Canadian Forest Service Publications

Improving carbon monitoring and reporting in forests using spatially‑explicit information. 2016. Boisvenue, C.; Smiley, B. P.; Kurz, W. A.; White, J. C.; Wulder, M. A. Carbon Balance Manage, 11:23.

Year: 2016

Available from: Pacific Forestry Centre

Catalog ID: 39152

Language: English

CFS Availability: PDF (download)

Available from the Journal's Web site.
DOI: 10.1186/s13021-016-0065-6

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Abstract

Background: Understanding and quantifying carbon (C) exchanges between the biosphere and the atmosphere— specifically the process of C removal from the atmosphere, and how this process is changing—is the basis for developing appropriate adaptation and mitigation strategies for climate change. Monitoring forest systems and reporting on greenhouse gas (GHG) emissions and removals are now required components of international efforts aimed at mitigating rising atmospheric GHG. Spatially-explicit information about forests can improve the estimates of GHG emissions and removals. However, at present, remotely-sensed information on forest change is not commonly integrated into GHG reporting systems. New, detailed (30-m spatial resolution) forest change products derived from satellite time series informing on location, magnitude, and type of change, at an annual time step, have recently become available. Here we estimate the forest GHG balance using these new Landsat-based change data, a spatial forest inventory, and develop yield curves as inputs to the Carbon Budget Model of the Canadian Forest Sector (CBMCFS3) to estimate GHG emissions and removals at a 30 m resolution for a 13 Mha pilot area in Saskatchewan, Canada. Results: Our results depict the forests as cumulative C sink (17.98 Tg C or 0.64 Tg C year−1) between 1984 and 2012 with an average C density of 206.5 (±0.6) Mg C ha−1. Comparisons between our estimates and estimates from Canada’s National Forest Carbon Monitoring, Accounting and Reporting System (NFCMARS) were possible only on a subset of our study area. In our simulations the area was a C sink, while the official reporting simulations, it was a C source. Forest area and overall C stock estimates also differ between the two simulated estimates. Conclusions: Both estimates have similar uncertainties, but the spatially-explicit results we present here better quantify the potential improvement brought on by spatially-explicit modelling. We discuss the source of the differences between these estimates. This study represents an important first step towards the integration of spatially-explicit information into Canada’s NFCMARS.

Plain Language Summary

The recent increase in atmospheric greenhouse gases (GHG) is cause for concern for human societies as it has widespread impacts on human and natural systems. The rate of build-up of carbon dioxide (CO2) in the atmosphere, the most important GHG, can be reduced by taking advantage of the fact that atmospheric CO2 can accumulate as carbon (C) in vegetation via photosynthesis, and through vegetation mortality, in soils in terrestrial ecosystems. Understanding C exchanges, specifically the process of C removal from the atmosphere, and how this process is changing or might change, is the basis for anticipating appropriate adaptation and developing mitigation strategies. Satellite observations of the earth surface present a new source of information that can provide more information on the status of our forests and potentially, the C they contain. Here, we present an improved GHG balance estimates for boreal forests that builds on newly available forest disturbances information, such as fire and harvesting, and new modelling capabilities for Canadian forests. Our results depict our pilot area in the boreal forests of Saskatchewan, Canada, as removing a total of 17.98 Tg of C between 1984 and 2012 with average C density of 206.5 Mg C (+/-0.6). These results do not quite match the previously reported values. We explain why this is, and discuss other improvements that may be possible in our GHG balance estimates for forests. This research is a an important step in making use of the additional information provided via remote sensing products towards producing increasingly accurate GHG balance estimations for forests.

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