河南省登封南部地区滑坡灾害早期识别与易发性评价

莫德国; 郑光明; 王超; 郑古月; 苗发盛

doi:10.19509/j.cnki.dzkq.tb20250169

河南省登封南部地区滑坡灾害早期识别与易发性评价

doi: 10.19509/j.cnki.dzkq.tb20250169

莫德国^{1, 2,},
郑光明^{1, 2},
王超^{3a, 3b},
郑古月^{3a, 3b},
苗发盛^{3a, 3b, ,}

1.
河南省自然资源监测和国土整治院，郑州 450003
2.
河南省地质灾害防治重点实验室，郑州 450003
3a.
中国地质大学（武汉）：地质探测与评估教育部重点实验室
3b.
中国地质大学（武汉）：工程学院，武汉 430074

基金项目: 地质探测与评估教育部重点实验室主任基金项目（GLAB2024ZR03）；中央高校基本科研业务费；河南省地质灾害风险综合调查评价项目（豫财环资[2023]136）；贵州省省级科技计划项目（黔科合支撑[2023]一般 127）

详细信息

作者简介:
莫德国：E-mail：ml87951@163.com

通讯作者:
E-mail：fsmiao@cug.edu.cn

中图分类号: P642.22；X43
计量
- 文章访问数: 235
- PDF下载量: 35
- 被引次数: 0
出版历程
- 收稿日期: 2025-04-11
- 录用日期: 2025-07-18
- 修回日期: 2025-07-14
- 网络出版日期: 2025-12-15

Early identification and susceptibility assessment of landslide hazards in southern Dengfeng, Henan Province

MO Deguo^{1, 2
,},
ZHENG Guangming^{1, 2},
WANG Chao^{3a, 3b},
ZHENG Guyue^{3a, 3b},
MIAO Fasheng^{3a, 3b
, ,}

1.
Institute of Natural Resources Monitoring and Comprehensive Land Improvement of Henan Province, Zhengzhou 450003, China
2.
Henan Key Laboratory of Geological Hazard Prevention and Control, Zhengzhou 450003, China
3a.
Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences (Wuhan)
3b.
Faculty of Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China

More Information

Author Bio:
E-mail：ml87951@163.com

Corresponding author: E-mail：fsmiao@cug.edu.cn

摘要

摘要:
河南省登封市南部地处嵩山山脉与黄河中下游平原过渡地带，地形地质条件复杂，滑坡灾害频发，对区域生产安全与居民生活构成严重威胁。开展滑坡隐患早期识别与高精度易发性评价，对区域地质灾害防治具有重要现实意义。综合运用光学遥感与小基线集合成孔径雷达干涉测量（SBAS-InSAR）技术开展滑坡隐患早期识别，选取高程、坡度、地层岩性等12项关键评价因子，基于信息量模型与机器学习方法（人工神经网络、随机森林及Stacking集成策略）进行滑坡易发性评价，并采用斜坡单元优化评价结果输出。结果表明：①通过多源遥感解译与野外验证，共识别滑坡隐患点33处，主要分布于研究区中部、西南及东南区域，空间分布与地形坡度、岩性软弱层及人类工程活动显著相关；②研究区滑坡易发性呈现北低南高分布特征，Stacking集成策略模型AUC值达0.96，预测精度最优，显著优于单一模型及传统信息量模型，8.60%的高易发区空间覆盖率即可捕获87.72%滑坡样本。本研究构建的多源遥感识别+集成学习评价技术框架，为登封南部地区滑坡风险精准防控提供了高精度数据支撑与技术范式，同时验证了集成学习在复杂地形区滑坡易发性评价中的显著优势。
- 滑坡灾害 /
- 早期识别 /
- 易发性评价 /
- SBAS-InSAR /
- Stacking集成策略模型 /
- 机器学习 /
- 河南省登封市
Abstract:
Objective
The southern region of Dengfeng City in Henan Province lies in the transitional zone between the Songshan Mountains and the Middle and Lower reaches of the Yellow River plain. Complex topographic and geological conditions in this region lead to frequent landslide hazards, posing serious threats to regional production safety and residents' lives. Conducting early identification of landslide hazards and high-precision susceptibility assessment is practically significant for the prevention and control of regional geological hazards.
Methods
This study applied optical remote sensing and small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) for the early identification of landslide hazards. Twelve key evaluation factors, including elevation, slope, and lithology, were selected. Landslide susceptibility assessment was carried out based on the information value model and machine learning methods (artificial neural network, random forest, and stacking ensemble model). Additionally, slope units were used to optimize the output of evaluation results.
Results
The results showed that: (1) A total of 33 landslide hazard sites were identified through multi-source remote sensing interpretation and field verification. They were mainly distributed in the central, southwestern, and southeastern parts of the study area, and their spatial distribution was significantly correlated with terrain slope, weak lithological layers, and human engineering activities. (2) Landslide susceptibility in the study area showed a distribution pattern of lower susceptibility in the north and higher susceptibility in the south. The stacking ensemble model achieved the highest prediction accuracy with an area under the curve (AUC) value of 0.96, which was significantly better than single models and the traditional information value model. The high susceptibility zone accounted for only 8.60% of the total area but captured 87.72% of the landslide samples.
Conclusion
The technical framework of multi-source remote sensing identification combined with ensemble learning evaluation constructed in this study provides high-precision data support and a technical paradigm for accurate prevention and control of landslide risks in the southern region of Dengfeng. It also verifies the significant advantages of ensemble learning for landslide susceptibility assessment in complex terrain areas.
- landslide hazard /
- early identification /
- susceptibility assessment /
- SBAS-InSAR /
- Stacking ensemble model /
- machine learning /
- Defeng City, Henan Province

