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Remote Sens., Volume 6, Issue 6 (June 2014), Pages 4647-5884

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Open AccessArticle Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel
Remote Sens. 2014, 6(6), 5868-5884; https://doi.org/10.3390/rs6065868
Received: 9 April 2014 / Revised: 5 June 2014 / Accepted: 6 June 2014 / Published: 20 June 2014
Cited by 19 | PDF Full-text (888 KB) | HTML Full-text | XML Full-text
Abstract
In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation
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In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean r = 0.78). The negative correlation between delays in SOS and CFAPAR is stronger (mean r = −0.71) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean r = 0.75). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship. Full article
(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)
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Open AccessArticle Assessment of Surface Urban Heat Islands over Three Megacities in East Asia Using Land Surface Temperature Data Retrieved from COMS
Remote Sens. 2014, 6(6), 5852-5867; https://doi.org/10.3390/rs6065852
Received: 10 March 2014 / Revised: 3 June 2014 / Accepted: 3 June 2014 / Published: 20 June 2014
Cited by 11 | PDF Full-text (1242 KB) | HTML Full-text | XML Full-text
Abstract
Surface urban heat island (SUHI) impacts control the exchange of sensible heat and latent heat between land and atmosphere and can worsen extreme climate events, such as heat waves. This study assessed SUHIs over three megacities (Seoul, Tokyo, Beijing) in East Asia using
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Surface urban heat island (SUHI) impacts control the exchange of sensible heat and latent heat between land and atmosphere and can worsen extreme climate events, such as heat waves. This study assessed SUHIs over three megacities (Seoul, Tokyo, Beijing) in East Asia using one-year (April 2011–March 2012) land surface temperature (LST) data retrieved from the Communication, Ocean and Meteorological Satellite (COMS). The spatio-temporal variations of SUHI and the relationship between SUHI and vegetation activity were analyzed using hourly cloud-free LST data. In general, the LST was higher in low latitudes, low altitudes, urban areas and dry regions compared to high latitudes, high altitudes, rural areas and vegetated areas. In particular, the LST over the three megacities was always higher than that in the surrounding rural areas. The SUHI showed a maximum intensity (10–13 °C) at noon during the summer, irrespective of the geographic location of the city, but weak intensities (4–7 °C) were observed during other times and seasons. In general, the SUHI intensity over the three megacities showed strong seasonal (diurnal) variations during the daytime (summer) and weak seasonal (diurnal) variations during the nighttime (other seasons). As a result, the temporal variation pattern of SUHIs was quite different from that of urban heat islands, and the SUHIs showed a distinct maximum at noon of the summer months and weak intensities during the nighttime of all seasons. The patterns of seasonal and diurnal variations of the SUHIs were clearly dependent on the geographic environment of cities. In addition, the intensity of SUHIs showed a strong negative relationship with vegetation activity during the daytime, but no such relationship was observed during the nighttime. This suggests that the SUHI intensity is mainly controlled by differences in evapotranspiration (or the Bowen ratio) between urban and rural areas during the daytime. Full article
Open AccessArticle Monitoring of Irrigation Schemes by Remote Sensing: Phenology versus Retrieval of Biophysical Variables
Remote Sens. 2014, 6(6), 5815-5851; https://doi.org/10.3390/rs6065815
Received: 18 February 2014 / Revised: 7 May 2014 / Accepted: 30 May 2014 / Published: 20 June 2014
Cited by 5 | PDF Full-text (1956 KB) | HTML Full-text | XML Full-text
Abstract
The appraisal of crop water requirements (CWR) is crucial for the management of water resources, especially in arid and semi-arid regions where irrigation represents the largest consumer of water, such as the Doukkala area, western Morocco. Simple and (semi) empirical approaches have been
