Southeast Asian seas span the largest archipelago in the global ocean and provide a complex oceanic pathway connecting the Pacific and Indian oceans. The Southeast Asian sea regional sea level trends are some of the highest observed in the modern satellite altimeter record that now spans almost 2 decades. Initial comparisons of global sea level reconstructions find that 17-year sea level trends over the past 60 years exhibit good agreement with decadal variability associated with the Pacific Decadal Oscillation and related fluctuations of trade winds in the region. The Southeast Asian sea region exhibits sea level trends that vary dramatically over the studied time period. This historical variation suggests that the strong regional sea level trends observed during the modern satellite altimeter record will abate as trade winds fluctuate on decadal and longer timescales. Furthermore, after removing the contribution of the Pacific Decadal Oscillation (PDO) to sea level trends in the past 20 years, the rate of sea level rise is greatly reduced in the Southeast Asian sea region. As a result of the influence of the PDO, the Southeast Asian sea regional sea level trends during the 2010s and 2020s are likely to be less than the global mean sea level (GMSL) trend if the observed oscillations in wind forcing and sea level persist. Nevertheless, long-term sea level trends in the Southeast Asian seas will continue to be affected by GMSL rise occurring now and in the future.

The traditional approach for determining potential fishing zones (PFZs) relies on oceanographic factors (biological, physical, and chemical) and fishermen's expertise. This approach has disadvantages particularly when it comes to the analysis of combining these factors to find an exact PFZ spatially and temporally. In this study, we proposed a framework for identifying PFZs based on a data-mining approach in the Eastern Indian Ocean. We utilized a spatio-temporal clustering method to identify clusters of zones with data on the largest number of fish catch, which were then integrated with the sea surface temperature (SST) and the sea surface chlorophyll a (SSC) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. The results of this data integration method were used as training data in the classification process, which was then used to determine PFZs. During the classification process, we utilized the $k$-nearest neighbor (KNN) classification method. The result gave an average accuracy of 87.11%, which showed that the proposed framework can be used effectively to determine PFZs. To validate the framework, we compared its performance against the heuristic rules taken from the knowledge-based expert system model on the SST and chlorophyll a data. The results showed that the proposed data-mining framework outperformed the heuristic rules from the knowledge-based expert system model.

Seagrass perform a variety of functions within ecosystems, and have both economic and ecological values, therefore it has to be kept sustainable. One of the stages to preserve seagrass ecosystems is monitoring by utilizing thespatial data accurately. The purpose of the study was to assess and compare the accuracy of DII and PCA transformationsfor mapping of seagrass ecosystems. Fieldstudy was carried out in Karang Bongkok and Kotok Island waters, in Agustus 2014 and in March 2015. A WorldView-2 image acquisition date of 5 October 2013 was used in the study. The transformations for image processing data were Depth Invariant Index (DII) and Principle Component Analysis (PCA) using Support Vector Machine (SVM) classification. The result shows that benthic habitat mapping of Karang Bongkok using DII and PCA transformations were 72%and 81% overall's accuracy respectively, whereas of Kotok Island were 83% and 84% overall's accuracy respectively. There were seven benthic habitat types found in karang Bongkok waters and in Kotok Island namely seagrass, sand, rubble, coral, logoon, sand mix seagrass, and sand mix rubble. PCA transformation was effectively to improve mapping accuracy of sea grass mapping in Kotok Island and Karang Bongkok.

Coral reef ecosystem is a typical ecosystem in tropical marine and important for coastal island communities, including in Owi Island. This study aims to know a condition of coral reef covered area in Owi Island using Rapideye imagery satellite. This study used the “depth invariant index” method using bands 1 and bands 2 is Y = ln (bands 1) – ki/kj ln (bands 2) with unsupervised classification and field observation directly using manta tow method. The results show that area or percent cover of live coral is 23ha (22%) and dead coral 21.46ha (20%). Meanwhile, percent cover live coral using manta tow method between 11%-30% and dead coral between 31%-50%. The overall accuracy satellite imagery for mapping benthic habitats is 73.42% and coefficient kappa is 0.67

Balikpapan Bay (BB) located 90 km southwest of Delta Mahakam is one of estuarine systems in eastern Kalimantan directly flows into Makassar Strait. Dimension of BB is about 36 km length and between 1-14 km width, with mouth is wider than head of the bay. Water depth is much deeper (18 m) at the middle part of the bay. More than 10 secondary rivers flow into the bay to contribute large amount of freshwater mass flux. To understand physical properties and water mass dynamics, field measurement during high and low tides were conducted. The results shows that the warmer and saltier water mass are dominant at the entrance of the bay at flood period. Warmer and less salty water mass remain in the inner bay. However, during ebb, salinity is slightly decreased near the entrance and freshwater are observed in the inner bay. During both high and low tides condition, vertical stratification of water mass is very weak. The model demonstrates that this is related to large seawater flux brought by flood current from coastal waters, as well as small freshwater influx from river runoffs. Hence, the tidal dynamics significantly controls stratification and dynamics of water mass in the bay.