| 工业区生态环境质量时空演变与驱动力分析 |
| 摘要点击 678 全文点击 9 投稿时间：2025-06-29 修订日期：2025-09-30 |
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| 中文关键词 遥感生态指数（RSEI） 重心迁移 地理探测器 灰色预测模型[GM（1，1）] GEE平台 生态环境质量 |
| 英文关键词 remote sensing ecological index （RSEI） migration of gravity center Geodetector grey prediction model[GM（1,1）] Google Earth Engine （GEE） ecological environment quality |
| DOI 10.13227/j.hjkx.202506349 |
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| 中文摘要 |
| 工业化进程显著影响了区域生态环境质量，针对工业区系统开展其时空演变过程分析与驱动因素识别，对于实现其可持续发展具有重要意义. 以典型工业区贵州茅台酒厂为研究对象，基于GEE平台和Landsat 8遥感影像数据，综合应用遥感生态指数（RSEI）、重心迁移模型、地理探测器与灰色预测模型[GM（1，1）]，通过揭示2013~2024年不同生态管理模式下研究区生态环境质量时空演变规律及其主要驱动因子，并预测未来发展趋势，为工业区生态规划与管理提供科学依据. 结果表明：①研究区生态环境质量存在显著空间分异，老厂区在持续管理下RSEI均值提升26.64%，但整体仍处于较差等级；中华片区因大规模扩建生态质量波动较大，西部退化明显，人工修复后RSEI良级面积于2021年增至20%以上；规划建设区无管理，其具有大面积自然环境，RSEI为良级，但近年出现生态退化迹象. 整个研究区RSEI改善的重心向东北方向移动，而其恶化的重心迁移方向则为西南方向. ②NDBSI、NDVI和LST为主要驱动因子，NDBSI驱动力最强（q=0.820），任意两因子交互作用驱动力均强于单因子，NDBSI与LST交互作用解释力最为显著（q=0.881）. ③预测结果显示，2031年老厂区RSEI将达0.39，中华片区可达0.521，规划建设区若无干预将继续退化. 构建适用于工业区生态环境动态监测与归因分析的方法框架，揭示不同生态管理模式下的生态演化路径与驱动机制，研究成果为工业区生态修复与绿色转型提供了理论支撑与决策参考，有利于帮助促进生态与工业协同发展. |
| 英文摘要 |
| 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. |
