Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
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DETEKSI LIMBAH ACID SLUDGE MENGGUNAKAN METODE RED EDGE BERBASIS DATA PENGINDERAAN JAUH
Full text linkIn line with the growing industry and population, the contamination of hazardous and toxic waste material increased. The increases is triggered by inappropriate handling of household and industry sector. The monitoring or detection of contaminated area or zone is very crucial to identify the areas of dispersion of the hazardous waste material. Remote sensing is one of applicable tool for detecting purposes. Several research has utilized remote sensing data to detect the contaminated areas by vegetation index, surface temperature as well as other indexes. This research proposes the red edge method from Landsat TM data to detect the hazardous waste material contamination in Pertamina RU-V Balikpapan. Based on the executed review, it is acknowledged that red edge method has a potential to detect the existence of hazardous and toxic waste, in the case where the acid sludge waste detection is correlated with the land rehabilitation such as neutralization, bioremediation, solidification and non-activation of acid sludge in the contaminated area which can be observed from its spectral displacement. The detection is related to bioremediation implementation and the indication of acid sludge in contaminated area. Based on the executed review, the red edge method is potentially applicable for this activity. The red edge pattern has defined the contaminated area in Pertamina RU-V Balikpapan. Based on the obtained and reviewed data, this research concluded that the monitoring of condition of hazardous waste could be implemented to identify which hazardous waste has been treated
PERBANDINGAN KLASIFIKASI BERBASIS OBJEK DAN KLASIFIKASI BERBASIS PIKSEL PADA DATA CITRA SATELIT SYNTHETIC APERTURE RADAR UNTUK PEMETAAN LAHAN
No full textUtilization of remote sensing data for land mapping has long been developed. In Indonesia, as a tropical region, the cloud becomes a classic problem in observing the Earth’s surface using optical remotely sensor satellite. Synthetic Aperture Radar (SAR) sensor satellite has the ability to penetrate clouds so it can solve cloud cover problems. In this study, the ALOS PALSAR data were used to assess object-based and pixel-based classification techniques. This data was chosen due to its capacity for object recognition based on backscatter characteristics. Object-based classification using the methods of Statistical Region Merging (SRM) for the object segmentation process and Support Vector Machine (SVM) for the classification process, whereas the pixel-based classification using SVM method. In the classification stage, several features of Target Decomposition and Image Decomposition of ALOS PALSAR data have been tested. The accuracy assessment of the classification was conducted using confusion matrix of the Region of Interest (ROI) data using the QuickBird data. Implementation of the object-based classification produced better result comparing to pixel-based classification. The number of optimal features is seven which consisted of three features Freeman Decomposition (Red, Green, Blue), Entropy, Alpha Angle, Anisotropy and Normalized Difference Polarization Index (NDPI). Overall accuracy reached 73.64% for the result of the object-based classification and 62.6% for the pixel-based classification
ANALISIS MATHEMATIK FRAKTAL UNTUK KLASIFIKASI MENGGUNAKAN CITRA PENGINDERAAN JAUH SPOT-4
Full text linkFractal is a mathematical set that typically displays self-similar patterns. Fractal have two basic characteristic suitable for modeling the topography of the earth surface self similarity and randomness; Applications of fractal geometry in remote sensing rely heavily on estimates of the non integer fractal dimension (D). The fractal dimension is calculated using the model of Surface Area Triangular Prism (TPSA). Fractal dimension is used to observe the spatial repetition (morphologie) of surface. In this study, fractal dimension is used to observe the relative height of a building / object of surface in urban area. This paper described image analysis using non integer fractal dimension used to determining the height of an object relative to the others, then do grouping of the object height by thresholding method. The result of the whole proses is presented after the density slicing proses. The analysis showed that the fractal dimension of the homogeneous object/surface is smaller than the heterogeneous objects. Based on it’s fractal dimensional objects/buildings in Jakarta city (covering 1600 ha), can be grouped in 3 classes: very high object, high object and rather high object and there are approximately 178 ha using 9 x 9 windows and approximately 80 ha using 17 x 17 windows very high object. However, the results of this study are still in the early stages that the fractal dimension can quantitatively interprets spatial structure and spatial complexity of remote sensing data. Therefore, research needs to be followed up with the field measurements and very high resolution resolution data (such as IKONOS)
