Feasibility study of forest wood-boring pest detection base on hyperspectral LiDAR

References

• Abdullah, H., R. Darvishzadeh, A. K. Skidmore, T. A. Groen, and M. Heurich. 2018. “European Spruce Bark Beetle (Ips Typographus, L.) Green Attack Affects Foliar Reflectance and Biochemical Properties.” International Journal of Applied Earth Observation and Geoinformation 64:199–209. https://doi.org/10.1016/j.jag.2017.09.009.
   Web of Science ®Google Scholar
• Ames, A., K. Meyer, and D. Medina. 2005. “Experimental Measurements of Radiometric LADAR Calibration Targets.” In Laser Source and System Technology for Defense and Security, edited by G. L. Wood, Vol. 5792, 120–128. SPIE. https://doi.org/10.1117/12.601668.
   Google Scholar
• Anderegg, W. R., A. T. Trugman, G. Badgley, C. M. Anderson, A. Bartuska, P. Ciais, D. Cullenward, et al. 2020. “Climate-Driven Risks to the Climate Mitigation Potential of Forests.” Science 368 (6497): eaaz7005. https://doi.org/10.1126/science.aaz7005.
   PubMed Web of Science ®Google Scholar
• Anderegg, W. R., C. Wu, N. Acil, N. Carvalhais, T. A. Pugh, J. P. Sadler, and R. Seidl. 2022. “A Climate Risk Analysis of Earth’s Forests in the 21st Century.” Science 377 (6610): 1099–1103. https://doi.org/10.1126/science.abp9723.
   PubMed Web of Science ®Google Scholar
• Boyd, M. A., L. T. Berner, P. Doak, S. J. Goetz, B. M. Rogers, D. Wagner, X. J. Walker, and M. C. Mack. 2019. “Impacts of Climate and Insect Herbivory on Productivity and Physiology of Trembling Aspen (Populus Tremuloides) in Alaskan Boreal Forests.” Environmental Research Letters 14 (8): 085010. https://doi.org/10.1088/1748-9326/ab215f.
   Web of Science ®Google Scholar
• Burges, C. J. 1998. “A Tutorial on Support Vector Machines for Pattern Recognition.” Data Mining and Knowledge Discovery 2 (2): 121–167. https://doi.org/10.1023/A:1009715923555.
   Web of Science ®Google Scholar
• Chen, Y., C. Jiang, J. Hyyppä, S. Qiu, Z. Wang, M. Tian, W. Li, et al. 2018. “Feasibility Study of Ore Classification Using Active Hyperspectral LiDAR.” IEEE Geoscience & Remote Sensing Letters 15 (11): 1785–1789. https://doi.org/10.1109/LGRS.2018.2854358.
   Web of Science ®Google Scholar
• Chen, Y., W. Li, J. Hyyppä, N. Wang, C. Jiang, F. Meng, L. Tang, E. Puttonen, and C. Li. 2019. “A 10-Nm Spectral Resolution Hyperspectral LiDAR System Based on an Acousto-Optic Tunable Filter.” Sensors 19 (7): 1620. https://doi.org/10.3390/s19071620.
   PubMed Web of Science ®Google Scholar
• Dalponte, M., Y. T. Solano-Correa, L. Frizzera, and D. Gianelle. 2022. “Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data.” Remote Sensing 14 (13): 3135. https://doi.org/10.3390/rs14133135.
   Web of Science ®Google Scholar
• Ding, J., Y. Wu, H. Zheng, W. Fu, R. Reardon, and M. Liu. 2006. “Assessing Potential Biological Control of Tree-Ofheaven, Ailanthus Altissima in North America.” Biocontrol Science and Technology 16:547–566. https://doi.org/10.1080/09583150500531909.
