Remote Sensing

12744 KiB

Open AccessArticle

Blending Satellite Observed, Model Simulated, and in Situ Measured Soil Moisture over Tibetan Plateau

by Yijian Zeng, Zhongbo Su, Rogier Van der Velde, Lichun Wang, Kai Xu, Xing Wang and Jun Wen

Remote Sens. 2016, 8(3), 268; https://doi.org/10.3390/rs8030268 - 22 Mar 2016

Cited by 78 | Viewed by 8381

Abstract

The inter-comparison of different soil moisture (SM) products over the Tibetan Plateau (TP) reveals the inconsistency among different SM products, when compared to in situ measurement. It highlights the need to constrain the model simulated SM with the in situ measured data climatology. [...] Read more.

The inter-comparison of different soil moisture (SM) products over the Tibetan Plateau (TP) reveals the inconsistency among different SM products, when compared to in situ measurement. It highlights the need to constrain the model simulated SM with the in situ measured data climatology. In this study, the in situ soil moisture networks, combined with the classification of climate zones over the TP, were used to produce the in situ measured SM climatology at the plateau scale. The generated TP scale in situ SM climatology was then used to scale the model-simulated SM data, which was subsequently used to scale the SM satellite observations. The climatology-scaled satellite and model-simulated SM were then blended objectively, by applying the triple collocation and least squares method. The final blended SM can replicate the SM dynamics across different climatic zones, from sub-humid regions to semi-arid and arid regions over the TP. This demonstrates the need to constrain the model-simulated SM estimates with the in situ measurements before their further applications in scaling climatology of SM satellite products. Full article

(This article belongs to the Special Issue Land Surface Processes and Interactions—From HCMM to Sentinel Missions and beyond)

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12141 KiB

Open AccessArticle

Anatomy of Subsidence in Tianjin from Time Series InSAR

by Peng Liu, Qingquan Li, Zhenhong Li, Trevor Hoey, Guoxiang Liu, Chisheng Wang, Zhongwen Hu, Zhiwei Zhou and Andrew Singleton

Remote Sens. 2016, 8(3), 266; https://doi.org/10.3390/rs8030266 - 22 Mar 2016

Cited by 41 | Viewed by 8641

Abstract

Groundwater is a major source of fresh water in Tianjin Municipality, China. The average rate of groundwater extraction in this area for the last 20 years fluctuates between 0.6 and 0.8 billion cubic meters per year. As a result, significant subsidence has been [...] Read more.

Groundwater is a major source of fresh water in Tianjin Municipality, China. The average rate of groundwater extraction in this area for the last 20 years fluctuates between 0.6 and 0.8 billion cubic meters per year. As a result, significant subsidence has been observed in Tianjin. In this study, C-band Envisat (Environmental Satellite) ASAR (Advanced Synthetic Aperture Radar) images and L-band ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) data were employed to recover the Earth’s surface evolution during the period between 2007 and 2009 using InSAR time series techniques. Similar subsidence patterns can be observed in the overlapping area of the ASAR and PALSAR mean velocity maps with a maximum radar line of sight rate of ~170 mm·year⁻¹. The west subsidence is modeled for ground water volume change using Mogi source array. Geological control by major faults on the east subsidence is analyzed. Storage coefficient of the east subsidence is estimated by InSAR displacements and temporal pattern of water level changes. InSAR has proven a useful tool for subsidence monitoring and displacement interpretation associated with underground water usage. Full article

(This article belongs to the Special Issue Earth Observations for Geohazards)

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8131 KiB

Open AccessArticle

Examining Urban Impervious Surface Distribution and Its Dynamic Change in Hangzhou Metropolis

by Longwei Li, Dengsheng Lu and Wenhui Kuang

Remote Sens. 2016, 8(3), 265; https://doi.org/10.3390/rs8030265 - 22 Mar 2016

Cited by 54 | Viewed by 8464

Abstract

Analysis of urban distribution and its expansion using remote sensing data has received increasing attention in the past three decades, but little research has examined spatial patterns of urban distribution and expansion with buffer zones in different directions. This research selected Hangzhou metropolis [...] Read more.

Analysis of urban distribution and its expansion using remote sensing data has received increasing attention in the past three decades, but little research has examined spatial patterns of urban distribution and expansion with buffer zones in different directions. This research selected Hangzhou metropolis as a case study to analyze spatial patterns and dynamic changes based on time-series urban impervious surface area (ISA) datasets. ISA was developed from Landsat imagery between 1991 and 2014 using a hybrid approach consisting of linear spectral mixture analysis, decision tree classifiers, and post-processing. The spatial patterns of ISA distribution and its dynamic changes in eight directions—east, southeast, south, southwest, west, northwest, north, and northeast—at the temporal scale were analyzed with a buffer zone-based approach. This research indicated that ISA can be extracted from Landsat imagery with both producer and user accuracies of over 90%. ISA in Hangzhou metropolis increased from 146 km² in 1991 to 868 km² in 2014. Annual ISA growth rates were between 15.6 km² and 48.8 km² with the lowest growth rate in 1994–2000 and the highest growth rate in 2005–2010. Urban ISA increase before 2000 was mainly due to infilling within the urban landscape, and, after 2005, due to urban expansion in the urban-rural interfaces. Urban expansion in this study area has different characteristics in various directions that are influenced by topographic factors and urban development policies. Full article

(This article belongs to the Special Issue Monitoring of Land Changes)

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8699 KiB

Open AccessArticle

Novel Approach to Unsupervised Change Detection Based on a Robust Semi-Supervised FCM Clustering Algorithm

by Pan Shao, Wenzhong Shi, Pengfei He, Ming Hao and Xiaokang Zhang

Remote Sens. 2016, 8(3), 264; https://doi.org/10.3390/rs8030264 - 22 Mar 2016

Cited by 61 | Viewed by 8513

Abstract

This study presents a novel approach for unsupervised change detection in multitemporal remotely sensed images. This method addresses the problem of the analysis of the difference image by proposing a novel and robust semi-supervised fuzzy C-means (RSFCM) clustering algorithm. The advantage of the [...] Read more.

