BS-Mamba for Black-Soil Area Detection On the Qinghai-Tibetan Plateau
This provides an efficient method for assessing black-soil areas to guide grassland restoration efforts on the Qinghai-Tibetan Plateau, addressing an environmental challenge, but it is incremental as it builds on existing detection methods.
The paper tackles the problem of detecting extremely degraded grassland (black-soil areas) on the Qinghai-Tibetan Plateau using UAV remote sensing imagery, and the result is that the novel BS-Mamba model achieves higher accuracy than state-of-the-art models on two independent test datasets.
Extremely degraded grassland on the Qinghai-Tibetan Plateau (QTP) presents a significant environmental challenge due to overgrazing, climate change, and rodent activity, which degrade vegetation cover and soil quality. These extremely degraded grassland on QTP, commonly referred to as black-soil area, require accurate assessment to guide effective restoration efforts. In this paper, we present a newly created QTP black-soil dataset, annotated under expert guidance. We introduce a novel neural network model, BS-Mamba, specifically designed for the black-soil area detection using UAV remote sensing imagery. The BS-Mamba model demonstrates higher accuracy in identifying black-soil area across two independent test datasets than the state-of-the-art models. This research contributes to grassland restoration by providing an efficient method for assessing the extent of black-soil area on the QTP.