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International Journal of Remote Sensing and Earth Sciences (IJReSES)
ISSN : 02166739     EISSN : 2549516X     DOI : -
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.
Articles 10 Documents
Search results for , issue " Vol 12, No 2 (2015)" : 10 Documents clear
DETECTION OF FOREST FIRE, SMOKE SOURCE LOCATIONS IN KALIMANTAN DURING THE DRY SEASON FOR THE YEAR 2015 USING LANDSAT 8 FROM THE THRESHOLD OF BRIGHTNESS TEMPERATURE ALGORITHM Kustiyo, .; Dewanti, Ratih; Lolitasari, Inggit
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (705.87 KB) | DOI: 10.30536/j.ijreses.2015.v12.a2692

Abstract

Almost every dry season, there are large forest/land fires in several regions in Indonesia, especially in Kalimantan and Sumatra in the dry season of August to September 2015 a forest fire in 6 provinces namely West Kalimantan, Central Kalimantan, South Kalimantan, Riau, Jambi, and South Sumatra. Even some parties proposed that the Government of Indonesia declares them as a national disaster. The low-resolution remote sensing data have been widely used for monitoring the occurrence of forest/land fires (hotspots), and mapping of  burnt scars. The hotspot detection was done by utilizing the data of NOAA-AVHRR and MODIS data which have a lower spatial resolution (1 km). In order to increase the level of detail and accuracy of product information, this research is done by using Landsat 8 TIRS (Thermal Infrared Sensor) band which has a greater spatial resolution of 100 m. The purpose of this research is to find and to determine the threshold value of the brightness temperature of the TIRS data to identify the source of fire smoke. The data used is the Landsat 8 of several parts of Borneo during the period of 24 August to 18 September 2015 recorded by the LAPAN's receiving station. Landsat - 8 TIRS band was converted into brightness temperature in degrees Celsius, then dots in a region that is considered the source of the smoke if the temperature of each pixel in the region > 43oC, and given the attributes with the highest temperatures of the pixels in the region. The source of the smoke was obtained through visual interpretation of the objects in the multispectral Natural Color Composite (NCC) and True Color Composite (TCC) images. Analysis of errors (commission error) is obtained by comparing the temperature detected by TIRS band with a visual appearance of the source of the smoke. The result of the experiment showed that there were detected 9 scenes with high temperatures over 43oC from the 27 scenes Kalimantan Landsat 8 data, which include 153 sites. The accuracy (commission error) of identification results using temperature ≥ 51°C is 0%, temperature ≥ 47°C is 10%, and temperature ≥ 43°C is 30.5%.
WATER CLARITY MAPPING IN KERINCI AND TONDANO LAKE WATERS USING LANDSAT 8 DATA Trisakti, Bambang; Suwargana, Nana; Parsa, I Made
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (967.704 KB) | DOI: 10.30536/j.ijreses.2015.v12.a2693

Abstract

Land conversion occurred in the lake catchment area caused the decreasing of water quality in many lakes of Indonesia. According to Lake Ecosystem Management Guidelines from Ministry of Environment, tropic state of lake water is one of parameters for assessing the lake ecosystem status. Tropic state can be indicated by the quantity of nitrogen, phosphorus, chlorophyll, and water clarity. The objective of this research is to develop the water quality algorithm and map the water clarity of lake water using Landsat 8 data. The data were standardized for sun geometry correction and atmospheric correction using Dark Object Subtraction method. In the first step, Total Suspended Solid (TSS) distributions in the lake were calculated using a semi empirical algorithm (Doxaran et al., 2002), which can be applied to a wide range of TSS values. Secchi Disk Transparency (SDT) distributions were calculated using our water clarity algorithm that was obtained from the relationship between TSS and SDT measured directly in the lake waters. The result shows that the water clarity algorithm developed in this research has the determination coefficient that reaches to 0,834. Implementation of the algorithm for Landsat 8 data in 2013 and 2014 showed that the water clarity in Kerinci Lake waters was around 2 m or less, but the water clarity in Tondano Lake waters was around 2 – 3 m. It means that Kerinci Lake waters had lower water clarity than Tondano Lake waters which is consistent with the field measurement results.
IDENTIFICATION OF LAND SURFACE TEMPERATURE DISTRIBUTION OF GEOTHERMAL AREA IN UNGARAN MOUNT BY USING LANDSAT 8 IMAGERY Nugroho, Udhi C.; Domiri, Dede Dirgahayu
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (1019.167 KB) | DOI: 10.30536/j.ijreses.2015.v12.a2708

