Detection of Annual Spruce Budworm Defoliation and Severity Classification Using Landsat Imagery

Abstract

Spruce budworm (SBW) is the most destructive forest pest in eastern forests of North America. Mapping annual current-year SBW defoliation is challenging because of the large landscape scale of infestations, high temporal/spatial variability, and the short period of time when detection is possible. We used Landsat-5 and Landsat-MSS data to develop a method to detect and map SBW defoliation, which can be used as ancillary or alternative information for aerial sketch maps (ASMs). Results indicated that Landsat-5 data were capable of detecting and classifying SBW defoliation into three levels comparable to ASMs. For SBW defoliation classification, a combination of three vegetation indices, including normalized difference moisture index (NDMI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI), were found to provide the highest accuracy (non-defoliated: 77%, light defoliation: 60%, moderate defoliation: 52%, and severe defoliation: 77%) compared to using only NDMI (non-defoliated: 76%, light defoliation: 40%, moderate defoliation: 43%, and severe defoliation: 67%). Detection of historical SBW defoliation was possible using Landsat-MSS NDVI data, and the produced maps were used to complement coarse-resolution aerial sketch maps of the past outbreak. The method developed for Landsat-5 data can be used for current SBW outbreak mapping in North America using Landsat-8 and Sentinel-2 imagery. Overall, the work highlights the potential of moderate resolution optical remote sensing data to detect and classify fine-scale patterns in tree defoliation.

A Conceptual Framework for the Spruce Budworm Early Intervention Strategy: Can Outbreaks be Stopped?

“…L2 monitoring efficiency could be further enhanced in the future through integration with modelling tools to predict moth dispersal patterns via weather forecasting [40] or radar [23], or through defoliation assessment using remote monitoring approaches [41,42].…”

Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling

“…Sentinel-2 data thus widen the possibility of using passive optical satellite data for vegetation monitoring, particularly in non-homogeneous and complex canopies (Lange et al, 2017). The temporal resolution of Sentinel-2 offers new opportunities to understand the trends of the vegetation affected by infective agents with higher accuracy than other satellites such as Landsat (Rahimzadeh-Bajgiran et al, 2018) or MODIS (Mura et al, 2018). Recent studies have investigated the actual capabilities of the sensor for monitoring temporal changes in vegetation activity in different canopy types such as wetlands (Araya-López et al, 2018;Whyte et al, 2018), grasslands (Hill, 2013) or forests (Castillo et al, 2017;Zarco-Tejada et al, 2018b).…”

Protection Strategy against Spruce Budworm

“…About one-quarter to one-half of plots had significantly clustered defoliation, and data on plot-level defoliation and tree basal area were sufficient for modeling individual tree defoliation [34]. 8 Rahimzadeh-Bajgiran et al [35] assessed the use of Landsat-5 and Landsat-MSS data to detect and map spruce budworm defoliation. A combination of three vegetation indices derived from Landsat data were able to detect and classify defoliation in three classes with an accuracy of 52%-77%.…”