Remote Sensing and GIS-Based Accuracy Assessment of LULC Map and Landslide Susceptibility Prediction for Meghalaya, India
Authors
-
Department of Civil Engineering, National Institute of Technology, Shillong - 793 003
Badavath Naveen -
Department of Civil Engineering, National Institute of Technology, Shillong - 793 003
Smrutirekha Sahoo
DOI:
https://doi.org/10.17491/jgsi/2024/173885Keywords:
Meghalaya, AHP, Landslide susceptibility, LULC, ROC.Abstract
Through this study, a Landslide Susceptibility Map (LSM) has been developed for the Meghalaya state, India using an Analytical Hierarchy Process (AHP). According to a 2012 Geological Survey of India report, the annual average number of landslides in Meghalaya is nearly 30, which is due to a combination of mountains, steep slopes, and excessive rainfall, leading the state to suffer a huge loss of life and property from landslides. For effective management of the current landslide situation, information about prior landslides is needed. Therefore, the landslide inventory map is prepared with 380 previously occurred events. The Landslide inventory records were separated into training samples (70%) and testing samples (30%) for the purpose of validation. In this regard, the present study has 15 conditioning factors, i.e., slope, rainfall, elevation, relative relief, aspect, distance from the road, curvature, distance from the stream, LULC, lineament density, geomorphology, geology, NDVI, MSAVI, NDWI, which are used to develop susceptibility map. Classification and accuracy assessment of LULC is carried out with segregation as 77% vegetation, 16.4% range land, 3.1% built area, 2.8% crops, 0.4% waterbodies, and 0.3% others (bare land, flooded vegetation, etc.). The Kappa for LULC categorization is 0.92, which is quite satisfactory and suggests that the LULC categorization is reliable. The developed susceptibility map is classified into four different classes, low susceptibility (35%), moderate susceptibility (41%), high susceptibility (20%), and very high susceptibility (4%), and has been verified using physical and Receiver Operating Characteristics (ROC) techniques. Results show that anticipated susceptibility classes are in good match with previous landslide events. The prepared map is reliable and can be used for land-use planning of the state in the future.
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