| 摘 要： |
The harsh climate and vast terrain of the Tibetan Plateau present significant challenges for large-scale remote sensing monitoring of grasslands, particularly in regions where the variation patterns of aboveground biomass (AGB) are influenced by high spatial complexity and pronounced environmental variability, further complicating the monitoring and estimation processes. To address this pressing issue, we propose a novel concept called the grassland key belt, which utilizes localized studies to reflect regional dynamics. Using AGB estimation in this key belt as a case study, we analyzed the gradient changes in AGB across different elevations. To achieve this, we used a bit-depth & residual quantization method for mining radiometric information from remote sensing images and integrated multimodal features, including remote sensing, terrain, and climate data, to accurately estimate AGB and generate an AGB distribution map. The experimental results demonstrate significant estimation accuracy, with an R-2 of 0.8728 and an RMSE of 17.4799 g/m(2). The study reveals an increasing trend in AGB as elevation decreases, with southern regions exhibiting higher AGB due to superior hydrothermal conditions. Notably, the southeastern grasslands display the highest AGB, consistent with previous studies, thus validating the reliability of the grassland key belt concept. This research advances the application of remote sensing technologies for monitoring vegetation dynamics in challenging environments, providing valuable insights for sustainable ecosystem management. |
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