International Journal of Remote Sensing and Earth Sciences (IJReSES)
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STUDY ON POTENTIAL FISHING ZONES (PFZ) INFORMATION BASED ON S-NPP VIIRS AND HIMAWARI-8 SATELLITES DATA
Get PDFSea surface temperature (SST) data from S-NPP VIIRS satellite has different spatial resolution with SST data from Himawari-8 satellite. In this study comparative analysis of potential fishing zones information from both satellites has been conducted. The analysis was conducted on three project areas (PA 7, PA 13, PA 19) as a representation Indonesian territorial waters. The data used were daily for both satellites with a period  time from August 2016 to December 2016. The method used was Single Image Detection (SIED) to detect thermal fronts. Method of mass center point for determining potential fishing zones coordinate point from result thermal front detection. Furthermore, an analysis of overlapping was done to compare the coordinate point information from both satellites. Based on data analysis that had been done, the result showed that potential fishing zones coordinate points of Himawari-8 satellite was mostly far from potential fishing zones coordinate point of S-NPP VIIRS. The coordinate points whose positionswere close together or nearly same from both satellites was only about 20 %. Differences in potential fishing zones coordinate positions occur due to the effect of different spatial resolutions of both satellite data and the size of the front thermal events that had high variability. The ideal potential fishing zones coordinate points information was probably a combination of the potential fishing zones coordinate points of S-NPP VIIRS and Himawari-8 by making two adjacent coordinate points to be a single coordinate point. Field validation testing was required to prove the accuracy of the coordinate point
LAPAN-A3 SATELLITE DATA ANALYSIS FOR LAND COVER CLASSIFICATION (CASE STUDY: TOBA LAKE AREA, NORTH SUMATRA)
Get PDFLAPAN-A3 is the 3rdgeneration satellite for remote sensing developed by National Institute of Aeronautics and Space (LAPAN). The camera provides imagery with 15 m spatial resolution and able to view a swath 120 km wide. This research analyzes the performance of LAPAN-A3 satellite data to classify land cover in Toba Lake area, North Sumatera. Data processing starts from the selection of region of interest up to the assessment of accuracy. Supervised classification with maximum likelihood approach and confusion matrix method was applied to classify and evaluate the assessment results. The land cover is classified into five classes; water, bare land, agriculture, forest and secondary forest. The result of accuracy test is 93.71%. It proves that LAPAN-A3 data could classify the land cover accurately. The data is expected to complement the need of the satellite data with medium spatial resolution
DETECTING THE AREA DAMAGE DUE TO COAL MINING ACTIVITIES USING LANDSAT MULTITEMPORAL (Case Study: Kutai Kartanegara, East Kalimantan)
Get PDFCoal is one of the most mining commodities to date, especially to supply both national and international energy needs. Coal mining activities that are not well managed will have an impact on the occurrence of environmental damage. This research tried to utilize the multitemporal Landsat data to analyze the land damage caused by coal mining activities. The research took place at several coal mine sites in East Kalimantan Province. The method developed in this research is the method of change detection. The study tried to know the land damage caused by mining activities using NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), NDWI (Normalized Difference Water Index) and GEMI (Global Environment Monitoring Index) parameter based change detection method. The results showed that coal mine area along with the damage that occurred in it can be detected from multitemporal Landsat data using NDSI value-based change detection method. The area damage due to coal mining activities can be classified into high, moderate, and low classes based on the mean and standard deviation of NDSI changes (ΔNDSI). The results of this study are expected to be used to support government efforts and mining managers in post-mining land reclamation activities
WATERMARKING METHOD OF REMOTE SENSING DATA USING STEGANOGRAPHY TECHNIQUE BASED ON LEAST SIGNIFICANT BIT HIDING
Get PDFRemote sensing satellite imagery is currently needed to support the needs of information in various fields. Distribution of remote sensing data to users is done through electronic media. Therefore, it is necessary to make security and identity on remote sensing satellite images so that its function is not misused. This paper describes a method of adding confidential information to medium resolution remote sensing satellite images to identify the image using steganography technique. Steganography with the Least Significant Bit (LSB) method is chosen because the insertion of confidential information on the image is performed on the rightmost bits in each byte of data, where the rightmost bit has the smallest value. The experiment was performed on three Landsat 8 images with different area on each composite band 4,3,2 (true color) and 6,5,3 (false color). Visually the data that has been inserted information does not change with the original data. Visually, the image that has been inserted with confidential information (or stego image) is the same as the original image. Both images cannot be distinguished on histogram analysis.  The Mean Squared Error value of stego images of  all three data less than 0.053 compared with the original image.  This means that information security with steganographic techniques using the ideal LSB method is used on remote sensing satellite imagery
BIOMASS ESTIMATION MODEL FOR MANGROVE FOREST USING MEDIUM-RESOLUTION IMAGERIES IN BSN CO LTD CONCESSION AREA, WEST KALIMANTAN
Get PDFMangrove forest is one of the forest ecosystem types that have the highest carbon stock in the tropics. Mangrove forests have a good assimilation capability with their environmental elements as well as on carbon sequestration. However, the availability of data and information on carbon storage, especially on tree biomass content of mangrove is still limited. Conventionally, an accurate estimation of biomass could be obtained from terrestrial measurements, but those methods are very costly and time-consuming. This study offered an alternative solution to overcome these limitations by using remote sensing technology, i.e. by using Landsat 8 and SPOT 5. The objective of this study is to formulate the biomass estimation model using medium resolution satellite imagery, as well as to develop a biomass distribution map based on the selected model. The study found that the NDVI of Landsat 8 and SPOT 5 have considerably high correlation coefficients with the standing biomass with a value of higher than 0.7071. On the basis of the values of aggregation deviation, mean deviation, bias, RMSE, χ², R², and s, the best model for estimating the mangrove stand biomass for Landsat 8 is B=0.00023404 e(20 NDVI) with the R² value of 77.1% and B=0.36+25.5 NDVI² with the R² value of 49.9% for SPOT 5. In general, the concession area of Bina Silva Nusa (BSN) Group (PT Kandelia Alam and PT Bina Ovivipari Semesta) have the potential of biomass ranging from 45 to 100 ton per ha
MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND
Get PDFMachine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for wide data observation. In remote sensing, machine learning method could be used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%
APPLICATION OF VAN HENGEL AND SPITZER ALGORITHM FOR INFORMATION ON BATHYMETRY EXTRACTION USING LANDSAT DATA
Get PDFRemote sensing technology provides an opportunity for effective and efficient bathymetry mapping, especially in areas which level of depth changes quickly. Bathymetry information is very useful for hydrographic and shipping safety. Landsat medium resolution satellite imagery can be used for the extraction of bathymetry information. This study aims to extract information from the Landsat bathymetry by using Van Hengel and Spitzer rotation algorithm transformation (1991) in the water of Menjangan Island, Bali. This study shows that Van Hengel and Spitzer rotation algorithm transformation (1991) can be used to extract information on the bathymetry of Menjangan Island. Extraction of bathymetric information generated from Landsat TM imagery data in March 19, 1997 had shown the depth interval of (-0.6) m to (-12.3) m and R2 value of 0.671. While Data LANDSAT ETM + dated June 23, 2000 resulted in depth interval of 0 m to (-19.1) m and R2 value of 0.796. Furthermore, data LANDSAT ETM + dated March 12, 2003 resulted in depth interval of 0 m to (-22.5) m and R2 value of 0.931