Analysis of Paddy Field Changes (1989–2021) Using Landsat Images and Flooding-Assisted MLC in an Urbanizing Tropical Watershed, Vientiane, Lao PDR

Iep Keovongsa, Atiqotun Fitriyah, Fumi Okura, Keigo Noda, Koshi Yoshida, Keoduangchai Keokhamphui and Tasuku Kato ()
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Iep Keovongsa: Department of Environmental and Agricultural Engineering, United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, 3-5-8 Saiwaicho, Fuchu, Tokyo 183-8509, Japan
Atiqotun Fitriyah: Research Center for Limnology and Water Resources, National Research and Innovation Agency, Cibinong, Bogor 16911, Indonesia
Fumi Okura: Japan International Research Center for Agricultural Sciences, 1-1 Owashi, Tsukuba 305-8686, Japan
Keigo Noda: Department of Biological and Environmental Engineering, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
Koshi Yoshida: Department of International Studies, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa Shi 277-8561, Japan
Keoduangchai Keokhamphui: Faculty of Water Resources, National University of Laos, Vientiane P.O. Box 7322, Laos
Tasuku Kato: Department of Environmental and Agricultural Engineering, United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, 3-5-8 Saiwaicho, Fuchu, Tokyo 183-8509, Japan

Sustainability, 2024, vol. 16, issue 22, 1-28

Abstract: Paddy fields are essential for food security and sustaining global dietary needs, yet urban expansion often encroaches on agricultural lands. Analyzing paddy fields and land use/land cover changes over time using satellite images provides critical insights for sustainable food production and balanced urban growth. However, mapping the paddy fields in tropical monsoon areas presents challenges due to persistent weather interference, monsoon-submerged fields, and a lack of training data. To address these challenges, this study proposed a flooding-assisted maximum likelihood classification (F-MLC) method. This approach utilizes accurate training datasets from intersecting flooded paddy field maps from the rainy and dry seasons, combined with the Automated Water Extraction Index (AWEI) to distinguish natural water bodies. The F-MLC method offers a robust solution for accurately mapping paddy fields and land use changes in challenging tropical monsoon climates. The classified images for 1989, 2000, 2013, and 2021 were produced and categorized into the following five major classes: urban areas, vegetation, paddy fields, water bodies, and other lands. The paddy field class derived for each year was validated using samples from various sources, contributing to the overall accuracies ranging from 83.6% to 90.4%, with a Kappa coefficient of between 0.80 and 0.88. The study highlights a significant decrease in paddy fields, while urban areas rapidly increased, replacing 23% of paddy fields between 1989 and 2021 in the watershed. This study demonstrates the potential of the F-MLC method for analyzing paddy fields and other land use changes over time in the tropical watershed. These findings underscore the urgent need for robust policy measures to protect paddy fields by clearly defining urban expansion boundaries, prioritizing paddy field preservation, and integrating these green spaces into urban development plans. Such measures are vital for ensuring a sustainable local food supply, promoting balanced urban growth, and maintaining ecological balance within the watershed.

Keywords: LULC change; urbanization; AWEI; maximum likelihood algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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