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Technical Note

Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science

Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada
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Remote Sens. 2019, 11(4), 463; https://doi.org/10.3390/rs11040463
Received: 11 January 2019 / Revised: 19 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
(This article belongs to the Section Forest Remote Sensing)
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. View Full-Text
Keywords: forests; forest modeling; carbon forests; forest modeling; carbon
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MDPI and ACS Style

Boisvenue, C.; White, J.C. Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science. Remote Sens. 2019, 11, 463. https://doi.org/10.3390/rs11040463

AMA Style

Boisvenue C, White JC. Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science. Remote Sensing. 2019; 11(4):463. https://doi.org/10.3390/rs11040463

Chicago/Turabian Style

Boisvenue, Céline, and Joanne C. White 2019. "Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science" Remote Sensing 11, no. 4: 463. https://doi.org/10.3390/rs11040463

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