the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Reviews and syntheses: One forest carbon model to rule them all? Utilizing ensembles of forest cover and biomass datasets to determine carbon budgets of the world’s forest ecosystems
Abstract. Understanding global forest carbon stocks is necessary to assess the world’s global carbon budget, with land cover change being estimated to contribute roughly 20 % of the emissions of greenhouse gases to the atmosphere. In the last decade or so, remote sensing has contributed estimates of above ground stocks of biomass – a key part of forest carbon stocks – with over twenty biomass maps available at pan-tropical and global scales. To further the understanding of forest carbon stocks, this research seeks to synthesize the findings of disparate data sources on: (i) forest cover, (ii) forest cover change, (iii) above ground biomass (AGB) / above ground carbon (AGC) stocks in forests. Satellite-derived forest cover and AGB estimates have substantial variability. In 2020, forests were estimated to cover between 22.6 million and 49.7 million km2 of the Earth’s land surface, thus ranging from 17.1 % to 37.6 % of total land cover. Likewise, examining forest cover change from available datasets, the estimated change in global forest cover between 2000 and 2020 was loss of approximately 88,734 to 124,184 km2 per year, combined with regrowth of forest cover of approximately 58,628 to 169,912 km2 per year. Combining that forest cover data with remotely sensed AGB estimates, total stocks of AGB for the year 2000 were estimated to be 325–697 Gt, while for the year 2020, the range was 401–580 Gt. The equivalent quantity of CO2 (i.e., CO2e) of that stock of forest biomass was therefore estimated to be 560 to 1,200 Gt for the year 2000, and 692–999 Gt for the year 2020. Our analysis found that the forest cover loss in tropics was the largest, at the rate of 1.4 % to 3.5 % net reduction between 2000 and 2020, whereas for the same time period, the temperate and boreal zones showed substantially lower forest cover loss (-2.5 % to 0.5 % and 1 % to 5.3 % respectively). This synthesis paper demonstrates that there is a fairly wide range of variability in estimates related to forest cover, forest cover change, and above ground biomass stocks, which are the main inputs for estimating forest carbon stocks and greenhouse gas emissions from land cover change.
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Status: open (until 25 Jul 2024)
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RC1: 'Comment on egusphere-2024-1179', Anonymous Referee #1, 17 Jun 2024
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General comments
This manuscript compares global and nearly-global datasets of land cover, forest cover and aboveground biomass and synthesizes information to understand their relevance. The work of collecting all these datasets and comparing them is per se tremendous and the analyses undertaken by the authors provide some interesting numbers. Nonetheless, the major issue with this manuscript is that they provide a long list of numbers. These numbers diverge and the conclusion is that there is not a single model, which was already known before. I would have appreciated if the authors had moved beyond synthesizing datasets and provided map producers with some guidelines of dos and don’ts from their perspective. In addition, there is no mention of the methods followed to synthesize data so I cannot judge whether these might have impacted the numbers that were derived from the maps. I also find the text superficially written (generic statements, varying numbers). Although, I believe that the manuscript has some scientific merit, it is currently not meeting the standards for publication in a peer-reviewed journal.
Below the authors can find a list of comments that they may consider should they pursue resubmission to another journal.
Specific comments
- The title seems to promise something that was not fulfilled by the article. What is a “forest carbon model” exactly?
- What is unique to this paper that has not been already discussed in similar review papers such as Lucas et al., and Rodriguez-Vega et al.?
Lucas, R.M., Mitchell, A.L., Armston, J., 2015. Measurement of Forest Above-Ground Biomass Using Active and Passive Remote Sensing at Large (Subnational to Global) Scales. Current Forestry Reports 1, 162–177. https://doi.org/10.1007/s40725-015-0021-9
Rodríguez-Veiga, P., Wheeler, J., Louis, V., Tansey, K., Balzter, H., 2017. Quantifying forest biomass carbon stocks from space. Current Forestry Reports 3, 1–18. https://doi.org/10.1007/s40725-017-0052-5
- What is your definition of “forest”?
- What is your definition of “biomass”?
- 4 is not introduced in the text.
- 5. Using values published by FAO in their resources assessment could help as reference to understand it. However, not knowing how each of the land cover datasets was re-labelled to forest/non-forest AND how maps were resampled to 1 km does not help to understand the differences in this figure. Actually, comparing maps of land cover at 1 km is introducing a lot of uncertainty in the results (too coarse resolution).
- The comparative analysis in Section IV is not really addressing the results from the comparison but providing a high-level presentation of concepts and potential explanations. In particular, the paragraph on lines 376-387 must be reconsidered. If one embarks in a study like the one envisaged in the title, one should also provide the answers. To me, these answers were not provided.
