The conversion of agricultural land to non-agricultural-purposes land had occurred in almost all areas in the Bali Province. This study aimed to explore sustainable food agricultural land control strategies in the Bali Province, by involving stakeholders: farmers, subak institutions, local agricultural board, agricultural business actors, agricultural financial institutions, NGOs, universities, and agricultural research and development bodies as study participants. SWOT analysis and Interpretive Structural Modeling (ISM) are used to analyze study data. The findings showed that the short, medium, and long-term strategies have great potential and are in line with some management policies to control the agricultural land sustainable conversion in the Bali Province. This strategy may bring essential changes for several areas: stimulating agricultural development policy formulation that align with Balinese local wisdom values, leading to a more selective agricultural land investment choice, procurement of special fund for legal protection of staple food agricultural land, and strengthening local to national-scale agricultural institutions.

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Indonesia is located right on the equator, which receives a lot of heat from the sun and rainfall. Therefore, Indonesia is prone to hydro meteorological natural disasters such as droughts, large sea waves, erosion, floods and landslides. The National Disaster Management Agency (BNPB) noted that floods are followed by landslides of the total hydro-meteorological disasters that most often occur in Indonesia. An inventory of the distribution of multi-year landslides is essential as a basis for disaster mitigation and disaster risk reduction. The research case study was carried out in an area prone to landslides around Mount Batur, Bali-Indonesia. Characteristics of areas with high rainfall and steep slopes (>45%). Detection of areas affected by landslides can be identified with multispectral remote sensing images such as Sentinel 2 Image with a spectral resolution of 13 bands and a spatial resolution ranging from 10-60 m. Data acquisition was carried out in the period 2017-2021. The Support Vector Machine (SVM) algorithm is an alternative for detecting landslide areas in this study. The result showed that the accuracy assessment of the SVM algorithm on the training and validation/testing models is more than 84%. We obtained carrying out a landslide inventory is 25.29 km2. Based on our analysis, the most extensive landslide distribution was found in Batur Village (South and Central), followed by Songan A, Sukawana, Kintamani, and Buahan Villages. This research can be used to develop the Landslide Susceptibility model so that entering the landslide inventory parameters gives good results. As well as a basis for disaster risk reduction (DRR), especially for the community, government, and tourists in this research location.

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Pemanfaatan teknologi penginderaan jauh dalam bidang pemetaan telah lama dikenal. Data penginderaan jauh menjadi salah satu sumber data untuk pemetaan, yang memberikan informasi rupa bumi, seperti Kawasan pemukiman, Kawasan hutan, Kawasan pertanian dan lain sebagainya. Salah satu jenis data penginderaan jauh adalah citra Synthetic Aperture Radar (SAR), SAR memancarkan gelombang elektromagnetik untuk mendapatkan informasi dari target/rupa bumi. Citra SAR memiliki kelebihan tidak terpengaruh awan, cuaca (hujan dengan intensitas ringan), dan dapat bekerja sepanjang hari serta malam, dibandingkan dengan citra optik. Citra SAR dengan metode komposit RGB dual polarimetric multiple menggunakan aplikasi SNAP Toolbox dari European Space Agency (ESA) dapat dimanfaatkan untuk membedakan lokasi persawahan dengan bukan sawah, perbedaan backscatter atau nilai reflektan digunakan untuk mengetahuinya. Penelitian lebih lanjut menggunakan metode klasifikasi non parametrik Random Forest, dengan membuat kelas menjadi 2, yaitu sawah dan non sawah, ke-2 kelas digunakan sebagai training data untuk metode tersebut. Data yang digunakan adalah citra satelit Sentinel-1 pada tanggal 30 Maret 2020 dengan mode IW Level-1 GRD Ascending direction beresolusi 20x22 m dan polarisasi ganda (VH dan VV) di Kecamatan Kediri, Kabupaten Tabanan, Provinsi Bali. Dari hasil penelitian diperoleh akurasi 96,90%.