International Journal of Remote Sensing and Earth Sciences (IJReSES)
International Journal of Remote Sensing and Earth Sciences (IJReSES) is expected to enrich the serial publications on earth sciences, in general, and remote sensing in particular, not only in Indonesia and Asian countries, but also worldwide.
This journal is intended, among others, to complement information on Remote Sensing and Earth Sciences, and also encourage young scientists in Indonesia and Asian countries to contribute their research results. This journal published by LAPAN.
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<![CDATA[Front Pages IJReSES Vol. 15, No. 1(2018) ]]>
<![CDATA[Journal Editor]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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<![CDATA[Front Pages IJReSES Vol. 15, No. 1(2018)]]>
<![CDATA[SPECTRAL ANALYSIS OF THE HIMAWARI-8 DATA FOR HOTSPOT DETECTION FROM LAND/FOREST FIRES IN SUMATRA ]]>
<![CDATA[Hana Listi Fitriana]]>;
<![CDATA[Sayidah Sulma]]>;
<![CDATA[nFN suwarsono]]>;
<![CDATA[Any Zubaidah]]>;
<![CDATA[Indah Prasasti]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a2836
<![CDATA[Himawari-8 is the last generation of the low spatial resolution satellite imagery that has capability to detect the thermal variation on the earth of every 10 minute. This must be very potential to be used for detecting land/forest fire. This paper has explored the spectral prospective of the Himawari-8 for detecting land/forest fire hotspot. The main objective for this study is to identify the potential use of Himawari-8 for detecting of land forest fire hotspot. The study area was performed in Ogan Komering Ilir, South of Sumatra, which on 2015 occur great forest/land fire event. The main process included in this study are image projection, training sample collection and spectral statistical analysis measured by calculate statistic, they are average values, standard deviation values from reflectance visible band value and brightness temperature value, beside that validation of data obtained from medium resolution data of Landsat 8 with the similar acquisition time. The study found that the Himawari-8 has good capacity to identify land/forest fire hotspot as expressed for high accuracy assessment using band 3 and band 7.]]>
<![CDATA[Back Pages IJReSES Vol. 15, No. 1(2018) ]]>
<![CDATA[Journal Editor]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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<![CDATA[Back Pages IJReSES Vol. 15, No. 1(2018)]]>
<![CDATA[STUDY ON POTENTIAL FISHING ZONES (PFZ) INFORMATION BASED ON S-NPP VIIRS AND HIMAWARI-8 SATELLITES DATA ]]>
<![CDATA[Sartono Marpaung]]>;
<![CDATA[Teguh Prayogo]]>;
<![CDATA[Kuncoro Teguh Setiawan]]>;
<![CDATA[Orbita Roswintiarti]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a2817
<![CDATA[Sea 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.]]>
<![CDATA[BIOMASS ESTIMATION MODEL FOR MANGROVE FOREST USING MEDIUM-RESOLUTION IMAGERIES IN BSN CO LTD CONCESSION AREA, WEST KALIMANTAN ]]>
<![CDATA[Sendi Yusandi]]>;
<![CDATA[I Nengah Surati Jaya]]>;
<![CDATA[Fairus Mulia]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a2683
<![CDATA[Mangrove 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.]]>
<![CDATA[LAPAN-A3 SATELLITE DATA ANALYSIS FOR LAND COVER CLASSIFICATION (CASE STUDY: TOBA LAKE AREA, NORTH SUMATRA) ]]>
<![CDATA[Jalu Tejo Nugroho]]>;
<![CDATA[Zylshal Zylshal]]>;
<![CDATA[Dony Kushardono]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a2782
<![CDATA[LAPAN-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.]]>
<![CDATA[COMPARISON OF MODEL ACCURACY IN TREE CANOPY DENSITY ESTIMATION USING SINGLE BAND, VEGETATION INDICES AND FOREST CANOPY DENSITY (FCD) BASED ON LANDSAT-8 IMAGERY (CASE STUDY: PEAT SWAMP FOREST IN RIAU PROVINCE) ]]>
<![CDATA[Faisal Ashaari]]>;
<![CDATA[Muhammad Kamal]]>;
<![CDATA[Dede Dirgahayu]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a2845
