DETECTING THE SPATIAL DISTRIBUTION OF SETTLEMENTS ON VOLCANIC REGION USING IMAGE LANDSAT-8 OLI IMAGERY

Geologically, Indonesia region is on track ring of fire, brings the consequence that the danger of volcanic eruption could occur at any time. Information sites where the settlement is located in the affected areas on emergency response process is needed in quick time. The availability of up to date data is important because it illustrates the actual condition of the region. Active volcanic landforms ranging from the crater to footslope in general is prone area to volcanic eruption, either by the threat of lava flows, pyroclastic falls, or lahars. This study aims to detect the spatial distribution of the settlement on volcanic region using Landsat-8 OLI. Parameters used for the detection of settlements is Normalized Difference Build-up Index (NDBI). Research methods include radiometric correction, delineation of the boundaries of volcanic landforms, NDBI value extraction, extraction of settlement areas, as well as the accuracy assesment.  Study area  is  Sinabung Volcano region located in the province of North Sumatera. Recently, the volcano experienced a devastating and catastrophic eruption. The results showed that the spatial distribution of settlements on volcanic landforms can be detected quickly from Landsat-8 OLI based on NDBI parameters with a sufficient degree of accuracy

VALIDASI HOTSPOT MODIS DI WILAYAH SUMATERA DAN KALIMANTAN BERDASARKAN DATA PENGINDERAAN JAUH SPOT-4 TAHUN 2012 (MODIS HOTSPOT VALIDATION OVER SUMATERA AND KALIMANTAN BASED ON REMOTE SENSING DATA SPOT-4 IN 2012)

Indikator kebakaran hutan dan lahan dapat ditunjukkan dengan adanya hotspot dan asap kebakaran. Saat ini informasi hotspot sebagai indikator kebakaran hutan/ lahan sudah digunakan dengan baik oleh masyarakat, namun masih diragukan akurasi dari informasi tersebut. Oleh karena itu informasi tentang hotspot yang tervalidasi sangat dibutuhkan dalam upaya penanggulangan kebakaran hutan/lahan secara tepat. Penelitian ini bertujuan untuk menguji akurasi titik hotspot dari beberapa sumber data, yaitu IndoFire Map Service (Indofire) dan Fire Information for Resource Management System (FIRMS). Validasi dilakukan dengan membandingkan data hotspot dengan kenampakan citra yang resolusinya lebih tinggi, yaitu SPOT-4. Hasil penelitian menunjukkan bahwa persentase hasil akurasi hostpot FIRMS sebesar 64% dengan tingkat Commision error dan Ommision error masing-masing 18%. Sedangkan persentase hasil akurasi hostpot Indofire ditemukan sebesar 42% dengan tingkat Commision error 20% dan Ommision error 38%. Analisis lebih lanjut di lahan gambut, telah diperoleh nilai akurasi hotspot Firms sebesar 66% dengan commision error 19% dan ommision error 15%, sedangkan hotspot Indofire ditemukan sebesar 46% dengan commision error 19% dan ommision error sekitar 35%. Nilai akurasi hotspot yang bersumber dari FIRMS lebih tinggi dibandingkan dengan hotspot Indofire. Hal ini dapat disebabkan oleh penggunaan semua tingkat kepercayaan hotspot (confidence level) mulai dari 5 hingga 100% yang berbeda dengan Indofire (confidence level>80%). Tingginya nilai ommision error disebabkan oleh kabut asap tebal dan awan yang tidak bisa dideteksi oleh algoritma MODIS. Disamping itu, tingginya nilai ommision error disebabkan oleh kebakaran asap kecil yang dideteksi di SPOT-4 dan juga kebakaran yang baru terjadi yang ditandai oleh asap yang belum menyebar luas, namun hotspot tidak terpantau oleh satelit. Berdasarkan hasil penelitian ini dapat disimpulkan bahwa penggunaan semua confidence level hotspot perlu dipertimbangkan untuk digunakan khususnya pada lahan gambut dibandingkan hanya menggunakan yang lebih besar dari 80% saja.Kata kunci: Hotspot, MODIS, Confidence level, Indofire, FIRMS-NASA, Penginderaan jau

THE USE OF HIGH RESOLUTION IMAGES TO EVALUATE THE EVENT OF FLOODS AND TO ANALYSIS THE RISK REDUCTION CASE STUDY: KAMPUNG PULO, JAKARTA

The flood hit Kampung Pulo region in almost every year. This disaster has caused the evacuation of some residents in weeks. Given the frequency of occurrence is quite high in the region it is necessary to do a study to support disaster risk reduction. This study aimed to evaluate the incidence of flooding that occurred in Kampung Pulo in terms of topography, river conditions, characteristics of the building, and socioeconomic conditions. Methods of study include geomorphology analysis, identification of areas of stagnant, the estimated number of people exposed, the estimation of socio-economic conditions of the population, as well as determining the location of an evacuation. The data used is high-resolution remote sensing imagery is QuickBird and SPOT-6. It also used the results of aerial photography using Unmanned Aerial Vehicle (UAV). Aerial photography was conducted on January 18, 2013, which is when the serious flooding that inundated almost the entire region of Kampung Pulo. Information risk level of buildings and population resulting from this study were obtained by using GIS. The results obtained from this study can be used to develop recommendations and strategies for flood mitigation in Kampung Pulo, Jakarta. One of them is the determination of the location for vertical evacuation plan in the affected areas

