Landslide Risk Analysis with Remote Sensing and GIS/AHP

Define a projection system for the study area using the utm coordinates, set the 1984 utm zone 44, apply to the data frame, and export the boundary.

Download rainfall data from free high-resolution CRU climatic datasets by searching on Google, copying the files, and pasting them into a precipitation layer, including long periods such as 100 years.

Learn how to download free land use land cover raster data for landslide risk analysis, using remote sensing, S3 land cover living atlas, Esri resources, and supervised classification.

Create an elevation map by mosaicking SRTM data, filling gaps, setting min and max values, and exporting a study-area elevation layer for landslide risk analysis with remote sensing and GIS/AHP.

Create a five-class slope map from SRTM elevation data using GIS tools, adjust the legend and layout, and export the final slope raster for landslide risk analysis.

learn to create a raw aspect map for landslide risk analysis using srtm data, generate an aspect input raster, and export the map with an adjusted legend for Pithoragarh.

Create a precipitation map for landslide risk analysis by processing NetCDF rainfall data, converting raster to points, and interpolating to generate a rainfall map for the project area.

Learn to create a relative relief map using hillshade to visualize elevation variation and its role in landslide risk, and prepare vector data such as distance to roads and streams.

Create a Landsat 8 raster for the study area and develop a land use and land cover map using supervised classification, labeling water, trees, crops, built areas, and clouds.

Create a stream distance map using hydrologic analysis of SRTM data, derive flow direction and accumulation, convert streams to vectors, and apply multi-ring buffers to identify landslide risk zones.

Create a road distance map for landslide risk by buffering primary, secondary, and tertiary roads in a study area with ring buffers at 200, 400, 600, 800, and 1000 meters.

Learn to compute ndvi from Landsat 8 bands 5 and 4, create a vegetation health map, classify into four categories, and export a ready-to-share ndvi map.

Learn to assign slope-based risk values by mapping a slope raster into classes and grid codes, showing how steeper slopes increase landslide risk, then editing and saving the risk attributes.

Assign land use and land cover risk values for landslide analysis using remote sensing and GIS; evaluate classes like water, trees, crops, built areas, clouds, and bare ground.

Assign relative risk values by inspecting a three-class attribute table, note the inverse relationship between relative values and landslide risk, and save the risk as a relative risk layer.

Prepare road distance data in a GIS workflow by creating risk value fields for road and root distance vectors, then assign and visualize landslide risk classes using symbology.

Assign risk values to stream distance vector data to model landslide risk, noting that risk decreases with distance and closer streams receive higher scores.

Convert vector data to raster for weighted overlay in landslide risk analysis, using analytic hierarchy model and risk values to produce aspect, curvature, slope, rainfall, and distance rasters.

Apply an excel-based analytic hierarchy process to create a pairwise matrix for landslide risk factors. Assign weights to srtm, slope, curvature, rainfall, aspect, and proximity to streams and roads.

Perform weighted overlay analysis in ArcGIS using raster criteria and HP-derived influence values to produce a landslide risk map, classify risk levels, and export the map.