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[IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES ]]>
<![CDATA[Anang Dwi Purwanto]]>;
<![CDATA[Wikanti Asriningrum]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3097
<![CDATA[The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites of 564 (NIR+SWIR+Red) from Landsat 8 and 8a114 (Vegetation Red Edge+SWIR+Red) from Sentinel 2A are the best RGB composites for identifying mangrove forest, in addition to those of 341 (Red+NIR+Blue) from SPOT 6. The near-infrared (NIR) and short-wave infrared (SWIR) bands play an important role in determining mangrove forests. The properties of vegetation are reflected strongly at the NIR wavelength and the SWIR band is very sensitive to evaporation and the identification of wetlands.]]>
<![CDATA[Back Pages IJReSES Vol. 16, No. 1 (2019) ]]>
<![CDATA[Editor Journal]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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<![CDATA[Back Pages IJReSES Vol. 16, No. 1 (2019)]]>
<![CDATA[BATHYMETRY EXTRACTION FROM SPOT 7 SATELLITE IMAGERY USING RANDOM FOREST METHODS ]]>
<![CDATA[Kuncoro Teguh Setiawan]]>;
<![CDATA[Nana Suwargana]]>;
<![CDATA[Devica Natalia Br. Ginting]]>;
<![CDATA[Masita Dwi Mandini Manessa]]>;
<![CDATA[Nanin Anggraini]]>;
<![CDATA[Syifa Wismayati Adawiah]]>;
<![CDATA[Atriyon Julzarika]]>;
<![CDATA[Surahman Surahman]]>;
<![CDATA[Syamsu Rosid]]>;
<![CDATA[Agustinus Harsono Supardjo]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3085
<![CDATA[The scope of this research is the application of the random forest method to SPOT 7 data to produce bathymetry information for shallow waters in Indonesia. The study aimed to analyze the effect of base objects in shallow marine habitats on estimating bathymetry from SPOT 7 satellite imagery. SPOT 7 satellite imagery of the shallow sea waters of Gili Matra, West Nusa Tenggara Province was used in this research. The estimation of bathymetry was carried out using two in-situ depth-data modifications, in the form of a random forest algorithm used both without and with benthic habitats (coral reefs, seagrass, macroalgae, and substrates). For bathymetry estimation from SPOT 7 data, the first modification (without benthic habitats) resulted in a 90.2% coefficient of determination (R2) and 1.57 RMSE, while the second modification (with benthic habitats) resulted in an 85.3% coefficient of determination (R2) and 2.48 RMSE. This research showed that the first modification achieved slightly better results than the second modification; thus, the benthic habitat did not significantly influence bathymetry estimation from SPOT 7 imagery.]]>
<![CDATA[THE UTILIZATION OF REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS FOR ANALYSIS OF LAND SUITABILITY FOR THE GROWING OF CIPLUKAN (PHYSALIS ANGULATA L.) ]]>
<![CDATA[Nur Adliani]]>;
<![CDATA[Nirmawana Simarmata]]>;
<![CDATA[Heriansyah Heriansyah]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3112
<![CDATA[Remote sensing data and geographic information systems are widely used for land suitability analysis for crops such as coffee and corn. This study aims to analyze and map suitable land for the plant known locally as ciplukan (Physalis angulata L.). As the cultivation of this plant is expected to be developed by the Institute of Technology of Sumatra, analysis of this type is needed. The parameters used in this study were slope, land use, rainfall and soil type. Information extraction from remote sensing data was carried out via visual interpretation of aerial photography used to create land-cover maps. Shuttle RADAR Topographic Mission (SRTM) data was converted from digital surface model (DSM) to digital terrain model (DTM) to provide elevation information. Land suitability analysis was performed using a scoring method and overlay analysis. The results obtained from the analysis identified several classes of land suitability for Physalis angulata L., categorized as suitable, less suitable, and not suitable. The less suitable class, scored at 9 to 11, comprised a total area of 180.96 ha, while the suitable area, scored at 12, comprised a total area of 49.1 ha.]]>
