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Remote Sens., Volume 8, Issue 9 (September 2016)

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Editorial

Jump to: Research, Review, Other

Open AccessEditorial Observation and Monitoring of Mangrove Forests Using Remote Sensing: Opportunities and Challenges
Remote Sens. 2016, 8(9), 783; doi:10.3390/rs8090783
Received: 22 August 2016 / Accepted: 11 September 2016 / Published: 21 September 2016
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Abstract
Mangrove forests, distributed in the tropical and subtropical regions of the world, are in a constant flux. They provide important ecosystem goods and services to nature and society. In recent years, the carbon sequestration potential and protective role of mangrove forests from natural
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Mangrove forests, distributed in the tropical and subtropical regions of the world, are in a constant flux. They provide important ecosystem goods and services to nature and society. In recent years, the carbon sequestration potential and protective role of mangrove forests from natural disasters is being highlighted as an effective option for climate change adaptation and mitigation. The forests are under threat from both natural and anthropogenic forces. However, accurate, reliable, and timely information of the distribution and dynamics of mangrove forests of the world is not readily available. Recent developments in the availability and accessibility of remotely sensed data, advancement in image pre-processing and classification algorithms, significant improvement in computing, availability of expertise in handling remotely sensed data, and an increasing awareness of the applicability of remote sensing products has greatly improved our scientific understanding of changing mangrove forest cover attributes. As reported in this special issue, the use of both optical and radar satellite data at various spatial resolutions (i.e., 1 m to 30 m) to derive meaningful forest cover attributes (e.g., species discrimination, above ground biomass) is on the rise. This multi-sensor trend is likely to continue into the future providing a more complete inventory of global mangrove forest distributions and attribute inventories at enhanced temporal frequency. The papers presented in this “Special Issue” provide important remote sensing monitoring advancements needed to meet future scientific objectives for global mangrove forest monitoring from local to global scales. Full article
(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)
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Research

Jump to: Editorial, Review, Other

Open AccessArticle Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data
Remote Sens. 2016, 8(9), 700; doi:10.3390/rs8090700
Received: 31 May 2016 / Revised: 28 July 2016 / Accepted: 15 August 2016 / Published: 25 August 2016
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Abstract
In this study, four models describing the interferometric coherence of the forest vegetation layer are proposed and compared with the TanDEM-X data. Our focus is on developing tools for hemiboreal forest height estimation from single-pol interferometric SAR measurements, suitable for wide area forest
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In this study, four models describing the interferometric coherence of the forest vegetation layer are proposed and compared with the TanDEM-X data. Our focus is on developing tools for hemiboreal forest height estimation from single-pol interferometric SAR measurements, suitable for wide area forest mapping with limited a priori information. The multi-temporal set of 19 TanDEM-X interferometric pairs and the 90th percentile forest height maps are derived from Airborne LiDAR Scanning (ALS), covering an area of 2211 ha of forests over Estonia. Three semi-empirical models along with the Random Volume over Ground (RVoG) model are examined for applicable parameter ranges and model performance under various conditions for over 3000 forest stands. This study shows that all four models performed well in describing the relationship between forest height and interferometric coherence. Use of an advanced model with multiple parameters is not always justified when modeling the volume decorrelation in the boreal and hemiboreal forests. The proposed set of semi-empirical models, show higher robustness compared to a more advanced RVoG model under a range of seasonal and environmental conditions during data acquisition. We also examine the dynamic range of parameters that different models can take and propose optimal conditions for forest stand height inversion for operationally-feasible scenarios. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification
Remote Sens. 2016, 8(9), 701; doi:10.3390/rs8090701
Received: 22 March 2016 / Revised: 16 August 2016 / Accepted: 17 August 2016 / Published: 26 August 2016
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Abstract
The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil
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The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in São Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400–2500 nm). The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA) to select relevant information on the spectra. Partial least squares regression (PLSR) was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR) region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R2 = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can help the development of computer programs that allow soil texture classification and subsequent digital soil mapping at detailed scales. In addition, it complies with requirements for sustainable land use and soil management. Full article
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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Open AccessArticle Dust Aerosol Optical Depth Retrieval and Dust Storm Detection for Xinjiang Region Using Indian National Satellite Observations
Remote Sens. 2016, 8(9), 702; doi:10.3390/rs8090702
Received: 1 April 2016 / Revised: 7 August 2016 / Accepted: 18 August 2016 / Published: 26 August 2016
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Abstract
The Xinjiang Uyghur Autonomous Region (Xinjiang) is located near the western border of China. Xinjiang has a high frequency of dust storms, especially in late winter and early spring. Geostationary satellite remote sensing offers an ideal way to monitor the regional distribution and
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The Xinjiang Uyghur Autonomous Region (Xinjiang) is located near the western border of China. Xinjiang has a high frequency of dust storms, especially in late winter and early spring. Geostationary satellite remote sensing offers an ideal way to monitor the regional distribution and intensity of dust storms, which can impact the regional climate. In this study observations from the Indian National Satellite (INSAT) 3D are used for dust storm detection in Xinjiang because of the frequent 30-min observations with six bands. An analysis of the optical properties of dust and its quantitative relationship with dust storms in Xinjiang is presented for dust events in April 2014. The Aerosol Optical Depth (AOD) derived using six predefined aerosol types shows great potential to identify dust events. Cross validation between INSAT-3D retrieved AOD and MODIS AOD shows a high coefficient of determination (R2 = 0.92). Ground validation using AERONET (Aerosol Robotic Network) AOD also shows a good correlation with R2 of 0.77. We combined the apparent reflectance (top-of-atmospheric reflectance) of visible and shortwave infrared bands, brightness temperature of infrared bands and retrieved AOD into a new Enhanced Dust Index (EDI). EDI reveals not only dust extent but also the intensity. EDI performed very well in measuring the intensity of dust storms between 22 and 24 April 2014. A visual comparison between EDI and Feng Yun-2E (FY-2E) Infrared Difference Dust Index (IDDI) also shows a high level of similarity. A good linear correlation (R2 of 0.78) between EDI and visibility on the ground demonstrates good performance of EDI in estimating dust intensity. A simple threshold method was found to have a good performance in delineating the extent of the dust plumes but inadequate for providing information on dust plume intensity. Full article
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Open AccessArticle A Method for Downscaling FengYun-3B Soil Moisture Based on Apparent Thermal Inertia
Remote Sens. 2016, 8(9), 703; doi:10.3390/rs8090703
Received: 29 March 2016 / Revised: 3 August 2016 / Accepted: 9 August 2016 / Published: 26 August 2016
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Abstract
FengYun-3B (FY-3B) soil moisture product, retrieved from passive microwave brightness temperature data based on the Qp model, has rarely been applied at the catchment and region scale. One of the reasons for this is its coarse spatial resolution (25-km). The study in this
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FengYun-3B (FY-3B) soil moisture product, retrieved from passive microwave brightness temperature data based on the Qp model, has rarely been applied at the catchment and region scale. One of the reasons for this is its coarse spatial resolution (25-km). The study in this paper presented a new method to obtain a high spatial resolution soil moisture product by downscaling FY-3B soil moisture product from 25-km to 1-km spatial resolution using the theory of Apparent Thermal Inertia (ATI) under bare surface or sparse vegetation covered land surface. The relationship between soil moisture and ATI was first constructed, and the coefficients were obtained directly from 25-km FY-3B soil moisture product and ATI derived from MODIS data, which is different from previous studies often assuming the same set of coefficients applicable at different spatial resolutions. The method was applied to Naqu area on the Tibetan Plateau to obtain the downscaled 1-km resolution soil moisture product, the latter was validated using ground measurements collected from Soil Moisture/Temperature Monitoring Network on the central Tibetan Plateau (TP-STMNS) in 2012. The downscaled soil moisture showed promising results with a coefficient of determination R2 higher than 0.45 and a root mean-square error (RMSE) less than 0.11 m3/m3 when comparing with the ground measurements at 5 sites out of the 9 selected sites. It was found that the accuracy of downscaled soil moisture was largely influenced by the accuracy of the FY-3B soil moisture product. The proposed method could be applied for both bare soil surface and sparsely vegetated surface. Full article
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Open AccessArticle A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas
Remote Sens. 2016, 8(9), 704; doi:10.3390/rs8090704
Received: 1 June 2016 / Revised: 19 August 2016 / Accepted: 23 August 2016 / Published: 26 August 2016
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Abstract
Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountainous areas present extreme topographic variability, and are characterized by more spatial heterogeneity and inaccessibility compared with flat terrain. It is difficult to collect representative ground-truth measurements, and the
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Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountainous areas present extreme topographic variability, and are characterized by more spatial heterogeneity and inaccessibility compared with flat terrain. It is difficult to collect representative ground-truth measurements, and the validation of LAI in mountainous areas is still problematic. A cost-constrained sampling strategy (CSS) in support of LAI validation was presented in this study. To account for the influence of rugged terrain on implementation cost, a cost-objective function was incorporated to traditional conditioned Latin hypercube (CLH) sampling strategy. A case study in Hailuogou, Sichuan province, China was used to assess the efficiency of CSS. Normalized difference vegetation index (NDVI), land cover type, and slope were selected as auxiliary variables to present the variability of LAI in the study area. Results show that CSS can satisfactorily capture the variability across the site extent, while minimizing field efforts. One appealing feature of CSS is that the compromise between representativeness and implementation cost can be regulated according to actual surface heterogeneity and budget constraints, and this makes CSS flexible. Although the proposed method was only validated for the auxiliary variables rather than the LAI measurements, it serves as a starting point for establishing the locations of field plots and facilitates the preparation of field campaigns in mountainous areas. Full article
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Open AccessArticle Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling
Remote Sens. 2016, 8(9), 705; doi:10.3390/rs8090705
Received: 3 May 2016 / Revised: 21 August 2016 / Accepted: 24 August 2016 / Published: 26 August 2016
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Abstract
Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study, we propose a bootstrap resampling and
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Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study, we propose a bootstrap resampling and reclassification approach that can be applied for assessing not only the uncertainty in classification results of the bootstrap-training data sets, but also the classification uncertainty of individual pixels in the study area. Two measures of pixel-specific classification uncertainty, namely the maximum class probability and Shannon entropy, were derived from the class probability vector of individual pixels and used for the identification of unclassified pixels. Unclassified pixels that are identified using the traditional chi-square threshold technique represent outliers of individual LULC classes, but they are not necessarily associated with higher classification uncertainty. By contrast, unclassified pixels identified using the equal-likelihood technique are associated with higher classification uncertainty and they mostly occur on or near the borders of different land-cover. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery
Remote Sens. 2016, 8(9), 706; doi:10.3390/rs8090706
Received: 6 May 2016 / Revised: 17 August 2016 / Accepted: 24 August 2016 / Published: 27 August 2016
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Abstract
Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned
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Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system—three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px−1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R2 values with the best model obtained at flowering (R2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle Ocean Wave Parameters Retrieval from Sentinel-1 SAR Imagery
Remote Sens. 2016, 8(9), 707; doi:10.3390/rs8090707
Received: 15 April 2016 / Revised: 5 August 2016 / Accepted: 23 August 2016 / Published: 27 August 2016
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Abstract
In this paper, a semi-empirical algorithm for significant wave height (Hs) and mean wave period (Tmw) retrieval from C-band VV-polarization Sentinel-1 synthetic aperture radar (SAR) imagery is presented. We develop a semi-empirical function for Hs retrieval, which describes
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In this paper, a semi-empirical algorithm for significant wave height (Hs) and mean wave period (Tmw) retrieval from C-band VV-polarization Sentinel-1 synthetic aperture radar (SAR) imagery is presented. We develop a semi-empirical function for Hs retrieval, which describes the relation between Hs and cutoff wavelength, radar incidence angle, and wave propagation direction relative to radar look direction. Additionally, Tmw can be also calculated through Hs and cutoff wavelength by using another empirical function. We collected 106 C-band stripmap mode Sentinel-1 SAR images in VV-polarization and wave measurements from in situ buoys. There are a total of 150 matchup points. We used 93 matchups to tune the coefficients of the semi-empirical algorithm and the rest 57 matchups for validation. The comparison shows a 0.69 m root mean square error (RMSE) of Hs with a 18.6% of scatter index (SI) and 1.98 s RMSE of Tmw with a 24.8% of SI. Results indicate that the algorithm is suitable for wave parameters retrieval from Sentinel-1 SAR data. Full article
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Open AccessArticle Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis
Remote Sens. 2016, 8(9), 708; doi:10.3390/rs8090708
Received: 24 April 2016 / Revised: 20 August 2016 / Accepted: 23 August 2016 / Published: 27 August 2016
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Abstract
Accurate building information plays a crucial role for urban planning, human settlements and environmental management. Synthetic aperture radar (SAR) images, which deliver images with metric resolution, allow for analyzing and extracting detailed information on urban areas. In this paper, we consider the problem
