Supplementing the Forest Health National Aerial Survey Program with Remote Sensing during the COVID-19 Pandemic: Lessons Learned from a Collaborative Approach

Abstract

The COVID-19 pandemic has created unprecedented challenges in the way the USDA Forest Service conducts business. Standard data collection methods were immediately challenged due to travel restrictions and due to uncertainty regarding when it would be safe to return to a “business as usual” approach. These challenges were met with an inspiring collaboration between forest health specialists directly involved in the annual Aerial Detection Survey (ADS) program and remote sensing specialists from the Forest Service and academia. This group worked together to generate informative training materials, new workflows, and weekly help sessions to directly address problems that arose during this capacity building exercise. Small ad hoc teams were created to identify regionally specific program resources to enhance remote sensing utilization while supplementing information gaps where aerial detection surveys were either limited or not possible. The lessons learned from this challenge provide an opportunity to continue the exploration of combining ADS, remote sensing, and field data to deliver comprehensive information for managing the nation’s forests, while applying what is working and learning and growing from both successes and limitations. Study Implications: The 2020 USDA Forest Service’s (USFS) Aerial Detection Survey (ADS) program faced unprecedented challenges resulting from the COVID-19 pandemic, which limited surveys across nearly all USFS regions. However, this pandemic created an unexpected positive outcome through an ongoing and wide-reaching collaboration between federal, state, academic, and private sectors that has allowed for a strong and lasting collaboration across USFS regions. Moreover, this collaboration has provided a unique opportunity to optimize a combination of ADS, remote sensing, and field visits to deliver a comprehensive, robust, and near-real-time assessment of the health of our nation’s forests.

A spectral three-dimensional color space model of tree crown health

“…Alternatively, attributes could be derived by datamining IDS or other existing forest health observational data sources [ 82 , 83 ], as well as information on host tree species [ 63 , 84 ], to probabilistically infer data labels at moderate spatial resolutions. Such integration of remote sensing with other traditional forest health survey data stands to play an important role in reducing risk exposure (i.e., of field surveyors) while also facilitating new and varied approaches to conducting surveys when traditional field activities are seriously curtailed, such as what occurred during the COVID-19 pandemic [ 85 ]. Furthermore, TCH results stand to complement existing IDS survey data by offering opportunities to refine or improve estimates of damage location (i.e., mapping damage more precisely and accurately) and intensity (e.g., TPA).…”

Denser forests across the USA experience more damage from insects and pathogens

“…The IDS data are geospatial in nature, with disturbances depicted by polygon or point features. The data are collected primarily through aerial survey of forestland in small aircraft by highly trained forest health specialists, although remotely sensed imagery from satellites is increasingly being used to monitor and map forest health conditions 63 – 65 . In either case, some ground survey for verification of the damage-causing agent by a specialist is necessary 66 .…”

Using Remote Sensing and Climate Data to Map the Extent and Severity of Balsam Woolly Adelgid Infestation in Northern Utah, USA

“…The data, collected during flight at considerable altitude and speed, are not without their limitations, including spatial uncertainty resulting from manual polygon delineation and thematic uncertainty resulting from surveyor bias and the difficulty in distinguishing between host species and causal agents [79,80]. Furthermore, some years lack complete coverage, such as in 2020 when COVID-19 limited the capacity of aerial surveys [81]. Accordingly, comparisons to other field-or remote-sensingbased data must be carried out cautiously with those limitations in mind.…”