Regional Change Prediction of Land Degradation Risks using Cellular Automata-Markov Modeling in Dhi Qar Province, Iraq
Received: 6 February 2025 | Revised: 28 February 2025 | Accepted: 7 March 2025 | Online: 15 April 2025
Corresponding author: Raad Abdulkadhim Abbood
Abstract
Land degradation, followed by desertification, poses a significant threat to countries in arid, semi-arid, and even humid regions. This issue has intensified dramatically since the middle of the last century. In Iraq, land degradation affects 69% of the agricultural land and is exacerbated by the high average temperatures and an annual precipitation of less than 100 mm, impacting over 72% of the country. It is considered one of the three main challenges humanity faces in the 21st century, alongside climate change and freshwater scarcity, both of which diminish land potential and deplete renewable resources. Understanding this phenomenon is particularly crucial for the Nasr region in Dhi Qar Province, southern Iraq, which spans an area of 95,919 ha. This research examines the land cover conversion, land use changes, and land degradation projections for the next 10 to 20 years. Satellite imagery from Landsat 5, 7, 8, and 9, taken at different times (1993, 2003, 2013, and 2024) with a spatial resolution of 30 × 30 m, using Thematic Mapper (TM) and Operational Land Imager [OLI] sensors, was obtained from the United States Geological Survey (USGS). The study area was first analyzed using satellite data and supervised classification. The data were then analyzed deploying a Cellular Automata (CA)-Markov model, revealing a significant increase in the sand dune area, which expanded from 8,128 ha to 18,240 ha during the study period. To predict future trends, the CA-Markov model was applied to classify maps for each Land Use/Land Cover (LU/LC) class through vector-based classification. The model simulated LU/LC changes by projecting the 1993 and 2024 patterns forward to the years 2034 and 2044. The former was calibrated by validating the simulated maps against actual LU/LC maps for the study area, with its accuracy being confirmed by a high kappa coefficient (>76%). The study projects similar land change patterns for 2034 and 2044, with ongoing desertification, declining vegetation cover, and stable agricultural lands throughout the 20-year period.
Keywords:
land degradation risks, multi-temporal landsat images, Cellular Automata-Markov model, Dhi Qar provinceDownloads
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