HTML全文

图 1 研究区地理位置及交通路线图

Figure 1. Geographical location and transportation routes in the study area

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图 2 研究区光学遥感解译滑坡隐患点(a)及部分滑坡展示(b～c)

Figure 2. Landslide hazard points interpreted from optical remote sensing (a) and partial landslide display (b-c) in the study area

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图 3 研究区InSAR解译滑坡隐患点位置(a)及部分滑坡展示(b, c)

Figure 3. Location of landslide hazard sites interpreted from InSAR (a) and display of partial landslides (b, c) in the study area

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图 4 研究区滑坡灾害隐患点综合解译对比图

a1～d1. 光学遥感解译图；a2～d2. 野外调查图；a3～d3. SBAS-InSAR解译图

Figure 4. Comprehensive interpretation comparison of lanslide hazard sites in the study area

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图 5 研究区滑坡灾害分布

Figure 5. Distribution of landslide hazards in the study area

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图 6 研究区斜坡单元划分

Figure 6. Zonation of slope units in the study area

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图 7 研究区滑坡各因子间的相关性分析

TWI. 地形湿度指数；SPI. 河流功率指数；NDVI. 归一化植被指数；下同

Figure 7. Correlation analysis among landslide factors in the study area

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图 8 研究区易发性评价因子

Qh. 第四系全新统；Qp³. 第四系上更新统；Qp². 第四系中更新统；T₁₊₂er. 三叠系二马营组；P₂sh. 上二叠统石千峰组；P₂s. 上二叠统上石盒子组；P₁x. 下二叠统下石盒子组；C₁₊₂. 中、上石炭统；∈₃c. 上寒武统长山组；∈₃g. 上寒武统崮山组；∈₂zh. 中寒武统张夏组；∈₂x. 中寒武统徐庄组；∈₂m. 中寒武统毛庄组；∈₁m. 下寒武统馒头组；∈₁x. 下寒武统辛集组；Pt₂ma. 中新元古界五佛山群马鞍山组；Pt₁s. 下元古界嵩山群嵩山组；下同。地形起伏度分类：<52 m为1类；[52, 101) m为2类；[196, 421] m为3类；>421 m为4类

Figure 8. Maps of landslide suspecptibility evaluation factors in the study area

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图 9 研究区滑坡易发性分区

Figure 9. Landslide susceptibility zoning in the study area

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图 10 研究区4种模型滑坡易发性结果ROC曲线对比

RF. 随机森林；ANN. 人工神经网络；AUC. 受试者工作特征曲线下面积；下同

Figure 10. Comparison of ROC curves of landslide susceptibility results from four models in the study area

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图 11 基于斜坡单元的研究区滑坡易发性分区图

Figure 11. Landslide susceptibility zoning based on slope units in the study area

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表 1 研究区遥感数据基本参数

Table 1. Basic parameters of remote sensing data in the study area

参数名称	参数
卫星名称（Satellite）	哨兵1号（Sentinel-1）
成像方向（Direction）	Ascending （升轨）
成像方式（Mode）	IW （干涉宽幅模式）
轨道（Path）	113
图幅（Frame）	106
极化方式（Polarization）	VV+VH （垂直发射−垂直接收+垂直发射−水平接收）

