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Remote Sens., Volume 6, Issue 5 (May 2014), Pages 3533-4646

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Open AccessArticle Multi-Polarization ASAR Backscattering from Herbaceous Wetlands in Poyang Lake Region, China
Remote Sens. 2014, 6(5), 4621-4646; https://doi.org/10.3390/rs6054621
Received: 13 December 2013 / Revised: 25 April 2014 / Accepted: 28 April 2014 / Published: 22 May 2014
Cited by 8 | PDF Full-text (1261 KB) | HTML Full-text | XML Full-text
Abstract
Wetlands are one of the most important ecosystems on Earth. There is an urgent need to quantify the biophysical parameters (e.g., plant height, aboveground biomass) and map total remaining areas of wetlands in order to evaluate the ecological status of wetlands. In this
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Wetlands are one of the most important ecosystems on Earth. There is an urgent need to quantify the biophysical parameters (e.g., plant height, aboveground biomass) and map total remaining areas of wetlands in order to evaluate the ecological status of wetlands. In this study, Environmental Satellite/Advanced Synthetic Aperture Radar (ENVISAT/ASAR) dual-polarization C-band data acquired in 2005 is tested to investigate radar backscattering mechanisms with the variation of hydrological conditions during the growing cycle of two types of herbaceous wetland species, which colonize lake borders with different elevation in Poyang Lake region, China. Phragmites communis (L.) Trin. is semi-aquatic emergent vegetation with vertical stem and blade-like leaves, and the emergent Carex spp. has rhizome and long leaves. In this study, the potential of ASAR data in HH-, HV-, and VV-polarization in mapping different wetland types is examined, by observing their dynamic variations throughout the whole flooding cycle. The sensitivity of ASAR backscattering coefficients to vegetation parameters of plant height, fresh and dry biomass, and vegetation water content is also analyzed for Phragmites communis (L.) Trin. and Carex spp. The research for Phragmites communis (L.) Trin. shows that HH polarization is more sensitive to plant height and dry biomass than HV polarization. ASAR backscattering coefficients are relatively less sensitive to fresh biomass, especially in HV polarization. However, both are highly dependent on canopy water content. In contrast, the dependence of HH- and HV- backscattering from Carex community on vegetation parameters is poor, and the radar backscattering mechanism is controlled by ground water level. Full article
Open AccessArticle External Validation of the ASTER GDEM2, GMTED2010 and CGIAR-CSI- SRTM v4.1 Free Access Digital Elevation Models (DEMs) in Tunisia and Algeria
Remote Sens. 2014, 6(5), 4600-4620; https://doi.org/10.3390/rs6054600
Received: 24 February 2014 / Revised: 24 April 2014 / Accepted: 13 May 2014 / Published: 21 May 2014
Cited by 25 | PDF Full-text (2147 KB) | HTML Full-text | XML Full-text
Abstract
Digital Elevation Models (DEMs) including Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Shuttle Radar Topography Mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available for nearly the entire earth’s surface. DEMs that are usually
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Digital Elevation Models (DEMs) including Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Shuttle Radar Topography Mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available for nearly the entire earth’s surface. DEMs that are usually subject to errors need to be evaluated using reference elevation data of higher accuracy. This work was performed to assess the vertical accuracy of the ASTER GDEM version 2, (ASTER GDEM2), the Consultative Group on International Agriculture Research-Consortium for Spatial Information (CGIAR-CSI) SRTM version 4.1 (SRTM v4.1) and the systematic subsample GMTED2010, at their original spatial resolution, using Global Navigation Satellite Systems (GNSS) validation points. Two test sites, the Anaguid Saharan platform in southern Tunisia and the Tebessa basin in north eastern Algeria, were chosen for accuracy assessment of the above mentioned DEMs, based on geostatistical and statistical measurements. Within the geostatistical approach, empirical variograms of each DEM were compared with those of the GPS validation points. Statistical measures were computed from the elevation differences between the DEM pixel value and the corresponding GPS point. For each DEM, a Root Mean Square Error (RMSE) was determined for model validation. In addition, statistical tools such as frequency histograms and Q-Q plots were used to evaluate error distributions in each DEM. The results indicate that the vertical accuracy of SRTM model is much higher than ASTER GDEM2 and GMTED2010 for both sites. In Anaguid test site, the vertical accuracy of SRTM is estimated 3.6 m (in terms of RMSE) 5.3 m and 4.5 m for the ASTERGDEM2 and GMTED2010 DEMs, respectively. In Tebessa test site, the overall vertical accuracy shows a RMSE of 9.8 m, 8.3 m and 9.6 m for ASTER GDEM 2, SRTM and GMTED2010 DEM, respectively. This work is the first study to report the lower accuracy of ASTER GDEM2 compared to the GMTED2010 data. Full article
Open AccessArticle Improving Classification of Airborne Laser Scanning Echoes in the Forest-Tundra Ecotone Using Geostatistical and Statistical Measures
Remote Sens. 2014, 6(5), 4582-4599; https://doi.org/10.3390/rs6054582
Received: 26 March 2014 / Revised: 12 May 2014 / Accepted: 13 May 2014 / Published: 21 May 2014
Cited by 3 | PDF Full-text (584 KB) | HTML Full-text | XML Full-text
Abstract
The vegetation in the forest-tundra ecotone zone is expected to be highly affected by climate change and requires effective monitoring techniques. Airborne laser scanning (ALS) has been proposed as a tool for the detection of small pioneer trees for such vast areas using
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The vegetation in the forest-tundra ecotone zone is expected to be highly affected by climate change and requires effective monitoring techniques. Airborne laser scanning (ALS) has been proposed as a tool for the detection of small pioneer trees for such vast areas using laser height and intensity data. The main objective of the present study was to assess a possible improvement in the performance of classifying tree and nontree laser echoes from high-density ALS data. The data were collected along a 1000 km long transect stretching from southern to northern Norway. Different geostatistical and statistical measures derived from laser height and intensity values were used to extent and potentially improve more simple models ignoring the spatial context. Generalised linear models (GLM) and support vector machines (SVM) were employed as classification methods. Total accuracies and Cohen’s kappa coefficients were calculated and compared to those of simpler models from a previous study. For both classification methods, all models revealed total accuracies similar to the results of the simpler models. Concerning classification performance, however, the comparison of the kappa coefficients indicated a significant improvement for some models both using GLM and SVM, with classification accuracies >94%. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
Open AccessArticle The Improved NRL Tropical Cyclone Monitoring System with a Unified Microwave Brightness Temperature Calibration Scheme
Remote Sens. 2014, 6(5), 4563-4581; https://doi.org/10.3390/rs6054563
Received: 14 November 2013 / Revised: 26 April 2014 / Accepted: 6 May 2014 / Published: 19 May 2014
Cited by 4 | PDF Full-text (3416 KB) | HTML Full-text | XML Full-text
Abstract
The near real-time NRL global tropical cyclone (TC) monitoring system based on multiple satellite passive microwave (PMW) sensors is improved with a new inter-sensor calibration scheme to correct the biases caused by differences in these sensor’s high frequency channels. Since the PMW sensor
