cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tika Hairani
Contact Email
jurnal@rmpi.brin.go.id
Phone
+6289674134425
Journal Mail Official
manessa@ui.ac.id
Editorial Address
Gedung S, BAKOSURTANAL, Jln. Raya Jakarta – Bogor Km 46 Cibinong, INDONESIA
Location
Kota bogor,
Jawa barat
INDONESIA
The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Published by Badan Riset dan Inovasi Nasional
ISSN : 02166739     EISSN : 2549516X     DOI : https://doi.org/10.55981/ijreses
Core Subject : Science,
Earth & Planetary Sciences
The International Journal of Remote Sensing and Earth Sciences (IJReSES), published by Badan Riset dan Inovasi Nasional (BRIN) in collaboration with the Ikatan Geografi Indonesia (IGI) and managed by the Department of Geography Universitas Indonesia, is a pivotal platform in the global dissemination of research in earth sciences and remote sensing. It aims to enrich the literature in these fields and serves as a key resource, particularly in Indonesia and Asian countries, while extending its reach worldwide. The journal is instrumental in complementing the body of knowledge in Remote Sensing and Earth Sciences and is committed to fostering the participation of young scientists, especially from Indonesia and Asian countries. Scope and Focus: IJReSES encompasses a wide spectrum of topics related to remote sensing and earth sciences, including but not limited to: - Remote sensing technologies and methodologies - Geospatial data acquisition, processing, and analysis - Earth observation and satellite imagery - Geographic Information Systems (GIS) - Environmental monitoring and management - Climate change and its impacts - Natural resource management - Land use and land cover change - Urban and rural development - Disaster risk reduction and response - Geology and geomorphology - Soil and water sciences - Biodiversity and ecosystem studies
Arjuna Subject : Ilmu Bumi dan Planet (semua kategori) - Ilmu Bumi dan Planet (Lain-Lain)
Articles 327 Documents
 13   14   15   16   17 
THE UTILIZATION OF REMOTE SENSING DATA TO SUPPORT GREEN OPEN SPACE MAPPING IN JAKARTA, INDONESIA Hana Listi Fitriana; Sayidah Sulma; Nur Febrianti; Jalu Tejo Nugroho; Nanik Suryo Haryani
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 2 (2018)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2018.v15.a2890

Abstract

Green open space becomes critical in maintaining the balance of the environment and improving the quality of urban living for a healthy life. The use of remote sensing data for calculation of green open space has been done notably using NDVI (Normalized Difference Vegetation Index) method from Landsat 8 and SPOT data. This research aims to calculate the accuracy of the green open space classification from multispectral data of Landsat 8 and SPOT 6 using the NDVI methods. Green open space could be assessed from the value NDVI. The value of NDVI generated from Landsat 8 and SPOT 6’s Red and NIR channels. The accuracy of NDVI values is then examined by comparing with Pleiades data. Pleiades data which has 50 cm panchromatic resolution and 2 m multispectral with 4 bands (B, G, R, NIR) can precisely visualize objects. So, it can be used as the reference in the calculation of the green open space based on NDVI. The results of the accuracy testing of Landsat 8 and SPOT 6 image could be used to identify the green open space by using NDVI SPOT of 6 can increase the accuracy of 5.36% from Landsat 8.
COMPARING ATMOSPHERIC CORRECTION METHODS FOR LANDSAT OLI DATA Esthi Kurnia Dewi; Bambang Trisakti
International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2016.v13.a2472

Abstract

Landsat data used for monitoring activities to land cover because it has spatial resolution and high temporal. To monitor land cover changes in an area, atmospheric correction is needed to be performed in order to obtain data with precise digital value picturing current condition. This study compared atmospheric correction methods namely Quick Atmospheric Correction (QUAC), Dark Object Subtraction (DOS) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH). The correction results then were compared to Surface Reflectance (SR) imagery data obtained from the United States Geological Survey (USGS) satelite. The three atmospheric correction methods were applied to Landsat OLI data path/row126/62 for 3 particular dates. Then, sample on vegetation, soil and bodies of water (waterbody) were retrieved from the image. Atmospheric correction results were visually observed and compared with SR sample on the absolute value, object spectral patterns, as well as location and time consistency. Visual observation indicates that there was a contrast change on images that had been corrected by using FLAASH method compared to SR, which mean that the atmospheric correction method was quite effective. Analysis on the object spectral pattern, soil, vegetation and waterbody of images corrected by using FLAASH method showed that it was not good enough eventhough the reflectant value differed greatly to SR image. This might be caused by certain variables of aerosol and atmospheric models used in Indonesia. QUAC and DOS made more appropriate spectral pattern of vegetation and water body than spectral library. In terms of average value and deviation difference, spectral patterns of soil corrected by using DOS was more compatible than QUAC.
Back Pages IJReSES Vol. 15, No. 2 (2018) Editor Journal
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 2 (2018)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Back Pages IJReSES Vol. 15, No. 2 (2018)
TECHNIQUE FOR IDENTIFYING BURNED VEGETATION AREA USING LANDSAT 8 DATA Bambang Trisakti; Udhi Catur Nugroho; Any Zubaidah
International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2016.v13.a2447

