考虑地表形变和土地利用变更的滑坡时空易发性差异分析

张锦瑞; 汪洋; 冯霄; 李远耀; 金必晶; 周超; 张鑫; 邓扬

doi:10.19509/j.cnki.dzkq.tb20240195

考虑地表形变和土地利用变更的滑坡时空易发性差异分析

doi: 10.19509/j.cnki.dzkq.tb20240195

张锦瑞^a,,
汪洋^b, ,,
冯霄^b,
李远耀^a,
金必晶^b,
周超^c,
张鑫^a,
邓扬^a

a.
中国地质大学(武汉)地质调查研究院, 武汉 430074
b.
中国地质大学(武汉)工程学院, 武汉 430074
c.
中国地质大学(武汉)地理与信息工程学院, 武汉 430074

基金项目:

国家自然科学基金项目 42371094

国家自然科学基金项目 41907253

详细信息

作者简介:
张锦瑞, E-mail: 1202221896@cug.edu.cn

通讯作者:
汪洋, E-mail: wangyangcug@126.com

中图分类号: P642.22;P642.26
计量
- 文章访问数: 42
- PDF下载量: 8
- 被引次数: 0
出版历程
- 收稿日期: 2024-04-26
- 录用日期: 2024-09-20
- 修回日期: 2024-08-28

Analysis of spatial-temporal variations in landslide susceptibility assessment considering surface deformation and land use dynamics

a.
Institute of Geological Survey, China University of Geosciences(Wuhan), Wuhan 430074, China
b.
Faculty of Engineering, China University of Geosciences(Wuhan), Wuhan 430074, China
c.
School of Geography and Information Engineering, China University of Geosciences(Wuhan), Wuhan 430074, China

摘要

摘要:
为探究移民迁建城区受人类工程活动影响而产生的滑坡易发性时空变化, 以三峡库区云阳县新城区为例, 引入滑坡易发性时变指标因子进行滑坡时空易发性差异制图, 从而探究迁建城区在城镇化过程中滑坡灾害的时空演化规律。选择Stacking集成模型作为静态易发性评价模型; 选取2017年1月16日-2018年8月27日(T1)、2018年9月20日-2021年7月30日(T2)、2021年8月23日-2023年11月17日(T3)3个不同时间跨度的InSAR形变速率和土地利用类型作为时变因子; 将时变因子与静态评价结果结合, 绘制了不同时间段的易发性差异分布图。研究表明, 引入时变因子进行滑坡时空易发性差异分析可以有效反映城市化对滑坡灾害的影响, 研究区土地类型由非工程用地转变为工程用地时, 滑坡易发性等级普遍上升, 2个变化阶段中栅格占比分别为61.3%和67.1%。城区所选典型滑坡的InSAR位移时序曲线的变化趋势与土地类型转变具有较高的时空相关性, 进一步验证了该方法的可靠性。本研究提出的研究思路能够在三峡库区移民迁建城区城市化进程中提供有效的防灾减灾依据和区域规划手段。
- 移民迁建城区 /
- 滑坡易发性 /
- 土地利用类型 /
- InSAR /
- 滑坡时空易发性差异分析 /
- 地表形变
Abstract: Objective
To investigate the spatial-temporal variations in landslide susceptibility due to human engineering activities in resettled urban areas.
Methods
This study focuses on the new urban area of Yunyang County in the Three Gorges Reservoir region. Landslide susceptibility time-varying index factors were introduced to map spatial-temporal susceptibility differences and explore the spatial-temporal evolution of landslide disasters during urbanization in resettled urban areas. First, the stacking ensemble model was selected as the static susceptibility evaluation model. Then, the InSAR deformation rates and land use types over three distinct time spans (namely, January 16, 2017, to August 27, 2018 (T1), September 20, 2018, to July 30, 2021 (T2), and August 23, 2021, to November 17, 2023 (T3)) were selected as time-varying factors. Last, the time-varying factors were combined with the static evaluation results to create susceptibility difference distribution maps for the different periods.
Results
The study revealed that introducing time-varying factors in the analysis of spatial-temporal susceptibility differences effectively reflects the impact of urbanization on landslide disasters. When the land type in the study area changed from non-engineering land to engineering land, the landslide susceptibility level generally increased, with grid shares of 61.3% and 67.1% in the two change stages, respectively. The temporal trends of the InSAR displacement time series curves for selected typical landslides in urban areas showed high spatial-temporal correlations with land type changes, further validating the reliability of this method.
Conclusion
The proposed research approach provides the basis for disaster prevention, mitigation, and regional planning during the urbanization process in resettled urban areas of the Three Gorges Reservoir region.
- resettled urban area /
- landslide susceptibility /
- land use type /
- InSAR /
- analysis of spatial-temporal variations in landslide susceptibility assessment /
- surface deformation
注释:

