Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
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OPTIMASI PARAMETER DALAM KLASIFIKASI SPASIAL PENUTUP PENGGUNAAN LAHAN MENGGUNAKAN DATA SENTINEL SAR
Full text linkIn this study, application of Sentinel-1 Synthetic Aperture Radar (SAR) data for the land use cover classification was investigated. The classification was implemented with supervised Neural Network classifier for Dual polarization (VH and VV) Sentinel-1 data using texture information of gray level co-occurance matrix (GLCM). The purpose of this study was to obtain the optimum parameters in the extraction of texture information of pixel window size, the orientation of neighboring relationships on the texture feature extraction, and the type of texture information feature used for the classification. The classification results showed that in the study area, the best accuracy obtained is 5 × 5 pixel window size, 00 orientation angle, and the use of entropy texture information as classification input. It was also found that more features texture information used as classification input can improve the accuracy, and with careful selection of appropriate texture information as classification input will give the best accuracy
PENGEMBANGAN LAYANAN WEB SPASIAL INFORMASI PEMANFAATAN PENGINDERAAN JAUH
Full text linkDissemination implementation of remote sensing application information through the management of National Earth Observation System at the Remote Sensing Application Center of National Institute of Aeronautics and Space could be achieved by expanding and completing the dissemination mechanism, from the traditional way to spatial web services. The service is a standard defined by the Open Geospatial Consortium. These standards are widely used for the dissemination of spatial information through Web Map Services, Web Feature Services and Web Coverage Services - based standards and greatly assist in the implementation function of data application and remote sensing information dissemination. This research aims to analyze and provide development methods of spatial web services for remote sensing application information. The research methods include setting the initial requirements, programming a map file and spatial web services testing. The results showed that the spatial web services based on University of Minnesota Mapserver has been successfully implemented and tested using Google Map real web map service client and QGIS Desktop in case study forest cover change in Indonesia
METODE DUAL KANAL UNTUK ESTIMASI KEDALAMAN DI PERAIRAN DANGKAL MENGGUNAKAN DATA SPOT 6 STUDI KASUS : TELUK LAMPUNG
Full text linkDepth data can be used to produce seabed profile, oceanography, biology, and sea level rise. Remote sensing technology can be used to estimate the depth of shallow marine waters characterized by the ability of light to penetrate water bodies. One image that can estimate the depth is SPOT 6 which has three visible canals and one NIR channel with 6 meter spatial resolution. This study used SPOT 6 image on March 22, 2015. The image was first being dark pixel atmospheric corrected by making 30 polygons. The originality of this method was to build a correlation between the dark pixel value of red and green channels with the depth of the field measurement results, made on June 3 to 9, 2015. The algorithm derived experimentally consisted of: thresholding which served to separate the land by the sea and the correlation function. The correlation function was obtained: first correlating the observation value with each band, then calculating the difference of minimum pixel darkness value and minimum for red and green channel was 0.056 and 0.0692. The model was then constructed by using the comparison proportions, so that the linear equations were obtained in two channels: Z (X1, X2) = 406.26 X1 + 327.21 X2 - 28.48. Depth estimation results were for a 5-meter scale, the most efficient estimation with the smallest error relative mean occurred in shallow water depth from 20 to 25 meters, while the result of 10 meters scale from 20 to 30 meters and the estimated depth had similar patterns or could be said close to reality. This method was able to detect sea depths up to 25 meters and had a small RMS error of 0.653246 meters. Thus the two-channel method could offer a fast, flexible, efficient, and economical solution to map topography of the ocean floor
UJI MODEL FASE PERTUMBUHAN PADI BERBASIS CITRA MODIS MULTIWAKTU DI PULAU LOMBOK
