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<![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 Vol. 14 No. 2 (2017) ]]>
Publisher : <![CDATA[BRIN ]]>
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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. Â]]>
<![CDATA[MONITORING OF LAKE ECOSYSTEM PARAMETER USING LANDSAT DATA (A CASE STUDY: LAKE RAWA PENING) ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Nana Suwargana]]>;
<![CDATA[Joko Santo Cahyono]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 1 (2015) ]]>
Publisher : <![CDATA[BRIN ]]>
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DOI: 10.30536/j.ijreses.2015.v12.a2674
<![CDATA[Most lakes in Indonesia have suffered (decrease in quality) caused by land conversion in the catchment area, soil erosion, and water pollution from agriculture and households. This study utilizes remote sensing data to monitor several parameters used as ecosystem status assessors in accordance with the guidelines of Lake Ecosystem Management provided by the Ministry of Environment. The monitoring was done at Lake Rawa Pening using Landsat TM/ETM+ satellite data over the period of 2000-2013. The data standardization was done for sun angle correction and also atmospheric correction by removing dark pixels using histogram adjustment method. RGB color composites (R: NIR + SWIR, G: NIR, B: NIR-RED) were used for water hyacinth identification; thus, the lake water surface area can be delineated. Further samples were collected for water hyacinth and water classification with Maximum Likelihood method. Total Suspended Matter (TSM) by Doxaran model and the water clarity from field measurement was correlated to build water clarity algorithm. The results show that Lake Rawa Pening was deterioting in term of quality during the period of 2000-2013; it can be seen from the dynamic rate of the shrinkage and the expansion of the lake water surface area, the uncontrolled distribution of water hyacinth which it covered 45% of the lake water surface area in 2013, the increased of TSM concentration, and the decreased of water clarity. Most parts of Rawa Pening’s water have clarity less than 2.5 m which indicated that the thropic status is hypertrophic class.]]>
<![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 Vol. 12 No. 2 (2015) ]]>
Publisher : <![CDATA[BRIN ]]>
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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[DEM GENERATION FROM STEREO ALOS PRISM AND ITS QUALITY IMPROVEMENT ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Atriyon Julzarika]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 8 (2011) ]]>
Publisher : <![CDATA[BRIN ]]>
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DOI: 10.30536/j.ijreses.2011.v8.a1740
<![CDATA[Digital elevation mode (DEM) is important data for supporting many activities. One of DEM generation methods is photogrametry of optical stereo data based on image matching and collinear correlation. The problem of DEM from optical stereo data is bullseye due to low contrast in relatively flat area and cloud cover. The research purpose is to generate DEM from ALOS PRISM stereo data level 1B2R and improve the quality of the DEM. DEM was generated using Leica Photogrametry Suite (LPS) software. The study area is located in Sragen district and its vicinity. The process needed three dimension of Ground Control Point (GCP) XYZ, as input data for collinear correlation. Ground measurement was conducted using differential GPS to collect 30 GCPs that used for input (21 GCPs) and for accuracy evaluation (9 GCPs). The generated DEM has good detail (10 m), but it has bullseye which mostly occurred in relatively flat area. The quality improvement was carried out by combining the DEM with SRTM DEM (30 m) using DEM fusion method. Both DEMs were processed for geoids correction (EGM 2008), co-registration and histogram normalization. The fusion method was conducted by considering height error map (HEM) of each DEM. The quality of fused DEM was evaluated by comparing HEM, the number of bullseye, and vertical accuracy before and after the fusion. The result shows that DEM fusion can preserve detail information of the DEM and significantly reduce the bullseye (decreasing more than 66% of bullseye). It also shows the improvement (from 7.6 m to 7.3 m) of vertical accuracy.]]>
<![CDATA[DETERMINATION OF STRATIFICATION BOUNDARY FOR FOREST AND NON FOREST MULTITEMPORAL CLASSIFICATION TO SUPPORT REDD+ IN SUMATERA ISLAN ]]>
<![CDATA[Tatik Kartika]]>;
<![CDATA[Inggit Lolita Sari]]>;
<![CDATA[Bambang Trisakti]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013) ]]>
Publisher : <![CDATA[BRIN ]]>
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DOI: 10.30536/j.ijreses.2013.v10.a1843