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The near real-time NRL global tropical cyclone (TC) monitoring system based on multiple satellite passive microwave (PMW) sensors is improved with a new inter-sensor calibration scheme to correct the biases caused by differences in these sensor’s high frequency channels. Since the PMW sensor 89 GHz channel is used in multiple current and near future operational and research satellites, a unified scheme to calibrate all satellite PMW sensor’s ice scattering channels to a common 89 GHz is created so that their brightness temperatures (TBs) will be consistent and permit more accurate manual and automated analyses. In order to develop a physically consistent calibration scheme, cloud resolving model simulations of a squall line system over the west Pacific coast and hurricane Bonnie in the Atlantic Ocean are applied to simulate the views from different PMW sensors. To clarify the complicated TB biases due to the competing nature of scattering and emission effects, a four-cloud based calibration scheme is developed (rain, non-rain, light rain, and cloudy). This new physically consistent inter-sensor calibration scheme is then evaluated with the synthetic TBs of hurricane Bonnie and a squall line as well as observed TCs. Results demonstrate the large TB biases up to 13 K for heavy rain situations before calibration between TMI and AMSR-E are reduced to less than 3 K after calibration. The comparison stats show that the overall bias and RMSE are reduced by 74% and 66% for hurricane Bonnie, and 98% and 85% for squall lines, respectively. For the observed hurricane Igor, the bias and RMSE decrease 41% and 25% respectively. This study demonstrates the importance of TB calibrations between PMW sensors in order to systematically monitor the global TC life cycles in terms of intensity, inner core structure and convective organization. A physics-based calibration scheme on TC’s TB corrections developed in this study is able to significantly reduce the biases between different PMW sensors. Full article
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Open AccessArticle Scattering Mechanisms for the “Ear” Feature of Lop Nur Lake Basin
Remote Sens. 2014, 6(5), 4546-4562; https://doi.org/10.3390/rs6054546
Received: 23 December 2013 / Revised: 25 April 2014 / Accepted: 29 April 2014 / Published: 16 May 2014
Cited by 7 | PDF Full-text (1305 KB) | HTML Full-text | XML Full-text
Abstract
Lop Nur is a famous dry lake in the arid region of China. It was an important section of the ancient “Silk Road”, famous in history as the prosperous communication channel between Eastern and Western cultures. At present, there is no surface water
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Lop Nur is a famous dry lake in the arid region of China. It was an important section of the ancient “Silk Road”, famous in history as the prosperous communication channel between Eastern and Western cultures. At present, there is no surface water in Lop Nur Lake basin, and on SAR (Synthetic Aperture Radar) images, it looks like an “Ear”. The objective of this paper is to interpret the Lop Nur phenomenon from the perspective of scattering mechanisms. Based on field investigation and analysis of sample properties, a two-layer scattering structure is proposed with detailed explanations of scattering mechanisms. In view of the rough surface, the MIEM (Modified Integral Equation Model) was introduced to represent air-surface scattering in Lop Nur. Then, a two-layer scattering model was developed which can describe surface scattering contribution. Using polarimetric decomposition, validations were carried out, and the RMSE (root mean square error) values for the HH and VV polarizations were found to be 1.67 dB and 1.06 dB, respectively. Furthermore, according to model parametric analysis, surface roughness was identified as an apparent reason for the “Ear” feature. In addition, the polarimetric decomposition result also showed that the volume scattering part had rich texture information and could portray the “Ear” feature exactly compared with the other two parts. It is maintained that subsurface properties, mainly generating volume scattering, can determine the surface roughness under the certain climate conditions, according to geomorphological dynamics, which can help to develop an inversion technology for Lop Nur. Full article
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Open AccessArticle Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality
Remote Sens. 2014, 6(5), 4515-4545; https://doi.org/10.3390/rs6054515
Received: 12 March 2014 / Revised: 4 May 2014 / Accepted: 7 May 2014 / Published: 16 May 2014
Cited by 23 | PDF Full-text (1686 KB) | HTML Full-text | XML Full-text
Abstract
Forest disturbances in central Europe caused by fungal pests may result in widespread tree mortality. To assess the state of health and to detect disturbances of entire forest ecosystems, up-to-date knowledge of the tree species diversity is essential. The German state Mecklenburg–Vorpommern is
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Forest disturbances in central Europe caused by fungal pests may result in widespread tree mortality. To assess the state of health and to detect disturbances of entire forest ecosystems, up-to-date knowledge of the tree species diversity is essential. The German state Mecklenburg–Vorpommern is severely affected by ash (Fraxinus excelsior) dieback caused by the fungal pathogen Hymenoscyphus pseudoalbidus. In this study, species diversity and the magnitude of ash mortality was assessed by classifying seven different tree species and multiple levels of damaged ash. The study is based on a multispectral WorldView-2 (WV-2) scene and uses object-based supervised classification methods based on multinomial logistic regressions. Besides the original multispectral image, a set of remote sensing indices (RSI) was derived, which significantly improved the accuracies of classifying different levels of damaged ash but only slightly improved tree species classification. The large number of features was reduced by three approaches, of which the linear discriminant analysis (LDA) clearly outperformed the more commonly used principal component analysis (PCA) and a stepwise selection method. Promising overall accuracies (83%) for classifying seven tree species and (73%) for classifying four different levels of damaged ash were obtained. Detailed tree damage and tree species maps were visually inspected using aerial images. The results are of high relevance for forest managers to plan appropriate cutting and reforestation measures to decrease ash dieback over entire regions. Full article
Open AccessArticle On the Atmospheric Correction of Antarctic Airborne Hyperspectral Data
Remote Sens. 2014, 6(5), 4498-4514; https://doi.org/10.3390/rs6054498
Received: 10 January 2014 / Revised: 9 May 2014 / Accepted: 9 May 2014 / Published: 16 May 2014
Cited by 5 | PDF Full-text (1851 KB) | HTML Full-text | XML Full-text
Abstract
The first airborne hyperspectral campaign in the Antarctic Peninsula region was carried out by the British Antarctic Survey and partners in February 2011. This paper presents an insight into the applicability of currently available radiative transfer modelling and atmospheric correction techniques for processing
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The first airborne hyperspectral campaign in the Antarctic Peninsula region was carried out by the British Antarctic Survey and partners in February 2011. This paper presents an insight into the applicability of currently available radiative transfer modelling and atmospheric correction techniques for processing airborne hyperspectral data in this unique coastal Antarctic environment. Results from the Atmospheric and Topographic Correction version 4 (ATCOR-4) package reveal absolute reflectance values somewhat in line with laboratory measured spectra, with Root Mean Square Error (RMSE) values of 5% in the visible near infrared (0.4–1 µm) and 8% in the shortwave infrared (1–2.5 µm). Residual noise remains present due to the absorption by atmospheric gases and aerosols, but certain parts of the spectrum match laboratory measured features very well. This study demonstrates that commercially available packages for carrying out atmospheric correction are capable of correcting airborne hyperspectral data in the challenging environment present in Antarctica. However, it is anticipated that future results from atmospheric correction could be improved by measuring in situ atmospheric data to generate atmospheric profiles and aerosol models, or with the use of multiple ground targets for calibration and validation. Full article