- The Assumptions in Section IV should also be reconsidered as they very much reduce the importance and the merit of the study.
- The purpose of the Caveats in Section IV is unclear. It is currently a mixture of speculations, work already published by others (lines 435-445) and possible work that was not undertaken (Lines 456-461).
- The Implications in Section IV could profit from some re-writing as well. 1) The authors should not ask questions (lines 469-474) but provide some indications. 2) Lines 486 – 492 are not related to this study.
Technical corrections
- Line 17: please correct “to further the understanding”
- Line 45: “15 years or so” were “last decade or so” in the abstract, “dozen sources” here were “20 sources” in the abstract. Please be precise and consistent.
- Line 46. Use “published” instead of “developed” since a few datasets have not been published.
- Lines 45-51. Near-field remote sensing is missing here (ALS, TLS).
- Line 54. Studies do not “advocate” but try to emphasize what is new to existing knowledge. As time goes by, new research demonstrates what the previous studies did not or could not consider.
- Line 70. Previous studies should be referred to.
- Line 72. “Implications” were not mentioned in the abstract, why?
- Lines 75-96. Move to the Introduction
- Lines 101-108. Why did you create your own classification since there are already official classifications around such the FAO Global Ecological Zones dataset, which is the basis of the IPCC classes as well? Classifying zone based on temperature can be misleading. Looking at Figure 2, Tibet for example belongs to the boreal zone, which is incorrect.
- Line 114. What is the impact of not distinguishing “tree” and “forest” cover on your study?
- Lines 120 and 129. The Hansen dataset is not a land cover dataset.
- Line 130. The JAXA FNF dataset is not land cover.
- Line 134. What does “For the most part” mean?
- Line 138. I would strongly suggest to avoid the Liu and Xu datasets at all because of the completely different spatial resolution.
- Line 139. What “statistical analysis”?
- Line 142. Where is this number “16” coming from considering that before you mentioned 22 and 29 datasets?
- Line 143. “were resampled” how?
- Line 145. Are you seriously thinking that nearest neighbor does not alter the statistics of the maps when going from 30 m to 1000 m?
- Line 146. Why resampling to the Mollweide projection? To my knowledge most (all?) datasets were in lat/long projection. Why not keeping this one?
- Line 150. The focus on the pan-tropical zone was already mentioned.
- Line 151. What is the “first level of analysis”?
- Line 151. And here we have “19” which is a new number apparently.
- Line 152. Shouldn’t it be 2010 instead of 2019?
- Line 153. What does “did not skew the analysis” mean here?
- Line 164. What are “widely accepted methods?” This should be made clear.
- Line 169. And here we have “11” datasets. Plus 3 one line below makes 14, again a new number.
- Lines 169 and 171. You may want to say “combinations” rather than “permutations”.
- Line 171. “those data”?
- Line 172. What does “tuning into nuances in the data” mean?
- Lines 174-176. Is this relevant?
- Line 180. Figure 3. The flowchart is redundant since what is done here is purely zonal statistics.
- Lines 184-190. Totally unclear what is done here.
- Lines 193-194. Text is redundant.
- Line 202. What are the “two outliers”?
- Lines 233 and 234. Where are these rates of km2/year from?
- Lines 244, 246 and 247. Are these % values for the totals?
- Figure 6 and Line 261. The reference should be to Santoro et al., 2021, ESSD.
- Figure 7. Fonts on the x-axis are too small. By the way, the Geocarbon dataset is a blend of 3 datasets: Saatchi et al (year 2000), and Baccini et al (year 2007) for the tropics and Santoro et al (year 2010) for temperate and boreal zones. Associating Geocarbon to 2000 can be misleading.
- Line 275. Why “As to be expected?”
- Line 279. This belt contains temperate rainforest (southeast Australia and Tasmania)
- Lines 280-282. Already mentioned, redundant.
- Lines 284. Your delineation does not correspond to the delineation adopted by IPCC.
- Line 294. Figs. 8-11 do not add information compared to the presentation of results with histograms unless it is explained in the text what these figures add with respect to Fig. 7.
- Line 304. With “models” do you refer to “estimates” perhaps?
- Line 304. Please reconsider this statement. A disagreement of 100% for an AGB = 1 ton/ha is less relevant than a disagreement of 20% for an AGB of 400 tons/ha.
- Figure 11. Is a CV based on 3 numbers a reliable figure for the level of agreement between maps?
Citation: https://doi.org/10.5194/egusphere-2024-1179-RC1
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