| 基于改进遥感生态指数的粤港澳大湾区生态环境质量监测及驱动力分析 |
| 摘要点击 1849 全文点击 17 投稿时间：2025-04-05 修订日期：2025-06-17 |
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| 中文关键词 生态质量 改进遥感生态指数（KRSEI） GEE云平台 最优参数地理探测器 粤港澳大湾区 |
| 英文关键词 ecological quality improved remote sensing ecological index （KRSEI） GEE cloud platform optimal parameter geographical detector Guangdong-Hong Kong-Macao Greater Bay area |
| DOI 10.13227/j.hjkx.202504062 |
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| 中文摘要 |
| 粤港澳大湾区是我国经济活力最强的区域之一，在经济高质量发展的过程中，剖析其生态环境质量时空变化态势和驱动机制，对践行生态文明建设战略意义重大. 基于GEE云平台和MODIS遥感数据，利用主成分分析基于绿度（KNDVI）、湿度（WET）、热度（LST）和干度（NDBSI）构建适用于高植被区的改进型遥感生态指数KRSEI，并采用Sen+Mann Kendall、Hurst指数、变异系数（CV）和参数最优地理探测器，分析2000~2020年间大湾区生态环境质量时空变化及未来趋势，并探究其影响机制. 结果表明:①模型PC1贡献度在83.91%以上，相较于RSEI能更好地集中各指标特征. ②2000~2020年研究区KRSEI均值分别为0.56、0.49、0.57、0.57和0.55，总体呈波动下降趋势. 生态等级“良”的面积占比最大，为23.31%~40.42%，“差”的面积占比为8.39%~15.65%，“优”和“良”的面积占比共增加6.62%，“差”和“较差”的面积占比共增加4.75%. 区域生境质量呈现“四周高、中部低”的空间格局，未来生态环境变化趋势以退化为主. ③区域生态质量整体上呈现良好稳定性，但经济带、自贸区等高强度开发区具有高变异性. ④最优参数地理探测分析表明，高程是生态环境质量空间分析的主要因素，且高程和土地利用的交互作用对KRSEI空间分异的驱动力最强. 研究可为大湾区可持续发展以及生态环境监测机制的健全提供科学参考，助力大湾区实现经济与生态环境的协同发展. |
| 英文摘要 |
| The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most economically vibrant regions in China. Analyzing the temporal-spatial variation patterns and driving mechanisms of its ecological environment quality is of significant importance for implementing the strategy of ecological civilization construction during the process of high-quality economic development. Based on the Google Earth Engine (GEE) cloud platform and MODIS remote sensing data, this study constructs an improved remote sensing ecological index (KRSEI) suitable for high-vegetation areas using principal component analysis (PCA), incorporating greenness (KNDVI), humidity (WET), heat (LST), and dryness (NDBSI). The Sen+Mann-Kendall method, Hurst index, coefficient of variation (CV), and parameter-optimized geographical detector model are employed to analyze the temporal-spatial changes and future trends of ecological environment quality in the GBA from 2000 to 2020 and to explore its influencing mechanisms. The results show that: ① The contribution rate of the model's first principal component (PC1) exceeded 83.91%, which better integrated the characteristics of each indicator compared to the traditional RSEI. ② The average KRSEI values in the study area from 2000 to 2020 were 0.56, 0.49, 0.57, 0.57, and 0.55, respectively, showing an overall fluctuating downward trend. The “good” ecological grade accounted for the largest area (23.31%-40.42%), while the “poor” grade accounted for 8.39%-15.65%. The combined area proportion of “excellent” and “good” regions increased by 6.62%, while that of “poor” and “very poor” regions increased by 4.75%. The regional habitat quality exhibited a spatial pattern of “high in the periphery, low in the center,” with ecological degradation expected to dominate future changes. ③ The overall ecological quality of the region showed good stability, but high-intensity development zones such as economic belts and free trade zones exhibited high variability. ④ Optimal parameter geographical detector analysis indicated that elevation was the primary factor in the spatial analysis of ecological environment quality, and the interaction between elevation and land use had the strongest driving force on the spatial differentiation of KRSEI. This study provides scientific references for the sustainable development of the GBA and the improvement of ecological environment monitoring mechanisms, facilitating the coordinated development of the economy and ecological environment in the region. |
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