Grassland vegetation cover inversion model based on random forest regression: A case study in Burqin County, Altay, Xinjiang Uygur Autonomous Region

As a large country with extensive grassland resources, China is facing severe grassland degradation. Studying trends in grassland vegetation cover change provides a basis for identifying the driving forces of grassland degradation and associated risk assessment. In previous studies, parametric regression models have typically been applied to estimate vegetation cover. However, the harsh assumptions of parametric regression have always been neglected. In the current study, vegetation cover monitoring data and vegetation indices ( NDVI, SAVI, MSAVI, EVI), extracted from Landsat remote sensing images, were used to build random forest regressions, which are non⁃parametric models. These models were subsequently compared with traditional linear regression models. To build and test these models, 205 samples were collected from 2010 to 2015 (data from 2012 were not included) in alpine meadow, mountain meadow, lower⁃flat meadow, temperate meadow steppe, desert steppe, steppe desert, and desert in Burqin County, Xinjiang Uygur Autonomous Region. Among these samples, 150 samples were used to build models, and the remainder was used as testing data. Two sets of Landsat remote sensing images, Level 1 Standard Product and Surface Reflectance Product, were applied separately, and both