所有作者声明不存在利益冲突。

HTML全文

图 1 研究区地理位置(a)、历史遥感影像(b~d)及土地利用类型(e~g)

Figure 1. Geographical location(a), historical remote sensing image(b-d) and land use types(e-g) in the study area

下载: 全尺寸图片幻灯片

图 2 研究区各影响因子分布图

Figure 2. Distribution diagram of different impact factors in the study area

下载: 全尺寸图片幻灯片

图 3 研究区各影响因子相关性

Figure 3. Correlation of influencing factors in the study area

下载: 全尺寸图片幻灯片

图 4 滑坡易发性结果图

Figure 4. Landslide susceptibility result diagram

下载: 全尺寸图片幻灯片

图 5 InSAR形变速率全区域预测结果

a~c.InSAR解译的初始结果，变形点存在局部缺失; g~i.将初始结果空间预测补全后的形变速率

Figure 5. InSAR deformation rate prediction results for the whole area

下载: 全尺寸图片幻灯片

图 6 滑坡时空易发性差异制图(T1, T2, T3分别为第一、二、三阶段，下同)

Figure 6. Mapping of spatial-temporal differences in susceptibility to landslides

下载: 全尺寸图片幻灯片

图 7 易发性栅格变化

Figure 7. Susceptibility grid changes

下载: 全尺寸图片幻灯片

图 8 张家湾滑坡(a~e)和梨园滑坡(f~j)案例分析

Figure 8. Analysis of Zhangjiawan landslide(a-e) and Liyuan landslide(f-j) in the study area

下载: 全尺寸图片幻灯片

表 1 滑坡易发性评价因子选择及依据

Table 1. Selection and basis of landslide susceptibility evaluation factors

一级评价因子		二级评价因子	选择依据
静态因子	地形地貌	高程	不同高程条件下土质滑坡临空条件存在差异
		地形粗糙度	反映地表起伏程度，与土质滑坡的稳定性密切相关
		剖面曲率	剖面曲率可以描述地形的复杂度，反映地表的侵蚀和沉积程度
		平面曲率	平面曲率可以反映地形在水平方向的特征
		坡向	坡向决定水分蒸散与入渗模式，进而影响土质滑坡的稳定性
		坡度	坡度对斜坡的松散物质堆积、应力分布和径流条件等因素产生影响，从而影响坡体稳定性
		斜坡结构	斜坡结构类型不仅反映了滑坡灾害所处的地层发育和斜坡交切关系的地质结构环境，还揭示了其所处的应力环境
	水文环境	地形湿度指数(TWI)	表示地表水分的积聚程度，高值区域更易发生滑坡
		距水系距离	水系邻近区域受库水渗透力和浮托力影响显著，降低土体稳定性
		归一化植被指数(NDVI)	植被覆盖可以加固土壤并减缓侵蚀，对坡体起保护作用
	基础地质	岩性	地层岩性是滑坡灾害发育的物质基础，主要影响作用表现在不同地层、不同岩性组合的滑坡、发育模式和发育程度存在差异
时变因子		土地利用类型	土地利用作为人类社会经济工程活动的表征之一，被视为衡量地区生态的重要因素，能够反映区域人类活动强度大小，同时，该因素会影响地表覆盖的情况，进而影响岩土体的水文与力学条件
时变因子		地表形变速率	监测地表形变动态变化，提供滑坡活动的早期预警信息，可以定量反映坡体稳定性，并识别潜在滑坡区域

下载: 导出CSV

参考文献(38)

