Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis

Abstract

Abstract. 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.

Assessment of torrential flood and landslide susceptibility of terrain: Case study - Mlava River Basin (Serbia)

“…Осим становништва у насељеним областима, богатство флоре и фауне на овом подручју може бити значајно угрожено природним и антропогеним процесима. Осим наведеног, ове непогоде значајно утичу и на привредне делатности и инфраструктуру (Dai et al, 2002;Tošić et al, 2014;Hao et al, 2020;Meena et al, 2021;Morar et al, 2021).…”

“…Apart from the population in populated areas, the richness of flora and fauna in this area can be significantly endangered by natural and anthropogenic processes. In addition to the above, these disasters significantly affect economic activities and infrastructure (Dai et al, 2002;Tošić et al, 2014;Hao et al, 2020;Meena et al, 2021;Morar et al, 2021).…”

Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting

“…During August of 2018, the district was affected by a number of landslides due to torrential rains [17]. A total of 388 landslides (Figure 1) were mapped within the district using government reports and pre-and post-event satellite images from Google Earth, and have been verified using a recently published dataset [18]. The inventory data were prepared separately for LSM, and are different from the dataset used for previous studies conducted by the authors [13] in which they derived the rainfall thresholds for the region.…”

Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria

“…Object-based image analysis (OBIA) has been widely used in the past two decades, such as in urban-land-cover mapping [1][2][3], ecological monitoring [4][5][6], disaster evaluation [7][8][9], and crop-type identification [10][11][12]. In recent years, OBIA has also been incorporated into the deep learning model to capture the precise outlines of ground objects and model spatial-context relationships [13][14][15].…”