International Journal of Remote Sensing and Earth Sciences (IJReSES)
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MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA
Get PDFMangrove 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
MODIS STANDARD (OC3) CHLOROPHYLL-A ALGORITHM EVALUATION IN INDONESIAN SEAS
Get PDFThe MODIS-estimated chlorophyll-a information was widely used in some operational application in Indonesia. However, there is no information about the performance of MODIS chlorophyll-a in Indonesian seas and there is no data used in development of algorithm was taken in Indonesian seas. Even the algorithm was validated in other area, it is important to know the performance of the algorithm work in Indonesian seas. Performance of MODIS Standard (OC3) algorithm at Indonesian seas was analyzed in this paper. The in-situ chlorophyll-a concentration data was collected during MOMSEI (Monsoon Offset Monitoring and Its Social and Ecosystem Impact) 2012 Cruise 25th April – 12th  May 2012 and also from archived data of the Research and Development Center for Marine Coastal Resources, Agency of Marine and Fisheries Research and Development, Indonesian Ministry of  Marine Affairs and Fisheries. The in-situ data used in this research is located in Indian Ocean the west of Sumatera part and Pacific Ocean the north of Papua Province part. Satellite data which is used is Ocean Color MODIS Level-2 Product that downloaded from NASA and MODIS L-0 from LAPAN Ground Station. MODIS Level 0 from LAPAN then processed to Level-2 using latest SeaDAS Software. The match-up resulted the MNB(%) is -4.8% that means satellite-estimated was underestimate in 4.8 % and RMSE is 0.058. When the data was separated following to the data source, the correlation and trend line equation became better. From MOMSEI Cruise data, the MNB(%) was -18.8% and RMSE 0.05. From Pacific Ocean Data, MNB (%) was -27 % and RMSE 0.049. From SONNE Cruise 2005, MNB (%) was -27 % and RMSE 0.049. MODIS standard algorithm is work well in Indonesia case-1 seawaters, which contain chlorophyll-a only, and derived that influence to the electromagnetic wave
TIME SERIES ANALYSIS OF TOTAL SUSPENDED SOLID (TSS) USING LANDSAT DATA IN BERAU COASTAL AREA, INDONESIA
Get PDFWater quality information is usually used for the first examination of the pollution.  One of the parameters of water quality is Total Suspended Solid (TSS), which describes the amount of matter of particles suspended in the water. TSS information is also used as initial information about waters condition of a region. TSS could be derive from Landsat data with several combinations of spectral channels to evaluate the condition of the observation area for both the waters and the surrounding land. The study aimed to evaluate Berau waters condition in Kalimantan, Indonesia, by utilizing TSS dynamics extracted from Landsat data. Validated TSS extraction algorithm was obtained by choosing the best correlation between  field data and image data. Sixty pairs of points had been used to build validated TSS algorithms for the Berau Coastal area. The algorithm was TSS = 3.3238 * exp (34 099 * Red Band Reflectance). The data used for this study were Landsat 5 TM, Landsat 7 ETM and Landsat 8 data acquisition in 1994, 1996, 1998, 2002, 2004, 2006, 2008 and 2013. For detailed evaluation, 20 regions were created along the watershed up to the coast. The results showed the fluctuation of TSS values in each selected region. TSS value increased if there was a change of any kind of land cover/land used into bareland, ponds, settlements or shrubs. Conversely, TSS value decreased if there was a wide increase of mangrove area or its position was very closed to the ocean
LINEAMENT DENSITY INFORMATION EXTRACTION USING DEM SRTM DATA TO PREDICT THE MINERAL POTENTIAL ZONES
Get PDFUtilization of remote sensing in geology is based on some identification of main parameters. They were the relief or morphology, flow patterns, and lineament. So it was necessary to study extraction method based on those parameters. This study aimed to obtain lineament density zone in the Geumpang area, Aceh, associated with mineral resource potential. Information of lineament density using remote sensing data was expected to help solve the problems that arised in the activities of early exploration, the difficulty of finding the prospect areas, so that the activities of pre-exploration always required a wide area and required a long time to determine the location of mineral prospect areas, it would have a direct impact on the financial of exploration activities. The used data was Landsat 8 and DEM SRTM of 30 m. The used method was processing of shaded relief on DEM data with the azimuth angle 0o, 45o, 90o, and 135o, then the result of hill shade process was done overlay, so DEM seen from all different azimuth angles. The results of the overlay were processed using the algorithm LINE with parameters such as the radius of the filter in pixels (RADI) 60, the threshold for edge gradient (GTHR) 120, the threshold for the curve length (LTHR) 100, the threshold for line fitting error (FTHR) 3, threshold for angular (ATHR) 30, and the threshold for linking distance (DTHR) 100. Vector lineament data from LINE algorithm process then performed density analysis to obtain lineament density zoning. Results from the study showed that the area has a high density lineament associated with mineral potency, so it was useful for exploration activities to minimize the survey area