This study presents a novel approach for unsupervised change detection in multitemporal remotely sensed images. This method addresses the problem of the analysis of the difference image by proposing a novel and robust semi-supervised fuzzy C-means (RSFCM) clustering algorithm. The advantage of the RSFCM is to further introduce the pseudolabels from the difference image compared with the existing change detection methods; these methods, mainly use difference intensity levels and spatial context. First, the patterns with a high probability of belonging to the changed or unchanged class are identified by selectively thresholding the difference image histogram. Second, the pseudolabels of these nearly certain pixel-patterns are jointly exploited with the intensity levels and spatial information in the properly defined RSFCM classifier in order to discriminate the changed pixels from the unchanged pixels. Specifically, labeling knowledge is used to guide the RSFCM clustering process to enhance the change information and obtain a more accurate membership; information on spatial context helps to lower the effect of noise and outliers by modifying the membership. RSFCM can detect more changes and provide noise immunity by the synergistic exploitation of pseudolabels and spatial context. The two main contributions of this study are as follows: (1) it proposes the idea of combining the three information types from the difference image, namely, (a) intensity levels, (b) labels, and (c) spatial context; and (2) it develops the novel RSFCM algorithm for image segmentation and forms the proposed change detection framework. The proposed method is effective and efficient for change detection as confirmed by six experimental results of this study. Full article

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2950 KiB

Open AccessArticle

A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation

by Martin Claverie, Jessica L. Matthews, Eric F. Vermote and Christopher O. Justice

Remote Sens. 2016, 8(3), 263; https://doi.org/10.3390/rs8030263 - 22 Mar 2016

Cited by 117 | Viewed by 14000

Abstract

In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers [...] Read more.

In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers daily temporal resolution, an improvement over previous products. This climate data record is based on a carefully calibrated and corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitable for climate studies. It spans from mid-1981 to the present. Further, this operational dataset is available in near real-time allowing use for monitoring purposes. The algorithm relies on artificial neural networks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparison with MODIS products and in situ data show the dataset is consistent and reliable with overall uncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect is observed in the broadleaf forest biomes with high LAI (>4.5) and FAPAR (>0.8) values. Full article

(This article belongs to the Special Issue Satellite Climate Data Records and Applications)

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13199 KiB

Open AccessArticle

A Geographically and Temporally Weighted Regression Model for Ground-Level PM_2.5 Estimation from Satellite-Derived 500 m Resolution AOD

by Yang Bai, Lixin Wu, Kai Qin, Yufeng Zhang, Yangyang Shen and Yuan Zhou

Remote Sens. 2016, 8(3), 262; https://doi.org/10.3390/rs8030262 - 22 Mar 2016

Cited by 136 | Viewed by 12211

Abstract

Regional haze episodes have occurred frequently in eastern China over the past decades. As a critical indicator to evaluate air quality, the mass concentration of ambient fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM_2.5) is involved in many [...] Read more.

Regional haze episodes have occurred frequently in eastern China over the past decades. As a critical indicator to evaluate air quality, the mass concentration of ambient fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM_2.5) is involved in many studies. To overcome the limitations of ground measurements on PM_2.5 concentration, which is featured in disperse representation and coarse coverage, many statistical models were developed to depict the relationship between ground-level PM_2.5 and satellite-derived aerosol optical depth (AOD). However, the current satellite-derived AOD products and statistical models on PM_2.5–AOD are insufficient to investigate PM_2.5 characteristics at the urban scale, in that spatial resolution is crucial to identify the relationship between PM_2.5 and anthropogenic activities. This paper presents a geographically and temporally weighted regression (GTWR) model to generate ground-level PM_2.5 concentrations from satellite-derived 500 m AOD. The GTWR model incorporates the SARA (simplified high resolution MODIS aerosol retrieval algorithm) AOD product with meteorological variables, including planetary boundary layer height (PBLH), relative humidity (RH), wind speed (WS), and temperature (TEMP) extracted from WRF (weather research and forecasting) assimilation to depict the spatio-temporal dynamics in the PM_2.5–AOD relationship. The estimated ground-level PM_2.5 concentration has 500 m resolution at the MODIS satellite’s overpass moments twice a day, which can be used for air quality monitoring and haze tracking at the urban and regional scale. To test the performance of the GTWR model, a case study was carried out in a region covering the adjacent parts of Jiangsu, Shandong, Henan, and Anhui provinces in central China. A cross validation was done to evaluate the performance of the GTWR model. Compared with OLS, GWR, and TWR models, the GTWR model obtained the highest value of coefficient of determination (R²) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE). Full article

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2950 KiB

Open AccessArticle

Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map

by Myroslava Lesiv, Elena Moltchanova, Dmitry Schepaschenko, Linda See, Anatoly Shvidenko, Alexis Comber and Steffen Fritz

Remote Sens. 2016, 8(3), 261; https://doi.org/10.3390/rs8030261 - 22 Mar 2016

Cited by 39 | Viewed by 8353

Abstract

Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived [...] Read more.

Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived from remote sensing. In the past, many different methods have been applied, without regard to their relative merit. In this study, we compared some of the most commonly-used methods to develop a hybrid forest cover map by combining available land cover/forest products and crowdsourced data on forest cover obtained through the Geo-Wiki project. The methods include: nearest neighbour, naive Bayes, logistic regression and geographically-weighted logistic regression (GWR), as well as classification and regression trees (CART). We ran the comparison experiments using two data types: presence/absence of forest in a grid cell; percentage of forest cover in a grid cell. In general, there was little difference between the methods. However, GWR was found to perform better than the other tested methods in areas with high disagreement between the inputs. Full article

(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)

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3321 KiB

Open AccessArticle

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

by Enrica Nestola, Carlo Calfapietra, Craig A. Emmerton, Christopher Y.S. Wong, Donnette R. Thayer and John A. Gamon

Remote Sens. 2016, 8(3), 260; https://doi.org/10.3390/rs8030260 - 19 Mar 2016

Cited by 31 | Viewed by 9514

Abstract

This study evaluated the seasonal productivity of a prairie grassland (Mattheis Ranch, in Alberta, Canada) using a combination of remote sensing, eddy covariance, and field sampling collected in 2012–2013. A primary objective was to evaluate different ways of parameterizing the light-use efficiency (LUE) [...] Read more.

