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International Journal of Remote Sensing and Earth Sciences (IJReSES)
Published by Lembaga Penerbangan dan Antariksa Nasional
ISSN : 02166739     EISSN : 2549516X     DOI : -
Core Subject : Science,
Earth & Planetary Sciences
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.
Arjuna Subject : -
Articles 11 Documents
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Search results for , issue "Vol 20, No 2 (2023)" : 11 Documents clear
THE RELATIONSHIP BETWEEN LAND USE AND LAND COVER TO RUN-OFF COEFFICIENT VALUE IN BRANTAS WATERSHED AREA, TULUNGAGUNG - EAST JAVA, INDONESIA Bowo Eko Cahyono; Asih Sumarlin; Nurul Priyantari; Katsunoshin Nishi
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3851

Abstract

The Ngrowo-Ngasinan sub-watershed is a part of Brantas Watershed which has an important role for the aquatic ecosystems in the Brantas watershed. Land cover changes in this sub-watershed can be identified by utilizing remote sensing technology. The use of remote sensing technology by applying Landsat 8 image data can be done by classifying several classes of land cover in the study area. Land cover affected the flow rate of a watershed because of its association with several problems due to the conversion of land. Land cover which influences the watershed ecosystems is forest. In addition to land cover, regional rainfall also affects the flow rate (runoff) in the area
Back Pages IJReSES Vol. 20, No. 2 (2023) Journal Manager
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3903

Abstract

Back Pages IJReSES Vol. 20, No. 2 (2023)
COMPARISON OF THE MANGROVE FOREST MAPPING ALGORITHMS IN KELABAT BAY USING RANDOM FOREST AND SUPPORT VECTOR MACHINES Rahmadi Rahmadi; Raldi Hendrotoro Seputro Koestoer
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3885

Abstract

One of the tropical ecosystems is the mangrove forest, which thrives on protected coastlines such as bays, estuaries, lagoons, and rivers. These are usually found in the intertidal zone. Mangroves are a valuable natural resource because they stabilize coastlines, prevent erosion, retain sediment and nutrients, protect against storms, regulate floods and currents, sequester carbon, maintain water quality, serve as spawning grounds for fish and other marine life, and provide food For plankton. With over 59.8% of the total area of mangroves on the planet, Indonesia has some of the largest mangrove forests in the world. With the case study of Kelabat Bay in Bangka Regency and the Bangka Belitung Islands, this study compares the use of random forest (RF) techniques and support vector machines (SVM) for mapping mangrove forests. Landsat-9 imagery from 2022, taken via the Google Earth Engine (GEE), is the data source used in this study. This study utilizes computer programming and accuracy testing. As a result, RF detected mangrove forests covering an area of approximately 67 ha (OA: 0.932), while SVM detected mangrove forests covering an area of approximately 62 ha (OA: 0.912).
ENVIRONMENT QUALITY IDENTIFICATION USING LANDSAT-8 IN THE PERIOD OF COVID-19 LOCKDOWN IN JAKARTA Khalifah Insan Nur Rahmi; Mangapul Parlindungan Tambunan; Rudy Tambunan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3850

Abstract

The quality of the urban environment during the Covid-19 lockdown became a concern because it was reported that it had improved but the spatial studies were still limited. Spatial information at regional scale can be extracted from Landsat-8 imagery. This study aims to spatially and temporally analyze environmental quality variables from Landsat-8 Imagery and compare environmental quality indices before, during and after the Covid-19 lockdown in Jakarta. Environmental quality variables extracted from Landsat-8 imagery are PM10, LST, NDVI, NDWI, NDMI. Radiometric correction and masking were applied to obtain Landsat-8 reflectance and radian values. PM10 concentrations were estimated using linear regression between station data and visible-near infrared (VNIR) reflectance band values. The variable land surface temperature (LST) is obtained from the brightness temperature band 10 extraction. NDVI, NDWI, and NDMI are extracted from the transformation of the reflectance band index. The environmental quality index is extracted from a weighted linear combination method where each variable has a weighted value of 50% PM10, 31% LST, 11% NDVI, 5% NDWI, and 3% NDMI. The results of the distribution of the environmental quality index before, during and after the Covid-19 lockdown show changes. Before the lockdown, some areas in Jakarta had a poor environmental quality index, while during the lockdown, only a few areas were still of poor quality, including the reclamation island and the Cilincing industrial area, North Jakarta. After the lockdown, the environmental quality index decreased again i.e. good, medium and bad categories but the distribution was not as wide as before the lockdown.
UTILIZING REMOTE SENSING AND MACHINE LEARNING FOR ECOSYSTEM SERVICES MAPPING AT GUNUNG MAS TEA PLANTATION Fitria, Annisa; Manessa, Masita Dwi Mandini; Tambunan, Rudy Parluhutan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3880

