21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West

Data-driven decision making is the key to providing effective and efficient wildfire protection and sustainable use of natural resources. Due to the complexity of natural systems, management decision(s) require clear justification based on substantial amounts of information that are both accurate an...

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Main Authors: John Hogland, Christopher J. Dunn, James D. Johnston
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Forests
Subjects:
Online Access:https://www.mdpi.com/1999-4907/12/8/1084
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author John Hogland
Christopher J. Dunn
James D. Johnston
author_facet John Hogland
Christopher J. Dunn
James D. Johnston
author_sort John Hogland
collection DOAJ
description Data-driven decision making is the key to providing effective and efficient wildfire protection and sustainable use of natural resources. Due to the complexity of natural systems, management decision(s) require clear justification based on substantial amounts of information that are both accurate and precise at various spatial scales. To build information and incorporate it into decision making, new analytical frameworks are required that incorporate innovative computational, spatial, statistical, and machine-learning concepts with field data and expert knowledge in a manner that is easily digestible by natural resource managers and practitioners. We prototyped such an approach using function modeling and batch processing to describe wildfire risk and the condition and costs associated with implementing multiple prescriptions for risk mitigation in the Blue Mountains of Oregon, USA. Three key aspects of our approach included: (1) spatially quantifying existing fuel conditions using field plots and Sentinel 2 remotely sensed imagery; (2) spatially defining the desired future conditions with regards to fuel objectives; and (3) developing a cost/revenue assessment (CRA). Each of these components resulted in spatially explicit surfaces describing fuels, treatments, wildfire risk, costs of implementation, projected revenues associated with the removal of tree volume and biomass, and associated estimates of model error. From those spatially explicit surfaces, practitioners gain unique insights into tradeoffs among various described prescriptions and can further weigh those tradeoffs against financial and logistical constraints. These types of datasets, procedures, and comparisons provide managers with the information needed to identify, optimize, and justify prescriptions across the landscape.
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spelling doaj.art-7b428388ecc9429695033580e8fb10d32023-11-22T07:43:19ZengMDPI AGForests1999-49072021-08-01128108410.3390/f1208108421st Century Planning Techniques for Creating Fire-Resilient Forests in the American WestJohn Hogland0Christopher J. Dunn1James D. Johnston2Rocky Mountain Research Station, U.S. Forest Service, Missoula, MT 59801, USACollege of Forestry, Oregon State University, Corvallis, OR 97331, USACollege of Forestry, Oregon State University, Corvallis, OR 97331, USAData-driven decision making is the key to providing effective and efficient wildfire protection and sustainable use of natural resources. Due to the complexity of natural systems, management decision(s) require clear justification based on substantial amounts of information that are both accurate and precise at various spatial scales. To build information and incorporate it into decision making, new analytical frameworks are required that incorporate innovative computational, spatial, statistical, and machine-learning concepts with field data and expert knowledge in a manner that is easily digestible by natural resource managers and practitioners. We prototyped such an approach using function modeling and batch processing to describe wildfire risk and the condition and costs associated with implementing multiple prescriptions for risk mitigation in the Blue Mountains of Oregon, USA. Three key aspects of our approach included: (1) spatially quantifying existing fuel conditions using field plots and Sentinel 2 remotely sensed imagery; (2) spatially defining the desired future conditions with regards to fuel objectives; and (3) developing a cost/revenue assessment (CRA). Each of these components resulted in spatially explicit surfaces describing fuels, treatments, wildfire risk, costs of implementation, projected revenues associated with the removal of tree volume and biomass, and associated estimates of model error. From those spatially explicit surfaces, practitioners gain unique insights into tradeoffs among various described prescriptions and can further weigh those tradeoffs against financial and logistical constraints. These types of datasets, procedures, and comparisons provide managers with the information needed to identify, optimize, and justify prescriptions across the landscape.https://www.mdpi.com/1999-4907/12/8/1084collaborative forest landscape restoration programfuelsquantitative risk assessmentremote sensingcost revenue assessmentestimation error
spellingShingle John Hogland
Christopher J. Dunn
James D. Johnston
21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West
Forests
collaborative forest landscape restoration program
fuels
quantitative risk assessment
remote sensing
cost revenue assessment
estimation error
title 21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West
title_full 21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West
title_fullStr 21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West
title_full_unstemmed 21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West
title_short 21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West
title_sort 21st century planning techniques for creating fire resilient forests in the american west
topic collaborative forest landscape restoration program
fuels
quantitative risk assessment
remote sensing
cost revenue assessment
estimation error
url https://www.mdpi.com/1999-4907/12/8/1084
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