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[SPATIAL AND TEMPORAL ANALYSIS OF LAND SURFACE TEMPERATURE CHANGE ON NEW BRITAIN ISLAND ]]>
<![CDATA[Devi, Rafika Minati]]>;
<![CDATA[Prasetya, Tofan Agung Eka]]>;
<![CDATA[Indriani, Diah]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2020.v17.a3342
<![CDATA[Land Surface Temperature (LST) is a parameter to estimate the temperature of the Earth’s surface and to detect climate change. Papua New Guinea is a tropical country with rainforests, the greatest proportion of which are located on the island of New Britain. Hectares of rainforests have been logged and deforested because of infrastructure construction. This study aims to investigate the change in land surface temperatures on the island from 2000 to 2019. The temperature data were taken from National Aeronautics and Space Administration (NASA) Terra satellites and were analysed using two statistical models: spatial and temporal. The spatial model used multivariate regression, while the temporal one used autoregression (AR). In this study, a cubic spline fitted curve was employed because this has the advantage of being smoother and providing good visuals. The results show that almost all the sub-regions of New Britain have experienced a significant increase in land surface temperature, with a Z value of 7.97 and a confidence interval (CI) of 0.264 – 0.437. The study only investigated land surface temperature change on New Britain Island using spatial and temporal analysis, so further analysis is needed which takes into account other variables such as vegetation and land cover, or which establishes correlations with other variables such as human health.]]>
<![CDATA[MAPPING BURNT AREAS USING THE SEMI-AUTOMATIC OBJECT-BASED IMAGE ANALYSIS METHOD ]]>
<![CDATA[Fitriana, Hana Listi]]>;
<![CDATA[Suwarsono, Suwarsono]]>;
<![CDATA[Kusratmoko, Eko]]>;
<![CDATA[Supriatna, Supriatna]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2020.v17.a3281
<![CDATA[Forest and land fires in Indonesia take place almost every year, particularly in the dry season and in Sumatra and Kalimantan. Such fires damage the ecosystem, and lower the quality of life of the community, especially in health, social and economic terms. To establish the location of forest and land fires, it is necessary to identify and analyse burnt areas. Information on these is necessary to determine the environmental damage caused, the impact on the environment, the carbon emissions produced, and the rehabilitation process needed. Identification methods of burnt land was made both visually and digitally by utilising satellite remote sensing data technology. Such data were chosen because they can identify objects quickly and precisely. Landsat 8 image data have many advantages: they can be easily obtained, the archives are long and they are visible to thermal wavelengths. By using a combination of visible, infrared and thermal channels through the semi-automatic object-based image analysis (OBIA) approach, the study aims to identify burnt areas in the geographical area of Indonesia. The research concludes that the semi-automatic OBIA approach based on the red, infrared and thermal spectral bands is a reliable and fast method for identifying burnt areas in regions of Sumatra and Kalimantan.]]>
<![CDATA[ASSESSMENT OF THE ACCURACY OF DEM FROM PANCHROMATIC PLEIADES IMAGERY (CASE STUDY: BANDUNG CITY. WEST JAVA) ]]>
<![CDATA[Nurtyawan, Rian]]>;
<![CDATA[fiscarina, Nadia]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2020.v17.a3329
<![CDATA[Pleiades satellite imagery is very high resolution. with 0.5 m spatial resolution in the panchromatic band and 2.5 m in the multispectral band. Digital elevation models (DEM) are digital models that represent the shape of the Earth's surface in three-dimensional (3D) form. The purpose of this study was to assess DEM accuracy from panchromatic Pleaides imagery. The process conducted was orthorectification using ground control points (GCPs) and the rational function model with rational polynomial coefficient (RFC) parameters. The DEM extraction process employed photogrammetric methods with different parallax concepts. Accuracy assessment was made using 35 independent check points (ICPs) with an RMSE accuracy of ± 0.802 m. The results of the Pleaides DEM image extraction were more accurate than the National DEM (DEMNAS) and SRTM DEM. Accuracy testing of DEMNAS results showed an RMSE of ± 0.955 m. while SRTM DEM accuracy was ± 17.740 m. Such DEM extraction from stereo Pleiades panchromatic images can be used as an element on base maps with a scale of 1: 5.000.]]>
<![CDATA[AN ENHANCEMENT TO THE QUANTITATIVE PRECIPITATION ESTIMATION USING RADAR-GAUGE MERGING ]]>
<![CDATA[Ali, Abdullah]]>;
<![CDATA[Deranadyan, Gumilang]]>;