Abstract

Indonesia located at the confluence of Eurasian tectonic plate, Australian tectonic plate and the Pacific tectonic plate. Therefore, Indonesia has big geothermal potential. One of the areas that has geothermal potential is Ungaran Mount. Remote sensing technology can have a role in geothermal exploration activity to map the distribution of land surface temperatures associated with geothermal manifestations. The advantages of remote sensing are able to get information without having to go directly to the field with a large area, and it takes quick, so that the information can be used as an initial reference exploration activities. This study aimed to obtain the distribution of land surface temperature as a regional analysis of geothermal potential. The method of this research was a correlation of brightness temperature (BT) Landsat 8 with land surface temperature (LST) MODIS. The results of correlation analysis showed the R2 value was equal to 0.87, it shows that between BT Landsat 8 and LST MODIS has a very high correlation. Based on Landsat 8 LST imagery correction, the average of fumarole temperature and hot spring is 240C. Fumarole and hot spring are located in dense vegetation land which has average temperature around 26.90C. Land surface temperature Landsat 8 can not be directly used to identify geothermal potential, especially in the dense vegetation area, due to the existence of dense vegetation which can absorb heat energy released by geothermal surface feature.
HEIGHT MODEL INTEGRATION USING ALOS PALSAR, X SAR, SRTM C, AND ICESAT/GLAS Julzarika, Atriyon
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (725.405 KB) | DOI: 10.30536/j.ijreses.2015.v12.a2691

Abstract

The scarcity of height models is one of the important issues in Indonesia. ALOS PALSAR, X SAR, SRTM C, and ICESAT/GLAS are free available global height models. Four data can be integrated the height models. Integration takes advantage of each characteristic data. The spatial resolution uses ALOS PALSAR. ICESAT/GLAS has a minimal height error because it is DTM. SAR has advantages of minimal error in the highland and need a low pass filter on the lowland. DSM uses X SAR and DEM from ALOS PALSAR. Characteristics and penetration of vegetation objects can be seen from the wavelength type of SAR data. This research aims to make height model integration in order to get the vertical accuracy better than vertical accuracy of global height models and minimum height error. The study area is located in Karo Regency. The first process is to crop the height models into Karo Regency, geoid undulation correction using EGM 2008. The next step is to detect pits and spires by using radius value 1000 m and depth +1.96σ (+5 m) with uncertainty 95,45%. Then generate HEM and height model integration. To know the accuracy of this height model, 100 reference points measured using GNSS, altimeter, and similar point observed on the height model integration are selected. The accuracy test covers RMSE, accuracy (z), and height difference test. The result of this study shows that the height model integration has a vertical accuracy in 1.14 m. This height model integration can be used for mapping scale 1: 10.0000.
THE EFFECT OF HYDROLOGIC RESPONSE UNIT ON CI RASEA WATERSHED STREAMFLOW BASED ON LANDSAT TM Emiyati, .; Kusratmoko, Eko; Sobirin, .
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (1040.223 KB) | DOI: 10.30536/j.ijreses.2015.v12.a2689

Abstract

. This paper discusses spatial pattern of Hydrologic Response Unit (HRU), which is a unit formed of hydrological analysis, including geology and soil type, elevation and slope, and also land cover in 2009. This paper also discusses the impact of HRU on streamflow of Ci Rasea watershed, West Java. Ci Rasea watershed is located at the upstream part of Ci Tarum watersheds in West Java Province, Indonesia. This research used SWAT (Soil and Water Assessment Tool) model to obtain spatial HRU and river flow. The method used Landsat TM data for land cover and daily rainfall for river flow modeling. The results have shown spatial pattern of HRU which was affected by land cover, soil type and slope. In 2009, accumulated surface runoff and streamflow changes were spatially affected by HRU changes. The large amount accumulation of river flow discharge happened in HRU with landcover paddy field, silty clay soil, and flat slope. While the low discharge of river flow happened in HRU with plantation, clay soil, and slightly steep slopes as HRU dominant. It was found that accumulation of surface runoff in Ci Rasea watershed can be reduced by changing the land cover type in some areas with clay and slightly steep slope to become plantation area and the areas with sandy loam soil and flat slope can be used for paddy fields. Beside affected by HRU, the river flow discharge was also affected by the distance of sub watershed to the outlet. By using NS model and statistical t-student for calibration and validation, it was obtained that the accuracy of river flow models with HRU was 70%. It meant that the model could better simulate water flows of the Ci Rasea watershed.
ANALYSIS ON THE QUALITY OF AEROSOL OPTICAL THICKNESS DATA DERIVED FROM NPP VIIRS AND AQUA MODIS OVER WESTERN REGION OF INDONESIA Adiningsih, Erna Sri; Indrajat, Andy; Salyasari, Noriandini D.
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (1445.501 KB) | DOI: 10.30536/j.ijreses.2015.v12.a2707