<![CDATA[DETECTING DEFORMATION DUE TO THE 2018 MERAPI VOLCANO ERUPTION USING INTERFEROMETRIC SYNTHETIC APERTURE RADAR (INSAR) FROM SENTINEL-1 TOPS ]]>
<![CDATA[Suwarsono Suwarsono]]>;
<![CDATA[Indah Prasasti]]>;
<![CDATA[Jalu Tejo Nugroho]]>;
<![CDATA[Jansen Sitorus]]>;
<![CDATA[Rahmat Arief]]>;
<![CDATA[Khalifah Insan Nur Rahmi]]>;
<![CDATA[Djoko Triyono]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3145
<![CDATA[This paper describes the application of Sentinel-1 TOPS (Terrain Observation with Progressive Scans), the latest generation of SAR satellite imagery, to detect displacement of the Merapi volcano due to the May–June 2018 eruption. Deformation was detected by measuring the vertical displacement of the surface topography around the eruption centre. The Interferometric Synthetic Aperture Radar (InSAR) technique was used to measure the vertical displacement. Furthermore, several Landsat-8 Thermal Infra Red Sensor (TIRS) imageries were used to confirm that the displacement was generated by the volcanic eruption. The increasing temperature of the crater was the main parameter derived using the Landsat-8 TIRS, in order to determine the increase in volcanic activity. To understand this phenomenon, we used Landsat-8 TIRS acquisition dates before, during and after the eruption. The results show that the eruption in the May–June 2018 period led to a small negative vertical displacement. This vertical displacement occurred in the peak of volcano range from -0.260 to -0.063 m. The crater, centre of eruption and upper slope of the volcano experienced negative vertical displacement. The results of the analysis from Landsat-8 TIRS in the form of an increase in temperature during the 2018 eruption confirmed that the displacement detected by Sentinel-1 TOPS SAR was due to the impact of volcanic activity. Based on the results of this analysis, it can be seen that the integration of SAR and thermal optical data can be very useful in understanding whether deformation is certain to have been caused by volcanic activity.]]>
<![CDATA[DETECTING AND COUNTING COCONUT TREES IN PLEIADES SATELLITE IMAGERY USING HISTOGRAM OF ORIENTED GRADIENTS AND SUPPORT VECTOR MACHINE ]]>
<![CDATA[Yudhi Prabowo]]>;
<![CDATA[Kenlo Nishida Nasahara]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3089
<![CDATA[This paper describes the detection of coconut trees using very-high-resolution optical satellite imagery. The satellite imagery used in this study was a panchromatic band of Pleiades imagery with a spatial resolution of 0.5 metres. The authors proposed the use of a histogram of oriented gradients (HOG) algorithm as the feature extractor and a support vector machine (SVM) as the classifier for this detection. The main objective of this study is to find out the parameter combination for the HOG algorithm that could provide the best performance for coconut-tree detection. The study shows that the best parameter combination for the HOG algorithm is a configuration of 3 x 3 blocks, 9 orientation bins, and L2-norm block normalization. These parameters provide overall accuracy, precision and recall of approximately 80%, 73% and 87%, respectively.]]>
<![CDATA[VARIABILITY OF SEA SURFACE TEMPERATURE (SST) AND CHLOROPHYLL-A (CHL-A) CONCENTRATIONS IN THE EASTERN INDIAN OCEAN DURING THE PERIOD 2002–2017 ]]>
<![CDATA[Michelia Mashita]]>;
<![CDATA[Jonson Lumban-Gaol]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3147
<![CDATA[We analysed the variability of sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) in the eastern Indian Ocean (EIO). We used monthly mean Chl-a and SST data with a 4-km spatial resolution derived from Level-3 Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) distributed by the Asia-Pacific Data-Research Center (APDRC) for the period 2002–2017. Wavelet analysis shows the annual and interannual variability of SST and Chl-a concentration in the EIO. The annual variability of SST and Chl-a is influenced by monsoon systems. During a southeast monsoon, SST falls while Chl-a increases due to upwelling. The annual variability of SST and Chl-a is also influenced by the Indian Ocean Dipole (IOD). During positive phases of the IOD (2006, 2012 and 2015), there was more intense upwelling in the EIO caused by the negative anomaly of SST and the positive anomaly of Chl-a concentration.]]>
