International Journal of Remote Sensing and Earth Sciences (IJReSES)
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MULTITEMPORAL LANDSAT DATA TO QUICK MAPPING OF PADDY FIELD BASED ON STATISTICAL PARAMETERS OF VEGETATION INDEX (CASE STUDY: TANGGAMUS, LAMPUNG)
Get PDFPaddy field has unique characteristics that distinguish it from other plants. Before it planting, paddy field is always flooded so that the appearance is dominated by water (aqueous phase). Within the growth of rice, field conditions will be increasingly dominated by greenish rice plants.While at the end, the rice plants will turn yellow indicating for harvesting. During flooding stage, the normalized difference vegetation index (NDVI) of pady field is negative. The negative value of NDVI of paddy field will ultimately increase to the maximum value at the maximum vegetative growth. TheNDVI of paddy field will decrease from generative phase until harvest and after harvest. The objective of this study was to perform the vegetation index analyses for multitemporal Landsat imagery of paddy field. The results showed that the difference of vegetation index values (maximum - minimum)of paddy field were greater than the difference of vegetation index values of other land uses. Such differences values can be used as indicator to map land for rice. The evaluation results with reference data showed that the mapping accuracy (overall accuracy) was of 87.4 percent
UTILIZATION OF MULTI TEMPORAL SAR DATA FOR FOREST MAPPING MODEL DEVELOPMENT
Get PDFUtilization 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
SITE SELECTION OF SEAWEED CULTURE USING SPOT AND LANDSAT SATELLITE DATA IN PARI ISLAND
Get PDFOne of several factors for seaweed culture success is to determine the suitable location for seaweed culture based on oceanographic parameters. The best location for seaweed culture is coastal waters with suitable requirements for total suspended solid (TSS), sea surface temperature (SST), and area with calm water that is sheltered from waves, strong current and predator, such as lagoon in the middle of an atoll. The purpose of this study was to locate the suitable area for seaweed culture in Pari island, Seribu island using SPOT and LANDSAT-TM data. The results showed that TSS in Pari island waters were in the range of 150 mg/l - 200 mg/l, SST in the range of 22-29°C, while coral reefs and lagoon was only available in some coastal locations. The analysis showed that most of Pari island waters were suitable for seaweed culture
DROUGHT AND FINE FUEL MOISTURE CODE EVALUATION: AN EARLY WARNING SYSTEM FOR FOREST/LAND FIRE USING REMOTE SENSING APPROACH
Get PDFThis study evaluated two parameters of fire danger rating system (FDRS) using remote sensing data i.e. drought code (DC) and fine fuel moisture code (FFMC) as an early warning program for forest/land fire in Indonesia. Using the reference DC and FFMC from observation data, we calculated the accuracy, bias, and error. The results showed that FFMC from satellite data had a fairly good correlation with FFMC observations (r=0.68, bias=7.6, and RMSE=15.7), while DC from satellite data had a better correlation with FFMC observations (r=0.88, bias=49.91, and RMSE=80.22). Both FFMC and DC from satellite and observation were comparable. Nevertheless, FFMC and DC satellite data showed an overestimation values than that observation data, particularly during dry season. This study also indicated that DC and FFMC could describe fire occurrence within a period of 3 months before fire occur, particularly for DC. These results demonstrated that remote sensing data can be used for monitoring and early warning fire in Indonesia
THE RELATIONSHIP BETWEEN TOTAL SUSPENDED SOLID (TSS) AND CORAL REEF GROWTH (CASE STUDY OF DERAWAN ISLAND, DELTA BERAU WATERS)
Get PDFTotal suspended solid (TSS) is one of the water quality parameters and limiting factor affecting coral reef growth. In this study, we used the algorithm of TSS= 3.3238*e(34.099* Green band) (where green band is reflectance band 2) to extract TSS from Landsat satellite data. The algorithm was validated with field data. Water column correction method developed by Lyzenga was used to map coral reef. The result showed that the coral reef area in Berau waters decreased significantly (about 12,805 ha or around 36 % ) from the year of 1979 to 2002. The most coral reef reduced area was detected around Derawan Island (about 5,685 ha). Further, some areas changed into sand dune. TSS concentration around Delta Berau and Derawan Island increased aproximately twice from 15- 35 mg/l in 1979 to 20-65 mg/l in 2002. The increase of TSS concentration was followed by the decrease of coral reef area
FISHPOND AQUACULTURE INVENTORY IN MAROS REGENCY OF SOUTH SULAWESI PROVINCE
Get PDFCurrently, fishpond aquaculture becomes an interesting business for investors because of its profit, and a source of livelihood for coastal communities. Inventory and monitoring of fishpond aquaculture provide important baseline data to determine the policy of expansion and revitalization of the fishpond. The aim of this research was to conduct an inventory and monitoring of fishpond area inMaros regency of South Sulawesi province using Satellite Pour l’Observation de la Terre (SPOT -4) and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Apeture Radar (PALSAR). SPOT image classification process was performed using maximum likelihood supervised classification method and the density slice method for ALOS PALSAR. Fishpond area from SPOT data was 9693.58 hectares (ha), this results have been through the process of validation and verification by the ground truth data. The fishponds area from PALSAR was 7080.5 Ha, less than the result from SPOT data. This was due to the classification result of PALSAR data showing someobjects around fishponds (dike, mangrove, and scrub) separately and were not combined in fishponds area calculation. Meanwhile, the result of SPOT -4 image classification combined object around fishponds area
LAND COVER CLASSIFICATION OF ALOS PALSAR DATA USING SUPPORT VECTOR MACHINE
Get PDFLand cover classification is one of the extensive used applications in the field of remote sensing. Recently, Synthetic Aperture Radar (SAR) data has become an increasing popular data source because its capability to penetrate through clouds, haze, and smoke. This study showed on an alternative method for land cover classification of ALOS-PALSAR data using Support Vector Machine (SVM) classifier. SVM discriminates two classes by fitting an optimal separating hyperplane to the training data in a multidimensional feature space, by using only the closest training samples. In order to minimize the presence of outliers in the training samples and to increase inter-class separabilities, prior to classification, a training sample selection and evaluation technique by identifying its position in a horizontal vertical–vertical horizontal polarization (HV-HH) feature space was applied. The effectiveness of our method was demonstrated using ALOS PALSAR data (25 m mosaic, dual polarization) acquired in Jambi and South Sumatra, Indonesia. There were nine different classes discriminated: forest, rubber plantation, mangrove & shrubs with trees, oilpalm & coconut, shrubs, cropland, bare soil, settlement, and water. Overall accuracy of 87.79% was obtained, with producer’s accuracies for forest, rubber plantation, mangrove & shrubs with trees, cropland, and water class were greater than 92%
