Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
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KLASIFIKASI PENUTUP LAHAN MENGGUNAKAN DATA LIDAR DENGAN PENDEKATAN MACHINE LEARNING
Full text linkLidar is a remote sensing technology. Lidar data is widely used and has been developed for mapping, detailed spatial planning, and natural disaster analysis. In its development for Lidar data management, software applications are widely used as well as by using built algorithms such as machine learning. The research aims to utilize Lidar data for land cover classification using machine learning, namely Support Vector Machine (SVM). The research location is Tanjung Karang village, Mataram City, Lombok. The classification applied is a supervised classification in which the training data is needed to perform the classification. The predicted land cover class in this study is limited to buildings, vegetation, roads, open land. The data used for classification is derived from Lidar, namely DTM, DSM, nDSM, and Intensity. The classification scheme used is one data input and a combination of data. The reference data used is a topographic map (Topographic map of Indonesia). The results showed that the classification with a data combination scheme had a better accuracy value than the one data classification scheme, which increased accuracy by about 15-20%. This shows that there are complementary factors between the data to be able to identify objects in the classification process
Analisis Perubahan Tutupan Lahan dari Citra TerraSAR-X Menggunakan Metode Analisis Texture dan Segmentasi di Jakarta
Full text linkRapid urban growth in Jakarta is indicated by the increases of building area, such as settlements, roads, commercial and others. Identification of land cover extent and its changes is a vital data for urban planning. One method for land cover mapping and its changes is obtained by the utilization of remote sensing data that characterize as having a continuity data, covers a vast area, and cost-effective. Remote sensing data can be obtained from optical and radar imagery. Radar data importance for mapping land cover and land cover changes because radar data does not constrain by time and weather. In early 2018, the TerraSAR-X (TSX) data can be acquired at the LAPAN Parepare ground station. This research uses the TSX Stripmap image mode with a spatial resolution of 3 m in 2010 and 2013. The TSX data will be used to map the land cover and land cover changes in Jakarta using method of the texture analysis and image segmentation. The accuracy assessment of the map will be assessed visually and quantitatively using the Pleiades images (0.5 m) and Google Earth images. The results show that the TSX images detect the current developments of settlements, new roads construction and provide information on the loss of green open space in Jakarta
IDENTIFIKASI MATERIAL PIROKLASTIK PASCA ERUPSI GUNUNG KELUD MENGGUNAKAN CITRA HYPERSPEKTRAL
Full text linkKOREKSI ATMOSFER DATA LANDSAT-8 MENGGUNAKAN PARAMETER ATMOSFER DARI DATA MODIS
Full text linkLandsat-8 data (level 1T) received by user are still in digital number and can be used directly for mapping land use / land cover. However, the data still has low radiometric accuracy when it is used to derive information such as vegetation index, biomass, land use/ land cover classification, etc. so that it requires radiometric / atmospheric correction. In this study, we use the second simulation of a satellite signal in the solar spectrum (6S) method to eliminate atmospheric disturbance and compare the results with field measurements. The atmospheric parameters used were aerosol optical depth (AOD), water vapor column and ozone thickness from MODIS data with the date and time of acquisition are close to Landsat-8 data acquisition. From the analysis conducted on the values of vegetation index (NDVI, EVI, SAVI and MSAVI) surface reflectance shows that the vegetation index that has high accuracy is NDVI (3-11) % and the lowest is MSAVI (11-24) %. Analysis of the spectral response of atmospheric corrected image shows that visible band have good accuracy with RMSE values ranging from (1 - 4) %. On the contrary the lowest accuracy is found on the near infrared channel (NIR) with values (14-27) %
EVALUASI REHABILITASI LAHAN KRITIS BERDASARKAN TREND NDVI LANDSAT-8 (Studi Kasus: DAS Serayu Hulu)
Full text linkThe use of remote sensing in vegetation monitoring has been widely applied, including vegetation density monitoring. However, the use to evaluate rehabilitation program on critical land is still limited. Evaluation of forest cover and land rehabilitation activities become important due to the increase of critical land. The current method to evaluate the land condition is conducted by ground check at the rehabilitation site held at the end of the year after the initial implementation of the rehabilitation program until the third year. This method requires a lot of time, labour, and money. Based on the standard regulation to evaluate the rehabilitation program, the program is successful if 90% the new vegetation planted can grows until the third year. Therefore, this research uses an effective and efficient method for evaluating land rehabilitation programs using remote sensing data by understanding vegetation conditions and their densities using multi-temporal analysis for large areas. A multi-temporal Landsat-8 images from 2015-2018 will be used to analyze the Normalized Difference Vegetation Index (NDVI) trend in the time-based sequence method using spatial analysis. The results show that the non-forest area in Serayu Hulu Watershed consist of non-critical land, moderate critical land, critical land, and severe ciritical land. According to the ground check and NDVI trend analysis, the rehabilitation in non-critical land of the non-forest area was generally unsuccessful due to the area rehabilitation plant were harvested before the rehabilitation evaluation time ended. On the otherhand, on critical land; moderate critical land; and severe critical land of the non-forest area, the success of rehabilitation program was indicated by the achievement of the NDVI threshold value at 0.4660; 0.4947. 0.4916, respectively
