Quantifying pine processionary moth defoliation in a pine-oak mixed forest using unmanned aerial systems and multispectral imagery

Pine processionary moth (PPM) feeds on conifer foliage and periodically result in outbreaks leading to large scale defoliation, causing decreased tree growth, vitality and tree reproduction capacity. Multispectral high-resolution imagery acquired from a UAS platform was successfully used to assess p...

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Bibliographic Details
Published in:PloS one 2019-03, Vol.14 (3), p.e0213027-e0213027
Main Authors: Cardil, Adrián, Otsu, Kaori, Pla, Magda, Silva, Carlos Alberto, Brotons, Lluis
Format: Article
Language:English
Subjects:
Accuracy
Animals
Biology and Life Sciences
Butterflies & moths
Coniferous trees
Damage assessment
Data collection
Defoliation
Diseases and pests
Ecology and Environmental Sciences
Engineering and Technology
Environmental Monitoring - methods
Foliage
Forests
Image acquisition
Image resolution
Moths
Normalized difference vegetative index
Open access
Outbreaks
Pest outbreaks
Photogrammetry
Pine
Pines
Pinus - parasitology
Quercus - parasitology
Remote sensing
Research and Analysis Methods
Satellite Imagery
Satellite remote sensing
Satellites
Scaling
Spain
Tree growth
Trees
Trees - parasitology
Unmanned aerial vehicles
Vegetation
Vegetation index
Vegetative index
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Description
Summary:Pine processionary moth (PPM) feeds on conifer foliage and periodically result in outbreaks leading to large scale defoliation, causing decreased tree growth, vitality and tree reproduction capacity. Multispectral high-resolution imagery acquired from a UAS platform was successfully used to assess pest tree damage at the tree level in a pine-oak mixed forest. We generated point clouds and multispectral orthomosaics from UAS through photogrammetric processes. These were used to automatically delineate individual tree crowns and calculate vegetation indices such as the normalized difference vegetation index (NDVI) and excess green index (ExG) to objectively quantify defoliation of trees previously identified. Overall, our research suggests that UAS imagery and its derived products enable robust estimation of tree crowns with acceptable accuracy and the assessment of tree defoliation by classifying trees along a gradient from completely defoliated to non-defoliated automatically with 81.8% overall accuracy. The promising results presented in this work should inspire further research and applications involving a combination of methods allowing the scaling up of the results on multispectral imagery by integrating satellite remote sensing information in the assessments over large spatial scales.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0213027