ESTIMASI LIMPAHAN PERMUKAAN DARI DATA SATELIT UNTUK MENDUKUNG PERINGATAN DINI BAHAYA BANJIR DI WILAYAH JABODETABEK
No full textThe study about runoff estimation based on soil moisture conditions was conducted using remote sensing data i.e., Landsat and Tropical Rainfall Measurement Mission during flood period January – February 2013 in Jakarta and its souroundings area. The Landsat data used to analyze the landcover/landuse which one of the basin characteristics. In this study, the TRMM has ability for representing the regional rainfall as 62.5 %. The Curve Number-Soil Conservation Service (CN-SCS) method was used in this study to estimate the runoff. The results of runoff estimation was shown in hydrograph unit in order to know when the flood will occur. The antecedent soil moisture condition in wet condition showed the best hydrograph unit. It had the peak point in January 17th 2013 exactly same with the time flood occurred in Jakarta and the souroundings area. This model has a good potential to be used as a flood early warning system. Spatially, the overall accuracy of the flood identification in Jakarta region compared with the flood map produced by Disaster Management Berau was 43 % with the producer’s accuracy 96 %, and user’s accuracy 42 %
PENGEMBANGAN METODE PENDUGAAN KEDALAMAN PERAIRAN DANGKAL MENGGUNAKAN DATA SATELIT SPOT-4. STUDI KASUS: TELUK RATAI, KABUPATEN PESAWARAN
Full text linkBathymetric estimation of shallow water depth using satellite remote sensing data becomes more prevalent. However, when these methods are implemented for areas with different environments, the results indicate the presence of irregularities. To minimize the deviation, conducted the merger of the information obtained from field measurements with reflectance values SPOT-4 satellite imagery. This paper proposed the method development for bathymetric estimation of shallow water depth based on the correlation function between the depth value of direct measurements using a "handheld echo-sounder" to the resultant of reflectance values (band 1 and band 3). The algorithm for bathymetric estimation of a shallow water depth consists of thresholding method and correlation functions. Threshold value (T) depth of 0.5 meters is determined from observations of the correlation function graph polynomial from five and magnitude is 0.35 <T <0.47. Based on the results of the calculations show that the SPOT-4 satellite data can be used to estimate the shallow water depths up to approximately 18 meters
MODEL DISEMINASI INFORMASI GEOSPASIAL PULAU-PULAU KECIL TERLUAR BERBASIS PEMANFAATAN PENGINDERAAN JAUH DAN GOOGLE MAPPING SYSTEM
Full text linkThis paper describes the implementation of Geospatial Information and Communication Technology (ICT-Geospatial) in the dissemination of geospatial information of outermost small islands in Indonesia. Dissemination models allow the user via the Internet and web media to easily interact and acquire geospatial information of outermost small islands that are needed through a web browser online. This study is a follow-up development of remote sensing applications of geospatial information "Outermost small islands in Indonesia Based on Three-Dimensional Maps Satellite Imagery and Land Cover Map". Geospatial information has been compiled and published using paper media in the form of albums. ICT-Geospatial has been growing very rapidly, especially the Internet, web media and geospatial information systems. Efforts to develop applications, allowing running processes of dissemination of geospatial information of outermost small islands in Indonesia to the general public through a network of electronic information. Those efforts are carried out through the construction of "Model of Dissemination of Geospatial Information of outermost small Islands Based on Remote Sensing Applications and Google Mapping System". With the establishment of model of dissemination of geospatial information of outermost small islands, it is expected to be a supporting complement of efforts to the dissemination of the information to the broader public and to benefit us all knowing the existence of small islands in the outer region of the Republic of Indonesia, so the society can participate in maintaining security, establishing and enforcing the boundaries; managing the natural resource/agriculture sustainably, taking part in the safety and preservation of natural resources
