USE OF A MULTISPECTRAL UAV PHOTOGRAMMETRY FOR DETECTION AND TRACKING OF FOREST DISTURBANCE DYNAMICS

14 citations as recorded by crossref.

1. Detection of green attack and bark beetle susceptibility in Norway Spruce: Utilizing PlanetScope Multispectral Imagery for Tri-Stage spectral separability analysis A. Trubin et al. https://doi.org/10.1016/j.foreco.2024.121838
2. Automatic Tree Crown Extraction from UAS Multispectral Imagery for the Detection of Bark Beetle Disturbance in Mixed Forests R. Minařík et al. https://doi.org/10.3390/rs12244081
3. Analysis of the Degree of Threat to Railway Infrastructure by Falling Tree Vegetation M. Kučera & Z. Dobesova https://doi.org/10.3390/ijgi10050292
4. Analysis of the Vigor of Pinus hartwegii Lindl. along an Altitudinal Gradient Using UAV Multispectral Images: Evidence of Forest Decline Possibly Associated with Climatic Change J. Gallardo-Salazar et al. https://doi.org/10.3390/f14061176
5. Monitoring the rehabilitation process of the windthrow area using UAS images and performance comparison of Sentinel-2A based different vegetation indexes T. Çinar et al. https://doi.org/10.1007/s12145-025-01701-7
6. Using Sentinel-2 Multispectral Images to Map the Occurrence of the Cossid Moth (Coryphodema tristis) in Eucalyptus Nitens Plantations of Mpumalanga, South Africa S. Kumbula et al. https://doi.org/10.3390/rs11030278
7. Unmanned aerial systems for agriculture and natural resources S. Hogan et al. https://doi.org/10.3733/ca.2017a0002
8. A review of major factors influencing the accuracy of mapping green-attack stage of bark beetle infestations using satellite imagery: Prospects to avoid data redundancy K. Zabihi et al. https://doi.org/10.1016/j.rsase.2021.100638
9. Evaluating Different Deep Learning Approaches for Tree Health Classification Using High-Resolution Multispectral UAV Data in the Black Forest, Harz Region, and Göttinger Forest J. Anwander et al. https://doi.org/10.3390/rs16030561
10. Radiometric and Atmospheric Corrections of Multispectral μMCA Camera for UAV Spectroscopy R. Minařík et al. https://doi.org/10.3390/rs11202428
11. Advancing Mediterranean Biodiversity Monitoring in South Africa Through Machine Learning and Cost‐Effective UAS Imagery M. Chaity et al. https://doi.org/10.1029/2025JG009096
12. Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data A. Fernandez-Carrillo et al. https://doi.org/10.3390/rs12213634
13. Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard J. Gallardo-Salazar & M. Pompa-García https://doi.org/10.3390/rs12244144
14. Järvselja metsade tormikahjustuste seire mehitamata õhusõidukitega K. Kokamägi et al. https://doi.org/10.2478/fsmu-2022-0007