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A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China.

, , , , , , , and . Remote. Sens., 11 (11): 1286 (2019)

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Assessing land degradation using rue and PMR based on remote sensing in Northeast Asia dryland regions., and . IGARSS, page 2842-2844. IEEE, (2016)Biomass and vegetation coverage survey in the Mu Us sandy land - based on unmanned aerial vehicle RGB images., , , , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2021)Monitoring Desertification Using Machine-Learning Techniques with Multiple Indicators Derived from MODIS Images in Mu Us Sandy Land, China., , , , , , and . Remote. Sens., 14 (11): 2663 (2022)Comparison of the backpropagation network and the random forest algorithm based on sampling distribution effects consideration for estimating nonphotosynthetic vegetation cover., , , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2021)A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China., , , , , , , and . Remote. Sens., 11 (11): 1286 (2019)Spatial and Temporal Differences in Alpine Meadow, Alpine Steppe and All Vegetation of the Qinghai-Tibetan Plateau and Their Responses to Climate Change., , , , , and . Remote. Sens., 13 (4): 669 (2021)The Response of Vegetation Phenology and Productivity to Drought in Semi-Arid Regions of Northern China., , and . Remote. Sens., 10 (5): 727 (2018)Spectral Response Assessment of Moss-Dominated Biological Soil Crust Coverage Under Dry and Wet Conditions., , , , , , , , and . Remote. Sens., 12 (7): 1158 (2020)