EVALUATION OF MANGROVE DAMAGE LEVEL BASED ON LANDSAT 8 IMAGE
Get PDFMonitoring of mangrove damage in Java requires special attention because the mangrove vegetation has been under pressure from various other land uses which are considered more productive. This paper applied quick-mangrove-damage-detection technique using Landsat 8. The purpose of this study is to develop mangrove damage identification algorithm using Landsat 8. The findings from field survey in Segara Anakan-Cilacap show that major mangrove logging generates the growth of minor mangrove, specifically Derris and Acanthus type; the minor mangrove cover area is categorized as high density based on NDVI value. The index use does not meet the actual condition in the field. This study proposes a new index as mangrove quality indicator. The new proposed mangrove index is derived from 2 bands that could differentiate mangrove vegetation where different digital number of two bands is higher from mangrove forest than non-mangrove forest. That phenomenon is caused the low of SWIR spectral on mangrove forest due to absorption by wet soil below the mangrove forest where flooded in high tide. The new mangrove index is formulated as (NIR – SWIR / NIR x SWIR) x 10000. The new mangrove index has good correlation with density of major mangrove in the field, and also good correlation with mangrove degradation map. Mangrove index has been functioning properly and can be applied in Segara Anakan, Cilacap and potentially can be applied in other locations
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
Get PDFAlmost 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%
A PARTIAL ACQUISITION TECHNIQUE OF SAR SYSTEM USING COMPRESSIVE SAMPLING METHOD
Get PDFIn line with the development of Synthetic Aperture Radar (SAR) technology, there is a serious problem when the SAR signal is acquired using high rate analog digital converter (ADC), that require large volumes data storage. The other problem on compressive sensing method,which frequently occurs, is a large measurement matrix that may cause intensive calculation. In this paper, a new approach was proposed, particularly on the partial acquisition technique of SAR system using compressive sampling method in both the azimuth and range direction. The main objectives of the study are to reduce the radar raw data by decreasing the sampling rate of ADC and to reduce the computational load by decreasing the dimension of the measurement matrix. The simulation results found that the reconstruction of SAR image using partial acquisition model has better resolution compared to the conventional method (Range Doppler Algorithm/RDA). On a target of a ship, that represents a low-level sparsity, a good reconstruction image could be achieved from a fewer number measurement. The study concludes that the method may speed up the computation time by a factor 4.49 times faster than with a full acquisition matrix
THE EFFECT OF DIFFERENT ATMOSPHERIC CORRECTIONS ON BATHYMETRY EXTRACTION USING LANDSAT 8 SATELLITE IMAGERY
Get PDFRemote sensing technology can be used to obtain information bathymetry. Bathymetric information plays an important role for fisheries, hydrographic and navigation safety. Bathymetric information derived from remote sensing data is highly dependent on the quality of satellite data use and processing. One of the processing to be done is the atmospheric correction process. The data used in this study is Landsat 8 image obtained on June 19, 2013. The purpose of this study was to determine the effect of different atmospheric correction on bathymetric information extraction from Landsat satellite image data 8. The atmospheric correction methods applied were the minimum radiant, Dark Pixels and ATCOR. Bathymetry extraction result of Landsat 8 uses a third method of atmospheric correction is difficult to distinguish which one is best. The calculation of the difference extraction results was determined from regression models and correlation coefficient value calculation error is generated
DEVELOPMENT OF LAND MOISTURE ESTIMATION MODEL USING MODIS INFRARED, THERMAL, AND EVI TO DETECT DROUGHT AT PADDY FIELD
Get PDFThe drought phenomena often occurs in summer season at paddy field of Java island. The drought phenomena causes decrease in rice production. This research was aimed to develop a model of land moisture (LM) estimation at agricultural field, especially for paddy field based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data which has seven reflectance and two thermal bands. The method used in this study included data correction, advance processing of MODIS data (land indices transformation), extraction of land indices value at location of field survey, and regression analysis to make the best model of land moisture estimation. The result showed that reflectance of 2nd channel (NIR) and rasio of Enhanced Vegetation Index (EVI) with Land Surface Temperature (LST) had high correlation with surface soil moisture (% weight) at 0 – 20 cm depth with formula: LM = 15.9*EVI/LST – 0.934*R2 – 16.8 (SE=9.6%; R2 =76.2%). Based on the model, land moisture was derived spatially at the agricultural field, especially at paddy field to detect andmonitor drought events. Information of land moisture can be used as an indicator to detect drought condition and early growing season of paddy crop