Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level

Remote Sensing

Roope Näsi²  Eija Honkavaara²  Päivi Lyytikäinen-Saarenmaa¹  Minna Blomqvist¹  Paula Litkey²  Teemu Hakala²  Niko Viljanen²  Tuula Kantola¹  Topi Tanhuanpää¹  Markus Holopainen¹  Cheng Wang³  Randolph H. Wynne³ 

[1] Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 Helsinki, Finland; E-Mails:;Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Geodeetinrinne 2, 02430 Masala, Finland; E-Mails:Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Geodeetinrinne 2, 02430 Masala, Finland;

关键词: bark beetle;    classification;    dense matching;    digital surface model;    hyperspectral;    insect outbreak;    photogrammetry;    radiometry;    UAV;   

DOI  :  10.3390/rs71115467

来源: mdpi

【 摘 要 】

Low-cost, miniaturized hyperspectral imaging technology is becoming available for small unmanned aerial vehicle (UAV) platforms. This technology can be efficient in carrying out small-area inspections of anomalous reflectance characteristics of trees at a very high level of detail. Increased frequency and intensity of insect induced forest disturbance has established a new demand for effective methods suitable in mapping and monitoring tasks. In this investigation, a novel miniaturized hyperspectral frame imaging sensor operating in the wavelength range of 500–900 nm was used to identify mature Norway spruce (Picea abies L. Karst.) trees suffering from infestation, representing a different outbreak phase, by the European spruce bark beetle (Ips typographus L.). We developed a new processing method for analyzing spectral characteristic for high spatial resolution photogrammetric and hyperspectral images in forested environments, as well as for identifying individual anomalous trees. The dense point clouds, measured using image matching, enabled detection of single trees with an accuracy of 74.7%. We classified the trees into classes of healthy, infested and dead, and the results were promising. The best results for the overall accuracy were 76% (Cohen’s kappa 0.60), when using three color classes (healthy, infested, dead). For two color classes (healthy, dead), the best overall accuracy was 90% (kappa 0.80). The survey methodology based on high-resolution hyperspectral imaging will be of a high practical value for forest health management, indicating a status of bark beetle outbreak in time.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.