Conference Agenda

Gorgan Bay in the southeast of the Caspian Sea faces significant issues with water volume reduction and water quality deterioration. The turbidity levels of this water body have increased recently owing to the ongoing decline in the Caspian Sea level and the increase in human activity. In this study, to monitor water quality of the bay, various machine learning models were used to retrieve turbidity levels from Sentinel-2 satellite imagery. In situ turbidity measurements acquired throughout the bay were correlated with Sentinel-2 reflectance data. A statistical evaluation was conducted to ascertain the prospective band combinations for estimating turbidity. Four regression methods, including Multiple Linear Regression (MLR), Random Forest (RF), Classification and Regression Trees (CART), and Gradient Tree Boost (GTB), were implemented to estimate turbidity levels using six different input scenarios. These models were tested on unseen test data, and it was found that the CART model with RMSE = 7.89 FTU, R² = 0.93, and Nash-Sutcliffe efficiency (NSE) = 0.74 exhibited superior performance. The generated turbidity maps across the bay showed sediment plumes next to southeastern river mouths, indicating increased turbidity levels in these areas compared to the rest of the bay, revealing intra-bay variability due to tidal and discharge dynamics. The applied methodology demonstrated superior performance compared to conventional empirical models in turbid coastal environments. The results indicated that the machine learning approaches coupled with satellite data provides water resource managers with a cost-effective and real-time tool for coastal water quality monitoring.

The formation of pit lakes in decommissioned open-pit mines has raised concerns regarding long-term water quality. Turbidity, a key indicator of suspended particulate matter, influences water clarity and aquatic ecological processes. This study estimates surface turbidity in the Águas Claras Mine (MAC) pit lake in Nova Lima, Brazil, using satellite imagery and in situ data to generate a continuous time series and assess compliance with thresholds established by current Brazilian environmental legislation (CONAMA Resolution No. 357/2005). Landsat 5 and 8 imagery were used to derive a spectral turbidity index. Based on the temporal overlap between satellite and field data, a linear regression model (R² = 0.77) was developed and applied to extend the turbidity time series. The results indicate that turbidity values remained below the legal limits for Class 1 freshwater. Higher turbidity levels were observed during the initial filling phase, associated with exposed slopes, as well as episodic increases during the rainy season due to sediment runoff. Over time, progressive revegetation and minimal anthropogenic disturbance contributed to the stabilization of water quality conditions. The integration of in situ measurements and remote sensing proved to be an effective approach for monitoring water quality in post-mining environments, supporting both environmental liability assessment and closure management.

Harmful algal blooms caused by Margalefidinium polykrikoides have intensified in Korean coastal waters, yet existing bio-optical models are not able to reproduce the species-specific spectral features required for quantitative bloom assessment. This study develops a dedicated semi-analytical bio-optical model by integrating multi-year field measurements collected from six campaigns between 2013 and 2022, including hyperspectral above-water radiometry, laboratory absorption spectra, and chlorophyll-a (Chla) observations.

The model formulation follows a standard absorption–backscattering reflectance framework, in which total absorption is decomposed into water, phytoplankton, NAP, and CDOM components, while phytoplankton backscattering is parameterized using two optimized species-dependent parameters. An iterative inversion procedure identifies the optimal backscattering structure by minimizing the spectral mismatch between modeled and measured hyperspectral Rrs. In addition, an empirical red-edge term is introduced to capture the distinct fluorescence-associated peak near ~700 nm that characterizes high-biomass M. polykrikoides waters.

The resulting model accurately reconstructs observed Rrs across low to high Chla conditions, reproducing key features such as strong blue absorption, the secondary blue rebound, and the pronounced red-edge peak. Comparisons with GIOP and Karenia-based models show substantially improved performance, particularly under extreme bloom conditions.

This work provides the first validated species-specific bio-optical parameterization for M. polykrikoides and offers a practical pathway for satellite-based HAB monitoring using upcoming hyperspectral missions such as PACE and GLIMR. The framework is extendable to additional HAB species and supports future development of physics-based, species-resolved coastal water-quality retrievals.

Coastal zones are highly vulnerable to multiple hazards, including tsunami, shoreline erosion, coral reef degradation, and escalating impacts of sea-level rise. These concerns illustrate the urgent need for accurate and high-resolution geospatial data in coastal areas are required to support coastal risk assessment and management. A seamless and accurate coastal digital elevation model (DEM) is a foundational dataset to support these needs.

However, the development of a seamless land-sea elevation surface remains challenging. The intertidal zone often forms a critical data gap between land DEMs and bathymetry grids. To address these limitations, the use of multi-sensor geospatial data has grown considerably in coastal science and hydrography, such as structure-from-motion (SfM) photogrammetry and satellite-derived bathymetry (SDB). Assimilating SfM and SDB offers a viable pathway for constructing seamless coastal DEM.

Therefore, understanding the quality of SfM and SDB data before integration is a critical step. This study addresses this gap by evaluating the vertical accuracy, effective spatial resolution, and internal consistency of SfM-derived coastal topography and SDB-derived shallow-water bathymetry in a challenging coastal environment commonly found in Indonesian waters, i.e., coral reef islands. In this study, the area of interest is Karimunjawa and Kemujan Islands, located in the Java Sea, Indonesia, approximately 80-90 km from the mainland.

Based on our preliminary results, SfM provides high spatial detail depths but introduces short wavelength oscillation while SDB shows smoother depth gradients. In addition, several depths derived from SfM and SDB indicate different vertical reference levels.