Geomatics

Precise lagoon bathymetry remains scarcely available for most tropical islands despite its importance for navigation, resource assessment, spatial planning, and numerical hydrodynamic modeling. Hydrodynamic models are increasingly used for instance to understand the ecological connectivity between marine populations of interest. Island remoteness and shallow waters complicate in situ bathymetric surveys, which are substantially costly. A multisource strategy using historical point sounding, multibeam surveys and well calibrated satellite-derived bathymetry (SDB) can offer the possibility to map entirely extensive and geomorphologically complex lagoons. The process is illustrated here for the rugose complex lagoon of Gambier Islands in French Polynesia. The targeted bathymetry product was designed to be used in priority for numerical larval dispersal modeling at 100 m spatial resolution. Spatial gaps in in situ data were filed with Sentinel-2 satellite images processed with the Iterative Multi-Band Ratio method that provided an accurate bathymetric model (1.42 m Mean Absolute Error in the 0–15 m depth range). Processing was optimized here, considering the specifications and the constraints related to the targeted hydrodynamic modeling application. In the near future, a similar product, possibly at higher spatial resolution, could improve spatial planning zoning scenarios and resource-restocking programs. For tropical island countries and for French Polynesia, in particular, the needs for lagoon hydrodynamic models remain high and solutions could benefit from such multisource coverage to fill the bathymetry gaps.

The Northeast region of Thailand covers approximately 16.89 million hectares, with about 6.17 million hectares of seasonal rice cultivation and 2.85 million hectares affected by soil salinity—a major constraint to agricultural productivity in this region. This study develops an integrated data fusion framework combining multi-temporal Landsat-8 and Sentinel-2 imagery to train machine learning (ML) models for the prediction of rice yield and soil salinity, allowing for an analysis of their relationship. The field data comprised 380 rice yield and 625 soil electrical conductivity (EC) samples collected in 2023. Three ML models—Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Regression (SVR)—were applied for variable reduction and optimal predictor selection. RF achieved the highest accuracy for yield prediction (R² = 0.86, RMSE = 0.19 t ha⁻¹) and salinity estimation (R² = 0.93, RMSE = 0.87 dS/m) when using fused Landsat–Sentinel data. Spatial analysis of 5000 matched points showed a strong negative relationship between seedling stage EC and yield (R² = 0.71), with yields declining sharply above 5 dS/m and remaining below 1.5 t ha⁻¹ beyond 15 dS/m. These results demonstrate the potential of multi-sensor fusion and ensemble ML approaches for precise soil salinity monitoring and sustainable rice production.

Accurate 3D reconstructions of architecture, engineering, and construction AEC structures using UAV photogrammetry are often hindered by occlusions, excessive image overlaps, or insufficient coverage, leading to inefficient flight paths and extended mission durations. This work presents a BIM-aware, autonomous UAV trajectory generation framework wherein a compact, geometrically valid viewpoint network is first derived as a foundation for path planning. The network is optimized via Integer Linear Programming (ILP) to ensure coverage of IFC-modeled components while penalizing poor stereo geometry, GSD, and triangulation uncertainty. The resulting minimal network is then sequenced into a global path using a TSP solver and partitioned into battery-feasible epochs for operation on active construction sites. Evaluated on two synthetic and one real-world case study, the method produces autonomous UAV trajectories that are 31–63% more compact in camera usage, 17–35% shorter in path length, and 28–50% faster in execution time, without compromising coverage or reconstruction quality. The proposed integration of BIM modeling, ILP optimization, TSP sequencing, and endurance-aware partitioning enables the framework for digital-twin updates and QA/QC monitoring, accordingly, offering a unified, geometry-adaptive solution for autonomous UAV inspection and remote sensing.