Assessing Pine Processionary Moth Defoliation Using Unmanned Aerial Systems

Abstract

Pine processionary moth (PPM) is one of the most destructive insect defoliators in the Mediterranean for many conifers, causing losses of growth, vitality and eventually the death of trees during outbreaks. There is a growing need for cost-effective monitoring of the temporal and spatial impacts of PPM in forest ecology to better assess outbreak spread patterns and provide guidance on the development of measures targeting the negative impacts of the species on forests, industry and human health. Remote sensing technology mounted on unmanned aerial systems (UASs) with high-resolution image processing has been proposed to assess insect outbreak impacts at local and forest stand levels. Here, we used UAS-acquired RGB imagery in two pine sites to quantify defoliation at the tree-level and to verify the accuracy of the estimates. Our results allowed the identification of healthy, infested and completely defoliated trees and suggested that pine defoliation estimates using UASs are robust and allow high-accuracy (79%) field-based infestation indexes to be derived that are comparable to those used by forest technicians. When compared to current field-based methods, our approach provides PPM impact assessments with an efficient data acquisition method in terms of time and staff, allowing the quantitative estimation of defoliation at tree-level scale. Furthermore, our method could be expanded to a number of situations and scaled up in combination with satellite remote sensing imagery or citizen science approaches.

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

“…Considering studies that use the UAV-RGB sensor applied to the detection of diseases, Cardil et al [145] assessed the insect outbreak impacts, more specifically the pine processionary moth, on a forest mostly covered by conifers (Pinus sylvestris, Pinus nigra) and deciduous species (Quercus ilex, Quercus faginea). According to the authors, it was possible to identify healthy, infested and completed defoliated trees, with an overall accuracy of 79 %.…”

“…Smigaj et al [148] proposed a system to detect Red Band Needle Blight infection levels caused by climate changes and its consequences such as the increase of pathogens. The remaining works [140,[145][146][147]149,150] concentrated efforts to detect damage made by insects such as oak splendour beetle, bark beetle and pine processionary moth. The studies covered several tree species such as oak trees, Norway spruces, scots and lodgepole pine and several other pie trees.…”

Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery

“…Consequently, the use of airborne-based UAS technology with enhanced spatial and temporal resolutions has significantly increased over the past decade for detecting and monitoring forest defoliation on host trees [10][11][12][13][14][15][16]. While spaceborne satellites have been more commonly used for defoliation detection and time-series monitoring over large areas, their images can be either free to the public at medium spatial resolutions (30-250 m) provided by Landsat and MODIS or costly at high spatial resolutions (0.3-10 m) available from WorldView-4, IKONOS, QuickBird, RapidEye, and TerraSAR-X [17].…”

“…While initial studies with UASs were focused on crop management for agriculture applications, the latest UAS technology has proved to be effective for forestry applications as a sampling tool to acquire ground-truth data [11,23]. To date, only a few studies have examined the classification accuracy of forest defoliation by insects using UAS imagery applied to methods such as Random Forest [11], object-based image analysis (OBIA) [12,13], k-nearest neighbor [10], maximum likelihood [14,15], and unsupervised classification [16], which demonstrated that the UAS technology enabled to examine their defoliation detection method at individual tree level with a high overall accuracy. An object-based classification approach with the Random Forest classifier was used by Dash et al [11] to predict discoloration classes of Pinus radiate in New Zealand, based on spectral indices such as normalized difference vegetation index (NDVI) and red edge NDVI with the kappa coefficient of 0.694.…”

“…In the United States, Hentz and Strager [15] combined RGB bands and elevation values to classify damage on deciduous trees using a pixel-based maximum likelihood classification (MLC) technique, with the kappa coefficient of 0.86. Cardil et al [14] also used the MLC based on RGB bands to distinguish infested or healthy trees of Pinus spp. in Spain with an overall accuracy of 79%, which was improved to 81% by adding an NIR band to classify three categories of defoliation and using unsupervised classification with NDVI [16].…”