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

18 citations as recorded by crossref.

1. High Spatial and Temporal Resolution Energy Flux Mapping of Different Land Covers Using an Off-the-Shelf Unmanned Aerial System J. Simpson et al. 10.3390/rs13071286
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. 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
4. UAV-Based Forest Health Monitoring: A Systematic Review S. Ecke et al. 10.3390/rs14133205
5. Early Detection of Dendroctonus valens Infestation at Tree Level with a Hyperspectral UAV Image B. Gao et al. 10.3390/rs15020407
6. Physically based illumination correction for sub-centimeter spatial resolution hyperspectral data O. Ihalainen et al. 10.1016/j.rse.2023.113810
7. What Is the Effect of Quantitative Inversion of Photosynthetic Pigment Content in Populus euphratica Oliv. Individual Tree Canopy Based on Multispectral UAV Images? Y. Kahaer et al. 10.3390/f13040542
8. Feasibility study of forest wood-boring pest detection base on hyperspectral LiDAR H. Shao et al. 10.1080/01431161.2024.2320180
9. Automatic Detection of Phytophthora pluvialis Outbreaks in Radiata Pine Plantations Using Multi-Scene, Multi-Temporal Satellite Imagery N. Camarretta et al. 10.3390/rs16020338
10. Early Detection of Dendroctonus valens Infestation with Machine Learning Algorithms Based on Hyperspectral Reflectance B. Gao et al. 10.3390/rs14061373
11. 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
12. Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data A. Fernandez-Carrillo et al. 10.3390/rs12213634
13. Spruce Bark Beetle Outbreak Prediction through Automatic Classification of Sentinel-2 Imagery A. Bozzini et al. 10.3390/f14061116
14. 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
15. 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
16. UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce B. Allen et al. 10.3390/rs14153830
17. Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review S. Marvasti-Zadeh et al. 10.1145/3625387
18. Hyperspectral reflectance measurements from UAS under intermittent clouds: Correcting irradiance measurements for sensor tilt C. Köppl et al. 10.1016/j.rse.2021.112719