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Open AccessArticle Time Series Analysis of Land Cover Change: Developing Statistical Tools to Determine Significance of Land Cover Changes in Persistence Analyses
Remote Sens. 2014, 6(5), 4473-4497; https://doi.org/10.3390/rs6054473
Received: 23 January 2014 / Revised: 22 April 2014 / Accepted: 4 May 2014 / Published: 14 May 2014
Cited by 13 | PDF Full-text (1579 KB) | HTML Full-text | XML Full-text
Abstract
Despite the existence of long term remotely sensed datasets, change detection methods are limited and often remain an obstacle to the effective use of time series approaches in remote sensing applications to Land Change Science. This paper establishes some simple statistical tests to
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Despite the existence of long term remotely sensed datasets, change detection methods are limited and often remain an obstacle to the effective use of time series approaches in remote sensing applications to Land Change Science. This paper establishes some simple statistical tests to be applied to NDVI-derived time series of remotely sensed data products. Specifically, the methods determine the statistical significance of three separate metrics of the persistence of vegetation cover or changes within a landscape by comparison to various forms of “benchmarks”; directional persistence (changes in sign relative to some fixed reference value), relative directional persistence (changes in sign relative to the preceding value), and massive persistence (changes in magnitude relative to the preceding value). Null hypotheses are developed on the basis of serially independent, normally distributed random variables. Critical values are established theoretically through consideration of the numeric properties of those variables, application of extensive Monte Carlo simulations, and parallels to random walk processes. Monthly pixel-level NDVI values for the state of Florida are analyzed over 25 years, illustrating the techniques’ abilities to identify areas and/or times of significant change, and facilitate a more detailed understanding of this landscape. The potential power and utility of such techniques is diverse within the area of remote sensing studies and Land Change Science, especially in the context of global change. Full article
Open AccessArticle Shallow-Water Benthic Identification Using Multispectral Satellite Imagery: Investigation on the Effects of Improving Noise Correction Method and Spectral Cover
Remote Sens. 2014, 6(5), 4454-4472; https://doi.org/10.3390/rs6054454
Received: 15 January 2014 / Revised: 6 May 2014 / Accepted: 6 May 2014 / Published: 14 May 2014
Cited by 7 | PDF Full-text (1555 KB) | HTML Full-text | XML Full-text
Abstract
Lyzenga’s method is used widely for radiative transfer analysis because of its simplicity of application to cases of shallow-water coral reef ecosystems with limited information of water properties. WorldView-2 imagery has been used previously to study bottom-type identification in shallow-water coral reef habitats.
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Lyzenga’s method is used widely for radiative transfer analysis because of its simplicity of application to cases of shallow-water coral reef ecosystems with limited information of water properties. WorldView-2 imagery has been used previously to study bottom-type identification in shallow-water coral reef habitats. However, this is the first time WorldView-2 imagery has been applied to bottom-type identification using Lyzenga’s method. This research applied both of Lyzenga’s methods: the original from 1981 and the one from 2006 with improved noise correction that uses the near-infrared (NIR) band. The objectives of this study are to examine whether the utilization of NIR bands in the correction of atmospheric and sea-surface scattering improves the accuracy of bottom classification, and whether increasing the number of visible bands also improves accuracy. Firstly, it has been determined that the improved 2006 correction method, which uses NIR bands, is only more accurate than the original 1981 correction method in the case of three visible bands. When applying six bands, the accuracy of the 1981 correction method is better than that of the 2006 correction method. Secondly, the increased number of visible bands, when applied to Lyzenga’s empirical radiative transfer model, improves the accuracy of bottom classification significantly. Full article
Open AccessArticle Estimating Canopy Nitrogen Content in a Heterogeneous Grassland with Varying Fire and Grazing Treatments: Konza Prairie, Kansas, USA
Remote Sens. 2014, 6(5), 4430-4453; https://doi.org/10.3390/rs6054430
Received: 30 January 2014 / Revised: 22 April 2014 / Accepted: 25 April 2014 / Published: 14 May 2014
Cited by 8 | PDF Full-text (2392 KB) | HTML Full-text | XML Full-text
Abstract
Quantitative, spatially explicit estimates of canopy nitrogen are essential for understanding the structure and function of natural and managed ecosystems. Methods for extracting nitrogen estimates via hyperspectral remote sensing have been an active area of research. Much of this research has been conducted
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Quantitative, spatially explicit estimates of canopy nitrogen are essential for understanding the structure and function of natural and managed ecosystems. Methods for extracting nitrogen estimates via hyperspectral remote sensing have been an active area of research. Much of this research has been conducted either in the laboratory, or in relatively uniform canopies such as crops. Efforts to assess the feasibility of the use of hyperspectral analysis in heterogeneous canopies with diverse plant species and canopy structures have been less extensive. In this study, we use in situ and aircraft hyperspectral data to assess several empirical methods for extracting canopy nitrogen from a tallgrass prairie with varying fire and grazing treatments. The remote sensing data were collected four times between May and September in 2011, and were then coupled with the field-measured leaf nitrogen levels for empirical modeling of canopy nitrogen content based on first derivatives, continuum-removed reflectance and ratio-based indices in the 562–600 nm range. Results indicated that the best-performing model type varied between in situ and aircraft data in different months. However, models from the pooled samples over the growing season with acceptable accuracy suggested that these methods are robust with respect to canopy heterogeneity across spatial and temporal scales. Full article
Open AccessArticle Estimation of the Image Interpretability of ZY-3 Sensor Corrected Panchromatic Nadir Data
Remote Sens. 2014, 6(5), 4409-4429; https://doi.org/10.3390/rs6054409
Received: 14 March 2014 / Revised: 21 April 2014 / Accepted: 6 May 2014 / Published: 14 May 2014
Cited by 6 | PDF Full-text (1519 KB) | HTML Full-text | XML Full-text
Abstract
Image quality is important for taking full advantage of satellite data. As a common indicator, the National Imagery Interpretability Scale (NIIRS) is widely used for image quality assessment and provides a comprehensive representation of image quality from the perspective of interpretability. The ZY-3
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Image quality is important for taking full advantage of satellite data. As a common indicator, the National Imagery Interpretability Scale (NIIRS) is widely used for image quality assessment and provides a comprehensive representation of image quality from the perspective of interpretability. The ZY-3 (Ziyuan-3) satellite is the first civil high resolution mapping satellite in China, which was established in 2012. So far, there has been no reports on adopting NIIRS as the common indicator for the quality assessment of that satellite image data. This lack of a common quality indicator results in a gap between satellite data users around the world and those in China regarding the understanding of the quality and usability of ZY-3 data. To overcome the gap, using the general image-quality equation (GIQE), this study evaluates the ZY-3 sensor-corrected (SC) panchromatic nadir (NAD) data in terms of the NIIRS. In order to solve the uncertainty resulting from the exceeding of the ground sample distance (GSD) of ZY-3 data (2.1 m) in GIQE (less than 2.03 m), eight images are used to establish the relationship between the manually obtained NIIRS and the GIQE predicted NIIRS. An adjusted GIQE is based on the relationship and verified by another five images. Our study demonstrates that the method of using adjusted GIQE for calculating NIIRS can be used for the quality assessment of ZY-3 satellite images and reveals that the NIIRS value of ZY-3 SC NAD data is about 2.79. Full article
