USING MULTITEMPORAL HYPER- AND MULTISPECTRAL UAV IMAGING FOR DETECTING BARK BEETLE INFESTATION ON NORWAY SPRUCE

20 citations as recorded by crossref.

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2. Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements F. Hellwig et al. 10.3390/rs13224659
3. European beech spring phenological phase prediction with UAV-derived multispectral indices and machine learning regression S. Krause & T. Sanders 10.1038/s41598-024-66338-w
4. Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season S. Junttila et al. 10.3390/rs14040909
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12. Drone-based early detection of bark beetle infested spruce trees differs in endemic and epidemic populations A. Bozzini et al. 10.3389/ffgc.2024.1385687
13. Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data A. Fernandez-Carrillo et al. 10.3390/rs12213634
14. Spruce Bark Beetle Outbreak Prediction through Automatic Classification of Sentinel-2 Imagery A. Bozzini et al. 10.3390/f14061116
15. Remote sensing of bark beetle damage in Norway spruce individual tree canopies using thermal infrared and airborne laser scanning data fusion A. Zakrzewska & D. Kopeć 10.1016/j.fecs.2022.100068
16. Uncovering Early Traces of Bark Beetle Induced Forest Stress via Semantically Enriched Sentinel-2 Data and Spectral Indices L. Mandl & S. Lang 10.1007/s41064-023-00240-4
17. UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce B. Allen et al. 10.3390/rs14153830
18. Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review S. Marvasti-Zadeh et al. 10.1145/3625387
19. Hyperspectral reflectance measurements from UAS under intermittent clouds: Correcting irradiance measurements for sensor tilt C. Köppl et al. 10.1016/j.rse.2021.112719
20. Early Detection of Southern Pine Beetle Attack by UAV-Collected Multispectral Imagery C. Kanaskie et al. 10.3390/rs16142608