TY - JOUR
T1 - Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis
AU - Hao, Lina
AU - Van Westen, C.J.
AU - Martha, Tapas Ranjan
AU - Jaiswal, Pankaj
AU - Mcadoo, Brian G.
N1 - Funding Information:
Acknowledgements. We thank the United Nations Environmental Program (UNEP) (Muralee Thummarukudy, Karen Sudmeier-Rieux, and Louise Schreyers) for initiating this work and the coor- dination, Sekhar Lukose Kuriakose and colleagues from the Kerala State Disaster Management Agency (KSDMA) for their support, the National Remote Sensing Centre (NRSC) for providing the landslide polygon inventory, the Geological Survey of India (GSI) for providing the landslide point inventory, and Google Earth for the use of multi-temporal HRSIs.
Funding Information:
Financial support. This research has been supported by the Chinese National Science Fund (grant nos. 41702358, 41790445, 41630640, 41771444) and the China Postdoctoral Science Foundation (grant no. 2017M622982).
Publisher Copyright:
© 2020 Royal Society of Chemistry. All rights reserved.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at https://doi.org/10.17026/dans-x6c-y7x2 (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies.
AB - Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at https://doi.org/10.17026/dans-x6c-y7x2 (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies.
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-GOLD
U2 - 10.5194/essd-12-2899-2020
DO - 10.5194/essd-12-2899-2020
M3 - Article
SN - 1866-3508
VL - 12
SP - 2899
EP - 2918
JO - Earth system science data
JF - Earth system science data
IS - 4
ER -
