International Journal of Remote Sensing and Earth Sciences (IJReSES)
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334 research outputs found
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EFFECT OF LOW PASS FILTER ON BATHYMETRIC DETECTION IN PULAU PUTRI SHALLOW SEA, KEPULAUAN SERIBU USING PLANETSCOPE SATELLITE IMAGERY
Full text linkSea depth measurements are usually only carried out at locations that can be passed by ships, so measurements in shallow waters are often not possible. Along with the development of remote sensing technology, shallow water bathymetry mapping can now be done using satellite imagery. The Stumpf method is a ratio model that compares two bands in order to reduce the effect of water albedo. The purpose of this research is to study the processing of satellite imagery data for the detection of bathymetry in shallow sea waters, to determine the effect of the low pass filter, and to find out the methods for obtaining detection results with high accuracy. In this study, the primary data used was PlanetScope imagery from the NICFI program. Bathymetry detection of shallow marine waters was carried out around the waters of Putri Island, Seribu Islands Regency. The results of the accuracy test for the detection of shallow sea bathymetry without the application of a low pass filter using the confusion matrix method and the RMSE calculation have higher accuracy with an overall accuracy value of 94.17% and an RMSE value of 1.6
DETECTION OF WATER-BODY BOUNDARIES FROM SENTINEL-2 IMAGERY FOR FLOODPLAIN LAKES
Full text linkThe impact of climate and human interaction has resulted in environmental degradation. Consistent observations of lakes in Indonesia are quite limited, especially for flood-exposure lake types. Satellite imagery data improves the ability to monitor water bodies of different scales and the efficiency of generating lake boundary information. This research aims to detect the boundaries of flood-exposure type lake water bodies from the detection model and calculate its accuracy in Semayang Melintang Lake using Sentinel-2 imagery data. The characteristics of water, soil, and vegetation objects were investigated based on the spectral values of the composite image bands from the Optimum Index Factor (OIF) calculation, to support the lake water body boundary detection model. The Object-Based Image Analysis (OBIA) method is used for water and non-water classification, by applying the machine learning algorithms random forest (RF), support vector machine (SVM), and decision tree (DT). Model validation was conducted by comparing spectral graphs and lake water body boundary model results. The accuracy test used the confusion matrix method and resulted in the highest accuracy value in the SVM algorithm with an Overall Accuracy of 95% and a kappa coefficient of 0.9. Based on the detection model, the area of Lake Semayang Melintang in 2021 is 23392.30 ha. This model can be used to estimate changes in the area of the flood-exposure lake consistently. Information on the boundaries of lake water bodies is needed to control the decline in the capacity and inundation area of flood-exposure lakes for management and monitoring plans
LAND USE/COVER CHANGE ON POTENTIAL LOSS OF SUMATRAN TIGERS IN KERINCI SEBLAT NATIONAL PARK BASED ON REMOTE SENSING DATA
Full text linkThe Sumatran tiger is an animal whose life is threatened due to land use changes and human activities. This study described the correlations between land cover/use change and the potential loss of Sumatran tigers in Kerinci Seblat National Park (KSNP) based on remote sensing data. Remote sensing technology was used due to the good historical data, and it can be used for land cover change analysis. The results of the land change analysis can be used to the analysis of the changes in the suitability level of the Sumatran tiger habitat. The analysis of land change in 2000 and 2020 with the random forest classification method and changes in the level of suitability of the Sumatran Tiger habitat has been carried out. The results of the analysis of land cover/use changes showed a very significant reduction in the area of primary forest, namely 282.58 km2, while the increase in the area of plantations and secondary forests was 186.52 km2 and 101.68 km2. This change affects the suitability level of the Sumatran tiger habitat from a very suitable level decreased from 164.42 km2 to suitable and not suitable. The declining suitability level class indicated the potential loss of Sumatran tigers in the Kerinci Seblat National Park. The increasing of plantation and settlement areas will increase the activity of humans. The conflict of human activity with Sumatran tigers’ life will impact the loss of Sumatran Tigers in KSN
COMPARISON OF DATA ASSIMILATION USING SURFACE OBSERVATION, UPPER AIR, AND SATELLITE RADIATION DATA ON RAINFALL PREDICTION IN THE JAMBI REGION (CASE STUDY OF HEAVY RAIN OCTOBER 20TH, 2020)
Full text linkWeather Research and Forecasting (WRF) is a mesoscale numerical weather prediction model that can provide good rainfall prediction information. The accuracy of the initial conditions and the accuracy of the parameterization scheme used in the WRF model affect the quality of the resulting rainfall prediction. Therefore it is necessary to assimilate to optimize the accuracy of the initial conditions in the model using the Three Dimensional Variational (3DVAR) assimilation technique. The purpose of this study was to determine the effect of applying the 3DVAR assimilation technique with the surface, upper air, and satellite radiation observations in predicting the occurrence of heavy rain on October 20th, 2020, in the Jambi region by first conducting a parameterization test of the cumulus and microphysical schemes. In this study, four experimental schemes were used, namely no assimilation (NON), observation data assimilation (OBS), satellite radiation data assimilation (SAT), and satellite radiation and observation data assimilation (BOTH). Each experimental model result was then verified statistically and spatially to determine the effect of the applied data assimilation. The results of this study indicate that the combination of Grell-3D and Thompson scheme shows the best performance in predicting rainfall. Then based on the spatial analysis of the SAT experiment, it is known that it can improve the model's initial conditions on the temperature and pressure parameters. Meanwhile, based on statistical verification, the SAT experiment improved the accuracy of rainfall predictions with a better forecast skill score than other experiments tested
