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[MONITORING OF MANGROVE GROWTH AND COASTAL CHANGES ON THE NORTH COAST OF BREBES, CENTRAL JAVA, USING LANDSAT DATA ]]>
<![CDATA[Susantoro, Tri Muji]]>;
<![CDATA[Wikantika, Ketut]]>;
<![CDATA[Yayusman, Lissa Fajri]]>;
<![CDATA[Tan, Alex]]>;
<![CDATA[Ghozali, M. Firman]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3221
<![CDATA[Severe abrasion occurred in the coastal area of Brebes Regency, Central Java between 1985 and 1995. Since 1997, mangroves have been planted around the location as a measure intended to prevent further abrasion. Between 1996 and 2018, monitoring has been carried out to assess coastal change in the area and the growth and development of the mangroves. This study aims to monitor mangrove growth and its impact on coastal area changes on the north coast of Brebes, Central Java Province using Landsat series data, which has previously proven suitable for wetland studies including mangrove growth and change. Monitoring of mangrove growth was analysed using the normalised difference vegetation index (NDVI) and the green normalised difference vegetation index (GNDVI) of the Landsat data, while the coastal change was analysed based on the overlaying of shoreline maps. Visual field observations of WorldView 2 images were conducted to validate the NDVI and GNDVI results. It was identified from these data that the mangroves had developed well during the monitoring period. The NDVI results showed that the total mangrove area increased between 1996 and 2018 about 9.82 km2, while the GNDVI showed an increase of 3.20 km2. Analysis of coastal changes showed that the accretion area about 9.17 km2 from 1996 to 2018, while the abrasion being dominant to the west of the Pemali River delta about 4.81 km2. It is expected that the results of this study could be used by government and local communities in taking further preventative actions and for sustainable development planning for coastal areas.]]>
<![CDATA[CLOUD IDENTIFICATION FROM MULTITEMPORAL LANDSAT-8 USING K-MEANS CLUSTERING ]]>
<![CDATA[Sunarmodo, Wismu]]>;
<![CDATA[Hayati, Anis Kamilah]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3285
<![CDATA[In the processing and analysis of remote-sensing data, cloud that interferes with earth-surface data is still a challenge. Many methods have already been developed to identify cloud, and these can be classified into two categories: single-date and multi-date identification. Most of these methods also utilize the thresholding method which itself can be divided into two categories: local thresholding and global thresholding. Local thresholding works locally and is different for each pixel, while global thresholding works similarly for every pixel. To determine the global threshold, two approaches are commonly used: fixed value as threshold and adapted threshold. In this paper, we propose a cloud-identification method with an adapted threshold using K-means clustering. Each related multitemporal pixel is processed using K-means clustering to find the threshold. The threshold is then used to distinguish clouds from non-clouds. By using the L8 Biome cloud-cover assessment as a reference, the proposed method results in Kappa coefficient of above 0.9. Furthermore, the proposed method has lower levels of false negatives and omission errors than the FMask method.]]>
<![CDATA[ROLLING MOSAIC METHOD TO SUPPORT THE DEVELOPMENT OF POTENTIAL FISHING ZONE FORECASTING FOR COASTAL AREAS ]]>
<![CDATA[Suniada, Komang Iwan]]>;
<![CDATA[Susilo, Eko]]>;
<![CDATA[Siwi, Wingking Era Rintaka]]>;
<![CDATA[Widagti, Nuryani]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3252
<![CDATA[The production of the Indonesian Institute for Marine Research and Observation’s mapping of forecast fishing areas (peta prakiraan daerah penangkapan ikan or PPDPI) based on passive satellite imagery is often constrained by high-cloud-cover issues, which lead to sub-optimal results. This study examines the use of the rolling mosaic method for providing geophysical variables, in particular, seasurface temperature (STT) together with minimum cloud cover, to enable clearer identification of oceanographic conditions. The analysis was carried out in contrasting seasons: dry season in July 2018 and rainy season in December 2018. In general, the rolling mosaic method is able to reduce cloud cover for sea-surface temperature (SST) data. A longer time range will increase the coverage percentage (CP) of SST data. In July, the CP of SST data increased significantly, from 15.3 % to 30.29% for the reference 1D mosaic and up to 84.19 % to 89.07% for the 14D mosaic. In contrast, the CP of SST data in December tended to be lower, from 4.93 % to 13.03% in the 1D mosaic to 41.48 % to 51.60% in the14D