia. However, the elevation changes on an individual catchment scale can be potentially influenced by supraglacial debris, ponds, lakes and ice cliffs besides regionally driven factors. Here, we present a detailed study on elevation changes of glaciers in the Lahaul-Spiti region derived from TanDEM-X and SRTM C-/X-band DEMs during 2000–2012 and 2012–2013. We observe three elevation change patterns during 2000–2012 among glaciers with different extent of supraglacial debris. The first pattern (<10% debris cover, type-1) indicates maximum thinning rates at the glacier terminus and is observed for glaciers with no or very low debris cover. In the second pattern (>10% debris cover, type-2), maximum thinning is observed up-glacier instead of glacier terminus. This is interpreted as the insulating effect of a thick debris cover. A third pattern, high elevation change rates near the terminus despite high debris cover (>10% debris cover, type-3) is most likely associated with either thinner debris thickness or enhanced melting at supraglacial ponds and lakes as well as ice cliffs. We empirically determined the SRTM C- and X-band penetration differences for debris-covered ice, clean ice/firn/snow and correct for this bias in our elevation change measurements. We show that this penetration bias, if uncorrected, underestimates the region-wide elevation change and geodetic mass balance by 20%. After correction, the region-wide elevation change (1712 km 2 ) was estimated to be −0.65 ± 0.43 m yr 1 during 2000–2012. Due to the short observation period, elevation change measurements from TanDEM-X for selected glaciers in the period 2012–2013 are subject to large uncertainties. However, similar spatial patterns were observed during 2000–2012 and 2012–2013, but at different magnitudes. This study reveals that the thinning patterns of debris-covered glaciers cannot be generalized and spatially detailed mapping of glacier elevation change is required to better understand the impact of different surface types under changing climatic conditions. Full article
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Open AccessArticle A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures
Remote Sens. 2016, 8(12), 1037; https://doi.org/10.3390/rs8121037
Received: 21 May 2016 / Revised: 19 November 2016 / Accepted: 6 December 2016 / Published: 21 December 2016
Cited by 1 | PDF Full-text (8063 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave
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Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer—Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration’s (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size. Full article
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Open AccessEditorial Towards an Integrated Global Land Cover Monitoring and Mapping System
Remote Sens. 2016, 8(12), 1036; https://doi.org/10.3390/rs8121036
Received: 9 December 2016 / Revised: 9 December 2016 / Accepted: 13 December 2016 / Published: 20 December 2016
Cited by 4 | PDF Full-text (363 KB) | HTML Full-text | XML Full-text
Abstract
Global land cover mapping has evolved in a number of ways over the past two decades including increased activity in the areas of map validation and inter-comparison, which is the main focus of this Special Issue in Remote Sensing. Here we describe
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Global land cover mapping has evolved in a number of ways over the past two decades including increased activity in the areas of map validation and inter-comparison, which is the main focus of this Special Issue in Remote Sensing. Here we describe the major trends in global land cover mapping that have occurred, followed by recent advances as exemplified by the papers in the Special Issue. Finally, we consider what the future holds for global land cover mapping. Full article
(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)
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Open AccessArticle Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared (Vis-NIR-SWIR) Spectroscopy Using Fractal-Based Feature Extraction
Remote Sens. 2016, 8(12), 1035; https://doi.org/10.3390/rs8121035
Received: 10 September 2016 / Revised: 9 December 2016 / Accepted: 14 December 2016 / Published: 19 December 2016
Cited by 5 | PDF Full-text (4291 KB) | HTML Full-text | XML Full-text
Abstract
Visible and near-infrared diffuse reflectance spectroscopy has been demonstrated to be a fast and cheap tool for estimating a large number of chemical and physical soil properties, and effective features extracted from spectra are crucial to correlating with these properties. We adopt a