   Web of Science ®Google Scholar
• Eitel, J. U., T. S. Magney, L. A. Vierling, and G. Dittmar. 2014. “Assessment of Crop Foliar Nitrogen Using a Novel Dual-Wavelength Laser System and Implications for Conducting Laser-Based Plant Physiology.” Isprs Journal of Photogrammetry & Remote Sensing 97:229–240. https://doi.org/10.1016/j.isprsjprs.2014.09.009.
   Web of Science ®Google Scholar
• Elsherif, A., R. Gaulton, and J. Mills. 2019. “Measuring Leaf Equivalent Water Thickness of Short-Rotation Coppice Willow Canopy Using Terrestrial Laser Scanning.” In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 6087–6090: IEEE. https://doi.org/10.1109/IGARSS.2019.8898665.
   Google Scholar
• Gaulton, R., F. M. Danson, F. A. Ramirez, and O. Gunawan. 2013. “The Potential of Dual-Wavelength Laser Scanning for Estimating Vegetation Moisture Content.” Remote Sensing of Environment 132:32–39. https://doi.org/10.1016/j.rse.2013.01.001.
   Web of Science ®Google Scholar
• Hais, M., J. Wild, L. Berec, J. Brůna, R. Kennedy, J. Braaten, and Z. Brož. 2016. “Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance.” Remote Sensing 8 (8): 687. https://doi.org/10.3390/rs8080687.
   Web of Science ®Google Scholar
• Hakala, T., J. Suomalainen, S. Kaasalainen, and Y. Chen. 2012. “Full Waveform Hyperspectral LiDAR for Terrestrial Laser Scanning.” Optics Express 20 (7): 7119–7127. https://doi.org/10.1364/OE.20.007119.
   PubMed Web of Science ®Google Scholar
• Hammond, W. M., A. P. Williams, J. T. Abatzoglou, H. D. Adams, T. Klein, R. López, C. Sáenz-Romero, H. Hartmann, D. D. Breshears, and C. D. Allen. 2022. “Global Field Observations of Tree Die-Off Reveal Hotter-Drought Fingerprint for Earth’s Forests.” Nature Communications 13 (1): 1761. https://doi.org/10.1038/s41467-022-29289-2.
   PubMed Web of Science ®Google Scholar
• Herrick, N. J., S. M. Salom, L. T. Kok, and T. J. McAvoy. 2011. “Life History, Development, and Rearing of Eucryptorrhynchus Brandti (Coleoptera: Curculionidae) in Quarantine.” Annals of the Entomological Society of America 104 (4): 718–725. https://doi.org/10.1603/AN11004.
   Web of Science ®Google Scholar
• Höfle, B., and N. Pfeifer. 2007. “Correction of Laser Scanning Intensity Data: Data and Model-Driven Approaches.” Isprs Journal of Photogrammetry & Remote Sensing 62 (6): 415–433. https://doi.org/10.1016/j.isprsjprs.2007.05.008.
   Web of Science ®Google Scholar
• Honkavaara, E., R. Näsi, R. Oliveira, N. Viljanen, J. Suomalainen, E. Khoramshahi, T. Hakala, et al. 2020. “Using Multitemporal Hyper-And Multispectral UAV Imaging for Detecting Bark Beetle Infestation on Norway Spruce.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 43:429–434. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-429-2020.
   Google Scholar
• Hu, Y., X. Kang, and J. Zhao. 2009. “Variable relationship between tree age and diameter at breast height for natural forests in Changbai Mountains.” Journal of Northeast Forestry University 37 (11): 38–42.
   Google Scholar
• Huo, L., E. Lindberg, J. Bohlin, and H. J. Persson. 2023. “Assessing the Detectability of European Spruce Bark Beetle Green Attack in Multispectral Drone Images with High Spatial-And Temporal Resolutions.” Remote Sensing of Environment 287:113484. https://doi.org/10.1016/j.rse.2023.113484.
   Web of Science ®Google Scholar
• Huo, L., H. J. Persson, and E. Lindberg. 2021. “Early Detection of Forest Stress from European Spruce Bark Beetle Attack, and a New Vegetation Index: Normalized Distance Red & SWIR (NDRS).” Remote Sensing of Environment 255:112240. https://doi.org/10.1016/j.rse.2020.112240.