mosaic. However, the longer time range decreases the relationship between the reference SST data and rolling mosaic method data. A strong relationship lies between the 1D mosaic and 3D mosaics, with correlation coefficients of 0.984 for July and 0.945 for December. Furthermore, a longer time range will decrease root mean square error (RMSE) values. In July, RMSE decreased from 0.288°C (3D mosaic) to 0.471°C (14D mosaic). The RMSE value in December decreased from 0.387°C (3D mosaic) to 0.477°C (14D mosaic). Based on scoring analysis of CP, correlation coefficient and RMSE value, results indicate that the 7D mosaic method is useful for providing low-cloud-coverage SST data for PPDPI production in the dry season, while the 14D mosaic method is suitable for the rainy season.]]>
<![CDATA[TENDENCY FOR CLIMATE-VARIABILITY-DRIVEN RISE IN SEA LEVEL DETECTED IN THE ALTIMETER ERA IN THE MARINE WATERS OF ACEH, INDONESIA ]]>
<![CDATA[Rahmawan, Guntur adhi]]>;
<![CDATA[wisha, ulung jantama]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3091
<![CDATA[Long-term sea level rise (SLR) leads to increasing frequency in overtopping events resulting from polar ice liquefaction triggered by rising global temperatures. Aceh province is directly bordered by the Indian Ocean, and is subject to the influence of ocean–atmosphere interactions which have a role in triggering temperature and sea level anomalies. Elevated sea level is possibly caused by temperature-induced water mass redistributions. This study aimed to prove that the Indian Ocean Dipole (IOD) and El-Nino–Southern Oscillation (ENSO) had an influence on sea level change in Aceh waters over the six years 2009–2015. Sea level anomaly (SLA) was identified using Jason-2 satellite data for the 2009–2015 period, to enable the mathematical prediction of SLR rate for further years. We found that SLR was approximately 0.0095 mm/year with an upward trend during the six years of observation. Overall, negative mode of IOD and positive phase of ENSO tend to trigger anomalies of sea level at certain times, and have a stronger influence on increasing SLA and sea surface temperature anomaly (SSTA) which takes place in a ‘see-saw’ fashion. Over the period of observation, the strongest evidence of IOD-correlated SLA, ENSO-correlated SLA and SSTA-correlated SLA were identified in second transitional seasons, with more than 50% of R2 value. The upward trend in SLA is influenced by climatic factors that successively control ocean–atmosphere interactions in Aceh’s marine waters. ]]>
<![CDATA[VERTICAL LAND MOTION AND INUNDATION PROCESSES BASED ON THE INTEGRATION OF REMOTELY SENSED DATA AND IPCC AR5 SCENARIOS IN COASTAL SEMARANG, INDONESIA ]]>
<![CDATA[Nandika, Muhammad Rizki]]>;
<![CDATA[Susilo, Setyo Budi]]>;
<![CDATA[Siregar, Vincentius]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3272
<![CDATA[Vertical land motion (VLM) is an important indicator in obtaining information about relative sea-level rise (SLR) in the coastal environment, but this remains an area of study poorly investigated in Indonesia. The purpose of this study is to investigate the significance of the influence of VLM and SLR on inundation. We address this issue for Semarang, Central Java, by estimating VLM using the small baseline subset time series interferometry SAR method for 24 Sentinel-1 satellite data for the period March 2017 to May 2019. The interferometric synthetic aperture radar (InSAR) method was used to reveal the phase difference between two SAR images with two repetitions of satellite track at different times. The results of this study indicate that the average land subsidence that occurred in Semarang between March 2017 and May 2019 was from (-121) mm/year to + 24 mm/year. Through a combination of VLM and SLR scenario data obtained from the Intergovernmental Panel on Climate Change (IPCC), it was found that the Semarang coastal zone will continue to shrink due to inundation (forecast at 7% in 2065 and 10% in 2100).]]>
<![CDATA[APPLICATION OF LAND SURFACE TEMPERATURE DERIVED FROM ASTER TIR TO IDENTIFY VOLCANIC GAS EMISSION AROUND BANDUNG BASIN ]]>
<![CDATA[Hilman, Zaki]]>;
<![CDATA[Saepuloh, Asep]]>;
<![CDATA[Susanto, Very]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3254
