The result of the accuracy by using cross-correlation matrix between the LULC model in 2015 with the LULC reference in 2015 is 75.88 %. The dynamics of LULC changes showed that area-class forest, dry land, paddy fields and shrubbery would be expected to experience an area decreases in the extent from the year 2015 to 2050, with the rate of change in average: 10.52, 13.22, 14.49 and 1.15 ha/year, respectively. Meanwhile, the area-class bare soil, plantation, settlement and water body would be expected to experience an area increases, with the rate of change in average: 6.79, 11.14, 11.49 and 9.7 ha/year, respectively. Furthermore, flood damage assessment can be calculated by estimating LULC area affected by the flood, which is determined based on the overlay between LULC maps from the result of Markov-CA with flood maps from the result of Monte Carlo algorithm. Under current conditions, estimated flood damage exposure to extreme flood events with return periods of 100 years for the water level scenario Hc = 3 m and Hc = 5 m is more than €520 and €958 million, respectively.
Remote sensing data can be used to help disaster management and environmental management because this data has advantages in terms of speed, is more efficient, can reach large and remote areas. Aside from that, it has consistency in measurement, can make repeated measurements, and has measurable accuracy. Floods in Lake Tempe occur almost every year due to overflowing of Lake Tempe. This research will detect flood inundation from Sentinel-1 data with single-polarization of flooding in Lake Tempe, South Sulawesi. The data used were Sentinel-1 data before flooding (2 May 2018) and after flooding (26 May 2018). The Single polarization of the Sentinel-1 can be used very well to identify floods event. The difference of input band uses the threshold of -6, and enables smoothing of 10. Utilization of tidal junction between Lake Tempe and Lake Sidenreng for agriculture and settlement has caused flooding due to lake water overflowing in the rainy season. This single polarization method can already be used in flooded areas, although in some locations it is still overestimates.
Landslide was one of natural disasters that affected by the weather. The intensity of landslide in Indonesia tended to increase from year to year with a larger area distribution. Remote sensing was a method that can be used to support disaster mitigation and response activities including landslide because this technology allows monitoring and analysis both spatially and temporally. One of the remote sensing satellites that can be used for monitoring landslide was Himawari-8. This weather satellite was launched in 2014 and had a temporal resolution of 10 minutes making it effective for meteorological, environmental and disaster observations. This research has used Himawari-8 rainfall data which extracted from cloud top temperature to determine the intensity of rainfall that causes landslide in Garut Regency. The daily accumulation of rainfall for five days before the landslide event up to five days after the landslide event has been investigated statistically to analyze the conditions of rainfall that trigger landslides. Rainfall thresholds for landslide was determined by the intensity maximum of daily accumulation. It was found that the intensity of rainfall that has potential to cause landslides based on the threshold value is as follows: Malangbong District 60.3 mm/day, Banjarwangi District 32.3 mm/day, Pasirwangi District 36.9 mm/day, Cisewu District 35.1 mm/day and Talegong District 52.8 mm/day. Landslide in four districts have corresponded with the day where the intensity of rainfall was maximum. Meanwhile for Talegong District, the landslide was occurred a day after its maximum.Keywords: rainfall, Himawari-8, landslide, remote sensing, thresholdLongsor merupakan salah satu bencana alam yang dipengaruhi oleh cuaca. Intensitas longsor di Indonesia cenderung meningkat dari tahun ke tahun dengan sebaran wilayah yang lebih luas. Penginderaan jauh merupakan metode yang dapat digunakan untuk mendukung kegiatan mitigasi dan tanggap bencana termasuk longsor karena teknologi ini memungkinkan pemantauan dan analisis baik secara spasial maupun temporal. Salah satu satelit penginderaan jauh yang dapat digunakan untuk pemantauan longsor adalah Himawari-8. Satelit cuaca ini diluncurkan pada tahun 2014 dan memiliki resolusi temporal 10 menit sehingga efektif untuk pengamatan meteorologi, lingkungan dan bencana. Penelitian ini menggunakan data curah hujan Himawari-8 yang diekstrak dari suhu puncak awan untuk mengetahui intensitas curah hujan penyebab longsor di Kabupaten Garut. Akumulasi curah hujan harian selama lima hari sebelum kejadian longsor sampai dengan lima hari setelah kejadian longsor diteliti secara statistik untuk menganalisis kondisi curah hujan yang memicu terjadinya longsor. Ambang batas curah hujan untuk longsor ditentukan oleh intensitas maksimum akumulasi harian. Diketahui bahwa intensitas curah hujan yang berpotensi menimbulkan longsor berdasarkan nilai ambang batas adalah sebagai berikut: Kecamatan Malangbong 60,3 mm / hari, Kecamatan Banjarwangi 32,3 mm / hari, Kecamatan Pasirwangi 36,9 mm / hari, Kecamatan Cisewu 35,1 mm / hari dan Kecamatan Talegong 52,8 mm / hari. Tanah longsor di empat kecamatan telah sesuai dengan hari dimana intensitas curah hujan maksimal. Sedangkan untuk Kecamatan Talegong, longsor terjadi sehari setelah maksimumnya.Kata kunci: curah hujan, Himawari-8, longsor, penginderaan jauh, ambang batas
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