Articles
<![CDATA[CAN THE PEAT THICKNESS CLASSES BE ESTIMATED FROM LAND COVER TYPE APPROACH? ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Atriyon Julzarika]]>;
<![CDATA[Udhi C. Nugroho]]>;
<![CDATA[Dipo Yudhatama]]>;
<![CDATA[Yudi Lasmana]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2017.v14.a2677
<![CDATA[Indonesia has been known as a home of the tropical peatlands. The peatlands are mainly in Sumatera, Kalimantan and Papua Islands. Spatial information on peatland depth is needed for the planning of agricultural land extensification. The research objective was to develop a preliminary estimation model of peat thickness classes based on land cover approach and analyse its applicability using Landsat 8 image. Ground data, including land cover, location and thickness of peat, were obtained from various surveys and peatlands potential map (Geology Map and Wetlands Peat Map). The land cover types were derived from Landsat 8 image. All data were used to build an initial model for estimating peat thickness classes in Merauke Regency. A table of relationships among land cover types, peat potential areas and peat thickness classes were made using ground survey data and peatlands potential maps of that were best suited to ground survey data. Furthermore, the table was used to determine peat thickness classes using land cover information produced from Landsat 8 image. The results showed that the estimated peat thickness classes in Merauke Regency consist of two classes, i.e., very shallow peatlands and shallow peatlands. Shallow peatlands were distributed at the upper part of Merauke Regency with mainly covered by forest. In comparison with Indonesia Peatlands Map, the number of classes was the two classes. The spatial distribution of shallow peatlands was relatively similar for its precision and accuracy, but the estimated area of shallow peatlands was greater than the area of shallow peatlands from Indonesia Peatlands Map. This research answered the question that peat thickness classes could be estimated by the land cover approach qualitatively. The precise estimation of peat thickness could not be done due to the limitation of insitu data. ]]>
<![CDATA[WATER CLARITY MAPPING IN KERINCI AND TONDANO LAKE WATERS USING LANDSAT 8 DATA ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Nana Suwargana]]>;
<![CDATA[I Made Parsa]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2015.v12.a2693
<![CDATA[Land conversion occurred in the lake catchment area caused the decreasing of water quality in many lakes of Indonesia. According to Lake Ecosystem Management Guidelines from Ministry of Environment, tropic state of lake water is one of parameters for assessing the lake ecosystem status. Tropic state can be indicated by the quantity of nitrogen, phosphorus, chlorophyll, and water clarity. The objective of this research is to develop the water quality algorithm and map the water clarity of lake water using Landsat 8 data. The data were standardized for sun geometry correction and atmospheric correction using Dark Object Subtraction method. In the first step, Total Suspended Solid (TSS) distributions in the lake were calculated using a semi empirical algorithm (Doxaran et al., 2002), which can be applied to a wide range of TSS values. Secchi Disk Transparency (SDT) distributions were calculated using our water clarity algorithm that was obtained from the relationship between TSS and SDT measured directly in the lake waters. The result shows that the water clarity algorithm developed in this research has the determination coefficient that reaches to 0,834. Implementation of the algorithm for Landsat 8 data in 2013 and 2014 showed that the water clarity in Kerinci Lake waters was around 2 m or less, but the water clarity in Tondano Lake waters was around 2 – 3 m. It means that Kerinci Lake waters had lower water clarity than Tondano Lake waters which is consistent with the field measurement results.]]>
<![CDATA[ANALISIS HUBUNGAN PENUTUP/PENGGUNAAN LAHAN DENGAN TOTAL SUSPENDED MATTER (TSM) KAWASAN PERAIRAN SEGARA ANAKAN MENGGUNAKAN DATA INDERAJA ]]>
<![CDATA[Ety Parwati]]>;
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Ita Carolita]]>;
<![CDATA[Tatik Kartika]]>;
<![CDATA[Sri Harini]]>;
<![CDATA[Ratih Dewanti]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 3 No. 1 (2006) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v3i1.3183
<![CDATA[Segara Anakan and its surrounding which is located in Cilacap Regency Central of Java, is the study area for this research. This region, like other estuaries, has a unique ecosystem which is protected and surrounded by the mangrove forest that can cause very dynamic development. In the upland, there are three big rivers flow; Citanduy, Cibeureum, and Cimeng. The main issue in this region is that the lagoon to become narrowing because of rapid sedimentation process. Landsat MSS, TM, and ETM of the years 1978, 1995, 1998, and 2003 are remote sensing data used in this research. An analysis in term of correlation between landuse/landcover changes and sedimentation was carried out by looking at their changes in the upper land especially along the rivers that have big contribution to the sedimentation in the lagoon. The result shows that there is high relation between landuse/landcover changes in the upper land and sedimentation around the lagoon.]]>
<![CDATA[SIMULASI JALUR EVAKUASI UNTUK BENCANA TSUNAMI BERBASIS DATA PENGINDERAAN JAUH (STUDI KASUS: KOTA PADANG, PROPINSI SUMATERA BARAT) ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Ita Carolita]]>;
<![CDATA[Mawardi Nur]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 4 No. 1 (2007) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v4i1.3191
