Tracking the Extent and Impacts of a Southern Pine Beetle (Dendroctonus frontalis) Outbreak in the Bienville National Forest

Abstract

The Bienville National Forest (BNF) in central Mississippi experienced an outbreak of southern pine beetle (SPB) beginning in 2015 and continuing through 2019. To assess the extent of the outbreak and subsequent treatments of impacted areas, high-resolution imagery was obtained from various sources and interpreted to determine the feasibility of this imagery for detecting SPB spots and tracking their spread and treatments. A negative binomial regression model then described the relationship between spot detection, year, and status (i.e., infestation/treatment) and then incidence rate ratios were calculated. The peak active infestation acreage occurred in 2017 with treatment and mitigation measures peaking in 2018. In total, over 4450 hectares (ha) were treated through 2019 in the BNF. Overall, it is possible to discern small areas of active SPB infestation and treatments. If used as a monitoring method, consistently available high-resolution imagery (e.g., from a satellite) provides an effective means of detecting, evaluating, and tracking infestations and related treatments.

The Use of High-Resolution Satellite Imagery to Determine the Status of a Large-Scale Outbreak of Southern Pine Beetle

“…The object classification using combined PC 1 and PC 2 provided the most accurate classification (assessed by F1 score; Figure 4a), though it was only marginally better than the classification using bands 1-5 (Figure 2b), which were most closely correlated with PC 2 (89.7% vs. 88.6%, respectively; Table 3). The area estimates are different, with PC 1 and PC 2 showing 1528 hectares and bands 1-5 showing 1271 hectares; both overestimated the area of active infestation (953 hectares; [16]) by 60 and 33%, respectively (Table 3). While bands 1-5 produced an area estimate closer to the accepted area, there were similar errors in the classification using all bands with misclassification in rights-of-ways.…”

“…The object classification of PC results produces a smoother map (Figure 6c) and is less susceptible to individual pixels with small groups of pixels showing up throughout an image (Figure 6b) that would lead to a waste of field resources, sending people to physically check or treat locations where there is no infestation. Classifying the PCA results is a more efficient method than the manual interpretation of imagery (e.g., digitizing; [16]). The combined visible and NIR data led to too much variance in the image for accurate classification [28].…”

“…A previous SPB outbreak impacted the forest in 2012 [5] with the most recent outbreak beginning in 2015. The spread of SPB in the BNF led to various treatment efforts in an attempt to control the outbreak [16]. These efforts include cut-and-leave, where trees are cut in a circular buffer around the infested trees to cover an area that is at least the height of the tallest infested tree.…”

“…The point locations were assessed visually using the visible image where the yellow-and/or red-attack stage could be identified. These validation points were compared to the manually classified infestation areas reported by Crosby and others [16]. From these locations, precision, recall, overall accuracy, and the F1 score were calculated for the seven different classifications performed.…”

“…Detection of insect infestation or damage improves with increased spatial or temporal resolution as greater temporal resolution allows for the examination of spectral signatures over finer time frames and a greater spatial resolution allows for the analysis of individual tree crowns. Using multi-date imagery was found to provide a greater likelihood of detecting intermediate damage [11] and a greater spatial resolution allows for infested trees and portions of stands to be digitized for the development of treatment protocols [11,12,15,16].…”