Please use this identifier to cite or link to this item: http://repository.ipb.ac.id/handle/123456789/170588
Title: Analisis Konversi Hutan Menjadi Perkebunan Sawit Terhadap Suhu Permukaan di Kabupaten Paser Tahun 2014–2024 Menggunakan Citra Landsat 8/9 dan MODIS
Other Titles: Analysis of Forest Conversion to Oil Palm Plantation on Surface Temperature in Paser Regency 2014-2024 Using Landsat 8/9 and MODIS Imagery
Authors: Liyantono
Abdullah, Muhamad Fikri
Issue Date: 2025
Publisher: IPB University
Abstract: Konversi hutan menjadi perkebunan kelapa sawit di Kabupaten Paser selama 2014–2024 berpotensi memengaruhi suhu permukaan lahan dan keseimbangan iklim mikro setempat. Penelitian ini bertujuan untuk menganalisis pola perubahan tutupan lahan hutan menjadi sawit selama periode 2014–2024 menggunakan citra satelit Landsat 8/9 serta mengevaluasi dampaknya terhadap suhu permukaan lahan (SPL) menggunakan data MODIS. Analisis dilakukan menggunakan platform Google Earth Engine dengan algoritma random forest untuk klasifikasi tutupan lahan dan ekstraksi statistik LST tahunan untuk area hutan tetap, sawit tetap, dan area konversi. Hasil penelitian menunjukkan uji akurasi rata-rata (overall accuracy) mencapai 90% dan indeks Kappa 82,2%. Hutan yang dikonversi menjadi sawit seluas 83.966,85 hektar di Kabupaten Paser berdampak terhadap peningkatan suhu permukaan sebesar 1–2°C, terutama di siang hari. Area sawit dan konversi menunjukkan suhu maksimum hingga 35°C, lebih tinggi dibandingkan hutan yang tetap di bawah 33°C. Hasil ini menegaskan peran penting hutan sebagai penstabil iklim mikro, serta implikasi ekologis dari konversi hutan yang perlu dipertimbangkan dalam kebijakan pengelolaan sumber daya lahan berkelanjutan.
The conversion of forests into oil palm plantations in Paser Regency during 2014–2024 has the potential to affect land surface temperature (LST) and the balance of the local microclimate. This study aimed to analyze the pattern of forest-to-oil-palm land cover change over the 2014–2024 period using Landsat 8/9 satellite imagery and to evaluate its impact on LST using MODIS data. The analysis was conducted on the Google Earth Engine platform employing the Random Forest algorithm for land cover classification and for extracting annual LST statistics across persistent forest, persistent oil palm, and conversion areas. The results showed an average overall accuracy of 90% with a Kappa index of 82.2%. A total of 83,966.85 hectares of forest were converted into oil palm plantations, resulting in an increase in surface temperature by 1–2°C, particularly during daytime. The oil palm and conversion areas exhibited maximum temperatures reaching up to 35°C, higher than persistent forest areas, which remained below 33°C. These findings highlight the crucial role of forests in stabilizing the microclimate and underscore the ecological implications of forest conversion that must be considered in sustainable land resource management policies.
URI: http://repository.ipb.ac.id/handle/123456789/170588
Appears in Collections:UT - Agricultural and Biosystem Engineering

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