This study evaluated the seasonal productivity of a prairie grassland (Mattheis Ranch, in Alberta, Canada) using a combination of remote sensing, eddy covariance, and field sampling collected in 2012–2013. A primary objective was to evaluate different ways of parameterizing the light-use efficiency (LUE) model for assessing net ecosystem fluxes at two sites with contrasting productivity. Three variations on the NDVI (Normalized Difference Vegetation Index), differing by formula and footprint, were derived: (1) a narrow-band NDVI (NDVI_680,800, derived from mobile field spectrometer readings); (2) a broad-band proxy NDVI (derived from an automated optical phenology station consisting of broad-band radiometers); and (3) a satellite NDVI (derived from MODIS AQUA and TERRA sensors). Harvested biomass, net CO₂ flux, and NDVI values were compared to provide a basis for assessing seasonal ecosystem productivity and gap filling of tower flux data. All three NDVIs provided good estimates of dry green biomass and were able to clearly show seasonal changes in vegetation growth and senescence, confirming their utility as metrics of productivity. When relating fluxes and optical measurements, temporal aggregation periods were considered to determine the impact of aggregation on model accuracy. NDVI values from the different methods were also calibrated against fAPAR_green (the fraction of photosynthetically active radiation absorbed by green vegetation) values to parameterize the APAR_green (absorbed PAR) term of the LUE (light use efficiency) model for comparison with measured fluxes. While efficiency was assumed to be constant in the model, this analysis revealed hysteresis in the seasonal relationships between fluxes and optical measurements, suggesting a slight change in efficiency between the first and second half of the growing season. Consequently, the best results were obtained by splitting the data into two stages, a greening phase and a senescence phase, and applying separate fits to these two periods. By incorporating the dynamic irradiance regime, the model based on APAR_green rather than NDVI best captured the high variability of the fluxes and provided a more realistic depiction of missing fluxes. The strong correlations between these optical measurements and independently measured fluxes demonstrate the utility of integrating optical with flux measurements for gap filling, and provide a foundation for using remote sensing to extrapolate from the flux tower to larger regions (upscaling) for regional analysis of net carbon uptake by grassland ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)

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5330 KiB

Open AccessArticle

Evaluation of an Airborne Remote Sensing Platform Consisting of Two Consumer-Grade Cameras for Crop Identification

by Jian Zhang, Chenghai Yang, Huaibo Song, Wesley Clint Hoffmann, Dongyan Zhang and Guozhong Zhang

Remote Sens. 2016, 8(3), 257; https://doi.org/10.3390/rs8030257 - 18 Mar 2016

Cited by 50 | Viewed by 10211

Abstract

Remote sensing systems based on consumer-grade cameras have been increasingly used in scientific research and remote sensing applications because of their low cost and ease of use. However, the performance of consumer-grade cameras for practical applications has not been well documented in related [...] Read more.

Remote sensing systems based on consumer-grade cameras have been increasingly used in scientific research and remote sensing applications because of their low cost and ease of use. However, the performance of consumer-grade cameras for practical applications has not been well documented in related studies. The objective of this research was to apply three commonly-used classification methods (unsupervised, supervised, and object-based) to three-band imagery with RGB (red, green, and blue bands) and four-band imagery with RGB and near-infrared (NIR) bands to evaluate the performance of a dual-camera imaging system for crop identification. Airborne images were acquired from a cropping area in Texas and mosaicked and georeferenced. The mosaicked imagery was classified using the three classification methods to assess the usefulness of NIR imagery for crop identification and to evaluate performance differences between the object-based and pixel-based methods. Image classification and accuracy assessment showed that the additional NIR band imagery improved crop classification accuracy over the RGB imagery and that the object-based method achieved better results with additional non-spectral image features. The results from this study indicate that the airborne imaging system based on two consumer-grade cameras used in this study can be useful for crop identification and other agricultural applications. Full article

(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)

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9315 KiB

Open AccessArticle

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

by Huai Yu, Wen Yang, Gui-Song Xia and Gang Liu

Remote Sens. 2016, 8(3), 259; https://doi.org/10.3390/rs8030259 - 17 Mar 2016

Cited by 64 | Viewed by 11580

Abstract

Scene classification plays an important role in understanding high-resolution satellite (HRS) remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been [...] Read more.

Scene classification plays an important role in understanding high-resolution satellite (HRS) remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been reported in the literature. One branch of research combines multiple complementary features based on various aspects such as texture, color and structure. Two methods are commonly used to combine these features: early fusion and late fusion. In this paper, we propose combining the two methods under a tree of regions and present a new descriptor to encode color, texture and structure features using a hierarchical structure-Color Binary Partition Tree (CBPT), which we call the CTS descriptor. Specifically, we first build the hierarchical representation of HRS imagery using the CBPT. Then we quantize the texture and color features of dense regions. Next, we analyze and extract the co-occurrence patterns of regions based on the hierarchical structure. Finally, we encode local descriptors to obtain the final CTS descriptor and test its discriminative capability using object categorization and scene classification with HRS images. The proposed descriptor contains the spectral, textural and structural information of the HRS imagery and is also robust to changes in illuminant color, scale, orientation and contrast. The experimental results demonstrate that the proposed CTS descriptor achieves competitive classification results compared with state-of-the-art algorithms. Full article

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Open AccessArticle

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

by Syed Ali Naqi Gilani, Mohammad Awrangjeb and Guojun Lu

Remote Sens. 2016, 8(3), 258; https://doi.org/10.3390/rs8030258 - 17 Mar 2016

Cited by 81 | Viewed by 10941

Abstract

The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on [...] Read more.

The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on object’s size, height, area, and orientation are generally benefited which adversely affect the detection performance. Often the buildings either small in size, under shadows or partly occluded are ousted during elimination of superfluous objects. To overcome the limitations, a methodology is developed to extract and regularise the buildings using features from point cloud and orthoimagery. The building delineation process is carried out by identifying the candidate building regions and segmenting them into grids. Vegetation elimination, building detection and extraction of their partially occluded parts are achieved by synthesising the point cloud and image data. Finally, the detected buildings are regularised by exploiting the image lines in the building regularisation process. Detection and regularisation processes have been evaluated using the ISPRS benchmark and four Australian data sets which differ in point density (1 to 29 points/m²), building sizes, shadows, terrain, and vegetation. Results indicate that there is 83% to 93% per-area completeness with the correctness of above 95%, demonstrating the robustness of the approach. The absence of over- and many-to-many segmentation errors in the ISPRS data set indicate that the technique has higher per-object accuracy. While compared with six existing similar methods, the proposed detection and regularisation approach performs significantly better on more complex data sets (Australian) in contrast to the ISPRS benchmark, where it does better or equal to the counterparts. Full article

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Open AccessArticle

Ash Decline Assessment in Emerald Ash Borer Infested Natural Forests Using High Spatial Resolution Images

by Justin Murfitt, Yuhong He, Jian Yang, Amy Mui and Kevin De Mille

Remote Sens. 2016, 8(3), 256; https://doi.org/10.3390/rs8030256 - 17 Mar 2016

Cited by 30 | Viewed by 11403

Abstract

The invasive emerald ash borer (EAB, Agrilus planipennis Fairmaire) infects and eventually kills endemic ash trees and is currently spreading across the Great Lakes region of North America. The need for early detection of EAB infestation is critical to managing the spread of [...] Read more.

