Vegetation restoration is an essential element of natural ecological adaptation and watershed management in ecologically degraded areas, and the scientific determination of vegetation restoration targets is critical for the regulation of regional ecological management patterns. The currently used sliding window-based similar-habitat potential mapping (SWSHPM) model relies heavily on subjective judgment in selecting overlay zoning variables and determining sliding window scales, resulting in high uncertainty in assessment outcomes. To address this issue, we propose an innovative methodological framework that couples the multiscale geographically weighted regression (MGWR) model with the SWSHPM model to achieve an objective and accurate assessment of vegetation restoration potential. The results obtained using Ningxia on the Chinese Loess Plateau as a case study indicate that MGWR effectively reveals the spatial heterogeneity and multiscale coupling mechanisms of the drivers of vegetation restoration. Precipitation and slope act as drivers at the local scale, whereas elevation, nighttime light intensity, and population density are the main drivers at the regional scale. The MGWR model outputs objectively identify significant driver variables—such as slope, nighttime light intensity, and population density—for SWSHPM model zoning. We further identified 90 km as the optimal sliding window scale, overcoming the previous limitation of subjectivity in parameter setting. The local vegetation restoration potential index (LVRPI) exhibited pronounced spatial differentiation. The LVRPI reached 40–60% (67.1–94.4 percentiles) in the southern hydrological conservation areas and the northern Helan Mountains, but only 20–40% (41.4–67.1 percentiles) in the central arid belt, highlighting water as a rigid constraint. This framework significantly enhances the scientific rigor and objectivity of vegetation restoration potential assessments, providing a reliable basis for differentiated management in ecologically fragile regions.
Cao et al. (Wed,) studied this question.