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基于优化遥感生态指数的长兴岛生态环境质量时空变化分析

Analysis of Temporal and Spatial Changes in Ecological Environment Quality on Changxing Island Using an Optimized Remote Sensing Ecological Index.

作者信息

Zhu Yuanyi, Hou Yingzi, Wang Fangxiong, Yu Haomiao, Liao Zhiying, Yu Qiao, Zhu Jianfeng

机构信息

School of Geographical Sciences, Liaoning Normal University, No. 850, Huanghe Road, Dalian 116029, China.

Liaoning Provincial Key Laboratory of Physical Geography and Geomatics, Liaoning Normal University, Street 15, Dalian 116029, China.

出版信息

Sensors (Basel). 2025 Mar 13;25(6):1791. doi: 10.3390/s25061791.

DOI:10.3390/s25061791
PMID:40292906
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11945366/
Abstract

In light of global climate change and accelerated urbanization, preserving and restoring island ecosystems has become critically important. This study focuses on Changxing Island in Dalian, China, evaluating the quality of its ecological environment. The research aims to quantify ecological changes since 2000, with an emphasis on land use transformations, coastline evolution, and the driving factors behind these changes. Using the Google Earth Engine (GEE) platform and remote sensing technology, an island remote sensing ecological index (IRSEI) was developed. The development of the IRSEI was grounded in several key ecological parameters, including the normalized difference vegetation index (NDVI), wetness index (WET), land surface temperature index (LST), multiband drought stress index (M-NDBSI), and land use intensity index (LUI). The research results show that, since 2002, land use types on Changxing Island have undergone significant changes, with a notable decrease in arable land and a significant increase in built-up areas, reflecting the ongoing urbanization process. With respect to coastline changes, the total coastline length of Changxing Island steadily increased from 2002 to 2022, with an average annual growth rate of 2.15 km. This change was driven mainly by reclamation and infrastructure construction. The IRSEI analysis further revealed a clear deterioration in the quality of the ecological environment of Changxing Island during the study period. The proportion of excellent ecological area decreased from 39.3% in 2002 to 8.89% in 2022, whereas the areas classified as poor and very poor increased to 56.23 km and 129.84 km, both of which set new historical records. These findings suggest that, as urbanization and coastline development intensify, the ecosystem of Changxing Island is at significant risk of degradation. The optimized IRSEI effectively captured the ecological environment quality of the island, improved the long-term stability of the index, and adequately met the requirements for large-scale and long-term ecological environment quality monitoring.

摘要

鉴于全球气候变化和城市化进程加速,保护和恢复岛屿生态系统变得至关重要。本研究聚焦于中国大连的长兴岛,评估其生态环境质量。该研究旨在量化2000年以来的生态变化,重点关注土地利用变化、海岸线演变以及这些变化背后的驱动因素。利用谷歌地球引擎(GEE)平台和遥感技术,开发了岛屿遥感生态指数(IRSEI)。IRSEI的开发基于几个关键生态参数,包括归一化植被指数(NDVI)、湿度指数(WET)、地表温度指数(LST)、多波段干旱胁迫指数(M-NDBSI)和土地利用强度指数(LUI)。研究结果表明,自2002年以来,长兴岛的土地利用类型发生了显著变化,耕地显著减少,建成区大幅增加,反映了城市化进程的持续推进。关于海岸线变化,长兴岛的海岸线总长度从2002年到2022年稳步增加,年均增长率为2.15千米。这种变化主要是由围垦和基础设施建设推动的。IRSEI分析进一步显示,在研究期间,长兴岛的生态环境质量明显恶化。优良生态区的比例从2002年的39.3%降至2022年的8.89%,而分类为差和极差的区域分别增加到56.23平方千米和129.84平方千米,均创下历史新高。这些发现表明,随着城市化和海岸线开发的加剧,长兴岛的生态系统面临着严重的退化风险。优化后的IRSEI有效捕捉了该岛的生态环境质量,提高了指数的长期稳定性,并充分满足了大规模、长期生态环境质量监测的需求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b2/11945366/7ddacde67db2/sensors-25-01791-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b2/11945366/29869fc03320/sensors-25-01791-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b2/11945366/a8510f2b8bce/sensors-25-01791-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b2/11945366/262c673e820f/sensors-25-01791-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b2/11945366/f03c26c508bf/sensors-25-01791-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b2/11945366/7ddacde67db2/sensors-25-01791-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b2/11945366/29869fc03320/sensors-25-01791-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b2/11945366/a8510f2b8bce/sensors-25-01791-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b2/11945366/262c673e820f/sensors-25-01791-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b2/11945366/f03c26c508bf/sensors-25-01791-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b2/11945366/7ddacde67db2/sensors-25-01791-g005.jpg

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7
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