(This article belongs to the Special Issue Satellite Mapping Technology and Application)
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Open AccessArticle On-Orbit Geometric Calibration Model and Its Applications for High-Resolution Optical Satellite Imagery
Remote Sens. 2014, 6(5), 4391-4408; https://doi.org/10.3390/rs6054391
Received: 13 January 2014 / Revised: 25 April 2014 / Accepted: 4 May 2014 / Published: 14 May 2014
Cited by 33 | PDF Full-text (757 KB) | HTML Full-text | XML Full-text
Abstract
On-orbit geometric calibration is a key technology to guarantee the geometric quality of high-resolution optical satellite imagery. In this paper, we present an approach for the on-orbit geometric calibration of high-resolution optical satellite imagery, focusing on two core problems: constructing an on-orbit geometric
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On-orbit geometric calibration is a key technology to guarantee the geometric quality of high-resolution optical satellite imagery. In this paper, we present an approach for the on-orbit geometric calibration of high-resolution optical satellite imagery, focusing on two core problems: constructing an on-orbit geometric calibration model and proposing a robust calculation method. First, a rigorous geometric imaging model is constructed based on the analysis of the major error sources. Second, we construct an on-orbit geometric calibration model through performing reasonable optimizing and parameter selection of the rigorous geometric imaging model. On this basis, the calibration parameters are partially calculated with a stepwise iterative method by dividing them into two groups: external and internal calibration parameters. Furthermore, to verify the effectiveness of the proposed calibration model and methodology, on-orbit geometric calibration experiments for ZY1-02C panchromatic camera and ZY-3 three-line array camera are conducted using the reference data of the Songshan calibration test site located in the Henan Province, China. The experimental results demonstrate a certain deviation of the on-orbit calibration result from the initial design values of the calibration parameters. Therefore, on-orbit geometric calibration is necessary for optical satellite imagery. On the other hand, by choosing multiple images, which cover different areas and are acquired at different points in time to verify their geometric accuracy before and after calibration, we find that after on-orbit geometric calibration, the geometric accuracy of these images without ground control points is significantly improved. Additionally, due to the effective elimination of the internal distortion of the camera, greater geometric accuracy was achieved with less ground control points than before calibration. Full article
(This article belongs to the Special Issue Satellite Mapping Technology and Application)
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Open AccessArticle Daily Evaporative Fraction Parameterization Scheme Driven by Day–Night Differences in Surface Parameters: Improvement and Validation
Remote Sens. 2014, 6(5), 4369-4390; https://doi.org/10.3390/rs6054369
Received: 31 March 2014 / Revised: 29 April 2014 / Accepted: 4 May 2014 / Published: 12 May 2014
Cited by 8 | PDF Full-text (1595 KB) | HTML Full-text | XML Full-text
Abstract
In a previous study, a daily evaporative fraction (EF) parameterization scheme was derived based on day–night differences in surface temperature, air temperature, and net radiation. Considering the advantage that incoming solar radiation can be readily retrieved from remotely sensed data in comparison with
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In a previous study, a daily evaporative fraction (EF) parameterization scheme was derived based on day–night differences in surface temperature, air temperature, and net radiation. Considering the advantage that incoming solar radiation can be readily retrieved from remotely sensed data in comparison with surface net radiation, this study simplified the daily EF parameterization scheme using incoming solar radiation as an input. Daily EF estimates from the simplified scheme were nearly equivalent to the results from the original scheme. In situ measurements from six Ameriflux sites with different land covers were used to validate the new simplified EF parameterization scheme. Results showed that daily EF estimates for clear skies were consistent with the in situ EF corrected by the residual energy method, showing a coefficient of determination of 0.586 and a root mean square error of 0.152. Similar results were also obtained for partly clear sky conditions. The non-closure of the measured energy and heat fluxes and the uncertainty in determining fractional vegetation cover were likely to cause discrepancies in estimated daily EF and measured counterparts. The daily EF estimates of different land covers indicate that the constant coefficients in the simplified EF parameterization scheme are not strongly site-specific. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
Open AccessArticle Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling
Remote Sens. 2014, 6(5), 4345-4368; https://doi.org/10.3390/rs6054345
Received: 27 November 2013 / Revised: 17 April 2014 / Accepted: 21 April 2014 / Published: 12 May 2014
Cited by 19 | PDF Full-text (1006 KB) | HTML Full-text | XML Full-text
Abstract
Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC
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Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC (Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean “dehesa” ecosystem) projects LST retrieved from Landsat data is required to integrate ground-based observations of energy, water, and carbon fluxes with multi-scale remotely-sensed data and assess water and carbon balance in ecologically fragile heterogeneous ecosystem of Mediterranean wooded grassland (dehesa). Thus, three methods based on the Radiative Transfer Equation were used to extract LST from a series of 2009–2011 Landsat-5 TM images to assess the applicability for temperature input generation to a Landsat-MODIS LST integration. When compared to surface temperatures simulated using MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) with atmospheric profiles inputs (LSTref), values from Single-Channel (SC) algorithm are the closest (root-mean-square deviation (RMSD) = 0.50 °C); procedure based on the online Radiative Transfer Equation Atmospheric Correction Parameters Calculator (RTE-ACPC) shows RMSD = 0.85 °C; Mono-Window algorithm (MW) presents the highest RMSD (2.34 °C) with systematical LST underestimation (bias = 1.81 °C). Differences between Landsat-retrieved LST and MODIS LST are in the range of 2 to 4 °C and can be explained mainly by differences in observation geometry, emissivity, and time mismatch between Landsat and MODIS overpasses. There is a seasonal bias in Landsat-MODIS LST differences due to greater variations in surface emissivity and thermal contrasts between landcover components. Full article
Open AccessArticle Tree Stem and Height Measurements using Terrestrial Laser Scanning and the RANSAC Algorithm
Remote Sens. 2014, 6(5), 4323-4344; https://doi.org/10.3390/rs6054323
Received: 3 March 2014 / Revised: 29 April 2014 / Accepted: 6 May 2014 / Published: 12 May 2014
Cited by 51 | PDF Full-text (745 KB) | HTML Full-text | XML Full-text
Abstract
Terrestrial laser scanning is a promising technique for automatic measurements of tree stems. The objectives of the study were (1) to develop and validate a new method for the detection, classification and measurements of tree stems and canopies using the Hough transformation and