<![CDATA[Multi-temporal classification is a method to determine forest and non-forest by considering a missing data, such as cloud cover using correlations value from the other data. This circumstances is frequently occured in a tropical area such as in Indonesia. To gain an optimum result of forest and non-forest classification, it is needed a stratification zone that describes the difference of vegetation condition due to different of vegetation type, soil type, climate, and land use/cover associations. This stratification zone will be useful to indicate the different biomass volume relating to carbon content for supporting the REDD+ project. The objective of this study was to determine stratification boundary by performing multi temporal classification in Sumatera Island using Landsat imagery in 25 meter resolution and Quick Bird imagery in 0.6 meter. Rough stratification was made by considering land use/cover, DEM and landform, using visual interpretation of moderate spatial resolution of satellitedata. High spatial resolution data was also provided in some areas to increase the accuracy level of stratification zone. The stratification boundary was evaluated using forest classification indices, and it was redetermined to obtain the final stratification zone. The indices was generated by CanonicalVariate Analysis (CVA) method, which was depend on training samples of forest and non-forest in each previous stratification zone. The amount of indices used in each zone were two or three indices depending on the separability of the forest and non-forest classification. The suitable indices used in each zone described forest as 100, non-forest as 0, and uncertain forest between 50-99. The result showed 20 stratification zones in Sumatera spreading out in coastal, mountain, flat area, and group of small islands. The stratification zone will improve the accuracy of forest and non-forest classification result and their change based on multi temporal classification.]]>
<![CDATA[UTILIZATION OF MULTI TEMPORAL SAR DATA FOR FOREST MAPPING MODEL DEVELOPMENT ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Rossi Hamzah]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013) ]]>
Publisher : <![CDATA[BRIN ]]>
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DOI: 10.30536/j.ijreses.2013.v10.a1844
<![CDATA[Utilization of optical satellite data in tropical region was limited to free cloud cover. Therefore, Synthetic Aperture Radar (SAR) becomes an alternative solution for forest mapping in Indonesia due to its capability to penetrate cloud. The objective of this research was to develop a forestmapping model based on multi temporal SAR data. Multi temporal ALOS PALSAR data for 2007 and 2008 were used for forest mapping, and one year mosaic LANDSAT data in 2008 was used as references data to obtain training sample and to verify the final forest classification. PALSAR processing was done using gamma naught conversion and Lee filtering. Samples were made in forest and water area, and the statistical values of the each object were calculated. Some thresholds were determined based on the average and standard deviation, and the best threshold was selected to classify forest and water in 2008. It was assumed that forest could not change in 1-2 years period. The classification of forest, water, and the change were combined to produce final forest in 2008, and then it was visually verified with mosaic LANDSAT in 2008. The result showed that forest, water, and the change could be well classified using threshold method. The forest derived from PALSAR was visually consistent with forest appearance in LANDSAT and forest produced from INCAS. It has better performance than forest derived from INCAS for separating oil palm plantation from the forest.]]>
<![CDATA[LAND COVER CLASSIFICATION ALOS AVNIR DATA USING IKONOS AS REFERENCE ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Dini Oktavia Ambarwati]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 9 No. 1 (2012) ]]>
Publisher : <![CDATA[BRIN ]]>
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DOI: 10.30536/j.ijreses.2012.v9.a1822
<![CDATA[Abstract. Advanced Land Observation Satellite (ALOS) is a Japanese satellite equipped with 3 sensors i.e., PRISM, AVNIR, and PALSAR. The Advanced Visible and Near Infrared Radiometer (AVNIR) provides multi spectral sensors ranging from Visible to Near Infrared to observe land and coastal zones. It has 10 meter spatial resolution, which can be used to map land cover with a scale of 1:25000. The purpose of this research was to determineclassification for land cover mapping using ALOS AVNIR data. Training samples were collected for 11 land cover classes from Bromo volcano by visually referring to very high resolution data of IKONOS panchromatic data. The training samples were divided into samples for classification input and samples for accuracy evaluation. Principal component analysis (PCA) was conducted for AVNIR data, and the generated PCA bands were classified using Maximum Likehood Enhanced Neighbor method. The classification result was filtered and re-classed into 8 classes. Misclassifications were evaluated and corrected in the post processing stage. The accuracy of classifications results, before and after post processing, were evaluated using confusion matrix method. The result showed that Maximum Likelihood Enhanced Neighbor classifier with post processing can produce land cover classification result of AVNIR data with good accuracy (total accuracy 94% and kappa statistic 0.92). ALOS AVNIR has been proven as a potential satellite data to map land cover in the study area with good accuracy.]]>
