Effects of Maduo Earthquake on Alpine Wetland Area and Habitat Quality Based on EL-InVEST Model

doi:10.16258/j.cnki.1674-5906.2025.02.004

Ecology and Environment ›› 2025, Vol. 34 ›› Issue (2): 209-221.DOI: 10.16258/j.cnki.1674-5906.2025.02.004

• Research Article【Ecology】 • Previous Articles Next Articles

Effects of Maduo Earthquake on Alpine Wetland Area and Habitat Quality Based on EL-InVEST Model

GU Tianjiang¹^,²(), DU Kai¹^,²^,³^,^*(), MAO Xufeng¹^,²^,^*(), JIN Xin¹^,², YU Hongyan⁴, TANG Wenjia⁵, WU Yi¹^,², LIU Zebi¹^,²

1. Key Laboratory of Surface Process and Ecological Conservation of Qinghai-Tibet Plateau/ Key Laboratory of Physical Geography and Environmental Process of Qinghai Province, Xining 810008, P. R. China
2. School of Geography Science, Qinghai Normal University, Xining 810008, P. R. China
3. Qinghai South of Qilian Mountain Forest Ecosystem Observation and Research Station, Huzhu 810500, P. R. China
4. Qinghai Qilian Mountain National Park Qinghai Service Guarantee Center, Xining 810008, P. R. China
5. Qinghai Provincial Department of Ecology and Environment, Xining 810008, P. R. China

Received:2024-09-06 Online:2025-02-18 Published:2025-03-03
Contact: DU Kai,MAO Xufeng

基于EL-InVEST模型的玛多地震对高寒湿地面积及生境质量影响研究

顾天江¹^,²(), 杜凯¹^,²^,³^,^*(), 毛旭锋¹^,²^,^*(), 金鑫¹^,², 于红妍⁴, 唐文家⁵, 吴艺¹^,², 刘泽碧¹^,²

1.青藏高原地表过程与生态保育教育部重点实验室/青海省自然地理与环境过程重点实验室，青海西宁 810008
2.青海师范大学地理科学学院，青海西宁 810008
3.青海祁连山南坡森林生态系统国家定位观测研究站，青海互助 810500
4.青海祁连山国家公园青海服务保障中心，青海西宁 810008
5.青海省生态环境厅，青海西宁 810008

通讯作者: 杜凯,毛旭锋
作者简介:顾天江（1998年生），男，硕士研究生，研究方向为湿地遥感。E-mail: 202247331019@stu.qhnu.edu.cn
基金资助:
青海省自然科学基金青年项目(2024-ZJ-960);青海师范大学中青年科研基金项目(KJQN2022002)

Abstract

Abstract:

On May 22, 2021, a 7.4-magnitude earthquake struck Maduo County, Guoluo Tibetan Autonomous Prefecture, Qinghai Province, China, causing damages to natural landscapes, such as wetlands, rivers, and lakes. Thus, this study aimed to accurately assess and quantify the impact of earthquakes on the alpine wetlands in the region. To address the research aim, we selected the 6-degree intensity zone of the Maduo earthquake as the research area, using the Google Earth Engine (GEE) as the platform, which was combined with Sentinel-2, Sentinel-1, and SRTM1 to extract research data. Remote sensing features, seven experimental schemes including the original spectrum, and vegetation index, original spectrum and water index, original spectrum and red edge index, original spectrum and texture feature, original spectrum and terrain feature, original spectrum and radar feature, and feature selection of random forest importance ranking. Finally, the habitat quality of the study area before and after the earthquake was systematically evaluated based on the classified alpine wetland data combined with the InVEST model. The influence of remote sensing features on alpine wetland classification was also explored. Changes in the alpine wetlands before and after the earthquake were quantified. Changes in habitat quality in the study area before and after the earthquake were quantitatively analyzed. The results showed that: 1) the integrated learning model constructed based on the stacking algorithm was superior to the stochastic forest in terms of classification accuracy, and the stochastic forest was superior to the support vector machine in terms of classification accuracy. When ensemble learning was used for classification, the classification accuracy of the alpine wetlands reached 92.741%, and the kappa coefficient was 0.902. Compared to a single support vector machine or random forest model, the classification accuracy was improved to a certain extent. 2) After adding textural features to the original spectrum, the classification accuracy of the lake wetland and river wetland reached 0.987 and 0.933, respectively, and by adding topographic features to the original spectrum, the classification accuracy of swamp wetlands reached 0.857. The results showed that the variance and correlation of texture features, as well as the slope and elevation of terrain features, had the most significant influence on the classification of alpine wetlands. 3) The Maduo earthquake resulted in a reduction in wetland area, among which the marsh wetland area decreased by 43.088 km², with a change rate of −0.254%; the river wetland area decreased by 31.654 km², with a change rate of −3.522%; and the lake wetland area decreased by 4.971 km², with a change rate of −0.303%. When the wetland area decreased, the bare land area increased by 36.160 km² and the rate of change was as high as 13.610%. The earthquake also caused many wetlands to undergo non-wetland transformation. 4) The average habitat quality decreased from 0.523 before the earthquake to 0.482 after the earthquake, and the high-grade habitat quality area decreased from 7.399% to 5.993%. The area of low-grade habitat quality increased from 2.191 to 7.658%. In summary, the research results show that the Maduo earthquake had a significant negative impact on alpine wetlands, which not only led to a decrease in wetland areas and an increase in non-wetland areas, but also led to an overall decline in habitat quality. This study provides a scientific basis for the ecological restoration and management of alpine wetlands after disasters and has important practical significance.

Key words: remote sensing classification, google earth engine, earthquake, alpine wetland, characterization preferred, sentinel-2

摘要：

为准确评估2021年玛多地震对高寒湿地的影响，选取震中6度烈度带为研究区，依托Google Earth Engine（GEE）平台，融合Sentinel-2、Sentinel-1以及SRTM1数据，构建原始光谱、植被指数、水体指数、红边指数、纹理特征、地形、雷达特征7个遥感特征集，利用Stacking算法构建集成学习模型实现震前、震后的高寒湿地分类;结合InVEST模型实现地震前后生境质量的定量评估。结果表明，1）集成学习分类精度优于支持向量机和随机森林，特征优选方案下分类精度最高（92.741%，Kappa系数为0.902），较支持向量机和随机森林有所提高。2）在原始光谱的基础上加入纹理特征，湖泊湿地和河流湿地的分类精度分别可达0.987、0.933，加入地形特征后沼泽湿地分类精度可达0.857，纹理特征中的方差和相关性以及地形特征中的坡度、高程对高寒湿地分类效果影响显著。3）地震造成湿地面积减小，其中沼泽湿地减少43.088 km²，变化率为−0.254%;河流湿地减少31.654 km²，变化率为−3.522%;湖泊湿地面积下降4.971 km²，变化率为−0.303%。而裸地面积增加了36.160 km²。4）生境质量均值从震前0.523下降到震后0.482，高等级生境质量面积占比从7.399%下降到5.993%，而低等级生境质量面积占比从2.191%上升到7.658%。该研究显示地震对高寒湿地产生负面影响，研究结果可为后续灾后生态恢复与管理提供依据。

关键词: 遥感分类, Google Earth Engine, 地震, 高寒湿地, 特征优选, 哨兵2号

CLC Number:

X14
TP79

GU Tianjiang, DU Kai, MAO Xufeng, JIN Xin, YU Hongyan, TANG Wenjia, WU Yi, LIU Zebi. Effects of Maduo Earthquake on Alpine Wetland Area and Habitat Quality Based on EL-InVEST Model[J]. Ecology and Environment, 2025, 34(2): 209-221.

顾天江, 杜凯, 毛旭锋, 金鑫, 于红妍, 唐文家, 吴艺, 刘泽碧. 基于EL-InVEST模型的玛多地震对高寒湿地面积及生境质量影响研究[J]. 生态环境学报, 2025, 34(2): 209-221.