[1]	唐辉明. 重大滑坡预测预报研究进展与展望[J]. 地质科技通报, 2022, 41(6): 1-13. doi: 10.19509/j.cnki.dzkq.2022.0203 TANG H M. Advance and prospects of major landslides prediction and forecasting[J]. Bulletin of Geological Science and Technology, 2022, 41(6): 1-13. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2022.0203
[2]	殷坤龙, 张宇, 汪洋. 水库滑坡涌浪风险研究现状和灾害链风险管控实践[J]. 地质科技通报, 2022, 41(2): 1-12. doi: 10.19509/j.cnki.dzkq.2022.0064 YIN K L, ZHANG Y, WANG Y. A review of landslide-generated waves risk and practice of management of hazard chain risk from reservoir landslide[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 1-12. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2022.0064
[3]	仝德富, 谭飞, 苏爱军, 等. 基于多源数据的谭家湾滑坡变形机制及稳定性评价[J]. 地质科技通报, 2021, 40(4): 162-170. doi: 10.19509/j.cnki.dzkq.2021.0432 TONG D F, TAN F, SU A J, et al. Deformation mechanism and stability evaluation of Tanjiawan landslide based on multi-source data[J]. Bulletin of Geological Science and Technology, 2021, 40(4): 162-170. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2021.0432
[4]	汤罗圣, 左清军, 岳敏, 等. 基于滑带完整性指标的三峡库区堆积层滑坡失稳破坏判据[J]. 地质科技通报, 2022, 41(6): 77-84. doi: 10.19509/j.cnki.dzkq.2022.0253 TANG L S, ZUO Q J, YUE M, et al. Instability failure criterion of debris landslide in the Three Gorges Reservoir area based on the sliding zone integrity index[J]. Bulletin of Geological Science and Technology, 2022, 41(6): 77-84. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2022.0253
[5]	冯霄, 王禹, 刘洋, 等. 考虑软弱夹层控滑机制及其空间不确定性的顺层岩质滑坡易发性评价: 万州区铁峰乡应用研究[J]. 地质科技通报, 2022, 41(2): 254-266. doi: 10.19509/j.cnki.dzkq.2022.0049 FENG X, WANG Y, LIU Y, et al. Susceptibility assessment of a translational rockslide considering the control mechanism and spatial uncertainty of a weak interlayer: Application study in Tiefeng Township, Wanzhou District[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 254-266. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2022.0049
[6]	田乃满, 兰恒星, 伍宇明, 等. 人工神经网络和决策树模型在滑坡易发性分析中的性能对比[J]. 地球信息科学学报, 2020, 22(12): 2304-2316. TIAN N M, LAN H X, WU Y M, et al. Performance comparison of BP artificial neural network and CART decision tree model in landslide susceptibility prediction[J]. Journal of Geo-information Science, 2020, 22(12): 2304-2316. (in Chinese with English abstract)
[7]	黄发明, 李金凤, 王俊宇, 等. 考虑线状环境因子适宜性和不同机器学习模型的滑坡易发性预测建模规律[J]. 地质科技通报, 2022, 41(2): 44-59. doi: 10.19509/j.cnki.dzkq.2022.0010 HUANG F M, LI J F, WANG J Y, et al. Modelling rules of landslide susceptibility prediction considering the suitability of linear environmental factors and different machine learning models[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 44-59. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2022.0010
[8]	殷跃平. 三峡库区边坡结构及失稳模式研究[J]. 工程地质学报, 2005, 13(2): 145-154. YIN Y P. Human-cutting slope structure and failure pattern at the Three Gorges Reservoir[J]. Journal of Engineering Geology, 2005, 13(2) : 145-154. (in Chinese with English abstract)
[9]	DILLE A, DEWITTE O, HANDWERGER A L, et al. Acceleration of a large deep-seated tropical landslide due to urbanization feedbacks[J]. Nature Geoscience, 2022, 15: 1048-1055. doi: 10.1038/s41561-022-01073-3
[10]	黄发明, 叶舟, 姚池, 等. 滑坡易发性预测不确定性: 环境因子不同属性区间划分和不同数据驱动模型的影响[J]. 地球科学, 2020, 45(12): 4535-4549. HUANG F M, YE Z, YAO C, et al. Uncertainties of landslide susceptibility prediction: Different attribute interval divisions of environmental factors and different data-based models[J]. Earth Science, 2020, 45(12): 4535-4549. (in Chinese with English abstract)
[11]	仉义星, 兰恒星, 李郎平, 等. 综合统计模型和物理模型的地质灾害精细评估: 以福建省龙山社区为例[J]. 工程地质学报, 2019, 27(3): 608-622. ZHANG Y X, LAN H X, LI L P, et al. Combining statistical model and physical model for refined assessment of geological disaster: A case study of Longshan community in Fujian Province[J]. Journal of Engineering Geology, 2019, 27(3): 608-622. (in Chinese with English abstract)