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Visible and near-infrared diffuse reflectance spectroscopy has been demonstrated to be a fast and cheap tool for estimating a large number of chemical and physical soil properties, and effective features extracted from spectra are crucial to correlating with these properties. We adopt a novel methodology for feature extraction of soil spectroscopy based on fractal geometry. The spectrum can be divided into multiple segments with different step–window pairs. For each segmented spectral curve, the fractal dimension value was calculated using variation estimators with power indices 0.5, 1.0 and 2.0. Thus, the fractal feature can be generated by multiplying the fractal dimension value with spectral energy. To assess and compare the performance of new generated features, we took advantage of organic soil samples from the large-scale European Land Use/Land Cover Area Frame Survey (LUCAS). Gradient-boosting regression models built using XGBoost library with soil spectral library were developed to estimate N, pH and soil organic carbon (SOC) contents. Features generated by a variogram estimator performed better than two other estimators and the principal component analysis (PCA). The estimation results for SOC were coefficient of determination (R2) = 0.85, root mean square error (RMSE) = 56.7 g/kg, the ratio of percent deviation (RPD) = 2.59; for pH: R2 = 0.82, RMSE = 0.49 g/kg, RPD = 2.31; and for N: R2 = 0.77, RMSE = 3.01 g/kg, RPD = 2.09. Even better results could be achieved when fractal features were combined with PCA components. Fractal features generated by the proposed method can improve estimation accuracies of soil properties and simultaneously maintain the original spectral curve shape. Full article
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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Open AccessArticle Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms
Remote Sens. 2016, 8(12), 1034; https://doi.org/10.3390/rs8121034
Received: 19 September 2016 / Revised: 12 December 2016 / Accepted: 14 December 2016 / Published: 19 December 2016
Cited by 2 | PDF Full-text (22299 KB) | HTML Full-text | XML Full-text
Abstract
Individual tree delineation using remotely sensed data plays a very important role in precision forestry because it can provide detailed forest information on a large scale, which is required by forest managers. This study aimed to evaluate the utility of airborne laser scanning
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Individual tree delineation using remotely sensed data plays a very important role in precision forestry because it can provide detailed forest information on a large scale, which is required by forest managers. This study aimed to evaluate the utility of airborne laser scanning (ALS) data for individual tree detection and species classification in Japanese coniferous forests with a high canopy density. Tree crowns in the study area were first delineated by the individual tree detection approach using a canopy height model (CHM) derived from the ALS data. Then, the detected tree crowns were classified into four classes—Pinus densiflora, Chamaecyparis obtusa, Larix kaempferi, and broadleaved trees—using a tree crown-based classification approach with different combinations of 23 features derived from the ALS data and true-color (red-green-blue—RGB) orthoimages. To determine the best combination of features for species classification, several loops were performed using a forward iteration method. Additionally, several classification algorithms were compared in the present study. The results of this study indicate that the combination of the RGB images with laser intensity, convex hull area, convex hull point volume, shape index, crown area, and crown height features produced the highest classification accuracy of 90.8% with the use of the quadratic support vector machines (QSVM) classifier. Compared to only using the spectral characteristics of the orthophotos, the overall accuracy was improved by 14.1%, 9.4%, and 8.8% with the best combination of features when using the QSVM, neural network (NN), and random forest (RF) approaches, respectively. In terms of different classification algorithms, the findings of our study recommend the QSVM approach rather than NNs and RFs to classify the tree species in the study area. However, these classification approaches should be further tested in other forests using different data. This study demonstrates that the synergy of the ALS data and RGB images could be a promising approach to improve species classifications. Full article
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Open AccessArticle A Direct and Fast Methodology for Ship Recognition in Sentinel-2 Multispectral Imagery
Remote Sens. 2016, 8(12), 1033; https://doi.org/10.3390/rs8121033
Received: 22 September 2016 / Revised: 10 December 2016 / Accepted: 14 December 2016 / Published: 19 December 2016
Cited by 3 | PDF Full-text (2156 KB) | HTML Full-text | XML Full-text
Abstract
The European Space Agency satellite Sentinel-2 provides multispectral images with pixel sizes down to 10 m. This high resolution allows for ship detection and recognition by determining a number of important ship parameters. We are able to show how a ship position, its
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The European Space Agency satellite Sentinel-2 provides multispectral images with pixel sizes down to 10 m. This high resolution allows for ship detection and recognition by determining a number of important ship parameters. We are able to show how a ship position, its heading, length and breadth can be determined down to a subpixel resolution. If the ship is moving, its velocity can also be determined from its Kelvin waves. The 13 spectrally different visual and infrared images taken using multispectral imagery (MSI) are “fingerprints” that allow for the recognition and identification of ships. Furthermore, the multispectral image profiles along the ship allow for discrimination between the ship, its turbulent wakes, and the Kelvin waves, such that the ship’s length and breadth can be determined more accurately even when sailing. The ship’s parameters are determined by using satellite imagery taken from several ships, which are then compared to known values from the automatic identification system. The agreement is on the order of the pixel resolution or better. Full article