Figures/Tables 16

References 52

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特征变量	指数全称	指数简称	特征说明
原始光谱特征	－	－	B₂，B₃，B₄，B₅，B₆，B₇，B₈，B_8a，B₁₁，B₁₂
植被指数	Normalized Difference Vegetation Index	NDVI	(B_8a−B₄)/(B_8a+B₄)
	Ratio Vegetation Index	RVI	B_8a/B₄
	Difference Vegetation Index	DVI	B_8a−B₄
	Green Chlorophyll Vegetation Index	GCVI	(B₈/B₃)−1
	Transformed Vegetation Index	TVI	0.5×(120×(B₈−B₃)×(B₈/B₄))
	Soil Adjusted Vegetation Index	SAVI	1.5×(B₈−B₄)/(B₈+B₄+0.5)
水体指数	Normalized Difference Water Index	NDWI	(B₃−B_8a)/(B₃+B_8a)
	Modified Normalized Difference Water Index	MNDWI	(B₃−B₁₁)/(B₃+B₁₁)
	Red-Near-Infrared Water Index	RNDWI	(B₁₂−B₄)/(B₁₂+B₄)
	Land Surface Water Index	LSWI	(B₈−B₁₁)/(B₈+B₁₁)
	Enhanced Water Index	EWI	(B₃−B₈−B₁₂)/(B₃+B₈+B₁₂)
	Surface Water Index	SWI	B₂+B₄−B₈
红边指数	Normalized Difference Vegetation Index red-edge 1	NDVIre1	(B_8a−B₅)/(B_8a+B₅)
	Normalized Difference Vegetation Index red-edge 2	NDVIre2	(B_8a−B₆)/(B_8a+B₆)
	Normalized Difference Vegetation Index red-edge 3	NDVIre3	(B_8a−B₇)/(B_8a+B₇)
	Normalized Difference red-edge 1	NDre1	(B₆−B₅)/(B₆+B₅)
	Normalized Difference red-edge 2	NDre2	(B₇−B₅)/(B₇+B₅)
	Chlorophyll Index red-edge	CIre	B₇/B₅−1
纹理特征	Angular second moment	GLCM_A	角二阶矩
	Correlation	GLCM_Cor	相关性
	Contrast	GLCM_Con	对比度
	Entropy	GLCM_E	熵
	Variance	GLCM_V	方差
地形	Digital Elevation Model	DEM	高程
	Slope	Slope	坡度
	Aspect	Aspect	坡向
雷达特征	VV	VV	Sentinel-1 VV
雷达特征	VH	VH	Sentinel-1 VH

特征变量	指数全称	指数简称	特征说明
原始光谱特征	－	－	B₂，B₃，B₄，B₅，B₆，B₇，B₈，B_8a，B₁₁，B₁₂
植被指数	Normalized Difference Vegetation Index	NDVI	(B_8a−B₄)/(B_8a+B₄)
	Ratio Vegetation Index	RVI	B_8a/B₄
	Difference Vegetation Index	DVI	B_8a−B₄
	Green Chlorophyll Vegetation Index	GCVI	(B₈/B₃)−1
	Transformed Vegetation Index	TVI	0.5×(120×(B₈−B₃)×(B₈/B₄))
	Soil Adjusted Vegetation Index	SAVI	1.5×(B₈−B₄)/(B₈+B₄+0.5)
水体指数	Normalized Difference Water Index	NDWI	(B₃−B_8a)/(B₃+B_8a)
	Modified Normalized Difference Water Index	MNDWI	(B₃−B₁₁)/(B₃+B₁₁)
	Red-Near-Infrared Water Index	RNDWI	(B₁₂−B₄)/(B₁₂+B₄)
	Land Surface Water Index	LSWI	(B₈−B₁₁)/(B₈+B₁₁)
	Enhanced Water Index	EWI	(B₃−B₈−B₁₂)/(B₃+B₈+B₁₂)
	Surface Water Index	SWI	B₂+B₄−B₈
红边指数	Normalized Difference Vegetation Index red-edge 1	NDVIre1	(B_8a−B₅)/(B_8a+B₅)
	Normalized Difference Vegetation Index red-edge 2	NDVIre2	(B_8a−B₆)/(B_8a+B₆)
	Normalized Difference Vegetation Index red-edge 3	NDVIre3	(B_8a−B₇)/(B_8a+B₇)
	Normalized Difference red-edge 1	NDre1	(B₆−B₅)/(B₆+B₅)
	Normalized Difference red-edge 2	NDre2	(B₇−B₅)/(B₇+B₅)
	Chlorophyll Index red-edge	CIre	B₇/B₅−1
纹理特征	Angular second moment	GLCM_A	角二阶矩
	Correlation	GLCM_Cor	相关性
	Contrast	GLCM_Con	对比度
	Entropy	GLCM_E	熵
	Variance	GLCM_V	方差
地形	Digital Elevation Model	DEM	高程
	Slope	Slope	坡度
	Aspect	Aspect	坡向
雷达特征	VV	VV	Sentinel-1 VV
雷达特征	VH	VH	Sentinel-1 VH

