顾及土地利用动态变化的滑坡易发性评估方法

doi:10.12082/dqxxkx.2023.220567

Abstract

Abstract:

The causes of landslide disasters are complex, and landslide susceptibility assessment is of great significance for disaster warning, prevention, and control management. In the previous mapping studies on landslide susceptibility assessment, land use change factor was not considered. This paper proposed a combination of factors for landslide susceptibility assessment by considering land use dynamic change factor. The landslide frequency ratio was used to quantitatively measure the correlation between land use change and landslide development. And Logistic Regression (LR) model was used to compare the prediction ability of the model before and after the introduction of land use change factor. We constructed three machine learning models: Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF). We used AUC and other indicators to compare model performance. Finally, we took Sanming City of Fujian Province as the study area and the whole Fujian Province as the verification area to conduct the landslide susceptibility assessment research. The results show that there is a strong correlation between land use change factor and landslide development. The inclusion of land use change factor improves model prediction accuracy, which indicates that it is necessary to introduce dynamic factor in the assessment of landslide susceptibility. The verification results show that RF model has higher prediction accuracy than DT and GBDT. The high landslide prone areas are mainly distributed in the west and central of Sanming City, where the land use change degree is high, and the impact of human activities is great. The low landslide prone areas basically locate in the high-altitude areas with little influence of human activities. This study provides a new research perspective for landslide susceptibility assessment and helps to explore the impact of human activities on disaster formation.

Key words: landslide, susceptibility, land use change, ensemble learning, decision tree, gradient boosting decision tree, random forest, Sanming

LIN Xuanxin, XIAO Guirong, ZHOU Houbo. Landslide Susceptibility Assessment Method Considering Land Use Dynamic Change[J].Journal of Geo-information Science, 2023, 25(5): 953-966.DOI:10.12082/dqxxkx.2023.220567

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基础数据	数据来源	数据类型	数据年份	分辨率
滑坡空间分布数据	资源环境科学与数据中心 (https://www.resdc.cn/data.aspx?DATAID=290)	矢量	积累至2019年	—
数字高程影像	ASTER Global Digital Elevation Model V003 (https://search.earthdata.nasa.gov/search)	栅格	成像时间为2000年3月1日—2013年11月30日	30 m×30 m
自然地理数据	全国地理信息资源目录服务系统1:100万全国基础地理数据库 (https://www.webmap.cn/commres.do?method=result100W)	矢量	2021年	—
地形数据	从数字高程影像中提取	栅格	—	30 m×30 m
降水量数据	温室数据共享平台(http://data.sheshiyuanyi.com/)	文本	2000—2019年	—
地质数据	地质科学数据出版系统1:100万福建省地质图数据 (http://dcc.cgs.gov.cn/)	矢量	2017年	—
土地利用类型数据	1990—2021年中国30 m年土地覆被数据集及其动态( https://doi.org/10.5281/zenodo.5816591)	栅格	2000—2020年	30 m×30 m

评估因子	高程	坡度	坡向	降水量	工程地质岩组	到断裂距离	到水系距离	到道路距离	20年土地利用变化
高程	1.0000	0.2510	0.0227	0.2012	-0.0861	0.1866	0.2701	0.3308	-0.2536
坡度	0.2510	1.0000	0.0363	0.0839	-0.0454	0.0378	0.0909	0.1345	-0.2725
坡向	0.0227	0.0363	1.0000	0.0047	-0.0061	-0.0003	0.0044	0.0083	-0.0182
降水量	0.2012	0.0839	0.0047	1.0000	0.1787	0.0899	0.0442	-0.0158	-0.0096
工程地质岩组	-0.0861	-0.0454	-0.0061	0.1787	1.0000	-0.1616	-0.0917	-0.0578	0.0961
到断裂距离	0.1866	0.0378	-0.0003	0.0899	-0.1616	1.0000	0.0607	0.1026	-0.0529
到水系距离	0.2701	0.0909	0.0044	0.0442	-0.0917	0.0607	1.0000	0.1630	-0.1508
到道路距离	0.3308	0.1345	0.0083	-0.0158	-0.0578	0.1026	0.1630	1.0000	-0.1626
20年土地利用变化	-0.2536	-0.2725	-0.0182	-0.0096	0.0961	-0.0529	-0.1508	-0.1626	1.0000

评估因子	TOL	VIF
高程	0.1138	8.7844
坡度	0.1715	5.8319
坡向	0.1749	5.7181
降水量	0.2970	3.3674
工程地质岩组	0.3091	3.2353
到断裂距离	0.3979	2.5134
到水系距离	0.3169	3.1551
到道路距离	0.3073	3.2540
20年土地利用变化	0.2533	3.9473

2020年土地利用类型	滑坡比	FR	2000—2020年地类变化	滑坡比	FR
植被区域	0.724	0.809	变为植被区域	0.014	2.541
农田	0.250	2.785	变为农田	0.113	2.812
水体	0.003	0.618	变为水体	0.001	1.073
裸地	0	0	变为裸地	0	0
不透水面	0.023	2.248	变为不透水面	0.010	1.689

项目	LR
项目	2000年土地利用类型	2020年土地利用类型	2000—2020年土地利用变化
训练数据集	0.9024	0.9037	0.9045
测试数据集	0.9042	0.9156	0.9156
完整数据集	0.9028	0.9060	0.9067
AUC值	0.9599	0.9611	0.9611

Landslide Susceptibility Assessment Method Considering Land Use Dynamic Change

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预测模型	主要参数	搜索范围	搜索步长	最优结果
DT	max_depth	[2,10]	2	6
	max_features	[2,8]	1	6
	max_leaf_nodes	[2,8]	1	7
GBDT	n_estimators	[20,300]	20	300
	max_depth	[2,10]	2	6
	max_features	[2,8]	1	8
	max_leaf_nodes	[2,8]	1	7
RF	max_depth	[2,10]	2	10
	max_features	[2,8]	1	5
	n_estimators	[20,300]	20	280

项目	DT	GBDT	RF
训练数据集	0.8829	0.9533	0.9634
测试数据集	0.8815	0.9481	0.9513
完整数据集	0.8826	0.9522	0.9610
AUC值	0.9315	0.9815	0.9922

易发性等级	滑坡点/数	滑坡比	面积比	频率比
极低	46	0.03	0.17	0.18
低	64	0.04	0.11	0.38
中	109	0.07	0.16	0.44
高	241	0.16	0.20	0.78
极高	1078	0.70	0.36	1.95

2020年土地利用类型	滑坡比	FR	2000—2020年地类变化	滑坡比	FR
植被区域	0.740	0.900	变为植被区域	0.021	1.258
农田	0.226	1.639	变为农田	0.099	2.432
水体	0.002	0.321	变为水体	0.000	0.338
裸地	0.000	0.000	变为裸地	0.000	0.000
不透水面	0.032	0.989	变为不透水面	0.013	0.828

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项目	LR
项目	2000年土地利用类型	2020年土地利用类型	2000—2020年土地利用变化
训练数据集	0.7239	0.7183	0.7294
测试数据集	0.7331	0.7323	0.7417
完整数据集	0.7258	0.7211	0.7319
AUC值	0.8032	0.7913	0.8082