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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 320 Documents
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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).
UTILIZATION OF SPOT 6/7 AND LANDSAT TO ANALYZE OPEN GREEN SPACE AND BUILT AREA IN SURABAYA CITY Ardha, Mohammad; Sari, Nurwita Mustika; Mukhoriyah, Mukhoriyah
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 21, No 1 (2024)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2024.v21.a3904

Abstract

The migration of people from rural to urban areas is a common phenomenon nowadays. One of the goals of urbanization is in the city of Surabaya. The increase in population causes the need for housing and the need for life to increase. One of the many changes in land use is the change of land into built-up land. The increase in the area of built-up land currently raises a new phenomenon where the area of open space is reduced due to changes in land use, one of the changes in land use is from green open space to built-up land. This study aims to see the extent to which the growth trend of green open space and built-up land in the city of Surabaya by using the NDVI method to see the trend of changes in green open space in the city of Surabaya and NDBI for the land built in the city of Surabaya. The data used in this study are SPOT 7 images for green open space and Landsat 8 for built land. Based on this method, green open space in the city of Surabaya in 2015 was 29.19%, in 2016 it was 21.22%, then in 2017 it was 24.54 %, and in 2018 it was 27.60%. While for Built land in 2015, it was 26.43%, in 2016 it was 26.44%, in 2017 it was 30.99% and in 2018 it was 42.88%. Other results were also obtained for the change of green open space into the land. awakened has increased every year, namely from 2015 to 2016 by 2.67%, from 2016 to 2017 by 4.43%, and from 2017 to 2018 by 8.08%. As for the land built into green open space, namely 2015 to 2016 of 2.01%, 2016 to 2017 of 2.84%, 2017 to 2018 of 2.72%. The conclusion from this activity is that NDVI can be used to see the level of vegetation density which can indicate the existence of green open space in urban areas. And NDBI can show the existence of built-up land. The city of Surabaya, has stable green open space, while the built land continues to increase every year.
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.
FUTURE SUITABILITY OF TEA PLANTS -CLIMATE ANALYSIS USING REMOTE ANALYSIS IN JAVA, INDONESIA Firdaus, Pramudhian; Manessa, Masita Dwi Mandini; Tambunan, Mangapul Parlindungan; Tambunan, Rudy Parlindungan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 1 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3833

Abstract

Tea 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.
COMPARATIVE ACCURACIES USING MACHINE LEARNING MODELS FOR MAPPING OF SUGARCANE PLANTATION BASED ON SENTINEL-2A IMAGERY IN KEDIRI AREA, EAST JAVA Pulungan, Ridson Alfarizal; Nooraeni, Rani
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 21, No 1 (2024)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2024.v21.a3840

Abstract

Data collection in smallholder sugarcane plantations is still very sensitive to the subjectivity of informants and data collectors. In the meantime, the problem with data collection on sugarcane plantation companies is a low response rate. This situation can reduce the precision of the estimates that are produced. Consequently, the goal of this research is to recognize sugarcane fields using the machine learning models on Sentinel-2A satellite imagery in Kediri Area that covering Kediri Regency and Kediri Municipality, East Java. Along with developing machine learning algorithms, this research will evaluate how well LightGBM performs when compared to other algorithms, including CART, SVM, Random Forest, and XGBoost. Each model employed hyperparameter tuning with random search and stratified 10-fold cross validation to avoid overfitting. The process of labelling satellite imagery using images from Google Street View, then predictor variables used are NDVI, NDWI, NDBI, EVI, and elevation. The most accurate classification model obtained was LightGBM, with a 98% accuracy and a cohen’s kappa of 97.7%. The estimated area of sugarcane plantations in the Kediri Regency and Kediri Municipality in September 2022 is 18,897.6 ha and 571.87 ha. 
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.
HYDRODYNAMICS MODELING IN KENDARI BAY, SOUTHEAST SULAWESI, INDONESIA Imalpen, Imalpen; Prartono, Tri; Rastina, Rastina; Koropitan, Alan Frendy; Yuliardi, Amir Yarkhasy
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 21, No 1 (2024)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2024.v21.a3906

Abstract

Kendari Bay is coastal water in the center of the capital city of Southeast Sulawesi province. It is shaped like a pocket with a narrow mouth and there is an estuary of a large river, namely the Wanggu river, which makes the dynamics of its waters very interesting to study. The focus of the study is the hydrodynamic factors in the Kendari Bay and Wanggu River areas. This study aims to examine the hydrodynamic conditions of Kendari Bay, mainly due to the existence of reclamation and the influence of the Wanggu River which has not been studied previously. This research method uses a two-dimensional model based on bathymetric data, tides, and the flow of the Wanggu River with a simulation time of 15 days (1 March to 15 March 2020). The modeling results were then verified with PUSHIDROSAL tidal elevation data showing an RMSE value of 0.07 indicating that the model was well constructed. The mixed tidal type with a tendency to double daily is the tidal type of Kendari Bay waters based on the Formzahl number value of 0.51. The current pattern generally moves in and out from east to west and vice versa with a varying elevation range following spring conditions of 1.75 m. The maximum tidal speed is 0.1784 m/s and the minimum value is 0.0057 m/s which is shown in the sample results of the model when the hing to low tide, and low to high tide. The results of the hydrodynamic modeling show that the current velocity increases when passing through a narrow path, namely the bay estuary and river estuary. The existence of the reclamation area affects the changes in the velocity of the current which is significantly larger and the direction of the current that undergoes a deflection follows the shape of the reclamation area. The current direction is to the southeast and then turns towards the northeast when low to high tide and eastward then turns towards the northeast when the high to low tide spring conditions compared to research before the reclamation
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.
SPATIAL TEMPORAL ANALYSIS OF LAND USE CHANGES IN AREAS VULNERABLE TO EARTHQUAKES AND LANDSLIDES, (Case Study: Cianjur Regency) Noer, Marwah; Mardalena, Ayu; Astuty, Yulia Indri; Rahmadi, Rahmad; Manessa, Masita Dwi Mandini
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 21, No 1 (2024)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2024.v21.a3896

Abstract

Cianjur Regency is a regency that is vulnerable to earthquakes and landslides. This is because the Cianjur Regency is crossed by the Cimandiri Fault which is actively moving. Meanwhile, the population growth rate in Cianjur district has increased based on data from Badan Pusat Statistik (BPS) for 2020-2021. Population growth causes many problems, especially the problem of space. Built-up land will be higher as the population increases. This study uses the temporal spatial analysis method of land use with variables of land use in 2013 and 2022, Earthquake Vulnerability Index, and Landslide Vulnerability Index. This variable was obtained based on the processing of Landsat 8 Satellite Imagery data in 2013 and 2022 and disaster vulnerability raster data from Badan Nasional Penanggulangan Bencana (BNPB). The results of this study are a temporal spatial analysis of changes in land use from 2013 - 2022 for earthquake-vulnerable areas and landslide-vulnerable areas. Changes in the use of built-up land to the Landslide Vulnerability Index experienced an increase in area in all categories. In contrast, the Earthquake Vulnerability Index only experienced an increase in the medium and high categories.

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