APLIKASI MODEL GEOBIOFISIK NDVI UNTUK IDENTIFIKASI HUTAN PADA DATA SATELIT LAPAN-A3
Full text linkThe LAPAN-A3 / IPB satellite is a micro satellite created by the nation's children in order to build the nation's independence in the field of Space. This satellite has 4 bands including 3 visible waves and 1 near infrared. Given that it is a new satellite, it is necessary to do a study and research on the ability of sensor characteristics to identify natural resources, one of which is forests. In this study besides using LAPAN-A3 satellite data, Landsat-8 data is also used as comparative data for testing the similarity of forest object classification results. Determination of extraction of geobiophysical parameters of forest identification using the Normalized Difference Vegetation Index (NDVI) model with a threshold value for forest identification. The results of the study with LAPAN-A3 satellite data show that the threshold range for forest identification is above 0.65 on the vegetation index scale -1 (minus one) to +1 (plus one). The results of the study after comparing NDVI values with Landsat-8 data have a 60% similarity
ANALISIS TINGKAT AKURASI TITIK HOTSPOT DARI S-NPP VIIRS DAN TERRA/AQUA MODIS TERHADAP KEJADIAN KEBAKARAN
Full text linkAccuracy analysis of the forest fire detection by using remote sensing data hotspots from SNPP and TERRA/AQUA has been carried out. The sensors used were MODIS sensors for TERRA/AQUA satellites and VIIRS sensors for S-NPP satellites. The detection of hotspots from remote sensing satellite data can be used as an early warning of forest fires. Hotspot can be derived from 2 sensors, namely MODIS and VIIRS sensors using algorithms that have been developed by science team from satellite developer. This hotspot information need to be accurately analysis by ground thruth of the fire events. This aims to analize the accuracy of hotspot information detection for forest fires. By comparing fire event data in 2018 and hotspot information data on hotspot databases owned by LAPAN. The results show that MODIS sensors are 39% and for VIIRS sensors are 20%. That result using 2 km of buffer radius which is the most significant result comparing others. It is clearly indicates that improvements are needed to improve the accuracy of hotspot derived from VIIRS data
ANALISIS SPASIAL KESESUAIAN BUDIDAYA KERAPU BERBASIS DATA PENGINDERAAN JAUH (STUDI KASUS: PULAU AMBON MALUKU)
Full text linkIndonesian waters have abundant marine aquaculture potential. This activity need to be maximized with remote sensing technology approach to determining locations that have the potential aquaculture areas. The research location is Ambon Island, Maluku Province. The method used for suitability site is Weighted Overlay Technique from biophysical parameters such as total suspended solids (TSS), sea surface temperature (SST), chlorophyll, and bathymetry. In addition, mangrove and coral reef data are used as a limiting factor for the suitability site. Based on the results of processing data, classes were quite suitable dominated in Piru Bay, Banguala Bay, and Ambon Bay; the appropriate classes were detected in Ambon Dalam Bay, and very suitable classes were detected in Piru Bay and Ambon Bay. The results of field measurement verification showed that the temperature of the image data with the insitu data correlated with the value of R2 0.74 and TSS image with insitu data shown R2 of 0.63
ANALISIS KONSENTRASI TSS DAN PENGARUHNYA PADA KINERJA PELABUHAN MENGGUNAKAN DATA REMOTE SENSING OPTIK DI TELUK KENDARI
Full text linkPENGEMBANGAN METODE KLASIFIKASI LAHAN SAWAH BERBASIS INDEKS CITRA LANDSAT MULTIWAKTU
Full text linkResearch on the development of a paddy field classification model based on Landsat remote sensing images aims to obtain a rapid classification of paddy field models. This study uses input multitemporal Landsat images (path/row 122/064) in 2017. The research was conducted in Subang regency, which is one of the center of West Java rice production. The method used in this study is the threshold method for the multi-temporal Landsat image index. As a reference, detailed scale spatial information on paddy fields base is used which is supplemented with data from field surveys using drones. First, an atmospheric correction of Landsat images was carried out using DOS (Dark Object Subtraction) Method, then transformation image to several indices: Enhance vegetation Index (EVI), Normal Difference Water Index (NDWI), and Normal Difference bare Index (NDBI) was carried out. For cloudy images, the index is filled with interpolation techniques from the index value before and after. The next step is smoothing index and statistical analysis to obtain minimum, maximum, mean, median, range (maximum - minimum), EVI_planting, EVI_harvesting, mean_planting-harvesting, mean_vegetative, mean_generative, NDWI_planting, NDWI_harvesting, NDBI_planting, and NDBI_harvesting. Classification accuracy is calculated by using the confusion matrix technique using detailed scale spatial information references. Based on the analysis and test of accuracy that has been done on several models, the highest accuracy is generated by the 1.5 stdev threshold model four index parameters (EVI_min, EVI_Max, EVI_range, and EVI_mean) with an accuracy of 86.56% and a kappa value of 0.716