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表 2 研究区滑坡信息量计算结果

Table 2. Information value calculation results of landslides in the study area

评价因子	因子分级	滑坡数量/个	滑坡数量比/%	栅格数量/个	栅格面积比/%	信息量	评价因子	因子分级	滑坡数量/个	滑坡数量比/%	栅格数量/个	栅格面积比/%	信息量
高程/m	<374	2	6.45	117043	37.81	−1.783450	NDVI	<−0.03	8	45.16	60927	19.80	0.815328
	[374, 482)	20	64.52	109631	35.42	0.584560		[−0.03, 0.12)	14	48.39	169622	55.12	−0.139570
	[482, 640]	9	29.03	68689	22.19	0.253583		[−0.12, 0.33]	15	6.45	45810	14.89	−0.845410
	640	0	0	14193	4.58	0		>0.33	2	0	31369	10.19	0
坡度/(°)	<8	3	9.68	141728	46.13	−1.569350	地层岩性	∈₁m	0	0	3439	1.12	0
	[8, 17)	14	45.16	101916	33.17	0.300856		∈₁x	0	0	5030	1.63	0
	[17, 30]	14	45.16	52553	17.10	0.963183		∈₂m	1	3.23	632	0.21	2.744813
	>30	0	0	11071	3.60	0		∈₂x	0	0	1718	0.56	0
坡向	平面	1	3.23	11460	3.73	−0.152920		∈₂zh	2	6.45	5457	1.77	1.282195
	N	7	22.58	61347	19.97	0.115311		∈₃c	1	3.23	5852	1.90	0.519164
	EN	6	19.35	41431	13.48	0.353678		∈₃g	3	9.68	13328	4.32	0.794693
	E	0	0	30724	10.00	0		C₁₊₂	1	3.23	5818	1.89	0.524991
	ES	4	12.90	30218	9.83	0.263804		P₁x	1	3.23	22864	7.42	−0.843620
	S	4	12.90	32641	10.62	0.186673		P₂s	13	41.94	56275	18.26	0.820646
	WS	1	3.23	24068	7.83	−0.894940		P₂sh	4	12.90	23283	7.55	0.524518
	W	4	12.90	31098	10.12	0.235098		Pt₁s	0	0	12341	4.00	0
	WN	4	12.90	44281	14.41	−0.118310		Pt₂ma	1	3.23	10806	3.51	−0.094150
平面曲率	<24.33	10	32.26	62641	20.53	0.451113		Qh	0	0	12288	3.99	0
	[24.33, 41.84)	7	22.58	85952	28.18	−0.221930		Qp³	3	9.68	99845	32.40	−1.219060
	[41.84, 59.69]	7	22.58	87117	28.56	−0.235390		Qp²	1	3.23	27997	9.08	−1.046150
	>59.69	7	22.58	69342	22.73	−0.007190		T₁₊₂er	0	0	1231	0.40	0
剖面曲率	<6.20	11	35.48	133098	43.63	−0.207240	距水系距离/m	<288	15	48.39	117978	38.28	0.223499
	[6.2, 12.8)	9	29.03	105269	34.51	−0.173350		[288,640)	3	9.68	100820	32.71	−1.228780
	[12.8, 22.3]	8	25.81	51315	16.82	0.427406		[640, 1105]	11	35.48	65932	21.39	0.495219
	>22.3	3	9.68	15370	5.04	0.652142		>1105	2	6.45	23474	7.62	−0.176800
地形起伏度/m	<52	2	6.45	142430	46.21	−1.979760	TWI	<5.9	14	45.16	138612	45.11	−0.006670
	[52, 101)	18	58.06	104917	34.04	0.523150		[5.9, 8.3)	12	38.71	120741	39.30	−0.022790
	[196, 421]	11	35.48	52023	16.88	0.732157		[8.3, 12.1]	3	9.68	35277	11.48	−0.178670
	>421	0	0	8834	2.87	0		>12.1	2	6.45	12638	4.11	0.442386
地表粗糙度	<1.07	22	70.97	264968	86.23	−0.202620	距道路距离/m	<546	10	32.26	126526	41.12	−0.251920
	[1.07, 1.33)	9	29.03	39131	12.74	0.816257		[546, 1207]	7	22.58	83842	27.25	−0.197080
	[1.33, 2.07]	0	0	2964	0.96	0		[1207, 1997]	9	29.03	66576	21.64	0.284828
	>2.07	0	0	205	0.07	0		>1997	5	16.13	30762	10.00	0.469105

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表 3 研究区各模型滑坡易发性分区统计

Table 3. Statistical results of landslide susceptibility zoning of different models in the study area

模型类别	易发性分级	分区栅格数	分区百分比/%	滑坡栅格数	占总滑坡栅格百分比/%	频率比值
信息量模型	非易发区	86768	28.46	0	0	0
	低易发区	60720	19.92	74	4.73	0.24
	中易发区	105066	34.46	616	39.39	1.14
	高易发区	52331	17.16	874	55.88	3.26
ANN模型	非易发区	206325	67.67	81	5.18	0.08
	低易发区	37629	12.34	65	4.16	0.34
	中易发区	21035	6.90	198	12.66	1.83
	高易发区	39896	13.09	1220	78.01	5.96
RF模型	非易发区	192354	63.09	66	4.22	0.07
	低易发区	28515	9.35	52	3.32	0.36
	中易发区	46218	15.16	233	14.90	0.98
	高易发区	37798	12.40	1213	77.56	6.26
Stacking集成策略模型	非易发区	166178	54.51	0	0	0
	低易发区	66077	21.67	48	3.07	0.14
	中易发区	46395	15.22	144	9.21	0.61
	高易发区	26235	8.60	1372	87.72	10.19

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