   Web of Science ®Google Scholar
• Jamali, S., P.-O. Olsson, A. Ghorbanian, and M. Müller. 2023. “Examining the Potential for Early Detection of Spruce Bark Beetle Attacks Using Multi-Temporal Sentinel-2 and Harvester Data.” Isprs Journal of Photogrammetry & Remote Sensing 205:352–366. https://doi.org/10.1016/j.isprsjprs.2023.10.013.
   Web of Science ®Google Scholar
• Jepsen, J. U., L. Kapari, S. B. Hagen, T. Schott, O. P. L. Vindstad, A. C. Nilssen, and R. A. Ims. 2011. “Rapid Northwards Expansion of a Forest Insect Pest Attributed to Spring Phenology Matching with Sub‐Arctic Birch.” Global Change Biology 17 (6): 2071–2083. https://doi.org/10.1111/j.1365-2486.2010.02370.x.
   Web of Science ®Google Scholar
• Jiang, C., Y. Chen, H. Wu, W. Li, H. Zhou, Y. Bo, H. Shao, S. Song, E. Puttonen, and J. Hyyppä. 2019. “Study of a High Spectral Resolution Hyperspectral LiDAR in Vegetation Red Edge Parameters Extraction.” Remote Sensing 11 (17): 2007. https://doi.org/10.3390/rs11172007.
   Web of Science ®Google Scholar
• Junttila, S., M. Holopainen, M. Vastaranta, P. Lyytikäinen-Saarenmaa, H. Kaartinen, J. Hyyppä, and H. Hyyppä. 2019. “The Potential of Dual-Wavelength Terrestrial Lidar in Early Detection of Ips Typographus (L.) Infestation–Leaf Water Content as a Proxy.” Remote Sensing of Environment 231:111264. https://doi.org/10.1016/j.rse.2019.111264.
   Web of Science ®Google Scholar
• Kaasalainen, S., E. Ahokas, J. Hyyppa, and J. Suomalainen. 2005. “Study of Surface Brightness from Backscattered Laser Intensity: Calibration of Laser Data.” IEEE Geoscience & Remote Sensing Letters 2 (3): 255–259. https://doi.org/10.1109/LGRS.2005.850534.
   Web of Science ®Google Scholar
• Kaasalainen, S., A. Jaakkola, M. Kaasalainen, A. Krooks, and A. Kukko. 2011. “Analysis of Incidence Angle and Distance Effects on Terrestrial Laser Scanner Intensity: Search for Correction Methods.” Remote Sensing 3 (10): 2207–2221. https://doi.org/10.3390/rs3102207.
   Web of Science ®Google Scholar
• Kaasalainen, S., T. Lindroos, and J. Hyyppa. 2007. “Toward Hyperspectral Lidar: Measurement of Spectral Backscatter Intensity with a Supercontinuum Laser Source.” IEEE Geoscience & Remote Sensing Letters 4 (2): 211–215. https://doi.org/10.1109/LGRS.2006.888848.
   Web of Science ®Google Scholar
• Kavaya, M. J., R. T. Menzies, D. A. Haner, U. P. Oppenheim, and P. H. Flamant. 1983. “Target Reflectance Measurements for Calibration of Lidar Atmospheric Backscatter Data.” Applied Optics 22 (17): 2619–2628. https://doi.org/10.1364/AO.22.002619.
   PubMed Web of Science ®Google Scholar
• Kluczek, M., B. Zagajewski, and T. Zwijacz-Kozica. 2023. “Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery.” Remote Sensing 15 (3): 844. https://doi.org/10.3390/rs15030844.
   Web of Science ®Google Scholar
• Kukko, A., S. Kaasalainen, and P. Litkey. 2008. “Effect of Incidence Angle on Laser Scanner Intensity and Surface Data.” Applied Optics 47 (7): 986–992. https://doi.org/10.1364/AO.47.000986.
