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Prediction of Greenhouse Area Expansion in an Agricultural Hotspot Using Landsat Imagery, Machine Learning and the Markov–FLUS Model

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  • Melis Inalpulat

    (Agricultural Remote Sensing Laboratory (AGRESEL), Department of Agricultural Structures and Irrigation, Faculty of Agriculture, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Türkiye
    Computer-Agriculture-Environment-Planning (ComAgEnPlan) Study Group, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Türkiye)

Abstract

Greenhouses (GHs) are important elements of agricultural production and help to ensure food security aligning with United Nations Sustainable Development Goals (SDGs). However, there are still environmental concerns due to excessive use of plastics. Therefore, it is important to understand the past and future trends on spatial distribution of GH areas, whereby use of remote sensing data provides rapid and valuable information. The present study aimed to determine GH area changes in an agricultural hotspot, Serik, Türkiye, using 2008 and 2022 Landsat imageries and machine learning, and to predict future patterns (2036 and 2050) via the Markov–FLUS model. Performances of random forest (RF), k-nearest neighborhood (KNN), and k-dimensional trees k-nearest neighborhood (KD-KNN) algorithms were compared for GH discrimination. Accordingly, the RF algorithm gave the highest accuracies of over 90%. GH areas were found to increase by 73% between 2008 and 2022. The majority of new areas were converted from agricultural lands. Markov-based predictions showed that GHs are likely to increase by 43% and 54% before 2036 and 2050, respectively, whereby reliable simulations were generated with the FLUS model. This study is believed to serve as a baseline for future research by providing the first attempt at the visualization of future GH conditions in the Turkish Mediterranean region.

Suggested Citation

  • Melis Inalpulat, 2024. "Prediction of Greenhouse Area Expansion in an Agricultural Hotspot Using Landsat Imagery, Machine Learning and the Markov–FLUS Model," Sustainability, MDPI, vol. 16(19), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8456-:d:1488237
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    References listed on IDEAS

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    1. Yusuyunjiang Mamitimin & Zibibula Simayi & Ayinuer Mamat & Bumairiyemu Maimaiti & Yunfei Ma, 2023. "FLUS Based Modeling of the Urban LULC in Arid and Semi-Arid Region of Northwest China: A Case Study of Urumqi City," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    2. Yang, Yuanyuan & Bao, Wenkai & Liu, Yansui, 2020. "Scenario simulation of land system change in the Beijing-Tianjin-Hebei region," Land Use Policy, Elsevier, vol. 96(C).
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