Landslide Susceptibility Mapping Using Landslide Numerical Risk Factor Model and Landslide Inventory Prepared Through OBIA in Chenab Valley, Jammu and Kashmir (India)

Patil, Abhijit S.; Bhadra, B. K.; Panhalkar, Sachin S.; Patil, Prasad

doi:10.1007/s12524-019-01092-5

Cited by 17 publications

(5 citation statements)

References 15 publications

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“…This is a widely employed vegetation index that serves to assess photosynthetic activity and is commonly utilized for determining the water and nitrogen consumption of vegetation cover. The relationship with landslides is very similar in comparison with the NDVI index [57].…”

Section: Gndvi (Green Normalized Difference Vegetation Index)mentioning

confidence: 66%

“…A spectral index that measures the density and health of vegetation based on the difference between the near-infrared and red bands [56] Green normalized difference vegetation index (GNDVI) Variant of NDVI using the green band instead of the red band for greater sensitivity to differences in chlorophyll [57] Enhanced vegetation index (EVI) Index similar to NDVI that corrects for some atmospheric and soil factors to improve vegetation monitoring [58] Normalized difference moisture index (NDMI) Index designed to detect water stress in vegetation and drought conditions [59] BI (Bare soil index) Index used to identify and quantify areas of bare soil [60] Normalized difference water index (NDWI) An index that uses spectral information to identify and monitor moisture content in vegetation and water bodies [61] Normalized difference glacier index (NDGI) Index used to detect and monitor glaciers and snow using spectral data [62] 2.…”

Section: Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 99%

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Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile

Parra,

González,

Chacón

et al. 2023

Sustainability

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Landslides represent one of the main geological hazards, especially in Chile. The main purpose of this study is to evaluate the application of machine learning algorithms (SVM, RF, XGBoost and logistic regression) and compare the results for the modeling of landslide susceptibility in the province of Chañaral, III region, Chile. A total of 86 sites are identified using various sources, in addition to 86 non-landslide sites. This spatial data management and analysis are conducted using QGIS software. The sites are randomly divided, and then a cross-validation process is applied to calculate the accuracy of the models. After that, from 22 conditioning factors, 12 are chosen based on the information gain ratio (IGR). Subsequently, five factors are excluded by the correlation criterion. After this analysis, two indices not previously utilized in the literature, the NDGI (normalized difference glacier index) and EVI (enhanced vegetation index), are employed for the final model. The performance of the models is evaluated through the area under the ROC (receiver operating characteristic) curve (AUC). To study the statistical behavior of the model, the Friedman nonparametric test is performed to compare the performance with the other algorithms and the Nemenyi test for pairwise comparison. Of the algorithms used, RF (AUC = 0.957) and XGBoost (AUC = 0.955) have the highest accuracy values measured in AUC compared to the other models and can be used for the same purpose in other geographic areas with similar characteristics. The findings of this investigation have the potential to assist in land use planning, landslide risk reduction, and informed decision making in the surrounding zones.

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Section: Gndvi (Green Normalized Difference Vegetation Index)mentioning

confidence: 66%

Section: Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 99%

Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile

Parra,

González,

Chacón

et al. 2023

Sustainability

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show abstract

“…It effectively addresses issues encountered in traditional pixel-based classification methods, such as "same object, different spectra" or "same spectra, different objects." OBIA offers advantages such as high interpretation accuracy and the ability to eliminate speckle noise (Martha et al, 2012;Scaioni et al, 2014;Patil et al, 2020). The OBIA data processing workflow is illustrated in Figure 2.…”

Section: Object Based Image Analysis Methodsmentioning

confidence: 99%

A comprehensive remote sensing identification model for ancient landslides in the Dadu river basin on the eastern margin of tibet plateau

Liu,

Zhang,

Ren

et al. 2023

Front. Earth Sci.

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The identification of ancient landslides has become a challenging task due to the long-term reconstruction and sediment cover, which obscure the original geomorphic characteristics of these landslides. To address this issue, a comprehensive remote sensing identification model, known as GTVI, is developed using the Object Based Image Analysis (OBIA) based on multi-source and high-resolution remote sensing data in the Dadu River Basin. The study reveals significant differences in texture, hue, shape, and adjacency topology between ancient landslides and reactivated landslides. The gray level co-occurrence matrix entropy (GLCM), terrain roughness index (TRI) and vegetation index (NDVI) effectively capture the information related to ancient landslides. The feasibility of the GTVI (GLCM and Terrain roughness and Vegetation index) model is confirmed through field investigations and remote sensing image analysis of typical landslides, demonstrating its high accuracy. This research provides a valuable method and technical reference for the rapid identification of ancient landslides in plateau canyon areas.

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“…Creating a map of landslides that have occurred in the past, also known as landslide inventory mapping, can be utilized to identify areas prone to landslides and assess their risks [60][61][62]. As stated in the Method section, data from three sources were gathered to form the landslide inventory of Isparta.…”

Section: Landslide Inventorymentioning

confidence: 99%

Determination and Evaluation of Landslide-Prone Regions of Isparta (Turkey): An Urban Planning View

Uluç Keçik,

Çiftçi,

Eren

et al. 2023

Sustainability

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Landslides represent a significant hazard affecting human life and property and threaten the sustainability of human settlements. They are among the most critical threats after earthquakes in Turkey. In 2020, 107 landslide events occurred in Turkey. Implementing proper policies, strategies, and tools for landslide risk mitigation remains challenging for urban planning institutions. In the plan preparation phase, urban planners and plan-making authorities, agencies, or institutions may overlook landslide risks due to a lack of data or related studies. Therefore, this article aims to develop a novel spatial analysis for identifying landslide-prone areas at the provincial level from an urban planning perspective. The analysis is compared to the approved upper-scale plan, and the results are used to build a more robust understanding of landslide risks for sustainable urban development. Isparta Province is selected as the study area, as it has active landslide areas. The methods used include a literature survey including internet sources, newspapers, plans, articles, and other research projects and a case study utilizing a GIS spatial analysis. The spatial analysis using GIS is based on three landslide inventories currently available in Turkey. This spatial analysis is developed to determine landslide-prone regions by considering thematic layers, triggering factors, and vulnerability inputs. As a result of this analysis, five landslide-prone areas in Isparta Province are determined. When these regions are compared to the upper-scale plan that covers the province, it is found that land use and planning decisions have neglected landslide risks, and urban areas are at high landslide risk. Several specific principles and strategies, such as a spatial inventory database and an integrated planning approach including landslide-prone areas, are stated with a reliable spatial analysis to assess landslide-prone areas on a regional scale, which can be applied later in any city and region of Turkey.

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Landslide Susceptibility Mapping Using Landslide Numerical Risk Factor Model and Landslide Inventory Prepared Through OBIA in Chenab Valley, Jammu and Kashmir (India)

Cited by 17 publications

References 15 publications

Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile

Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile

A comprehensive remote sensing identification model for ancient landslides in the Dadu river basin on the eastern margin of tibet plateau

Determination and Evaluation of Landslide-Prone Regions of Isparta (Turkey): An Urban Planning View

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