   PubMed Web of Science ®Google Scholar
• Långström, B., L. Lisha, L. Hongpin, C. Peng, L. Haoran, C. Hellqvist, and F. Lieutier. 2002. “Shoot Feeding Ecology of Tomicus Piniperda and T. Minor (Col., Scolytidae) in Southern China.” Journal of Applied Entomology 126 (7–8): 333–342. https://doi.org/10.1046/j.1439-0418.2002.00651.x.
   Web of Science ®Google Scholar
• Liang, X., P. Litkey, J. Hyyppa, H. Kaartinen, M. Vastaranta, and M. Holopainen. 2011. “Automatic stem mapping using single-scan terrestrial laser scanning.” IEEE Transactions on Geoscience & Remote Sensing 50 (2): 661–670. https://doi.org/10.1109/TGRS.2011.2161613.
   Web of Science ®Google Scholar
• Li, W., C. Jiang, Y. Chen, J. Hyyppä, L. Tang, C. Li, and S. W. Wang. 2018. “A Liquid Crystal Tunable Filter-Based Hyperspectral LiDAR System and Its Application on Vegetation Red Edge Detection.” IEEE Geoscience & Remote Sensing Letters 16 (2): 291–295. https://doi.org/10.1109/LGRS.2018.2870143.
   Web of Science ®Google Scholar
• Lin, Q., H. Huang, L. Yu, and J. Wang. 2018. “Detection of Shoot Beetle Stress on Yunnan Pine Forest Using a Coupled LIBERTY2-INFORM Simulation.” Remote Sensing 10 (7): 1133. https://doi.org/10.3390/rs10071133.
   Web of Science ®Google Scholar
• Linnakoski, R., and K. M. Forbes. 2019. “Pathogens—The Hidden Face of Forest Invasions by Wood-Boring Insect Pests.” Frontiers in Plant Science 10:90. https://doi.org/10.3389/fpls.2019.00090.
   PubMed Web of Science ®Google Scholar
• Luo, Y., H. Huang, and A. Roques. 2023. “Early Monitoring of Forest Wood-Boring Pests with Remote Sensing.” Annual Review of Entomology 68:277–298. https://doi.org/10.1146/annurev-ento-120220-125410.
   PubMed Web of Science ®Google Scholar
• Marvasti-Zadeh, S. M., D. Goodsman, N. Ray, and N. Erbilgin. 2023. “Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review.” ACM Computing Surveys 56 (4): 1–40. https://doi.org/10.1145/3625387.
   Web of Science ®Google Scholar
• Migas-Mazur, R., M. Kycko, T. Zwijacz-Kozica, and B. Zagajewski. 2021. “Assessment of Sentinel-2 Images, Support Vector Machines and Change Detection Algorithms for Bark Beetle Outbreaks Mapping in the Tatra Mountains.” Remote Sensing 13 (16): 3314. https://doi.org/10.3390/rs13163314.
   Web of Science ®Google Scholar
• Minařík, R., J. Langhammer, and T. Lendzioch. 2021. “Detection of Bark Beetle Disturbance at Tree Level Using UAS Multispectral Imagery and Deep Learning.” Remote Sensing 13 (23): 4768. https://doi.org/10.3390/rs13234768.
   Web of Science ®Google Scholar
• Muller, J., and P. C. Yuen. 2008. Imaging Lidar Simulator Interface, NERC-CEOI: Hyperspectral Imaging Lidar (LADAR).
   Google Scholar
• Netherer, S., D. Kandasamy, A. Jirosová, B. Kalinová, M. Schebeck, and F. Schlyter. 2021. “Interactions Among Norway Spruce, the Bark Beetle Ips Typographus and Its Fungal Symbionts in Times of Drought.” Journal of Pest Science 94 (3): 591–614. https://doi.org/10.1007/s10340-021-01341-y.