Abstract

Preliminary analysis on quality data of Aerosol Optical Thickness/Depth or AOT/AOD derived from NPP VIIRS EDR (Environmental Data Record) has been done in previous work. Qualitative analysis of the previous work revealed that AOT data of VIIRS had insufficient quality due to some factors such as sun glint and cloud cover. However the accuracy of AOT VIIRS data over western area of Indonesia has not been investigated. Therefore this paper describes further analysis on AOT VIIRS data quality and accuracy. Comparison with AOT derived from Aqua MODIS data was implemented since AOT of MODIS has verified well with AOT data from field observation. Examination on cloud masking intermediate product of VIIRS was done for its importance in AOT data processing and persistent cloud cover obstacle over Indonesia. We used VIIRS and MODIS data archieved by LAPAN ground station. Further analysis on sun glint and cloud masking images indicates that these two intermediate products predominantly affect the quality of AOT from VIIRS and MODIS over the study areas. Compared with AOT of MODIS, AOT of VIIRS seems to result more pixels consisting AOT information over the same area and date. The statistical results showed that AOT values of VIIRS highly correlated with AOT values of MODIS with R2 of 78%. The accuracy of AOT derived from VIIRS was adequate as indicated by RMSE of  0.0977 or less than 0.5 for the samples over Sumatra, Borneo, and Java islands. Visual comparison of AOT images indicates that VIIRS data could result more detailed AOT values than MODIS data. Therefore the AOT of VIIRS data could be recommended for further applications in western area of Indonesia.  
Front Pages IJReSES Vol. 12, No. 2(2015) Journal, Editorial
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (3007.507 KB)

Abstract

Front Pages IJReSES Vol. 12, No. 2(2015)
OZONE VARIABILITY AND OZONE DEPLETING SUBSTANCES (ODS) IN INDONESIA BASED ON MLS-AURA DATA Komala, Ninong; Ambarsari, Novita
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (768.41 KB) | DOI: 10.30536/j.ijreses.2015.v12.a2706

Abstract

Research and characterizing the ozone profiles and Ozone Depleting Substances (ODS) in Indonesia is a satellite data-based research activities. The aim of the study was to obtain the characteristics of ozone in Indonesia as well as the contribution of ODS to the variability of ozone. By performing a data inventory based on satellite data, analyze the pattern of annual, seasonal and perform linkage analysis of the contribution of ODS changes to the conditions of ozone. Daily data of vertical profiles of ozone and  in the form of volume mixing ratio (vmr) with format HDF (Hierarchical Data Format) is extracted to the territory of Indonesia to take parameters as latitude, longitude, and concentration. Then converted to Excel format with the help of data processing software of MATLAB. Results obtained in the form of ozone characteristics in Indonesia, the percentage of contribution to the variability of ozone also contribution to the variability of ozone in Indonesia in several levels of height. By using Microwave Limb Sounders (MLS) AURA satellite data in the period of 2005 to 2013 characteristics of monthly vertical profiles of ozone in Indonesia has been obtained. The ODS studied were ClO and BrO. Peak of vertical profiles of ozone occurs at a pressure of 10 hPa or altitude of 25.9 km. ClO peak occurs at a pressure of 2.1 hPa or altitude of 30.6 km and BrO reached the peak at 14 hPa or altitude of 24.5 km. When ClO and BrO reach a maximum concentration at stratosphere then ozone molecules is potentially damaging or decrease in the stratosphere. Temporal variations of ozone showed decrease when  ODS concentrations increased (particularly ClO and BrO). Linear regression of ozone with ozone showed a negative correlation coefficient which indicates there is a strong relationship between ozone concentrations decline in pressure of 14 hPa when BrO reach the maximum. Likewise for ClO which also showed a negative correlation with the decrease in ozone concentration. ClO contribution to the decreasing of ozone in Indonesia was marked by every addition of 0.01 ppb ClO will reduce ozone of  0.00583 ppm (5.83 ppb). While any increase of  0.01 ppb of BrO will decrease 0.03 ppb of ozone.
Back Pages IJReSES Vol. 12, No. 2(2015) Journal, Editorial
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (3499.224 KB)

Abstract

Back Pages IJReSES Vol. 12, No. 2(2015)
MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA Winarso, Gathot; Vetrita, Yenni; Purwanto, Anang D.; Anggraini, Nanin; Darmawan, Soni; Yuwono, Doddy M.
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (624.791 KB) | DOI: 10.30536/j.ijreses.2015.v12.a2687

Abstract

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan.  Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.

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