Fine Mapping and Dynamic Analysis of Grassland–Livestock Balance in Alpine Grassland From 2019 to 2022 Based on RAV-Satellite Spectral Fusion and Statistical Data Spatialization

Assessing the state of the grassland–livestock balance is of great practical significance for continuously optimizing grazing management and ensuring the sustainable use of alpine grasslands. However, obtaining fine, dynamic, and continuous information of the grassland-livestock balance in alpine grassland is greatly hindered due to limitations in precision, timeliness, and spatial scale of two key indicators, theoretical livestock carrying capacity (TLCC) and actual livestock carrying capacity (ALCC). On the one hand, this study constructed grassland aboveground biomass (AGB) estimation models using the multispectral remote aerial vehicle (RAV) data and Sentinel-2 data in the northeast of Tibetan Plateau based on the nonnegative matrix factorization (NMF)-based differentiated fusion method. The annual high-precision TLCC spatial distribution dataset was obtained through simulation from 2019 to 2023. On the other hand, this study constructed livestock density estimation models based on the livestock statistical data of the smallest administrative unit and various gridded environmental and socioeconomic data from 2019 to 2022; it thus achieved the fine spatialization of the regional-scale ALCC. The spatiotemporal dynamics of TLCC and ALCC sequences were comprehensively analyzed, and the grassland-livestock balance status and its driving factors from 2019 to 2022 were further evaluated in the study area. We concluded that: 1) the ${R} ^{2}$ of the optimal RAV-based and Sentinel-2-based grassland AGB models reached 0.88 and 0.77, with corresponding root mean square error (RMSE) of 552.39 and 692.41 kg/ha, respectively; 2) differentiated fusion with RAV spectral data significantly enhanced the estimation accuracy of Sentinel-2 data for regional-scale grassland AGB ( ${R} ^{2}$ improved from 0.77 to 0.80); 3) the RF-based livestock density model had the best spatialization effect on ALCC ( ${R} ^{2} =0.80$ and RMSE =77.34 sheep/km2); 4) from 2019 to 2022, the overall status of the grassland-livestock balance was good, with over 65% of the grassland area consistently nonoverloaded or balanced, while severely overloaded grassland area remained below 16.14%; large interannual fluctuations were observed in nonoverloaded and severely overloaded grasslands; the overloaded areas mainly concentrated in the core pasture and the periphery of high population density areas; and 5) livestock density, temperature, and distance to river were the main driving factors of livestock-grassland balance dynamics. Innovatively, an annual 10-m spatial resolution grassland-livestock balance dataset was developed.