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The appraisal of crop water requirements (CWR) is crucial for the management of water resources, especially in arid and semi-arid regions where irrigation represents the largest consumer of water, such as the Doukkala area, western Morocco. Simple and (semi) empirical approaches have been applied to estimate CWR: the first one is called Kc-NDVI method, based on the correlation between the Normalized Difference Vegetation Index (NDVI) and the crop coefficient (Kc); the second one is the analytical approach based on the direct application of the Penman-Monteith equation with reflectance-based estimates of canopy biophysical variables, such as surface albedo (r), leaf area index (LAI) and crop height (hc). A time series of high spatial resolution RapidEye (REIS), SPOT4 (HRVIR1) and Landsat 8 (OLI) images acquired during the 2012/2013 agricultural season has been used to assess the spatial and temporal variability of crop evapotranspiration ETc and biophysical variables. The validation using the dual crop coefficient approach (Kcb) showed that the satellite-based estimates of daily ETc were in good agreement with ground-based ETc, i.e., R2 = 0.75 and RMSE = 0.79 versus R2 = 0.73 and RMSE = 0.89 for the Kc-NDVI, respective of the analytical approach. The assessment of irrigation performance in terms of adequacy between water requirements and allocations showed that CWR were much larger than allocated surface water for the entire area, with this difference being small at the beginning of the growing season. Even smaller differences were observed between surface water allocations and Irrigation Water Requirements (IWR) throughout the irrigation season. Finally, surface water allocations were rather close to Net Irrigation Water Requirements (NIWR). Full article
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Open AccessArticle Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine
Remote Sens. 2014, 6(6), 5795-5814; https://doi.org/10.3390/rs6065795
Received: 31 March 2014 / Revised: 26 May 2014 / Accepted: 27 May 2014 / Published: 19 June 2014
Cited by 74 | PDF Full-text (1833 KB) | HTML Full-text | XML Full-text
Abstract
Extreme learning machine (ELM) is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization. In this paper, we propose to integrate spectral-spatial information for hyperspectral
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Extreme learning machine (ELM) is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization. In this paper, we propose to integrate spectral-spatial information for hyperspectral image classification and exploit the benefits of using spatial features for the kernel based ELM (KELM) classifier. Specifically, Gabor filtering and multihypothesis (MH) prediction preprocessing are two approaches employed for spatial feature extraction. Gabor features have currently been successfully applied for hyperspectral image analysis due to the ability to represent useful spatial information. MH prediction preprocessing makes use of the spatial piecewise-continuous nature of hyperspectral imagery to integrate spectral and spatial information. The proposed Gabor-filtering-based KELM classifier and MH-prediction-based KELM classifier have been validated on two real hyperspectral datasets. Classification results demonstrate that the proposed methods outperform the conventional pixel-wise classifiers as well as Gabor-filtering-based support vector machine (SVM) and MH-prediction-based SVM in challenging small training sample size conditions. Full article
Open AccessArticle Crop Condition Assessment with Adjusted NDVI Using the Uncropped Arable Land Ratio
Remote Sens. 2014, 6(6), 5774-5794; https://doi.org/10.3390/rs6065774
Received: 19 January 2014 / Revised: 31 May 2014 / Accepted: 3 June 2014 / Published: 19 June 2014
Cited by 10 | PDF Full-text (5230 KB) | HTML Full-text | XML Full-text
Abstract
Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics
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Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics (based on crop condition profiles). Since this type of method will generate false information if there are changes in crop rotation, cropping area or crop phenology, information on cropped/uncropped arable land is integrated to improve the accuracy of crop condition monitoring. The study proposes a new method to retrieve adjusted NDVI for cropped arable land during the growing season of winter crops by integrating 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data at 250-m resolution with a cropped and uncropped arable land map derived from the multi-temporal China Environmental Satellite (Huan Jing Satellite) charge-coupled device (HJ-1 CCD) images at 30-m resolution. Using the land map’s data on cropped and uncropped arable land, a pixel-based uncropped arable land ratio (UALR) at 250-m resolution was generated. Next, the UALR-adjusted NDVI was produced by assuming that the MODIS reflectance value for each pixel is a linear mixed signal composed of the proportional reflectance of cropped and uncropped arable land. When UALR-adjusted NDVI data are used for crop condition assessment, results are expected to be more accurate, because: (i) pixels with only uncropped arable land are not included in the assessment; and (ii) the adjusted NDVI corrects for interannual variation in cropping area. On the provincial level, crop growing profiles based on the two kinds of NDVI data illustrate the difference between the regular and the adjusted NDVI, with the difference depending on the total area of uncropped arable land in the region. The results suggested that the proposed method can be used to improve the assessment of early crop condition, but additional evaluation in other major crop producing regions is needed to better assess the method’s application in other regions and agricultural systems. Full article
(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)
Open AccessArticle 3D Ground Penetrating Radar to Detect Tree Roots and Estimate Root Biomass in the Field
Remote Sens. 2014, 6(6), 5754-5773; https://doi.org/10.3390/rs6065754
Received: 25 April 2014 / Revised: 11 June 2014 / Accepted: 12 June 2014 / Published: 18 June 2014
Cited by 13 | PDF Full-text (1457 KB) | HTML Full-text | XML Full-text
Abstract
The objectives of this study were to detect coarse tree root and to estimate root biomass in the field by using an advanced 3D Ground Penetrating Radar (3D GPR) system. This study obtained full-resolution 3D imaging results of tree root system using 500
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The objectives of this study were to detect coarse tree root and to estimate root biomass in the field by using an advanced 3D Ground Penetrating Radar (3D GPR) system. This study obtained full-resolution 3D imaging results of tree root system using 500 MHz and 800 MHz bow-tie antennas, respectively. The measurement site included two larch trees, and one of them was excavated after GPR measurements. In this paper, a searching algorithm, based on the continuity of pixel intensity along the root in 3D space, is proposed, and two coarse roots whose diameters are more than 5 cm were detected and delineated correctly. Based on the detection results and the measured root biomass, a linear regression model is proposed to estimate the total root biomass in different depth ranges, and the total error was less than 10%. Additionally, based on the detected root samples, a new index named “magnitude width” is proposed to estimate the root diameter that has good correlation with root diameter compared with other common GPR indexes. This index also provides direct measurement of the root diameter with 13%–16% error, providing reasonable and practical root diameter estimation especially in the field. Full article
(This article belongs to the Special Issue Close-Range Remote Sensing by Ground Penetrating Radar)
Open AccessArticle A Novel Clustering-Based Feature Representation for the Classification of Hyperspectral Imagery
Remote Sens. 2014, 6(6), 5732-5753; https://doi.org/10.3390/rs6065732
Received: 21 January 2014 / Revised: 30 May 2014 / Accepted: 4 June 2014 / Published: 18 June 2014
Cited by 8 | PDF Full-text (1845 KB) | HTML Full-text | XML Full-text
Abstract
In this study, a new clustering-based feature extraction algorithm is proposed for the spectral-spatial classification of hyperspectral imagery. The clustering approach is able to group the high-dimensional data into a subspace by mining the salient information and suppressing the redundant information. In this
[...] Read more.
