Mapping of groundwater zones in deserts in ArcGIS with AHP

Register on Earthdata, download aster dem for your study area, outline your desert study area with a polygon, download multiple tiff tiles, and mosaic them to cover the area.

Apply a fill operation to each digital elevation model image, then mosaic them into a single raster using ArcGIS Model Builder; avoid mosaicking before filling to prevent errors.

Create a mosaic of raw DEM images using mosaic tools in ArcGIS, compare statistics and symbology with the previous field mosaic.

Create a depression map by subtracting the raw digital elevation model from the field mosaic, then reclassify into seven classes and apply unique-value styling to highlight depression zones.

Reproject the study area to a UTM zone 41 northern hemisphere, set the output coordinate system, adjust the cell size to 30 meters, and extract the area by mask.

Download Landsat images for your study area from Earth Explorer, outline the area, set vegetation peak dates, limit cloud cover to 10%, and bulk download Landsat 8/9 collection 2 data.

Extract Landsat images within your study area, add spectral bands to ArcGIS, and group them by year. Overlay the study area shapefile and prepare for per-image composites.

Stack Landsat multispectral bands into a single composite image, create true color and false color displays, and mosaic multiple images to map groundwater zones in desert areas using ArcGIS.

Mosaic eight-band raster layers into a single image using ArcGIS data management tools, then view a true-color composite to delineate groundwater potential zones in desert areas.

Perform supervised classification of the mosaic using training samples collected via polygon, separating water, barren land, agriculture, built up areas, rocks and asphalt; apply post-classification correction to improve accuracy.

Extract the study area from the mosaic with extract by mask, project the DEM to the UTM system, and export data at 30-meter cells in ArcGIS for AHP groundwater mapping.

Create hillshade from a digital elevation model in ArcGIS, configure light source azimuth and altitude, and compare two hillshades to delineate aligned areas in the study area.

Delineate lineament areas in desert groundwater mapping by creating an alignment polyline layer in ArcGIS, editing sketches, and outlining major alignments for subsequent mapping.

Create a lineament density map to delineate groundwater zones in deserts using ArcGIS, aligning features to a predefined projection and study area, then reclassify to highlight high potential zones.

Learn to reclassify a lineament density map in ArcGIS using AHP analysis, converting continuous data to six discrete raster classes, with zero as a separate class, for groundwater potential.

Download CRU precipitation data from the climate research unit, select decadal precipitation for your study area, save and unzip the file, then open it in RJ Armour for analysis.

Create a raster from a NetCDF precipitation dataset in ArcGIS and extract data for the study area. Sum all 120 bands to obtain total precipitation.

Sum precipitation across a decade using local statistics, convert raster to point data, and interpolate to produce a higher resolution precipitation map for desert study areas in ArcGIS.

Apply kriging interpolation in ArcGIS by projecting to the proper UTM system, selecting ordinary kriging on precipitation points, and generating a 30-meter raster for subsequent clipping and weighted overlay.

Extract the study area in UTM, classify precipitation into five natural breaks classes, reclassify the data, and map groundwater potential zones in desert landscapes using ArcGIS.

Utilize the make raster layer tool to extract band 4 and band 5 from the mosaic, then combine the two bands with the drastic calculator to create a map.

Create a savi map in arcgis using raster calculator on band five minus band four, applying l factor values 0.5 to 1.1 to analyze arid and semi-arid valleys.

Create a slope map for the study area using the slope tool in spatial analysis, deriving slope in degrees from a digital elevation model and reclassifying to discrete classes.

Apply the analytical hierarchy process to structure complex decisions, quantify criteria via pairwise comparisons, and derive weights for alternatives while verifying the consistency ratio on a 1-9 scale.

Explore how the analytic hierarchy process, a multi-criteria decision analysis tool, uses pairwise comparisons with a one-to-nine scale and derives insights from eigenvectors and eigenvalues, with a fruit example.

Create a reciprocal pairwise comparison matrix for AHP by filling the upper triangle with judgments and deriving the lower triangle with reciprocals, ensuring all values are positive.

Compute the priority vector as the normalized eigenvector of a 3 by 3 reciprocal matrix by column normalization and row averaging; illustrated with apple, banana, and cherry comparisons.

Evaluate the consistency of subjective judgments using the consistency index and the consistency ratio, comparing options with paired judgments and the random consistency index to assess transitive preferences in AHP.

Create a six-layer AHP comparison matrix with 15 comparisons, using a one to nine odd-number scale to rank slope, depression, alignment, precipitation, land use, and soil types.

Create a reciprocal matrix for AHP in ArcGIS by listing layers land use, slope, depression, alignment, precipitation, and land cover, with diagonals set to one and columns summed for weights.

Calculate layer percentages in ArcGIS with AHP by normalizing reciprocal metric values to column sums, then convert to percentages for a six-layer weighted overlay.

Learn to assess AHP consistency by computing column sums, forming new columns, and deriving the principal eigenvalue and consistency ratio for pairwise comparisons.

Learn to compute the principal eigenvalue, derive the consistency index and ratio with the random consistency index, and decide acceptability of results in ArcGIS with AHP.

Apply weighted overlay in ArcMap to integrate slope, soil types, precipitation, line density, and other thematic layers with AHP-based weights to map desert groundwater potential zones in ArcGIS.