[12]	RABONZA M L, FELIX R P, LAGMAY A M F A, et al. Shallow landslide susceptibility mapping using high-resolution topography for areas devastated by super typhoon Haiyan[J]. Landslides, 2016, 13(1): 201-210.
[13]	魏冠军, 高茂宁. 结合TRIGRS模型的黄土滑坡危险性评价粒子滤波数据同化方法[J]. 地球信息科学学报, 2023, 25(10): 2084-2092. WEI G J, GAO M N. Particle filter data assimilation method for loess landslide risk assessment combined with TRIGRS model[J]. Journal of Geo-information Science, 2023, 25(10): 2084-2092. (in Chinese with English abstract)
[14]	邬礼扬, 殷坤龙, 曾韬睿, 等. 不同栅格尺寸下输电线路地质灾害易发性评价[J]. 地质科技通报, 2024, 43(1): 241-252. doi: 10.19509/j.cnki.dzkq.tb20220307 WU L Y, YIN K L, ZENG T R, et al. Evaluation of geological disaster susceptibility of transmission lines under different grid resolutions[J]. Bulletin of Geological Science and Technology, 2024, 43(1): 241-252. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.tb20220307
[15]	MERGHADI A, ABDERRAHMANE B, TIEN BUI D. Landslide susceptibility assessment at Mila Basin (Algeria): A comparative assessment of prediction capability of advanced machine learning methods[J]. ISPRS International Journal of Geo-Information, 2018, 7(7): 268.
[16]	POURGHASEMI H R, KERLE N. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran[J]. Environmental Earth Sciences, 2016, 75(3): 185.
[17]	CAN R, KOCAMAN S, GOKCEOGLU C. A comprehensive assessment of XGBoost algorithm for landslide susceptibility mapping in the upper basin of Ataturk Dam, Turkey[J]. Applied Sciences, 2021, 11(11): 4993.
[18]	DI NAPOLI M, CAROTENUTO F, CEVASCO A, et al. Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability[J]. Landslides, 2020, 17(8): 1897-1914.
[19]	金必晶, 曾韬睿, 桂蕾, 等. 考虑未来土地利用动态情景的滑坡易发性制图[J]. 地球信息科学学报, 2024, 26(6): 1486-1499. JIN B J, ZENG T R, GUI L, et al. Mapping the landslide susceptibility considering future land use dynamics scenario[J]. Journal of Geo-information Science, 2024, 26(6): 1486-1499. (in Chinese with English abstract)
[20]	周超, 甘露露, 王悦, 等. 综合非滑坡样本选取指数与异质集成机器学习的区域滑坡易发性建模[J]. 地球信息科学学报, 2023, 25(8): 1570-1585. ZHOU C, GAN L L, WANG Y, et al. Landslide susceptibility prediction based on non-landslide samples selection and heterogeneous ensemble machine learning[J]. Journal of Geo-information Science, 2023, 25(8): 1570-1585. (in Chinese with English abstract)
[21]	吴宏阳, 周超, 梁鑫, 等. 基于XGBoost模型的三峡库区燕山乡滑坡易发性评价与区划[J]. 中国地质灾害与防治学报, 2023, 34(5): 141-152. WU H Y, ZHOU C, LIANG X, et al. Assessment of landslide susceptibility mapping based on XGBoost model: A case study of Yanshan Township[J]. The Chinese Journal of Geological Hazard and Control, 2023, 34(5): 141-152. (in Chinese with English abstract)
[22]	谢小旭, 李德营, 许方党, 等. 基于模糊相似优先比的滑坡易发性评价及动态更新[J/OL]. 工程地质学报: 1-13[2024-10-09]. http://kns.cnki.net/kcms/detail/11.3249.P.20240126.1345.002.html. XIE X X, LI D Y, XU F D, et al. Landslide susceptibility assessment and dynamic updating based on the fuzzy priority ratio[J/OL]. Journal of Engineering Geology: 1-13[2024-10-09]. http://kns.cnki.net/kcms/detail/11.3249.P.20240126.1345.002.html. (in Chinese with English abstract)
[23]	王佳佳, 殷坤龙, 肖莉丽. 基于GIS和信息量的滑坡灾害易发性评价: 以三峡库区万州区为例[J]. 岩石力学与工程学报, 2014, 33(4): 797-808. WANG J J, YIN K L, XIAO L L. Landslide susceptibility assessment based on GIS and weighted information value: A case study of Wanzhou District, Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering, 2014, 33(4): 797-808. (in Chinese with English abstract)
[24]	乔治, 蒋玉颖, 贺曈, 等. 土地利用变化模拟研究进展[J]. 生态学报, 2022, 42(13): 5165-5176. QIAO Z, JIANG Y Y, HE T, et al. Land use change simulation: Progress, challenges, and prospects[J]. Acta Ecologica Sinica, 2022, 42(13): 5165-5176. (in Chinese with English abstract)