   PubMed Web of Science ®Google Scholar
• Nevalainen, O., T. Hakala, J. Suomalainen, R. Mäkipää, M. Peltoniemi, A. Krooks, and S. Kaasalainen. 2014. “Fast and Nondestructive Method for Leaf Level Chlorophyll Estimation Using Hyperspectral LiDAR.” Agricultural and Forest Meteorology 198:250–258. https://doi.org/10.1016/j.agrformet.2014.08.018.
   Web of Science ®Google Scholar
• Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12:2825–2830.
   Web of Science ®Google Scholar
• Rock, B. N., J. E. Vogelmann, D. L. Williams, A. F. Vogelmann, and T. Hoshizaki. 1986. “Remote Detection of Forest Damage.” Bioscience 36 (7): 439–445. https://doi.org/10.2307/1310339.
   Web of Science ®Google Scholar
• Rohde, M., R. Waldmann, and J. Lunderstädt. 1996. “Induced Defence Reaction in the Phloem of Spruce (Picea abies) and Larch (Larix Decidua) After Attack by Ips Typographus and Ips Cembrae.” Forest Ecology and Management 86 (1–3): 51–59. https://doi.org/10.1016/S0378-1127(96)03802-9.
   Web of Science ®Google Scholar
• Rullán-Silva, C., A. E. Olthoff, V. Pando, J. A. Pajares, and J. A. Delgado. 2015. “Remote Monitoring of Defoliation by the Beech Leaf-Mining Weevil Rhynchaenus Fagi in Northern Spain.” Forest Ecology and Management 347:200–208. https://doi.org/10.1016/j.foreco.2015.03.005.
   Web of Science ®Google Scholar
• Schaepman-Strub, G., M. E. Schaepman, T. H. Painter, S. Dangel, and J. V. Martonchik. 2006. “Reflectance Quantities in Optical Remote Sensing—Definitions and Case Studies.” Remote Sensing of Environment 103 (1): 27–42. https://doi.org/10.1016/j.rse.2006.03.002.
   Web of Science ®Google Scholar
• Seidl, R., D. Thom, M. Kautz, D. Martin-Benito, M. Peltoniemi, G. Vacchiano, J. Wild, et al. 2017. “Forest Disturbances Under Climate Change.” Nature Climate Change 7 (6): 395–402. https://doi.org/10.1038/nclimate3303.
   PubMed Web of Science ®Google Scholar
• Senf, C., A. Buras, C. S. Zang, A. Rammig, and R. Seidl. 2020. “Excess Forest Mortality is Consistently Linked to Drought Across Europe.” Nature Communications 11 (1): 6200. https://doi.org/10.1038/s41467-020-19924-1.
   PubMed Web of Science ®Google Scholar
• Shao, H., Z. Cao, W. Li, Y. Chen, C. Jiang, J. Hyyppä, J. Chen, and L. Sun. 2021. “Feasibility Study of Wood-Leaf Separation Based on Hyperspectral LiDAR Technology in Indoor Circumstances.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 15:729–738. https://doi.org/10.1109/JSTARS.2021.3134651.
   Web of Science ®Google Scholar
• Shao, H., Y. Chen, W. Li, C. Jiang, H. Wu, J. Chen, B. Pan, and J. Hyyppä. 2020. “An Investigation of Spectral Band Selection for Hyperspectral LiDAR Technique.” Electronics 9 (1): 148. https://doi.org/10.3390/electronics9010148.
   Web of Science ®Google Scholar
• Shao, H., Y. Chen, Z. Yang, C. Jiang, W. Li, H. Wu, Z. Wen, S. Wang, E. Puttnon, and J. Hyyppä. 2019. “A 91-Channel Hyperspectral LiDAR for Coal/Rock Classification.” IEEE Geoscience & Remote Sensing Letters 17 (6): 1052–1056. https://doi.org/10.1109/LGRS.2019.2937720.
