[Spatiotemporal Evolution and Driving Force Analysis of Ecological Environment Quality in an Industrial Area]

Industrialization has significantly affected the regional ecological environment quality. It is of great significance to analyze the spatial and temporal evolution process and identify the driving factors of the industrial area system for its sustainable development. In this study， Landsat 8 remote sensing imageries on GEE were used to construct the remote sensing ecological index （RSEI）. The migration of the gravity center， Geodetector， and the GM（1，1） grey prediction model were applied to analyze the spatiotemporal evolution of ecological environment quality in the study area from 2013 to 2024 under different ecological management strategies. The main driving factors were identified， and the future development trend was predicted， providing a scientific basis for ecological planning and management in industrial areas. The results showed that： ① There was a significant spatial differentiation in the ecological environment quality of the study area. A 26.64% increase was shown in the average RSEI of the Old Factory Area with good management， but its level was still "relatively poor." Due to the large-scale expansion in the Zhonghua Area， fluctuations were observed in the ecological environment quality， and degradation was observed in the western region. After artificial restoration， an increase in the RSEI "good" area to more than 20% was shown in 2021. In the Planning-Construction Area， characterized by extensive natural coverage and "good" RSEI， management had been absent， leading to emerging signs of ecological degradation observed in recent years. Moreover， a northeastward migration of the RSEI improvement center was exhibited across the study area， while a southwestward shift of deterioration was demonstrated. ② NDBSI， NDVI， and LST were identified as the primary driving factors， with NDBSI exhibiting the strongest driving force （q=0.820）. The explanatory power generated by any two-factor interaction was observed to exceed that of a single factor， and the most significant explanatory effect was demonstrated through the interaction between NDBSI and LST （q=0.881）. ③ Based on the prediction results， the RSEI was projected to reach 0.39 in the Old Factory Area and 0.521 in the Zhonghua Area by 2031. Without intervention， continued degradation was anticipated in the Planning-Construction Area. A methodological framework was constructed in this study to facilitate dynamic monitoring and attribution analysis of industrial area ecosystems， through which ecological evolution and driving mechanisms were revealed under varying ecological managements. These findings provide theoretical support and decision-making references for ecological restoration and green transformation in industrial areas， contributing to the coordinated development of ecological and industrial systems.