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Improving carbon monitoring and reporting in forests using spatially-explicit information
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. Monitor...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5082575/ https://www.ncbi.nlm.nih.gov/pubmed/27853482 http://dx.doi.org/10.1186/s13021-016-0065-6 |
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author | Boisvenue, Céline Smiley, Byron P. White, Joanne C. Kurz, Werner A. Wulder, Michael A. |
author_facet | Boisvenue, Céline Smiley, Byron P. White, Joanne C. Kurz, Werner A. Wulder, Michael A. |
author_sort | Boisvenue, Céline |
collection | PubMed |
description | 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 (CBM-CFS3) 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. |
format | Online Article Text |
id | pubmed-5082575 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-50825752016-11-14 Improving carbon monitoring and reporting in forests using spatially-explicit information Boisvenue, Céline Smiley, Byron P. White, Joanne C. Kurz, Werner A. Wulder, Michael A. Carbon Balance Manag Research 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 (CBM-CFS3) 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. Springer International Publishing 2016-10-26 /pmc/articles/PMC5082575/ /pubmed/27853482 http://dx.doi.org/10.1186/s13021-016-0065-6 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Boisvenue, Céline Smiley, Byron P. White, Joanne C. Kurz, Werner A. Wulder, Michael A. Improving carbon monitoring and reporting in forests using spatially-explicit information |
title | Improving carbon monitoring and reporting in forests using spatially-explicit information |
title_full | Improving carbon monitoring and reporting in forests using spatially-explicit information |
title_fullStr | Improving carbon monitoring and reporting in forests using spatially-explicit information |
title_full_unstemmed | Improving carbon monitoring and reporting in forests using spatially-explicit information |
title_short | Improving carbon monitoring and reporting in forests using spatially-explicit information |
title_sort | improving carbon monitoring and reporting in forests using spatially-explicit information |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5082575/ https://www.ncbi.nlm.nih.gov/pubmed/27853482 http://dx.doi.org/10.1186/s13021-016-0065-6 |
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