Abstract. Sea surface height anomaly is a oceanographic parameter that has spatial and temporal variability. This paper aims to determine the characters of sea surface height anomaly in the south and north seas of Java Island. To find these characters, a descriptive analysis of monthly anomaly data is performed spatially, zonally and temporally. Based on satellite altimetry data from 1993 to 2010, the analysis shows that the average of sea surface height anomaly varies, ranging from -15 cm to 15 cm. Spatially and zonally, there are three patterns that can be concidered as sea surface height 1 INTRODUCTIONIndonesia is a maritime continent, about 70 percent of its territory consists of water and flanked by two great oceans; Indian and Pacific Ocean. The very vast oceanic zone has great potential and influence on various sectors of human life. Those potentials and influences should be studied and identified for public welfare. An understanding of oceanic physical dynamic or circulation through data analysis can be used to improve human welfare (Dwi, 2010). Adequate availability of temporal and spatial data from oceanographic parameters are required in research activities.The presence of satellite altimetry becomes the appropriate solution in meeting both regional and global needs of oceanographic data (Handoko, 2004). The analysed data generated from satellite altimetry shows pictures of the occurring processes of ocean dynamics and the factors or parameters that are dominant in the ocean dynamics (Digby, 1999 et al., 2001). As a result of global warming and polar ice melting, ocean water volume increases so that the sea surface height rises. One of oceanographic parameters associated with physical dynamics that are discussed in this study are sea surface height anomaly. Sea surface height anomaly is the magnitude of the deviation on the average sea surface height condition (Steward, 2008). Sea surface height is the distance between the sea surfaces to the reference ellipsoid. With the availability of satellite altimetry data from remote sensing technology monitoring, analysis of sea surface height anomaly on seas surrounding Java Island can be done. The purpose of this study is to compare the characteristics of sea surface height anomalies spatially (different areas) and temporally (different time). Teh obtained characteristics of sea surface height anomaly can be used to determine the upwelling zone. Upwelling zones is a potential reservoir for fishing operations. METHODThis study used sea surface height anomaly data from combined (merged) monitoring of multiple satellite altimetry: Topex/Poseidon, Jason-1, Envisat, Jason-2 and Cryosat-2. The data has a spatial resolution of 0.33 ° x 0.33 ° and monthly temporal. The data used in the analysis are from 1993 to 2010. The data sources can be found at ftp://aviso.oceanobs. com/pub/seadatanet/. The study area is the south and north sea of Java Island with zonal boundary from longitude: E 105.33 ⁰ to 114.67 ⁰ and meridional boundary of latitude: S 3.42 ⁰ to 12.01 ⁰. F...

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.

Model testing is a step that must be done before operational activities. This testing aimed to test rice growth phase models based on MODIS in Lombok using multitemporal LANDSAT imagery and 4eld data. This study was carried out by the method of analysis and evaluation in several stages, these are : evaluation of accuracy by multitemporal Landsat 8 image analysis, then evaluation by using 4eld data, and analysis of growth phase information to calculate model consistency. The accuracy of growth phase model was calculated using Confusion Matrix. The results of stage I analysis for phase of April 30 and July 19 showed the accuracy of the model is 58-59 %, while the evaluation of stage II for phase of period July 19 with survey data indicated that the overall accuracy is 53 %. However, the results of model consistency analysis show that the resulting phase of the smoothed MODIS imagery shows a consistent pattern as well as the EVI pattern of rice plants with an 86% accuracy, but not for pattern data without smoothing. This testing give conclusion is the model is good, but for operational MODIS input data must be smoothed 4rst before index value extraction.