DETECTING THE AFFECTED AREAS OF MOUNT SINABUNG ERUPTION USING LANDSAT 8 IMAGERIES BASED ON REFLECTANCE CHANGE

The position of Indonesia as part of a "ring of fire" bringing the consequence that the life of the nation and the state will also be influenced by volcanism. Therefore, it is necessary to map rapidly the affected areas of a volcano eruption. Objective of the research is to detect the affected areas of Mount Sinabung eruption recently in North Sumatera by using optical images Landsat 8 Operational Land Imager (OLI). A pair of Landsat 8 images in 2013 and 2014, period before and after eruption, was used to analysis the reflectance change from that period. Affected areas of eruption was separated based on threshold value of reflectance change. The research showed that the affected areas of Mount Sinabung eruption can be detected and separated by using Landsat 8 OLI images based on the change of reflectance value band 4, 5 and NDVI. Band 5 showed  the highest values of decreasing and band 4 showed the highest values of increasing. Compared with another uses of single band, the combination of both bands (NDVI) give the best result for detecting the affected areas of  volcanic eruption

ANALISIS KARAKTERISTIK TEMPERATUR AREA TERBAKAR (BURNED AREA) MENGGUNAKAN DATA LANDSAT-8 TIRS DI KALIMANTAN (ANALYZING THE TEMPERATURE CHARACTERISTICS OF BURNED AREA USING LANDSAT-8 TIRS IN KALIMANTAN)

Biomass burning in an area will leave traces of fire such as charcoal, ash, and outcrop of land in the area known as the burned area. The burnt area is thought to have a relatively higher temperature than the surrounding area were not burned. This study aims to determine the characteristics of the temperature of the burned area using remote sensing data of Landsat-8 TIRS (Thermal Infra Red Sensor). The selected research locations are parts of Central Kalimantan and South Kalimantan incoming Landsat scene-8 path / row 118/062. The research method is a data processing Landsat-8 TIRS (channels 10 and 11) to produce an image of the brightness temperature as well as data analysis includes a statistical analysis of central tendency of the values of the brightness temperature of the sample (calculation of mean and standard deviation) as well as distance calculation (D-value). The results showed that the brightness temperature data either channel 10 or channel 11 Landsat-8 TIRS has good ability in separating the burned area and bare soil, but has a low ability to separate the burned areas and settlements. Thus, the brightness temperature parameter cannot be used as a single variable for the extraction of burned areas in a scene image of a single acquisition. ABSTRAKPeristiwa kebakaran biomassa pada suatu daerah akan menyisakan bekas-bekas kebakaran seperti arang, abu, serta singkapan tanah pada daerah tersebut yang dikenal dengan burned area. Daerah bekas kebakaran tersebut diduga memiliki temperatur yang relatif lebih tinggi dibandingkan dengan daerah sekitarnya yang tidak terbakar. Penelitian ini bertujuan untuk mengetahui karakteristik temperatur burned area menggunakan data penginderaan jauh Landsat-8 Thermal Infra Red Sensor (TIRS). Lokasi penelitian yang dipilih adalah sebagian wilayah Kalimantan Tengah dan Kalimantan Selatan yang masuk scene Landsat-8 path/row 118/062. Metode penelitian yang dilakukan adalah pengolahan data Landsat-8 TIRS (kanal 10 dan 11) untuk menghasilkan citra suhu kecerahan serta analisis data yang meliputi analisis statistik tendensi sentral dari nilai-nilai suhu kecerahan dari sampel (perhitungan rerata dan standar deviasi) serta perhitungan jarak (D-value). Hasil penelitian menunjukkan bahwa data suhu kecerahan baik kanal 10 maupun kanal 11 Landsat-8 TIRS memiliki kemampuan yang baik dalam memisahkan burned area dan lahan terbuka, namun memiliki kemampuan yang rendah untuk memisahkan burned area dan permukiman. Dengan demikian, parameter suhu kecerahan belum bisa dipergunakan sebagai variabel tunggal untuk ekstraksi burned area pada suatu scene citra perekaman tunggal

DETECTING THE AREA DAMAGE DUE TO COAL MINING ACTIVITIES USING LANDSAT MULTITEMPORAL (Case Study: Kutai Kartanegara, East Kalimantan)

Coal is one of the most mining commodities to date, especially to supply both national and international energy needs. Coal mining activities that are not well managed will have an impact on the occurrence of environmental damage. This research tried to utilize the multitemporal Landsat data to analyze the land damage caused by coal mining activities. The research took place at several coal mine sites in East Kalimantan Province. The method developed in this research is the method of change detection. The study tried to know the land damage caused by mining activities using NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), NDWI (Normalized Difference Water Index) and GEMI (Global Environment Monitoring Index) parameter based change detection method. The results showed that coal mine area along with the damage that occurred in it can be detected from multitemporal Landsat data using NDSI value-based change detection method. The area damage due to coal mining activities  can be classified into high, moderate, and low classes based on the mean and standard deviation of NDSI changes (ΔNDSI). The results of this study are expected to be used to support government efforts and mining managers in post-mining land reclamation activities

Analisis kerapatan jaringan stasiun hujan untuk menunjang prediksi iklim di Indonesia

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