<![CDATA[Identification of a tree canopy density information may use remote sensing data such as Landsat-8 imagery. Remote sensing technology such as digital image processing methods could be used to estimate the tree canopy density. The purpose of this research was to compare the results of accuracy of each method for estimating the tree canopy density and determine the best method for mapping the tree canopy density at the site of research. The methods used in the estimation of the tree canopy density are Single band (green, red, and near-infrared band), vegetation indices (NDVI, SAVI, and MSARVI), and Forest Canopy Density (FCD) model. The test results showed that the accuracy of each method: green 73.66%, red 75.63%, near-infrared 75.26%, NDVI 79.42%, SAVI 82.01%, MSARVI 82.65%, and FCD model 81.27%. Comparison of the accuracy results from the seventh methods indicated that MSARVI is the best method to estimate tree canopy density based on Landsat-8 at the site of research. Estimation tree canopy density with MSARVI method showed that the canopy density at the site of research predominantly 60-70% which spread evenly.]]>
<![CDATA[MAPPING APATITE-ILMENITE RARE EARTH ELEMENT MINERALIZED ZONE USING FUZZY LOGIC METHOD IN SIJUK DISTRICT, BELITUNG ]]>
<![CDATA[Muhamad Iqbal Januadi Putra]]>;
<![CDATA[nFN Sobirin]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a2828
<![CDATA[District of Sijuk located in Belitung Island is rich with non-lead mineral content. As the part of Southeast Asia’s Lead Belt, the presence of Apatite-Ilmenite Rare Earth Element formed by the region’s geological condition is very likely. However, there has not been any activity to map and identify the apatite-ilmenite distribution in this region. Therefore, the objective of this study was to map the mineralized apatite-ilmenite in Sijuk District. Using remote sensing technology, Landsat 8 OLI were utilized to map the distribution of mineralized apatite-ilmenite rare earth element. Alteration mineral carrier, geological structure, and lithology data were all used as variables. Landsat-8 was pre-processed using band ratio and Directed Principal Component Analysis (DPCA) method for gaining alteration variable. The fuzzy logic method was then deployed for integrating all data. The result of this research showed the potential distribution of mineralized apatite-ilmenite with a total area of 1,617 ha. The most prioritized areas for apatite-ilmenite mineral exploitation are located in Air Seruk Village’s IUP (Izin Usaha Pertambangan/Mining Business License), Sijuk Village’s IUP, and Batu Itam Village’s IUP. This study also illustrates the orientation of the metal utilization of apatite-ilmenite in district Sijuk.]]>
<![CDATA[WATERMARKING METHOD OF REMOTE SENSING DATA USING STEGANOGRAPHY TECHNIQUE BASED ON LEAST SIGNIFICANT BIT HIDING ]]>
<![CDATA[Destri Yanti Hutapea]]>;
<![CDATA[Octaviani Hutapea]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a2824
<![CDATA[Remote 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.]]>
<![CDATA[ANALYSIS OF LAND USE SPATIAL PATTERN CHANGE OF TOWN DEVELOPMENT USING REMOTE SENSING ]]>
<![CDATA[Samsul Arifin]]>;
<![CDATA[nFN Mukhoriyah]]>;
<![CDATA[Dipo Yudhatama]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a2795
<![CDATA[The Assessment of the physical character of a city is considered relatively easier than the social-cultural aspects. It is important to recognize the type of city form and to predict the behavior of people in the city and its surrounding. Due to those characteristics, the study of the pattern of physical development of the city is required. The objective of research is to analyze the change of spatial pattern of the city due to the city growing by remote sensing. The multitemporal data of Landsat 5/7/8 year 2000, 2006 and 2015 in Jabodetabek area were used. The classification technique had been done and it produced five classes of land uses. Those are water, built-up area, vegetation, other land use and no data. The results of the analysis in Jabodetabek area (Jakarta, Bogor, Depok, Tangerang and Bekasi) show that there was land use changes from vegetation and other land use area to built-up area with an average accuracy of 78% in each year. The pattern of physical development of the city looks linear from year 2000 until year 2006, which is confirmed as concentric pattern from year 2006 to 2015. Based on those analysis, it confirmed that the city development in Jakarta as the center was influenced by the spatial land development of the surrounding cities of Depok, Bogor, Bekasi and Tangerang. The pattern of spatial development from 2000 to 2006 in Bogor, Bekasi and Depok areas is Linear pattern, whereas from 2006 - 2015 the pattern of spatial development shows Propagation Concentric pattern. For Tangerang Region in 2000-2015 its development is patterned Propagation Concentric.]]>
