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

Date Published: March 19, 2019

Publisher: Public Library of Science

Author(s): Adrián Cardil, Kaori Otsu, Magda Pla, Carlos Alberto Silva, Lluis Brotons, Tayyab Ikram Shah.


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.

Partial Text

The area covered by forest ecosystems in the Mediterranean has increased during the last century due to land abandonment and climate change impacts, which have led to significant changes in forest dynamics [1–3]. These forest changes an increase in the effects of pests on trees, partially due to more frequent large-scale outbreaks becoming an increasingly important disturbance in forest dynamics [4,5]. Amongst these pests we can highlight the increasing impact of the pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff., Lepidoptera: Notodontidae; henceforth PPM), one of the main pests of Pinus sp., a native species of the Mediterranean region including North Africa, southern Europe and some areas of the Middle East [6]. Life cycle is characterized by a one-year development cycle for short-lived female moths which typically live for 1 or 2 days and longer-lived males [7]. The cycle involves adult emergence in summer (June–September), larval feeding during fall and winter, and pupation in soil followed by a short or prolonged diapause up to several years under specific circumstances [4,7–9]. The area affected by PPM in Europe is expanding northwards to higher latitudes and upwards to higher altitudes from where it was absent, probably as a result of increasing winter temperatures [6].

Under a global change context with more frequent extreme climatic events [9,55–57] PPM outbreaks are expected to become more frequent on Mediterranean coniferous and mixed forests. In this context, effective monitoring techniques are urgently required over a large spatial scales. The use of UAS-based image acquisition technology is emerging research in assessment of forest pests such as the PPM over representative spatial and temporal scales. Furthermore, the acquisition of data through UAS may also be useful to complement, or even substitute field assessments with large scale quantitative evaluations when detailed results are required. Several authors have suggested low-cost image acquisition with UAS platforms as an alternative option to assess the percentage of defoliated trees and the level of defoliation in each tree quantitatively, and obtain promising validation results with field measurements [5,27]. In this study, we have used RGB and NIR imagery to account for tree species and degree of defoliation in mix pine-oak stands. We identified pine and holm oak and classified pines as non-defoliated, partially defoliated and completely defoliated, after being identified and delineated by a local maximum algorithm on a CHM, as well as assessing the percentage of defoliation of each tree.

In this study, we investigated the use of multispectral high-resolution imagery acquired from a UAS platform and image processing techniques to quantitatively assess PPM impact on a pine-oak mixed forest at tree level. Overall, this research suggested that UAS imagery and its derived products, such as canopy height model and normalized difference vegetation index, enabled us to estimate tree species, count individual trees with acceptable accuracy and assess defoliation using canopy cover at tree level by classifying pines non-defoliated, partially defoliated and completely defoliated automatically with high accuracy. Moreover, the accuracy of our proposed methodology at tree level was higher than previous studies. This proposed framework highlights the future potential of UAS, multispectral imagery and structure-from-motion algorithms for individual tree detection, PPM quantification, qualification and monitoring. Thus, we believe that the promising results presented here in should inspire further research and applications to the forest health assessments.




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