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Terrestrial laser scanning is a promising technique for automatic measurements of tree stems. The objectives of the study were (1) to develop and validate a new method for the detection, classification and measurements of tree stems and canopies using the Hough transformation and the RANSAC algorithm and (2) assess the influence of distance to the scanner on the measurement accuracy. Tree detection and stem diameter estimates were validated for 16 circular plots with 20 m radius. The three dominating tree species were Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.) and birch (Betula spp.). The proportion of detected trees decreased as the distance to the scanner increased and followed the trend of decreasing visible area. Within 10 m from the scanner, the proportion of detected trees was 87% on average for the plots and the diameter at breast height was estimated with a relative root-mean-square-error (RMSE) of 14%. The most accurate diameter measurements were obtained for pine, which had a RMSE of 7% for all the full 20 m radius plots. The RANSAC algorithm reduced noise and made it possible to obtain reliable estimates. Full article
Open AccessArticle Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes
Remote Sens. 2014, 6(5), 4305-4322; https://doi.org/10.3390/rs6054305
Received: 21 January 2014 / Revised: 14 April 2014 / Accepted: 29 April 2014 / Published: 9 May 2014
Cited by 10 | PDF Full-text (1135 KB) | HTML Full-text | XML Full-text
Abstract
The transferability of a visible and near-infrared (VNIR) model for soil organic matter (SOM) estimation in riparian landscapes is explored. The results indicate that for the soil samples with air-drying, grinding and 2-mm sieving pretreatment, the model calibrated from the soil sample set
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The transferability of a visible and near-infrared (VNIR) model for soil organic matter (SOM) estimation in riparian landscapes is explored. The results indicate that for the soil samples with air-drying, grinding and 2-mm sieving pretreatment, the model calibrated from the soil sample set with mixed land-use types can be applied in the SOM prediction of cropland soil samples (r2Pre = 0.66, RMSE = 2.78 g∙kg−1, residual prediction deviation (RPD) = 1.45). The models calibrated from cropland soil samples, however, cannot be transferred to the SOM prediction of soil samples with diverse land-use types and different SOM ranges. Wavelengths in the region of 350–800 nm and around 1900 nm are important for SOM estimation. The correlation analysis reveals that the spectral wavelengths from the soil samples with and without the air-drying, grinding and 2-mm sieving pretreatment are not linearly correlated at each wavelength in the region of 350–1000 nm, which is an important spectral region for SOM estimation in riparian landscapes. This result explains why the models calibrated from samples without pretreatment fail in the SOM estimation. The Kennard–Stone algorithm performed well in the selection of a representative subset for SOM estimation using the spectra of soil samples with pretreatment, but failed in soil samples without the pretreatment. Our study also demonstrates that a widely applicable SOM prediction model for riparian landscapes should be based on a wide range of SOM content. Full article
Open AccessArticle Quantifying Responses of Spectral Vegetation Indices to Dead Materials in Mixed Grasslands
Remote Sens. 2014, 6(5), 4289-4304; https://doi.org/10.3390/rs6054289
Received: 5 December 2013 / Revised: 29 April 2014 / Accepted: 4 May 2014 / Published: 8 May 2014
Cited by 5 | PDF Full-text (724 KB) | HTML Full-text | XML Full-text
Abstract
Spectral vegetation indices have been the primary resources for characterizing grassland vegetation based on remotely sensed data. However, the use of spectral indices for vegetation characterization in grasslands has been challenged by the confounding effects from external factors, such as soil properties, dead
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Spectral vegetation indices have been the primary resources for characterizing grassland vegetation based on remotely sensed data. However, the use of spectral indices for vegetation characterization in grasslands has been challenged by the confounding effects from external factors, such as soil properties, dead materials, and shadowing of vegetation canopies. Dead materials refer to the dead component of vegetation, including fallen litter and standing dead grasses accumulated from previous years. The abundant dead materials have been presenting challenges to accurately estimate green vegetation using spectral vegetation indices (VIs) derived from remote sensing data in mixed grasslands. Therefore, a close investigation of the relationship between VIs and dead materials is needed. The identified relationships could provide better insight into not only using remote sensing data for quantitative estimation of dead materials, but also the improvement of green vegetation estimation in the mixed grassland that has a high proportion of dead materials. In this article, the spectral reflectance of dead materials and green vegetation mixtures and dead material cover were measured in mixed grasslands located in Grassland National Park (GNP), Saskatchewan, Canada. Nine VIs were derived from the measured spectral reflectance. The relationship between dead material cover and VIs was quantified using the regression model and sensitivity analysis. Results indicated that the relationship between dead material cover and VIs is a function of the amount of dead material cover. Weak positive relationship was found between VIs and dead materials where the cover was less than 50%, and a significant high negative relationship was evident when cover was greater than 50%. When the combined exponential and linear model was applied to fit the negative relationships, more than 90% variation in dead material cover could be explained by VIs. Sensitivity analysis was further applied to the developed models, indicating that sensitivities of all VIs were significant over the entire range of dead material cover except for the triangular vegetation index (TVI), which has insignificant sensitivity when dead material cover was greater than 94%. Among all VIs, the weighted difference vegetation index (WDVI) had the highest sensitivity to changes in dead material cover higher than 50%. The results from this study indicated that vegetation indices based on combination of reflectance in red and NIR bands can be used to estimate dead material cover that is greater than 50%. Full article
Open AccessArticle Global Ecosystem Response Types Derived from the Standardized Precipitation Evapotranspiration Index and FPAR3g Series
Remote Sens. 2014, 6(5), 4266-4288; https://doi.org/10.3390/rs6054266
Received: 16 January 2014 / Revised: 14 April 2014 / Accepted: 21 April 2014 / Published: 8 May 2014
Cited by 4 | PDF Full-text (1661 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Observing trends in global ecosystem dynamics is an important first step, but attributing these trends to climate variability represents a further step in understanding Earth system changes. In the present study, we classified global Ecosystem Response Types (ERTs) based on common spatio-temporal patterns
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Observing trends in global ecosystem dynamics is an important first step, but attributing these trends to climate variability represents a further step in understanding Earth system changes. In the present study, we classified global Ecosystem Response Types (ERTs) based on common spatio-temporal patterns in time-series of Standardized Precipitation Evapotranspiration Index (SPEI) and FPAR3g anomalies (1982–2011) by using an extended Principal Component Analysis. The ERTs represent region specific spatio-temporal patterns of ecosystems responding to drought or ecosystems with decreasing severity in drought events as well as ecosystems where drought was not a dominant factor in a 30-year period. Highest explanatory values in the SPEI12-FPAR3g anomalies and strongest SPEI12-FPAR3g correlations were seen in the ERTs of Australia and South America whereas lowest explanatory value and lowest correlations were observed in Asia and North America. These ERTs complement traditional pixel based methods by enabling the combined assessment of the location, timing, duration, frequency and severity of climatic and vegetation anomalies with the joint assessment of wetting and drying climatic conditions. The ERTs produced here thus have potential in supporting global change studies by mapping reference conditions of long term ecosystem changes. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Forest Fire Severity Assessment Using ALS Data in a Mediterranean Environment