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Accurate building information plays a crucial role for urban planning, human settlements and environmental management. Synthetic aperture radar (SAR) images, which deliver images with metric resolution, allow for analyzing and extracting detailed information on urban areas. In this paper, we consider the problem of extracting individual buildings from SAR images based on domain ontology. By analyzing a building scattering model with different orientations and structures, the building ontology model is set up to express multiple characteristics of individual buildings. Under this semantic expression framework, an object-based SAR image segmentation method is adopted to provide homogeneous image objects, and three categories of image object features are extracted. Semantic rules are implemented by organizing image object features, and the individual building objects expression based on an ontological semantic description is formed. Finally, the building primitives are used to detect buildings among the available image objects. Experiments on TerraSAR-X images of Foshan city, China, with a spatial resolution of 1.25 m × 1.25 m, have shown the total extraction rates are above 84%. The results indicate the ontological semantic method can exactly extract flat-roof and gable-roof buildings larger than 250 pixels with different orientations. Full article
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Open AccessArticle Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion
Remote Sens. 2016, 8(9), 709; doi:10.3390/rs8090709
Received: 3 June 2016 / Revised: 19 August 2016 / Accepted: 24 August 2016 / Published: 27 August 2016
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Abstract
With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR) has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation
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With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR) has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF). In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP), gray level co-occurrence (GLCM), maximal response 8 (MR8), and scale-invariant feature transform (SIFT). In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM) dataset and the Wuhan University (WH) dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches. Full article
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Open AccessArticle Edge Detection and Feature Line Tracing in 3D-Point Clouds by Analyzing Geometric Properties of Neighborhoods
Remote Sens. 2016, 8(9), 710; doi:10.3390/rs8090710
Received: 10 March 2016 / Revised: 23 August 2016 / Accepted: 24 August 2016 / Published: 1 September 2016
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Abstract
This paper presents an automated and effective method for detecting 3D edges and tracing feature lines from 3D-point clouds. This method is named Analysis of Geometric Properties of Neighborhoods (AGPN), and it includes two main steps: edge detection and feature line tracing. In
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This paper presents an automated and effective method for detecting 3D edges and tracing feature lines from 3D-point clouds. This method is named Analysis of Geometric Properties of Neighborhoods (AGPN), and it includes two main steps: edge detection and feature line tracing. In the edge detection step, AGPN analyzes geometric properties of each query point’s neighborhood, and then combines RANdom SAmple Consensus (RANSAC) and angular gap metric to detect edges. In the feature line tracing step, feature lines are traced by a hybrid method based on region growing and model fitting in the detected edges. Our approach is experimentally validated on complex man-made objects and large-scale urban scenes with millions of points. Comparative studies with state-of-the-art methods demonstrate that our method obtains a promising, reliable, and high performance in detecting edges and tracing feature lines in 3D-point clouds. Moreover, AGPN is insensitive to the point density of the input data. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Using VIIRS Day/Night Band to Measure Electricity Supply Reliability: Preliminary Results from Maharashtra, India
Remote Sens. 2016, 8(9), 711; doi:10.3390/rs8090711
Received: 24 May 2016 / Revised: 28 July 2016 / Accepted: 15 August 2016 / Published: 29 August 2016
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Abstract
Unreliable electricity supplies are common in developing countries and impose large socio-economic costs, yet precise information on electricity reliability is typically unavailable. This paper presents preliminary results from a machine-learning approach for using satellite imagery of nighttime lights to develop estimates of electricity
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Unreliable electricity supplies are common in developing countries and impose large socio-economic costs, yet precise information on electricity reliability is typically unavailable. This paper presents preliminary results from a machine-learning approach for using satellite imagery of nighttime lights to develop estimates of electricity reliability for western India at a finer spatial scale. We use data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Partnership (SNPP) satellite together with newly-available data from networked household voltage meters. Our results point to the possibilities of this approach as well as areas for refinement. With currently available training data, we find a limited ability to detect individual outages identified by household-level measurements of electricity voltage. This is likely due to the relatively small number of individual outages observed in our preliminary data. However, we find that the approach can estimate electricity reliability rates for individual locations fairly well, with the predicted versus actual regression yielding an R2 > 0.5. We also find that, despite the after midnight overpass time of the SNPP satellite, the reliability estimates derived are representative of daytime reliability. Full article
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Open AccessArticle Diel and Spatial Dependence of Humpback Song and Non-Song Vocalizations in Fish Spawning Ground
Remote Sens. 2016, 8(9), 712; doi:10.3390/rs8090712
Received: 10 June 2016 / Revised: 9 August 2016 / Accepted: 23 August 2016 / Published: 30 August 2016
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Abstract
The vocalization behavior of humpback whales was monitored over vast areas of the Gulf of Maine using the passive ocean acoustic waveguide remote sensing technique (POAWRS) over multiple diel cycles in Fall 2006. The humpback vocalizations comprised of both song and non-song are
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The vocalization behavior of humpback whales was monitored over vast areas of the Gulf of Maine using the passive ocean acoustic waveguide remote sensing technique (POAWRS) over multiple diel cycles in Fall 2006. The humpback vocalizations comprised of both song and non-song are analyzed. The song vocalizations, composed of highly structured and repeatable set of phrases, are characterized by inter-pulse intervals of 3.5 ± 1.8 s. Songs were detected throughout the diel cycle, occuring roughly 40% during the day and 60% during the night. The humpback non-song vocalizations, dominated by shorter duration (≤3 s) downsweep and bow-shaped moans, as well as a small fraction of longer duration (∼5 s) cries, have significantly larger mean and more variable inter-pulse intervals of 14.2 ± 11 s. The non-song vocalizations were detected at night with negligible detections during the day, implying they probably function as nighttime communication signals. The humpback song and non-song vocalizations are separately localized using the moving array triangulation and array invariant techniques. The humpback song and non-song moan calls are both consistently localized to a dense area on northeastern Georges Bank and a less dense region extended from Franklin Basin to the Great South Channel. Humpback cries occur exclusively on northeastern Georges Bank and during nights with coincident dense Atlantic herring shoaling populations, implying the cries are feeding-related. Full article
(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)
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Open AccessArticle Comparison of Arctic Sea Ice Thickness from Satellites, Aircraft, and PIOMAS Data
Remote Sens. 2016, 8(9), 713; doi:10.3390/rs8090713
Received: 31 March 2016 / Revised: 20 August 2016 / Accepted: 25 August 2016 / Published: 30 August 2016
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Abstract
In this study, six Arctic sea ice thickness products are compared: the AVHRR Polar Pathfinder-extended (APP-x), ICESat, CryoSat-2, SMOS, NASA IceBridge aircraft flights, and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). The satellite products are based on three different retrieval methods:
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In this study, six Arctic sea ice thickness products are compared: the AVHRR Polar Pathfinder-extended (APP-x), ICESat, CryoSat-2, SMOS, NASA IceBridge aircraft flights, and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). The satellite products are based on three different retrieval methods: an energy budget approach, measurements of ice freeboard, and the relationship between passive microwave brightness temperatures and thin ice thickness. Inter-comparisons are done for the periods of overlap from 2003 to 2013. Results show that ICESat sea ice is thicker than APP-x and PIOMAS overall, particularly along the north coast of Greenland and Canadian Archipelago. The relative differences of APP-x and PIOMAS with ICESat are −0.48 m and −0.31 m, respectively. APP-x underestimates thickness relative to CryoSat-2, with a mean difference of −0.19 m. The biases for APP-x, PIOMAS, and CryoSat-2 relative to IceBridge thicknesses are 0.18 m, 0.18 m, and 0.29 m. The mean difference between SMOS and CryoSat-2 for 0~1 m thick ice is 0.13 m in March and −0.24 m in October. All satellite-retrieved ice thickness products and PIOMAS overestimate the thickness of thin ice (1 m or less) compared to IceBridge for which SMOS has the smallest bias (0.26 m). The spatial correlation between the datasets indicates that APP-x and PIOMAS are the most similar, followed by APP-x and CryoSat-2. Full article
(This article belongs to the Special Issue Sea Ice Remote Sensing and Analysis)
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Open AccessArticle Agricultural Soil Alkalinity and Salinity Modeling in the Cropping Season in a Spectral Endmember Space of TM in Temperate Drylands, Minqin, China
Remote Sens. 2016, 8(9), 714; doi:10.3390/rs8090714
Received: 4 July 2016 / Revised: 23 August 2016 / Accepted: 25 August 2016 / Published: 31 August 2016
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Abstract
This paper presents the potential of the four-image spectral endmember (EM) space comprising sand (SL), green vegetation (GV), saline land (SA), and dark materials (DA), unmixed from Landsat TM/ETM+ to map dryland agricultural soil alkalinity and salinity (i.e., soil alkalinity (pH) and soil
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This paper presents the potential of the four-image spectral endmember (EM) space comprising sand (SL), green vegetation (GV), saline land (SA), and dark materials (DA), unmixed from Landsat TM/ETM+ to map dryland agricultural soil alkalinity and salinity (i.e., soil alkalinity (pH) and soil electrical conductivity (EC)) in the shallow root zone (0–20 cm) using partial least squares regression (PLSR) and an artificial neural network (ANN). The results reveal that SA, SL, and GV fractions at the subpixel level, and land surface temperature (LST) are necessary independent variables for soil EC modeling in Minqin Oasis, a temperate-arid system in China. The R2 (coefficient of determination) of the optimized parameters with the ANN model was 0.79, the root mean squared error (RMSE) was 0.13, and the ratio of prediction to deviation (RPD) was 1.95 when evaluated against all sampled data. In addition to the aforementioned four variables, the DA fraction and the recent historical SA fraction (SAH) in the spring dry season in 2008 were also helpful for soil pH modeling. The model performance is R2 = 0.76, RMSE = 0.24, and RPD = 1.96 for all sampled data. In summary, the stable EMs and LST space of TM imagery with an ANN approach can generate near-real-time regional soil alkalinity and salinity estimations in the cropping period. This is the case even in the critical agronomic range (EC of 0–20 dS·m−1 and pH of 7–9) at which researchers and policy-makers require near-real-time crop management information. Full article
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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Open AccessArticle Cloud Detection for High-Resolution Satellite Imagery Using Machine Learning and Multi-Feature Fusion
Remote Sens. 2016, 8(9), 715; doi:10.3390/rs8090715
Received: 9 June 2016 / Revised: 18 August 2016 / Accepted: 24 August 2016 / Published: 31 August 2016
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Abstract
The accurate location of clouds in images is prerequisite for many high-resolution satellite imagery applications such as atmospheric correction, land cover classifications, and target recognition. Thus, we propose a novel approach for cloud detection using machine learning and multi-feature fusion based on a
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The accurate location of clouds in images is prerequisite for many high-resolution satellite imagery applications such as atmospheric correction, land cover classifications, and target recognition. Thus, we propose a novel approach for cloud detection using machine learning and multi-feature fusion based on a comparative analysis of typical spectral, textural, and other feature differences between clouds and backgrounds. To validate this method, we tested it on 102 Gao Fen-1(GF-1) and Gao Fen-2(GF-2) satellite images. The overall accuracy of our multi-feature fusion method for cloud detection was more than 91.45%, and the Kappa coefficient for all the tested images was greater than 80%. The producer and user accuracy were also higher at 93.67% and 95.67%, respectively; both of these values were higher than the values for the other tested feature fusion methods. Our results show that this novel multi-feature approach yields better accuracy than other feature fusion methods. In post-processing, we applied an object-oriented method to remove the influence of highly reflective ground objects and further improved the accuracy. Compared to traditional methods, our new method for cloud detection is accurate, exhibits good scalability, and produces consistent results when mapping clouds of different types and sizes over various land surfaces that contain natural vegetation, agriculture land, built-up areas, and water bodies. Full article
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Open AccessArticle Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia
Remote Sens. 2016, 8(9), 716; doi:10.3390/rs8090716
Received: 20 June 2016 / Revised: 17 August 2016 / Accepted: 24 August 2016 / Published: 31 August 2016
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Abstract
Numerous studies have identified associations between the surface urban heat island (SUHI) effect (i.e., SUHI, hereinafter is referred to as UHI) and urban growth, particularly changes in land cover patterns. This research questions their causal links to answer a key policy question: If
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Numerous studies have identified associations between the surface urban heat island (SUHI) effect (i.e., SUHI, hereinafter is referred to as UHI) and urban growth, particularly changes in land cover patterns. This research questions their causal links to answer a key policy question: If cities restrict urban expansion and encourage people to live within existing urban areas, will that help in controlling UHI? The question has been answered by estimating four models using data from Brisbane, Australia: Model 1—cross-sectional ordinary least square (OLS) regression—to examine the association between the UHI effect and land cover patterns in 2013; Model 2—cross-sectional geographically weighted regression (GWR)—to examine whether the outputs generated from Model 1 possess significant spatial variations; Model 3—longitudinal OLS—to examine whether changes in land cover patterns led to changes in UHI effects between 2004 and 2013; and Model 4—longitudinal GWR—to examine whether the outputs generated from Model 3 vary significantly over space. All estimations were controlled for potential confounding effects (e.g., population, employment and dwelling densities). Results from the cross-sectional OLS and GWR models were consistent with previous findings and showed that porosity is negatively associated with the UHI effect in 2013. In contrast, population density has a positive association. Results from the longitudinal OLS and GWR models confirm their causal linkages and showed that an increase in porosity level reduced the UHI effect, whereas an increase in population density increased the UHI effect. The findings suggest that even a containment of population growth within existing urban areas will lead to the UHI effect. However, this can be significantly minimized through proper land use planning, by creating a balance between urban and non-urban uses of existing urban areas. Full article
