Spatially Refined Ecosystem Service Valuation Using an Improved Remote Sensing Ecological Index: A Case Study of the Qionglai Mountains Section of Giant Panda National Park, China

Land-cover-based ecosystem service valuation commonly assigns a uniform value coefficient to pixels within the same land-cover class, thereby overlooking ecological heterogeneity in mountainous protected areas. This study developed an improved remote sensing ecological index (IRSEI) and applied it as a spatial adjustment factor in the equivalent factor method to assess ecosystem service value (ESV) changes in the Qionglai Mountains section of Giant Panda National Park, China, between 2017 and 2022. IRSEI integrated the normalized difference vegetation index, wetness, normalized difference built-up and soil index, land surface temperature, and cumulative dynamic habitat index derived from the fraction of absorbed photosynthetically active radiation. DHI-cum was included as a proxy for annual cumulative vegetation productivity and habitat energy availability rather than a direct measure of biodiversity or giant panda habitat quality. Total ESV increased from 3.27 × 108 CNY in 2017 to 4.25 × 108 CNY in 2022, representing an increase of 9.79 × 107 CNY, or 29.98%. Water bodies contributed the largest absolute increase, rising by 4.79 × 107 CNY, or 42.45%, whereas farmland showed the highest relative increase of 45.35%. Woodland remained the dominant contributor to total ESV. Spatially, ESV was higher in the northern and southern parts and lower in the central region. All corrected sensitivity coefficients were below one, indicating that total ESV responded inelastically to ±50% perturbations of individual land-cover value coefficients. The framework improves within-class spatial differentiation of ESV and may support targeted management of mountainous protected areas, although field-based habitat and biodiversity data are needed for further validation.