威胁源	权重	最大影响距离/km	衰退类型
裸地	0.8	8	线性
其他	0.5	3	线性
道路	0.5	5	线性

威胁源	权重	最大影响距离/km	衰退类型
裸地	0.8	8	线性
其他	0.5	3	线性
道路	0.5	5	线性

土地利用类型	生境适宜度	敏感度
土地利用类型	生境适宜度	裸地	其他	道路
草地	0.80	0.6	0.5	0.5
沼泽湿地	0.80	0.6	0.4	0.65
湖泊湿地	0.90	0.8	0.4	0.7
河流湿地	0.80	0.6	0.4	0.65
裸地	0	0	0.1	0
其他	0.20	0.2	0	0.3

Effects of Maduo Earthquake on Alpine Wetland Area and Habitat Quality Based on EL-InVEST Model

基于EL-InVEST模型的玛多地震对高寒湿地面积及生境质量影响研究

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数据	时间	分辨率/m	影像/景
Sentinel-2	20200501－20200901 20220501－20220901	10	73 70
Sentinel-1	2020、2022	10	79、72
SRTM1	－	30	－

方案	模型
方案	支持向量机	随机森林	集成学习
1	原始光谱
2	原始光谱+植被指数
3	原始光谱+水体指数
4	原始光谱+红边指数
5	原始光谱+纹理特征
6	原始光谱+地形
7	原始光谱+雷达特征
8	特征优选

方案	支持向量机		随机森林		集成学习
方案	总体精度/ %	Kappa	总体精度/ %	Kappa	总体精度/ %	Kappa
方案1	86.883	0.831	87.424	0.832	88.821	0.852
方案2	85.274	0.814	89.032	0.845	90.012	0.861
方案3	87.741	0.838	88.058	0.842	89.031	0.849
方案4	87.847	0.869	89.027	0.893	89.143	0.853
方案5	89.143	0.863	89.346	0.889	90.640	0.869
方案6	88.062	0.842	90.108	0.901	92.465	0.903
方案7	87.848	0.838	87.959	0.836	88.708	0.848
方案8	88.817	0.846	91.293	0.883	92.741	0.902

地类	2020年面积/ km²	2022年面积/ km²	变化量/ km²	变化率/ %
草地	29389.719	29264.393	−125.326	−0.426
沼泽湿地	16952.379	16909.291	−43.088	−0.254
湖泊湿地	1643.060	1638.089	−4.971	−0.303
河流湿地	898.803	867.150	−31.654	−3.522
冰川与积雪	200.049	190.976	−9.072	−4.535
裸地	265.692	301.852	36.160	13.610
其他	4285.789	4463.740	177.950	4.152

2022年	2020年
2022年	草地	沼泽湿地	湖泊湿地	河流湿地	冰川与积雪	裸地	其他
草地	－	27.123	0.891	5.768	6.000	28.888	41.622
沼泽湿地	15.866	－	0.538	29.466	0.515	5.978	15.328
湖泊湿地	0.018	0.078	－	4.969	0.004	0.153	0.256
河流湿地	0.266	0.990	2.54	－	0.125	3.484	3.086
冰川与积雪	0.069	0.009	0.011	0.250	－	0.616	1.017
裸地	0.106	0.084	0.064	0.692	0.053	－	3.213
其他	6.344	5.085	0.827	10.142	32.491	18.858	－