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Open AccessArticle Precise Measurement of Stem Diameter by Simulating the Path of Diameter Tape from Terrestrial Laser Scanning Data
Remote Sens. 2016, 8(9), 717; doi:10.3390/rs8090717
Received: 27 May 2016 / Revised: 18 August 2016 / Accepted: 26 August 2016 / Published: 31 August 2016
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Abstract
Accurate measurement of stem diameter is essential to forest inventory. As a millimeter-level measuring tool, terrestrial laser scanning (TLS) has not yet reached millimeter-level accuracy in stem diameter measurements. The objective of this study is to develop an accurate method for deriving the
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Accurate measurement of stem diameter is essential to forest inventory. As a millimeter-level measuring tool, terrestrial laser scanning (TLS) has not yet reached millimeter-level accuracy in stem diameter measurements. The objective of this study is to develop an accurate method for deriving the stem diameter from TLS data. The methodology of stem diameter measurement by diameter tape was adopted. The stem cross-section at a given height along the stem was determined. Stem points for stem diameter retrieval were extracted according to the stem cross-section. Convex hull points of the extracted stem points were calculated in a projection plane. Then, a closed smooth curve was interpolated onto the convex hull points to simulate the path of the diameter tape, and stem diameter was calculated based on the length of the simulated path. The stems of different tree species with different properties were selected to verify the presented method. Compared with the field-measured diameter, the RMSE of the method was 0.0909 cm, which satisfies the accuracy requirement for forest inventory. This study provided a method for determining the stem cross-section and an efficient and precise curve fitting method for deriving stem diameter from TLS data. The importance of the stem cross-section and convex hull points in stem diameter retrieval was demonstrated. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Submerged and Emergent Land Cover and Bathymetric Mapping of Estuarine Habitats Using WorldView-2 and LiDAR Imagery
Remote Sens. 2016, 8(9), 718; doi:10.3390/rs8090718
Received: 25 May 2016 / Revised: 23 August 2016 / Accepted: 24 August 2016 / Published: 31 August 2016
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Abstract
Tidal creeks are small estuarine watersheds characterized by low freshwater input, marine to brackish salinity, and subtidal, intertidal, and supratidal habitats. Most people are familiar with large rivers and estuaries, but the smaller tidal watersheds comprise a greater percentage of the coastline. As
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Tidal creeks are small estuarine watersheds characterized by low freshwater input, marine to brackish salinity, and subtidal, intertidal, and supratidal habitats. Most people are familiar with large rivers and estuaries, but the smaller tidal watersheds comprise a greater percentage of the coastline. As the population along coasts rises there is growing concern about water quality and increased sedimentation rates. Therefore, these smaller tidal creek watersheds are at risk to pollution, decreased environmental health, and deterioration of protective salt marshes. The purpose of this study was to test methods for high spatial resolution mapping of benthic (submerged) and emergent habitats as well as the derivation of bathymetry using DigitalGlobe’s WorldView-2 imagery. An intensive field effort was conducted to test and assess several image processing techniques. Results concluded that: (1) supervised habitat classification produced the highest map accuracy (95%); (2) sand, water, scrub/shrub, and docks/rubble were mapped the most accurately at greater than 95%; (3) saltmarsh habitats (high and low density cordgrass, Spartina alterniflora, and black needlerush, Juncus roemerianus), mud, and oyster beds were between 80 and 85% accurate; (4) pan-sharpening and atmospheric correction did not improve map accuracy; (5) LiDAR (light detection and ranging) data increased habitat map accuracy; and (6) WorldView-2 imagery was capable of deriving water depth and these data increased the map accuracy of benthic habitats. The project produced habitat maps for benthic and emergent species at high spatial resolution (4 m2) which will be useful for studying the dynamic processes in this tidal environment. The data and methods developed here could be used by state and local government planning agencies to assess potential long-term changes and develop appropriate management strategies. Full article
(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)
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Open AccessArticle Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images
Remote Sens. 2016, 8(9), 719; doi:10.3390/rs8090719
Received: 22 May 2016 / Revised: 18 August 2016 / Accepted: 18 August 2016 / Published: 31 August 2016
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Abstract
Forest health is an important variable that we need to monitor for forest management decision making. However, forest health is difficult to assess and monitor based merely on forest field surveys. In the present study, we first derived a comprehensive forest health indicator
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Forest health is an important variable that we need to monitor for forest management decision making. However, forest health is difficult to assess and monitor based merely on forest field surveys. In the present study, we first derived a comprehensive forest health indicator using 15 forest stand attributes extracted from forest inventory plots. Second, Pearson’s correlation analysis was performed to investigate the relationship between the forest health indicator and the spectral and textural measures extracted from SPOT-5 images. Third, all-subsets regression was performed to build the predictive model by including the statistically significant image-derived measures as independent variables. Finally, the developed model was evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). Additionally, the produced model was further validated for its performance using the leave-one-out cross-validation approach. The results indicated that our produced model could provide reliable, fast and economic means to assess and monitor forest health. A thematic map of forest health was finally produced to support forest health management. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Health)
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Open AccessArticle Detecting Terrain Stoniness From Airborne Laser Scanning Data †
Remote Sens. 2016, 8(9), 720; doi:10.3390/rs8090720
Received: 29 June 2016 / Revised: 7 August 2016 / Accepted: 17 August 2016 / Published: 31 August 2016
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Abstract
Three methods to estimate the presence of ground surface stones from publicly available Airborne Laser Scanning (ALS) point clouds are presented. The first method approximates the local curvature by local linear multi-scale fitting, and the second method uses Discrete-Differential Gaussian curvature based on
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Three methods to estimate the presence of ground surface stones from publicly available Airborne Laser Scanning (ALS) point clouds are presented. The first method approximates the local curvature by local linear multi-scale fitting, and the second method uses Discrete-Differential Gaussian curvature based on the ground surface triangulation. The third baseline method applies Laplace filtering to Digital Elevation Model (DEM) in a 2 m regular grid data. All methods produce an approximate Gaussian curvature distribution which is then vectorized and classified by logistic regression. Two training data sets consisted of 88 and 674 polygons of mass-flow deposits, respectively. The locality of the polygon samples is a sparse canopy boreal forest, where the density of ALS ground returns is sufficiently high to reveal information about terrain micro-topography. The surface stoniness of each polygon sample was categorized for supervised learning by expert observation on the site. The leave-pair-out (L2O) cross-validation of the local linear fit method results in the area under curve A U C = 0 . 74 and A U C = 0 . 85 on two data sets, respectively. This performance can be expected to suit real world applications such as detecting coarse-grained sediments for infrastructure construction. A wall-to-wall predictor based on the study was demonstrated. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Hurricane Wind Speed Estimation Using WindSat 6 and 10 GHz Brightness Temperatures
Remote Sens. 2016, 8(9), 721; doi:10.3390/rs8090721
Received: 4 August 2016 / Revised: 4 August 2016 / Accepted: 23 August 2016 / Published: 31 August 2016
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Abstract
The realistic and accurate estimation of hurricane intensity is highly desired in many scientific and operational applications. With the advance of passive microwave polarimetry, an alternative opportunity for retrieving wind speed in hurricanes has become available. A wind speed retrieval algorithm for wind
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The realistic and accurate estimation of hurricane intensity is highly desired in many scientific and operational applications. With the advance of passive microwave polarimetry, an alternative opportunity for retrieving wind speed in hurricanes has become available. A wind speed retrieval algorithm for wind speeds above 20 m/s in hurricanes has been developed by using the 6.8 and 10.7 GHz vertically and horizontally polarized brightness temperatures of WindSat. The WindSat measurements for 15 category 4 and category 5 hurricanes from 2003 to 2010 and the corresponding H*wind analysis data are used to develop and validate the retrieval model. In addition, the retrieved wind speeds are also compared to the Remote Sensing Systems (RSS) global all-weather product and stepped-frequency microwave radiometer (SFMR) measurements. The statistical results show that the mean bias and the overall root-mean-square (RMS) difference of the retrieved wind speeds with respect to the H*wind analysis data are 0.04 and 2.75 m/s, respectively, which provides an encouraging result for retrieving hurricane wind speeds over the ocean surface. The retrieved wind speeds show good agreement with the SFMR measurements. Two case studies demonstrate that the mean bias and RMS difference are 0.79 m/s and 1.79 m/s for hurricane Rita-1 and 0.63 m/s and 2.38 m/s for hurricane Rita-2, respectively. In general, the wind speed retrieval accuracy of the new model in hurricanes ranges from 2.0 m/s in light rain to 3.9 m/s in heavy rain. Full article
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Open AccessArticle MOD2SEA: A Coupled Atmosphere-Hydro-Optical Model for the Retrieval of Chlorophyll-a from Remote Sensing Observations in Complex Turbid Waters
Remote Sens. 2016, 8(9), 722; doi:10.3390/rs8090722
Received: 20 June 2016 / Revised: 11 August 2016 / Accepted: 27 August 2016 / Published: 1 September 2016
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Abstract
An accurate estimation of the chlorophyll-a (Chla) concentration is crucial for water quality monitoring and is highly desired by various government agencies and environmental groups. However, using satellite observations for Chla estimation remains problematic over coastal waters due to their optical complexity and
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An accurate estimation of the chlorophyll-a (Chla) concentration is crucial for water quality monitoring and is highly desired by various government agencies and environmental groups. However, using satellite observations for Chla estimation remains problematic over coastal waters due to their optical complexity and the critical atmospheric correction. In this study, we coupled an atmospheric and a water optical model for the simultaneous atmospheric correction and retrieval of Chla in the complex waters of the Wadden Sea. This coupled model called MOD2SEA combines simulations from the MODerate resolution atmospheric TRANsmission model (MODTRAN) and the two-stream radiative transfer hydro-optical model 2SeaColor. The accuracy of the coupled MOD2SEA model was validated using a matchup data set of MERIS (MEdium Resolution Imaging SpectRometer) observations and four years of concurrent ground truth measurements (2007–2010) at the NIOZ jetty location in the Dutch part of the Wadden Sea. The results showed that MERIS-derived Chla from MOD2SEA explained the variations of measured Chla with a determination coefficient of R2 = 0.88 and a RMSE of 3.32 mg·m−3, which means a significant improvement in comparison with the standard MERIS Case 2 regional (C2R) processor. The proposed coupled model might be used to generate a time series of reliable Chla maps, which is of profound importance for the assessment of causes and consequences of long-term phenological changes of Chla in the turbid Wadden Sea area. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle Study of the Variations of Archaeological Marks at Neolithic Site of Lucera, Italy Using High-Resolution Multispectral Datasets
Remote Sens. 2016, 8(9), 723; doi:10.3390/rs8090723
Received: 17 July 2016 / Revised: 17 August 2016 / Accepted: 26 August 2016 / Published: 1 September 2016
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Abstract
Satellite images have been systematically explored by archaeologists to detect crop marks, which are considered as a proxy for the identification of buried archaeological remains. Even though several existing algorithms are frequently applied, such as histogram enhancements and vegetation indices, the detection of
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Satellite images have been systematically explored by archaeologists to detect crop marks, which are considered as a proxy for the identification of buried archaeological remains. Even though several existing algorithms are frequently applied, such as histogram enhancements and vegetation indices, the detection of crop marks still remains a difficult task, while the final interpretation results can be very poor. This paper aims to present some of the current difficulties of “remote sensing archaeology” in terms of detection and interpretation of crop marks due to the crops’ phenological variations. At the same time, the presented work seeks to evaluate the recently proposed linear equations for the enhancement of crop marks, initially developed for the eastern Mediterranean region. These linear equations re-project the initial n-space spectral into a new 3D orthogonal space determined by three components: a crop mark component, a vegetation component, and a soil component. For the aims of this study, the Lucera archaeological site (southern Italy), where several Neolithic trenches have been identified, was selected. QuickBird and GeoEye high-resolution satellite images were analysed, indicating that vegetation indices may mismatch some crop marks depending on the phenological stage of the vegetation cultivated in the area of the archaeological site. On the contrary, ratios from linear equations were able to spot these crop marks even in shadow areas, indicating that improvements and developments of novel methodologies and equations based on remote sensing datasets can further assist archaeological research. Full article
(This article belongs to the Special Issue Remote Sensing for Cultural Heritage)
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Open AccessArticle Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images
Remote Sens. 2016, 8(9), 724; doi:10.3390/rs8090724
Received: 3 May 2016 / Revised: 22 August 2016 / Accepted: 29 August 2016 / Published: 1 September 2016
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Abstract
Aquatic vegetation has important ecological and regulatory functions and should be monitored in order to detect ecosystem changes. Field data collection is often costly and time-consuming; remote sensing with unmanned aircraft systems (UASs) provides aerial images with sub-decimetre resolution and offers a potential
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Aquatic vegetation has important ecological and regulatory functions and should be monitored in order to detect ecosystem changes. Field data collection is often costly and time-consuming; remote sensing with unmanned aircraft systems (UASs) provides aerial images with sub-decimetre resolution and offers a potential data source for vegetation mapping. In a manual mapping approach, UAS true-colour images with 5-cm-resolution pixels allowed for the identification of non-submerged aquatic vegetation at the species level. However, manual mapping is labour-intensive, and while automated classification methods are available, they have rarely been evaluated for aquatic vegetation, particularly at the scale of individual vegetation stands. We evaluated classification accuracy and time-efficiency for mapping non-submerged aquatic vegetation at three levels of detail at five test sites (100 m × 100 m) differing in vegetation complexity. We used object-based image analysis and tested two classification methods (threshold classification and Random Forest) using eCognition®. The automated classification results were compared to results from manual mapping. Using threshold classification, overall accuracy at the five test sites ranged from 93% to 99% for the water-versus-vegetation level and from 62% to 90% for the growth-form level. Using Random Forest classification, overall accuracy ranged from 56% to 94% for the growth-form level and from 52% to 75% for the dominant-taxon level. Overall classification accuracy decreased with increasing vegetation complexity. In test sites with more complex vegetation, automated classification was more time-efficient than manual mapping. This study demonstrated that automated classification of non-submerged aquatic vegetation from true-colour UAS images was feasible, indicating good potential for operative mapping of aquatic vegetation. When choosing the preferred mapping method (manual versus automated) the desired level of thematic detail and the required accuracy for the mapping task needs to be considered. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Sea Surface Temperature Retrieval from MODIS Radiances Using Truncated Total Least Squares with Multiple Channels and Parameters
