International Journal of Remote Sensing and Earth Sciences (IJReSES)
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COASTLINE CHANGE ANALYSIS ON BALI ISLAND USING SENTINEL-1 SATELLITE IMAGERY
Full text linkBali is well-known as a popular tourism location for both local and foreign tourists. There are nine areas designated for tourism, eight of which are coastal. However, due to coastal erosion, the coastline of Bali is changing every year. The purpose of this study is to determine the changes that took place between 2015 and 2020 using Sentinel-1 satellite imagery. The study was conducted along the coastline of Bali Island at coordinates 08° 53' 35.5648" S, 114° 24' 41.8359" E and 08° 00' 46.7865" S, 115° 44' 17.5928" E. The coastlines were identified using the Otsu image thresholding method and linear tidal correction was performed. The coastline change analysis was made using the transect method. Ground truths were conducted in representative areas where major changes had occurred, either as a result of abrasion or accretion. According to the Sentinel-1 analysis, the coastline changes in Bali during the period 2015 – 2020 were mainly caused by abrasion, apart from at Buleleng, which were generally caused by accretion. Abrasion in Bali is dominantly affected by strong currents and high waves meanwhile accretion which having weak currents and low waves was more affected by human factor such as the construction in this study area
MACHINE LEARNING APPLIED TO SENTINEL-2 AND LANDSAT-8 MULTISPECTRAL AND MEDIUM-RESOLUTION SATELLITE IMAGERY FOR THE DETECTION OF RICE PRODUCTION AREAS IN NGANJUK, EAST JAVA, INDONESIA
Full text linkStatistics Indonesia (BPS) has been introducing the use of Area Sampling Frame (ASF) surveys from 2018 to estimate rice production areas, although the process continues to suffer from the high costs of human and other resources. To support this type of conventional field survey, a more scalable and inexpensive approach using publicly-available remote sensing data, for example from the Sentinel-2 and Landsat-8 satellites, has been explored. In this research, we compare the performance gain from Sentinel-2 and Landsat-8 images using a multiple composite-index enriched machine learning classifier to detect rice production areas located in Nganjuk, East Java, Indonesia as a case study area. We build a detection model from a set of machine learning classifiers, Decision Tree (CART), Support Vector Machine, Logistic Regression, Ensemble Bagging Methods (Random Forest and Extra Trees), and Ensemble Boosting Methods (AdaBoost and XGBoost). The composite indices consist of the NDVI and EVI for agricultural and forest areas, NDWI for water and cloud, and NDBI, NDTI, and BSI for built-up areas, fallows, and asphalt-based roads. Validated by k-fold cross-validation, Sentinel-2 and Landsat-8 achieved F1-scores of 0.930 and 0.919 respectively at the scale of 30 meters per pixel. Using a 10 meter resolution per pixel for the Sentinel-2 imagery showed an increased F1-score of up to 0.971. Our evaluation shows that the higher spatial resolution imagery of Sentinel-2 achieves a better prediction, not only performance-wise, but also as a better representation of actual conditions
FISHING BOAT DISTRIBUTION ESTABLISHED BY COMPARING VMS AND VIIRS DATA AROUND THE ARU ISLANDS IN MALUKU INDONESIA
Full text linkMarine protected areas (MPAs) and no take zones (NTZs) are essential for the preservation of marine ecosystems. However, these important areas can be severely harmed by illegal fishing. All vessels above 30 gross tons are required to use vessel monitoring systems (VMSs) that enable vessel tracking by sending geographic data to satellites in each specific time period. The Visible Infrared Radiometer Suite (VIIRS) is a sensor on the National Oceanic and Atmospheric Administration (NOAA)-20 satellite that can detect the light-emitting diode (LED) light used by fishing vessels from space during the night time. In this research, VMS and VIIRS fishery data were combined in order to identify fishing vessels that were detected by the VIIRS sensor of the NOAA-20 satellite. The research was focused on an area near the Aru Islands in the Arafura Sea in Indonesia. Data on LED light used by the fishing techniques of purse seine and bouke ami were obtained for the whole of 2018. The data were then processed using R software. An R package called LLFI (LED Light Fisheries Identifier) was created, containing several R-functions that calculate VMS vessel position during satellite overpass time and then combine the VMS and VIIRS data attributes, resulting in a dataset comprising vessels identified from the VIIRS dataset. Out of all the estimated VMS fishing vessel positions during the VIIRS satellite overpass, approximately 51% could be assigned to fishing vessels detected from the VIIRS dataset. For bouke ami, the identification rate was approximately 87%, while that for small purse seine was around 39%. Ultimately, the LLFI package created daily paths for each identified fishing vessel, displaying all its movements during the day of its’identification. These daily paths did not show any activity within MPA or NTZ. The LLFI package was successful in combining VMS and VIIRS data, estimating VMS vessel positions during the VIIRS satellite overpass, identifying a percentage of the vessels, and creating a daily path for each identified vessel.Â
VERTICAL LAND MOTION AND INUNDATION PROCESSES BASED ON THE INTEGRATION OF REMOTELY SENSED DATA AND IPCC AR5 SCENARIOS IN COASTAL SEMARANG, INDONESIA
Full text linkVertical 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)
APPLICATION OF LAND SURFACE TEMPERATURE DERIVED FROM ASTER TIR TO IDENTIFY VOLCANIC GAS EMISSION AROUND BANDUNG BASIN
Full text linkGas 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
MONITORING OF MANGROVE GROWTH AND COASTAL CHANGES ON THE NORTH COAST OF BREBES, CENTRAL JAVA, USING LANDSAT DATA
Full text linkSevere 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
HOTSPOT VALIDATION OF THE HIMAWARI-8 SATELLITE BASED ON MULTISOURCE DATA FOR CENTRAL KALIMANTAN
Full text linkThe 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%
TENDENCY FOR CLIMATE-VARIABILITY-DRIVEN RISE IN SEA LEVEL DETECTED IN THE ALTIMETER ERA IN THE MARINE WATERS OF ACEH, INDONESIA
Full text linkLong-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.Â