International Journal of Remote Sensing and Earth Sciences (IJReSES)
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TSUNAMI HAZARD MODELING IN THE COASTAL AREA OF KULON PROGO REGENCY
Full text linkKulon Progo Regency is located in the southern part of Java Island, one of Indonesia's areas that is prone to tsunami disasters. Kulon Progo Regency is prone to tsunamis because it faces a subduction zone in the Indian Ocean. Therefore, it is necessary to model tsunami inundation and map the tsunami hazard zone in the Kulon Progo coastal area. This study aims to model tsunami inundation and produce a tsunami hazard map with a tsunami height scenario of 5 meters and 10 meters. The method used in modeling tsunami inundation is using a mathematical calculation developed by Berryman-2006 using the parameters of the coefficient of surface roughness, slope, and the height of the tsunami at the coastline. The estimated tsunami inundation area is classified into a tsunami hazard index using the fuzzy logic method resulting in an index of 0 – 1, which is then divided into three hazard classes. The results of the tsunami hazard mapping with the 5 meters scenario are 15 villages in 4 sub-districts included in the hazard zone with a total area of 20672,34 Ha affected. The results of the tsunami hazard mapping with a 10 meters scenario are 26 villages in 4 sub-districts with a total area of 53042,66 Ha affected. The results of this research can be used as basic information for disaster mitigation
PLATFORM REEF LAGOON DETECTION FROM SENTINEL-2 IN PANGGANG ISLAND AND SEMAKDAUN ISLAND
Full text linkProcessing of satellite image data for the detection of platform reef lagoons is intended as one of the geo-physical parameters of the reef landform. Panggang Island and Semakdaun Island were chosen to make the detection model because they are ideal for lagoon reef landforms and tapulang court reefs. This model is only valid in the continental shelf area and the back arc and small island tectonic type. Determination of this location is done to improve the accuracy of spectral-based data processing. Platform reefs are one of four classes of reef landforms. Sentinel-2A data with a spatial resolution of 10m, blue, green, red, and near infrared bands were selected to investigate their ability to detect lagoons. Processing of data by calculating the Optimum Index Factor (OIF) to produce a composite image and drawing transect lines to produce pixel values and spectral graphics of the lagoon. The results of data processing in the form of graphs, composite images and pixel values were built to realize a digital lagoon detection model. These results are used for lagoon growth stage analysis for the classification of three reef platform landforms, visually and digitally interpretation. This digital and visual detection system design is useful for monitoring coral reef ecosystems
VEGETATION INDICES FROM LANDSAT-8 DATA IN PALABUHANRATU
Full text linkLand cover will change due to population pressure, resource use, and human interest in space. Measuring the land area is important to determine how much-converted land is positive and negative. The vegetation on land was determined by how densely the plants were spread out. This study is conducted in Palabuhanratu, Sukabumi Regency. Aims to test and compare how accurate EVI and SAVI are at seeing vegetation density. The images used are from Landsat 8 in 2018 and 2022. Calibration is performed using high-resolution images, followed by field surveys with 98 points from polygon sampling. The average accuracy of the results from EVI is 49%, while the average accuracy of the results from SAVI is 45%. So, we can say that the EVI or SAVI based-input gives a similar result on observing the vegetation density in Palabuhanratu
FUTURE SUITABILITY OF TEA PLANTS -CLIMATE ANALYSIS USING REMOTE ANALYSIS IN JAVA, INDONESIA
Full text linkTea production is highly dependent on the geographical and climatic conditions of the environment where the plants are grown and on the crisis of climate change from time to time. Therefore, an analysis is needed to determine the impact of climatic conditions on the tea production industry, especially in Indonesia. Precipitation and temperature are the contributing factors to the productivity of tea. This phenomenon can be understood through analysis and projection of climate. This analysis can be utilized for mitigation and adaptation to applied climate in Indonesia's agriculture sector, especially in the industrial production of tea. By comparing the analysis of climate for tea in the past 1991 – 2020 period and the projection of future climate in the period 2051 – 2070, this study explains climate analysis to the production of tea, especially in Gunung Mas and Java Island, Indonesia. The result shows that climate analysis in the past in period 1991 – 2020, obtained existence influence and trend change to bulk available rain and temperature for the region Gunung Mas and its surroundings. Projection suitability land industry plant tea based on scenario future climate seen the impact with decrease suitable area as land growth plant tea. Climate scenarios RCP 4.5 and RCP 8.5 for 2070 show the influence of climate impact on the suitability of the tea plantation land industry
AUTOMATION OF DAILY LANDSLIDE POTENTIAL INFORMATION BASED ON REMOTE SENSING SATELLITE IMAGERY USING OPEN-SOURCE SOFTWARE TECHNOLOGY
Full text linkThis automation system automatically generated landslide potential information based on daily precipitation data. This system is essential to replace the previous manual processing system with an automated and integrated system. The results of the developed system are the distribution of areas with landslide potential based on daily precipitation data. The system was built using geographic information systems and web service techniques. This allows the automation process to be performed quickly and accurately. The landslide susceptibility map used is from the National Disaster Management Agency, so the information is more reliable. Himawari-8 is used to determine the potential for extreme precipitation in 10 minutes because this satellite has a very high temporal resolution. The system is already in use and has proven to replace manual processing and is faster. Further development will be more challenging if the system can be connected to the sensors installed on site so that the sensors on site can issue a landslide warning in case of extreme precipitation so that the surrounding communities can respond immediately. Opportunities for future development of the system may also be incorporated into landslide potential prediction based on the precipitation forecast mode
EFFECT OF ATMOSPHERIC CORRECTION ALGORITHM ON LANDSAT-8 AND SENTINEL-2 CLASSIFICATION ACCURACY IN PADDY FIELD AREA
Full text linkLandsat-8 and Sentinel-2 satellite imageries are widely used for various remote sensing applications because they are easy to access and free to download. A precise atmospheric correction is necessary to be applied to the optical satellite imageries so that the derived information becomes more accurate and reliable. In this study, the performance of atmospheric correction algorithms (i.e., 6S, FLAASH, DOS, LaSRC, and Sen2Cor) was evaluated by comparing the object's spectral response, vegetation index, and classification accuracy in the paddy field area before and after the implementation of atmospheric correction. Overall, the results show that each algorithm has varying accuracy. Nevertheless, all atmospheric correction algorithms can improve the classification accuracy, whereby those derived by the 6S and FLAASH yielded the highest accuracy
COMPARISON OF MACHINE LEARNING ALGORITHMS FOR LAND USE AND LAND COVER ANALYSIS USING GOOGLE EARTH ENGINE (CASE STUDY: WANGGU WATERSHED)
Full text linkHuman population growth and land use and land cover (LULC) change have always developed side by side. Considering selection of a good Machine Learning (ML) classifier algorithm is needed considering the high estimation of LULC maps based on remote sensing. This study aims to produce a LULC classification of Landsat-8 and Sentinel-2 images by comparing the accuracy performance of three ML algorithms, namely: Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM). Dataset comparison ratios were also explored to find the LULC classification results with the best accuracy. Sentinel-2 is better than Landsat-8 regarding Overall Accuracy (OA) and Coefficient Kappa. The comparison ratio of the training and testing datasets with a good level of accuracy is 70:30 on both images with the average OA Landsat-8 and Sentinel-2 being 92.09% and 94.21%, respectively. The RF algorithm outperforms CART and SVM in both types of satellite imagery. The mean OA of the CART, RF, and SVM classifiers was 92.03%, 94.74%, 83.54% on Landsat-8, 93.14%, 96.15%, and 93.34% on Sentinel-2, respectively