UAV-BORNE THERMAL IMAGING FOR FOREST HEALTH MONITORING: DETECTION OF DISEASE-INDUCED CANOPY TEMPERATURE INCREASE

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1. Characteristics in Regional Climate Change Over South Korea for Regional Climate Policy Measures: Based on Long-Term Observations J. Moon et al. https://doi.org/10.15531/KSCCR.2020.11.6.755
2. Advancements in Automated Plant Disease Detection: a Comprehensive Review of Imaging Technologies and Deep Learning Applications R. Raju & T. Thasleema https://doi.org/10.1007/s10343-025-01247-0
3. Tools and methods for monitoring the health of the urban greenery A. Gupta et al. https://doi.org/10.1038/s41893-024-01295-w
4. Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques J. Abdulridha et al. https://doi.org/10.1007/s11119-019-09703-4
5. Termal Konfor Açısından RTEÜ Zihni Derin Yerleşkesinin Görüntü İşleme Yöntemleri ile Analizi G. ERCAN OĞUZTÜRK & C. MURAT https://doi.org/10.58816/duzceod.1401102
6. Relative Radiometric Calibration Using Tie Points and Optimal Path Selection for UAV Images J. Shin et al. https://doi.org/10.3390/rs12111726
7. Three-dimensional thermal characterization of forest canopies using UAV photogrammetry C. Webster et al. https://doi.org/10.1016/j.rse.2017.09.033
8. Monitoring phases of plant stress in juvenile commercial forest cuttings using contemporary nursery sensor technologies R. Ferreira et al. https://doi.org/10.1007/s11056-026-10208-y
9. Unveiling drought stress in conifers: canopy temperature and transpiration monitoring in a controlled setting M. Gräf et al. https://doi.org/10.1093/forestry/cpaf056
10. Extraction of tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu via spectral-spatial classification using UAV-based hyperspectral images N. Zhang et al. https://doi.org/10.1186/s13007-020-00678-2
11. Design of a Distance-Compensated Infrared Thermometer for Canopy Temperature Monitoring L. Nguyen et al. https://doi.org/10.3390/s26144380
12. The estimation of crop emergence in potatoes by UAV RGB imagery B. Li et al. https://doi.org/10.1186/s13007-019-0399-7
13. Integration of field measurements with unmanned aerial vehicle to predict forest inventory metrics at tree and stand scales in natural pure Crimean pine forests S. Bulut et al. https://doi.org/10.1080/01431161.2024.2357837
14. Response of mature Norway spruce to experimental thermal and drought stress in relation to Ips typographus attack: Crown temperatures and sap flow A. Karpov et al. https://doi.org/10.1016/j.foreco.2025.123290
15. Determination of differences in temperature regimes on healthy and bark-beetle colonised spruce trees using a handheld thermal camera A. Majdák et al. https://doi.org/10.3832/ifor3531-014
16. Orthomosaicking Thermal Drone Images of Forests via Simultaneously Acquired RGB Images R. Kapil et al. https://doi.org/10.3390/rs15102653
17. Forestry applications of UAVs in Europe: a review C. Torresan et al. https://doi.org/10.1080/01431161.2016.1252477
18. A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses J. Barbedo https://doi.org/10.3390/drones3020040
19. Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning C. Zhang et al. https://doi.org/10.3389/fpls.2022.1077403
20. Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review K. Neupane & F. Baysal-Gurel https://doi.org/10.3390/rs13193841
21. Thermal Infrared Remote Sensing of Stress Responses in Forest Environments: a Review of Developments, Challenges, and Opportunities M. Smigaj et al. https://doi.org/10.1007/s40725-023-00207-z
22. Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning H. Nguyen et al. https://doi.org/10.3390/rs13020260
23. Application of multiple spectral systems for the tree disease detection: A review S. Fang et al. https://doi.org/10.1080/05704928.2021.1930552
24. High resolution thermal and multispectral remote sensing for detecting devitalized trees in Monarch Butterfly Biosphere Reserve R. Guadarrama-León et al. https://doi.org/10.5424/fs/2025342-20940
25. Quantifying Water Use Efficiency in Strawberry Production Under Climatic Stress: A Review of Equations, Trends, and Modeling Tools M. Maskey https://doi.org/10.3390/horticulturae12081015
26. Analysis of solar radiation and thermography data using tree crowns parameters for Norway spruce A. Karpov et al. https://doi.org/10.1016/j.foreco.2025.122557