Remote Sens. 2016, 8(9), 725; doi:10.3390/rs8090725
Received: 5 June 2016 / Revised: 10 August 2016 / Accepted: 26 August 2016 / Published: 1 September 2016
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Abstract
Global sea-surface temperatures (SST) from MODIS measured brightness temperatures generated using the regression methods, have been available to users for more than a decade, and are used extensively for a wide range of atmospheric and oceanic studies. However, as evidenced by a number
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Global sea-surface temperatures (SST) from MODIS measured brightness temperatures generated using the regression methods, have been available to users for more than a decade, and are used extensively for a wide range of atmospheric and oceanic studies. However, as evidenced by a number of studies, there are indications that the retrieval quality and cloud detection are somewhat sub-optimal. To improve the performance of both of these aspects, we endorse a new physical deterministic algorithm, based on truncated total least squares (TTLS), using multiple channels and parameters, in conjunction with a hybrid cloud detection scheme using a radiative transfer model atop a functional spectral difference method. The TTLS method is a new addition that improves the information content of the retrieval compared to our previous work using modified total least squares (MTLS), which is feasible because more measurements are available, allowing a larger retrieval vector. A systematic study is conducted to ascertain the appropriate channel selection for SST retrieval from the 16 thermal infrared channels available from the MODIS instrument. Additionally, since atmospheric aerosol is a well-known source of degraded quality of SST retrieval, we include aerosol profiles from numerical weather prediction in the forward simulation and include the total column density of all aerosols in the retrieval vector of our deterministic inverse method. We used a slightly modified version of our earlier reported cloud detection algorithm, namely CEM (cloud and error mask), for this study. Time series analysis of more than a million match-ups shows that our new algorithm (TTLS+CEM) can reduce RMSE by ~50% while increasing data coverage by ~50% compared to the operationally available MODIS SST. Full article
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Open AccessArticle Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing
Remote Sens. 2016, 8(9), 726; doi:10.3390/rs8090726
Received: 26 July 2016 / Revised: 18 August 2016 / Accepted: 23 August 2016 / Published: 1 September 2016
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Abstract
The impact of a rapidly changing climate on the biosphere is an urgent area of research for mitigation policy and management. Plant phenology is a sensitive indicator of climate change and regulates the seasonality of carbon, water, and energy fluxes between the land
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The impact of a rapidly changing climate on the biosphere is an urgent area of research for mitigation policy and management. Plant phenology is a sensitive indicator of climate change and regulates the seasonality of carbon, water, and energy fluxes between the land surface and the climate system, making it an important tool for studying biosphere–atmosphere interactions. To monitor plant phenology at regional and continental scales, automated near-surface cameras are being increasingly used to supplement phenology data derived from satellite imagery and data from ground-based human observers. We used imagery from a network of phenology cameras in a citizen science project called Season Spotter to investigate whether information could be derived from these images beyond standard, color-based vegetation indices. We found that engaging citizen science volunteers resulted in useful science knowledge in three ways: first, volunteers were able to detect some, but not all, reproductive phenology events, connecting landscape-level measures with field-based measures. Second, volunteers successfully demarcated individual trees in landscape imagery, facilitating scaling of vegetation indices from organism to ecosystem. And third, volunteers’ data were used to validate phenology transition dates calculated from vegetation indices and to identify potential improvements to existing algorithms to enable better biological interpretation. As a result, the use of citizen science in combination with near-surface remote sensing of phenology can be used to link ground-based phenology observations to satellite sensor data for scaling and validation. Well-designed citizen science projects targeting improved data processing and validation of remote sensing imagery hold promise for providing the data needed to address grand challenges in environmental science and Earth observation. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Reconstruction of MODIS Spectral Reflectance under Cloudy-Sky Condition
Remote Sens. 2016, 8(9), 727; doi:10.3390/rs8090727
Received: 27 June 2016 / Revised: 20 July 2016 / Accepted: 29 August 2016 / Published: 9 September 2016
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Abstract
Clouds usually cause invalid observations for sensors aboard satellites, which corrupts the spatio-temporal continuity of land surface parameters retrieved from remote sensing data (e.g., MODerate Resolution Imaging Spectroradiometer (MODIS) data) and prevents the fusing of multi-source remote sensing data in the field of
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Clouds usually cause invalid observations for sensors aboard satellites, which corrupts the spatio-temporal continuity of land surface parameters retrieved from remote sensing data (e.g., MODerate Resolution Imaging Spectroradiometer (MODIS) data) and prevents the fusing of multi-source remote sensing data in the field of quantitative remote sensing. Based on the requirements of spatio-temporal continuity and the necessity of methods to restore bad pixels, primarily resulting from image processing, this study developed a novel method to derive the spectral reflectance for MODIS band of cloudy pixels in the visual–near infrared (VIS–NIR) spectral channel based on the Bidirectional Reflectance Distribution Function (BRDF) and multi-spatio-temporal observations. The proposed method first constructs the spatial distribution of land surface reflectance based on the corresponding BRDF and the solar-viewing geometry; then, a geographically weighted regression (GWR) is introduced to individually derive the spectral surface reflectance for MODIS band of cloudy pixels. A validation of the proposed method shows that a total root-mean-square error (RMSE) of less than 6% and a total R2 of more than 90% are detected, which indicates considerably better precision than those exhibited by other existing methods. Further validation of the retrieved white-sky albedo based on the spectral reflectance for MODIS band of cloudy pixels confirms an RMSE of 3.6% and a bias of 2.2%, demonstrating very high accuracy of the proposed method. Full article
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Open AccessArticle Environmental Concerns of Deforestation in Myanmar 2001–2010
Remote Sens. 2016, 8(9), 728; doi:10.3390/rs8090728
Received: 11 June 2016 / Revised: 25 August 2016 / Accepted: 30 August 2016 / Published: 2 September 2016
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Abstract
Deforestation in Myanmar has recently attracted much attention worldwide. This study examined spatio-temporal patterns of deforestation and forest carbon flux in Myanmar from 2001 to 2010 and environmental impacts at the regional scale using land products of the Moderate Resolution Imaging Spectroradiometer (MODIS).
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Deforestation in Myanmar has recently attracted much attention worldwide. This study examined spatio-temporal patterns of deforestation and forest carbon flux in Myanmar from 2001 to 2010 and environmental impacts at the regional scale using land products of the Moderate Resolution Imaging Spectroradiometer (MODIS). The results suggest that the total deforestation area in Myanmar was 21,178.8 km2, with an annual deforestation rate of 0.81%, and that the total forest carbon release was 20.06 million tons, with an annual rate of 0.37%. Mangrove forests had the highest deforestation and carbon release rates, and deciduous forests had both the largest deforestation area and largest amount of carbon release. During the study period, the south and southwestern regions of Myanmar, especially Ayeyarwady and Rakhine, were deforestation hotspots (i.e., the highest deforestation and carbon release rates occurred in these regions). Deforestation caused significant carbon release, reduced evapotranspiration (ET), and increased land surface temperatures (LSTs) in deforested areas in Myanmar during the study period. Constructive policy recommendations are put forward based on these research results. Full article
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Open AccessArticle Aboveground Biomass Estimation of Individual Trees in a Coastal Planted Forest Using Full-Waveform Airborne Laser Scanning Data
Remote Sens. 2016, 8(9), 729; doi:10.3390/rs8090729
Received: 6 May 2016 / Revised: 11 August 2016 / Accepted: 26 August 2016 / Published: 1 September 2016
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Abstract
The accurate estimation of individual tree level aboveground biomass (AGB) is critical for understanding the carbon cycle, detecting potential biofuels and managing forest ecosystems. In this study, we assessed the capability of the metrics of point clouds, extracted from the full-waveform Airborne Laser
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The accurate estimation of individual tree level aboveground biomass (AGB) is critical for understanding the carbon cycle, detecting potential biofuels and managing forest ecosystems. In this study, we assessed the capability of the metrics of point clouds, extracted from the full-waveform Airborne Laser Scanning (ALS) data, and of composite waveforms, calculated based on a voxel-based approach, for estimating tree level AGB individually and in combination, over a planted forest in the coastal region of east China. To do so, we investigated the importance of point cloud and waveform metrics for estimating tree-level AGB by all subsets models and relative weight indices. We also assessed the capability of the point cloud and waveform metrics based models and combo model (including the combination of both point cloud and waveform metrics) for tree-level AGB estimation and evaluated the accuracies of these models. The results demonstrated that most of the waveform metrics have relatively low correlation coefficients (<0.60) with other metrics. The combo models (Adjusted R2 = 0.78–0.89), including both point cloud and waveform metrics, have a relatively higher performance than the models fitted by point cloud metrics-only (Adjusted R2 = 0.74–0.86) and waveform metrics-only (Adjusted R2 = 0.72–0.84), with the mostly selected metrics of the 95th percentile height (H95), mean of height of median energy (HOMEμ) and mean of the height/median ratio (HTMRμ). Based on the relative weights (i.e., the percentage of contribution for R2) of the mostly selected metrics for all subsets, the metric of 95th percentile height (H95) has the highest relative importance for AGB estimation (19.23%), followed by 75th percentile height (H75) (18.02%) and coefficient of variation of heights (Hcv) (15.18%) in the point cloud metrics based models. For the waveform metrics based models, the metric of mean of height of median energy (HOMEμ) has the highest relative importance for AGB estimation (17.86%), followed by mean of the height/median ratio (HTMRμ) (16.23%) and standard deviation of height of median energy (HOMEσ) (14.78%). This study demonstrated benefits of using full-waveform ALS data for estimating biomass at tree level, for sustainable forest management and mitigating climate change by planted forest, as China has the largest area of planted forest in the world, and these forests contribute to a large amount of carbon sequestration in terrestrial ecosystems. Full article
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Open AccessArticle Deep-Learning-Based Classification for DTM Extraction from ALS Point Cloud
Remote Sens. 2016, 8(9), 730; doi:10.3390/rs8090730
Received: 20 May 2016 / Revised: 21 August 2016 / Accepted: 29 August 2016 / Published: 5 September 2016
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Abstract
Airborne laser scanning (ALS) point cloud data are suitable for digital terrain model (DTM) extraction given its high accuracy in elevation. Existing filtering algorithms that eliminate non-ground points mostly depend on terrain feature assumptions or representations; these assumptions result in errors when the
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Airborne laser scanning (ALS) point cloud data are suitable for digital terrain model (DTM) extraction given its high accuracy in elevation. Existing filtering algorithms that eliminate non-ground points mostly depend on terrain feature assumptions or representations; these assumptions result in errors when the scene is complex. This paper proposes a new method for ground point extraction based on deep learning using deep convolutional neural networks (CNN). For every point with spatial context, the neighboring points within a window are extracted and transformed into an image. Then, the classification of a point can be treated as the classification of an image; the point-to-image transformation is carefully crafted by considering the height information in the neighborhood area. After being trained on approximately 17 million labeled ALS points, the deep CNN model can learn how a human operator recognizes a point as a ground point or not. The model performs better than typical existing algorithms in terms of error rate, indicating the significant potential of deep-learning-based methods in feature extraction from a point cloud. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Integration of Ground and Multi-Resolution Satellite Data for Predicting the Water Balance of a Mediterranean Two-Layer Agro-Ecosystem
Remote Sens. 2016, 8(9), 731; doi:10.3390/rs8090731
Received: 8 June 2016 / Revised: 26 August 2016 / Accepted: 29 August 2016 / Published: 5 September 2016
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Abstract
The estimation of site water budget is important in Mediterranean areas, where it represents a crucial factor affecting the quantity and quality of traditional crop production. This is particularly the case for spatially fragmented, multi-layer agricultural ecosystems such as olive groves, which are
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The estimation of site water budget is important in Mediterranean areas, where it represents a crucial factor affecting the quantity and quality of traditional crop production. This is particularly the case for spatially fragmented, multi-layer agricultural ecosystems such as olive groves, which are traditional cultivations of the Mediterranean basin. The current paper aims at demonstrating the effectiveness of spatialized meteorological data and remote sensing techniques to estimate the actual evapotranspiration (ETA) and the soil water content (SWC) of an olive orchard in Central Italy. The relatively small size of this orchard (about 0.1 ha) and its two-layer structure (i.e., olive trees and grasses) require the integration of remotely sensed data with different spatial and temporal resolutions (Terra-MODIS, Landsat 8-OLI and Ikonos). These data are used to drive a recently proposed water balance method (NDVI-Cws) and predict ETA and then site SWC, which are assessed through comparison with sap flow and soil wetness measurements taken in 2013. The results obtained indicate the importance of integrating satellite imageries having different spatio-temporal properties in order to properly characterize the examined olive orchard. More generally, the experimental evidences support the possibility of using widely available remotely sensed and ancillary datasets for the operational estimation of ETA and SWC in olive tree cultivation systems. Full article
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Open AccessArticle Mapping Daily Air Temperature for Antarctica Based on MODIS LST
