Abstract. 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 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.

Abstract. 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 R 2 = 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 R 2 = 0.663 and R 2 = 0.712. Keywords: bathymetry, Landsat 8, reflectance, Van Hengel and Spitzer algorithm INTRODUCTIONBathymetric mapping is important for the coastal zone and watershed management. Bathymetry measurement conventionally in the shallow area and wavy as in a reef area is very difficult and expensive even sometimes very dangerous. (Kanno et al., 2011). The bathymetry intertidal zone is needed to study the morphology of the seabed, the environment management of coastal resources and oceanographic modeling (Stumpf et al., 2003). The coastal oceanographic research and the protection of the water environment require the use of the high accuracy bathymetric data. The use of Lidar and Radar Bathymetry for the collection of bathymetric data with high resolution and accuracy is expensive (Kaimaris et al., 2012). Siregar et al., 2009 conducted a bathymetric study using three methods Kriging interpolation method, namely, the method of inverse distance to power, and minimum curvature method.One of the satellites that can be used for mapping shallow water bathymetry is Landsat 8. Landsat imagery has a spatial resolution of 30 meters are equipped with visible channel that required in the extraction of bathymetry map. Visible channel (blue, red and green) has the ability to penetrate the water to a certain depth, the blue channel has the ability to penetrate deeper into the water body. Jupp (1988) concluded that Landsat imagery can be used in Determining the water depth, band 2 (blue channel) has the ability to penetrate up to 25 meters of water depth, Kuncoro Teguh Setiawan et al. 80International Journal of Remote Sensing and Earth Science Vol. FORMOSAT (Kholil et al., 2007), Landsat and Quickbird (Nurlidiasari, 2004). The coefficient of determination (R 2 ) is used to give a more detailed picture of the ability of channel 1,2 and 3 of Quickbird imagery in explaining variations in water depth (Siregar et al., 2010).The maximum depth th...

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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