首页期刊导航|International journal of applied earth observation and geoinformation
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International journal of applied earth observation and geoinformation
International Institute for Aerospace Survey and Earth Sciences
International Institute for Aerospace Survey and Earth Sciences
1569-8432
International journal of applied earth observation and geoinformation/Journal International journal of applied earth observation and geoinformationISTPSCIAHCI
查看更多>>摘要：In this study, we report on the assessment of elevation-dependent warming processes in the Andean region between 7 degrees S and 20 degrees S, using Land Surface Temperature (LST). Remotely sensed LST data were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor in an 8-day composite, at a 1 km resolution, and from 2000 to 2017 during austral winter (June-July-August, JJA). We analysed the relation between mean monthly daytime LST and mean monthly maximum air temperature. This relation is analysed for different types of coverage, obtaining a significant correlation that varies from 0.57 to 0.82 (p < 0.01). However, effects of change in land cover were ruled out by a previous comparative assessment of trends in daytime LST and normalized difference vegetation index (NDVI). The distribution of the winter daytime LST trend was found to be increasing in most areas, while decreasing in only a few areas. This trend shows that winter daytime LST is increasing at an average rate of 1.0 degrees C/decade. We also found that the winter daytime LST trend has a clear dependence on elevation, with strongest warming effects at higher elevations: 0.50 degrees C/decade at 1000-1500 masl, and 1.7 degrees C/decade above 5000 masl. However, the winter nighttime LST trend shows a steady increase with altitude increase. The dependence of rising temperature trends on elevation could have severe implications for water resources and high Andean ecosystems.
查看更多>>摘要：Invasive plants constitute one of the major causes of biodiversity loss, and its monitoring plays an important role in the management of coastal ecological systems. This study aimed to apply high precision 3D mesh-model and digital orthophoto map (DOM) derived from unmanned aerial vehicle (UAV) multi view images to monitor the invasive plants over coastal mountain region in Shenzhen, China. To overcome the limitations of RGB images, the Gray-level Co-occurrence Matrix (GLCM) features of images were analyzed and combined with spectral features to obtain the 2-dimentional distribution of invasive plants first, using an object-based image analysis technique. A fine analysis was then introduced to obtain a more accurate 3-dimentional distribution of invasive plant by combining 2-dimentional distribution of invasive plant and 3D mesh model. the results have shown that: (1) Although the UAV RGB image has limited spectral information, the low-altitude makes the spatial resolution very high, which can effectively enhance the effectiveness of the texture in mapping invasive plants, and finally achieved an overall accuracy of 93.25%. (2) The use of 3D mesh model, on the one hand, could significantly alleviate the impact of undulatory terrain over mountain area and improve the classification result; on the other hand, it could better visualize the final results, helping us more intuitively understanding the distribution of invasive plants. This study demonstrated the great potential of UAV-derived 3D mesh model in accurate natural resource management over mountain areas.
查看更多>>摘要：Proper calibration of airborne hyperspectral imagery is essential for maximizing the quantitative utility of remotely-sensed imagery, especially when distinguishing subtle changes in spectral curves related to specific plant physiological properties (e.g. chlorophyll and water). Many studies use the empirical line approach with reference reflectance taken from dark and bright targets to calibrate airborne images. However, few have paid attention to the issue of sensor oversaturation due to the exposure setting of the imaging sensor, and no studies have investigated the effects this has placed on image calibration. With limited radiometric resolution, a sensor would become saturated by energy reflected from bright targets when its exposure is set to maximize signals reflected from a feature of interest, for example vegetation. This would result in large bias in the reflectance calibration process, and should be addressed for enormous amounts of high spatial and spectral resolution data that have been increasingly taken by manned or unmanned aircraft. In this study, we test the exposure setting of a hyperspectral sensor for maximizing vegetation signal and investigate potential reference targets in an airborne scene, and propose a more suitable airborne hyperspectral imaging and an empirical line atmospheric correction procedure by taking into account: 1) imaging sensor exposure setting, 2) spectral extrapolation, 3) sensor saturation of targets' signal, and 4) optimal materials and grey levels for field reference reflectance for the empirical line method. The imaging experiment was conducted over a grassland field with the Micro-Hyperspec VNIR sensor. Using field hyperspectral data to validate the calibration results, we found that our proposed empirical line calibration approach improved the reflectance accuracy significantly. Vegetation indices calculated from the calibrated spectra were able to estimate chlorophyll content with success. Our work offers insights into image calibration and describes a feasible method to maximize quantitative utilities of airborne Hyperspectral imagery for vegetation studies.
查看更多>>摘要：The relationship between vegetation optical depth (VOD) retrieved by L-band SMOS radiometer and forest above ground biomass (AGB) was investigated in tropical areas of Africa and South America. VOD was retrieved from the latest version of level 2 SMOS algorithm, while reference AGB was obtained from a pantropical database, encompassing a large number of ground plot data derived from field surveys conducted on both continents. In Africa and South-America, VOD increased with AGB, reaching saturation at about 350 Mg ha(-1). The strength of the relation was improved selecting VOD data in appropriate seasons, characterized by a higher dynamic range of values. The capability of VOD data to estimate AGB was further investigated using Random Forest decision trees, adding to VOD selected climate variables from the Climatic Research Unit (temperature, potential evapotranspiration, and precipitation) and water deficit data, and validating regression tests with ground data from the reference AGB database. The results for the five analyzed years indicate that the best estimates of AGB are obtained by the joined use of VOD and potential evapotranspiration input data, but all climate variables brought an improvement in AGB estimates. AGB estimates were relatively stable for the considered period, with limited variations possibly due to changes in biomass and to data quality of VOD and of climate variables. The VOD signal and estimated AGB were also analyzed according to ecological homogeneous units (ecoregions), evidencing data clusters, partially overlapped to each other, in the VOD - AGB plane.
NSTL
Elsevier