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Open AccessArticle Ground-Based Hyperspectral Image Analysis of the Lower Mississippian (Osagean) Reeds Spring Formation Rocks in Southwestern Missouri
Remote Sens. 2016, 8(12), 1018; https://doi.org/10.3390/rs8121018
Received: 17 August 2016 / Revised: 3 December 2016 / Accepted: 6 December 2016 / Published: 19 December 2016
Cited by 3 | PDF Full-text (19555 KB) | HTML Full-text | XML Full-text
Abstract
Ground-based hyperspectral imaging is fairly new for studying near-vertical rock exposures where airborne or satellite-based imaging fail to provide useful information. In this study, ground-based hyperspectral image analysis was performed on a roadcut, where diagenetic tripolite facies is observed in southwestern Missouri. Laboratory-based
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Ground-based hyperspectral imaging is fairly new for studying near-vertical rock exposures where airborne or satellite-based imaging fail to provide useful information. In this study, ground-based hyperspectral image analysis was performed on a roadcut, where diagenetic tripolite facies is observed in southwestern Missouri. Laboratory-based reflectance spectroscopy and hyperspectral image analyses were also performed on collected samples. Image classification was performed using Spectral Feature Fitting (SFF) and Mixture-tuned Match Filtering (MTMF) with laboratory- and image-derived end-member spectra. SFF provided thorough yet detailed classification, whereas MTMF provided information on the relative abundances of the lithologies. Ground-based hyperspectral imaging demonstrated its potential to aid geological studies providing valuable information on mineralogical and lithological variations rapidly and with two-dimensional continuity in inaccessible rock faces of near-vertical outcrops. The results showed decreasing tripolite abundance going downward in the investigated vertical succession. Also, a leaching pattern has been observed such that persistent and continuous limestone layers become lenses and patches towards the upper portion of the outcrop. These observations show that the effect of tripolitization decreases when going deeper in the succession, suggesting that the fluid responsible for the weathering of siliceous precursors may have been flowing from top to bottom and thus have had a meteoric origin. Full article
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Open AccessArticle Detection of the Coupling between Vegetation Leaf Area and Climate in a Multifunctional Watershed, Northwestern China
Remote Sens. 2016, 8(12), 1032; https://doi.org/10.3390/rs8121032
Received: 18 September 2016 / Revised: 1 December 2016 / Accepted: 14 December 2016 / Published: 18 December 2016
Cited by 3 | PDF Full-text (4135 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Accurate detection and quantification of vegetation dynamics and drivers of observed climatic and anthropogenic change in space and time is fundamental for our understanding of the atmosphere–biosphere interactions at local and global scales. This case study examined the coupled spatial patterns of vegetation
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Accurate detection and quantification of vegetation dynamics and drivers of observed climatic and anthropogenic change in space and time is fundamental for our understanding of the atmosphere–biosphere interactions at local and global scales. This case study examined the coupled spatial patterns of vegetation dynamics and climatic variabilities during the past three decades in the Upper Heihe River Basin (UHRB), a complex multiple use watershed in arid northwestern China. We apply empirical orthogonal function (EOF) and singular value decomposition (SVD) analysis to isolate and identify the spatial patterns of satellite-derived leaf area index (LAI) and their close relationship with the variability of an aridity index (AI = Precipitation/Potential Evapotranspiration). Results show that UHRB has become increasingly warm and wet during the past three decades. In general, the rise of air temperature and precipitation had a positive impact on mean LAI at the annual scale. At the monthly scale, LAI variations had a lagged response to climate. Two major coupled spatial change patterns explained 29% and 41% of the LAI dynamics during 1983–2000 and 2001–2010, respectively. The strongest connections between climate and LAI were found in the southwest part of the basin prior to 2000, but they shifted towards the north central area afterwards, suggesting that the sensitivity of LAI to climate varied over time, and that human disturbances might play an important role in altering LAI patterns. At the basin level, the positive effects of regional climate warming and precipitation increase as well as local ecological restoration efforts overwhelmed the negative effects of overgrazing. The study results offer insights about the coupled effects of climatic variability and grazing on ecosystem structure and functions at a watershed scale. Findings from this study are useful for land managers and policy makers to make better decisions in response to climate change in the study region. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing
Remote Sens. 2016, 8(12), 1031; https://doi.org/10.3390/rs8121031
Received: 20 September 2016 / Revised: 5 December 2016 / Accepted: 14 December 2016 / Published: 18 December 2016
Cited by 28 | PDF Full-text (11116 KB) | HTML Full-text | XML Full-text
Abstract
There is a growing need to increase global crop yields, whilst minimising use of resources such as land, fertilisers and water. Agricultural researchers use ground-based observations to identify, select and develop crops with favourable genotypes and phenotypes; however, the ability to collect rapid,
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There is a growing need to increase global crop yields, whilst minimising use of resources such as land, fertilisers and water. Agricultural researchers use ground-based observations to identify, select and develop crops with favourable genotypes and phenotypes; however, the ability to collect rapid, high quality and high volume phenotypic data in open fields is restricting this. This study develops and assesses a method for deriving crop height and growth rate rapidly from multi-temporal, very high spatial resolution (1 cm/pixel), 3D digital surface models of crop field trials produced via Structure from Motion (SfM) photogrammetry using aerial imagery collected through repeated campaigns flying an Unmanned Aerial Vehicle (UAV) with a mounted Red Green Blue (RGB) camera. We compare UAV SfM modelled crop heights to those derived from terrestrial laser scanner (TLS) and to the standard field measurement of crop height conducted using a 2 m rule. The most accurate UAV-derived surface model and the TLS both achieve a Root Mean Squared Error (RMSE) of 0.03 m compared to the existing manual 2 m rule method. The optimised UAV method was then applied to the growing season of a winter wheat field phenotyping experiment containing 25 different varieties grown in 27 m2 plots and subject to four different nitrogen fertiliser treatments. Accuracy assessments at different stages of crop growth produced consistently low RMSE values (0.07, 0.02 and 0.03 m for May, June and July, respectively), enabling crop growth rate to be derived from differencing of the multi-temporal surface models. We find growth rates range from −13 mm/day to 17 mm/day. Our results clearly display the impact of variable nitrogen fertiliser rates on crop growth. Digital surface models produced provide a novel spatial mapping of crop height variation both at the field scale and also within individual plots. This study proves UAV based SfM has the potential to become a new standard for high-throughput phenotyping of in-field crop heights. Full article
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Open AccessReview Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics
Remote Sens. 2016, 8(12), 1029; https://doi.org/10.3390/rs8121029
Received: 6 September 2016 / Revised: 1 December 2016 / Accepted: 5 December 2016 / Published: 18 December 2016
Cited by 14 | PDF Full-text (3812 KB) | HTML Full-text | XML Full-text
Abstract
Anthropogenic stress and disturbance of forest ecosystems (FES) has been increasing at all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health
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Anthropogenic stress and disturbance of forest ecosystems (FES) has been increasing at all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health (FH). Remote sensing (RS) bridges the gaps of these limitations, by monitoring indicators of FH on different spatio-temporal scales, and in a cost-effective, rapid, repetitive and objective manner. In this paper, we provide an overview of the definitions of FH, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how we can observe FH with RS. We introduce the concept of spectral traits (ST) and spectral trait variations (STV) in the context of FH monitoring and discuss the prospects, limitations and constraints. Stress, disturbances and resource limitations can cause changes in FES taxonomic, structural and functional diversity; we provide examples how the ST/STV approach can be used for monitoring these FES characteristics. We show that RS based assessments of FH indicators using the ST/STV approach is a competent, affordable, repetitive and objective technique for monitoring. Even though the possibilities for observing the taxonomic diversity of animal species is limited with RS, the taxonomy of forest tree species can be recorded with RS, even though its accuracy is subject to certain constraints. RS has proved successful for monitoring the impacts from stress on structural and functional diversity. In particular, it has proven to be very suitable for recording the short-term dynamics of stress on FH, which cannot be cost-effectively recorded using in-situ methods. This paper gives an overview of the ST/STV approach, whereas the second paper of this series concentrates on discussing in-situ terrestrial monitoring, in-situ RS approaches and RS sensors and techniques for measuring ST/STV for FH. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Health)
