PRELIMINARY DETECTION OF GEOTHERMAL MANIFESTATION POTENTIAL USING MICROWAVE SATELLITE REMOTE SENSING

The satellite technology has developed significantly. The sensors of remote sensing satellites are in the form of optical, Microwave, and LIDAR. These sensors can be used for energy and mineral resources applications. The example of those applications are height model and the potential of geothermal manifestation detection. This study aims to detect the potential of geothermal manifestation using remote sensing. The study area is the Northern of the Inverse Arc of Sulawesi. The method used is remote sensing approach for its preliminary detection with 4 steps as follow (a) mining land identification, (b) geological parameter extraction, (c) preparation of standardized spatial data, and (d) geothermal manifestation. Mining lands identification is using Vegetation Index Differencing method. Geological parameters include structural geology, height model, and gravity model. The integration method is used for height model. The height model integration use ALOS PALSAR data, Icesat/GLAS, SRTM, and X SAR. Structural geology use dip and strike method. Gravity model use physical geodesy approach. Preparation of standardized spatial data with re-classed and analyzed using Geographic Information System between each geological parameter, whereas physical geodesy methods are used for geothermal manifestation detection. Geothermal manifestation using physical geodesy approach in Barthelmes method. Grace and GOCE data are used for gravity model. The geothermal manifestation detected from any parameter is analyzed by using geographic information system method. The result of this study is 10 area of geothermal manifestation potential. The accuracy test of this research is 87.5 % in 1.96 σ. This research can be done efficiently and cost-effectively in the process. The results can be used for various geological and mining applications

TECHNIQUE FOR IDENTIFYING BURNED VEGETATION AREA USING LANDSAT 8 DATA

During the last two decades, forest and land fire is a catastrophic event that happens almost every year in Indonesia.  Therefore, it is necessary to develop a technic to monitor forest fires using satellite data to obtain the latest information of burned area in a large scale area. The objective of this research is to develop a method for burned area mapping that happened between two Landsat 8 data recording on August 13rd and September 14th 2015. Burned area was defined as a burned area of vegetation. The hotspot distribution during the period August - September 2015 was used to help visual identification of burned area on the Landsat image and to verify the burned area resulted from this research. Samples were taken at several land covers to determine the spectral pattern differences among burned area, bare area and other land covers, and then the analysis was performed to determine the suitable spectral bands or indices and threshold values that will be used in the model. Landsat recorded on August 13rd before the fire was extracted for soil, while Landsat recorded on September 14th after the fire was extracted for burned area. Multi-temporal analysis was done to get the burned area occurring during the certain period. The results showed that the clouds could be separated using combination of ocean blue and cirrus bands, the burned area was extracted using a combination of NIR and SWIR band, while soil was extracted using ratio SWIR / NIR. Burned area obtained in this study had high correlation with the hotspot density of MODIS with the accuracy was around 82,4 %

DETECTING THE SURFACE WATER AREA IN CIRATA DAM UPSTREAM CITARUM USING A WATER INDEX FROM SENTINEL-2

This paper describes the detection of the surface water area in Cirata dam,  upstream Citarum, using a water index derived from Sentinel-2. MSI Level 1C (MSIL1C) data from 16 November 2018 were extracted into a water index such as the NDWI (Normalized Difference Water Index) model of Gao (1996), McFeeters (1996), Roger and Kearney (2004), and Xu (2006). Water index were analyzed based on the presence of several objects (water, vegetation, soil, and built-up). The research resulted in the ability of each water index to separate water and non-water objects. The results conclude that the NDWI of McFeeters (1996) derived from Sentinel-2 MSI showed the best results in detecting the surface water area of the reservoir

Ekstraksi Kelurusan (Linement) Secara Otomatis Menggunakan Data DEM SRTM Studi Kasus: Pulau Bangka

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Pemetaan Indeks Resiko Gerakan Tanah Menggunakan Citra DEM SRTM Dan Data Geologi Di Kecamatan Pejawaran, Kabupaten Banjarnegara

Wilayah Kabupaten Banjarnegara yang didominasi oleh wilayah pegunungan dan perbukitan memiliki potensi bencana, salahsatunya adalah bencana tanah longsor/gerakan tanah. Penelitian ini bertujuan untuk melakukanpemetaan indeks ancaman bencana gerakan tanah. Informasi ini sangat diperlukan sebagai input dalam penyusunan peta resiko bencana yang dipergunakan sebagai pedoman penanggulangan dan pencegahan bencana bagi pemerintahdaerah. Penelitian mengambil lokasi di Kecamatan Pejawaran Kabupaten Banjarnegara. Indeks ancaman bencana gerakan tanah disusun menggunakan metode analythical hierarchy process (AHP). Kriteria yang digunakan yaitu geologi daerah, kemiringan lereng, morfologi wilayah, dan penggunaan lahan, serta curah hujan. Hasil penelitian menunjukkan bahwa faktor yang paling tinggi dalam mempengaruhi ancaman longsor di Kecamatan Pejawaran Kabupaten Banjarnegara adalah kemiringan lereng dan litologi atau jenis batuan penyusun lapisan tanah.Hlm. 529-54

Preliminary Detection of Geothermal Manifestation Potential Using Microwave Satellite Remote Sensing