The invasive emerald ash borer (EAB, Agrilus planipennis Fairmaire) infects and eventually kills endemic ash trees and is currently spreading across the Great Lakes region of North America. The need for early detection of EAB infestation is critical to managing the spread of this pest. Using WorldView-2 (WV2) imagery, the goal of this study was to establish a remote sensing-based method for mapping ash trees undergoing various infestation stages. Based on field data collected in Southeastern Ontario, Canada, an ash health score with an interval scale ranging from 0 to 10 was established and further related to multiple spectral indices. The WV2 image was segmented using multi-band watershed and multiresolution algorithms to identify individual tree crowns, with watershed achieving higher segmentation accuracy. Ash trees were classified using the random forest classifier, resulting in a user’s accuracy of 67.6% and a producer’s accuracy of 71.4% when watershed segmentation was utilized. The best ash health score-spectral index model was then applied to the ash tree crowns to map the ash health for the entire area. The ash health prediction map, with an overall accuracy of 70%, suggests that remote sensing has potential to provide a semi-automated and large-scale monitoring of EAB infestation. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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Open AccessArticle

Remote Sensing of Deformation of a High Concrete-Faced Rockfill Dam Using InSAR: A Study of the Shuibuya Dam, China

by Wei Zhou, Shaolin Li, Zhiwei Zhou and Xiaolin Chang

Remote Sens. 2016, 8(3), 255; https://doi.org/10.3390/rs8030255 - 17 Mar 2016

Cited by 42 | Viewed by 8362

Abstract

Settlement is one of the most important deformation characteristics of high concrete faced rockfill dams (CFRDs, >100 m). High CFRDs safety would pose a great threat to the security of people’s lives and property downstream if this kind of deformation were not to [...] Read more.

Settlement is one of the most important deformation characteristics of high concrete faced rockfill dams (CFRDs, >100 m). High CFRDs safety would pose a great threat to the security of people’s lives and property downstream if this kind of deformation were not to be measured correctly, as traditional monitoring approaches have limitations in terms of durability, coverage, and efficiency. It has become urgent to develop new monitoring techniques to complement or replace traditional monitoring approaches for monitoring the safety and operation status of high CFRDs. This study examines the Shuibuya Dam (up to 233.5 m in height) in China, which is currently the highest CFRD in the world. We used space-borne Interferometric Synthetic Aperture Radar (InSAR) time series to monitor the surface deformation of the Shuibuya Dam. Twenty-one ALOS PALSAR images that span the period from 28 February 2007 to 11 March 2011 were used to map the spatial and temporal deformation of the dam. A high correlation of 0.93 between the InSAR and the in-situ monitoring results confirmed the reliability of the InSAR method; the deformation history derived from InSAR is also consistent with the in-situ settlement monitoring system. In addition, the InSAR results allow continuous investigation of dam deformation over a wide area that includes the entire dam surface as well as the surrounding area, offering a clear picture continuously of the dam deformation. Full article

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Open AccessArticle

Spectral Dependent Degradation of the Solar Diffuser on Suomi-NPP VIIRS Due to Surface Roughness-Induced Rayleigh Scattering

by Xi Shao, Changyong Cao and Tung-Chang Liu

Remote Sens. 2016, 8(3), 254; https://doi.org/10.3390/rs8030254 - 17 Mar 2016

Cited by 34 | Viewed by 8183

Abstract

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar Orbiting Partnership (SNPP) uses a solar diffuser (SD) as its radiometric calibrator for the reflective solar band calibration. The SD is made of Spectralon™ (one type of fluoropolymer) and was chosen because [...] Read more.

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar Orbiting Partnership (SNPP) uses a solar diffuser (SD) as its radiometric calibrator for the reflective solar band calibration. The SD is made of Spectralon™ (one type of fluoropolymer) and was chosen because of its controlled reflectance in the Visible/Near-Infrared/Shortwave-Infrared region and its near-Lambertian reflectance property. On-orbit changes in VIIRS SD reflectance as monitored by the Solar Diffuser Stability Monitor showed faster degradation of SD reflectance for 0.4 to 0.6 µm channels than the longer wavelength channels. Analysis of VIIRS SD reflectance data show that the spectral dependent degradation of SD reflectance in short wavelength can be explained with a SD Surface Roughness (length scale << wavelength) based Rayleigh Scattering (SRRS) model due to exposure to solar UV radiation and energetic particles. The characteristic length parameter of the SD surface roughness is derived from the long term reflectance data of the VIIRS SD and it changes at approximately the tens of nanometers level over the operational period of VIIRS. This estimated roughness length scale is consistent with the experimental result from radiation exposure of a fluoropolymer sample and validates the applicability of the Rayleigh scattering-based model. The model is also applicable to explaining the spectral dependent degradation of the SDs on other satellites. This novel approach allows us to better understand the physical processes of the SD degradation, and is complementary to previous mathematics based models. Full article

(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)

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Open AccessArticle

Estimating Evapotranspiration of an Apple Orchard Using a Remote Sensing-Based Soil Water Balance

by Magali Odi-Lara, Isidro Campos, Christopher M. U. Neale, Samuel Ortega-Farías, Carlos Poblete-Echeverría, Claudio Balbontín and Alfonso Calera

Remote Sens. 2016, 8(3), 253; https://doi.org/10.3390/rs8030253 - 17 Mar 2016

Cited by 66 | Viewed by 10539

Abstract

The main goal of this research was to estimate the actual evapotranspiration (ET_c) of a drip-irrigated apple orchard located in the semi-arid region of Talca Valley (Chile) using a remote sensing-based soil water balance model. The methodology to estimate ET_c [...] Read more.