<![CDATA[Gas emission in volcanic areas is one of the features that can be used for geothermal exploration and to monitor volcanic activity. Volcanic gases are usually emitted in permeable zones in geothermal fields. The use of thermal infrared radiometers (TIR) onboard of advanced spaceborne thermal emission and reflection radiometers (ASTER) aims to detect thermal anomalies at the ground surface related to gas emissions from permeable zones. The study area is located around Bandung Basin, West Java (Indonesia), particularly the Papandayan and Domas craters. This area was chosen because of the easily detected land surface temperature (LST) following emissivity and vegetation corrections (Tcveg). The ASTER TIR images used in this study were acquired by direct night and day observation, including observations made using visible to near-infrared radiometers (VNIR). Field measurements of volcanic gases composed of SO2 and CO2 were performed at three different zones for each of the craters. The measured SO2 concentration was found to be constant over time, but CO2 concentration showed some variation in the craters. We obtained results suggesting that SO2 gas measurements and Tcveg are highly correlated. At Papandayan crater, the SO2 gas concentration was 334.34 ppm and the Tcveg temperature was 35.67 °C, results that are considered highly anomalous. The same correlation was also found at Domas crater, which showed an increased SO2 gas concentration of 35.39 ppm located at a high-anomaly Tcveg of 30.65 °C. Therefore, the ASTER TIR images have potential to identify volcanic gases as related to high Tcveg.]]>
<![CDATA[FISHING-VESSEL DETECTION USING SYNTHETIC APERTURE RADAR (SAR) SENTINEL-1 (CASE STUDY: JAVA SEA) ]]>
<![CDATA[Fitriani, Sarah Putri]]>;
<![CDATA[Gaol, Jonson Lumban]]>;
<![CDATA[Kushardono, Dony]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3235
<![CDATA[The synthetic aperture radar (SAR) instrument of Sentinel-1 is a remote sensing technology being developed to enable the detection of vessel distribution. The purpose of this research is to study fishing-vessel detection using SAR Sentinel-1 data. In this study, the constant false alarm rate method (CFAR) for Sentinel-1 data is used for the detection of fishing vessels in Indramayu sea waters. The data used to detect ships includes SAR Sentinel-1A images and vessel monitoring system (VMS) data acquired on 8 March and 20 March 2018. SAR Sentinel-1 imagery data is obtained through pre-processing and object identification using Sentinel Application Platform (SNAP) software. Overlay analysis is then used to enable discrimination of immovable and movable objects and validation of ships detected from SAR Sentinel-1 imagery is performed using VMS data. From overlay analysis, 46 ships were detected on 8 March 2018 and 39 ships on 20 March 2018. Of all the ship points detected using SAR Sentinel-1, 7.06% could be detected by VMS data while 92.94% could not. The number of ships detected by SAR Sentinel-1 is greater than those detected by VMS because not all ships use VMS devices. ]]>
<![CDATA[CLASSIFICATION OF RICE-PLANT GROWTH PHASE USING SUPERVISED RANDOM FOREST METHOD BASED ON LANDSAT-8 MULTITEMPORAL DATA ]]>
<![CDATA[Triscowati, Dwi Wahyu]]>;
<![CDATA[Sartono, Bagus]]>;
<![CDATA[Kurnia, Anang]]>;
<![CDATA[Dirgahayu, Dede]]>;
<![CDATA[Wijayanto, Arie Wahyu]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3217
<![CDATA[Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.]]>
<![CDATA[HOTSPOT VALIDATION OF THE HIMAWARI-8 SATELLITE BASED ON MULTISOURCE DATA FOR CENTRAL KALIMANTAN ]]>
<![CDATA[Rahmi, Khalifah Insan Nur]]>;
<![CDATA[Sulma, Sayidah]]>;
<![CDATA[Prasasti, Indah]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3293
<![CDATA[The Advanced Himawari Imager (AHI) is the sensor aboard the remote-sensing satellite Himawari-8 which records the Earth’s weather and land conditions every 10 minutes from a geostationary orbit. The imagery produced known as Himawari-8 has 16 bands which cover visible, near infrared, middle infrared and thermal infrared wavelength potentials to monitor forestry phenomena. One of these is forest/land fires, which frequently occur in Indonesia in the dry season. Himawari-8 can detect hotspots in thermal bands 5 and band 7 using absolute fire pixel (AFP) and possible fire pixel (PFP) algorithms. However, validation has not yet been conducted to assess the accuracy of this information. This study aims to validate hotspots identified from Himawari images based on information from Landsat 8 images, field surveys and burnout data. The methodology used to validate hotspots comprises AFP and PFP extraction, determining firespots from Landsat 8, buffering at 2 km from firespots, field surveys, burnout data, and calculation of accuracy. AFP and PFP hotspot validation of firespots from Landsat-8 is found to have higher accuracy than the other options. In using Himawari-8 hotspots to detect land/forest fires in Central Kalimantan, the AFP algorithm with 2km radius has accuracy of 51.33% while the PFP algorithm has accuracy of 27.62%.]]>
<![CDATA[Front Pages IJReSES Vol. 16, No. 2 (2019) ]]>
<![CDATA[Editor, Journal]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2019.v16.a3343
<![CDATA[Front Pages IJReSES Vol. 16, No. 2 (2019)]]>