In this study, a new clustering-based feature extraction algorithm is proposed for the spectral-spatial classification of hyperspectral imagery. The clustering approach is able to group the high-dimensional data into a subspace by mining the salient information and suppressing the redundant information. In this way, the relationship between neighboring pixels, which was hidden in the original data, can be extracted more effectively. Specifically, in the proposed algorithm, a two-step process is adopted to make use of the clustering-based information. A clustering approach is first used to produce the initial clustering map, and, subsequently, a multiscale cluster histogram (MCH) is proposed to represent the spatial information around each pixel. In order to evaluate the robustness of the proposed MCH, four clustering techniques are employed to analyze the influence of the clustering methods. Meanwhile, the performance of the MCH is compared to three other widely used spatial features: the gray-level co-occurrence matrix (GLCM), the 3D wavelet texture, and differential morphological profiles (DMPs). The experiments conducted on four well-known hyperspectral datasets verify that the proposed MCH can significantly improve the classification accuracy, and it outperforms other commonly used spatial features. Full article
Open AccessArticle Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity
Remote Sens. 2014, 6(6), 5717-5731; https://doi.org/10.3390/rs6065717
Received: 31 December 2013 / Revised: 4 May 2014 / Accepted: 13 May 2014 / Published: 18 June 2014
Cited by 12 | PDF Full-text (2731 KB) | HTML Full-text | XML Full-text
Abstract
Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of
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Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth’s human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data
Remote Sens. 2014, 6(6), 5696-5716; https://doi.org/10.3390/rs6065696
Received: 14 March 2014 / Revised: 9 June 2014 / Accepted: 9 June 2014 / Published: 18 June 2014
Cited by 20 | PDF Full-text (2234 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data
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Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data gaps of long-term infestation monitoring, but the elimination of observational noise and attributing changes quantitatively are two main challenges in its effective application. Here, we present a forest growth trend analysis method that integrates Landsat temporal trajectories and decision tree techniques to derive annual forest disturbance maps over an 11-year period. The temporal trajectory component successfully captures the disturbance events as represented by spectral segments, whereas decision tree modeling efficiently recognizes and attributes events based upon the characteristics of the segments. Validated against a point set sampled across a gradient of MPB mortality, 86.74% to 94.00% overall accuracy was achieved with small variability in accuracy among years. In contrast, the overall accuracies of single-date classifications ranged from 37.20% to 75.20% and only become comparable with our approach when the training sample size was increased at least four-fold. This demonstrates that the advantages of this time series work flow exist in its small training sample size requirement. The easily understandable, interpretable and modifiable characteristics of our approach suggest that it could be applicable to other ecoregions. Full article
Open AccessEditorial Calibration and Verification of Remote Sensing Instruments and Observations
Remote Sens. 2014, 6(6), 5692-5695; https://doi.org/10.3390/rs6065692
Received: 10 June 2014 / Accepted: 11 June 2014 / Published: 17 June 2014
PDF Full-text (88 KB) | HTML Full-text | XML Full-text
Abstract
Satellite instruments are nowadays a very important source of information. The physical quantities (essential variables) derived from satellites are utilized in a wide field of applications, in particular in atmospheric physics and geoscience. In contrast to ground measurements the physical quantities are not
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Satellite instruments are nowadays a very important source of information. The physical quantities (essential variables) derived from satellites are utilized in a wide field of applications, in particular in atmospheric physics and geoscience. In contrast to ground measurements the physical quantities are not directly measured, but have to be retrieved from satellite observations. Satellites observe hereby the reflection or emission of radiation by the Earth's surface or atmosphere, which enables the retrieval of respective physical quantities (essential variables). The physical basis for the retrieval is the interaction of the radiation with the Earth’s atmosphere and surface. This interaction is defined by radiative transfer, which favors the use of radiances and their respective units within retrieval methods. [...] Full article
Open AccessArticle A Photogrammetric and Computer Vision-Based Approach for Automated 3D Architectural Modeling and Its Typological Analysis
Remote Sens. 2014, 6(6), 5671-5691; https://doi.org/10.3390/rs6065671
Received: 13 March 2014 / Revised: 12 June 2014 / Accepted: 12 June 2014 / Published: 17 June 2014