The main goal of this research was to estimate the actual evapotranspiration (ET_c) of a drip-irrigated apple orchard located in the semi-arid region of Talca Valley (Chile) using a remote sensing-based soil water balance model. The methodology to estimate ET_c is a modified version of the Food and Agriculture Organization of the United Nations (FAO) dual crop coefficient approach, in which the basal crop coefficient (K_cb) was derived from the soil adjusted vegetation index (SAVI) calculated from satellite images and incorporated into a daily soil water balance in the root zone. A linear relationship between the K_cb and SAVI was developed for the apple orchard K_cb = 1.82·SAVI − 0.07 (R² = 0.95). The methodology was applied during two growing seasons (2010–2011 and 2012–2013), and ET_c was evaluated using latent heat fluxes (LE) from an eddy covariance system. The results indicate that the remote sensing-based soil water balance estimated ET_c reasonably well over two growing seasons. The root mean square error (RMSE) between the measured and simulated ET_c values during 2010–2011 and 2012–2013 were, respectively, 0.78 and 0.74 mm·day⁻¹, which mean a relative error of 25%. The index of agreement (d) values were, respectively, 0.73 and 0.90. In addition, the weekly ET_c showed better agreement. The proposed methodology could be considered as a useful tool for scheduling irrigation and driving the estimation of water requirements over large areas for apple orchards. Full article

(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)

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Open AccessArticle

Assessing Earthquake-Induced Tree Mortality in Temperate Forest Ecosystems: A Case Study from Wenchuan, China

by Hongcheng Zeng, Tao Lu, Hillary Jenkins, Robinson I. Negrón-Juárez and Jiceng Xu

Remote Sens. 2016, 8(3), 252; https://doi.org/10.3390/rs8030252 - 17 Mar 2016

Cited by 6 | Viewed by 6817

Abstract

Earthquakes can produce significant tree mortality, and consequently affect regional carbon dynamics. Unfortunately, detailed studies quantifying the influence of earthquake on forest mortality are currently rare. The committed forest biomass carbon loss associated with the 2008 Wenchuan earthquake in China is assessed by [...] Read more.

Earthquakes can produce significant tree mortality, and consequently affect regional carbon dynamics. Unfortunately, detailed studies quantifying the influence of earthquake on forest mortality are currently rare. The committed forest biomass carbon loss associated with the 2008 Wenchuan earthquake in China is assessed by a synthetic approach in this study that integrated field investigation, remote sensing analysis, empirical models and Monte Carlo simulation. The newly developed approach significantly improved the forest disturbance evaluation by quantitatively defining the earthquake impact boundary and detailed field survey to validate the mortality models. Based on our approach, a total biomass carbon of 10.9 Tg∙C was lost in Wenchuan earthquake, which offset 0.23% of the living biomass carbon stock in Chinese forests. Tree mortality was highly clustered at epicenter, and declined rapidly with distance away from the fault zone. It is suggested that earthquakes represent a significant driver to forest carbon dynamics, and the earthquake-induced biomass carbon loss should be included in estimating forest carbon budgets. Full article

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Open AccessArticle

Correction of Incidence Angle and Distance Effects on TLS Intensity Data Based on Reference Targets

by Kai Tan and Xiaojun Cheng

Remote Sens. 2016, 8(3), 251; https://doi.org/10.3390/rs8030251 - 16 Mar 2016

Cited by 79 | Viewed by 9491

Abstract

The original intensity value recorded by terrestrial laser scanners is influenced by multiple variables, among which incidence angle and distance play a crucial and dominant role. Further studies on incidence angle and distance effects are required to improve the accuracy of currently available [...] Read more.

The original intensity value recorded by terrestrial laser scanners is influenced by multiple variables, among which incidence angle and distance play a crucial and dominant role. Further studies on incidence angle and distance effects are required to improve the accuracy of currently available methods and to implement these methods in practical applications. In this study, the effects of incidence angle and distance on intensity data of the Faro Focus^3D 120 terrestrial laser scanner are investigated. A new method is proposed to eliminate the incidence angle and distance effects. The proposed method is based on the linear interpolation of the intensity values of reference targets previously scanned at various incidence angles and distances. Compared with existing methods, a significant advantage of the proposed method is that estimating the specific function forms of incidence angle versus intensity and distance versus intensity is no longer necessary; these are canceled out when the scanned and reference targets are measured at the same incidence angle and distance. Results imply that the proposed method has high accuracy and simplicity in eliminating incidence angle and distance effects and can significantly reduce the intensity variations caused by these effects on homogeneous surfaces. Full article

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Open AccessArticle

Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery

by Ruyi Feng, Yanfei Zhong, Yunyun Wu, Da He, Xiong Xu and Liangpei Zhang

Remote Sens. 2016, 8(3), 250; https://doi.org/10.3390/rs8030250 - 16 Mar 2016

Cited by 22 | Viewed by 6973

Abstract

Subpixel mapping is a method of enhancing the spatial resolution of images, which involves dividing a mixed pixel into subpixels and assigning each subpixel to a definite land-cover class. Traditionally, subpixel mapping is based on the assumption of spatial dependence, and the spatial [...] Read more.

Subpixel mapping is a method of enhancing the spatial resolution of images, which involves dividing a mixed pixel into subpixels and assigning each subpixel to a definite land-cover class. Traditionally, subpixel mapping is based on the assumption of spatial dependence, and the spatial correlation information among pixels and subpixels is considered in the prediction of the spatial locations of land-cover classes within the mixed pixels. In this paper, a novel subpixel mapping method for hyperspectral remote sensing imagery based on a nonlocal method, namely nonlocal total variation subpixel mapping (NLTVSM), is proposed to use the nonlocal self-similarity prior to improve the performance of the subpixel mapping task. Differing from the existing spatial regularization subpixel mapping technique, in NLTVSM, the nonlocal total variation is used as a spatial regularizer to exploit the similar patterns and structures in the image. In this way, the proposed method can obtain an optimal subpixel mapping result and accuracy by considering the nonlocal spatial information. Compared with the classical and state-of-the-art subpixel mapping approaches, the experimental results using a simulated hyperspectral image, two synthetic hyperspectral remote sensing images, and a real hyperspectral image confirm that the proposed algorithm can obtain better results in both visual and quantitative evaluations. Full article

(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)

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Open AccessArticle

Investigation and Mitigation of the Crosstalk Effect in Terra MODIS Band 30

by Junqiang Sun, Sriharsha Madhavan and Menghua Wang

Remote Sens. 2016, 8(3), 249; https://doi.org/10.3390/rs8030249 - 16 Mar 2016

Cited by 17 | Viewed by 4846

Abstract

It has been previously reported that thermal emissive bands (TEB) 27–29 in the Terra (T-) MODerate resolution Imaging Spectroradiometer (MODIS) have been significantly affected by electronic crosstalk. Successful linear theory of the electronic crosstalk effect was formulated, and it successfully characterized the effect [...] Read more.

