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Guangxing Wang
Researcher at Southern Illinois University Carbondale
Publications - 175
Citations - 4588
Guangxing Wang is an academic researcher from Southern Illinois University Carbondale. The author has contributed to research in topics: Thematic Mapper & Spatial variability. The author has an hindex of 31, co-authored 163 publications receiving 3362 citations. Previous affiliations of Guangxing Wang include Central South University Forestry and Technology & Fugro.
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A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems
TL;DR: A survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables fromRemote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure.
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Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates
Dengsheng Lu,Qi Chen,Guangxing Wang,Emilio F. Moran,Mateus Batistella,Maozhen Zhang,Gaia Vaglio Laurin,David Saah +7 more
TL;DR: Li et al. as mentioned in this paper provided a brief overview of current forest biomass estimation methods using both Landsat Thematic mapper (TM) image and LiDAR data, and demonstrated that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems.
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Mapping Multiple Variables for Predicting Soil Loss by Geostatistical Methods with TM Images and a Slope Map
TL;DR: In this article, the authors compared two geostatistical methods and a traditional stratification to map the factors and to estimate soil loss and found that the co-simulation model performed significantly better than the traditional stratified method in terms of overall and spatially explicit estimate.
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Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation
TL;DR: The results indicate that pine forest and mixed forest have the highest AGB saturation values and Chinese fir and broadleaf forest have lower saturation values, and bamboo forest and shrub have the lowest saturation values.
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Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with Landsat Thematic Mapper images
TL;DR: In this article, the authors compared three traditional and three geostatistical methods for mapping the universal soil loss equation (USLE): vegetation classification with average, linear and log-linear regression for C factor assignment, sequential Gaussian cosimulations with and without Thematic Mapper (TM) images, and colocated cokriging with TM images.