Analytic Networking Process Based on Geomantic and Remote Sensing for Land Degradation Monitoring of Mosul City

Abstract

Land degradation has recently emerged as a major environmental concern as a result of human activity and climate change. A wide range of land use/cover changes are taking place in many parts of Iraq. The primary causes of land degradation in the City of Mosul are rapid population growth, severe soil erosion, deforestation, insufficient vegetative cover, and uneven agriculture and livestock production. In the present work, remote sensing data, GIS tools, and Analytic Networking Process (ANP) were conducted for modeling land degradation. Landsat images were used for two periods, namely period between 2013 and 2021. However, Maximum Likelihood classification (ML) was used to classify images. The overall accuracy of post-classification images in different satellite data in 2013 and 2021 were 97.55 %, 98.91%, with kappa coefficients, 0.9627, and 0.9840 respectively. The finding revealed that the extremely degraded area was 64.546200 km² which is changing of land cover from vegetation to urban. This is while the very raw degraded area was 167.342400 km² which is changing of land cover from soil to bare soil. Areas of degradation of extreme regions that have been transformed from vegetation class to urban and bare soil classes, as well as those that have changed from soil class to urban and bare soil classes, are considered the most degraded areas.