<![CDATA[Tsunami disaster caused great damages and very large victims especially when occurs in urban area along. Therefore information of evacuation in a map is very important for disaster preparedness in order to minimize the number of victims in affected area. Here, information generated from remote sensing satellite data (Landsat, SPOT-5 nad DEM) and secondary data (administration boundary and field survey data) are used ti simulate evacuation route and to produce a map for Padang City. Vulnerability and evacuation areas are determined based on information of maximum tsunami height in shoreline and topography condition. Landuse/landcover and infrastructure (road, bridge and building) are extracted from SPOT data. All the data obtained from remote sensing and secondary data are integrated using geospatial modeling to simulate tsunami evacuation routes. The simulation of evacuation route in Padang City for tsunami preparedness is provided by considering river line, shelters and save zone, available infrastructur (road), the shortest distance (to shelters and save zone) and local community experiences.]]>
<![CDATA[EKSTRAKSI OTOMATIS INFORMASI DEM DARI CITRA STEREO PRISM-ALOS ]]>
<![CDATA[Bambang Trisakti]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 4 No. 1 (2007) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v4i1.3194
<![CDATA[ALOS satellite was launched on January 24th 2006 and is equipped by PRISM sensor which has a mission to produce stereoscopic image. PRISM is a panchromatic radiometer with 2.5 spatial resolution, and it has 3 telescopes for recording the image from nadir, forward and backward view is known as stereoscopic image which is usefull to generate earth surface height or DEM (Digital Elevation Model). Automatic DEM extraction was done by area based matching technique using PRISM DEM software. This technique correlates area/pixel in master image with same are/pixel in target image based based on grey value similarity of pixel. Relief displacement (parallax) of each area/pixel was extracted from the correlation process, and then it was used to generate earth surface height or DEM. The generated DEM was compared with reference data (SRTM X and C band) to analyze the level of DEM accuracy. The result shows that DEM from automatic extraction needs geoids correction (Eart surface relief correction). After doing the correction, the DEM has similar distribution height but smoother DEM pattern than referenced DEM. Finally, RMSE of PRISMDEM are around 16 m relative to the referenced DEM.]]>
<![CDATA[KAJIAN DISTRIBUSI SPASIAL DEBIT ALIRAN PERMUKAAN DI DAERAH ALIRAN SUNGAI (DAS) BERBASIS DATA SATELIT PENGINDERAAN JAUH ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Kuncoro Teguh]]>;
<![CDATA[Susanto Susanto]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 5 No. 1 (2008) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v5i1.3236
<![CDATA[The aim of this paper is to analyze the spatial distribution of the water discharge in the Ciliwung watershed based on the remote sensing satellite data. Digital elevation model (DEM) is processed to determine the watershed border using the steepest slope method and also used to calculate pixels area. The calculated pixels area are used to determine the watershed and landcover areas in 3 dimension perspective, the landcover of the Ciliwung watershed is mapped by using SPOT-4 image acquired in 2007. All generated information are used as the input to determine the spatial distribution of the water discharge using the run-off coefficient table produced by related institutions. In the next step, the total water discharge in some outlets (Katulampa, Depok and Muara Ciliwung) are compared and the relationship between the landcover condition and the water discharge is evaluated. The result shows that DEM can be used to determine the watershed border and calculate the watershed area, which the results are almost same with the real condition. The spatial distribution of the water discharge is useful to analyse the water discharge contribution of each part of watershed to the total water discharge in the Ciliwung watershed. It shows that the water discharge in Katulampa outlet contributes 44 % of the total water discharge in the Ciliwung watershed. Further, some landcover types are identified in the high water discharge area. This kind of information is very useful for the regional planning and flood management activities.]]>
<![CDATA[KAJIAN KOREKSI TERRAIN PADA CITRA LANDSAT THEMATIC MAPPER (TM) ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Mahdi Kartasasmita]]>;
<![CDATA[Kustiyo Kustiyo]]>;
<![CDATA[Tatik Kartika]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 6 No. 1 (2009) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v6i1.3239
<![CDATA[Terrain correction is used to minimize the shadow effect due to variation of earth’s topography. So, the process is very useful to correct the distortion of the pixel value at the mountainous area in the satellite image. The aim of this paper is to study the terrain correction process and its implementation for Landsat TM. The algorithm of the terrain correction was built by determining the pixel normal angle which is defined as an angle between the sun and surface normal directions. The calculation of the terrain correction needs the information of sun zenith angle, sun elevation angle (obtained from header data), pixel slope, and pixel aspect derived from digital elevation model (DEM). The C coefficient from each band was determined by calculating the gradient and the intercept of the correlation between the Cos pixel normal angle and the pixel reflectance in each band. Then, the Landsat TM image was corrected by the algorithm using the pixel normal angle and C coefficient. C Coefficients used in this research were obtained from our calculation and from Indonesia National Carbon Accounting System (INCAS). The result shows that without the C coefficient, pixels value increases very high when the pixel normal angle approximates 90°. The C coefficient prevents that condition, so the implementation of the C coefficient obtained from INCAS in the algorithm can produce the image which has the same topography appearance. Further, each band of the corrected image has a good correlation with the corrected band from the INCAS result. The implementation of the C coefficient from our calculation still needs some evaluation, especially for the method to determine the training sample for calculating the C coefficient.]]>