<![CDATA[Hairuly Umam, Iddam]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2020.v17.a3316
<![CDATA[Quantitative Precipitation Estimation (QPE) is quite important information for the hydrology fields and has many advantages for many purposes. Its dense spatial and temporal resolution can be combined with the surface observation to enhance the accuracy of the estimation. This paper presents an enhancement to the QPE product from BMKG weather radar network at Surabaya by adjusting the estimation value form radar to the real data observation from rain gauge. A total of 58 rain gauge is used. The Mean Field Bias (MFB) method used to determine the correction factor through the difference between radar estimation and rain gauge observation value. The correction factor obtained at each gauge points are interpolated to the entire radar grid in a multiplicative adjustment. Radar-gauge merging results a significant improvement revealed by the decreasing of mean absolute error (MAE) about 40% and false alarm ratio (FAR) as well an increasing of possibility of detection (POD) more than 50% at any rain categories (light rain, moderate rain, heavy rain, and very heavy rain). This performance improvement is very beneficial for operational used in BMKG and other hydrological needs.]]>
<![CDATA[UTILISATION OF NASA - GFWED AND FIRMS SATELLITE DATA IN DETERMINING THE PROBABILITY OF HOTSPOTS USING THE FIRE WEATHER INDEX (FWI) IN OGAN KOMERING ILIR REGENCY, SOUTH SUMATRA ]]>
<![CDATA[Nainggolan, Hermanto Asima]]>;
<![CDATA[Veanti, Desak Putu Okta]]>;
<![CDATA[Akbar, Dzikrullah]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2020.v17.a3202
<![CDATA[Prevention and mitigation of forest and land fires have important roles considering its various negative impacts. Throughout 2018, in Ogan Komering Ilir District, 864 hectares of land burned. This data increased significantly compared to the burned area in the previous year. Lack of field meteorological observation is still a problem in solving the problem of forest fire in the region. Consequently, we utilize NASA - GFWED and FIRMS satellite data to analyze the hotspots probabilities in Ogan Komering Ilir District, South Sumatra. Conditional probability analysis will be used to find out the likelihood of hotspots based on FWI and FFMC from 2001 to 2016. More than 50 percent of hotspots appear during extreme FFMC class and high to extreme FWI class. The probability of hotspots for extreme FFMC class and extreme FWI class varied between 0.3 to 10.4 % and 0.1 to 3.8 % respectively. Meanwhile, fire-prone areas with the highest density of fires are in the sub-district of Tulung Selapan, and the safest region is the Cengal sub-district.]]>
<![CDATA[DETECTING THE SURFACE WATER AREA IN CIRATA DAM UPSTREAM CITARUM USING A WATER INDEX FROM SENTINEL-2 ]]>
<![CDATA[Suwarsono, Suwarsono]]>;
<![CDATA[Yulianto, Fajar]]>;
<![CDATA[Fitriana, Hana Listi]]>;
<![CDATA[Nugroho, Udhi Catur]]>;
<![CDATA[Sukowati, Kusumaning Ayu Dyah]]>;
<![CDATA[Khomarudin, Muhammad Rokhis]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2020.v17.a3286
<![CDATA[This paper describes the detection of the surface water area in Cirata dam, Â upstream Citarum, using a water index derived from Sentinel-2. MSI Level 1C (MSIL1C) data from 16 November 2018 were extracted into a water index such as the NDWI (Normalized Difference Water Index) model of Gao (1996), McFeeters (1996), Roger and Kearney (2004), and Xu (2006). Water index were analyzed based on the presence of several objects (water, vegetation, soil, and built-up). The research resulted in the ability of each water index to separate water and non-water objects. The results conclude that the NDWI of McFeeters (1996) derived from Sentinel-2 MSI showed the best results in detecting the surface water area of the reservoir.]]>
<![CDATA[OPTIMIZATION OF RICE FIELD CLASSIFICATION MODEL BASED ON THRESHOLD INDEX OF MULTITEMPORAL LANDSAT IMAGES ]]>
<![CDATA[Dirgahayu, Dede]]>;
<![CDATA[Parsa, Made]]>;
<![CDATA[Harini, Sri]]>;
<![CDATA[Kurhardono, Dony]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2020.v17.a3333
<![CDATA[The development of rice land classification models in 2018 has shown that the phenology-based threshold of rice crops from the multi-temporal Landsat image index can be used to classify rice fields relatively well. The weakness of the models was the limitations of the research area, which was confined to the Subang region, West Java, so it is was deemed necessary to conduct further research in other areas. The objective of this study is to obtain optimal parameters of classification model of rice and land based on multi-temporal Landsat image indexes. The study was conducted in several districts of rice production centers in South Sulawesi and West Java (besides Subang). The threshold method was employed for the Landsat Image Enhanced Vegetation Index (EVI). Classification accuracy was calculated in two stages, the first using detailed scale reference information on rice field base, and the second using field data (from a survey). Based on the results of the analysis conducted on several models, the highest accuracy is generated by the three index parameter models (EVI_min, EVI_max, and EVI_range) and adjustable threshold with 94.8% overall accuracy. Therefore this model was acceptable for used for nationally rice fields mapping.]]>
