基于Landsat长时间序列影像的城市生态品质时空演变及驱动因素分析

doi:10.11947/j.AGCS.2025.20240147

摘要/Abstract

摘要：

分析城市生态质量的时空演变及其驱动因素，对区域环境保护与高质量可持续发展至关重要。本文基于多时相Landsat卫星遥感影像及相关辅助数据，利用遥感生态指数（remote sensing ecological index，RSEI），并结合Theil-Sen估计、Mann-Kendall趋势检验、Hurst指数、最优参数地理探测器（optimal parameters-based geographical detectors，OPGD）及多尺度地理加权回归（multi-scale geographically weighted regression，MGWR）模型构建了系统化精细化的城市生态品质评价分析框架。为了验证该框架在快速城市化背景下的适用性和有效性，本文将其用于探究过去20年东莞市的生态质量变化及其影响因素。研究表明：过去20 a间，东莞市生态环境质量经历了恶化-改善-再恶化的复杂动态变化过程。此外，前瞻性预测分析进一步明确了未来生态质量变化的重点区域，并识别出面临严重生态风险及具备较大改善潜力的区域。

关键词: 时空演变, 驱动因素, 最优参数地理探测器, 遥感生态指数, 多尺度地理加权回归

Abstract:

Analyzing the spatio-temporal evolution of urban ecological quality and its driving factors is crucial for regional environmental protection and sustainable development. This paper presents a robust analytical framework for urban ecological quality evaluation based on multi-temporal Landsat satellite remote sensing imagery and auxiliary data. The framework integrates the remote sensing ecological index (RSEI) with Theil-Sen estimation, Mann-Kendall trend test, Hurst index, optimal parameters-based geographical detectors (OPGD), and multi-scale geographically weighted regression (MGWR). To verify its applicability and effectiveness in the context of rapid urbanization, this framework was used to investigate ecological quality changes and influencing factors in Dongguan city over the past 20 years. The results show that Dongguan's ecological environment has undergone a complex dynamic process of deterioration-improvement-re-deterioration during the 20-year period. Furthermore, forward-looking predictive analysis identified key areas of future ecological change and regions facing severe ecological risks as well as those with significant improvement potential. This comprehensive analysis provides scientific support for ecological management in Dongguan and offers valuable insights for similar regions undergoing rapid urbanization.

Key words: spatio-temporal evolution, driving factors, OPGD, RSEI, MGWR

中图分类号:

P237

高永刚, 刘雨婷, 徐涵秋. 基于Landsat长时间序列影像的城市生态品质时空演变及驱动因素分析[J]. 测绘学报, 2025, 54(3): 510-522.

Yonggang GAO, Yuting LIU, Hanqiu XU. Analysis of the spatio-temporal evolution and driving factors of urban ecological quality based on long-term Landsat image time series[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(3): 510-522.

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数据类型	分辨率/m	数据来源
Landsat 5 TM（2000—2012年7—8月） Landsat 7 ETM+（2013年7—8月） Landsat 8 OLI（2014—2020年7—8月）	30	National Aeronautics and Space Administration（NASA）（https://scarch.carthdata.nasa.gov/search）
temperature（TEMP） precipitation（PREP）	1000	China Meteorological Data Service Centre（https://data.cma.cn/data/）
digital elevation model（DEM）、fractional vegetation cover（FVC）	30	Geospatial Data Cloud（http://www.gscloud.cn/）
land use and land cover change（LUCC）	30	Published by Prof. Yang Jie and Huang Xin of Wuhan University（https://doi.org/10.5281/zenodo.5816591）
night light intensity data（NLID）	500	An extended time-series（2000—2020）of global NPP-VIIRS-like nighttime light data（https://doi.org/10.7910/DVN/YGIVCD）

年份	指标	PC1	PC2	PC3	PC4
2000	NDVI	0.582 33	0.669 79	0.075 41	0.454 51
	WET	0.398 88	-0.695 99	0.393 17	0.449 35
	NDBSI	-0.571 99	0.007 56	-0.285 24	0.769 03
	LST	-0.417 87	0.258 70	0.870 84	0.009 66
	特征值	0.235 04	0.022 74	0.017 24	0.001 96
	贡献率/（%）	84.859 5	8.209 1	6.224 1	0.707 3
2010	NDVI	0.563 80	-0.609 32	0.422 26	0.364 08
	WET	0.411 54	0.742 74	0.071 03	0.523 38
	NDBSI	-0.572 79	-0.205 85	-0.195 31	0.769 02
	LST	-0.429 74	0.186 24	0.882 33	-0.046 14
	特征值	0.233 04	0.023 90	0.014 76	0.002 21
	贡献率/（%）	85.079 1	8.72 45	5.388 1	0.808 3
2020	NDVI	0.483 63	-0.735 42	-0.234 80	-0.412 47
	WET	0.427 41	0.671 25	-0.26870	-0.542 71
	NDBSI	-0.627 27	-0.086 34	0.259 41	-0.729 23
	LST	-0.435 83	-0.033 53	-0.897 43	0.059 62
	特征值	0.173 97	0.034 36	0.017 09	0.002 48
	贡献率/（%）	76.335 4	15.078 8	7.498 9	1.087 0

年份	RSEI	EI	年份	RSEI	EI
2000	0.538	63.3	2011	0.414	57.9
2001	0.460	60.8	2012	0.472	59.8
2002	0.474	61.3	2013	0.467	61.1
2003	0.511	62.4	2014	0.439	59.6
2004	0.430	58.9	2015	0.417	60.4
2005	0.419	58.2	2016	0.485	62.4
2006	0.446	59.7	2017	0.447	60.3
2007	0.463	60.1	2018	0.444	60.2
2008	0.435	58.7	2019	0.461	60.9
2009	0.427	59.4	2020	0.474	59.6
2010	0.447	60.8

β	z	趋势	面积/km²	百分比/（%）
≥0.000 5	≥1.96	显著增加	41.092	1.7
≥0.000 5	-1.96≤z<1.96	没有显著增加	611.516	25.2
-0.000 5~0.000 5	-1.96<z<1.96	无重大变化	491.955	20.3
≤-0.000 5	-1.96<z≤1.96	无显著退化	687.464	28.3
≤-0.000 5	≤-1.96	显著退化	594.231	24.5

Hurst值	β	未来变化	面积/km²	百分比/（%）
<0.5	≤-0.000 5	未来改进	18.153	0.7
<0.5	-0.000 5<β<0.000 5	随机变化	429.782	17.7
<0.5	≥-0.000 5	未来退化	740.072	30.5
>0.5	≤-0.000 5	持续退化	524.012	21.6
>0.5	-0.000 5<β<0.000 5	持续不变	666.527	27.5
>0.5	≥0.000 5	持续改进	47.753 1	2.0