<![CDATA[KAJIAN DAMPAK PERUBAHAN IKLIM TERHADAP KEBAKARAN HUTAN DAN DEFORESTASI DI PROVINSI KALIMANTAN BARAT ]]>
<![CDATA[Nanin Anggraini]]>;
<![CDATA[Bambang Trisakti]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 8 No. 1 (2011) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v8i1.3247
<![CDATA[Increasing or decreasing of rainfall intensity, due to the climate change, affects the environment condition in many Indonesia areas. For instance: low rainfall intensity causes high number of forest fire occurrence in Kalimantan Island. The impact of climate change is studied by analyzing the correlation among rainfall intensity, number of forest fire occurrence and forest area change in West Kalimantan Province. The rainfall is extracted using Tropical Rainfall Measurement Mission (TRMM) data for 2001-2008. The number of forest fire occurrence is identified by the number of hotspot extracted from thermal sensor of satellite data MODIS for 2001 - 2008. The forest area is calculated from MODIS data for 2003, 2005, 2007 and 2009. Pixel which has Normalized Difference Vegetation Index (NDVI) value more than 0,7 along a year round is assumed as forest pixel. The NDVI value is obtained by doing training sample in forest area. The result shows that the rainfall has slightly upward trend in Kalimantan. The rainfall has negative correlation with the number of hotspot. When the rainfall was the lowest and the number of hotspot was the highest in 2004, the forest area between 2003 and 2005 decreased (deforestation) significantly. On the other hand, when the rainfall was high and the hotspot was low in 2008, no decreasing in forest area otherwise we found the increasing of forest area. It is probably due to reforestation and expansion of plantation area (such as oil palm).]]>
<![CDATA[STANDARISASI KOREKSI DATA SATELIT MULTIWAKTU DAN MULTISENSOR (LANDSAT TM/ETM+ DAN SPOT-4) ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Gagat Nugroho]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 9 No. 1 (2012) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v9i1.3257
<![CDATA[Remote sensing satellite data has been widely used to support watershed and lake managements. However researches conducted in Indonesia are facing common problems related with standardization of data pre-processing, particularly that are related to orthorectification and radiometric correction. The objective of this research is to standardize the satellite data correction to monitor Total Suspended Material (TSM) in Limboto lake along 1990-2010 period using Landsat TM/ETM+ and SPOT-4. The data correction process was performed included orthorectification, sun correction, terrain correction and normalization of data with different time and different sensor. The result of each correction process was examined to evaluate the quality improvement before and after correction. The corrected data was then used to monitor the degree of turbidity of Limboto Lake during 1990-2010 periods. The study results show that data correction reduces position error and object spectral difference due to differences in acquisition time and sensor. The examined correction provides more accurate and consistent results. The quality of Limboto Lake was monitored decreases gradually, where the higher TSM concentration was found during the period of 1990-2010.]]>
<![CDATA[PEMANFAATAN DATA SATELIT UNTUK ANALISIS POTENSI GENANGAN DAN DAMPAK KERUSAKAN AKIBAT KENAIKAN MUKA AIR LAUT ]]>
<![CDATA[Nanin Anggraini]]>;
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Tri Edhi Budhi Soesilo]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 9 No. 2 (2012) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v9i2.3266
<![CDATA[Increasing of ocean water volume caused sea level rise (SLR) that threatens the existence of small islands and coastal areas, such as North Jakarta. Besides the SLR, North Jakarta is also threatened by land subsidence. This study aims to predict the height of SLR in 2030 and to analyze the impact of SLR on the coastal areas of North Jakarta. The total height of SLR in 2030 was predicted using tidal data, land subsidence data, and SLR prediction by B2 scenario from the Intergovernmental Panel on Climate Change (IPCC). Potential inundation area due to SLR was estimated using Digital Elevation Model Shuttle Radar Topography Mission (DEM SRTM) X-C band with spatial resolution 30 m. The damage was analyzed by doing the overlay between the inundation areas with the land use information extracted from QuickBird data. The result shows that the SLR predictions in 2030 are 2.88 m caused by the tide, 2.28 m caused by the land subsidence, and 1.29 m caused by the B2 scenario IPCC. The total height of SLR prediction is 6.45 m. The potential damages of land use are dominated by urban area (1045 ha) and industrial area (563 ha). The most inundated areas are located in Penjaringan sub-district for urban (523 ha) and in Cilincing sub-district for industrial area (311 ha).]]>
