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THE EFFECT OF DIFFERENT ATMOSPHERIC CORRECTIONS ON BATHYMETRY EXTRACTION USING LANDSAT 8 SATELLITE IMAGERY Kuncoro Teguh Setiawan; Yennie Marini; Johannes Manalu; Syarif Budhiman
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 1 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Remote 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.

MODIS STANDARD (OC3) CHLOROPHYLL-A ALGORITHM EVALUATION IN INDONESIAN SEAS Gathot Winarso; Yennie Marini
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 11, No 1 (2014)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

The 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.

BATHYMETRY DATA EXTRACTION ANALYSIS USING LANDSAT 8 DATA Kuncoro Teguh Setiawan; Syifa Wismayati Adawiah; Yennie Marini; Gathot Winarso
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 13, No 2 (2016)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

The remote sensing technique can be used to produce bathymetric map. Bathymetric mapping is important for the coastal zone and watershed management. In the previous study conducted in Menjangan Island of Bali, bathymetric extractin information from the top of the atmosphere (TOA) reflectance image of Landsat ETM+ data has R2 = 0.620. Not optimal correlation value produced is highly influenced by the reflectance image of Landsat ETM+ data, were used, hence the lack of the research which became the basis of the present study. The study was on the Karang Lebar water of Thousand Islands, Jakarta. And the aim was to determine whether there was an increased correlation coefficient value of bathymetry extraction information generated from Surface reflectance and TOA reflectance imager of Landsat 8 data acquired on August 12, 2014. The method of extraction was done using algorithms Van Hengel and Spitzer (1991). Extraction absolute depth information obtained from the model logarithm of Landsat 8 surface reflectance images and pictures TOA produce a correlation value of R2 = 0.663 and R2 = 0.712.

BATHYMETRY DATA EXTRACTION ANALYSIS USING LANDSAT 8 DATA Kuncoro Teguh Setiawan; Syifa Wismayati Adawiah; Yennie Marini; Gathot Winarso
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.a2448

The remote sensing technique can be used to produce bathymetric map. Bathymetric mapping is important for the coastal zone and watershed management. In the previous study conducted in Menjangan Island of Bali, bathymetric extractin information from the top of the atmosphere (TOA) reflectance image of Landsat ETM+Â data has R2 = 0.620. Not optimal Â correlation value produced is highly influenced by the reflectance image of Landsat ETM+ data, were used, hence the lack of the research which became the basis of the present study. The study was on the Karang Lebar water of Thousand Islands, Jakarta. And the aim was to determine whether there was an increased correlation coefficient value of bathymetry extraction information generated from Surface reflectance and TOA reflectance imager of Landsat 8 data acquired on August 12, 2014. The method of extraction was done using algorithms Van Hengel and Spitzer (1991). Extraction Â Â absolute depth information obtained from the model logarithm of Landsat 8 surface reflectance images and pictures TOA produce a correlation value of R2 = 0.663 and R2 = 0.712.

MODIS STANDARD (OC3) CHLOROPHYLL-A ALGORITHM EVALUATION IN INDONESIAN SEAS Gathot Winarso; Yennie Marini
International Journal of Remote Sensing and Earth Sciences Vol. 11 No. 1 (2014)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2014.v11.a2597

The 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 â€“ 12thÂ Â 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 Â 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-2Â 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.

THE EFFECT OF DIFFERENT ATMOSPHERIC CORRECTIONS ON BATHYMETRY EXTRACTION USING LANDSAT 8 SATELLITE IMAGERY Kuncoro Teguh Setiawan; Yennie Marini; Johannes Manalu; Syarif Budhiman
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 1 (2015)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2015.v12.a2668

Remote 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

SITE SELECTION OF SEAWEED CULTURE USING SPOT AND LANDSAT SATELLITE DATA IN PARI ISLAND Bidawi Hasyim; Wawan K. Harsanugraha; Yennie Marini; Anneke K.S. Manoppo
International Journal of Remote Sensing and Earth Sciences Vol. 9 No. 2 (2012)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2012.v9.a1833

One of several factors for seaweed culture success is to determine the suitable location for seaweed culture based on oceanographic parameters. The best location for seaweed culture is coastal waters with suitable requirements for total suspended solid (TSS), sea surface temperature (SST), and area with calm water that is sheltered from waves, strong current and predator, such as lagoon in the middle of an atoll. The purpose of this study was to locate the suitable area for seaweed culture in Pari island, Seribu island using SPOT and LANDSAT-TM data. The results showed that TSS in Pari island waters were in the range of 150 mg/l - 200 mg/l, SST in the range of 22-29°C, while coral reefs and lagoon was only available in some coastal locations. The analysis showed that most of Pari island waters were suitable for seaweed culture.

FISHPOND AQUACULTURE INVENTORY IN MAROS REGENCY OF SOUTH SULAWESI PROVINCE Yennie Marini; Emiyati; Teguh Prayogo; Rossi Hamzah; Bidawi Hasyim
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1839

Currently, fishpond aquaculture becomes an interesting business for investors because of its profit, and a source of livelihood for coastal communities. Inventory and monitoring of fishpond aquaculture provide important baseline data to determine the policy of expansion and revitalization of the fishpond. The aim of this research was to conduct an inventory and monitoring of fishpond area inMaros regency of South Sulawesi province using Satellite Pour l’Observation de la Terre (SPOT -4) and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Apeture Radar (PALSAR). SPOT image classification process was performed using maximum likelihood supervised classification method and the density slice method for ALOS PALSAR. Fishpond area from SPOT data was 9693.58 hectares (ha), this results have been through the process of validation and verification by the ground truth data. The fishponds area from PALSAR was 7080.5 Ha, less than the result from SPOT data. This was due to the classification result of PALSAR data showing someobjects around fishponds (dike, mangrove, and scrub) separately and were not combined in fishponds area calculation. Meanwhile, the result of SPOT -4 image classification combined object around fishponds area.

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