Remote Sens. 2016, 8(9), 732; doi:10.3390/rs8090732
Received: 18 May 2016 / Revised: 4 August 2016 / Accepted: 31 August 2016 / Published: 5 September 2016
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Abstract
Spatial predictions of near-surface air temperature (Tair) in Antarctica are required as baseline information for a variety of research disciplines. Since the network of weather stations in Antarctica is sparse, remote sensing methods have large potential due to
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Spatial predictions of near-surface air temperature ( T a i r ) in Antarctica are required as baseline information for a variety of research disciplines. Since the network of weather stations in Antarctica is sparse, remote sensing methods have large potential due to their capabilities and accessibility. Based on the MODIS land surface temperature (LST) data, T a i r at the exact time of satellite overpass was modelled at a spatial resolution of 1 km using data from 32 weather stations. The performance of a simple linear regression model to predict T a i r from LST was compared to the performance of three machine learning algorithms: Random Forest (RF), generalized boosted regression models (GBM) and Cubist. In addition to LST, auxiliary predictor variables were tested in these models. Their relevance was evaluated by a Cubist-based forward feature selection in conjunction with leave-one-station-out cross-validation to reduce the impact of spatial overfitting. GBM performed best to predict T a i r using LST and the month of the year as predictor variables. Using the trained model, T a i r could be estimated with a leave-one-station-out cross-validated R 2 of 0.71 and a RMSE of 10.51 C. However, the machine learning approaches only slightly outperformed the simple linear estimation of T a i r from LST ( R 2 of 0.64, RMSE of 11.02 C). Using the trained model allowed creating time series of T a i r over Antarctica for 2013. Extending the training data by including more years will allow developing time series of T a i r from 2000 on. Full article
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Open AccessArticle Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets
Remote Sens. 2016, 8(9), 733; doi:10.3390/rs8090733
Received: 30 April 2016 / Revised: 12 August 2016 / Accepted: 29 August 2016 / Published: 6 September 2016
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Abstract
Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM)
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Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June–August) of 2012 from 48 1 m × 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW) interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by selecting 24 1 m × 1 m plots that were well and poorly represented. Ground-truthing indicated that including phenology had a better accuracy ( R 2 = 0.75 , R M S E = 9.94 ) than the single image upscaling ( R 2 = 0.63 , R M S E = 12.05 ) predicted from IDW. We also updated our upscaling approach to include the 24 ground-truthing plots, and a second ground-truthing campaign in late August of 2014 indicated a better accuracy for the phenology model ( R 2 = 0.61 , R M S E = 13.78 ) than only using the original 48 plots for the phenology model ( R 2 = 0.23 , R M S E = 17.49 ). We believe that the cluster-based IDW upscaling approach and the representativeness analysis offer new insights for upscaling high-resolution data in fragmented landscapes. This analysis and approach provides PFT maps needed to inform land surface models in Arctic ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series
Remote Sens. 2016, 8(9), 734; doi:10.3390/rs8090734
Received: 30 June 2016 / Revised: 25 August 2016 / Accepted: 30 August 2016 / Published: 7 September 2016
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Abstract
Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image time-series (SITS) to
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Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image time-series (SITS) to discriminate tree species in temperate forests is investigated. Based on a 17-date SITS acquired across one year, thirteen major tree species (8 broadleaves and 5 conifers) are classified in a study area of southwest France. The performance of parametric (GMM) and nonparametric (k-NN, RF, SVM) methods are compared at three class hierarchy levels for different versions of the SITS: (i) a smoothed noise-free version based on the Whittaker smoother; (ii) a non-smoothed cloudy version including all the dates; (iii) a non-smoothed noise-free version including only 14 dates. Noise refers to pixels contaminated by clouds and cloud shadows. The results of the 108 distinct classifications show a very high suitability of the SITS to identify the forest tree species based on phenological differences (average κ = 0 . 93 estimated by cross-validation based on 1235 field-collected plots). SVM is found to be the best classifier with very close results from the other classifiers. No clear benefit of removing noise by smoothing can be observed. Classification accuracy is even improved using the non-smoothed cloudy version of the SITS compared to the 14 cloud-free image time series. However conclusions of the results need to be considered with caution because of possible overfitting. Disagreements also appear between the maps produced by the classifiers for complex mixed forests, suggesting a higher classification uncertainty in these contexts. Our findings suggest that time-series data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrates the potential contribution of the recently launched Sentinel-2 satellite for studying forest ecosystems. Full article
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Open AccessArticle A New Temperature-Vegetation Triangle Algorithm with Variable Edges (TAVE) for Satellite-Based Actual Evapotranspiration Estimation
Remote Sens. 2016, 8(9), 735; doi:10.3390/rs8090735
Received: 8 May 2016 / Revised: 19 August 2016 / Accepted: 27 August 2016 / Published: 7 September 2016
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Abstract
The estimation of spatially-variable actual evapotranspiration (AET) is a critical challenge to regional water resources management. We propose a new remote sensing method, the Triangle Algorithm with Variable Edges (TAVE), to generate daily AET estimates based on satellite-derived land surface temperature and the
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The estimation of spatially-variable actual evapotranspiration (AET) is a critical challenge to regional water resources management. We propose a new remote sensing method, the Triangle Algorithm with Variable Edges (TAVE), to generate daily AET estimates based on satellite-derived land surface temperature and the vegetation index NDVI. The TAVE captures heterogeneity in AET across elevation zones and permits variability in determining local values of wet and dry end-member classes (known as edges). Compared to traditional triangle methods, TAVE introduces three unique features: (i) the discretization of the domain as overlapping elevation zones; (ii) a variable wet edge that is a function of elevation zone; and (iii) variable values of a combined-effect parameter (that accounts for aerodynamic and surface resistance, vapor pressure gradient, and soil moisture availability) along both wet and dry edges. With these features, TAVE effectively addresses the combined influence of terrain and water stress on semi-arid environment AET estimates. We demonstrate the effectiveness of this method in one of the driest countries in the world—Jordan, and compare it to a traditional triangle method (TA) and a global AET product (MOD16) over different land use types. In irrigated agricultural lands, TAVE matched the results of the single crop coefficient model (−3%), in contrast to substantial overestimation by TA (+234%) and underestimation by MOD16 (−50%). In forested (non-irrigated, water consuming) regions, TA and MOD16 produced AET average deviations 15.5 times and −3.5 times of those based on TAVE. As TAVE has a simple structure and low data requirements, it provides an efficient means to satisfy the increasing need for evapotranspiration estimation in data-scarce semi-arid regions. This study constitutes a much needed step towards the satellite-based quantification of agricultural water consumption in Jordan. Full article
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Open AccessArticle Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data
Remote Sens. 2016, 8(9), 736; doi:10.3390/rs8090736
Received: 22 June 2016 / Revised: 26 August 2016 / Accepted: 30 August 2016 / Published: 6 September 2016
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Abstract
In this study, the potential of using very high resolution Pléiades imagery to estimate a number of common forest attributes for 10-m plots in boreal forest was examined, when a high-resolution terrain model was available. The explanatory variables were derived from three processing
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In this study, the potential of using very high resolution Pléiades imagery to estimate a number of common forest attributes for 10-m plots in boreal forest was examined, when a high-resolution terrain model was available. The explanatory variables were derived from three processing alternatives. Height metrics were extracted from image matching of the images acquired from different incidence angles. Spectral derivatives were derived by performing principal component analysis of the spectral bands and lastly, second order textural metrics were extracted from a gray-level co-occurrence matrix, computed with an 11 × 11 pixels moving window. The analysis took place at two Swedish test sites, Krycklan and Remningstorp, containing boreal and hemi-boreal forest. The lowest RMSE was estimated with 1.4 m (7.7%) for Lorey’s mean height, 1.7 m (10%) for airborne laser scanning height percentile 90, 5.1 m2·ha−1 (22%) for basal area, 66 m3·ha−1 (27%) for stem volume, and 26 tons·ha−1 (26%) for above-ground biomass, respectively. It was found that the image-matched height metrics were most important in all models, and that the spectral and textural metrics contained similar information. Nevertheless, the best estimations were obtained when all three explanatory sources were used. To conclude, image-matched height metrics should be prioritised over spectral metrics when estimation of forest attributes is concerned. Full article
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Open AccessArticle A Semantic Modelling Framework-Based Method for Building Reconstruction from Point Clouds
Remote Sens. 2016, 8(9), 737; doi:10.3390/rs8090737
Received: 12 June 2016 / Revised: 10 August 2016 / Accepted: 30 August 2016 / Published: 8 September 2016
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Abstract
Over the past few years, there has been an increasing need for semantic information in automatic city modelling. However, due to the complexity of building structure, the semantic reconstruction of buildings is still a challenging task because it is difficult to extract architectural
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Over the past few years, there has been an increasing need for semantic information in automatic city modelling. However, due to the complexity of building structure, the semantic reconstruction of buildings is still a challenging task because it is difficult to extract architectural rules and semantic information from the data. To improve the insufficiencies, we present a semantic modelling framework-based approach for automated building reconstruction using the semantic information extracted from point clouds or images. In this approach, a semantic modelling framework is designed to describe and generate the building model, and a workflow is established for extracting the semantic information of buildings from an unorganized point cloud and converting the semantic information into the semantic modelling framework. The technical feasibility of our method is validated using three airborne laser scanning datasets, and the results are compared with other related works comprehensively, which indicate that our approach can simplify the reconstruction process from a point cloud and generate 3D building models with high accuracy and rich semantic information. Full article
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
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Open AccessArticle A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters
Remote Sens. 2016, 8(9), 738; doi:10.3390/rs8090738
Received: 22 June 2016 / Revised: 15 August 2016 / Accepted: 15 August 2016 / Published: 9 September 2016
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Abstract
Extraction of vegetation information from remotely sensed images has remained a long-term challenge due to the influence of soil background. To reduce this effect, the slope and intercept of the soil line (SL) should be known to calculate SL-related vegetation indices (VIs). These
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Extraction of vegetation information from remotely sensed images has remained a long-term challenge due to the influence of soil background. To reduce this effect, the slope and intercept of the soil line (SL) should be known to calculate SL-related vegetation indices (VIs). These VIs can be used to estimate the biophysical parameters of agricultural crops. However, it is a difficult task to retrieve the SL parameters under the vegetation canopy. A feasible method for retrieving these parameters involves extracting the bottom boundary line in two-dimensional spectral spaces (i.e., red and near-infrared bands). In this study, the slope and intercept of the SL was extracted from Landsat 8 OLI images of a test site in northeastern Germany. Different statistical methods, including the Red-NIRmin method, quantile regression method (using a floating tau with the smallest p-value), and a new approach proposed in this paper using a fixed quantile tau known as the diffuse non-interceptance (DIFN) value, were applied to retrieve the SL parameters. The DIFN value describes the amount of light visible below the canopy that reaches the soil surface. Therefore, this value can be used as a threshold for retrieving the bottom soil line. The simulated SLs were compared with actual ones extracted from ground truth data, as recorded by a handheld spectrometer, and were also compared with the SL retrieved from bare soil pixels of the Landsat 8 image collected after harvest. Subsequently, the SL parameters were used to separately estimate the dry biomasses of winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.) at the local and field scales using different SL-related vegetation indices. The SL can be retrieved more accurately at the local scale compared with the field scale, and its simulation can be critical in the field due to significant differences from the actual SL. Moreover, the slope and intercept of the simulated SLs found using the floating and fixed quantile tau (slope ≈ 1.1 and intercept ≈ 0.05) show better agreement with the actual SL parameters (slope ≈ 1.2 and intercept ≈ 0.03) in the late growing stages (i.e., end of ripening and senescence stages) of crops. The slope and intercept of the soil line extracted from bare soil pixels of the Landsat 8 OLI data after harvest (slope = 1.3, intercept = 0.03, and R2 = 0.94) are similar to those of the simulated SL. The correlation coefficient (R2) of the simulated SLs are greater than 0.97 during different growing stage and all of the SL parameters are statistically significant (p < 0.05) at the local scale. The results also imply the need for different vegetation indices to best retrieve the crop biomass depending on the growing stage, but relatively small differences in performances were observed in this study. Full article
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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Open AccessArticle Global Surface Net-Radiation at 5 km from MODIS Terra
Remote Sens. 2016, 8(9), 739; doi:10.3390/rs8090739
Received: 20 April 2016 / Revised: 17 August 2016 / Accepted: 23 August 2016 / Published: 6 September 2016
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Abstract
Reliable and fine resolution estimates of surface net-radiation are required for estimating latent and sensible heat fluxes between the land surface and the atmosphere. However, currently, fine resolution estimates of net-radiation are not available and consequently it is challenging to develop multi-year estimates
