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• W2616303630 startingPage "189" @default.
• W2616303630 abstract "Vegetation height is an important parameter for biomass assessment and vegetation classification. However, vegetation height data over large areas are difficult to obtain. The existing vegetation height data derived from the Ice, Cloud and land Elevation Satellite (ICESat) data only include laser footprints in relatively flat forest regions (<5°). Thus, a large portion of ICESat data over sloping areas has not been used. In this study, we used a new slope correction method to improve the accuracy of estimates of vegetation heights for regions where slopes fall between 5° and 15°. The new method enabled us to use more than 20% additional laser data compared with the existing vegetation height data which only uses ICESat data in relatively flat areas (slope < 5°) in China. With the vegetation height data extracted from ICESat footprints and ancillary data including Moderate Resolution Imaging Spectroradiometer (MODIS) derived data (canopy cover, reflectances and leaf area index), climate data, and topographic data, we developed a wall to wall vegetation height map of China using the Random Forest algorithm. We used the data from 416 field measurements to validate the new vegetation height product. The coefficient of determination (R2) and RMSE of the new vegetation height product were 0.89 and 4.73 m respectively. The accuracy of the product is significantly better than that of the two existing global forest height products produced by Lefsky (2010) and Simard et al. (2011), when compared with the data from 227 field measurements in our study area. The new vegetation height data demonstrated clear distinctions among forest, shrub and grassland, which is promising for improving the classification of vegetation and above-ground forest biomass assessment in China." @default.
• W2616303630 created "2017-05-26" @default.
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• W2616303630 date "2017-07-01" @default.
• W2616303630 modified "2023-10-16" @default.
• W2616303630 title "Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data" @default.
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• W2616303630 doi "https://doi.org/10.1016/j.isprsjprs.2017.04.020" @default.
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