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Open AccessArticle Building Change Detection Using Old Aerial Images and New LiDAR Data
Remote Sens. 2016, 8(12), 1030; https://doi.org/10.3390/rs8121030
Received: 11 September 2016 / Revised: 12 December 2016 / Accepted: 14 December 2016 / Published: 17 December 2016
Cited by 3 | PDF Full-text (8311 KB) | HTML Full-text | XML Full-text
Abstract
Building change detection is important for urban area monitoring, disaster assessment and updating geo-database. 3D information derived from image dense matching or airborne light detection and ranging (LiDAR) is very effective for building change detection. However, combining 3D data from different sources is
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Building change detection is important for urban area monitoring, disaster assessment and updating geo-database. 3D information derived from image dense matching or airborne light detection and ranging (LiDAR) is very effective for building change detection. However, combining 3D data from different sources is challenging, and so far few studies have focused on building change detection using both images and LiDAR data. This study proposes an automatic method to detect building changes in urban areas using aerial images and LiDAR data. First, dense image matching is carried out to obtain dense point clouds and then co-registered LiDAR point clouds using the iterative closest point (ICP) algorithm. The registered point clouds are further resampled to a raster DSM (Digital Surface Models). In a second step, height difference and grey-scale similarity are calculated as change indicators and the graph cuts method is employed to determine changes considering the contexture information. Finally, the detected results are refined by removing the non-building changes, in which a novel method based on variance of normal direction of LiDAR points is proposed to remove vegetated areas for positive building changes (newly building or taller) and nEGI (normalized Excessive Green Index) is used for negative building changes (demolish building or lower). To evaluate the proposed method, a test area covering approximately 2.1 km2 and consisting of many different types of buildings is used for the experiment. Results indicate 93% completeness with correctness of 90.2% for positive changes, while 94% completeness with correctness of 94.1% for negative changes, which demonstrate the promising performance of the proposed method. Full article
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
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Open AccessArticle Joint Time-Frequency Signal Processing Scheme in Forward Scattering Radar with a Rotational Transmitter
Remote Sens. 2016, 8(12), 1028; https://doi.org/10.3390/rs8121028
Received: 8 September 2016 / Revised: 2 December 2016 / Accepted: 2 December 2016 / Published: 17 December 2016
Cited by 2 | PDF Full-text (4580 KB) | HTML Full-text | XML Full-text
Abstract
This paper explores the concept of a Forward Scattering Radar (FSR) system with a rotational transmitter for target detection and localization. Most of the research and development in FSR used a fixed dedicated transmitter; therefore, the detection of stationary and slow moving target
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This paper explores the concept of a Forward Scattering Radar (FSR) system with a rotational transmitter for target detection and localization. Most of the research and development in FSR used a fixed dedicated transmitter; therefore, the detection of stationary and slow moving target is very difficult. By rotating the transmitter, the received signals at the receiver contain extra information carried by the Doppler due to the relative movement of the transmitter-target-receiver. Hence, rotating the transmitter enhances the detection capability especially for a stationary and slow-moving target. In addition, it increases the flexibility of the transmitter to control the signal direction, which broadens the coverage of FSR networks. In this paper, a novel signal processing for the new mode of FSR system based on the signal’s joint time-frequency is proposed and discussed. Additionally, the concept of the FSR system with the rotational transmitter is analyzed experimentally for the detection and localization of a stationary target, at very low speed and a low profile target crossing the FSR baseline. The system acts as a virtual fencing of a remote sensor for area monitoring. The experimental results show that the proposed mode with the new signal processing scheme can detect a human intruder. The potential applications for this system could be used for security and border surveillance, debris detection on an airport runway, ground aerial monitoring, intruder detection, etc. Full article
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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Open AccessArticle Two Component Decomposition of Dual Polarimetric HH/VV SAR Data: Case Study for the Tundra Environment of the Mackenzie Delta Region, Canada
Remote Sens. 2016, 8(12), 1027; https://doi.org/10.3390/rs8121027
Received: 11 July 2016 / Revised: 5 December 2016 / Accepted: 8 December 2016 / Published: 16 December 2016
