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Nasi, R., Honkavaara, E., Lyytikainen-Saarenmaa, P., Blomqvist, M., Litkey, P., Hakala, T., Viljanen, N., Kantola, T., Tanhuanpaa, T. and Holopainen, M. (2015) Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level. Remote Sensing, 7, 15467-15493.
https://doi.org/10.3390/rs71115467

has been cited by the following article:

  • TITLE: Cicada (Magicicada) Tree Damage Detection Based on UAV Spectral and 3D Data

    AUTHORS: Ângela Maria Klein Hentz, Michael P. Strager

    KEYWORDS: Photogrammetry, Insect Damage, 3D Dense Point Cloud

    JOURNAL NAME: Natural Science, Vol.10 No.1, January 30, 2018

    ABSTRACT: The periodical cicadas appear in regions of the United States in intervals of 13 or 17 years. During these intervals, deciduous trees are often impacted by the small cuts and eggs they lay in the outer branches which soon die off. Because this is such an infrequent occurrence and it isso difficult to assess the damage across large forested areas, there is little information about the extent of this impact. The use of remote sensing techniques has been proven to be useful in forest health management to monitor large areas. In addition, the use of Unmanned Aerial Vehicles (UAVs) has become a valuable tool for analysis. In this study,we evaluated the impact of the periodical cicada occurrence on a mixed hardwood forest using UAV imagery.The goal was to evaluate the potential of this technology as a tool for forest health monitoring. We classified the cicada impact using two Maximum Likelihood classifications, one using only the high resolution spectral derived from leaf-on imagery (MLC 1), and in the second we included the Canopy Height Model (CHM)—derived fromleaf-on Digital Surface Model (DSM) and leaf-off Digital Terrain Model (DTM)—information in the classification process (MLC 2). We evaluated the damage percentage in relation to the total forest area in 15 circular plots and observed a range from 1.03%-22.23% for MLC 1, and 0.02%-10.99% for MLC 2. The accuracy of the classification was 0.35 and 0.86, for MLC 1 and MLC 2, based on the kappa index. The results allow us to highlight the importance of combining spectral and 3D information to evaluate forest health features. We believe this approach can be applied in many forest monitoring objectives in order todetect disease or pest impacts.