Remote Sens. 2014, 6(5), 4240-4265; https://doi.org/10.3390/rs6054240
Received: 2 January 2014 / Revised: 8 April 2014 / Accepted: 15 April 2014 / Published: 8 May 2014
Cited by 15 | PDF Full-text (4928 KB) | HTML Full-text | XML Full-text
Abstract
Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities which result in diverse socio-ecological consequences. In order to predict fire severity, spectral indices derived from remotely sensed images have been used extensively. Such spectral indices are usually
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Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities which result in diverse socio-ecological consequences. In order to predict fire severity, spectral indices derived from remotely sensed images have been used extensively. Such spectral indices are usually used in combination with ground sampling to relate detected radiometric changes to actual fire effects. However, the potential of the tridimensional information captured by Airborne Laser Scanners (ALS) to severity mapping has been less explored. With the objective of addressing this question, in this paper, explanatory variables extracted from ALS point clouds are related to field estimations of the Composite Burn Index collected in four fires located in Aragón (Spain). Logistic regression models were developed and statistically tested and validated to map fire severity with up to 85.5% accuracy. The canopy relief ratio and the percentage of all returns above one meter height were the most significant variables and were therefore used to create a continuous map of severity levels. Full article
(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)
Open AccessArticle Evaluation of Spatiotemporal Variations of Global Fractional Vegetation Cover Based on GIMMS NDVI Data from 1982 to 2011
Remote Sens. 2014, 6(5), 4217-4239; https://doi.org/10.3390/rs6054217
Received: 19 December 2013 / Revised: 27 March 2014 / Accepted: 16 April 2014 / Published: 5 May 2014
Cited by 29 | PDF Full-text (3230 KB) | HTML Full-text | XML Full-text
Abstract
Fractional vegetation cover (FVC) is an important biophysical parameter of terrestrial ecosystems. Variation of FVC is a major problem in research fields related to remote sensing applications. In this study, the global FVC from 1982 to 2011 was estimated by GIMMS NDVI data,
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Fractional vegetation cover (FVC) is an important biophysical parameter of terrestrial ecosystems. Variation of FVC is a major problem in research fields related to remote sensing applications. In this study, the global FVC from 1982 to 2011 was estimated by GIMMS NDVI data, USGS global land cover characteristics data and HWSD soil type data with a modified dimidiate pixel model, which considered vegetation and soil types and mixed pixels decomposition. The evaluation of the robustness and accuracy of the GIMMS FVC with MODIS FVC and Validation of Land European Remote sensing Instruments (VALERI) FVC show high reliability. Trends of the annual FVCmax and FVCmean datasets in the last 30 years were reported by the Mann–Kendall method and Sen’s slope estimator. The results indicated that global FVC change was 0.20 and 0.60 in a year with obvious seasonal variability. All of the continents in the world experience a change in the annual FVCmax and FVCmean, which represents biomass production, except for Oceania, which exhibited a significant increase based on a significance level of p = 0.001 with the Student’s t-test. Global annual maximum and mean FVC growth rates are 0.14%/y and 0.12%/y, respectively. The trends of the annual FVCmax and FVCmean based on pixels also illustrated that the global vegetation had turned green in the last 30 years. A significant trend on the p = 0.05 level was found for 15.36% of the GIMMS FVCmax pixels on a global scale (excluding permanent snow and ice), in which 1.8% exhibited negative trends and 13.56% exhibited positive trends. The GIMMS FVCmean similarly produced a total of 16.64% significant pixels with 2.28% with a negative trend and 14.36% with a positive trend. The North Frigid Zone represented the highest annual FVCmax significant increase (p = 0.05) of 25.17%, which may be caused mainly by global warming, Arctic sea-ice loss and an advance in growing seasons. Better FVC predictions at large regional scales, with high temporal resolution (month) and long time series, would advance our ability to understand the characteristics of the global FVC changes in the last 30 years and predict the response of vegetation to global climate change. Full article
Open AccessArticle On Line Validation Exercise (OLIVE): A Web Based Service for the Validation of Medium Resolution Land Products. Application to FAPAR Products
Remote Sens. 2014, 6(5), 4190-4216; https://doi.org/10.3390/rs6054190
Received: 25 February 2014 / Revised: 18 April 2014 / Accepted: 21 April 2014 / Published: 5 May 2014
Cited by 26 | PDF Full-text (1566 KB) | HTML Full-text | XML Full-text
Abstract
The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth
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The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth Observation Satellites) Land Product Validation (LPV) sub-group. OLIVE has three main objectives: (i) to provide a consistent and centralized information on the definition of the biophysical variables, as well as a description of the main available products and their performances (ii) to provide transparency and traceability by an online validation procedure compliant with the CEOS LPV and QA4EO (Quality Assurance for Earth Observation) recommendations (iii) and finally, to provide a tool to benchmark new products, update product validation results and host new ground measurement sites for accuracy assessment. The functionalities and algorithms of OLIVE are described to provide full transparency of its procedures to the community. The validation process and typical results are illustrated for three FAPAR products: GEOV1 (VEGETATION sensor), MGVIo (MERIS sensor) and MODIS collection 5 FPAR. OLIVE is available on the European Space Agency CAL/VAL portal), including full documentation, validation exercise results, and product extracts. Full article
Open AccessArticle Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery
Remote Sens. 2014, 6(5), 4173-4189; https://doi.org/10.3390/rs6054173
Received: 21 January 2014 / Revised: 21 April 2014 / Accepted: 22 April 2014 / Published: 5 May 2014
Cited by 86 | PDF Full-text (1167 KB) | HTML Full-text | XML Full-text
Abstract
Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation
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Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000–2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs) was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000–2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously. Full article
Open AccessArticle Determination of Carbonate Rock Chemistry Using Laboratory-Based Hyperspectral Imagery
Remote Sens. 2014, 6(5), 4149-4172; https://doi.org/10.3390/rs6054149
Received: 6 December 2013 / Revised: 11 April 2014 / Accepted: 28 April 2014 / Published: 5 May 2014
Cited by 22 | PDF Full-text (4474 KB) | HTML Full-text | XML Full-text
Abstract
The development of advanced laboratory-based imaging hyperspectral sensors, such as SisuCHEMA, has created an opportunity to extract compositional information of mineral mixtures from spectral images. Determining proportions of minerals on rock surfaces based on spectral signature is a challenging approach due to naturally-occurring
