International Journal of Remote Sensing and Earth Sciences (IJReSES)
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VARIABILITY AND VALIDATION OF SEA SURFACE TEMPERATURE ESTIMATED BY PATHFINDER ALGORITHM OF NOAA-AVHRR SATELLITE IN THE NORTH PAPUA WATERS
Get PDFVariability and validation of sea surface temperatures (SST) in north Papua waters were conducted using SST estimated by Pathfinder algorithm of NOAA AVHRR satellite and SST measurements from TAO buoy in 2001-2009. Satellite data (SST Pathfinder) were daily, weekly, and monthly composite with 4x4 km2 resolution and downloaded from http://poet.jpl.nasa.gov. In situ data (SST measurement from buoy TAO) were measured at a depth of 1.5 m and recorded every hour (http://www.pmel.noaa.gov/tao_deliv). The in situ data then converted into daily, weekly, and monthly average data. In general, the SST values of both satellite and in situ SST in the north Papua waters ranged between 27.10 - 31.90 °C. During the east season (June-September), SST values (27.90-31.90 °C) were generally higher than the SST values ( 27.10-30.13 °C) during the west season (December-February). In general, the SST values both day-time and night-time from in situ and the satellite measurements showed no significant differences except in waters close to the shore. The results also showed that the coefficient of determination values (R2) between the satellite and the in situ SST measurements were relatively low (65%) and up to 5% of RMSE. The relatively low correlation between in situ dan satellite SST measurements may be due to high cloud coverage (90-96%) in the north Papua waters so that SST satellite data become less representative of the in situ data. These results also indicated that the Pathfinder algorithm can not be used as a valid estimate of SST NOAA AVHRR satellite for the north Papua waters.
Keywords: SST Pathfinder, NOAA AVHRR, Validation, TAO buoy, North Papua Water
CO2 FLUX IN INDONESIAN WATER DETERMINED BY SATELLITE DATA
Get PDFThe oceans was considered to be a major sink for CO2. The improving of quantitative and qualitative description about the ability of sea in uptaking or emitting CO2 is a great scientific concern in meteorological and climatological science. Measurement of the ability of sea in uptake or emitting CO2 could determined by measuring the CO2 exchange coefficient on sea interface and the measuring the different partial pressure of CO2 between the air and sea. In this study, CO2 flux distribution of Indonesian waters in 2007 to 2009 was computed using monthly CO2 exchange and the different partial pressure of CO2 estimated from wind speed, salinity, SST, and sea characteristic, which were obtained from satellite data. The carbon dioxide flux thus was estimated and discussed by two different designs of transfer velocity (k), of Wanninkhof (1992), kW92 relationship and by Nightingale et al. (2000), kN, relationship. The result indicated that generally, Indonesian water was emitting the CO2 to the air. Average CO2 emitting from sea to the air for recent year in 2007 to 2009 are 3.80 (mol m-2year-1) and 2.85 (mol m-2year-1) with kW92 relationship and kN relationship calculation, respectively. The total average CO2 emission from sea to the air in 2007 to 2009 for the Indonesian waters areas are 0.15 (PgC year-1) and 0.12 (PgC year-1) based on kW92 relationship and kN relationship calculations, respectively.
Keywords: CO2 flux, salinity, SST, sink and sources of CO2
ANALYSIS OF CRITICAL LAND IN THE MUSI WATERSHED USING GEOGRAPHIC INFORMATION SYSTEMS
Get PDFCritical land is a land that is no longer functioning as a regulator of water, agricultural production elements and environmental protection elements. Owing to the fact that the analysis of critical land is usually carried out manually, the probability of errors in processing (human error) is very high. This research utilizes the Geographic Information System (GIS) technology to analyze critical area in protected forest area of Musi Watershed. The application of GIS technology, enables the analysis of critical land according to standard of critical land criteria. The results show that the very critical level area in protected forest area of Musi Watershed is 1.7%. The dominant level is in critical potential area (53.34%).
Keywords: Critical Land, Watershed, Remote Sensing, GIS, Weighting Method, SPO-4
RED TIDE DETECTION USING Seawifs STANDARD CHOLOROPHYLL-a ALGORITHM IN SOUTHEAST KOREAN WATERS
No full textCochlodinium polykrikoides red tides have occurred in summer every year at coastal waters of the South Korea. Chlorophyll-a concentration data estimated from ocean color satellite SeaWiFS (Sea-viewing Wide Field-of-view Sensor) were used to detect the red tide in this study. The high value of chlorophyll-a concentration used to detect red tide was analyzed and compared with red tide map produced by National Fisheries Research and Development Institute of Korea (NFRDI). Based on SeaWiFS data and NFRDI red tide map, it was found that high chlorophyll-a concentration of ≥ 5 mg/m3in SeaWiFS images corresponded to the red-tide occurrence with some limitations.