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Reliable and fine resolution estimates of surface net-radiation are required for estimating latent and sensible heat fluxes between the land surface and the atmosphere. However, currently, fine resolution estimates of net-radiation are not available and consequently it is challenging to develop multi-year estimates of evapotranspiration at scales that can capture land surface heterogeneity and are relevant for policy and decision-making. We developed and evaluated a global net-radiation product at 5 km and 8-day resolution by combining mutually consistent atmosphere and land data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra. Comparison with net-radiation measurements from 154 globally distributed sites (414 site-years) from the FLUXNET and Surface Radiation budget network (SURFRAD) showed that the net-radiation product agreed well with measurements across seasons and climate types in the extratropics (Wilmott’s index ranged from 0.74 for boreal to 0.63 for Mediterranean sites). Mean absolute deviation between the MODIS and measured net-radiation ranged from 38.0 ± 1.8 W∙m−2 in boreal to 72.0 ± 4.1 W∙m−2 in the tropical climates. The mean bias was small and constituted only 11%, 0.7%, 8.4%, 4.2%, 13.3%, and 5.4% of the mean absolute error in daytime net-radiation in boreal, Mediterranean, temperate-continental, temperate, semi-arid, and tropical climate, respectively. To assess the accuracy of the broader spatiotemporal patterns, we upscaled error-quantified MODIS net-radiation and compared it with the net-radiation estimates from the coarse spatial (1° × 1°) but high temporal resolution gridded net-radiation product from the Clouds and Earth’s Radiant Energy System (CERES). Our estimates agreed closely with the net-radiation estimates from the CERES. Difference between the two was less than 10 W·m−2 in 94% of the total land area. MODIS net-radiation product will be a valuable resource for the science community studying turbulent fluxes and energy budget at the Earth’s surface. Full article
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Open AccessArticle An Algorithm for Automatic Road Asphalt Edge Delineation from Mobile Laser Scanner Data Using the Line Clouds Concept
Remote Sens. 2016, 8(9), 740; doi:10.3390/rs8090740
Received: 10 July 2016 / Revised: 24 August 2016 / Accepted: 30 August 2016 / Published: 7 September 2016
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Abstract
Accurate road asphalt extent delineation is needed for road and street planning, road maintenance, and road safety assessment. In this article, a new approach for automatic roadside delineation is developed based on the line clouds concept. The method relies on line cloud grouping
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Accurate road asphalt extent delineation is needed for road and street planning, road maintenance, and road safety assessment. In this article, a new approach for automatic roadside delineation is developed based on the line clouds concept. The method relies on line cloud grouping from point cloud laser data. Using geometric criteria, the initial 3D LiDAR point data is structured in lines covering the road surface. These lines are then grouped according to a set of quasi-planar restriction rules. Road asphalt edge limits are extracted from the end points of lines belonging to these groups. Finally a two-stage smoothing procedure is applied to correct for edge occlusions and other anomalies. The method was tested on a 2.1 km stretch of road, and the results were checked using a RTK-GNSS measured dataset as ground truth. Correctness and completeness were 99% and 97%, respectively. Full article
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Open AccessArticle Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series
Remote Sens. 2016, 8(9), 741; doi:10.3390/rs8090741
Received: 17 June 2016 / Revised: 24 August 2016 / Accepted: 4 September 2016 / Published: 8 September 2016
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Abstract
Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated
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Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated fused normalized-difference vegetation index (NDVI) data with high spatial and temporal resolution by utilizing the STARFM algorithm to produce a fused GF-1 and MODIS NDVI dataset. We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude) from a fused NDVI time-series after reconstruction using the TIMESAT software. We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. We then classified the land cover using a support vector machine and analyzed the classification accuracies. We found that the proposed method achieved satisfactory classification results, and that the combination of the fused NDVI data with the extracted phenological parameters significantly improved classification accuracy. The classification accuracy based on the composited GF-1 multi-spectral bands combined with the phenological parameters was the highest among the four scenarios, with an overall classification accuracy of 88.8% and a Kappa coefficient of 0.8714, which represent increases of 9.3 percentage points and 0.1073, respectively, compared with GF-1 spectral data alone. The producer’s and user’s accuracy for different land cover types improved, with a few exceptions, and cropland and broadleaf forest had the largest increase. Full article
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Open AccessArticle Assessment of Spatial Representativeness of Eddy Covariance Flux Data from Flux Tower to Regional Grid
Remote Sens. 2016, 8(9), 742; doi:10.3390/rs8090742
Received: 6 June 2016 / Revised: 31 August 2016 / Accepted: 1 September 2016 / Published: 8 September 2016
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Abstract
Combining flux tower measurements with remote sensing or land surface models is generally regarded as an efficient method to scale up flux data from site to region. However, due to the heterogeneous nature of the vegetated land surface, the changing flux source areas
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Combining flux tower measurements with remote sensing or land surface models is generally regarded as an efficient method to scale up flux data from site to region. However, due to the heterogeneous nature of the vegetated land surface, the changing flux source areas and the mismatching between ground source areas and remote sensing grids, direct use of in-situ flux measurements can lead to major scaling bias if their spatial representativeness is unknown. Here, we calculate and assess the spatial representativeness of 15 flux sites across northern China in two aspects: first, examine how well a tower represents fluxes from the specific targeted vegetation type, which is called vegetation-type level; and, second, examine how representative is the flux tower footprint of the broader landscape or regional extents, which is called spatial-scale level. We select fraction of target vegetation type (FTVT) and Normalized Difference Vegetation Index (NDVI) as key indicators to calculate the spatial representativeness of 15 EC sites. Then, these sites were ranked into four grades based on FTVT or cluster analysis from high to low in order: (1) homogeneous; (2) representative; (3) acceptable; and (4) disturbed measurements. The results indicate that: (1) Footprint climatology for each site was mainly distributed in an irregular shape, had similar spatial pattern as spatial distribution of prevailing wind direction; (2) At vegetation-type level, the number of homogeneous, representative, acceptable and disturbed measurements is 8, 4, 1 and 2, respectively. The average FTVT was 0.83, grass and crop sites had greater representativeness than forest sites; (3) At spatial-scale level, flux sites with zonal vegetation had greater representativeness than non-zonal vegetation sites, and the scales were further divided into three sub-scales: (a) in flux site scale, the average of absolute NDVI bias was 4.34%, the number of the above four grades is 9, 4, 1 and 1, respectively; (b) in remote sensing pixel scale, the average of absolute NDVI bias was 8.27%, the number is 7, 2, 2 and 4, respectively; (c) in land model grid scale, the average of absolute NDVI bias was 12.13%, the number is 5, 4, 3 and 3. These results demonstrate the variation of spatial representativeness of flux measurements among different application levels and scales and highlighted the importance of proper interpretation of EC flux measurements. These results also suggest that source area of EC flux should be involved in model validation and/or calibration with EC flux measurements. Full article
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Open AccessArticle Rigorous Line-Based Transformation Model Using the Generalized Point Strategy for the Rectification of High Resolution Satellite Imagery
Remote Sens. 2016, 8(9), 743; doi:10.3390/rs8090743
Received: 8 June 2016 / Revised: 2 September 2016 / Accepted: 5 September 2016 / Published: 8 September 2016
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Abstract
High precision geometric rectification of High Resolution Satellite Imagery (HRSI) is the basis of digital mapping and Three-Dimensional (3D) modeling. Taking advantage of line features as basic geometric control conditions instead of control points, the Line-Based Transformation Model (LBTM) provides a practical and
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High precision geometric rectification of High Resolution Satellite Imagery (HRSI) is the basis of digital mapping and Three-Dimensional (3D) modeling. Taking advantage of line features as basic geometric control conditions instead of control points, the Line-Based Transformation Model (LBTM) provides a practical and efficient way of image rectification. It is competent to build the mathematical relationship between image space and the corresponding object space accurately, while it reduces the workloads of ground control and feature recognition dramatically. Based on generalization and the analysis of existing LBTMs, a novel rigorous LBTM is proposed in this paper, which can further eliminate the geometric deformation caused by sensor inclination and terrain variation. This improved nonlinear LBTM is constructed based on a generalized point strategy and resolved by least squares overall adjustment. Geo-positioning accuracy experiments with IKONOS, GeoEye-1 and ZiYuan-3 satellite imagery are performed to compare rigorous LBTM with other relevant line-based and point-based transformation models. Both theoretic analysis and experimental results demonstrate that the rigorous LBTM is more accurate and reliable without adding extra ground control. The geo-positioning accuracy of satellite imagery rectified by rigorous LBTM can reach about one pixel with eight control lines and can be further improved by optimizing the horizontal and vertical distribution of control lines. Full article
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Open AccessArticle Statistical and Spectral Features of Corrugated Seafloor Shaped by the Hans Glacier in Svalbard
Remote Sens. 2016, 8(9), 744; doi:10.3390/rs8090744
Received: 11 March 2016 / Revised: 29 August 2016 / Accepted: 5 September 2016 / Published: 10 September 2016
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Abstract
High-resolution images of the seabed obtained with the use of hydroacoustic measurements allow a detailed identification of inaccessible seabed areas such as the Hans Glacier foreland in the Hornsund Fjord on Spitsbergen. Analyses presented in the paper were carried out on a Digital
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High-resolution images of the seabed obtained with the use of hydroacoustic measurements allow a detailed identification of inaccessible seabed areas such as the Hans Glacier foreland in the Hornsund Fjord on Spitsbergen. Analyses presented in the paper were carried out on a Digital Elevation Model (DEM) of the bay’s seafloor exposed in the process of deglaciation, obtained from bathymetric data recorded by a multibeam echosounder. The main objective of this study was to show the relevance of the autocorrelation length parameter used to describe the roughness of the bottom surface based on the example of seafloor postglacial forms in the Hans Glacier foreland. The resulting parameter reflects the scale of the terrain roughness, which varies between geomorphologic forms. Maps of the autocorrelation length were derived from successive tiles of the data, overlapping by 90%. Based on this, the two-dimensional Fourier transform (2D FFT) was successively conducted, and the power spectral density and autocorrelation were calculated following the Wiener–Khinchin theorem. The thus obtained parameter describes the scale of the glacial bay seafloor roughness, which was assigned to the geomorphological features observed. Full article
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Open AccessArticle A Scale-Driven Change Detection Method Incorporating Uncertainty Analysis for Remote Sensing Images
Remote Sens. 2016, 8(9), 745; doi:10.3390/rs8090745
Received: 22 June 2016 / Revised: 27 August 2016 / Accepted: 5 September 2016 / Published: 12 September 2016
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Abstract
Change detection (CD) based on remote sensing images plays an important role in Earth observation. However, the CD accuracy is usually affected by sunlight and atmospheric conditions and sensor calibration. In this study, a scale-driven CD method incorporating uncertainty analysis is proposed to
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Change detection (CD) based on remote sensing images plays an important role in Earth observation. However, the CD accuracy is usually affected by sunlight and atmospheric conditions and sensor calibration. In this study, a scale-driven CD method incorporating uncertainty analysis is proposed to increase CD accuracy. First, two temporal images are stacked and segmented into multiscale segmentation maps. Then, a pixel-based change map with memberships belonging to changed and unchanged parts is obtained by fuzzy c-means clustering. Finally, based on the Dempster-Shafer evidence theory, the proposed scale-driven CD method incorporating uncertainty analysis is performed on the multiscale segmentation maps and the pixel-based change map. Two experiments were carried out on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and SPOT 5 data sets. The ratio of total errors can be reduced to 4.0% and 7.5% for the ETM+ and SPOT 5 data sets in this study, respectively. Moreover, the proposed approach outperforms some state-of-the-art CD methods and provides an effective solution for CD. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle Detecting Slope and Urban Potential Unstable Areas by Means of Multi-Platform Remote Sensing Techniques: The Volterra (Italy) Case Study
Remote Sens. 2016, 8(9), 746; doi:10.3390/rs8090746
Received: 4 July 2016 / Revised: 30 August 2016 / Accepted: 30 August 2016 / Published: 9 September 2016
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Abstract
Volterra (Central Italy) is a town of great historical interest, due to its vast and well-preserved cultural heritage, including a 2.6 km long Etruscan-medieval wall enclosure representing one of the most important elements. Volterra is located on a clayey hilltop prone to landsliding,
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Volterra (Central Italy) is a town of great historical interest, due to its vast and well-preserved cultural heritage, including a 2.6 km long Etruscan-medieval wall enclosure representing one of the most important elements. Volterra is located on a clayey hilltop prone to landsliding, soil erosion, therefore the town is subject to structural deterioration. During 2014, two impressive collapses occurred on the wall enclosure in the southwestern urban sector. Following these events, a monitoring campaign was carried out by means of remote sensing techniques, such as space-borne (PS-InSAR) and ground-based (GB-InSAR) radar interferometry, in order to analyze the displacements occurring both in the urban area and the surrounding slopes, and therefore to detect possible critical sectors with respect to instability phenomena. Infrared thermography (IRT) was also applied with the aim of detecting possible criticalities on the wall-enclosure, with special regards to moisture and seepage areas. PS-InSAR data allowed a stability back-monitoring on the area, revealing 19 active clusters displaying ground velocity higher than 10 mm/year in the period 2011–2015. The GB-InSAR system detected an acceleration up to 1.7 mm/h in near-real time as the March 2014 failure precursor. The IRT technique, employed on a double survey campaign, in both dry and rainy conditions, permitted to acquire 65 thermograms covering 23 sectors of the town wall, highlighting four thermal anomalies. The outcomes of this work demonstrate the usefulness of different remote sensing technologies for deriving information in risk prevention and management, and the importance of choosing the appropriate technology depending on the target, time sampling and investigation scale. In this paper, the use of a multi-platform remote sensing system permitted technical support of the local authorities and conservators, providing a comprehensive overview of the Volterra site, its cultural heritage and landscape, both in near-real time and back-analysis and at different scales of investigation. Full article
(This article belongs to the Special Issue Remote Sensing for Cultural Heritage)
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Open AccessArticle Weak Environmental Controls of Tropical Forest Canopy Height in the Guiana Shield
Remote Sens. 2016, 8(9), 747; doi:10.3390/rs8090747
Received: 1 July 2016 / Revised: 16 August 2016 / Accepted: 30 August 2016 / Published: 9 September 2016
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Abstract
Canopy height is a key variable in tropical forest functioning and for regional carbon inventories. We investigate the spatial structure of the canopy height of a tropical forest, its relationship with environmental physical covariates, and the implication for tropical forest height variation mapping.
