Predicting and mapping land cover/land use changes in Erbil /Iraq using CA-Markov synergy model

M. Fadhil Al-Quraishi, Ayad (2020) Predicting and mapping land cover/land use changes in Erbil /Iraq using CA-Markov synergy model. Earth Science Informatics. ISSN 18650473

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One of the most dynamic components of the environment is land use land cover (LULC), which have been changing remarkably since after the industrial revolution at various scales. Frequent monitoring and quantifying LULC change dynamics provide a better understanding of the function and health of ecosystems. This study aimed at modelling the future changes of LULC for the Erbil governorate in the Kurdistan region of Iraq (KRI) using the synergy Cellular Automata (CA)-Markov model. For this aim, three consecutive-year Landsat imagery (i.e., 1988, 2002, and 2017) were classified using the Maximum Likelihood Classifier. From the classification, three LULC maps with several class categories were generated, and then change-detection analysis was executed. Using the classified (1988–2002) and (2002–2017) LULC maps in the hybrid model, LULC maps for 2017 and 2050 were modelled respectively. The model output (modelled 2017) was validated with the classified 2017 LULC map. The accuracy of agreements between the classified and the modelled maps were Kno = 0.8339, Klocation = 0.8222, Kstandard = 0.7491, respectively. Future predictions demonstrate between 2017 and 2050, built-up land, agricultural land, plantation, dense vegetation and water body will increase by 173.7% (from 424.1 to 1160.8 km2), 79.5% (from 230 to 412.9 km2), 70.2% (from 70.2 to 119.5 km2), 48.9% (from 367.2 to 546.9 km2) and 132.7% (from 10.7 to 24.9 km2), respectively. In contrast, sparse vegetation, barren land will decrease by 9.7% (2274.6 to 2052.8 km2), 18.4% (from 9463.9-7721 km2), respectively. The output of this study is invaluable for environmental scientists, conservation biologists, nature-related NGOs, decision-makers, and urban planners.

Item Type: Article
Uncontrolled Keywords: CA-Markov , Change-detection , Prediction , Classification , Remote sensing , GIS
Subjects: T Technology > TE Highway engineering. Roads and pavements
Depositing User: ePrints deposit
Date Deposited: 14 Jan 2021 13:09
Last Modified: 14 Jan 2021 13:09

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