Abstract:

Grassland aboveground biomass （AGB） is an important indicator in grassland monitoring. It is an important index when designing strategies for the ecological protection and rational utilization of grassland resources. In addition， it is of great significance for the sustainable development and scientific management of grassland. In this study， shrub grassland in Xing’an County， Guangxi was the subject of the research， and data were obtained from airborne LiDAR data and high-resolution multispectral satellite images. The retrieval of grassland AGB was investigated using data collected from 89 field quadrats in 2021 and five basic regression models. The accuracy of different indicators and models was evaluated by root mean square error （RMSE）， mean absolute error （MAE）， and R-square values. It was found that grass height metrics were very important information for grassland AGB retrieval. We calculated correlation coefficients between pairs of indexes. In terms of vegetation indexes， the highest correlation coefficient was between the enhanced vegetation index （EVI） and AGB （0.666）. In terms of vegetation height indexes， the highest correlation coefficient was between average grass height （CHM_mean） and AGB （0.686）. In terms of combined indexes， the highest correlation coefficient was between the ratio vegetation index （RVI）×CHM_mean and AGB （0.735）. The accuracy and verification results showed that the minimum RMSE of the EVI models was 292.047 g·m^-2， the minimum RMSE of CHM_mean models was 245.084 g·m^-2， and the minimum RMSE of RVI×CHM_mean models was 225.872 g·m^-2. Our results show that grass height information can be effectively extracted from airborne LiDAR data， and although there is an obvious underestimation， it still has great application potential in research on grassland AGB.

Key words: grassland aboveground biomass, airborne LiDAR, grass height, high-resolution satellite image, remotely sensed retrieval model