It has been previously reported that thermal emissive bands (TEB) 27–29 in the Terra (T-) MODerate resolution Imaging Spectroradiometer (MODIS) have been significantly affected by electronic crosstalk. Successful linear theory of the electronic crosstalk effect was formulated, and it successfully characterized the effect via the use of lunar observations as viable inputs. In this paper, we report the successful characterization and mitigation of the electronic crosstalk for T-MODIS band 30 using the same characterization methodology. Though the phenomena of the electronic crosstalk have been well documented in previous works, the novel for band 30 is the need to also apply electronic crosstalk correction to the non-linear term in the calibration coefficient. The lack of this necessity in early works thus demonstrates the distinct difference of band 30, and, yet, in the same instances, the overall correctness of the characterization formulation. For proper result, the crosstalk correction is applied to the band 30 calibration coefficients including the non-linear term, and also to the earth view radiance. We demonstrate that the crosstalk correction achieves a long-term radiometric correction of approximately 1.5 K for desert targets and 1.0 K for ocean scenes. Significant striping removal in the Baja Peninsula earth view imagery is also demonstrated due to the successful amelioration of detector differences caused by the crosstalk effect. Similarly significant improvement in detector difference is shown for the selected ocean and desert targets over the entire mission history. In particular, band 30 detector 8, which has been flagged as “out of family” is restored by the removal of the crosstalk contamination. With the correction achieved, the science applications based on band 30 can be significantly improved. The linear formulation, the characterization methodology, and the crosstalk effect correction coefficients derived using lunar observations are once again demonstrated to work remarkably well. Full article

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Open AccessArticle

A Two-Source Model for Estimating Evaporative Fraction (TMEF) Coupling Priestley-Taylor Formula and Two-Stage Trapezoid

by Hao Sun

Remote Sens. 2016, 8(3), 248; https://doi.org/10.3390/rs8030248 - 16 Mar 2016

Cited by 25 | Viewed by 6288

Abstract

Remotely sensed land surface temperature and fractional vegetation coverage (LST/FVC) space has been widely used in modeling and partitioning land surface evaporative fraction (EF) which is important in managing water resources. However, most of such models are based on conventional trapezoid and simply [...] Read more.

Remotely sensed land surface temperature and fractional vegetation coverage (LST/FVC) space has been widely used in modeling and partitioning land surface evaporative fraction (EF) which is important in managing water resources. However, most of such models are based on conventional trapezoid and simply determine the wet edge as air temperature (T_a) or the lowest LST value in an image. We develop a new Two-source Model for estimating EF (TMEF) based on a two-stage trapezoid coupling with an extension of the Priestly-Taylor formula. Latent heat flux on the wet edge is calculated with the Priestly-Taylor formula, whereas that on the dry edge is set to 0. The wet and dry edges are then determined by solving radiation budget and energy balance equations. The model was evaluated by comparing with other two models that based on conventional trapezoid (i.e., the Two-source Trapezoid Model for Evapotranspiration (TTME) and a One-source Trapezoid model for EF (OTEF)) in how well they simulate and partition EF using MODIS products and field observations from HiWATER-MUSOEXE in 2012. Results show that the TMEF outperforms the other two models, where EF mean absolute relative deviations are 9.57% (TMEF), 15.03% (TTME), and 30.49% (OTEF). Full article

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Open AccessArticle

LiDAR-Based Solar Mapping for Distributed Solar Plant Design and Grid Integration in San Antonio, Texas

by Tuan B. Le, Danial Kholdi, Hongjie Xie, Bing Dong and Rolando E. Vega

Remote Sens. 2016, 8(3), 247; https://doi.org/10.3390/rs8030247 - 16 Mar 2016

Cited by 11 | Viewed by 9083

Abstract

This study represents advancements in the state-of-the-art of the solar energy industry by leveraging LiDAR-based building characterization for city-wide, distributed solar photovoltaics, solar maps, highlighting the distribution of solar energy across the city of San Antonio. A methodology is implemented to systematically derive [...] Read more.

This study represents advancements in the state-of-the-art of the solar energy industry by leveraging LiDAR-based building characterization for city-wide, distributed solar photovoltaics, solar maps, highlighting the distribution of solar energy across the city of San Antonio. A methodology is implemented to systematically derive the tilt and azimuth angles of each rooftop and to quantify solar direct, diffuse, and global horizontal irradiance for hundreds of buildings in a LiDAR tile scale, by using already established methodologies that are typically only applied to a single location or building rooftop. The methodology enables the formulation of typical meteorological data, measured or forecasted time series of irradiances over distributed assets. A new concept on the subject of distributed solar plant (DSP) design is also introduced, by using the building rooftop tilt and azimuth angles, to strategically optimize the use and adoption of solar incentives according to the grid age and its vulnerabilities to solar variability in the neighborhoods. The method presented here shows that on an hourly basis DSP design could provide a 5% and 9% of net load capacity support per hour in the afternoon and morning times, respectively. Our results show that standard building rooftop tilt angles in the south Texas region has significant impact on the total amount of the energy over the course of a day, though its impact on the shapes of the daily energy profile is relatively insignificant when compared to the azimuth angle. Building surfaces’ azimuth angle is the most important factor to determine the shape of daily energy profile and its peak location within a day. The methodology developed in this study can be employed to study the potential solar energy in other regions and to match the design of distributed solar plants to the capacity needs on specified distribution grids. Full article

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Open AccessArticle

Automated Extraction and Mapping for Desert Wadis from Landsat Imagery in Arid West Asia

by Yongxue Liu, Xiaoyu Chen, Yuhao Yang, Chao Sun and Siyu Zhang

Remote Sens. 2016, 8(3), 246; https://doi.org/10.3390/rs8030246 - 16 Mar 2016

Cited by 8 | Viewed by 7224

Abstract

Wadis, ephemeral dry rivers in arid desert regions that contain water in the rainy season, are often manifested as braided linear channels and are of vital importance for local hydrological environments and regional hydrological management. Conventional methods for effectively delineating wadis from heterogeneous [...] Read more.

Wadis, ephemeral dry rivers in arid desert regions that contain water in the rainy season, are often manifested as braided linear channels and are of vital importance for local hydrological environments and regional hydrological management. Conventional methods for effectively delineating wadis from heterogeneous backgrounds are limited for the following reasons: (1) the occurrence of numerous morphological irregularities which disqualify methods based on physical shape; (2) inconspicuous spectral contrast with backgrounds, resulting in frequent false alarms; and (3) the extreme complexity of wadi systems, with numerous tiny tributaries characterized by spectral anisotropy, resulting in a conflict between global and local accuracy. To overcome these difficulties, an automated method for extracting wadis (AMEW) from Landsat-8 Operational Land Imagery (OLI) was developed in order to take advantage of the complementarity between Water Indices (WIs), which is a technique of mathematically combining different bands to enhance water bodies and suppress backgrounds, and image processing technologies in the morphological field involving multi-scale Gaussian matched filtering and a local adaptive threshold segmentation. Evaluation of the AMEW was carried out in representative areas deliberately selected from Jordan, SW Arabian Peninsula in order to ensure a rigorous assessment. Experimental results indicate that the AMEW achieved considerably higher accuracy than other effective extraction methods in terms of visual inspection and statistical comparison, with an overall accuracy of up to 95.05% for the entire area. In addition, the AMEW (based on the New Water Index (NWI)) achieved higher accuracy than other methods (the maximum likelihood classifier and the support vector machine classifier) used for bulk wadi extraction. Full article