The satellite technology has developed significantly. The sensors of remote sensing satellites are in the form of optical, Microwave, and LIDAR. These sensors can be used for energy and mineral resources applications. The example of those applications are height model and the potential of geothermal manifestation detection. This study aims to detect the potential of geothermal manifestation using remote sensing. The study area is the Northern of the Inverse Arc of Sulawesi. The method used is remote sensing approach for its preliminary detection with 4 steps as follow (a) mining land identification, (b) geological parameter extraction, (c) preparation of standardized spatial data, and (d) geothermal manifestation. Mining lands identification is using Vegetation Index Differencing method. Geological parameters include structural geology, height model, and gravity model. The integration method is used for height model. The height model integration use ALOS PALSAR data, Icesat/GLAS, SRTM, and X SAR. Structural geology use dip and strike method. Gravity model use physical geodesy approach. Preparation of standardized spatial data with re-classed and analyzed using Geographic Information System between each geological parameter, whereas physical geodesy methods are used for geothermal manifestation detection. Geothermal manifestation using physical geodesy approach in Barthelmes method. Grace and GOCE data are used for gravity model. The geothermal manifestation detected from any parameter is analyzed by using geographic information system method. The result of this study is 10 area of geothermal manifestation potential. The accuracy test of this research is 87.5 % in 1.96 ?. This research can be done efficiently and cost-effectively in the process. The results can be used for various geological and mining applications.Hlm. 187-19

Burned Area Identification Using Landsat 8

Forest and land fire is a catastrophic event that always happens every year in Indonesia. In 2015, forest and land fires occured in many islands of Indonesia especially in Sumatera, Kalimantan, Sulawesi and Papua island. Therefore, it is necessary to monitor forest fires using satellite data to obtain the latest information of burned area in a large scale area. This research aims to develop a method for burned area mapping that happened between two Landsat 8 data recording on August 13rd and September 14th 2015. The information of hotspot distribution during the period August - September 2015 was used to help visual identification of burned area on the Landsat image and to verify the burned area resulted using the method. Samples were taken at several land covers to determine the spectral pattern differences among burned area, bare area and other land covers, and then perform the analysis to determine the suitable spectral bands or indexs and threshold values that will be used in the model. Landsat recorded on August 13rd 2016 was extracted for an bare area, while Landsat recorded on September 14th was extracted for burned area. Multi-temporal analysis was done to get the burned area occurring during the period August 13rd to September 14th 2015. The results showed that the clouds could be separated by using a combination of ocean blue and cirrus band, the burned area by using a combination of NIR and SWIR band, while bare area by using ratio SWIR / NIR. Burned area Obtained in this study had a high correlation with the hotspot density and visual appearance of burned area in the Landsat images.Hlm. 406-41

Spatial-Temporal Dynamics Land Use/Land Cover Change and Flood Hazard Mapping in the Upstream Citarum Watershed, West Java, Indonesia

This study presents the information on the dynamics of changes in land use/land cover (LULC) spatially and temporally related to the causes of flooding in the study area. The dynamics of LULC changes have been derived based on the classification of Landsat imagery for the period between 1990 and 2016. Terrain surface classification (TSC) was proposed as a micro-landform classification approach in this study to create flood hazard assessment and mapping that was produced based on the integration of TSC with a probability map for flood inundation, and flood depth information derived from field observation. TSC as the micro-landform classification approach was derived from SRTM30 DEM data. Multi-temporal Sentinel-1 data were used to construct a pattern of historical inundation or past flooding in the study area and also to produce the flood probability map. The results of the study indicate that the proposed flood hazard mapping (FHM) from the TSC as a micro-landform classification approach has the same pattern with the results of the integration of historical inundation or previous floods, as well as field investigations in the study area. This research will remain an important benchmark for planners, policymakers and researchers regarding spatial planning in the study area. In addition, the results can provide important input for sustainable land use plans and strategies for mitigating flood hazards

Spatial-temporal dynamics land use/land cover change and flood hazard mapping in the upstream citarum watershed, west Java, Indonesia

This study presents the information on the dynamics of changes in land use/land cover (LULC) spatially and temporally related to the causes of flooding in the study area. The dynamics of LULC changes have been derived based on the classification of Landsat imagery for the period between 1990 and 2016. Terrain surface classification (TSC) was proposed as a micro-landform classification approach in this study to create flood hazard assessment and mapping that was produced based on the integration of TSC with a probability map for flood inundation, and flood depth information derived from field observation. TSC as the micro-landform classification approach was derived from SRTM30 DEM data. Multi-temporal Sentinel-1 data were used to construct a pattern of historical inundation or past flooding in the study area and  also to produce the flood probability map. The results of the study indicate that the proposed flood hazard mapping (FHM) from the TSC as a micro-landform classification approach has the same pattern with the results of the integration of historical inundation or previous floods, as well as field investigations in the study area. This research will remain an important benchmark for planners, policymakers and  researchers regarding spatial planning in the study area. In addition, the results can provide important input for sustainable land use plans and strategies for mitigating flood hazards

Machine Learning-Based Local Knowledge Approach to Mapping Urban Slums in Bandung City, Indonesia

Rapid urban population growth in Bandung City has led to the development of slums due to inadequate housing facilities and urban planning. However, it remains unclear how these slums are distributed and evolve spatially and temporally. Therefore, it is necessary to map their distribution and trends effectively. This study aimed to classify slum areas in Bandung City using a machine learning-based local knowledge approach; this classification exercise contributes towards Sustainable Development Goal 11 related to sustainable cities and communities. The methods included settlement and commercial/industrial classification from 2021 SPOT-6 satellite data by the Random Forest classifier. A knowledge-based classifier was used to derive slum and non-slum settlements from the settlement and commercial/industrial classification, as well as railway, river, and road buffering. Our findings indicate that these methods achieved an overall accuracy of 82%. The producer’s accuracy for slum areas was 70%, while the associated user’s accuracy was 92%. Meanwhile, the Kappa coefficient was 0.63. These findings suggest that local knowledge could be a potent option in the machine learning algorithm