Application of environmental variables in statistically-based landslide susceptibility mapping: A review
Environmental variables are crucial factors affecting the development and distribution of landslides, and they also provide vitally important information for statistically-based landslide susceptibility mapping (SLSM). The acquisition and utilization of appropriate and the most influential environme...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1147427/full |
| _version_ | 1797860966539460608 |
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| author | Xin Zhao Xin Zhao Xin Zhao Zhifang Zhao Faming Huang Jiangcheng Huang Zhiquan Yang Qi Chen Dingyi Zhou Liuyang Fang Liuyang Fang Xian Ye Xian Ye Jiangqin Chao |
| author_facet | Xin Zhao Xin Zhao Xin Zhao Zhifang Zhao Faming Huang Jiangcheng Huang Zhiquan Yang Qi Chen Dingyi Zhou Liuyang Fang Liuyang Fang Xian Ye Xian Ye Jiangqin Chao |
| author_sort | Xin Zhao |
| collection | DOAJ |
| description | Environmental variables are crucial factors affecting the development and distribution of landslides, and they also provide vitally important information for statistically-based landslide susceptibility mapping (SLSM). The acquisition and utilization of appropriate and the most influential environmental variables and their combinations are crucial for improving the quality of SLSM results. However, compared with the construction of SLSM models based on machine learning, the acquisition and utilization of high-quality environmental variables have received very little attention. In order to further clarify the research status of the application of environmental variables and possible development directions in future research, this study systematically analyzed the application of environmental variables in SLSM. To this end, a literature database was constructed by collecting 261 peer-reviewed articles (from 2002 to 2021) on SLSM from the Web of Science and CNKI platform (www.cnki.net) based on the keywords of “landslide susceptibility” and “environmental variable.” We found that existing methods for determining environmental variables do not consider the regional representativeness and geomorphological significance of the variables. We also found that at present, environmental variables are utilized generally without the realization and understanding of their spatial heterogeneity. Accordingly, this study raises two major scientific issues: 1) Effective identification of important environmental variables required in SLSM. 2) Effective representation of the spatial heterogeneity of environmental variables in SLSM modeling. From the perspective of the identification of dominant variables and their geospatial pattern of heterogeneity, targeted solutions for future research are also preliminarily discussed, including the method for identifying dominant variables from qualitative and quantitative perspectives and SLSM model construction considering the specific geospatial patterns. In addition, the applicability and limitation of the mentioned methods are discussed. |
| first_indexed | 2024-04-09T21:55:28Z |
| format | Article |
| id | doaj.art-9fba12e4087f4077b2f80657544234f0 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-09T21:55:28Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-9fba12e4087f4077b2f80657544234f02023-03-24T06:09:01ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-03-011110.3389/feart.2023.11474271147427Application of environmental variables in statistically-based landslide susceptibility mapping: A reviewXin Zhao0Xin Zhao1Xin Zhao2Zhifang Zhao3Faming Huang4Jiangcheng Huang5Zhiquan Yang6Qi Chen7Dingyi Zhou8Liuyang Fang9Liuyang Fang10Xian Ye11Xian Ye12Jiangqin Chao13Broadvision Engineering Consultants Co., Ltd., Kunming, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming, ChinaYunnan Key Laboratory of Digital Communications, Kunming, ChinaSchool of Earth Sciences, Yunnan University, Kunming, ChinaSchool of Infrastructure Engineering, Nanchang University, Nanchang, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming, ChinaSchool of Public Security and Emergency Management, Kunming University of Science and Technology, Kunming, ChinaSchool of Earth Sciences, Yunnan University, Kunming, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming, ChinaBroadvision Engineering Consultants Co., Ltd., Kunming, ChinaYunnan Key Laboratory of Digital Communications, Kunming, ChinaBroadvision Engineering Consultants Co., Ltd., Kunming, ChinaYunnan Key Laboratory of Digital Communications, Kunming, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming, ChinaEnvironmental variables are crucial factors affecting the development and distribution of landslides, and they also provide vitally important information for statistically-based landslide susceptibility mapping (SLSM). The acquisition and utilization of appropriate and the most influential environmental variables and their combinations are crucial for improving the quality of SLSM results. However, compared with the construction of SLSM models based on machine learning, the acquisition and utilization of high-quality environmental variables have received very little attention. In order to further clarify the research status of the application of environmental variables and possible development directions in future research, this study systematically analyzed the application of environmental variables in SLSM. To this end, a literature database was constructed by collecting 261 peer-reviewed articles (from 2002 to 2021) on SLSM from the Web of Science and CNKI platform (www.cnki.net) based on the keywords of “landslide susceptibility” and “environmental variable.” We found that existing methods for determining environmental variables do not consider the regional representativeness and geomorphological significance of the variables. We also found that at present, environmental variables are utilized generally without the realization and understanding of their spatial heterogeneity. Accordingly, this study raises two major scientific issues: 1) Effective identification of important environmental variables required in SLSM. 2) Effective representation of the spatial heterogeneity of environmental variables in SLSM modeling. From the perspective of the identification of dominant variables and their geospatial pattern of heterogeneity, targeted solutions for future research are also preliminarily discussed, including the method for identifying dominant variables from qualitative and quantitative perspectives and SLSM model construction considering the specific geospatial patterns. In addition, the applicability and limitation of the mentioned methods are discussed.https://www.frontiersin.org/articles/10.3389/feart.2023.1147427/fullliterature reviewlandslidesusceptibility zonationstatistically-based methodenvironmental variablegeomorphological significance |
| spellingShingle | Xin Zhao Xin Zhao Xin Zhao Zhifang Zhao Faming Huang Jiangcheng Huang Zhiquan Yang Qi Chen Dingyi Zhou Liuyang Fang Liuyang Fang Xian Ye Xian Ye Jiangqin Chao Application of environmental variables in statistically-based landslide susceptibility mapping: A review Frontiers in Earth Science literature review landslide susceptibility zonation statistically-based method environmental variable geomorphological significance |
| title | Application of environmental variables in statistically-based landslide susceptibility mapping: A review |
| title_full | Application of environmental variables in statistically-based landslide susceptibility mapping: A review |
| title_fullStr | Application of environmental variables in statistically-based landslide susceptibility mapping: A review |
| title_full_unstemmed | Application of environmental variables in statistically-based landslide susceptibility mapping: A review |
| title_short | Application of environmental variables in statistically-based landslide susceptibility mapping: A review |
| title_sort | application of environmental variables in statistically based landslide susceptibility mapping a review |
| topic | literature review landslide susceptibility zonation statistically-based method environmental variable geomorphological significance |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1147427/full |
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