Cited by 9 | PDF Full-text (1372 KB) | HTML Full-text | XML Full-text
Abstract
Thanks to the advances in integrating photogrammetry and computer vision, as well as in some numeric algorithms and methods, it is possible to aspire to turn 2D (images) into 3D (point clouds) in an automatic, flexible and good-quality way. This article presents a
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Thanks to the advances in integrating photogrammetry and computer vision, as well as in some numeric algorithms and methods, it is possible to aspire to turn 2D (images) into 3D (point clouds) in an automatic, flexible and good-quality way. This article presents a new method through the development of PW (Photogrammetry Workbench) (and how this could be useful for architectural modeling). This tool enables the user to turn images into scale 3D point cloud models, which have a better quality than those of laser systems. Moreover, the point clouds may include the respective orthophotos with photographic texture. The method allows the study of the typology of architecture and has been successfully tested on a sample of ten religious buildings located in the region of Aliste, Zamora (Spain). Full article
Open AccessArticle A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data
Remote Sens. 2014, 6(6), 5650-5670; https://doi.org/10.3390/rs6065650
Received: 6 March 2014 / Revised: 6 June 2014 / Accepted: 9 June 2014 / Published: 17 June 2014
Cited by 9 | PDF Full-text (987 KB) | HTML Full-text | XML Full-text
Abstract
Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a
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Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a logistic function to fit the leaf area index (LAI) curve. We applied the resulting asymptotic lines and the curvature extrema to derive the vegetation phenophases of germination, green-up, maturity, senescence, defoliation and dormancy. The new proposed S-curve function has been tested in a specific area (Shangdong Province, China), characterized by a specific pattern in leaf area index (LAI) time course due to the dominant presence of crops. The function has not yet received any global testing. The identified phenophases were validated against measurement stations in Shandong Province. (i) From the site-scale comparison, we find that the detected phenophases using the S-curve (SC) algorithm are more consistent with the observations than using the logistic (LC) algorithm and the asymmetric Gaussian (AG) algorithm, especially for the germination and dormancy. The phenological recognition rates (PRRs) of the SC algorithm are obviously higher than those of two other algorithms. The S-curve function fits the LAI curve much better than the logistic function and asymmetric Gaussian function; (ii) The retrieval results of the SC algorithm are reliable and in close proximity to the green-up observed data whether using the AVHRR LAI or the improved MODIS LAI. Three inversion algorithms shows the retrieval results based on AVHRR LAI are all later than based on improved MODIS LAI. The bias statistics reveal that the retrieval results based on the AVHRR LAI datasets are more reasonable than based on the improved MODIS LAI datasets. Overall, the S-curve algorithm has the advantage of deriving vegetation phenophases across time and space as compared to the LC algorithm and the AG algorithm. With the SC algorithm, the vegetation phenophases can be extracted more effectively. Full article
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Open AccessArticle Remote Sensing Assessment of Forest Disturbance across Complex Mountainous Terrain: The Pattern and Severity of Impacts of Tropical Cyclone Yasi on Australian Rainforests
Remote Sens. 2014, 6(6), 5633-5649; https://doi.org/10.3390/rs6065633
Received: 29 January 2014 / Revised: 3 June 2014 / Accepted: 4 June 2014 / Published: 17 June 2014
Cited by 5 | PDF Full-text (1548 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Topography affects the patterns of forest disturbance produced by tropical cyclones. It determines the degree of exposure of a surface and can alter wind characteristics. Whether multispectral remote sensing data can sense the effect of topography on disturbance is a question that deserves
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Topography affects the patterns of forest disturbance produced by tropical cyclones. It determines the degree of exposure of a surface and can alter wind characteristics. Whether multispectral remote sensing data can sense the effect of topography on disturbance is a question that deserves attention given the multi-scale spatial coverage of these data and the projected increase in intensity of the strongest cyclones. Here, multispectral satellite data, topographic maps and cyclone surface wind data were used to study the patterns of disturbance in an Australian rainforest with complex mountainous terrain produced by tropical cyclone Yasi (2011). The cyclone surface wind data (H*wind) was produced by the Hurricane Research Division of the National Oceanic and Atmospheric Administration (HRD/NOAA), and this was the first time that this data was produced for a cyclone outside of United States territory. A disturbance map was obtained by