[25]	林炫歆, 肖桂荣, 周侯伯. 顾及土地利用动态变化的滑坡易发性评估方法[J]. 地球信息科学学报, 2023, 25(5): 953-966. LIN X X, XIAOG R, ZHOU H B. Landslide susceptibility assessment method considering land use dynamic change[J]. Journal of Geo-information Science, 2023, 25(5): 953-966. (in Chinese with English abstract)
[26]	MOREIRA A, PRATS-IRAOLA P, YOUNIS M, et al. A tutorial on synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(1): 6-43.
[27]	HE Y, WANG W H, ZHANG L F, et al. An identification method of potential landslide zones using InSAR data and landslide susceptibility[J]. Geomatics, Natural Hazards and Risk, 2023, 14(1): 2185120.
[28]	曾韬睿, 邬礼扬, 金必晶, 等. 基于stacking集成策略和SBAS-InSAR的滑坡动态易发性制图[J]. 岩石力学与工程学报, 2023, 42(9): 2266-2282. ZENG T R, WU L Y, JIN B J, et al. Landslide dynamic susceptibility mapping based on stacking ensemble strategy and SBAS-InSAR[J]. Chinese Journal of Rock Mechanics and Engineering, 2023, 42(9): 2266-2282. (in Chinese with English abstract)
[29]	张伟, 陈宏, 纪成亮, 等. 基于升降轨InSAR数据的高山峡谷区滑坡易发性评价[J/OL]. 地质科技通报: 1-10[2024-10-09]. https://doi.org/10.19509/j.cnki.dzkq.tb20230560. ZHANG W, CHEN H, JI C L, et al. Landslide susceptibility assessment in the alpine and canyon areas based on ascending and descending InSAR data[J]. Bulletin of Geological Science and Technology: 1-10[2024-10-09]. https://doi.org/10.19509/j.cnki.dzkq.tb20230560. . (in Chinese with English abstract)
[30]	梅乐, 马星, 连鑫龙, 等. 城市化过程中土地利用变化对地质灾害易发性影响的研究[J]. 自然灾害学报, 2023, 32(5): 186-196. MEI L, MA X, LIAN X L, et al. Influence of land use change on the susceptibility of geological disasters in the process of urbanization[J]. Journal of Natural Disasters, 2023, 32(5): 186-196. (in Chinese with English abstract)
[31]	周超, 殷坤龙, 曹颖, 等. 基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价[J]. 地球科学, 2020, 45(6): 1865-1876. ZHOU C, YIN K L, CAO Y, et al. Landslide susceptibility assessment by applying the coupling method of radial basis neural network and adaboost: A case study from the Three Gorges Reservoir area[J]. Earth Science, 2020, 45(6): 1865-1876. (in Chinese with English abstract)
[32]	贾琳, 蔡静森, 晏鄂川, 等. 基于地质环境分区的南漳县城区滑坡易发性评价[J]. 人民长江, 2021, 52(5): 86-94. JIA L, CAI J S, YAN E C, et al. Assessment of landslide susceptibility in Nanzhang County based on geological environment zoning[J]. Yangtze River, 2021, 52(5): 86-94. (in Chinese with English abstract)
[33]	HONG H Y, LIU J Z, ZHU A X. Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble[J]. Science of the Total Environment, 2020, 718: 137231.
[34]	DOU J, YUNUS A P, BUI D T, et al. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan[J]. Landslides, 2020, 17(3): 641-658.
[35]	HAKIM W, ACHMAD A, LEE C W. Land subsidence susceptibility mapping in jakarta using functional and meta-ensemble machine learning algorithm based on time-series InSAR data[J]. Remote Sensing, 2020, 12(21): 3627.
[36]	郭子正, 殷坤龙, 黄发明, 等. 基于滑坡分类和加权频率比模型的滑坡易发性评价[J]. 岩石力学与工程学报, 2019, 38(2): 287-300. GUO Z Z, YIN K L, HUANG F M, et al. Evaluation of landslide susceptibility based on landslide classification and weighted frequency ratio model[J]. Chinese Journal of Rock Mechanics and Engineering, 2019, 38(2): 287-300. (in Chinese with English abstract)
[37]	陈丹璐, 孙德亮, 文海家, 等. 基于不同因子筛选方法的LightGBM-SHAP滑坡易发性研究[J]. 北京师范大学学报(自然科学版), 2024, 60(1): 148-158. CHEN D L, SUN D L, WEN H J, et al. LightGBM-SHAP landslide susceptibility by different factor screening methods[J]. Journal of Beijing Normal University (Natural Science), 2024, 60(1): 148-158. (in Chinese with English abstract)
[38]	NAGHIBI S A, KHODAEI B, HASHEMI H. An integrated InSAR-machine learning approach for ground deformation rate modeling in arid areas[J]. Journal of Hydrology, 2022, 608: 127627.