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Open AccessArticle

Potential of High Spatial and Temporal Ocean Color Satellite Data to Study the Dynamics of Suspended Particles in a Micro-Tidal River Plume

by Anouck Ody, David Doxaran, Quinten Vanhellemont, Bouchra Nechad, Stefani Novoa, Gaël Many, François Bourrin, Romaric Verney, Ivane Pairaud and Bernard Gentili

Remote Sens. 2016, 8(3), 245; https://doi.org/10.3390/rs8030245 - 16 Mar 2016

Cited by 58 | Viewed by 9200

Abstract

Ocean color satellite sensors are powerful tools to study and monitor the dynamics of suspended particulate matter (SPM) discharged by rivers in coastal waters. In this study, we test the capabilities of Landsat-8/Operational Land Imager (OLI), AQUA&TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) and MSG-3/Spinning [...] Read more.

Ocean color satellite sensors are powerful tools to study and monitor the dynamics of suspended particulate matter (SPM) discharged by rivers in coastal waters. In this study, we test the capabilities of Landsat-8/Operational Land Imager (OLI), AQUA&TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) and MSG-3/Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensors in terms of spectral, spatial and temporal resolutions to (i) estimate the seawater reflectance signal and then SPM concentrations and (ii) monitor the dynamics of SPM in the Rhône River plume characterized by moderately turbid surface waters in a micro-tidal sea. Consistent remote-sensing reflectance (Rrs) values are retrieved in the red spectral bands of these four satellite sensors (median relative difference less than ~16% in turbid waters). By applying a regional algorithm developed from in situ data, these Rrs are used to estimate SPM concentrations in the Rhône river plume. The spatial resolution of OLI provides a detailed mapping of the SPM concentration from the downstream part of the river itself to the plume offshore limits with well defined small-scale turbidity features. Despite the low temporal resolution of OLI, this should allow to better understand the transport of terrestrial particles from rivers to the coastal ocean. These details are partly lost using MODIS coarser resolutions data but SPM concentration estimations are consistent, with an accuracy of about 1 to 3 g·m⁻³ in the river mouth and plume for spatial resolutions from 250 m to 1 km. The MODIS temporal resolution (2 images per day) allows to capture the daily to monthly dynamics of the river plume. However, despite its micro-tidal environment, the Rhône River plume shows significant short-term (hourly) variations, mainly controlled by wind and regional circulation, that MODIS temporal resolution failed to capture. On the contrary, the high temporal resolution of SEVIRI makes it a powerful tool to study this hourly river plume dynamics. However, its coarse resolution prevents the monitoring of SPM concentration variations in the river mouth where SPM concentration variability can reach 20 g·m⁻³ inside the SEVIRI pixel. Its spatial resolution is nevertheless sufficient to reproduce the plume shape and retrieve SPM concentrations in a valid range, taking into account an underestimation of about 15%–20% based on comparisons with other sensors and in situ data. Finally, the capabilities, advantages and limits of these satellite sensors are discussed in the light of the spatial and temporal resolution improvements provided by the new and future generation of ocean color sensors onboard the Sentinel-2, Sentinel-3 and Meteosat Third Generation (MTG) satellite platforms. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Environments)

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Open AccessArticle

Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana

by Ibrahim Fayad, Nicolas Baghdadi, Jean-Stéphane Bailly, Nicolas Barbier, Valéry Gond, Bruno Hérault, Mahmoud El Hajj, Frédéric Fabre and José Perrin

Remote Sens. 2016, 8(3), 240; https://doi.org/10.3390/rs8030240 - 16 Mar 2016

Cited by 45 | Viewed by 10652

Abstract

LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne [...] Read more.

LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)

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Open AccessArticle

Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

by Alim Samat, Paolo Gamba, Jilili Abuduwaili, Sicong Liu and Zelang Miao

Remote Sens. 2016, 8(3), 234; https://doi.org/10.3390/rs8030234 - 16 Mar 2016

Cited by 32 | Viewed by 7548

Abstract

In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA) in the context of hyperspectral image classification [...] Read more.

In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA) in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM). To show the superior performance of the proposed approach, conventional support vector machines (SVMs) and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC), joint distribution adaptation (JDA), and joint transfer matching (JTM), are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA), randomized nonlinear principal component analysis (rPCA), factor analysis (FA) and non-negative matrix factorization (NNMF) are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images. Full article

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Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

by Zhixin Cheng, Xiao Hua Wang, David Paull and Jianhua Gao

Remote Sens. 2016, 8(3), 244; https://doi.org/10.3390/rs8030244 - 15 Mar 2016

Cited by 38 | Viewed by 7996

Abstract

Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant natural variations. The understanding of such variations is of great significance in coastal waters. The aim of this study is to investigate and assess the diurnal and seasonal variations of [...] Read more.

Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant natural variations. The understanding of such variations is of great significance in coastal waters. The aim of this study is to investigate and assess the diurnal and seasonal variations of surface TSM distribution and its mechanisms in coastal waters based on Geostationary Ocean Color Imager (GOCI) data. As a case study, dynamic variations of TSM in the macro-tidal Yalu River estuary (YRE) of China were analysed. With regard to diurnal variability, there were usually two peaks of TSM in a tidal cycle corresponding to the maximum flood and ebb current. Tidal action appears to play a vital role in diurnal variations of TSM. Both the processes of tidal re-suspension and advection could be identified; however, the diurnal variation of TSM was mainly affected by a re-suspension process. In addition, spring-neap tides can affect the magnitude of TSM diurnal variations in the YRE. The GOCI-retrieved TSM results clearly showed the seasonal variability of surface TSM in this area, with the highest level occurring in winter and the lowest in summer. Moreover, although river discharge to the YRE was much greater in the wet season than the dry season, TSM concentrations were significantly higher in the dry season. Wind waves were considered to be the main factor affecting TSM seasonal variation in the YRE. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Environments)

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Open AccessArticle

A Stochastic Geometry Method for Pylon Reconstruction from Airborne LiDAR Data

by Bo Guo, Xianfeng Huang, Qingquan Li, Fan Zhang, Jiasong Zhu and Chisheng Wang

Remote Sens. 2016, 8(3), 243; https://doi.org/10.3390/rs8030243 - 15 Mar 2016

Cited by 26 | Viewed by 7313

Abstract

Object detection and reconstruction from remotely sensed data are active research topic in photogrammetric and remote sensing communities. Power engineering device monitoring by detecting key objects is important for power safety. In this paper, we introduce a novel method for the reconstruction of [...] Read more.