Abstract

During the last two decades, forest and land fire is a catastrophic event that happens almost every year in Indonesia. Therefore, it is necessary to develop a technic to monitor forest fires using satellite data to obtain the latest information of burned area in a large scale area. The objective of this research is to develop a method for burned area mapping that happened between two Landsat 8 data recording on August 13rd and September 14th 2015. Burned area was defined as a burned area of vegetation. The hotspot distribution during the period August - September 2015 was used to help visual identification of burned area on the Landsat image and to verify the burned area resulted from this research. Samples were taken at several land covers to determine the spectral pattern differences among burned area, bare area and other land covers, and then the analysis was performed to determine the suitable spectral bands or indices and threshold values that will be used in the model. Landsat recorded on August 13rd before the fire was extracted for soil, while Landsat recorded on September 14th after the fire was extracted for burned area. Multi-temporal analysis was done to get the burned area occurring during the certain period. The results showed that the clouds could be separated using combination of ocean blue and cirrus bands, the burned area was extracted using a combination of NIR and SWIR band, while soil was extracted using ratio SWIR / NIR. Burned area obtained in this study had high correlation with the hotspot density of MODIS with the accuracy was around 82,4 %.
TECHNIQUE TO RECONSTRUCT BAND 6 REFLECTANCE INFORMATION OF AQUA MODIS Andy Indradjad; Noriandini Dewi Salyasari; Rahmat Arief
International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2016.v13.a2449

Abstract

Remote sensing data could experience damage due to sensor failure or atmospheric condition. Reconstruction technique to retrieve the missing information had been widely developed in the past few years. This writing aimed to provide a technique to recover reflectance information of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Band 6. Since Band 6 Aqua MODIS experienced sensor failure, lots of information would be missing. There were three kinds of methods used in repairing such damage. Two of which were categorized as spatial-based methods, i.e. NaN interpolation method and tensor completion method. Whereas, another method was a spectral-based one. NaN was an interpolation method to reconstruct missing value; while tensor completion method utilized low rank approximation, and spectral method used correlation between Band 6 and Band 7 which had near wavelength. Implementation of these methods was resulted in reconstruction of Aqua Modis Band 6 data which was damaged due to detector disfunction on Aqua Satellite. Peak Signal to Noise Ratio (PSNR) value of this method was 41 dB, meaning that reconstruction technique provided positive impacts for data improvement.
IDENTIFICATION AND CLASSIFICATION OF FOREST TYPES USING DATA LANDSAT 8 IN KARO, DAIRI, AND SAMOSIR DISTRICTS, NORTH SUMATRA Heru Noviar; Tatik Kartika
International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2016.v13.a2477

Abstract

Forests have important roles in terms of carbon storage and other values. Various studies have been conducted to identify and distinguish the forest from non-forest classes. Several forest types classes such as secondary forests and plantations should be distinguished related to the restoration and rehabilitation program for dealing with climate change. The study was carried out to distinguish several classes of important forests such as the primary dryland forests, secondary dryland forest, and plantation forests using Landsat 8 to develop identification techniques of specific forests classes. The study areas selected were forest areas in three districts, namely Karo, Dairi, and Samosir of North Sumatera Province. The results showed that using composite RGB 654 of Landsat 8 imagery based on test results OIF for the forest classification, the forests could be distinguished with other land covers. Digital classification can be combined with the visual classification known as a hybrid classification method, especially if there are difficulties in border demarcation between the two types of forest classes or two classes of land covers.
HAZE REMOVAL IN THE VISIBLE BANDS OF LANDSAT 8 OLI OVER SHALLOW WATER AREA Kustiyo; Kamilah Hayati
International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2016.v13.a2445

Abstract

Haze is one of radiometric quality parameters in remote sensing imagery. With certain atmospheric correction, haze is possible to be removed. Nevertheless, an efficient method for haze removal is still a challenge. Many methods have been developed to remove or to minimize the haze disruption. While most of the developed methods deal with removing haze over land areas, this paper tried to focus to remove haze from shallow water areas. The method presented in this paper is a simple subtraction algorithm between a band that reflected by water and a band that absorbed by water. This paper used data from Landsat 8 with visible bands as a band that reflected by water while the band that absorbed by water represented by NIR, SWIR-1, and SWIR-2 bands. To validate the method, a reference data which relatively clear of cloud and haze contamination is selected. The pixel numbers from certain points are selected and collected from data scene, results scene and reference scene. Those pixel numbers, then being compared each other to get a correlation number between data scene to reference scene and between result scene and reference scene. The comparison shows that the method using NIR, SWIR-1, and SWIR-2 all significantly improved correlations numbers between result scene with reference scene to higher than 0.9. The comparison also indicates that haze removal result using NIR band had the highest correlation with reference data.
Back Pages IJReSES Vol. 13, No. 2(2016) Editorial Journal
International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Back Pages IJReSES Vol. 13, No. 2(2016)
COMPARISON OF MODEL ACCURACY IN TREE CANOPY DENSITY ESTIMATION USING SINGLE BAND, VEGETATION INDICES AND FOREST CANOPY DENSITY (FCD) BASED ON LANDSAT-8 IMAGERY (CASE STUDY: PEAT SWAMP FOREST IN RIAU PROVINCE) Faisal Ashaari; Muhammad Kamal; Dede Dirgahayu
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 1 (2018)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2018.v15.a2845