等级	2020年		2022年
等级	面积/km²	占比/%	面积/km²	占比/%
低	1175.153	2.191	4107.405	7.658
较低	15096.779	28.147	15488.854	28.878
中	27080.554	50.490	24677.148	46.009
较高	6314.505	11.773	6147.162	11.461
高	3968.489	7.399	3214.374	5.993

类型	年份	NP	PD	TE	ED	LSI	IJI	SPLIT
沼泽	2020	46367	8819690429	14.976	1251.087	2379752.850	244.654	42.344
沼泽	2022	64749	12337772459	15.875	1833.864	3494385.512	361.581	37.634
湖泊	2020	939	178611713	1.164	19.340	36787.546	12.498	4060.071
湖泊	2022	1966	374616761	1.166	26.886	51230.652	17.162	4131.102
河流	2020	11004	2093123848	0.096	153.226	291458.556	130.364	385751.218
河流	2022	18849	3591633432	0.055	189.616	361308.910	164.058	992477.949
裸地	2020	3148	598796244	0.082	38.395	73032.979	60.698	1275973.018
裸地	2022	7948	1514473050	0.086	64.576	123048.077	94.431	1154555.653

[1]	WEN Ni, WANG Chongyang, CHEN Xingda, CHEN Shuisen, ZHOU Xia, YU Guorong. High-Resolution Remote Sensing Estimation of Ammonia Nitrogen Concentrations in Coastal Urban River Networks Based on Machine Learning Models [J]. Ecology and Environment, 2024, 33(11): 1737-1747.
[2]	LI Yang, HOU Zhiyong, CHEN Wei, YU Xiaoying, XIE Yonghong, HUANG Xin, TAN Peiyang, LI Jicheng, LI Shanglin, YANG Hui. Plant Diversity and Systematic Composition of Alpine Wetlands in Dawei Mountain [J]. Ecology and Environment, 2023, 32(4): 643-650.
[3]	HAO Jinhu, WEI Wei, LI Shengnan, MA Muyuan, LI Xiaoxia, YANG Hongguo, JIANG Qiyu, CHAI Peidong. GEE Based Evaluation of the Spatial-temporal Pattern and Drivers of Long-term Water Body in Beijing-Tianjin-Hebei [J]. Ecology and Environment, 2023, 32(3): 556-566.
[4]	ZHU Jinfu, HUANG Ruiling, DONG Zhiqiang, MAO Xiaoning, ZHOU Huakun. Response of the Soil Bacterial Community to Nitrogen Addition in Alpine Wetland of Qinghai Lake [J]. Ecology and Environment, 2022, 31(6): 1101-1109.

方案	精度
方案	草地	沼泽湿地	湖泊湿地	河流湿地	冰川与积雪	裸地	其他
方案1	0.872	0.807	0.943	0.917	0.973	0.965	0.888
方案2	0.880	0.817	0.947	0.913	0.958	0.990	0.897
方案3	0.870	0.820	0.955	0.925	0.960	0.978	0.905
方案4	0.883	0.825	0.948	0.923	0.973	0.978	0.892
方案5	0.897	0.843	0.987	0.933	0.917	0.923	0.877
方案6	0.903	0.857	0.950	0.923	0.972	0.970	0.895
方案7	0.870	0.825	0.937	0.922	0.948	0.990	0.898
方案8	0.930	0.867	0.982	0.920	0.988	0.920	0.830

方案	精度
方案	草地	沼泽湿地	湖泊湿地	河流湿地	冰川与积雪	裸地	其他
方案1	0.872	0.807	0.943	0.917	0.973	0.965	0.888
方案2	0.880	0.817	0.947	0.913	0.958	0.990	0.897
方案3	0.870	0.820	0.955	0.925	0.960	0.978	0.905
方案4	0.883	0.825	0.948	0.923	0.973	0.978	0.892
方案5	0.897	0.843	0.987	0.933	0.917	0.923	0.877
方案6	0.903	0.857	0.950	0.923	0.972	0.970	0.895
方案7	0.870	0.825	0.937	0.922	0.948	0.990	0.898
方案8	0.930	0.867	0.982	0.920	0.988	0.920	0.830