   Web of Science ®Google Scholar
• Vapnik, V. N. 1999. “An Overview of Statistical Learning Theory.” IEEE Transactions on Neural Networks 10 (5): 988–999. https://doi.org/10.1109/72.788640.
   PubMed Web of Science ®Google Scholar
• Wagner, W. 2010. “Radiometric Calibration of Small-Footprint Full-Waveform Airborne Laser Scanner Measurements: Basic Physical Concepts.” ISPRS Journal of Photogrammetry & Remote Sensing 65 (6): 505–513. https://doi.org/10.1016/j.isprsjprs.2010.06.007.
   Web of Science ®Google Scholar
• Wagner, W., A. Ullrich, V. Ducic, T. Melzer, and N. Studnicka. 2006. “Gaussian Decomposition and Calibration of a Novel Small-Footprint Full-Waveform Digitising Airborne Laser Scanner.” ISPRS Journal of Photogrammetry & Remote Sensing 60 (2): 100–112. https://doi.org/10.1016/j.isprsjprs.2005.12.001.
   Web of Science ®Google Scholar
• Wang, S. 1981. “The Target Reflection Characteristics of Laser.” Laser Technology 6 (9): 15–24.
   Google Scholar
• Wang, L., H. Huang, and Y. Luo. 2010. “Remote Sensing of Insect Pests in Larch Forest Based on Physical Model.” In 2010 IEEE International Geoscience and Remote Sensing Symposium, edited by C. Ruf, 3299–3302. IEEE. https://doi.org/10.1109/IGARSS.2010.5649528.
   Google Scholar
• Wang, J., Q. Lin, S. Meng, H. Huang, and Y. Liu. 2024. “Individual Tree-Level Monitoring of Pest Infestation Combining Airborne Thermal Imagery and Light Detection and Ranging.” Forests 15 (1): 112. https://doi.org/10.3390/f15010112.
   Web of Science ®Google Scholar
• Wang, J., S. Meng, Q. Lin, Y. Liu, and H. Huang. 2022. “Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR.” Applied Sciences 12 (9): 4372. https://doi.org/10.3390/app12094372.
   Google Scholar
• Yu, L., J. Huang, S. Zong, H. Huang, and Y. Luo. 2018. “Detecting Shoot Beetle Damage on Yunnan Pine Using Landsat Time-Series Data.” Forests 9 (1): 39. https://doi.org/10.3390/f9010039.
   Web of Science ®Google Scholar
• Zakrzewska, A., D. Kopeć, A. Ochtyra, and M. Potůčková. 2023. “Can Canopy Temperature Acquired from an Airborne Level Be a Tree Health Indicator in an Urban Environment?” Urban Forestry & Urban Greening 79:127807. https://doi.org/10.1016/j.ufug.2022.127807.
   Web of Science ®Google Scholar
• Zhang, A., J. E. Oliver, J. R. Aldrich, B. Wang, and V. C. Mastro. 2002. “Stimulatory Beetle Volatiles for the Asian Longhorned Beetle, Anoplophora Glabripennis (Motschulsky).” Zeitschrift für Naturforschung C 57 (5–6): 553–558. https://doi.org/10.1515/znc-2002-5-626.
   PubMedGoogle Scholar
• Zhang, Y., and X. Shen. 2013. “Quantitative Analysis on Geometric Size of LiDAR Footprint.” IEEE Geoscience & Remote Sensing Letters 11 (3): 701–705. https://doi.org/10.1109/LGRS.2013.2276126.
   Web of Science ®Google Scholar
• Zhao, F., X. Yang, M. A. Schull, M. O. Román-Colón, T. Yao, Z. Wang, Q. Zhang, et al. 2011. “Measuring Effective Leaf Area Index, Foliage Profile, and Stand Height in New England Forest Stands Using a Full-Waveform Ground-Based Lidar.” Remote Sensing of Environment 115 (11): 2954–2964. https://doi.org/10.1016/j.rse.2010.08.030.
   Web of Science ®Google Scholar