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The ecological quality of a region is significantly influenced by its geographical conditions, which can yield different effects on ecosystems. Nevertheless, the lack of adequate technology has impeded quantitative investigations into these differences. Consequently, there is an increasing demand for effective techniques to quantitatively measure differences in ecological quality resulting from variations in geographical conditions. This study applied the novel Remote Sensing-based Ecological Index (RSEI) concurrently to two distinct provincial-level regions in China, Fujian and Ningxia, to quantitatively detect their ecological differences. These two regions possess contrasting geographical conditions, with Fujian having high forest coverage and abundant rainfall, while Ningxia features low forest coverage and extensive loess plateau and desert terrain. By linking geographical factors with their corresponding ecological responses, we conducted a comprehensive analysis to determine whether the contrasting geographical conditions between the two regions had caused significant disparities in their ecological status. The results indicate that the contrasting geographical conditions have indeed led to marked ecological differences, with Fujian exhibiting excellent ecological status, while Ningxia lags behind due to unfavorable geographical conditions. In terms of RSEI scores, Fujian consistently achieved higher RSEI values (>0.8) in the study years, reaching an excellent ecological level, whereas Ningxia recorded scores lower than 0.45 during the comparable years, corresponding to a poor to moderate ecological level. Regarding the impact of geographical factors on ecological conditions, the positive contributions of greenness and wetness indicators to the ecology in Fujian were significantly greater than those in Ningxia (58% vs. 39%), whereas the contributions of negative indicators, dryness and hotness, were notably higher in Ningxia compared to Fujian (|–61|% vs. |–42|%). The successful concurrent application of RSEI to these two geographically distant regions also demonstrates the robustness of the RSEI technique.
Xu et al. (Wed,) studied this question.