Spatiotemporal Land-Use Changes of Batticaloa Municipal Council in Sri Lanka from 1990 to 2030 Using Land Change Modeler

Ibra Lebbe Mohamed Zahir, Sunethra Thennakoon, Rev. Pinnawala Sangasumana, Jayani Herath, Buddhika Madurapperuma and Atham Lebbe Iyoob
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Ibra Lebbe Mohamed Zahir: Department of Geography, South Eastern University of Sri Lanka, University Park, Oluvil 32360, Sri Lanka
Sunethra Thennakoon: Department of Geography, University of Sri Jayewardenepura, Gangodawila, Nugegoda 10250, Sri Lanka
Rev. Pinnawala Sangasumana: Department of Geography, University of Sri Jayewardenepura, Gangodawila, Nugegoda 10250, Sri Lanka
Jayani Herath: Department of Geography, University of Sri Jayewardenepura, Gangodawila, Nugegoda 10250, Sri Lanka
Buddhika Madurapperuma: Department of Forestry and Wildland Resources, Humboldt State University, 1st Harpst Street, Arcata, CA 95521, USA
Atham Lebbe Iyoob: Land Use Policy Planning Department, District Secretariat, Ampara 32000, Sri Lanka

Geographies, 2021, vol. 1, issue 3, 1-12

Abstract: Land-use change is a predictable and principal driving force of potential environmental changes on all spatial and temporal scales. A land-use change model is a tool that supports the analysis of the sources and consequences of land-use dynamics. This study aims to assess the spatiotemporal land-use changes that occurred during 1990–2020 in the municipal council limits of Batticaloa. A land change modeler has been used as an innovative land planning and decision support system in this study. The main satellite data were retrieved from Landsat in 1990, 2000, 2010, and 2020. For classification, the supervised classification method was employed, particularly with the medium resolution satellite images. Land-use classes were analyzed by the machine learning algorithm in theland change modeler. The Markov chain method was also used to predict future land-use changes. The results of the study reveal that only one land-use type, homestead, has gradually increased, from 12.1% to 34.1%, during the above-mentioned period. Agriculture land use substantially declined from 26.9% to 21.9%. Bare lands decreased from 11.5% to 5.0%, and wetlands declined from 13.9% to 9.6%.

Keywords: land-use change; spatiotemporal; supervised classification; machine learning; Markov chain (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2021
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