Object detection and reconstruction from remotely sensed data are active research topic in photogrammetric and remote sensing communities. Power engineering device monitoring by detecting key objects is important for power safety. In this paper, we introduce a novel method for the reconstruction of self-supporting pylons widely used in high voltage power-line systems from airborne LiDAR data. Our work constructs pylons from a library of 3D parametric models, which are represented using polyhedrons based on stochastic geometry. Firstly, laser points of pylons are extracted from the dataset using an automatic classification method. An energy function made up of two terms is then defined: the first term measures the adequacy of the objects with respect to the data, and the second term has the ability to favor or penalize certain configurations based on prior knowledge. Finally, estimation is undertaken by minimizing the energy using simulated annealing. We use a Markov Chain Monte Carlo sampler, leading to an optimal configuration of objects. Two main contributions of this paper are: (1) building a framework for automatic pylon reconstruction; and (2) efficient global optimization. The pylons can be precisely reconstructed through energy optimization. Experiments producing convincing results validated the proposed method using a dataset of complex structure. Full article

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A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

by Weiwei Sun, Man Jiang, Weiyue Li and Yinnian Liu

Remote Sens. 2016, 8(3), 238; https://doi.org/10.3390/rs8030238 - 15 Mar 2016

Cited by 37 | Viewed by 5780

Abstract

A novel Symmetric Sparse Representation (SSR) method has been presented to solve the band selection problem in hyperspectral imagery (HSI) classification. The method assumes that the selected bands and the original HSI bands are sparsely represented by each other, i.e., symmetrically represented. [...] Read more.

A novel Symmetric Sparse Representation (SSR) method has been presented to solve the band selection problem in hyperspectral imagery (HSI) classification. The method assumes that the selected bands and the original HSI bands are sparsely represented by each other, i.e., symmetrically represented. The method formulates band selection into a famous problem of archetypal analysis and selects the representative bands by finding the archetypes in the minimal convex hull containing the HSI band points (i.e., one band corresponds to a band point in the high-dimensional feature space). Without any other parameter tuning work except the size of band subset, the SSR optimizes the band selection program using the block-coordinate descent scheme. Four state-of-the-art methods are utilized to make comparisons with the SSR on the Indian Pines and PaviaU HSI datasets. Experimental results illustrate that SSR outperforms all four methods in classification accuracies (i.e., Average Classification Accuracy (ACA) and Overall Classification Accuracy (OCA)) and three quantitative evaluation results (i.e., Average Information Entropy (AIE), Average Correlation Coefficient (ACC) and Average Relative Entropy (ARE)), whereas it takes the second shortest computational time. Therefore, the proposed SSR is a good alternative method for band selection of HSI classification in realistic applications. Full article

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Open AccessArticle

Monitoring Riverbank Erosion in Mountain Catchments Using Terrestrial Laser Scanning

by Laura Longoni, Monica Papini, Davide Brambilla, Luigi Barazzetti, Fabio Roncoroni, Marco Scaioni and Vladislav Ivov Ivanov

Remote Sens. 2016, 8(3), 241; https://doi.org/10.3390/rs8030241 - 14 Mar 2016

Cited by 56 | Viewed by 10896

Abstract

Sediment yield is a key factor in river basins management due to the various and adverse consequences that erosion and sediment transport in rivers may have on the environment. Although various contributions can be found in the literature about sediment yield modeling and [...] Read more.

Sediment yield is a key factor in river basins management due to the various and adverse consequences that erosion and sediment transport in rivers may have on the environment. Although various contributions can be found in the literature about sediment yield modeling and bank erosion monitoring, the link between weather conditions, river flow rate and bank erosion remains scarcely known. Thus, a basin scale assessment of sediment yield due to riverbank erosion is an objective hard to be reached. In order to enhance the current knowledge in this field, a monitoring method based on high resolution 3D model reconstruction of riverbanks, surveyed by multi-temporal terrestrial laser scanning, was applied to four banks in Val Tartano, Northern Italy. Six data acquisitions over one year were taken, with the aim to better understand the erosion processes and their triggering factors by means of more frequent observations compared to usual annual campaigns. The objective of the research is to address three key questions concerning bank erosion: “how” erosion happens, “when” during the year and “how much” sediment is eroded. The method proved to be effective and able to measure both eroded and deposited volume in the surveyed area. Finally an attempt to extrapolate basin scale volume for bank erosion is presented. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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Open AccessArticle

Frescoed Vaults: Accuracy Controlled Simplified Methodology for Planar Development of Three-Dimensional Textured Models

by Marco Giorgio Bevilacqua, Gabriella Caroti, Isabel Martínez-Espejo Zaragoza and Andrea Piemonte

Remote Sens. 2016, 8(3), 239; https://doi.org/10.3390/rs8030239 - 14 Mar 2016

Cited by 17 | Viewed by 7305

Abstract

In the field of documentation and preservation of cultural heritage, there is keen interest in 3D metric viewing and rendering of architecture for both formal appearance and color. On the other hand, operative steps of restoration interventions still require full-scale, 2D metric surface [...] Read more.

In the field of documentation and preservation of cultural heritage, there is keen interest in 3D metric viewing and rendering of architecture for both formal appearance and color. On the other hand, operative steps of restoration interventions still require full-scale, 2D metric surface representations. The transition from 3D to 2D representation, with the related geometric transformations, has not yet been fully formalized for planar development of frescoed vaults. Methodologies proposed so far on this subject provide transitioning from point cloud models to ideal mathematical surfaces and projecting textures using software tools. The methodology used for geometry and texture development in the present work does not require any dedicated software. The different processing steps can be individually checked for any error introduced, which can be then quantified. A direct accuracy check of the planar development of the frescoed surface has been carried out by qualified restorers, yielding a result of 3 mm. The proposed methodology, although requiring further studies to improve automation of the different processing steps, allowed extracting 2D drafts fully usable by operators restoring the vault frescoes. Full article

(This article belongs to the Special Issue Remote Sensed Data and Processing Methodologies for 3D Virtual Reconstruction and Visualization of Complex Architectures)

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Remote Sens., Volume 8, Issue 3 (March 2016) – 104 articles

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