Optimal Survey Design for Forest Carbon Monitoring in Remote Regions Using Multi-Objective Mathematical Programming

Cost-effective monitoring of forest carbon resources is critical to the development of national policies and enforcement of international agreements aimed at reducing carbon emissions and mitigating the impacts of climate change. While carbon monitoring systems are often based on national forest inv...

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Vydáno v:Forests Ročník 13; číslo 7; s. 972
Hlavní autoři: Tóth, Sándor F., Oken, Kiva L., Stawitz, Christine C., Andersen, Hans-Erik
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.07.2022
Témata:
Airborne sensing
Alaska
Biomass
Boreal forests
Budgets
Carbon
Carbon cycle
Carbon sequestration
Case studies
Climate change
cost effectiveness
Costs
Ecosystems
Emissions
Environmental aspects
Errors
Forest carbon
Forest ecosystems
Forest resources
Forests
infrastructure
International agreements
Lidar
Linear programming
Mathematical analysis
Mathematical models
Mathematical optimization
Mathematical programming
Measurement
Methods
Monitoring systems
Multiple criteria decision making
Multiple objective analysis
national forests
Nuclear factor I
Optimization techniques
Permafrost
Plot (Narrative)
Pools
Regression analysis
Regression models
Remote monitoring
Remote regions
Remote sensing
soil
soil carbon
Soils
Terrestrial ecosystems
transportation
trees
United States
United States > US
Alaska
ISSN:1999-4907, 1999-4907
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Shrnutí:Cost-effective monitoring of forest carbon resources is critical to the development of national policies and enforcement of international agreements aimed at reducing carbon emissions and mitigating the impacts of climate change. While carbon monitoring systems are often based on national forest inventories (NFI) utilizing a large sample of field plots, in remote regions the lack of transportation infrastructure often requires heavier reliance on remote sensing technologies, such as airborne lidar. The challenge motivating our research is that the efficacy of estimating carbon with lidar varies across the various carbon pools within forest ecosystems. Lidar measurements are typically highly correlated with aboveground tree carbon but are less strongly correlated with other carbon pools, such as down woody materials (DWM) and soil. Field measurements are essential to both (1) estimate soil and DWM carbon directly and (2) develop regression models to estimate tree carbon indirectly using lidar. With limited budgets and time, however, decision makers must find an optimal way to combine field measurements with lidar to minimize standard errors in carbon estimates for the various pools. We introduce a multi-objective binary programming formulation that quantifies the tradeoffs behind the competing objectives of minimizing standard errors for tree carbon, DWM carbon, and soil carbon. Using NFI and airborne lidar data from a remote boreal forest region of interior Alaska, we demonstrate the operational feasibility of the method and suggest that it is generalizable to other carbon sampling projects because of its generic mathematical structure.
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ISSN:1999-4907
1999-4907
DOI:10.3390/f13070972