20 citations as recorded by crossref.

1. Retrospective Analysis of a Large-Scale Gypsy Moth Outbreak in Hungary Combining Multi-Source Satellite and In Situ Data T. Molnár et al. https://doi.org/10.3390/f16091472
2. Long‐term comparison shows protected and non‐protected forests differ in harvesting, but not in wildfires or drought‐driven dieback J. Espelta et al. https://doi.org/10.1111/1365-2664.70282
3. Mapping forest tree species and their uncertainty using Earth observation and National Forest Inventory data: towards operational monitoring in Sweden A. Abdi & F. Wang https://doi.org/10.1080/01431161.2026.2625513
4. Is forest structure better explained by present or historical forest cover? A case study in Brașov, Romania D. Rajakaruna et al. https://doi.org/10.1016/j.tfp.2026.101212
5. Mechanisms of Spatial Coupling Between Plantation Species Distribution and Historical Disturbance in the Complex Topography of Eastern Yunnan X. Zhang et al. https://doi.org/10.3390/rs17172925
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. Forest canopy cover loss dynamics in Germany between 2017 and 2024: Revealing regional differences F. Thonfeld et al. https://doi.org/10.1016/j.jag.2026.105157
8. Combining multitemporal remote sensing data and repeated ground observations from the Spanish National Forest Inventory to estimate site index and diameter distribution for uneven-aged management of Juniperus thurifera L. forests F. Mauro et al. https://doi.org/10.1093/forestry/cpaf080
9. Long-term recovery of canopy 3D structural diversity following wildfires in the world’s largest temperate woodland B. Zhang et al. https://doi.org/10.1098/rspb.2025.1095
10. Growth of residual forest stands after severe density reduction due to natural disturbances and silvicultural interventions H. Pretzsch https://doi.org/10.1016/j.foreco.2026.123627
11. Integrated analysis of harvest statistics provided by remote sensing, national forest inventories and administrative survey systems: An example from Italy R. Pilli et al. https://doi.org/10.1016/j.jag.2025.104871
12. Tree species–specific forest canopy cover loss in Germany (2018–2024): A national-scale remote sensing assessment M. Wegler et al. https://doi.org/10.1016/j.foreco.2026.123630
13. Mapping megatrends shaping European forests J. Langenbacher et al. https://doi.org/10.1007/s11625-025-01747-y
14. 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
15. Forest Disturbance Classification Under Imbalanced and Small-Sample Conditions Based on Collaborative Semi-Supervised Learning and Sample Generation Y. Liu et al. https://doi.org/10.3390/rs18101579
16. Unravelling the spatial and temporal variability of natural disturbances in European forests S. Miguel et al. https://doi.org/10.1111/1365-2664.70344
17. On-demand, semantic EO data cubes – knowledge-based, semantic querying of multimodal data for mesoscale analyses anywhere on Earth F. Kröber et al. https://doi.org/10.1016/j.isprsjprs.2025.07.015
18. Mapping tree dieback using Sentinel-2 time series and generalized regression-based unmixing models reveals species-specific patterns across Germany J. Alsleben et al. https://doi.org/10.1016/j.rse.2026.115464
19. Hidden forest loss: challenges in detecting wind- and insect-driven forest disturbances with global forest change landsat-based products in mixed southern boreal forests K. Korznikov et al. https://doi.org/10.1016/j.rsase.2025.101798
20. Assessing disturbance risks of the European spruce bark beetle under climate change: a review of model approaches and future perspectives T. Hallas et al. https://doi.org/10.1007/s10342-025-01841-x