The physical development of an area causes Land Use Land Cover (LULC) changes due to land requirement increases. Kendari City is the capital city of Southeast Sulawesi Province with extensive urbanization and extensive LULC changes. This study aims to analyze LULC changes in Kendari City (2000-2021) using multi-temporal Landsat imageries data. Landsat-5, Landsat -7, and Landsat-8 imageries spanning 20 years obtained from the Google Earth Engine (GEE) database. LULC classification based on machine learning using random forest method. The pattern of LULC of 2000 - 2010 spread to the west and increased the settlements in Kadia District. In 2010 - 2021 settlement developments to the west and south, namely in Kadia and Wua-wua Districts. An increase in built-up land or settlements in Kendari City has occurred about two times over the last 20 years has led to agricultural land. The increase in built-up land or settlements in Kendari City reached 1,920.44 Ha, and at the same time, there was a decrease in agricultural land in rice fields by 1,866.86 ha in the last 20 years.
Agricultural drought is one of the hydrometeorological disasters that cause significant losses because it affects food stocks. In addition, agricultural droughts, impact the physical and socio-economic development of the community. Remote sensing technology is used to monitor agricultural droughts spatially and temporally for minimizing losses. This study reviewed the literatures related to remote sensing and GIS for monitoring drought vulnerability in Indonesia. The study was conducted on an island-scale on Java Island, a provincial-scale in East Java and Bali, and a district-scale in Indramayu and Kebumen. The dominant method was the drought index, which involves variable land surface temperature (LST), vegetation index, land cover, wetness index, and rainfall. Each study has a strong point and a weak point. Low-resolution satellite imagery has been used to assess drought vulnerability. At the island scale, it provides an overview of drought conditions, while at the provincial scale, it focuses on paddy fields and has little detailed information. In-situ measurements at the district scale detect meteorological drought accurately, but there were limitations in the mapping unit's detailed information. Drought mapping using GIS and remote sensing at the district scale has detailed spatial information on climate and physiographic aspects, but it needs temporal data monitoring.
Metode untuk deteksi tumpahan minyak menggunakan data SAR telah berkembang dari metode manual hingga metode otomatis. Penelitian ini bertujuan untuk membandingkan metode analisis tekstur dan adaptive threshold untuk deteksi tumpahan minyak menggunakan citra SAR Sentinel 1. Wilayah kajian meliputi perairan utara Bintan yang hampir rutin terjadi kasus tumpahan minyak khususnya pada musim barat/utara, serta perairan Teluk Balikpapan yang mengalami kejadian tumpahan minyak yang cukup besar pada akhir Maret 2018. Tahap awal dilakukan koreksi data meliputi koreksi atau kalibrasi radiometrik, filtering dan land masking. Tahap selanjutnya adalah deteksi dark spot yang dilakukan menggunakan dua pendekatan dan dibandingkan metode yang memberikan hasil terbaik. Metode pertama adalah analisis tekstur menggunakan Grey Level cooccurrence matrix (GLCM) dengan perhitungan homogenity, entropi dan Angular Second Moment (ASM), kemudian dilakukan klasifikasi menggunakan Maximum Likelihood, sedangkan pendekatan kedua adalah menggunakan adaptive threshold. Hasil kajian menunjukkan bahwa metode tekstur analisis GLCM dan adaptive threshold pada citra SAR Sentinel 1 memberikan hasil yang cukup baik untuk area tumpahan minyak yang cukup tebal. Namun untuk area tumpahan minyak yang tipis atau pada wilayah pencampuran air, metode adaptive threshold memberikan hasil yang lebih baik. Modifikasi berupa masking kapal (atau objek dengan backscatter tinggi) sebelum diterapkan metode adaptive threshold dapat mengurangi kesalahan seperti terdeteksinya objek minyak di sekitar kapal.
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