A multi-temporal landslide inventory and hazard zonation using relative effect method along the Mughal road Shopian, India

2021 ◽  
Vol 14 (7) ◽  
pp. 42-51
Author(s):  
Bashir Subiaya ◽  
T. Ramkumar
Keyword(s):  
Causative Factor ◽  
Statistical Technique ◽  
Relative Effect ◽  
Landslide Hazard ◽  
Landslide Inventory ◽  
Hazard Mapping ◽  
Mountain Range ◽  
Jammu And Kashmir ◽  
Hazard Zone ◽  
Effect Method

Landslide inventory and thematic data are of utmost importance in the domain of landslide hazard mapping. The union territory of Jammu and Kashmir, India surrounded by the Himalayan and the Pir-Panjal mountain range is prone to landslides and has already caused havoc at many places. The present study aims to provide the landslide inventory of the Mughal Road, Shopian, which lies in the Pir Panjal range of Kashmir valley. Multidate satellite data of the years 2008 to 2020 are utilized to create an inventory of landslides in this area.The use of high-resolution satellite imagery made it possible to delineate the shallow as well as the deep landslides along the roadside where they occur frequently. To understand the landslide causes, a statistical technique, relative effect method has been implemented in this study. This method helped in mapping the hazard zone areas. The relative effect of each causative factor on landslides is determined by calculating the ratio of coverage and slide which were analyzed in GIS environment. The resulting landslide hazard zone map has been classified as very low, low, moderate, high and very high zones. Out of the total area, 12.62% is critical to landslides, 21.45% is highly prone and 24.84% is moderately prone while 21.94% is low and 19.13% is very low prone to landslides. The outcome of this susceptibility modeling will be beneficial for handling and monitoring the forthcoming landslides as well as the fortification of the general public and environmental hazards of the study area. It will also help the planners in the development around the study area.

scholarly journals SPATIO TEMPORAL DETECTION AND VIRTUAL MAPPING OF LANDSLIDE USING HIGH-RESOLUTION AIRBORNE LASER ALTIMETRY (LIDAR) IN DENSELY VEGETATED AREAS OF TROPICS

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 21-30 ◽  
Author(s):  
T. Bibi ◽  
K. Azahari Razak ◽  
A. Abdul Rahman ◽  
A. Latif
Keyword(s):  
High Resolution ◽  
Landslide Hazard ◽  
Airborne Lidar ◽  
Landslide Inventory ◽  
Hazard Mapping ◽  
High Resolution Data ◽  
Geological Conditions ◽  
Hazard And Risk ◽  
Spatio Temporal ◽  
Virtual Mapping

Landslides are an inescapable natural disaster, resulting in massive social, environmental and economic impacts all over the world. The tropical, mountainous landscape in generally all over Malaysia especially in eastern peninsula (Borneo) is highly susceptible to landslides because of heavy rainfall and tectonic disturbances. The purpose of the Landslide hazard mapping is to identify the hazardous regions for the execution of mitigation plans which can reduce the loss of life and property from future landslide incidences. Currently, the Malaysian research bodies e.g. academic institutions and government agencies are trying to develop a landslide hazard and risk database for susceptible areas to backing the prevention, mitigation, and evacuation plan. However, there is a lack of devotion towards landslide inventory mapping as an elementary input of landslide susceptibility, hazard and risk mapping. <br><br> The developing techniques based on remote sensing technologies (satellite, terrestrial and airborne) are promising techniques to accelerate the production of landslide maps, shrinking the time and resources essential for their compilation and orderly updates. The aim of the study is to provide a better perception regarding the use of virtual mapping of landslides with the help of LiDAR technology. The focus of the study is spatio temporal detection and virtual mapping of landslide inventory via visualization and interpretation of very high-resolution data (VHR) in forested terrain of Mesilau river, Kundasang. However, to cope with the challenges of virtual inventory mapping on in forested terrain high resolution LiDAR derivatives are used. This study specifies that the airborne LiDAR technology can be an effective tool for mapping landslide inventories in a complex climatic and geological conditions, and a quick way of mapping regional hazards in the tropics.

Landslide hazard zone mapping using Information Value model: the case of Gidole Landslide, Southern Ethiopia

2020 ◽  
Author(s):  
Filagot Mengistu Walle ◽  
Karuturi Venkata Suryabhagavan ◽  
Tarun Raghuvanshi ◽  
Elias Lewi
Keyword(s):  
Causative Factor ◽  
Landslide Hazard ◽  
Information Value ◽  
Southern Ethiopia ◽  
Causative Factors ◽  
Hazard Zone ◽  
Information Value Model ◽  
High Hazard ◽  
Value Model ◽  
Factor Maps

