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

Abstract

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.

Plant phenotyping relevance

UASマルチスペクトル画像から樹冠を抽出し、植食による樹木の落葉・被害状態を自動定量化する手法が研究の中心であり、精度評価も行っている。

Code and data availability

The paper's UAS multispectral imagery, derived point clouds/orthomosaics, and field validation data were deposited in open access on Zenodo (DOI 10.5281/zenodo.2539199), directly supporting this paper's defoliation phenotyping analysis. Other URLs (Pix4D, rLiDAR, FAO) are generic tools or cited references, not paper-