<![CDATA[INTEGRATION OF GIS AND REMOTE SENSING FOR HOTSPOT DISTRIBUTION ANALYSIS IN BERBAK SEMBILANG NATIONAL PARK ]]>
<![CDATA[Andita Minda Mora]]>;
<![CDATA[Bambang Hero Saharjo]]>;
<![CDATA[Lilik Budi Prasetyo]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3194
<![CDATA[Abstract. Remote sensing is composed of many interrelated processes to be able to consider physical objects such as buildings, land, and plants which are objects that can be discussed by applications discussed in various disciplines that discuss geology, forestry, soil science, and geography. The use of GIS and remote sensing for fire monitoring has been widely used. However, this study is the first study conducted in the TNBS area after the Berbak National Park (TNB) in Jambi to join the Sembilang National Park (TNS) in South Sumatra. Hotspot distribution in this study was obtained using Getis-Ord-Gi * statistics, hotspot data collected from 2000-2018 in the TNBS area. The results of the hotspot distribution during the 2000-2018 recorded by MODIS satellites with time acquisition and statistical analysis using Gi* show the results that the hotspots gathered (80% confidence level) outside the TNBS area, which is a mixed fields area. Further studies on causes of fire in terms of socio-economic and cultural needs to be done to get the right advice in reducing the risk of loss of forest cover and diversity in TNBS. Keywords: mitigation, hydrology, DAS]]>
<![CDATA[THE USE OF C-BAND SYNTHETIC APERTURE RADAR SATELLITE DATA FOR RICE PLANT GROWTH PHASE IDENTIFICATION ]]>
<![CDATA[Anugrah Indah Lestari]]>;
<![CDATA[Dony Kushardono]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3171
<![CDATA[Identification of the rice plant growth phase is an important step in estimating the harvest season and predicting rice production. It is undertaken to support the provision of information on national food availability. Indonesia’s high cloud coverage throughout the year means it is not possible to make optimal use of optical remote sensing satellite systems. However, the Synthetic Aperture Radar (SAR) remote sensing satellite system is a promising alternative technology for identifying the rice plant growth phase since it is not influenced by cloud cover and the weather. This study uses multi-temporal C-Band SAR satellite data for the period May–September 2016. VH and VV polarisation were observed to identify the rice plant growth phase of the Ciherang variety, which is commonly planted by farmers in West Java. Development of the rice plant growth phase model was optimized by obtaining samples spatially from a rice paddy block in PT Sang Hyang Seri, Subang, in order to acquire representative radar backscatter values from the SAR data on the age of certain rice plants. The Normalised Difference Polarisation Index (NDPI) and texture features, namely entropy, homogeneity and the Grey-Level Co-occurrence Matrix (GLCM) mean, were included as the samples. The results show that the radar backscatter value (σ0) of VH polarisation without the texture feature, with the entropy texture feature and GLCM mean texture feature respectively exhibit similar trends and demonstrate potential for use in identifying and monitoring the rice plant growth phase. The rice plant growth phase model without texture feature on VH polarisation is revealed as the most suitable model since it has the smallest average error.]]>
<![CDATA[Front Pages IJReSES Vol. 16, No. 1 (2019) ]]>
<![CDATA[Editor Journal]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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<![CDATA[Front Pages IJReSES Vol. 16, No. 1 (2019)]]>