SPATIAL ANALYSIS OF THE TSUNAMI RISK IN PALABUHANRATU SUB-DISTRICT, SUKABUMI REGENCY, INDONESIA BASED ON THE DISASTER CRUNCH MODELSPATIAL ANALYSIS OF THE TSUNAMI RISK IN PALABUHANRATU SUB-DISTRICT, SUKABUMI REGENCY, INDONESIA BASED ON THE DISASTER CRUNCH
No full textPalabuhanratu Sub-District is one of the southern coastal areas of Java that has the potential to be exposed to tsunamis, with an estimated run-up of between 12-20 meters. Accordingly, it is necessary to conduct tsunami disaster mitigation by analysing the level of tsunami risk in the district to reduce potential losses if a tsunami occurs. This study aims to map the level of tsunami risk in Palabuhanratu Sub-District based on the disaster crunch model, which is a risk model that integrates vulnerability and tsunami hazard factors. The tsunami vulnerability analysis uses a weighted overlay quantitive approach, while the tsunami hazard analysis employs simulation of tsunami propagation by COMCOT V.1.7; the tsunami inundation reduction model; cost distance analysis; and fuzzy membership analysis. The results of the tsunami risk analysis show that villages included in the high-, medium-, and low-risk categories are Citepus, Palabuhanratu, and Jayanti. The percentage of high-risk areas in the three villages are 10% (139 hectares), 20.3% (114 hectares), and 0.01% (0.13 hectares) respectively. The higher the risk of a tsunami in an area, the higher the losses that will be incurred by the local population
A NEW INTERPRETATION OF THE EXISTENCE OF THE PANJANG REGIONAL FAULT BASED ON DEM AND FIELD OBSERVATIONS IN LAMPUNG, SUMATRA, INDONESIAD LAMPUNG, SUMATRA, INDONESIAOBSERVATION AT LAMPUNG, SUMATRA, INDONESIA
No full textReferring to the regional geological map sheet of Tanjung Karang, the existence of the Panjang regional fault in the Sukarame area (the research area) is still debated. This can be seen from the dashed line on the map, which indicates that the existence of the fault is still unclear. The objective of this research is to ascertain the existence of the Panjang Fault, together with information on its type and direction. The method used was to integrate the tectonic geomorphological sections through Digital Elevation Model (DEM) interpretations and field observations result. Field observations were made to confirm the existence of these structures. We found that the Panjang regional fault in the research area does exist. From the south of research area, the fault apparently continues into the research area. It is a normal fault in a northwest-southeast direction. The existence of the fault is also supported by the discovery of water springs during the field observations. The fault has cut aquifers so that the groundwater appears on the surface as water springs
GROUNDWATER LEVEL ESTIMATION MODEL ON PEATLANDS USING SAR SENTINEL-1 DATA IN PART OF RIAU, INDONESIA
Full text linkThe character of peatlands has the ability to store large amounts of water, but the surface of the peatlands dries quickly and easy to burn during the dry season. Research aims to build a model to estimate groundwater level of peatland. Statistical analysis of Karl Pearson Product Moment correlation test was used to determine the relationship between the back scatter values and the Surface Soil Moisture (SSM) values from the Sentinel-1 SAR data processing with the groundwater level values measured using the Sipalaga instrument. Regression analysis was used to determine the model that could be used to estimate the groundwater level of peatlands in the study area based on the results of Sentinel-1 SAR data processing. The results showed that the Sentinel-1 SAR data with the Sigma_0 format in decibel (db) units with VV polarization had the highest correlation value with the groundwater level data of peatlands measured using the Sipalaga instrument, with a value of r -0.648 (moderate correlation). Model to estimate water level of peatlands was Y = -101.629 + (-7.414 x), where 'Y' was the groundwater level of peatlands in the study area and 'x' was the Sentinel-1 SAR data with Sigma_0 format in decibel (db) units with VV polarization. The spatial and temporal patterns of peatlands groundwater level in the study area from Sentinel-1 SAR data showed peatlands that to survive at a water level <40 cm was in the area around of the Rokan River and also in plantation areas, especially Acacia plantations, where canals were made to irrigate and land management
ESTIMATION OF OIL PALM PLANT PRODUCTIVITY USING SENTINEL-2A IMAGERY AT CIKASUNGKA PLANATION PTPN VIII, BOGOR, WEST JAVA
Full text linkPalm oil is one of the commodities that is growing well in Indonesia with a high commercial value which makes the demand for processed palm oil products increase, it is necessary to have data and technology to estimate the productivity of oil palm plantations more efficiently. Remote sensing technology is one of the technologies that can be used to decision problems spatially and accurately, efficiently, and dynamically. One of them is remote sensing using Sentinel-2A imagery. This study aims to analyze the distribution and the accuracy of the NDVI and ARVI algorithms for the estimation of oil palm productivity at the Cikasungka Plantation PTPN VIII. The estimated productivity of oil palm plantations at Cikasungka Plantation varies in each block with an estimated productivity of oil palm plantations of 35,061 Kg/Ha/Month using the algorithm NDVI and ARVI algorithm is 35,431 Kg/Ha/Month. Oil palm productivity was regressed by vegetation index and plant age to generate a model. Based on modeling with these two algorithms, the accuracy of the ARVI algorithm model has a lower RMSE value than NDVI, so it can be said that it is better in estimation of oil palm plant productivity at the Cikasungka Plantation