Abstract. This study evaluates the synergy of NASA’s Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR, Sentinel-2 multispectral imagery, and L-band Argentine Satellite System for Emergency Management (SAOCOM) 1A SAR data for aboveground biomass (AGB) estimation in the Belgrade Forest, Istanbul. Utilizing 1,356 GEDI L4A footprints as reference data, the research incorporates ten Sentinel-2 bands, five optical indices (NDVI, NDVIred, EVI, LSWI, CIre), SAR backscattering coefficients (σ°HH and σ°HV), polarimetric H/A/α polarimetric decomposition parameters and dual polarimetric radar vegetation indices, namely the Dual-Pol Radar Vegetation Index (DpRVI). High-dimensional feature spaces were optimized through ensemble-based, correlation-based, and hybrid RFECV selection strategies before evaluating four machine learning architectures: Multi-layer Perceptron (MLP), Kernel Ridge, Lasso, and Elastic Net. The MLP model achieved the highest predictive accuracy (R2 = 0.20, RMSE = 62.93 Mg/ha, MAE = 51.31 Mg/ha), outperforming linear regularization models, which exhibited R2 values between 0.15 and 0.16. Sensitivity analysis identified red-edge and SWIR bands, alongside indices such as NDVIred, LSWI, and CIre, as the most robust predictors, while the contribution of SAR-derived features remained comparatively limited. These findings underscore the efficacy of non-linear deep learning architectures and multi-source data fusion in resolving complex biophysical interactions within heterogeneous forest environments.
Eren Gursoy Ozdemir, Omer Gokberk Narin, S. Abdikan· The International Archives o...· 0 citations
Results confirm that systematic feature engineering and algorithm tuning are as critical as classifier selection in SAR-based land cover mapping and lay the foundation for scalable operational workflows applicable to rapidly urbanizing regions.
Melih Altay, B. Tavus, Fatih Fehmi Şi̇mşek et al.· The International Archives o...· 0 citations
Abstract. Large cities are complex areas containing densely populated areas along with their infrastructure. Surface movements can pose risks to the safety of structures, such as subway lines, in urban areas. Advanced multi-temporal Interferometric Synthetic Aperture Radar (InSAR) methods are used to obtain surface deformations, specifically those covering large areas. In this study, the new metro line under construction in the Koceeli Gebze region, adjacent to Istanbul, was analyzed with the multi-time InSAR method. In this context, PS/DS-based phase-linking approach is applied on Sentinel-1 that was acquired between 2019 and 2025. The displacement can reach up to about 13.5 mm/yr specifically where the maximum movement is obtained at the first station. Several time series indicated the evolution of the movement over the surface at the stations. The affected buildings were also examined surrounding of the first stations. The results provide information on monitoring the structural health of infrastructure under construction and also on the impact on surrounding structures. InSAR monitoring methods contribute to achieving the Sustainable Development Goals (SDG 9) and Sustainable Cities and Communities (SDG 11) by providing information about safe and sustainable building and settlement areas.
Suat Coskun, Ç. Bayık, Fusun Balik Sanli et al.· The International Archives o...· 0 citations
A zero-shot burned-area mapping framework based on the Segment Anything Model (SAM) and multispectral Sentinel-2 imagery and integrating index-based composites with SAM outputs significantly enhances the discrimination between burned and unburned surfaces by reducing boundary fragmentation and spectral confusion in heterogeneous landscapes.
Melih Altay, Fatih Fehmi Şi̇mşek, S. Abdikan· The International Archives o...· 0 citations
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