Abstract

Land use and land cover changes are one of the main factors affecting ecosystems and the services they provide. Conversion from natural vegetation to agricultural and urban land can lead to the degradation of ecosystem services and loss of biodiversity. Puncak area, Bogor, which is a highland area, has become an area that is synonymous with tea plantations because it has an ecosystem that is suitable for being a tea plantation area. Gunung Mas tea plantation managed by PTPN VIII is one of the largest tea plantations and a contributor to foreign exchange in Indonesia. The tourism potential in the plantation and agricultural business sectors has a high selling value as a tourist object and attraction. The purpose of this study is to find out the distribution of ecosystem services for climate regulation, water flow and flood regulation, and ecotourism and cultural recreation services at Gunung Mas tea plantation which is displayed in the form of an Ecosystem Service Map. The land cover classification was extracted from the Sentinel 2A image, which was then scored based on expert judgment. The scoring results are then processed using the AHP Pairwise Comparison method. The results of the study show that the research area has very high climate regulation ecosystem services, very high water flow and flood regulation, and high cultural recreation and ecotourism ecosystem services. Keywords: AHP, Ecosystem Services, Land Use and Land Cover, Supervised classification, Tea Plantation.
ASSESSING THE POSSIBILITY OF LAND SUBSIDENCE DUE TO GEOTHERMAL PRODUCTION IN SARULLA GEOTHERMAL FIELD USING SENTINEL-1 Mochamad Iqbal; Panggea Ghiyats Sabrian
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3843

Abstract

Sarulla geothermal field is one of the largest geothermal fields in the world which has a 330 MW installed capacity. The field consists of three areas, namely Namora Langit (NIL)-1, NIL-2, and Silangkitang (SIL) which operated from 2017 and 2018. It is situated precisely at the Sarulla graben which is an active tectonic area composed of Quaternary Toba tuff and intermediate lava and extrusive felsic pyroclastic Toru. This study aims to see whether land subsidence may emerge in the Sarulla geothermal field and its environs in addition to determining whether the geothermal activity or anthropogenic is responsible for the deformation. We used the persistent scatterer (PS) interferometry synthetic aperture radar (InSAR) method to calculate the rate of subsidence in the area. 30 ascending images from Sentinel-1 were gathered from 5 January to 18 December 2020 with a separation of 12 days to run the analysis. The results demonstrate that Sarulla is undergoing subsidence occurring at NIL and SIL with a velocity of 0 to -32.9 mm/year. Although negative deformation occurs in the geothermal area, there is no solid evidence indicating geothermal fluid extraction is the cause of subsidence.
SPATIAL ANALYSIS OF LAND USE AND LAND COVER VARIATIONS AFFECTING TEA PRODUCTION IN GUNUNGMAS PLANTATION THROUGH REMOTE SENSING TECHNIQUES Paramita, Elok Lestari; Manessa, Masita Dwi Mandini; Tambunan, Mangapul Parlindungan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3888