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Canopy height is a key variable in tropical forest functioning and for regional carbon inventories. We investigate the spatial structure of the canopy height of a tropical forest, its relationship with environmental physical covariates, and the implication for tropical forest height variation mapping. Making use of high-resolution maps of LiDAR-derived Digital Canopy Model (DCM) and environmental covariates from a Digital Elevation Model (DEM) acquired over 30,000 ha of tropical forest in French Guiana, we first show that forest canopy height is spatially correlated up to 2500 m. Forest canopy height is significantly associated with environmental variables, but the degree of correlation varies strongly with pixel resolution. On the whole, bottomland forests generally have lower canopy heights than hillslope or hilltop forests. However, this global picture is very noisy at local scale likely because of the endogenous gap-phase forest dynamic processes. Forest canopy height has been predictively mapped across a pixel resolution going from 6 m to 384 m mimicking a low resolution case of 3 points·km 2 . Results of canopy height mapping indicated that the error for spatial model with environment effects decrease from 8.7 m to 0.91 m, depending of the pixel resolution. Results suggest that, outside the calibration plots, the contribution of environment in shaping the global canopy height distribution is quite limited. This prevents accurate canopy height mapping based only on environmental information, and suggests that precise canopy height maps, for local management purposes, can only be obtained with direct LiDAR monitoring. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model
Remote Sens. 2016, 8(9), 748; doi:10.3390/rs8090748
Received: 21 July 2016 / Revised: 19 August 2016 / Accepted: 5 September 2016 / Published: 11 September 2016
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Abstract
Hyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In this
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Hyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In this method, a novel technique for labeling regions obtained by the spectral-spatial segmentation process is presented. Our method includes the following steps. First, the probabilistic support vector machines (SVM) classifier is used to estimate probabilities belonging to each information class. Second, an extended JBF is employed to perform image smoothing on the probability maps. By using our JBF process, salt-and-pepper classification noise in homogeneous regions can be effectively smoothed out while object boundaries in the original image are better preserved as well. Third, a sequence of modified bi-labeling graph cut models is constructed for each information class to extract the desirable object belonging to the corresponding class from the smoothed probability maps. Finally, a classification map is achieved by merging the segmentation maps obtained in the last step using a simple and effective rule. Experimental results based on three benchmark airborne hyperspectral datasets with different resolutions and contexts demonstrate that our method can achieve 8.56%–13.68% higher overall accuracies than the pixel-wise SVM classifier. The performance of our method was further compared to several classical hyperspectral image classification methods using objective quantitative measures and a visual qualitative evaluation. Full article
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Open AccessArticle A Novel Tri-Training Technique for Semi-Supervised Classification of Hyperspectral Images Based on Diversity Measurement
Remote Sens. 2016, 8(9), 749; doi:10.3390/rs8090749
Received: 27 June 2016 / Revised: 2 September 2016 / Accepted: 4 September 2016 / Published: 12 September 2016
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Abstract
This paper introduces a novel semi-supervised tri-training classification algorithm based on diversity measurement for hyperspectral imagery. In this algorithm, three measures of diversity, i.e., double-fault measure, disagreement metric and correlation coefficient, are applied to select the optimal classifier combination from different classifiers, e.g.,
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This paper introduces a novel semi-supervised tri-training classification algorithm based on diversity measurement for hyperspectral imagery. In this algorithm, three measures of diversity, i.e., double-fault measure, disagreement metric and correlation coefficient, are applied to select the optimal classifier combination from different classifiers, e.g., support vector machine (SVM), multinomial logistic regression (MLR), extreme learning machine (ELM) and k-nearest neighbor (KNN). Then, unlabeled samples are selected using an active learning (AL) method, and consistent results of any other two classifiers combined with a spatial neighborhood information extraction strategy are employed to predict their labels. Moreover, a multi-scale homogeneity (MSH) method is utilized to refine the classification result with the highest accuracy in the classifier combination, generating the final classification result. Experiments on three real hyperspectral data indicate that the proposed approach can effectively improve classification performance. Full article
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Open AccessArticle Impact of Sensor Zenith Angle on MOD10A1 Data Reliability and Modification of Snow Cover Data for the Tarim River Basin
Remote Sens. 2016, 8(9), 750; doi:10.3390/rs8090750
Received: 17 March 2016 / Revised: 1 September 2016 / Accepted: 8 September 2016 / Published: 12 September 2016
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Abstract
Snow in the mountainous watersheds of the Tarim River Basin is the primary source of water for western China. The Snow Cover Daily L3 Global 500-m Grid (MOD10A1) remote sensing dataset has proven extremely valuable for monitoring the changing snow cover patterns over
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Snow in the mountainous watersheds of the Tarim River Basin is the primary source of water for western China. The Snow Cover Daily L3 Global 500-m Grid (MOD10A1) remote sensing dataset has proven extremely valuable for monitoring the changing snow cover patterns over large spatial areas; however, inherent uncertainty associated with large sensor zenith angles (SZAs) has called its reliability into question. Comparative analysis that utilized a paired-date difference method for parameters such as snow cover frequency, snow cover percentage, and normalized difference snow index (NDSI) has shown that overestimation of snow cover in the Tarim River Basin correlates with high values of SZA. Hence, such overestimation was associated with an increase in the NDSI, attributable to the change in reflectance between Band 4 and Band 6 imagery. A maximum threshold value of SZA of 22.37° was used alongside a multiday refilling method to modify the MOD10A1 dataset to produce a new daily snow cover map of the Tarim River Basin, spanning a 10-year period. A comparison of benchmark results of snow cover classification produced by the HJ-1A/B satellite revealed an increase in the overall accuracy of up to 4%, confirming the usefulness of our modified MOD10A1 data. Full article
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Open AccessArticle Engaging with the Canopy—Multi-Dimensional Vegetation Mark Visualisation Using Archived Aerial Images
Remote Sens. 2016, 8(9), 752; doi:10.3390/rs8090752
Received: 18 July 2016 / Revised: 24 August 2016 / Accepted: 5 September 2016 / Published: 12 September 2016
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Abstract
Using Montarice in central Adriatic Italy as a case study, this paper focuses on the extraction of the spectral (i.e., plant colour) and geometrical (i.e., plant height) components of a crop canopy from archived aerial photographs, treating both parameters as proxies for archaeological
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Using Montarice in central Adriatic Italy as a case study, this paper focuses on the extraction of the spectral (i.e., plant colour) and geometrical (i.e., plant height) components of a crop canopy from archived aerial photographs, treating both parameters as proxies for archaeological prospection. After the creation of orthophotographs and a canopy height model using image-based modelling, new archaeological information is extracted from this vegetation model by applying relief-enhancing visualisation techniques. Through interpretation of the resulting data, a combination of the co-registered spectral and geometrical vegetation dimensions clearly add new depth to interpretative mapping, which is typically based solely on colour differences in orthophotographs. Full article
(This article belongs to the Special Issue Archaeological Prospecting and Remote Sensing)
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Open AccessArticle Effects of Different Methods on the Comparison between Land Surface and Ground Phenology—A Methodological Case Study from South-Western Germany
Remote Sens. 2016, 8(9), 753; doi:10.3390/rs8090753
Received: 27 June 2016 / Revised: 25 August 2016 / Accepted: 8 September 2016 / Published: 13 September 2016
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Abstract
Several methods exist for extracting plant phenological information from time series of satellite data. However, there have been only a few successful attempts to temporarily match satellite observations (Land Surface Phenology or LSP) with ground based phenological observations (Ground Phenology or GP). The
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Several methods exist for extracting plant phenological information from time series of satellite data. However, there have been only a few successful attempts to temporarily match satellite observations (Land Surface Phenology or LSP) with ground based phenological observations (Ground Phenology or GP). The classical pixel to point matching problem along with the temporal and spatial resolution of remote sensing data are some of the many issues encountered. In this study, MODIS-sensor’s Normalised Differenced Vegetation Index (NDVI) time series data were smoothed using two filtering techniques for comparison. Several start of season (SOS) methods established in the literature, namely thresholds of amplitude, derivatives and delayed moving average, were tested for determination of LSP-SOS for broadleaf forests at a site in southwestern Germany using 2001–2013 time series of NDVI data. The different LSP-SOS estimates when compared with species-rich GP dataset revealed that different LSP-SOS extraction methods agree better with specific phases of GP, and the choice of data processing or smoothing strongly affects the LSP-SOS extracted. LSP methods mirroring late SOS dates, i.e., 75% amplitude and 1st derivative, indicated a better match in means and trends, and high, significant correlations of up to 0.7 with leaf unfolding and greening of late understory and broadleaf tree species. GP-SOS of early understory leaf unfolding partly were significantly correlated with earlier detecting LSP-SOS, i.e., 20% amplitude and 3rd derivative. Early understory SOS were, however, more difficult to detect from NDVI due to the lack of a high resolution land cover information. Full article
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Open AccessArticle The Variations and Trends of MODIS C5 & C6 Products’ Errors in the Recent Decade over the Background and Urban Areas of North China
Remote Sens. 2016, 8(9), 754; doi:10.3390/rs8090754
Received: 20 June 2016 / Revised: 22 August 2016 / Accepted: 8 September 2016 / Published: 13 September 2016
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Abstract
With ten-year (2004–2013) ground-based observations of Beijing Forest (BJF) and Beijing City (BJC) sites in North China, we validated the high-quality MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 (C5) and Collection 6 (C6) Aerosol Optical Depth (AOD) products’ precision and discussed the sensors
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With ten-year (2004–2013) ground-based observations of Beijing Forest (BJF) and Beijing City (BJC) sites in North China, we validated the high-quality MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 (C5) and Collection 6 (C6) Aerosol Optical Depth (AOD) products’ precision and discussed the sensors degradation issues. The annual mean AOD and Angstrom exponent (α) were 0.20 ± 0.02 and 0.83 ± 0.15 in the background over the past ten years, and they were 0.59 ± 0.07 and 1.13 ± 0.08 in the urban, respectively. Ground-based AOD had both slightly declining trends, with variations of 0.023 and 0.057 over the past decade in the background and urban, respectively. There were large differences among the eight kinds of MODIS AOD products (Terra vs. Aqua, C5 vs. C6, DT (Deep Target) vs. DB (Deep Blue), and DTDB in the background and urban areas), but all the products’ monthly errors had larger variations in the spring and summer, and smaller ones in the autumn and winter. In the background, more than 62% of DT matchups for C5 and C6 products were within NASA’s expected error (EE) envelope. In the urban, 69%~72% of C6 DB retrievals were falling within EE envelope. The new dataset named C6 DTDB had better performance in the background, whereas it overestimated by 37%~41% in the urban caused by surface reflectivity estimation error. The range of monthly average error varied from −0.21 to 0.28 in the background and from −0.63 to 0.48 in the urban. From the background to the urban areas, the retrieval errors of Terra and Aqua had slightly increased by 0.0023~0.0158 and 0.0011~0.0124 per year, respectively, which implied that the two MODIS instruments had degraded slowly. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy
Remote Sens. 2016, 8(9), 755; doi:10.3390/rs8090755
Received: 19 July 2016 / Revised: 5 September 2016 / Accepted: 8 September 2016 / Published: 14 September 2016
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Abstract
Soil organic carbon (SOC) is an essential property for soil function, fertility and sustainability of agricultural systems. It can be measured with visible and near-infrared reflectance (VIS-NIR) spectroscopy efficiently based on empirical equations and spectra data for air/oven-dried samples. However, the spectral signal
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Soil organic carbon (SOC) is an essential property for soil function, fertility and sustainability of agricultural systems. It can be measured with visible and near-infrared reflectance (VIS-NIR) spectroscopy efficiently based on empirical equations and spectra data for air/oven-dried samples. However, the spectral signal is interfered with by soil moisture content (MC) under in situ conditions, which will affect the accuracy of measurements and calibration transfer among different areas. This study aimed to (1) quantify the influences of MC on SOC prediction by VIS-NIR spectroscopy; and (2) explore the potentials of orthogonal signal correction (OSC) and generalized least squares weighting (GLSW) methods in the removal of moisture interference. Ninety-eight samples were collected from the Jianghan plain, China, and eight MCs were obtained for each sample by a rewetting process. The VIS-NIR spectra of the rewetted soil samples were measured in the laboratory. Partial least squares regression (PLSR) was used to develop SOC prediction models. Specifically, three validation strategies, namely moisture level validation, transferability validation and mixed-moisture validation, were designed to test the potentials of OSC and GLSW in removing the MC effect. Results showed that all of the PLSR models generated at different moisture levels (e.g., 50–100, 250–300 g·kg−1) were moderately successful in SOC predictions (r2pre = 0.58–0.85, RPD = 1.55–2.55). These models, however, could not be transferred to soil samples with different moisture levels. OSC and GLSW methods are useful filter transformations improving model transferability. The GLSW-PLSR model (mean of r2pre = 0.77, root mean square error for prediction (RMSEP) = 3.08 g·kg−1, and residual prediction deviations (RPD) = 2.09) outperforms the OSC-PLSR model (mean of r2pre = 0.67, RMSEP = 3.67 g·kg−1, and RPD = 1.76) when the moisture-mixed protocol is used. Results demonstrated the use of OSC and GLSW combined with PLSR models for efficient estimation of SOC using VIS-NIR under different soil MC conditions. Full article
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Open AccessArticle Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
Remote Sens. 2016, 8(9), 756; doi:10.3390/rs8090756
Received: 25 February 2016 / Revised: 30 August 2016 / Accepted: 5 September 2016 / Published: 15 September 2016
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Abstract
The objectives of the study were to determine the spatial rate of CO2 flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization)
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The objectives of the study were to determine the spatial rate of CO2 flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ°) calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R2 = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ° VH and σ° VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ° Sentinel-1 VH, σ° Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R2 = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO2 to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring. Full article
(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)
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Open AccessArticle Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis
Remote Sens. 2016, 8(9), 757; doi:10.3390/rs8090757
Received: 14 May 2016 / Revised: 21 July 2016 / Accepted: 5 September 2016 / Published: 15 September 2016
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Abstract
We developed a method to enhance compositional mapping from spectral remote sensing through the integration of visible to near infrared (VNIR, ~0.4–1 µm), shortwave infrared (SWIR, ~1–2.5 µm), and longwave infrared (LWIR, ~8–13 µm) data. Spectral information from the individual ranges was first
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We developed a method to enhance compositional mapping from spectral remote sensing through the integration of visible to near infrared (VNIR, ~0.4–1 µm), shortwave infrared (SWIR, ~1–2.5 µm), and longwave infrared (LWIR, ~8–13 µm) data. Spectral information from the individual ranges was first analyzed independently and then the resulting compositional information in the form of image endmembers and apparent abundances was integrated using ISODATA cluster analysis. Independent VNIR, SWIR, and LWIR analyses of a study area near Mountain Pass, California identified image endmembers representing vegetation, manmade materials (e.g., metal, plastic), specific minerals (e.g., calcite, dolomite, hematite, muscovite, gypsum), and general lithology (e.g., sulfate-bearing, carbonate-bearing, and silica-rich units). Integration of these endmembers and their abundances produced a final full-range classification map incorporating much of the variation from all three spectral ranges. The integrated map and its 54 classes provide additional compositional information that is not evident in the VNIR, SWIR, or LWIR data alone, which allows for more complete and accurate compositional mapping. A supplemental examination of hyperspectral LWIR data and comparison with the multispectral LWIR data used in the integration illustrates its potential to further improve this approach. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Open AccessArticle Assessing the Severity of Wind Gusts with Lidar
Remote Sens. 2016, 8(9), 758; doi:10.3390/rs8090758
Received: 12 July 2016 / Revised: 2 September 2016 / Accepted: 8 September 2016 / Published: 14 September 2016
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Abstract
Lidars have gained a lot of popularity in the field of wind energy, partly because of their potential to be used for wind turbine control. By scanning the oncoming wind field, any threats such as gusts can be detected early and high loads