27. Enhanced flight planning and calibration for UAV based thermal imaging: implications for canopy temperature and transpiration analysis M. Gerchow et al. https://doi.org/10.3389/ffgc.2025.1457762
28. Use of Miniature Thermal Cameras for Detection of Physiological Stress in Conifers M. Smigaj et al. https://doi.org/10.3390/rs9090957
29. Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities N. Guimarães et al. https://doi.org/10.3390/rs12061046
30. Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture A. Abbas et al. https://doi.org/10.3390/agronomy13061524
31. Relationships between canopy surface temperature measured from drones and below-canopy forest microclimate in a tree diversity experiment L. Wei et al. https://doi.org/10.1007/s00484-026-03173-w
32. Remote sensing forest health assessment – a comprehensive literature review on a European level J. Drechsel & M. Forkel https://doi.org/10.2478/forj-2024-0022
33. A band selection method for consumer-grade camera modification for UAV-based rapeseed growth monitoring C. Wang et al. https://doi.org/10.1016/j.atech.2025.100830
34. Identification of damage severity in Fraxinus excelsior L. trees caused by ash dieback using multisensory and multitemporal UAV imagery L. Buchner et al. https://doi.org/10.1016/j.foreco.2025.122660
35. Linking High-Resolution UAV-Based Remote Sensing Data to Long-Term Vegetation Sampling—A Novel Workflow to Study Slow Ecotone Dynamics F. Döweler et al. https://doi.org/10.3390/rs16050840
36. Multi-Branch Channel-Gated Swin Network for Wetland Hyperspectral Image Classification R. Liu et al. https://doi.org/10.3390/rs17162862
37. Investigating the capabilities of thermal technologies to detect pathogen stress in commercial forest compartments R. Ferreira et al. https://doi.org/10.1007/s11056-026-10177-2
38. The Effects of Clear-Cutting on Ground Thermal Regimes After a Wildfire Using Hand-Held Thermal Imaging Camera in a Semi-arid Forest Ecosystems T. Dindaroglu et al. https://doi.org/10.1007/s41742-022-00504-8
39. Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera J. Kelly et al. https://doi.org/10.3390/rs11050567
40. The Conception of Test Fields for Fast Geometric Calibration of the FLIR VUE PRO Thermal Camera for Low-Cost UAV Applications A. Fryskowska-Skibniewska et al. https://doi.org/10.3390/s22072468
41. Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture A. Upadhyay et al. https://doi.org/10.1007/s10462-024-11100-x
42. Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective R. Hall et al. https://doi.org/10.4039/tce.2016.11
43. The Application of UASs in Forest Management and Monitoring: Challenges and Opportunities for Use in the Miombo Woodland H. Shamaoma et al. https://doi.org/10.3390/f13111812
44. Canopy temperature from an Unmanned Aerial Vehicle as an indicator of tree stress associated with red band needle blight severity M. Smigaj et al. https://doi.org/10.1016/j.foreco.2018.11.032
45. The Evaluation of Photogrammetry-Based DSM from Low-Cost UAV by LiDAR-Based DSM M. Gašparović et al. https://doi.org/10.15177/seefor.17-16
46. Vehículos aéreos no tripulados para el monitoreo del estado nutricional y fitosanitario de cultivos forestales M. Guevara Bonilla et al. https://doi.org/10.14483/2256201X.19250
47. Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma P. Ahmadi et al. https://doi.org/10.3390/rs14051239
48. Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands O. Brovkina et al. https://doi.org/10.1080/10095020.2017.1416994
49. GreenScan: Toward Large-Scale Terrestrial Monitoring the Health of Urban Trees Using Mobile Sensing A. Gupta et al. https://doi.org/10.1109/JSEN.2024.3397490
50. Detection of Eucalyptus Leaf Disease with UAV Multispectral Imagery K. Liao et al. https://doi.org/10.3390/f13081322
51. Mekansal Termal Konfor Açısından Atatürk Üniversitesi Yerleşkesi Termal Kamera Görüntülerinin Analizi S. YILMAZ et al. https://doi.org/10.17097/ataunizfd.535209
52. Evaluating the feasibility and potential of unmanned aerial vehicles to monitor implementation of forestry best management practices in the coastal plain of the southeastern United States A. Rijal et al. https://doi.org/10.1016/j.foreco.2023.121280