Cicada (<i>Magicicada</i>) Tree Damage Detection Based on UAV Spectral and 3D Data

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.

Blue Water Visitor Monitoring Potential: A Literature Review and Alternative Proposal

“…Sensors may be used on UAVs which collect normal imagery (RGB lens), near-infrared (NIR) light, 3D point clouds, digital elevation and surface models (DEM/DSM), and light detection and ranging (LiDAR) models, among others. The precision of these spectral tools can be quite specific and has been demonstrated in forestry applications [90,91], as well as fine-scale riverine habitat assessment [89,92].…”

UAV-Based Forest Health Monitoring: A Systematic Review

“…In some cases, data acquisition was carried out not only during the summer (leaf-on) but also in the winter (leaf-off) season to optimize the detection of the evergreen mistletoe (Viscum album) [113]. Others used the opportunity to get a clear view of the ground, enabling the creation of an improved photogrammetric digital terrain model (DTM) [114]. Only in six papers was the collection of UAV data over a period of more than a year described [97,98,105,106,108,115].…”