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Lidars have gained a lot of popularity in the field of wind energy, partly because of their potential to be used for wind turbine control. By scanning the oncoming wind field, any threats such as gusts can be detected early and high loads can be avoided by taking preventive actions. Unfortunately, lidars suffer from some inherent weaknesses that hinder measuring gusts; e.g., the averaging of high-frequency fluctuations and only measuring along the line of sight). This paper proposes a method to construct a useful signal from a lidar by fitting a homogeneous Gaussian velocity field to a set of scattered measurements. The output signal, an along-wind force, acts as a measure for the damaging potential of an oncoming gust and is shown to agree with the rotor-effective wind speed (a similar control input, but derived directly from the wind turbine’s shaft torque). Low data availability and the disadvantage of not knowing the velocity between the lidar beams is translated into uncertainty and integrated in the output signal. This allows a designer to establish a control strategy based on risk, with the ultimate goal to reduce the extreme loads during operation. Full article
(This article belongs to the Special Issue Remote Sensing of Wind Energy)
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Open AccessArticle Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake
Remote Sens. 2016, 8(9), 759; doi:10.3390/rs8090759
Received: 14 July 2016 / Revised: 27 August 2016 / Accepted: 9 September 2016 / Published: 14 September 2016
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Abstract
Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing
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Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes’ theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle Scale Effects of the Relationships between Urban Heat Islands and Impact Factors Based on a Geographically-Weighted Regression Model
Remote Sens. 2016, 8(9), 760; doi:10.3390/rs8090760
Received: 8 July 2016 / Revised: 14 August 2016 / Accepted: 9 September 2016 / Published: 15 September 2016
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Abstract
Urban heat island (UHI) effect, the side effect of rapid urbanization, has become an obstacle to the further healthy development of the city. Understanding its relationships with impact factors is important to provide useful information for climate adaptation urban planning strategies. For this
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Urban heat island (UHI) effect, the side effect of rapid urbanization, has become an obstacle to the further healthy development of the city. Understanding its relationships with impact factors is important to provide useful information for climate adaptation urban planning strategies. For this purpose, the geographically-weighted regression (GWR) approach is used to explore the scale effects in a mountainous city, namely the change laws and characteristics of the relationships between land surface temperature and impact factors at different spatial resolutions (30–960 m). The impact factors include the Soil-adjusted Vegetation Index (SAVI), the Index-based Built-up Index (IBI), and the Soil Brightness Index (NDSI), which indicate the coverage of the vegetation, built-up, and bare land, respectively. For reference, the ordinary least squares (OLS) model, a global regression technique, is also employed by using the same dependent variable and explanatory variables as in the GWR model. Results from the experiment exemplified by Chongqing showed that the GWR approach had a better prediction accuracy and a better ability to describe spatial non-stationarity than the OLS approach judged by the analysis of the local coefficient of determination (R2), Corrected Akaike Information Criterion (AICc), and F-test at small spatial resolution (< 240 m); however, when the spatial scale was increased to 480 m, this advantage has become relatively weak. This indicates that the GWR model becomes increasingly global, revealing the relationships with more generalized geographical patterns, and then spatial non-stationarity in the relationship will tend to be neglected with the increase of spatial resolution. Full article
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
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Open AccessArticle Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods
Remote Sens. 2016, 8(9), 761; doi:10.3390/rs8090761
Received: 11 May 2016 / Revised: 31 August 2016 / Accepted: 9 September 2016 / Published: 16 September 2016
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Abstract
Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived
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Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI) information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD) always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle A Simple Harmonic Model for FAPAR Temporal Dynamics in the Wetlands of the Volga-Akhtuba Floodplain
Remote Sens. 2016, 8(9), 762; doi:10.3390/rs8090762
Received: 27 May 2016 / Revised: 12 August 2016 / Accepted: 29 August 2016 / Published: 17 September 2016
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Abstract
The paper reports a technique used to construct a reference time series for the fraction of absorbed photosynthetically-active radiation (FAPAR) based on remotely-sensed data in the largest Russian arid wetland territory. For the arid Volga-Akhtuba wetlands, FAPAR appears to be an informative spectral
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The paper reports a technique used to construct a reference time series for the fraction of absorbed photosynthetically-active radiation (FAPAR) based on remotely-sensed data in the largest Russian arid wetland territory. For the arid Volga-Akhtuba wetlands, FAPAR appears to be an informative spectral index for estimating plant cover health and its seasonal and annual dynamics. Since FAPAR algorithms are developed for multiple satellite sensors, all FAPAR-based models are suggested to be universal and useful for future studies and long-term monitoring of plant cover, particularly in wetlands. The model developed in the present work for FAPAR temporal dynamics clearly reflects the field-observed seasonal and annual changes of plant cover in the Volga-Akhtuba floodplain wetlands. Various types of wetland plant communities were categorized by the specific parameters of the model seasonal vegetation curve. In addition, the values derived from the model function allow quantitative estimation of wetland plant cover health. This information is particularly important for the Volga-Akhtuba floodplain, because its hydrological regime is regulated by the Volzhskaya hydropower plant. The ecosystem is extremely fragile and sensitive to human impact, and wetland plant cover health is a key indicator of regulatory efficiency. The present study is another step towards developing a methodology focused on arid wetland vegetation monitoring and conservation of its biodiversity and natural conditions. Full article
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Open AccessArticle Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms
Remote Sens. 2016, 8(9), 763; doi:10.3390/rs8090763
Received: 15 May 2016 / Revised: 20 August 2016 / Accepted: 8 September 2016 / Published: 16 September 2016
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Abstract
Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify
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Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify land cover components of African savanna in wet and dry season. We compared the performance of Object-Based Image Analysis (OBIA) and pixel-based approach with several algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification and regression trees (CART) and support vector machines (SVM). Results showed that classifications of WV-2 imagery produce high accuracy results (>77%) regardless of the applied classification approach. However, OBIA had a significantly higher accuracy for almost every classifier with the highest overall accuracy score of 93%. Amongst tested classifiers, SVM and RF provided highest accuracies. Overall classifications of the wet season image provided better results with 93% for RF. However, considering woody leaf-off conditions, the dry season classification also performed well with overall accuracy of 83% (SVM) and high producer accuracy for the tree cover (91%). Our findings demonstrate the potential of imagery like WorldView-2 with OBIA and advanced supervised machine-learning algorithms in seasonal fine-scale land cover classification of African savanna. Full article
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Open AccessArticle Effect of High-Frequency Sea Waves on Wave Period Retrieval from Radar Altimeter and Buoy Data
Remote Sens. 2016, 8(9), 764; doi:10.3390/rs8090764
Received: 4 July 2016 / Revised: 19 August 2016 / Accepted: 12 September 2016 / Published: 17 September 2016
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Abstract
Wave periods estimated from satellite altimetry data behave differently from those calculated from buoy data, especially in low-wind conditions. In this paper, the geometric mean wave period Ta is calculated from buoy data, rather than the commonly used zero-crossing wave period T
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Wave periods estimated from satellite altimetry data behave differently from those calculated from buoy data, especially in low-wind conditions. In this paper, the geometric mean wave period T a is calculated from buoy data, rather than the commonly used zero-crossing wave period T z . The geometric mean wave period uses the fourth moment of the wave frequency spectrum and is related to the mean-square slope of the sea surface measured using altimeters. The values of T a obtained from buoys and altimeters agree well (root mean square difference: 0.2 s) only when the contribution of high-frequency sea waves is estimated by a wavenumber spectral model to complement the buoy data, because a buoy cannot obtain data from waves having wavelengths that are shorter than the characteristic dimension of the buoy. Full article
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Open AccessArticle The Sensitivity of AOD Retrieval to Aerosol Type and Vertical Distribution over Land with MODIS Data
Remote Sens. 2016, 8(9), 765; doi:10.3390/rs8090765
Received: 26 July 2016 / Revised: 10 September 2016 / Accepted: 13 September 2016 / Published: 17 September 2016
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Abstract
This study is to evaluate the sensitivity of Aerosol Optical Depth (AOD τ) to aerosol vertical profile and type, using the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 algorithm over land. Four experiments were performed, using different aerosol properties including 3 possible
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This study is to evaluate the sensitivity of Aerosol Optical Depth (AOD τ) to aerosol vertical profile and type, using the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 algorithm over land. Four experiments were performed, using different aerosol properties including 3 possible non-dust aerosol models and 14 vertical distributions. The algorithm intrinsic uncertainty was investigated as well as the interplay effect of aerosol vertical profile and type on the retrieval. The results show that the AOD retrieval is highly sensitive to aerosol vertical profile and type. With 4 aerosol vertical distributions, the algorithm with a fixed vertical distribution gives about 5% error in the AOD retrieval with aerosol loading τ 0 . 5 . With pure aerosols (smoke and dust), the retrieval of AOD shows errors ranging from 2% to 30% for a series of vertical distributions. Errors in aerosol type assumption in the algorithm can lead to errors of up to 8% in the AOD retrieval. The interplay effect can give the AOD retrieval errors by over 6%. In addition, intrinsic algorithm errors were found, with a value of >3% when τ> 3.0. This is due to the incorrect estimation of the surface reflectance. The results suggest that the MODIS algorithm can be improved by considering a realistic aerosol model and its vertical profile, and even further improved by reducing the algorithm intrinsic errors. Full article
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Open AccessArticle Estimating Ladder Fuels: A New Approach Combining Field Photography with LiDAR
Remote Sens. 2016, 8(9), 766; doi:10.3390/rs8090766
Received: 28 April 2016 / Accepted: 12 September 2016 / Published: 17 September 2016
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Abstract
Forests historically associated with frequent fire have changed dramatically due to fire suppression and past harvesting over the last century. The buildup of ladder fuels, which carry fire from the surface of the forest floor to tree crowns, is one of the critical
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Forests historically associated with frequent fire have changed dramatically due to fire suppression and past harvesting over the last century. The buildup of ladder fuels, which carry fire from the surface of the forest floor to tree crowns, is one of the critical changes, and it has contributed to uncharacteristically large and severe fires. The abundance of ladder fuels makes it difficult to return these forests to their natural fire regime or to meet management objectives. Despite the importance of ladder fuels, methods for quantifying them are limited and imprecise. LiDAR (Light Detection and Ranging), a form of active remote sensing, is able to estimate many aspects of forest structure across a landscape. This study investigates a new method for quantifying ladder fuel in the field (using photographs with a calibration banner) and remotely (using LiDAR data). We apply these new techniques in the Klamath Mountains of Northern California to predict ladder fuel levels across the study area. Our results demonstrate a new utility of LiDAR data to identify fire hazard and areas in need of fuels reduction. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessFeature PaperArticle Evaluation of Single Photon and Geiger Mode Lidar for the 3D Elevation Program
Remote Sens. 2016, 8(9), 767; doi:10.3390/rs8090767
Received: 28 June 2016 / Revised: 19 August 2016 / Accepted: 8 September 2016 / Published: 19 September 2016
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Abstract
Data acquired by Harris Corporation’s (Melbourne, FL, USA) Geiger-mode IntelliEarth™ sensor and Sigma Space Corporation’s (Lanham-Seabrook, MD, USA) Single Photon HRQLS sensor were evaluated and compared to accepted 3D Elevation Program (3DEP) data and survey ground control to assess the suitability of these
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Data acquired by Harris Corporation’s (Melbourne, FL, USA) Geiger-mode IntelliEarth™ sensor and Sigma Space Corporation’s (Lanham-Seabrook, MD, USA) Single Photon HRQLS sensor were evaluated and compared to accepted 3D Elevation Program (3DEP) data and survey ground control to assess the suitability of these new technologies for the 3DEP. While not able to collect data currently to meet USGS lidar base specification, this is partially due to the fact that the specification was written for linear-mode systems specifically. With little effort on part of the manufacturers of the new lidar systems and the USGS Lidar specifications team, data from these systems could soon serve the 3DEP program and its users. Many of the shortcomings noted in this study have been reported to have been corrected or improved upon in the next generation sensors. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle High-Resolution NDVI from Planet’s Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture
Remote Sens. 2016, 8(9), 768; doi:10.3390/rs8090768
Received: 5 July 2016 / Revised: 11 September 2016 / Accepted: 12 September 2016 / Published: 19 September 2016
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Abstract
Planet Labs (“Planet”) operate the largest fleet of active nano-satellites in orbit, offering an unprecedented monitoring capacity of daily and global RGB image capture at 3–5 m resolution. However, limitations in spectral resolution and lack of accurate radiometric sensor calibration impact the utility
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Planet Labs (“Planet”) operate the largest fleet of active nano-satellites in orbit, offering an unprecedented monitoring capacity of daily and global RGB image capture at 3–5 m resolution. However, limitations in spectral resolution and lack of accurate radiometric sensor calibration impact the utility of this rich information source. In this study, Planet’s RGB imagery was translated into a Normalized Difference Vegetation Index (NDVI): a common metric for vegetation growth and condition. Our framework employs a data mining approach to build a set of rule-based regression models that relate RGB data to atmospherically corrected Landsat-8 NDVI. The approach was evaluated over a desert agricultural landscape in Saudi Arabia where the use of near-coincident (within five days) Planet and Landsat-8 acquisitions in the training of the regression models resulted in NDVI predictabilities with an r2 of approximately 0.97 and a Mean Absolute Deviation (MAD) on the order of 0.014 (~9%). The MAD increased to 0.021 (~14%) when the Landsat NDVI training image was further away (i.e., 11–16 days) from the corrected Planet image. In these cases, the use of MODIS observations to inform on the change in NDVI occurring between overpasses was shown to significantly improve prediction accuracies. MAD levels ranged from 0.002 to 0.011 (3.9% to 9.1%) for the best performing 80% of the data. The technique is generic and extendable to any region of interest, increasing the utility of Planet’s dense time-series of RGB imagery. Full article
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Open AccessArticle Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea
Remote Sens. 2016, 8(9), 769; doi:10.3390/rs8090769
Received: 4 April 2016 / Revised: 24 August 2016 / Accepted: 12 September 2016 / Published: 20 September 2016
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Abstract
We study the wind climate and its long-term variability in the North Sea and South China Sea, areas relevant for offshore wind energy development, using satellite-based wind data, because very few reliable long-term in-situ sea surface wind observations are available. The Special Sensor
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We study the wind climate and its long-term variability in the North Sea and South China Sea, areas relevant for offshore wind energy development, using satellite-based wind data, because very few reliable long-term in-situ sea surface wind observations are available. The Special Sensor Microwave Imager (SSM/I) ocean winds extrapolated from 10 m to 100 m using the Charnock relationship and the logarithmic profile method are compared to Weather Research and Forecasting (WRF) model results in both seas and to in-situ observations in the North Sea. The mean wind speed from SSM/I and WRF differ only by 0.1 m/s at Fino1 in the North Sea, while west of Hainan in the South China Sea the difference is 1.0 m/s. Linear regression between SSM/I and WRF winds at 100 m show correlation coefficients squared of 0.75 and 0.67, standard deviation of 1.67 m/s and 1.41 m/s, and mean difference of −0.12 m/s and 0.83 m/s for Fino1 and Hainan, respectively. The WRF-derived winds overestimate the values in the South China Sea. The inter-annual wind speed variability is estimated as 4.6% and 4.4% based on SSM/I at Fino1 and Hainan, respectively. We find significant changes in the seasonal wind pattern at Fino1 with springtime winds arriving one month earlier from 1988 to 2013 and higher winds in June; no yearly trend in wind speed is observed in the two seas. Full article
(This article belongs to the Special Issue Remote Sensing of Wind Energy)
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Open AccessArticle Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed
Remote Sens. 2016, 8(9), 770; doi:10.3390/rs8090770
Received: 1 July 2016 / Revised: 15 August 2016 / Accepted: 9 September 2016 / Published: 19 September 2016
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Abstract
Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the
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Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellites were combined with population variables to be statistically compared against reported cases of Dengue Fever in the Río Magdalena watershed, Colombia. Three-factor analysis models were investigated to analyze variable patterns, including a population, population density, and empirical Bayesian estimation model. Results identified varying levels of Dengue Fever transmission risk, and environmental characteristics which support, and advance, the research literature. Multiple temperature metrics, elevation, and vegetation composition were among the more contributory variables found to identify future potential outbreak locations. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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