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.