Landslide Extraction From Sentinel-2 Image in Siwalik of Surkhet District, Nepal

Abstract

Abstract. Locating landslides and determining its extent is deemed an important task in estimating loss and damage and carry out mitigation works. As landslides are recurring phenomena in the research site, Siwalik Hills of western Nepal, freely available Sentinel-2 satellite images were considered to delineate landslides. The method employed in this process was Object-Based Image Analysis carried out in eCognition software using multiresolution segmentation algorithm. Parameters taken for segmentation were a scale of 20, the shape of 0.3, and compactness of 0.5. When a threshold value of < 0.35 in NDVI was used to distinguish landslides from image objects, some non-landslide objects were also selected. These false positives were removed successively using the threshold values on different bands, band ratios, slope information, hillshade and geometrical properties of image objects. There were altogether 264 landslides detected in the study area with size ranging from 300 m2 to 1675 m2 and landslide density of approximately 2 per km2. The accuracy, when compared to reference inventory, showed correctness and completeness measuring 80.28% and 66.27% respectively. These results showed semi-automatic landslide extraction was successful and Sentinel-2 can be used for similar tasks in other areas of Siwalik.

Extraction of Water Bodies from Sentinel-2 Images in the Foothills of Nepal Himalaya

“…Commonly used classification accuracy does not justify the water bodies in thematic and geometric dimensions (Budha and Bhardwaj, 2019). Thus, object-based classification accuracy (STEP method) was used to define the thematic and geometric accuracies.…”

“…Thus, object-based classification accuracy (STEP method) was used to define the thematic and geometric accuracies. STEP method was used to understand the reliability of thematic attributes (shape, theme, edge, and positional similarity) carried by classified objects (Budha and Bhardwaj, 2019;Lizarazo, 2014). The STEP accuracy analysis presented that thematically larger water bodies (snow fed Mahakali River, natural lakes) can be extracted more accurately than smaller water bodies (irrigation canals, artificial ponds).…”

Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan

“…Object-oriented classification technology segments homogeneous images and collects adjacent pixels as analysis objects, and uses high-resolution and multiple-spectral data to conduct high-precision classification [21]. Many scholars used the object-oriented classification method to extract landslides automatically, and the accuracy is higher than the traditional statistical analysis and machine learning methods [5,[22][23][24][25][26][27][28][29][30]. In order to improve the accuracy of automatic extraction, some scholars combined traditional statistical analysis and machine learning methods to optimize the object-oriented method [31][32][33][34][35][36].…”