LAND COVER CLASSIFICATION ALOS AVNIR DATA USING IKONOS AS REFERENCE
No full textAbstract. Advanced Land Observation Satellite (ALOS) is a Japanese satellite equipped with 3 sensors i.e., PRISM, AVNIR, and PALSAR. The Advanced Visible and Near Infrared Radiometer (AVNIR) provides multi spectral sensors ranging from Visible to Near Infrared to observe land and coastal zones. It has 10 meter spatial resolution, which can be used to map land cover with a scale of 1:25000. The purpose of this research was to determineclassification for land cover mapping using ALOS AVNIR data. Training samples were collected for 11 land cover classes from Bromo volcano by visually referring to very high resolution data of IKONOS panchromatic data. The training samples were divided into samples for classification input and samples for accuracy evaluation. Principal component analysis (PCA) was conducted for AVNIR data, and the generated PCA bands were classified using Maximum Likehood Enhanced Neighbor method. The classification result was filtered and re-classed into 8 classes. Misclassifications were evaluated and corrected in the post processing stage. The accuracy of classifications results, before and after post processing, were evaluated using confusion matrix method. The result showed that Maximum Likelihood Enhanced Neighbor classifier with post processing can produce land cover classification result of AVNIR data with good accuracy (total accuracy 94% and kappa statistic 0.92). ALOS AVNIR has been proven as a potential satellite data to map land cover in the study area with good accuracy
INDENTIFYING PATTERNS OF SATTELITE IMAGERY USING AN ARTIFICIAL NEURAL NETWORK
No full textAn artificial neural network analysis based on the self-organizing map (SOM) was used to examine patterns of satellite imagery. This study used 3 × 4 SOM array to extract patterns of satellite-observed chlorophyll-a (chl-a) along the southern coast of the Lesser Sunda Islands from 1998 to 2006. The analyses indicated two characteristic spatial patterns, namely the northwest and the southeast monsoon patterns. The northwest monsoon pattern was characterized by a low chl-a concentration. In contrast, the southeast monsoon pattern was indicated by a high chl-a distributed along the southern coast of the Lesser Sunda Islands. Furthermore, this study demonstrated that the seasonal variations of those two patterns were related to the variations of winds and sea surface temperature (SST). The winds were predominantly southeasterly (northwesterly) during southeast (northwest) monsoon, drived offshore (onshore) Ekman transport and produced upwelling (downwelling) along the southern coasts of the Lesser Sunda Islands. Consequently, upwelling reduce dSST and helped replenish the surface water nutrients, thus supporting high chl-a concentration. Finally, this study demonstrated that the SOM method was very useful for the identifications of patterns in various satellite imageries
ESTIMATION OF RADIOMETRIC PERFORMANCE OF ELEKCTRO-OPTICAL IMAGING SENSOR OF LOW EARTH EQUATORIAL ORBIT LAPAN SATTELITE
No full textStudy of spectro-radiometric performance of electro-optical imager which is planned to be launched on low earth equatorial orbit LAPAN satellite was conducted through simulative calculation of image irradiance and its associated generated voltage at the image detector output. Simulative calculation was applied to three scenarios of selected spectral bands. Those spectral bands were selected spectra (1), which consisted of spectral bands B = (390-540 and 790-900) nm, G = (470-610 and 700-900 ) nm, and R = (590-650 and 650-900) nm; selected spectra (2) consisted of B1 = (390-540) nm, G1 = (470-610) nm, and R1 = (590-650) nm; and selected spectra (3) consisted of B1(Green) = (525-605) nm, B2(Red) = (630-690) nm, and B3(NIR) = (750-900) nm, on three scenarios of optical aperture or f-number (N) 2.8, 4.0, and 5.6. Green grasses, dry grasses, and conifers were examples of the imaged target, chosen as representation of vegetations. Kodak KLI-8023 was used as the optical detector. The integration time was assumed 3 miliseconds which correspond to about 20 m ground sampling distance (GSD). Solar zenith angle were varying from 90ï‚° (early morning) to 0ï‚° (solar noon). The results showed that option (3) of selected spectra, as proposed for pushbroom imager of LAPAN satellite, was relatively accepted to be implemented and complemented with f-number 4.0 of optical system used. Whereas simulation RGB color displayed composed by R = B2(Red), G = B3(NIR), B = B1(Green) also showed a greenish color sense as expected for vegetation imaged target
ORTORECTIFICATION OF SPOT-4 DATA USING RATIONAL POLYNOMIAL COEFFICIENTS
No full textOrthorectification of satellite imagery can be done in two ways i.e., rigorous sensor model and the approximation model of the satellite’s orbit. Dependence on physicalparameters, to make rigorous sensor model is more complicated and difficult to apply. The approximation model can be either Rational Polynomial Coefficients (RPC) model or parallel projection system. RPC is a mathematical model which is not depends on the sensor. It is used to improve the positioning accuracy when the parameter of the physical sensor model is unknown. This study assessed orthorectification of SPOT-4 using the RPC model with 7 coefficients. Root Mean Square Error (RMSE) of GCPs obtained from the study was less than 1 pixel. RPC did not depend on physical and satellite orbit parameters. Thus the RPC was simpler and easier to apply