KLASIFIKASI FASE PERTUMBUHAN PADI BERDASARKAN CITRA HIPERSPEKTRAL DENGAN MODIFIKASI LOGIKA FUZZY
Full text linkRemote sensing is a technology that is capable of overcoming the problems of measurement data for fast and accurate information. One of implementation of remote sensing technology in the field of agriculture is in hyperspectral image data retrieval to find out the condition and age of the rice plant. It is necessary for the estimation of rice yield in order to support Government policy in conducting imports rice to meet food needs in Indonesia. To have a good prediction model in estimation of rice yield that has high accuracy must be preceded by the determination of the phase of the rice plant. The selection of the appropriate classifier must also supported the selection of just the right features to get the optimum accuracy. In this study, we conducted a comparison between Fuzzy Logic and Modified Fuzzy Logic to perform the classification on nine rice growth stages based on hyperspectral image. Modified Fuzzy Logic have the same procedure with Fuzzy Logic but with extra crisp rules given in Fuzzy Rules which is expected to increase the accuracy achievement. In this study, Modified Fuzzy Logic proved to be able to improve the accuracy of up to 10% compared to Fuzzy Logic
PENGEMBANGAN MODEL IDENTIFIKASI DAERAH BEKAS KEBAKARAN HUTAN DAN LAHAN (BURNED AREA) MENGGUNAKAN CITA MODIS DI KALIMANTAN
No full textOPTIMALISASI PARAMETER SEGMENTASI UNTUK PEMETAAN LAHAN SAWAH MENGGUNAKAN CITRA SATELIT LANDSAT (STUDI KASUS PADANG PARIAMAN, SUMATERA BARAT DAN TANGGAMUS, LAMPUNG)
Full text linkPixel-based digital-image classification results often contain of salt and pepper effects, while the visual classification has weakness because frequently provide inconsistent results. Due to the above, this study describes "Segmentation Parameter Optimization for Wetland Mapping Using Landsat Satellite Image" with object-based classification. The main objective of this study is to find out the optimal combination of segmentation parameters for paddy field mapping. The study was carried out in two sites namely in Pariaman, -West Sumatera Province and in Tangamus, -Lampung Province using segmentation of Landsat acquired in 2008 and visual interpretation of multitemporal Landsat images acquired in 2000-2009. Landsat segmentation covers two steps, firstly segmentation to optimize the parameter of color and compactness values, secondly to optimize the segmentation scale parameter. For validation, the study used both the visual-based and quantitative-based classification results of 2005 and 2007 derived from Quickbird image. Qualitative test includes object separation and segmentation accuracy of the first step of segmentation, while quantitative test is performed using confusion matrix on the second step of segmentation. This study results show that within the combinations of parameter values analyzed, the combination of parameter color value of 0.9, shape of 0.1, compactness of 0.5, and smoothness of 0.5 provides the most similar segmentation to the data reference. Meanwhile, the best case that the rules of cartography is scale of 8 for Pariaman study area and scale of 6 for Tangamus study area having accuracy ranges from 90.7% to 96.3%. This study concluded that the effect of the uncertainty of geometry of Landsat images against Quickbird images shows the maximum error of segment tolerance the origin of 4 ha to 16.70 ha for Pariaman site and 13.32 ha for Tangamus site. This results are still acceptable in segmentation results. Finally it was found that, the most optimal combination of parameters for mapping paddy field is at a scale of 1:1, color of 0.9.
 
PENGARUH PENGAMBILAN TRAINING SAMPLE SUBSTRAT DASAR BERBEDA PADA KOREKSI KOLOM AIR MENGGUNAKAN DATA PENGINDERAAN JAUH
Full text linkLyzen ga (1978, 1981) developed a method to correct the water column using a ratio of bottom waters substrates reflectance on 2 (two) different bands, assuming that the ratio is the same for a different bottom type. The problem arise when the Lyzenga method was being simplified. In this case by sampling different bottom substrates as input. This study aims to compare the effects of the simplification process with the result of the calculation using the actual Lyzenga method. The calculation of water column correction followed the process described in the guide by UNESCO (1999) and Green et al (2000). The results showed that samples from two different substrates which has a very different radiance (reflectance) increased the index value of the substrate in deeper water