To address the issue of inaccurate migration of rocky desertification evaluation results from a large scale to the micro scale of parcels, this study introduces a series of methods. Firstly, it employs deep learning techniques to assess the level of rocky desertification in arable land units and to accurately identify the boundaries of these arable lands. Subsequently, the study conducts a time-series reconstruction of indexes used to discriminate rocky desertification, all while adhering to the constraints of these boundaries. Finally, it calculates the degree of rocky desertification within each parcel unit using an entropy weighting method. This approach is well-suited for the complex arable land scenarios typically found in mountainous areas. In the study area, a total of 49,961 cultivated lands have been identified. Notably, rocky desertified cultivated land accounts for 58% of the total, with a predominant distribution of mildly rocky desertified cultivated land.
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