Full text linkModel testing is a step that must be done before operational activities. This testing aimed to test rice growth phase models based on MODIS in Lombok using multitemporal LANDSAT imagery and field data. This study was carried out by the method of analysis and evaluation in several stages, these are: evaluation of accuracy by multitemporal Landsat 8 image analysis, then evaluation by using field data, and analysis of growth phase information to calculate model consistency. The accuracy of growth phase model was calculated using Confusion Matrix. The results of stage I analysis for phase of April 30 and July 19 showed the accuracy of the model is 58-59%, while the evaluation of stage II for phase of period July 19 with survey data indicated that the overall accuracy is 53%. However, the results of model consistency analysis show that the resulting phase of the smoothed MODIS imagery shows a consistent pattern as well as the EVI pattern of rice plants with an 86% accuracy, but not for pattern data without smoothing. This testing give conclusion is the model is good, but for operational MODIS input data must be smoothed first before index value extraction
KLASIFIKASI MULTISKALA UNTUK PEMETAAN ZONA GEOMORFOLOGI DAN HABITAT BENTIK MENGGUNAKAN METODE OBIA DI PULAU PARI
No full textThis study used multiscale classification and applied object-based image analysis (OBIA) for geomorphic zone and benthic habitats mapping in Pari islands. An optimized segmentation was performed to get optimum classification result. Classification methods for level 1 and 2 used contextual editing classification and for level 3 used support vector machines classifier. The results showed that overall accuracy for level 1 was 97% (reef level), level 2 was 87% (geomorphic zone), and level 3 was 75% (benthic habitats). Accuracy achieved by support vector machines classification was performed only in level 3 and optimum scale value achieved was 50 in compare with other scale values, i.e. 5, 25, 50, 75, 95. OBIA methods can be used as an alternative for geomorphic zone and benthic habitats map
KLASIFIKASI PENUTUP/PENGGUNAAN LAHAN DENGAN DATA SATELIT PENGINDERAAN JAUH HIPERSPEKTRAL (HYPERION) MENGGUNAKAN METODE NEURAL NETWORK TIRUAN
Full text linkHyperspectral remote sensing data has numerous spectral information for the land-use/landcover (LULC) classification, but a large number of hyperspectral band data is becoming a problem in the LULC classification. This research proposes the use of the back propagation neural network for LULC classification with hyperspectral remote sensing data. Neural network used in this study is three layers, in which to test input layer has a number of neurons as many as 242 to process all band data, 163 neurons, and 50 neurons to process the data band has a high average digital number, and data bands at wavelengths of visible to near infrared. The results showed the use of all the data band hyperspectral on classification with the neural network has the highest classification accuracy of up to 98% for 18 LULC class, but it takes a very long time. Selecting a number of bands of precise data for classification with a neural network, in addition to speeding up data processing time, can also provide sufficient accuracy classification results
ALGORITMA DUA DIMENSI UNTUK ESTIMASI MUATAN PADATAN TERSUSPENSI MENGGUNAKAN DATA SATELIT LANDSAT-8, STUDI KASUS: TELUK LAMPUNG
Full text linkRemote sensing technique is a powerful tool for monitoring the coastal zone. Optical sensors can be used to measure water quality parameters Total Suspended Matter (MPT). In order to be able to extract information MPT, the satellite data need to be validated with in situ measurements that make the relationship between the reflectance band with concentration MPT measurement results. In this model, do the correlation between the measurement results with the reflectance values band 3 and band 4. then obtained a linear equation, then calculated using the argument of a ratio of 60:75 to each of the correlation coefficient, the obtained linear equation two Dimension T (X3, X4) = 2313.77 X3 + 4741.11 X4 + 314.95. Based on the concentration MPT of dated June 3, 2015 was lower than in the west to the east. this is because the east is already contaminated with the plant, effluent solids by humans, while the west for still many floating net fish, and mangrove. Based on the results of measurement and calculation results, is still far from perfect (accuracy 60%), one factor is the value thresholding, when determining the boundary between: clouds, sea, and land. Generally indicates that the model is still in need for repair