Cited by 5 | PDF Full-text (5579 KB) | HTML Full-text | XML Full-text
Abstract
This study investigates a two component decomposition technique for HH/VV-polarized PolSAR (Polarimetric Synthetic Aperture Radar) data. The approach is a straight forward adaption of the Yamaguchi decomposition and decomposes the data into two scattering contributions: surface and double bounce under the assumption of
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This study investigates a two component decomposition technique for HH/VV-polarized PolSAR (Polarimetric Synthetic Aperture Radar) data. The approach is a straight forward adaption of the Yamaguchi decomposition and decomposes the data into two scattering contributions: surface and double bounce under the assumption of a negligible vegetation scattering component in Tundra environments. The dependencies between the features of this two and the classical three component Yamaguchi decomposition were investigated for Radarsat-2 (quad) and TerraSAR-X (HH/VV) data for the Mackenzie Delta Region, Canada. In situ data on land cover were used to derive the scattering characteristics and to analyze the correlation among the PolSAR features. The double bounce and surface scattering features of the two and three component scattering model (derived from pseudo-HH/VV- and quad-polarized data) showed similar scattering characteristics and positively correlated-R2 values of 0.60 (double bounce) and 0.88 (surface scattering) were observed. The presence of volume scattering led to differences between the features and these were minimized for land cover classes of low vegetation height that showed little volume scattering contribution. In terms of separability, the quad-polarized Radarsat-2 data offered the best separation of the examined tundra land cover types and will be best suited for the classification. This is anticipated as it represents the largest feature space of all tested ones. However; the classes “wetland” and “bare ground” showed clear positions in the feature spaces of the C- and X-Band HH/VV-polarized data and an accurate classification of these land cover types is promising. Among the possible dual-polarization modes of Radarsat-2 the HH/VV was found to be the favorable mode for the characterization of the aforementioned tundra land cover classes due to the coherent acquisition and the preserved co-pol. phase. Contrary, HH/HV-polarized and VV/VH-polarized data were found to be best suited for the characterization of mixed and shrub dominated tundra. Full article
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Open AccessArticle Dryland Vegetation Functional Response to Altered Rainfall Amounts and Variability Derived from Satellite Time Series Data
Remote Sens. 2016, 8(12), 1026; https://doi.org/10.3390/rs8121026
Received: 3 November 2016 / Revised: 28 November 2016 / Accepted: 8 December 2016 / Published: 16 December 2016
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Abstract
Vegetation productivity is an essential variable in ecosystem functioning. Vegetation dynamics of dryland ecosystems are most strongly determined by water availability and consequently by rainfall and there is a need to better understand how water limited ecosystems respond to altered rainfall amounts and
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Vegetation productivity is an essential variable in ecosystem functioning. Vegetation dynamics of dryland ecosystems are most strongly determined by water availability and consequently by rainfall and there is a need to better understand how water limited ecosystems respond to altered rainfall amounts and variability. This response is partly determined by the vegetation functional response to rainfall (β) approximated by the unit change in annual vegetation productivity per unit change in annual rainfall. Here, we show how this functional response from 1983 to 2011 is affected by below and above average rainfall in two arid to semi-arid subtropical regions in West Africa (WA) and South West Africa (SWA) differing in interannual variability of annual rainfall (higher in SWA, lower in WA). We used a novel approach, shifting linear regression models (SLRs), to estimate gridded time series of β. The SLRs ingest annual satellite based rainfall as the explanatory variable and annual satellite-derived vegetation productivity proxies (NDVI) as the response variable. Gridded β values form unimodal curves along gradients of mean annual precipitation in both regions. β is higher in SWA during periods of below average rainfall (compared to above average) for mean annual precipitation <600 mm. In WA, β is hardly affected by above or below average rainfall conditions. Results suggest that this higher β variability in SWA is related to the higher rainfall variability in this region. Vegetation type-specific β follows observed responses for each region along rainfall gradients leading to region-specific responses for each vegetation type. We conclude that higher interannual rainfall variability might favour a more dynamic vegetation response to rainfall. This in turn may enhance the capability of vegetation productivity of arid and semi-arid regions to better cope with periods of below average rainfall conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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