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The development of advanced laboratory-based imaging hyperspectral sensors, such as SisuCHEMA, has created an opportunity to extract compositional information of mineral mixtures from spectral images. Determining proportions of minerals on rock surfaces based on spectral signature is a challenging approach due to naturally-occurring minerals that exist in the form of intimate mixtures, and grain size variations. This study demonstrates the application of SisuCHEMA hyperspectral data to determine mineral components in hand specimens of carbonate rocks. Here, we applied wavelength position, spectral angle mapper (SAM) and linear spectral unmixing (LSU) approaches to estimate the chemical composition and the relative abundance of carbonate minerals on the rock surfaces. The accuracy of these classification methods and correlation between mineral chemistry and mineral spectral characteristics in determining mineral constituents of rocks are also analyzed. Results showed that chemical composition (Ca-Mg ratio) of carbonate minerals at a pixel (e.g., sub-grain) level can be extracted from the image pixel spectra using these spectral analysis methods. The results also indicated that the spatial distribution and the proportions of calcite-dolomite mixtures on the rock surfaces vary between the spectral methods. For the image shortwave infrared (SWIR) spectra, the wavelength position approach was found to be sensitive to all compositional variations of carbonate mineral mixtures when compared to the SAM and LSU approaches. The correlation between geochemical elements and spectroscopic parameters also revealed the presence of these carbonate mixtures with various chemical compositions in the rock samples. This study concludes that the wavelength position approach is a stable and reproducible technique for estimating carbonate mineral chemistry on the rock surfaces using laboratory-based hyperspectral data. Full article
Open AccessArticle A Non-MLE Approach for Satellite Scatterometer Wind Vector Retrievals in Tropical Cyclones
Remote Sens. 2014, 6(5), 4133-4148; https://doi.org/10.3390/rs6054133
Received: 3 July 2013 / Revised: 15 April 2014 / Accepted: 21 April 2014 / Published: 5 May 2014
PDF Full-text (1398 KB) | HTML Full-text | XML Full-text
Abstract
Satellite microwave scatterometers are the principal source of global synoptic-scale ocean vector wind (OVW) measurements for a number of scientific and operational oceanic wind applications. However, for extreme wind events such as tropical cyclones, their performance is significantly degraded. This paper presents a
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Satellite microwave scatterometers are the principal source of global synoptic-scale ocean vector wind (OVW) measurements for a number of scientific and operational oceanic wind applications. However, for extreme wind events such as tropical cyclones, their performance is significantly degraded. This paper presents a novel OVW retrieval algorithm for tropical cyclones which improves the accuracy of scatterometer based ocean surface winds when compared to low-flying aircraft with in-situ and remotely sensed observations. Unlike the traditional maximum likelihood estimation (MLE) wind vector retrieval technique, this new approach sequentially estimates scalar wind directions and wind speeds. A detailed description of the algorithm is provided along with results for ten QuikSCAT hurricane overpasses (from 2003–2008) to evaluate the performance of the new algorithm. Results are compared with independent surface wind analyses from the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division’s H*Wind surface analyses and with the corresponding SeaWinds Project’s L2B-12.5 km OVW products. They demonstrate that the proposed algorithm extends the SeaWinds capability to retrieve wind speeds beyond the current range of approximately 35 m/s (minimal hurricane category-1) with improved wind direction accuracy, making this new approach a potential candidate for current and future conically scanning scatterometer wind retrieval algorithms. Full article
Open AccessArticle Calibrated Full-Waveform Airborne Laser Scanning for 3D Object Segmentation
Remote Sens. 2014, 6(5), 4109-4132; https://doi.org/10.3390/rs6054109
Received: 31 January 2014 / Revised: 1 April 2014 / Accepted: 15 April 2014 / Published: 2 May 2014
Cited by 1 | PDF Full-text (8380 KB) | HTML Full-text | XML Full-text
Abstract
Segmentation of urban features is considered a major research challenge in the fields of photogrammetry and remote sensing. However, the dense datasets now readily available through airborne laser scanning (ALS) offer increased potential for 3D object segmentation. Such potential is further augmented by
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Segmentation of urban features is considered a major research challenge in the fields of photogrammetry and remote sensing. However, the dense datasets now readily available through airborne laser scanning (ALS) offer increased potential for 3D object segmentation. Such potential is further augmented by the availability of full-waveform (FWF) ALS data. FWF ALS has demonstrated enhanced performance in segmentation and classification through the additional physical observables which can be provided alongside standard geometric information. However, use of FWF information is not recommended without prior radiometric calibration, taking into account all parameters affecting the backscatter energy. This paper reports the implementation of a radiometric calibration workflow for FWF ALS data, and demonstrates how the resultant FWF information can be used to improve segmentation of an urban area. The developed segmentation algorithm presents a novel approach which uses the calibrated backscatter cross-section as a weighting function to estimate the segmentation similarity measure. The normal vector and the local Euclidian distance are used as criteria to segment the point clouds through a region growing approach. The paper demonstrates the potential to enhance 3D object segmentation in urban areas by integrating the FWF physical backscattered energy alongside geometric information. The method is demonstrated through application to an interest area sampled from a relatively dense FWF ALS dataset. The results are assessed through comparison to those delivered from utilising only geometric information. Validation against a manual segmentation demonstrates a successful automatic implementation, achieving a segmentation accuracy of 82%, and out-performs a purely geometric approach. Full article
Open AccessArticle Monitoring Changes in Rice Cultivated Area from SAR and Optical Satellite Images in Ben Tre and Tra Vinh Provinces in Mekong Delta, Vietnam
Remote Sens. 2014, 6(5), 4090-4108; https://doi.org/10.3390/rs6054090
Received: 13 January 2014 / Revised: 8 April 2014 / Accepted: 24 April 2014 / Published: 2 May 2014
Cited by 10 | PDF Full-text (3823 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this study was to obtain up-to-date information on land use and to identify long term changes in land use, especially rice, aquaculture and other crops in Ben Tre and Tra Vinh provinces in Vietnam’s Mekong Delta. Long-term changes in land-use
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The objective of this study was to obtain up-to-date information on land use and to identify long term changes in land use, especially rice, aquaculture and other crops in Ben Tre and Tra Vinh provinces in Vietnam’s Mekong Delta. Long-term changes in land-use of the study area have not been studied using long time series of SAR and optical Earth observation (EO) data before. EO data from 1979–2012 was used: ENVISAT ASAR Wide Swath Mode, SPOT and Landsat imagery. An unsupervised ISODATA classification was performed on multitemporal SAR images. The results were validated using ground truth data. Using the Synthetic Aperture Radar (SAR) imagery maps for 2005, 2009 and 2011 were obtained. Different rice crops, aquaculture and fruit trees could be distinguished with an overall accuracy of 80%. Using available optical imagery the time series was extended from 2005 to 1979. Long-term decrease in the rice acreage and increase in the aquaculture acreage could be detected. Full article