<![CDATA[ANALYSIS OF POTENTIAL FISHING ZONES IN COASTAL WATERS: A CASE STUDY OF NIAS ISLAND WATERS ]]>
<![CDATA[Purwanto, Anang Dwi]]>;
<![CDATA[Prayogo, Teguh]]>;
<![CDATA[Marpaung, Sartono]]>;
<![CDATA[Suhada, Argo Galih]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2020.v17.a3298
<![CDATA[The need for information on potential fishing zones based on remote sensing satellite data (ZPPI) in coastal waters is increasing. This study aims to create an information model of such zones in coastal waters (coastal ZPPI). The image data used include GHRSST, SNPP-VIIRS and MODIS-Aqua images acquired from September 1st-30th, 2018 and September 1st-30th, 2019, together with other supporting data. The coastal ZPPI information is based on the results of thermal front SST detection and overlaying this with chlorophyll-a. The method of determining the thermal front sea surface temperature (SST) used Single Image Edge Detection (SIED). The chlorophyll-a range used was in the mesotropic area (0.2-0.5 mg/m3). Coastal ZPPI coordinates were determined using the polygon centre of mass, while the coastal ZPPI information generated was only for coastal areas with a radius of between 4-12 nautical miles and was divided into two criteria, namely High Potential (HP) and Low Potential (LP). The results show that the coastal ZPPI models were suitable to determine fishing locations around Nias Island. The percentage of coastal ZPPI information generated was around 90% information monthly. In September 2018, 27 days of information were produced, consisting of 11 HP sets of coastal ZPPI information and 16 sets of LP information, while in September 2019 it was possible to produce 29 days of such information, comprising 11 sets of HP coastal ZPPI information and 18 LP sets. The use of SST parameters of GHRSST images and the addition of chlorophyll-a parameters to MODIS-Aqua images are very effective and efficient ways of supporting the provision of coastal ZPPI information in the waters of Nias Island and its surroundings.]]>
<![CDATA[ANALYSIS OF WATER PRODUCTIVITY IN THE BANDA SEA BASED ON REMOTE SENSING SATELLITE DATA ]]>
<![CDATA[Marpaung, Sartono]]>;
<![CDATA[Faristyawan, Risky]]>;
<![CDATA[Purwanto, Anang Dwi]]>;
<![CDATA[Asriningrum, Wikanti]]>;
<![CDATA[Suhada, Argo Galih]]>;
<![CDATA[Prayogo, Teguh]]>;
<![CDATA[Sitorus, Jansen]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2020.v17.a3280
<![CDATA[Abstract. This study examines the density of potential fishing zone (PFZ) points and chlorophyll-a concentration in the Banda Sea. The data used are those on chlorophyll-a from the Aqua MODIS satellite, PFZ points from ZAP and the monthly southern oscillation index. The methods used are single image edge detection, polygon center of mass, density function and a Hovmoller diagram. The result of the analysis show that productivity of chlorophyll-a in the Banda Sea is influenced by seasonal factors (dry season and wet season) and ENSO phenomena (El Niño and La Niña). High productivity of chlorophyll-a  occurs during in the dry season with the peak in August, while low productivity occurs in the wet season and the transition period, with the lowest levels in April and December. The variability in chlorophyll-a production is influenced by the global El Niño and La Niña phenomena; production increases during El Niño and decreases during La Niña. Tuna conservation areas have as lower productivity of chlorophyll-a and PFZ point density compared to the northern and southern parts of the Banda Sea. High density PFZ point regions are associated with regions that have higher productivity of chlorophyll-a, namely the southern part of the Banda Sea, while low density PFZ point areas are associated with regions that have a low productivity of chlorophyll-a, namely tuna conservation areas. The effect of the El Niño phenomenon in increasing chlorophyll-a concentration is stronger in the southern part of study area than in the tuna conservation area. On the other hand, the effect of La Niña phenomenon in decreasing chlorophyll-a concentration is stronger in the tuna conservation area than in the southern and northern parts of the study area. ]]>