&lt;p&gt;Landslide hazard is becoming serious environmental constraints for the developmental activities in the highlands of Ethiopia. With the current infrastructure development, urbanization, rural development, and with the present landslide management system, it is predictable that the frequency and magnitude of landslide and losses due to such hazards would continue to increase. In the present study landslide hazard zone mapping were carried out in and around Gidole Town in Southern Ethiopia. The main objective of the study was to map landslide hazard zone using Information Value Bi-variant statistical model.&amp;#160; For landslide hazard zonation of the study area six causative factors namely; aspect, slope angle, elevation, Lithology, Normalized Deference Vegetation Index (NDVI) and land-use and land-cover were considered. The landslide inventory mapping for the present study area was carried out through field observations and Google Earth image interpretation. Later, Information value was calculated based on the influence of causative factors on past landslide. The distribution of landslide over each causative factor maps was obtained and analyzed. Weights for the class with in these causative factor maps was obtained using information value model. Distribution of landslide in the study area was largely governed by aspect of southwest facing, slope angel of 30-45&lt;sup&gt;o&lt;/sup&gt;, elevation of 1815&amp;#8211;2150m, NDVI of 0.27&amp;#8722;0.37, Lithology of colluvial deposit and land-use and land-cover of agricultural land. The landslide hazard zonation map shows that 78.38km&lt;sup&gt;2&lt;/sup&gt; (36.3%) area fall within very low hazard (VLH) zone, 72.85km&lt;sup&gt;2&lt;/sup&gt; (34.2%) of the area fall within low hazard (LH) zone, 12.78 km&lt;sup&gt;2&lt;/sup&gt; (6.6%), 32.72 km&lt;sup&gt;2&lt;/sup&gt; (15.4%) and 15.89 km&lt;sup&gt;2&lt;/sup&gt; (7.5%) of the area falls into very high hazard (VHH), high hazard (HH) and moderate hazard (MH), respectively. Further, validation of LHZ map with past landslide inventory data shows that 92.3% of the existing landslides fall in very high hazard (VHH) and high hazard (VHH) zone. Thus, it can safely be concluded that the hazard zones delineated in the present study validates with the past landslide data and the potential zone depicted can reasonably be applied for the safe planning of the area.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Key words&lt;/strong&gt;: Landslide, Gidole, Landslide hazard zone, Information Value model&lt;/p&gt;

Research on Non-Landslide Selection Method for Landslide Hazard Mapping

2021 ◽  
Author(s):  
Xia Li ◽  
Jiulong Cheng ◽  
Dehao Yu ◽  
Yangchun Han
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Selection Method ◽  
Landslide Hazard ◽  
River Channel ◽  
Information Value ◽  
Hazard Mapping ◽  
Landslide Prediction ◽  
Conditioning Factors ◽  
Landslide Hazard Mapping

Abstract Most landslide prediction models need to select non-landslides. At present, non-landslides mainly use subjective inference or random selection method, which makes it easy to select non-landslides in high-risk areas. To solve this problem and improve the accuracy of landslide prediction, the method of selecting non-landslide by Information value (IV) is proposed in this study. Firstly, 230 historical landslides and 10 landslide conditioning factors are extracted and interpreted by using Remote Sensing (RS) image, Geographic Information System (GIS) and field survey. Secondly, random, buffer, river channel or slope, and IV methods are used to obtain non-landslides, and the obtained non-landslides are applied to the popular SVM model for landslide hazard mapping (LHM) in western area of Tumen City. The landslide hazard map based on the river channel or slope method is seriously inconsistent with the actual situation of study area, Therefore, the three methods of random, buffer, and IV are verified and compared by accuracy, receiver operating characteristic (ROC) curve and the area under curves (AUC). The results show that the landslide prediction accuracy of the three methods is more than 80%, and the prediction accuracy is high, but the IV is higher. In addition, IV can identify the very high hazard regions with smaller area. Therefore, it is more reasonable to use IV to select non-landslides, and IV method is more practical in landslide prevention and engineering construction. The research results may be useful to provide basic information of landslide hazard for decision makers and planners.

scholarly journals Remote sensing and GIS technology in landslide hazard mapping in the Colombian Andes

Episodes ◽  
1992 ◽  
Vol 15 (1) ◽  
pp. 32-35 ◽  
Author(s):  
E. Leroi ◽  
O. Rouzeau ◽  
J. -Y. Scanvic ◽  
C.C. Weber ◽  
G. Vargas C.
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Landslide Hazard ◽  
Hazard Mapping ◽  
Gis Technology ◽  
Colombian Andes ◽  
Landslide Hazard Mapping

scholarly journals Mapping landslides from EO data using deep-learning methods

2020 ◽  
Author(s):  
Nikhil Prakash ◽  
Andrea Manconi ◽  
Simon Loew
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Landslide Hazard ◽  
Probability Of Detection ◽  
Landslide Inventory ◽  
Economic Losses ◽  
Learning Methods ◽  
High Resolution Data ◽  
Horizon 2020 ◽  
Mountainous Regions

&lt;p&gt;Landslide hazard has always been a significant source of economic losses and fatalities in the mountainous regions. Knowledge of the spatial extent of the past and present landslide activity, compiled in the form of a landslide inventory map, is essential for effective risk management. High-resolution data acquired by Earth observation (EO) satellites are often used to map landslides by identifying morphological expressions that can be associated with past and/or recent deformation. This is a slow and difficult process as it requires extensive manual efforts. As a result, such maps are not readily available for all the landslide hazard affected regions. Fully automated methods are required to exploit the exponentially increasing amount of EO data available for landslide hazard assessments. In this context, conventional methods like pixel-based and object-based machine learning strategies have been studied extensively in the last decade. Recent advances in convolutional neural network (CNN), a type of deep-learning method, has outperformed other conventional learning methods in similar image interpretation tasks. In this work, we present a deep-learning based method for semantic segmentation of landslides from EO images. We present the results from a study area in the south of Portland in Oregon, USA. The landslide inventory for training and ground truth was extracted from the Statewide Landslide Information Database of Oregon (SLIDO). We were able to achieve a probability of detection (POD) greater than 0.70. This method can also be extended to be used for rapid mapping of landslides after a major triggering event (like earthquake or extreme metrological event) has occurred.&lt;/p&gt;&lt;p&gt;This work is done in the framework of European Commission's Horizon 2020 project &quot;BETTER&amp;#8221;. More information is available on the website https://www.ec-better.eu/.&lt;/p&gt;

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