applying spectral mixture analyses on satellite data and presented a significant correlation with field-measured tree mortality. Our results showed that, consistent with cyclones in the southern hemisphere, multispectral data revealed that forest disturbance was higher on the left side of the cyclone track. The highest level of forest disturbance occurred in forests along the path of the cyclone track (±30°). Levels of forest disturbance decreased with decreasing slope and with an aspect facing off the track of the cyclone or away from the dominant surface winds. An increase in disturbance with surface elevation was also observed. However, areas affected by the same wind intensity presented increased levels of disturbance with increasing elevation suggesting that complex terrain interactions act to speed up wind at higher elevations. Yasi produced an important offset to Australia’s forest carbon sink in 2010. We concluded that multispectral data was sensitive to the main effects of complex topography on disturbance patterns. High resolution cyclone wind surface data are needed in order to quantify the effects of topographic accelerations on cyclone related forest disturbances. Full article
Open AccessArticle Estimation of Mass Balance of the Grosser Aletschgletscher, Swiss Alps, from ICESat Laser Altimetry Data and Digital Elevation Models
Remote Sens. 2014, 6(6), 5614-5632; https://doi.org/10.3390/rs6065614
Received: 19 November 2013 / Revised: 30 May 2014 / Accepted: 30 May 2014 / Published: 17 June 2014
Cited by 16 | PDF Full-text (936 KB) | HTML Full-text | XML Full-text
Abstract
Traditional glaciological mass balance measurements of mountain glaciers are a demanding and cost intensive task. In this study, we combine data from the Ice Cloud and Elevation Satellite (ICESat) acquired between 2003 and 2009 with air and space borne Digital Elevation Models (DEMs)
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Traditional glaciological mass balance measurements of mountain glaciers are a demanding and cost intensive task. In this study, we combine data from the Ice Cloud and Elevation Satellite (ICESat) acquired between 2003 and 2009 with air and space borne Digital Elevation Models (DEMs) in order to derive surface elevation changes of the Grosser Aletschgletscher in the Swiss Alps. Three different areas of the glacier are covered by one nominal ICESat track, allowing us to investigate the performance of the approach under different conditions in terms of ICESat data coverage, and surface characteristics. In order to test the sensitivity of the derived trend in surface lowering, several variables were tested. Employing correction for perennial snow accumulation, footprint selection and adequate reference DEM, we estimated a mean mass balance of −0.92 ± 0.18 m w.e. a−1. for the whole glacier in the studied time period. The resulting mass balance was validated by a comparison with another geodetic approach based on the subtraction of two DEMs for the years 1999 and 2009. It appears that the processing parameters need to be selected depending on the amount of available ICESat measurements, quality of the elevation reference and character of the glacier surface. Full article
(This article belongs to the Special Issue Cryospheric Remote Sensing)
Open AccessArticle A Bayesian Based Method to Generate a Synergetic Land-Cover Map from Existing Land-Cover Products
Remote Sens. 2014, 6(6), 5589-5613; https://doi.org/10.3390/rs6065589
Received: 27 February 2014 / Revised: 4 June 2014 / Accepted: 10 June 2014 / Published: 16 June 2014
Cited by 5 | PDF Full-text (2003 KB) | HTML Full-text | XML Full-text
Abstract
Global land cover is an important parameter of the land surface and has been derived by various researchers based on remote sensing images. Each land cover product has its own disadvantages and limitations. Data fusion technology is becoming a notable method to fully
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Global land cover is an important parameter of the land surface and has been derived by various researchers based on remote sensing images. Each land cover product has its own disadvantages and limitations. Data fusion technology is becoming a notable method to fully integrate existing land cover information. In this paper, we developed a method to generate a synergetic global land cover map (synGLC) based on Bayes theorem. A state probability vector was defined to precisely and quantitatively describe the land cover classification of every pixel and reduce the errors caused by legends harmonization and spatial resampling. Simple axiomatic approaches were used to generate the prior land cover map, in which pixels with high consistency were regarded to be correct and then used as benchmark to obtain posterior land cover map. Validation results show that our hybrid land cover map (synGLC, the dataset is available on request) has the best overall performance compared with the existing global land cover products. Closed shrub-lands and permanent wetlands have the highest uncertainty in our fused land cover map. This novel method can be extensively applied to fusion of land cover maps with different legends, spatial resolutions or geographic ranges. Full article
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