Using CA-Markov Model and Landsat 8 Imagery Data for Land Use Prediction in Thale Noi Non-hunting Area
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Abstract
Both present and future land use planning can utilize remote sensing technology to monitor changes in land use, especially in areas with worldwide importance. This research studied land use types derived from Landsat 8 satellite imagery in 2015, 2019 and 2023 and land use changes during 2015–2019 and 2019–2023. Land use prediction for 2023 was developed based on a CA-Markov model in the Thale Noi Non-hunting Area and then was applied to predict land use change in 2027. The Landsat 8 imagery was classified based on a visual interpretation technique into 5 land use categories: 1) urban and built-up land; 2) agricultural land; 3) forest land; 4) water body; and 5) miscellaneous land.
Based on the results of the study, land use in 2015, 2019 and 2023 was predicted with overall accuracy levels of 94.59, 97.30 and 97.30%, respectively with Kappa statistics of 0.92 0.96 and 0.96, respectively. During 2015–2019, the greatest decrease in area was for forest land (939.03 ha), whereas the greatest increase was for miscellaneous land (966.99 ha). Similarly, during 2019–2023, the greatest decrease in area was for forest land (75.96 ha), with the greatest increase being again for miscellaneous (79.03 ha). Land use prediction in 2023 in the Thale Noi Non-hunting Area, calculated using the CA-Markov model, had an overall accuracy of 91.89%, with a Kappa statistic of 0.89. Analysis of the trends driving predicted land use change in 2027 indicated that agricultural land, forest land and water bodies tended to continue to decrease, whereas miscellaneous land and urban and built-up land tended to continue to increase.
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ข้าพเจ้าและผู้เขียนร่วม (ถ้ามี) ขอรับรองว่า ต้นฉบับที่เสนอมานี้ยังไม่เคยได้รับการตีพิมพ์และไม่ได้อยู่ในระหว่างกระบวนการพิจารณาตีพิมพ์ลงในวารสารหรือสิ่งตีพิมพ์อื่นใด ข้าพเจ้าและผู้เขียนร่วม (ถ้ามี) ยอมรับหลักเกณฑ์และเงื่อนไขการพิจารณาต้นฉบับ ทั้งยินยอมให้กองบรรณาธิการมีสิทธิ์พิจารณาและตรวจแก้ต้นฉบับได้ตามที่เห็นสมควร พร้อมนี้ขอมอบลิขสิทธิ์ผลงานที่ได้รับการตีพิมพ์ให้แก่วารสารวนศาสตร์ คณะวนศาสตร์ มหาวิทยาลัยเกษตรศาสตร์ กรณีมีการฟ้องร้องเรื่องการละเมิดลิขสิทธิ์เกี่ยวกับภาพ กราฟ ข้อความส่วนใดส่วนหนึ่ง หรือ ข้อคิดเห็นที่ปรากฏในผลงาน ให้เป็นความรับผิดชอบของข้าพเจ้าและผู้เขียนร่วม (ถ้ามี) แต่เพียงฝ่ายเดียว และหากข้าพเจ้าและผู้เขียนร่วม (ถ้ามี) ประสงค์ถอนบทความในระหว่างกระบวนการพิจารณาของทางวารสาร ข้าพเจ้าและผู้เขียนร่วม (ถ้ามี) ยินดีรับผิดชอบค่าใช้จ่ายทั้งหมดที่เกิดขึ้นในกระบวนการพิจารณาบทความนั้น”
References
Congalton, R.G., Green, K. 2008. Assessing the accuracy of remotely sensed data. Principles and Practices, 2nd ed. CRC Press. Boca Raton, FL, USA.
Food and Agriculture Organization of the United Nations (FAO). 2022. Thale Noi Wetland Buffalo Pastoral Agro-eco-system in Thailand. https://www.fao.org/giahs/giahs aroundtheworld/designated-sites/asia-and-the-pacific/thailand-thale-noi/detailed-information/en/, 14 January 2024.
Department of National Park, Wildlife and Plant Conservation (DNP). 2016. Report on the Biodiversity Durvey of the Thale Noi Wildlife Non-Hunting Area, Phatthalung Province, Songkhla Province, Nakhon Si Thammarat Province. Department of National Park, Wildlife and Plant Conservation, Bangkok, Thailand. (in Thai)
Department of National Park, Wildlife and Plant Conservation (DNP). 2018. Annual Report 2018. Department of National Park, Wildlife and Plant Conservation, Bangkok, Thailand. (in Thai)
Department of National Park, Wildlife and Plant Conservation (DNP). 2021. Annual Report 2021. Department of National Park, Wildlife and Plant Conservation, Bangkok, Thailand. (in Thai).
Department of National Park, Wildlife and Plant Conservation (DNP). 2023. Draft Plan for Conservation and Protection of the Thale Noi Non-Hunting Area in 2023-2032. Department of National Park, Wildlife and Plant Conservation, Bangkok, Thailand. (in Thai).
Department of Province Administration. 2023. Population in Thailand. https://stat.bora.dopa.go.th/stat/statnew/statyear/#/TableTemplate/Area/statpop, 20 April 2023.
Geo-Informatics and Space Technology Development Agency (Public Organization). 2009. Space Technplogy and Geo-informatics. Geo-Informatics and Space Technology Development Agency (Public Organization), Bangkok, Thailand. (in Thai)
Geo-Informatics and Space Technology Development Agency (Public Organization). 2013. Fundamental Geographic Data Set (FGDS) Land Use. Geo-Informatics and Space Technology Development Agency (Public Organization), Bangkok, Thailand. (in Thai)
Kan-in, K. 2020. Comparative Study on CA-Markov Model and CLUE-S Model for Land Use Changed Prediction in National Reserved Forest, Nan Province. M.S. Thesis, Kasetsart University, Bangkok, Thailand. (in Thai)
Landis, J.R., Koch, G.G. 1977. The measurement of observer agreement for categorical data. Biometrics, 33(1): 159-174.
Office of Natural Resources and Environmental Policy and Planning. 2021. Project to Increase Efficiency of Eetland Management in Thailand: Creating Guidelines for the Management of Wetlands of International Importance (Ramsar Site) of Thailand. Office of Natural Resources and Environmental Policy and Planning, Bangkok, Thailand. (in Thai)
Royal Forest Department (RFD). 2023. Final Report of Forest area Information Project 2023. Royal Forest Department, Bangkok, Thailand. (in Thai)
Sukyuang, N., Chuchip ,K., Tongdeenok, P. 2023. Prediction of land use changes for the Mae Soi river sub-basin, Lampang province, using a neural network model. Thai Journal of Forestry, 42(1): 1-11. (in Thai)
Tonsiri, S., Arunpraparut, W., Khunrattanasiri, W. 2018. Application of CA-Markov model to predict land use changes in Khao Soi Dao Wildlife Sanctuary, Chanthaburi province. Thai Journal of Forestry, 37(2): 138-150. (in Thai)
Wanthongchai, K., Saguantam, P. 2013. Evaluation of fire damages from the 2012 peat fires at Kuan Kreng peat forest. In: 8 Decades of Forestry the Science of Life. Forestry Research Center, Faculty of Forestry, Kasetsart University, Bangkok, Thailand, pp. 168-169. (in Thai)