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Open AccessArticle A Decade Long, Multi-Scale Map Comparison of Fire Regime Parameters Derived from Three Publically Available Satellite-Based Fire Products: A Case Study in the Central African Republic
Remote Sens. 2014, 6(5), 4061-4089; https://doi.org/10.3390/rs6054061
Received: 30 December 2013 / Revised: 23 April 2014 / Accepted: 24 April 2014 / Published: 2 May 2014
Cited by 9 | PDF Full-text (5888 KB) | HTML Full-text | XML Full-text
Abstract
Although it is assumed that satellite-derived descriptions of fire activity will differ depending on the dataset selected for analysis, as of yet, the effects of failed and false detections at the pixel level and on an instantaneous basis have not been propagated through
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Although it is assumed that satellite-derived descriptions of fire activity will differ depending on the dataset selected for analysis, as of yet, the effects of failed and false detections at the pixel level and on an instantaneous basis have not been propagated through space and time to determine their cumulative impact on the characterization of individual fire regime parameters. Here we perform the first ever decade long, multi-scale map comparison of fire chronologies and fire seasonality derived from three publicly available satellite-based fire products: the MODIS active fire product (MCD14ML), the ATSR nighttime World Fire Atlas (WFA), and the MODIS burned area product (MCD45A1). Results indicate that: (i) the agreement between fire chronologies derived from two dissimilar satellite products improves as fire pixels are aggregated into coarser grid cells, but diminishes as the number of years included in the time series increases; and (ii) all three datasets provide distinctly different portraits of the onset, peak, and duration of the fire season regardless of the map resolution. Differences in regional, long-term fire regime parameters derived from the three datasets are attributed to the unique capability of each sensor and detection algorithm to recognize geographical gradients, seasonal oscillations, decadal trends, and interannual variability in active fire characteristics and burned area patterns. Since different satellite sensors and detection algorithm strategies are sensitive to different types of fires, we demonstrate that disagreements in fire regime maps derived from dissimilar satellite-based fire products can be used as an advantage to highlight spatial and temporal transitions in landscape fire activity. Given access to multiple, publically available datasets, we caution against describing fire regimes using a single satellite-based active fire or burned area product. Full article
Open AccessArticle Object-Based Classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR
Remote Sens. 2014, 6(5), 4043-4060; https://doi.org/10.3390/rs6054043
Received: 7 February 2014 / Revised: 15 April 2014 / Accepted: 24 April 2014 / Published: 2 May 2014
Cited by 6 | PDF Full-text (3070 KB) | HTML Full-text | XML Full-text
Abstract
LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created
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LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created from a LiDAR derived DEM. Multiresolution segmentation was applied to the slope model and road seed objects were iteratively grown into candidate objects. A road classification accuracy of 86% was achieved using this fully automated procedure and post processing increased this accuracy to 90%. In order to assess the sensitivity of the road classification to LiDAR ground point spacing, the LiDAR ground point cloud was repeatedly thinned by a fraction of 0.5 and the classification procedure was reapplied. The producer’s accuracy of the road classification declined from 79% with a ground point spacing of 0.91 to below 50% with a ground point spacing of 2, indicating the importance of high point density for accurate classification of abandoned logging roads. Full article
Open AccessArticle A Three-Dimensional Index for Characterizing Crop Water Stress
Remote Sens. 2014, 6(5), 4025-4042; https://doi.org/10.3390/rs6054025
Received: 14 October 2013 / Revised: 24 March 2014 / Accepted: 15 April 2014 / Published: 2 May 2014
Cited by 5 | PDF Full-text (1110 KB) | HTML Full-text | XML Full-text
Abstract
The application of remotely sensed estimates of canopy minus air temperature (Tc-Ta) for detecting crop water stress can be limited in semi-arid regions, because of the lack of full ground cover (GC) at water-critical crop stages. Thus, soil background may restrict
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The application of remotely sensed estimates of canopy minus air temperature (Tc-Ta) for detecting crop water stress can be limited in semi-arid regions, because of the lack of full ground cover (GC) at water-critical crop stages. Thus, soil background may restrict water stress interpretation by thermal remote sensing. For partial GC, the combination of plant canopy temperature and surrounding soil temperature in an image pixel is expressed as surface temperature (Ts). Soil brightness (SB) for an image scene varies with surface soil moisture. This study evaluates SB, GC and Ts-Ta and determines a fusion approach to assess crop water stress. The study was conducted (2007 and 2008) on a commercial scale, center pivot irrigated research site in the Texas High Plains. High-resolution aircraft-based imagery (red, near-infrared and thermal) was acquired on clear days. The GC and SB were derived using the Perpendicular Vegetation Index approach. The Ts-Ta was derived using an array of ground Ts sensors, thermal imagery and weather station air temperature. The Ts-Ta, GC and SB were fused using the hue, saturation, intensity method, respectively. Results showed that this method can be used to assess water stress in reference to the differential irrigation plots and corresponding yield without the use of additional energy balance calculation for water stress in partial GC conditions. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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Open AccessArticle Spatial Co-Registration of Ultra-High Resolution Visible, Multispectral and Thermal Images Acquired with a Micro-UAV over Antarctic Moss Beds
Remote Sens. 2014, 6(5), 4003-4024; https://doi.org/10.3390/rs6054003
Received: 17 January 2014 / Revised: 15 April 2014 / Accepted: 15 April 2014 / Published: 2 May 2014
Cited by 44 | PDF Full-text (2308 KB) | HTML Full-text | XML Full-text
Abstract
In recent times, the use of Unmanned Aerial Vehicles (UAVs) as tools for environmental remote sensing has become more commonplace. Compared to traditional airborne remote sensing, UAVs can provide finer spatial resolution data (up to 1 cm/pixel) and higher temporal resolution data. For
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In recent times, the use of Unmanned Aerial Vehicles (UAVs) as tools for environmental remote sensing has become more commonplace. Compared to traditional airborne remote sensing, UAVs can provide finer spatial resolution data (up to 1 cm/pixel) and higher temporal resolution data. For the purposes of vegetation monitoring, the use of multiple sensors such as near infrared and thermal infrared cameras are of benefit. Collecting data with multiple sensors, however, requires an accurate spatial co-registration of the various UAV image datasets. In this study, we used an Oktokopter UAV to investigate the physiological state of Antarctic moss ecosystems using three sensors: (i) a visible camera (1 cm/pixel), (ii) a 6 band multispectral camera (3 cm/pixel), and (iii) a thermal infrared camera (10 cm/pixel). Imagery from each sensor was geo-referenced and mosaicked with a combination of commercially available software and our own algorithms based on the Scale Invariant Feature Transform (SIFT). The validation of the mosaic’s spatial co-registration revealed a mean root mean squared error (RMSE) of 1.78 pixels. A thematic map of moss health, derived from the multispectral mosaic using a Modified Triangular Vegetation Index (MTVI2), and an indicative map of moss surface temperature were then combined to demonstrate sufficient accuracy of our co-registration methodology for UAV-based monitoring of Antarctic moss beds. Full article
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