Canadian Forest Service Publications
Information needs of next-generation forest carbon models: Opportunities for remote sensing science. 2019. Boisvenue, C., White, J.C. Remote Sens., 11(4), 463;
Available from: Pacific Forestry Centre
Catalog ID: 39537
CFS Availability: PDF (download)
Available from the Journal's Web site. †
† This site may require a fee.
Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from multiple research domains, none of which currently offer a complete understanding of forest carbon dynamics. New large-area forest information products derived from remotely sensed data provide unprecedented spatial and temporal information about our forests, which is information that is currently underutilized in forest carbon models. Our goal in this communication is to articulate the information needs of next-generation forest carbon models in order to enable the remote sensing community to realize the best and most useful application of its science, and perhaps also inspire increased collaboration across these research fields. While remote sensing science currently provides important contributions to large-scale forest carbon models, more coordinated efforts to integrate remotely sensed data into carbon models can aid in alleviating some of the main limitations of these models; namely, low sample sizes and poor spatial representation of field data, incomplete population sampling (i.e., managed forests exclusively), and an inadequate understanding of the processes that influence forest carbon accumulation and fluxes across spatiotemporal scales. By articulating the information needs of next-generation forest carbon models, we hope to bridge the knowledge gap between remote sensing experts and forest carbon modelers, and enable advances in large-area forest carbon modeling that will ultimately improve estimates of carbon stocks and fluxes.
Plain Language Summary
Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key priorities for remote sensing science. Forest carbon, however, is the result of multiple overlaying processes ranging from molecular gas exchanges, to plant interactions, and global circulation. No single model, or modeling approach, currently provides a complete assessment of carbon stocks and fluxes, at any scale. There are too many unknown processes and interactions. While remote sensing science currently provides important contributions to large-scale forest carbon models, the next-generation of forest carbon models needs to be built on the joint expertise from the remote sensing community and the forest modelling community. Our article presents a summary of current forest-carbon science and an overview of large-area forest carbon modelling, including their main limitations. We present examples of well-used information and suggest directions for further joint research development.
- Date modified: