9 citations as recorded by crossref.

1. Determining vertical structure of forests in Poland using a semi-automated approach based on ALS data P. Janiec et al. https://doi.org/10.1016/j.ecolind.2025.113825
2. An improved forest canopy height mapping method by combining GAN-enhanced optical imagery, SAR, and GEDI data R. Wang et al. https://doi.org/10.1016/j.tfp.2025.101131
3. Straightforward model-based approach using only field data and open-source maps to improve carbon stock estimates for REDD + projects L. Haneda et al. https://doi.org/10.1038/s41598-026-37201-x
4. Retrieving yearly forest growth from satellite data: A deep learning based approach M. Schwartz et al. https://doi.org/10.1016/j.rse.2025.114959
5. Mapping and understanding the regional farmland SOC distribution in southern China using a Bayesian spatial model B. Hu et al. https://doi.org/10.1016/j.geoderma.2025.117446
6. An outlook on the rapid decline of carbon sequestration and perspectives for an improved monitoring of French forests P. Ciais et al. https://doi.org/10.5802/crgeos.309
7. Wood density variation in European forest species: drivers and implications for multiscale biomass and carbon assessment in France H. Cuny et al. https://doi.org/10.5194/bg-23-2365-2026
8. Synergistic use of ICESat-2 lidar data and Sentinel-2 imagery for assessing hurricane-driven forest changes A. Gautam et al. https://doi.org/10.1007/s10661-025-14749-1
9. Digital Twins in Forest Management Using Scalable Deep Learning Pipeline M. Keskes & M. Niţă https://doi.org/10.1109/JSTARS.2026.3666924