Abstract

Identification of a tree canopy density information may use remote sensing data such as Landsat-8 imagery. Remote sensing technology such as digital image processing methods could be used to estimate the tree canopy density. The purpose of this research was to compare the results of accuracy of each method for estimating the tree canopy density and determine the best method for mapping the tree canopy density at the site of research. The methods used in the estimation of the tree canopy density are Single band (green, red, and near-infrared band), vegetation indices (NDVI, SAVI, and MSARVI), and Forest Canopy Density (FCD) model. The test results showed that the accuracy of each method: green 73.66%, red 75.63%, near-infrared 75.26%, NDVI 79.42%, SAVI 82.01%, MSARVI 82.65%, and FCD model 81.27%. Comparison of the accuracy results from the seventh methods indicated that MSARVI is the best method to estimate tree canopy density based on Landsat-8 at the site of research. Estimation tree canopy density with MSARVI method showed that the canopy density at the site of research predominantly 60-70% which spread evenly.
ANALYSIS OF LAND USE SPATIAL PATTERN CHANGE OF TOWN DEVELOPMENT USING REMOTE SENSING Samsul Arifin; Mukhoriyah; Dipo Yudhatama
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 1 (2018)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2018.v15.a2795

Abstract

The Assessment of the physical character of a city is considered relatively easier than the social-cultural aspects. It is important to recognize the type of city form and to predict the behavior of people in the city and its surrounding. Due to those characteristics, the study of the pattern of physical development of the city is required. The objective of research is to analyze the change of spatial pattern of the city due to the city growing by remote sensing. The multitemporal data of Landsat 5/7/8 year 2000, 2006 and 2015 in Jabodetabek area were used. The classification technique had been done and it produced five classes of land uses. Those are water, built-up area, vegetation, other land use and no data. The results of the analysis in Jabodetabek area (Jakarta, Bogor, Depok, Tangerang and Bekasi) show that there was land use changes from vegetation and other land use area to built-up area with an average accuracy of 78% in each year. The pattern of physical development of the city looks linear from year 2000 until year 2006, which is confirmed as concentric pattern from year 2006 to 2015. Based on those analysis, it confirmed that the city development in Jakarta as the center was influenced by the spatial land development of the surrounding cities of Depok, Bogor, Bekasi and Tangerang. The pattern of spatial development from 2000 to 2006 in Bogor, Bekasi and Depok areas is Linear pattern, whereas from 2006 - 2015 the pattern of spatial development shows Propagation Concentric pattern. For Tangerang Region in 2000-2015 its development is patterned Propagation Concentric.

Page 15 of 33 | Total Record : 327

 13   14   15   16   17 

Filter by Year

2004 2024


Reset
Filter By Issues
All Issue Vol. 21 No. 2 (2024) Vol. 21 No. 1 (2024) Vol. 20 No. 2 (2023) Vol. 20 No. 1 (2023) Vol. 19 No. 2 (2022) Vol. 19 No. 1 (2022) Vol. 18 No. 2 (2021) Vol. 18 No. 1 (2021) Vol. 17 No. 2 (2020) Vol. 17 No. 1 (2020) Vol. 16 No. 2 (2019) Vol. 16 No. 1 (2019) Vol. 15 No. 2 (2018) Vol. 15 No. 1 (2018) Vol. 14 No. 2 (2017) Vol. 14 No. 1 (2017) Vol. 13 No. 2 (2016) Vol. 13 No. 1 (2016) Vol. 12 No. 2 (2015) Vol. 12 No. 1 (2015) Vol. 11 No. 2 (2014) Vol. 11 No. 1 (2014) Vol. 10 No. 2 (2013) Vol. 10 No. 1 (2013) Vol. 9 No. 2 (2012) Vol. 9 No. 1 (2012) Vol. 8 (2011) Vol. 7 No. 1 (2010) Vol. 6 (2009) Vol. 5 (2008) Vol. 4 (2007) Vol. 3 (2006) Vol. 2 (2005) Vol. 1 No. 1 (2004) More Issue