| 基于GEE的环鄱阳湖城市群长时序生态环境质量时空变化及预测分析 |
| 摘要点击 522 全文点击 8 投稿时间：2025-07-25 修订日期：2025-09-10 |
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| 中文关键词 生态环境质量 遥感生态指数 GEE平台 时空变化 预测分析 |
| 英文关键词 ecological environment quality remote sensing ecological index GEE platform spatial-temporal change predictive analysis |
| DOI 10.13227/j.hjkx.202507335 |
| 作者 | 单位 | E-mail | | 龚循强 | 东华理工大学测绘与空间信息工程学院, 南昌 330013
东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室, 南昌 330013 | xqgong1988@ecut.edu.cn | | 邓居豪 | 东华理工大学测绘与空间信息工程学院, 南昌 330013
东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室, 南昌 330013
江西省生态环境科学研究与规划院, 生态环境部通江湖泊(鄱阳湖)保护与修复重点实验室, 南昌 330039 | | | 朱煜峰 | 东华理工大学测绘与空间信息工程学院, 南昌 330013
东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室, 南昌 330013 | | | 张园眼 | 江西省生态环境科学研究与规划院, 生态环境部通江湖泊(鄱阳湖)保护与修复重点实验室, 南昌 330039 | | | 徐佩 | 东华理工大学测绘与空间信息工程学院, 南昌 330013
东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室, 南昌 330013
江西省生态环境科学研究与规划院, 生态环境部通江湖泊(鄱阳湖)保护与修复重点实验室, 南昌 330039 | | | 张萌 | 江西省生态环境科学研究与规划院, 生态环境部通江湖泊(鄱阳湖)保护与修复重点实验室, 南昌 330039 | tomdeshiye@126.com |
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| 中文摘要 |
| 客观评估环鄱阳湖城市群生态环境质量的时空演变及未来变化趋势，对实现该区域经济与生态平衡至关重要. 基于GEE平台筛选2000~2020年MODIS遥感影像提取生态指标，通过主成分分析构建遥感生态指数模型来表征生态环境质量，并结合Theil-Sen斜率估计、CV变异系数和Mann-Kendall检验等方法分析环鄱阳湖城市群的生态环境质量时空变化，在此基础上引入Hurst指数分析生态环境质量的未来变化趋势，最后进一步应用CA-Markov模型模拟和预测2030年环鄱阳湖城市群的生态环境质量. 结果表明：环鄱阳湖城市群生态环境质量在2000~2014年波段上升，2014年往后小幅度下降，21 a间生态环境质量处于中等偏上水平，整体呈现改善趋势；等级为优的区域主要集中在东北部和西部地区，中部及南部地区主要以差和较差为主，生态环境质量的变化趋势主要以不显著改善为主；各生态等级重心均分布在南昌市及其周边区域，近5 a生态环境质量等级重心以东南方向迁移为主，幅度较小；生态环境质量整体上较稳定，以低波动及较低波动为主，未来变化表现为持续改善和反持续改善，2030年环鄱阳湖城市群生态环境质量将得到一定改善，差和较差的部分仍集中在研究区的中部、西南部及东南部地区. 研究可为快速准确评价区域生态环境质量、探究生态环境质量的长时序变化及未来发展趋势提供依据，对平衡区域发展与自然的关系至关重要. |
| 英文摘要 |
| Objectively assessing the spatiotemporal evolution and future trends of the ecological environmental quality （EEQ） of the urban agglomeration around Poyang Lake is crucial for achieving a balance between economic development and ecological preservation in the region. Utilizing the Google Earth Engine （GEE） cloud platform， this study filtered MODIS remote sensing images from 2000 to 2020 to extract ecological indicators. The remote sensing ecological index （RSEI） model was constructed via principal component analysis （PCA） to characterize EEQ. The spatiotemporal changes in EEQ within the urban agglomeration around Poyang Lake were analyzed using Theil-Sen slope estimation， coefficient of variation （CV）， and Mann-Kendall tests. Furthermore， the Hurst exponent was introduced to analyze future trends in EEQ. Finally， the CA-Markov model was applied to simulate and predict the EEQ for the year 2030. The results indicate that： The EEQ in the urban agglomeration around Poyang Lake exhibited a fluctuating upward trend from 2000 to 2014， followed by a slight decline thereafter. Over the 21-year period， the overall EEQ remained at a moderate to high level， showing an overall improvement trend. Areas classified as “excellent” were primarily concentrated in the northeastern and western regions， while the central and southern areas were predominantly characterized by “poor” and “fair” grades. The dominant trend of EEQ change was non-significant improvement， covering the largest areal proportion. The centers of gravity for all EEQ grades were located within Nanchang City and its surrounding areas. Over the past five years， these centers exhibited a minor shift predominantly towards the southeast. EEQ demonstrated overall stability， characterized mainly by low and relatively low fluctuations. Future changes are projected to consist primarily of persistent improvement and anti-persistent improvement. By 2030， the EEQ of the urban agglomeration around Poyang Lake is predicted to show some improvement； however， areas classified as “Poor” and “Fair” are expected to remain concentrated in the central， southwestern， and southeastern parts of the study area. This study provides a methodological foundation for rapidly and accurately evaluating regional EEQ， investigating its long-term dynamics， and forecasting its future trajectory. It is essential for reconciling regional development with the natural environment. |
