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 is so 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 from leaf-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 to detect disease or pest impacts.
Highlights
This study was performed at the West Virginia University Research Forest (WVURF), which is composed of approximately 3075 ha of mixed hardwood forest
The ground resolution obtained from the leaf-on imagery was 3.03 cm/pix, and the Digital Surface Model (DSM) presented a resolution of 12.1 cm/pix with 68.2 points/m2
The leaf-off imagery presented a ground resolution of 4.85 cm/pix, the Digital Terrain Model (DTM) presented a resolution of 19.4 cm/pix, and a dense point density of 26.5 points/m2
Summary
The damage to the trees can cause problems such as reduced growth, or even tree death [6], resulting in an impact to forest production and ecological services. There are at least three major strategies for using remote sensing to assess forest damage: early damage detection, extent mapping, and damage quantification [6]. In forest health, most studies have used remote sensing techniques to map forest conditions at a regional or stand level [9,10,11,12,13,14,15]. Individual tree damage is often investigated for disturbance across stand level extents [16,17,18]. Few studies have been able to examine individual branch scale disturbance because of the high spatial resolution needed for detection
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