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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improving carbon monitoring and reporting in forests using spatially-explicit information
|
|---|---|
| Published in |
Carbon Balance and Management, October 2016
|
| DOI | 10.1186/s13021-016-0065-6 |
| Pubmed ID | |
| Authors |
Céline Boisvenue, Byron P. Smiley, Joanne C. White, Werner A. Kurz, Michael A. Wulder |
| Abstract |
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. 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. 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. |
X Demographics
The data shown below were collected from the profiles of 20 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 8 | 40% |
| United States | 2 | 10% |
| South Africa | 1 | 5% |
| Germany | 1 | 5% |
| Unknown | 8 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 15 | 75% |
| Scientists | 4 | 20% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 1% |
| Unknown | 72 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 23% |
| Student > Master | 11 | 15% |
| Student > Ph. D. Student | 10 | 14% |
| Other | 8 | 11% |
| Student > Bachelor | 3 | 4% |
| Other | 7 | 10% |
| Unknown | 17 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Environmental Science | 28 | 38% |
| Agricultural and Biological Sciences | 9 | 12% |
| Earth and Planetary Sciences | 4 | 5% |
| Business, Management and Accounting | 1 | 1% |
| Nursing and Health Professions | 1 | 1% |
| Other | 7 | 10% |
| Unknown | 23 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 April 2017.
All research outputs
#2,540,902
of 25,392,205 outputs
Outputs from Carbon Balance and Management
#51
of 219 outputs
Outputs of similar age
#42,433
of 321,152 outputs
Outputs of similar age from Carbon Balance and Management
#3
of 4 outputs
Altmetric has tracked 25,392,205 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 219 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.0. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 321,152 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.