Machine Learning-Based Remote Sensing Inversion of Non-Photosynthetic/Photosynthetic Vegetation Coverage in Desertified Areas and Its Response to Drought Analysis.
Remote. Sens., September, 2024
Combining the SHAP Method and Machine Learning Algorithm for Desert Type Extraction and Change Analysis on the Qinghai-Tibetan Plateau.
Remote. Sens., 2024
Monitoring Desertification Using Machine-Learning Techniques with Multiple Indicators Derived from MODIS Images in Mu Us Sandy Land, China.
Remote. Sens., 2022
Spatial and Temporal Differences in Alpine Meadow, Alpine Steppe and All Vegetation of the Qinghai-Tibetan Plateau and Their Responses to Climate Change.
Remote. Sens., 2021
Biomass and vegetation coverage survey in the Mu Us sandy land - based on unmanned aerial vehicle RGB images.
Int. J. Appl. Earth Obs. Geoinformation, 2021
Comparison of the backpropagation network and the random forest algorithm based on sampling distribution effects consideration for estimating nonphotosynthetic vegetation cover.
Int. J. Appl. Earth Obs. Geoinformation, 2021
Spectral Response Assessment of Moss-Dominated Biological Soil Crust Coverage Under Dry and Wet Conditions.
Remote. Sens., 2020
A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China.
Remote. Sens., 2019
The Response of Vegetation Phenology and Productivity to Drought in Semi-Arid Regions of Northern China.
Remote. Sens., 2018
Assessing land degradation using rue and PMR based on remote sensing in Northeast Asia dryland regions.
Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, 2016