<![CDATA[UTILIZATION OF IKONOS IMAGE AND SRTM AS ALTERNATIVE CONTROL POINT REFERENCE FOR ALOS DEM GENERATION ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Gathot Winarso]]>;
<![CDATA[Atriyon Julzarika]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 7 No. 1 (2010) ]]>
Publisher : <![CDATA[BRIN ]]>
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DOI: 10.30536/j.ijreses.2010.v7.a1539
<![CDATA[Abstract. Digital Elevation Model (DEM) was generated from Advanced LandObservation Satellite - The Panchromatic Remote-Sensing Instrument for Stereo Mapping(ALOS PRISM) stereo data using image matching and collinear correlation based on LeicaPhotogrametry Suite (LPS) software. The process needs three dimension of Ground ControlPoint (GCP) or Control Point (CP) XYZ as input data for collinear correlation to determineexterior orientation coefficient. The main problem of the DEM generation is the difficultyto obtain the accurate field measurement GCP in many areas. Therefore, another alternativeCP sources are needed. The aim of this research was to study the possibility of (IKONOS)image and Shuttle Radar Topography Mission (SRTM) X-C band to be used as CPreference for ALOS PRISM DEM generation. The study area was Sragen and Bandungregion. The DEM of each study area was generated using 2 methods: generated using fieldmeasurement GCPs taken by differential GPS and generated using CPs from IKONOSimage (XY coordinat) and SRTM for (Z elevation). The generated DEMs were compared.The accuracy of both DEMs were evaluated using another field measurement GCPs. Theresult showed that the generated DEM using CPs from IKONOS and SRTM X-C hadrelatively same height pattern and height distribution along transect line with the DEMusing GCPs. The absolute accuracy of the DEM using CPs was about 60% - 80% lessaccuracy comparing to the DEM using GCPs. This research showed that IKONOS imageand SRTM X-C band can be considered as good alternative CP source to generate highaccuracy DEM from ALOS PRISM stereo data.]]>
<![CDATA[STUDY OF MODIS-AQUA DATA FOR MAPPING TOTAL SUSPENDED MATTER (TSM) IN COASTAL WATERS ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Parwati]]>;
<![CDATA[Syarif Budhiman]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 2 (2005) ]]>
Publisher : <![CDATA[BRIN ]]>
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DOI: 10.30536/j.ijreses.2005.v2.a1355
<![CDATA[The MODIS-Aqua data have been studied to map TSM distribution in coastal waters. TSM algorithm model for MODIS data with spatial resolution of 250 m, 500 m and 1000 m was developed by correlating the TSM derived from spectral values of MODIS and the TSM derived from Landsat-7 ETM data using the calibrated algorithm. Statistical test was conducted to see normality of data and level of influence from both parameters. Analysis was conducted to see the change of spectral value from bands of MODIS data with resolution of 1000 m towards the change of level of TSM concentration. The results showsthat the TSM algorithm model is in the form of power (Xa) with the highest correlation coefficient is obtained from the correlation between the Landsat TSM value with the quantification of band 1 and band 2 of MODIS data for spatial resolution 250 m, ratio of band 4 and band 3 for spatial resolution 500 m, and ratio of band 13 and 11 for spatial resolution 1000 m. The pattern of TSM distribution in coastal waters can be identified in more accurate using MODIS data with resolution of 250 m and 500 m. The analysis result of the curve of MODIS spectral value data with resolution 1000 m shows that the change of TSM concentration influences significantly to the form of curve of spectral value, especially for band 11 - 16 ( visible green, red and NIR).]]>
<![CDATA[ENVIRONMENTAL QUALITY CHANGES OF SINGKARAK WATER CATCHMENT AREA USING REMOTE SENSING DATA ]]>
<![CDATA[Ita Carolita]]>;
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Heru Noviar]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 2 (2013) ]]>
Publisher : <![CDATA[BRIN ]]>
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DOI: 10.30536/j.ijreses.2013.v10.a1853
<![CDATA[Lake Singkarak in west Sumatera is currently in very poor condition and become one of the priorities in the government lake rescue program. High sedimentation rate from soil erosion has caused siltation, decreasing of quality and quantity of lake water. Monitoring of the environment quality changes of the lake and its surrounding are required. This study used Landsat and SPOT satellite data in periods of 2000-2011 to evaluate environmental quality parameters of the lake such as land cover, lake water quality (total suspended solid), water run-off, and water discharge in Singkarak lake catchment area. Maximum likelihood classifier was used to obtain land cover. Total suspended solid was extracted using Doxaran algorithm. The look up table and rational method were used to estimate run-off and water discharge. The results showed that the decreasing of forest area and the increasing of settlement were consistent with the increasing of average run-off and water discharge in Paninggahan and Sumpur sub-catchment area. The results were also consistent with the increasing of TSS in Singkarak lake, where TSS increased from around 2-3 mg/l up to 5-6 mg/l in the periods of 2000-2011.]]>