PENERAPAN ALGORITMA SPECTRAL ANGLE MAPPER (SAM) UNTUK KLASIFIKASI LAMUN MENGGUNAKAN CITRA SATELIT WORLDVIEW-2
Full text linkRemote sensing technology has been developed for monitoring and identification of coastal environment and resources, such as seagrasses. In Indonesia, particularly seagrass mapping spectrometer utilizing spectral library has not been done. This study aimed to determine the spectral signature based in situ measurement and image analysis, analyze the implementation of the algorithm Spectral Angle Mapper (SAM) and test accuracy in mapping seagrass to species level based on spectral libraries. Research conducted in seagrass Tunda Island, Banten. Satellite imagery used is WorldView2 and the seagrass spectral reflectance was measured using a spectrometer USB4000. SAM classification algorithm utilizing spectral libraries and classify objects in a single pixel can be homogeneous. Classification results in the form of class Enhalus acoroides, Cymodocea rotundata, Thalassia hemprichii, and Halophila ovalis. The resulting accuracy of 35.6%. The area of each class is 0.8 hectares for the class Cymodocea rotundata, 2.79 hectares for Enhalus acoroides, class Thalassia hemprichii 3.7 hectares, and 3.5 hectares for Halophila ovalis. Classification of seagrass to species level yet produce good accuracy. Seagrass area with a variety of species and number of channels on a multispectral satellite image is assumed to be the cause of the low value of accuracy
ANALISIS TEMPERATUR DAN UAP AIR BERBASIS SATELIT TERRA/AQUA (MODIS, LEVEL-2)
Full text linkTerra and Aqua satellites that consist of multiple sensors including MODIS instruments, which is operated to detect the phenomena that exist on land, sea and atmosphere. Not a lot of data extracted especially for Indonesia region the associated with atmospheric data, because the product is still in the raw data (level-0). For data extraction of level-0 to level-2 needed software IMAPP (International MODIS/airs Processing Package) so displays some data atmospheric parameters including MOD 04 - Aerosol, MOD 05 - Total precipitable Water (Water Vapor), MOD 06 - Cloud, MOD 07 - Atmospheric Profiles, MOD 08 - gridded Atmospheric and MOD 35 in HDF4 format (Hierarchical Data Format-4) swath. This paper discussed only MOD07/MYD07 atmospheric profiles level-2 related parameters such as the temperature of the atmosphere at an altitude of 780 hPa and water vapor at a height of 700 hPa. This study aimed to analyze the phenomena in the atmosphere, based on extraction method Atmospheric Profiles in the resolution 1km, that consists of temperature and moisture level 2, in the format hdf4 daily swath into data daily and monthly grid in .dat format, in the period of December 2014, January, July, and August 2015, especially in the area of Indonesia. The comparison between the results of the extraction swath and grid data from Terra/Aqua MODIS, that parameter atmospheric for the temperature has R-sqare an average of 0.72 and water vapor 0.74, while the RMSE temperature and water vapor are 0.88 and 0.29
PERBANDINGAN HASIL KLASIFIKASI LIMBAH LUMPUR ASAM DENGAN METODE SPECTRAL ANGLE MAPPER DAN SPECTRAL MIXTURE ANALYSIS BERDASARKAN CITRA LANDSAT - 8
Full text linkThe utilization of remote sensing data is an alternative way that could be used for rapid detection of large coverage hazardous waste area. This study aims to classify the acid sludge contaminated area using Landsat 8 by applying Spectral Angle Mapper (SAM) classification method with two spectral reference sources, namely field spectral measurement using a spectrometer and endmember spectral from the image, and then compare the classification results. The accuracy level of SAM classification result showed that classification using endmember spectral from the image as the reference spectral reached 66,7%, whereas classification using field spectral measurement as spectral reference only reached 33,3%. The accuracy level of Spectral Mixture Analysis (SMA) classification result showed that classification using endmember spectral from the image as the reference spectral reached 62,5%. The affecting factors for the low accuracy is the significant differences of the spectral profiles obtained from spectrometer with spectral Landsat-8 due to differences of spatial and altitude