Abstract

Tea is a manufactured beverage that is popular around the world. In value chain analysis to increase efficiency, remote sensing technology can be developed to monitor the phenomenon of land use land cover (LULC) change and vegetation health conditions. This study aims to identify LULC in tea plantations, identify the health condition of tea plantations, then analyze spatial trends of changes in tea productivity in Gunungmas Afdeling-1 due to changes in tea area or tea vegetation health condition. Identification of changes in LULC in tea plantations can be carried out using remote sensing technology and machine learning, in this study, Google Earth Engine (GEE) LULC identification was generated using a supervised classification with the random forest algorithm on the GEE. Tea productivity trends decreased from 2019 to 2020, but increased from 2020 to 2021. They show that the trend of changes in the area of tea plantation classification is decreasing. According to the NDVI result, most of the reduced area of tea plantations is in areas with healthy vegetation. The trends in tea productivity changes are not in line with changes in the LULC area of tea plantation classification class and tea vegetation health condition.
RESIDENTIAL CLASSIFICATION USING GEOBIA IN PART OF JAKARTA SUBURBAN AREA Akmal Hafiudzan; Prima Widayani; Nurwita Mustika Sari
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3862

Abstract

The increasing of urban population followed by socioeconomic problems leads to emerging various number of researchs in urban area, especially in Jakarta Metropolitan Area. One of them are escalated tension-conflict due to rise of newly Gated Communities residential that sprawl across local residents (Kampung Kota). There is urgency to map all 3 types of residential (Kampung Kota, Perumnas, Cluster) through satellite imagery on a wide-scale. This study uses WorldView-2 imagery data recorded for 2020. The method used is an object-based method, namely GEOBIA using the eCognition Developer 64 software. The GEOBIA process is carried out through three stages, firstly the segmentation to separate residential blocks from surrounding land cover objects (bodies of water, vegetation, open land, non-residential built-up land) as well as exploring the variable values of each object, then sample-based classification using the SVM algorithm on Google Earth Engine application, and accuracy test to evaluate semantic and geometric accuracy levels. The results of the mapping are 3 classes of residential types followed by 4 classes of land cover. The overall accuracy of the three types of residential is 80% which means that the GEOBIA approach is able to show good performance.
EFFECT OF LOW PASS FILTER ON BATHYMETRIC DETECTION IN PULAU PUTRI SHALLOW SEA, KEPULAUAN SERIBU USING PLANETSCOPE SATELLITE IMAGERY Alberto Junior Hutagaol; Kuncoro Teguh Setiawan; Muhammad Sulaiman Nur Ubay; Hastuadi Harsa
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3897

Abstract

Sea 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 1.61
SPATIAL ANALYSIS OF QUANTITATIVE PRECIPITATION FORECAST ACCURACY BASED ON STRUCTURE AMPLITUDE LOCATION (SAL) TECHNIQUE Abdullah Ali; Achmad Rifani; Supriatna Supriatna; Yunus Subagyo Swarinoto; Umi Sa'adah
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3854

Abstract

Quantitative Precipitation Forecast (QPF) is the final product of a short-term forecasting algorithm (nowcasting) based on weather radar data which is widely used in hydrometeorological aspects. The calculation of the accuracy value using point data on a rainfall gauge often causes a double penalty problem because the QPF prediction results are in the form of spatial objects. This study aims to apply object-based spatial verification in analyzing the accuracy of QPF based on the Short Term Ensemble Prediction System (STEPS) algorithm using the SAL technique. The verification process is carried out by calculating the index value of the structure component (S), amplitude (A), and location (L) in the QPF prediction results based on the results of weather radar observations. The index values for components S and A have a range of -2 to 2, and 0 to 1 for component L with a perfect value of 0. The case study used is the occurrence of heavy rains that caused flooding in Bogor Regency in 2020. SAL verification results from 26 case studies used shows the average value of the components S, A, and L, respectively 0.51, 0.38, and 0.21. As many as 75% of all case studies have S and L component values less than 0.5 which indicate the structure and location of the QPF prediction object is close to the structure and location of the object of observation. A positive value in component A indicates that the QPF prediction results based on the STEPS algorithm tend to be overestimated but on a low scale, namely 0.38 out of 2.

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