Remote Sensing Estimation of Grassland Aboveground Biomass based on Random Forest

Grassland is the largest terrestrial ecosystem in China. Biomass is a key indicator of ecosystem quality and ecosystem function. It is of great significance for us to accurately estimate the grassland biomass for the effective and rational use of grassland resources, the restoration of damaged grassland ecosystem, and the highquality development of animal husbandry. In this study, we took Xilinguole league of Inner Mongolia autonomous region as the research area. We used GF-1 satellite images, ground sample data of 216 sites, and Random Forest (RF) algorithm to estimate Grassland Aboveground Biomass (AGB) and explore the applicability of the algorithm in grassland biomass estimation. Moreover, in order to evaluate the applicability of random forest algorithm in aboveground biomass estimation, we carried out a series of analysis when using the algorithm, such as k- fold cross validation, multicollinearity diagnosis, partial effect and so on. Based this, we completed the construction of the random forest model and compared the modeling results with those from other models. Then, we selected the best model to realize the inversion estimation of grassland aboveground biomass in Xilinguole league. The main conclusions are as follows: (1) In the process of biomass model construction in Xilinguole league, random forest algorithm can avoid multicollinearity problem if there are multiple input variables; (2) The random forest model has better applicability than other models in the estimation of grassland biomass. The accuracy of the random forest model is 85% while the RMSE is 202.13 kg/hm2; (3) Using the random forest model, we estimated the grassland aboveground biomass of the whole study area in 2017. The results indicated that the spatial distribution had a decreasing trend from east to west. When grassland types are concerned, the grassland aboveground biomass yield of mountain meadow was the highest among all grassland types while the total yield of temperate grassland was the highest among all grassland types. The results are not only beneficial to the monitoring and evaluation of grassland ecosystem, but also have a certain reference value for grassland macro management. © 2021, Science Press. All right reserved.