Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests

Zhiyu Zhang; Wenjian Ni; Shaun Quegan; Jingming Chen; Peng Gong; Luiz Carlos Estraviz Rodriguez; Huadong Guo; Jiancheng Shi; Liangyun Liu; Zengyuan Li; Yating He; Qinhuo Liu; Yosio Shimabukuro; Guoqing Sun

doi:10.1016/j.xinn.2024.100610

The Innovation > 2024 Vol. 5 > No. 3

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Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests

1.
Key Laboratory of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2.
University of Chinese Academy of Sciences, Shijingshan District, Beijing 100049, China
3.
Chinal of Mathematics and Statistics, University of Sheffield, Sheffield S3 7RH, UK
4.
Department of Geography and Program in Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
5.
Department of Earth Sciences and Department of Geography, University of Hong Kong, Hong Kong, China
6.
Forest Science Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-900, Brazil
7.
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
8.
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
9.
Research Institute of Forest Policy and Information, Chinese Academy of Forestry, Beijing 100091, China
10.
Remote Sensing Department, National Institute for Space Research (INPE), Av. dos Astronautas 1758, São José dos Campos 12227-010, Brazil
11.
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

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Corresponding author: niwj@aircas.ac.cn
Received Date: July 25, 2023

Accepted Date: March 08, 2024

Published Online: March 12, 2024

The Innovation 5(3), https://doi.org/10.1016/j.xinn.2024.100610 | Cite this article

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PUBLIC SUMMARY
1. ■ The accuracy of existing carbon density maps is limited owing to the lack of direct measures of vertical forest structures.
2. ■ The synergy of interferometric synthetic aperture radar (InSAR) data acquired before and after deforestation has been proposed as a direct measure of vertical forest structures.
3. ■ An accurate map of carbon density in the deforested areas was produced using light detection and ranging (lidar) data, forestloss maps, and vertical forest structures.
4. ■ New results have revealed that carbon loss caused by deforestation in the 2000s in Latin America was severely overestimated.
5. ■ Knowledge of the role of Latin American forests in the 2000s in global carbon budgets has been challenged by this new result.

ABSTRACT

ABSTRACT

The role of tropical forests in the global carbon budget remains controversial, as carbon emissions from deforestation are highly uncertain. This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation. New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar. We found that lost forests are special cases, and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography. Thus, using biomass mapping, we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions. Here, using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests, we found that deforestation in the 2000s in Latin America, one of the severely deforested regions, mainly occurred in forests with a significantly lower carbon stock density than typical mature forests. Deforestation areas with carbon stock densities lower than 20.0, 50.0, and 100.0 Mg C/ha accounted for 42.1%, 62.0%, and 83.3% of the entire deforested area, respectively. The average carbon stock density of lost forests was only 49.13 Mg C/ha, which challenges the current knowledge on the carbon stock density of lost forests (with a default value 100 Mg C/ha according to the Intergovernmental Panel on Climate Change Tier 1 estimates, or approximately 112 Mg C/ha used in other studies). This is demonstrated over both the entire region and the footprints of the spaceborne lidar. Consequently, our estimate of carbon loss from deforestation in Latin America in the 2000s was 253.0 ± 21.5 Tg C/year, which was considerably less than existing remote-sensing-based estimates, namely 400–600 Tg C/year. This indicates that forests in Latin America were most likely not a net carbon source in the 2000s compared to established carbon sinks. In previous studies, considerable effort has been devoted to rectify the underestimation of carbon sinks; thus, the overestimation of carbon emissions should be given sufficient consideration in global carbon budgets. Our results also provide solid evidence for the necessity of renewing knowledge on the role of tropical forests in the global carbon budget in the future using observations from new space missions.

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REFERENCES

[1]	Friedlingstein, P., Jones, M.W., O’Sullivan, M., et al. (2019). Global Carbon Budget 2019. Earth Syst. Sci. Data 11: 1783–1838. https://doi.org/10.5194/essd-11-1783-2019. View in Article CrossRef Google Scholar
[2]	Zhu, Y., Wang, D., Smith, P., et al. (2022). What can the Glasgow Declaration on Forests bring to global emission reduction? Innovation 3: 100307. https://doi.org/10.1016/j.xinn.2022.100307. View in Article CrossRef Google Scholar
[3]	Hansen, M.C., Potapov, P.V., Moore, R., et al. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science *342*: 850–853. https://doi.org/10.1126/science.1244693. View in Article CrossRef Google Scholar
[4]	Hong, C., Burney, J.A., Pongratz, J., et al. (2021). Global and regional drivers of land-use emissions in 1961-2017. Nature *589*: 554–561. https://doi.org/10.1038/s41586-020-03138-y. View in Article CrossRef Google Scholar
[5]	Eggleston, H., Buendia, L., Miwa, K., et al. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. View in Article Google Scholar
[6]	Harris, N.L., Brown, S., Hagen, S.C., et al. (2012). Baseline Map of Carbon Emissions from Deforestation in Tropical Regions. Science *336*: 1573–1576. https://doi.org/10.1126/science.1217962. View in Article CrossRef Google Scholar
[7]	Baccini, A., Walker, W., Carvalho, L., et al. (2017). Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science *358*: 230–234. https://doi.org/10.1126/science.aam5962. View in Article CrossRef Google Scholar
[8]	Mitchard, E.T.A. (2018). The tropical forest carbon cycle and climate change. Nature *559*: 527–534. https://doi.org/10.1038/s41586-018-0300-2. View in Article CrossRef Google Scholar
[9]	Wang, H., Jiang, F., Liu, Y., et al. (2022). Global Terrestrial Ecosystem Carbon Flux Inferred from TanSat XCO₂ Retrievals. J. Remote Sens. *2022*. https://doi.org/10.34133/2022/9816536. View in Article CrossRef Google Scholar
[10]	Peters, G.P., Andrew, R.M., Boden, T., et al. (2013). The challenge to keep global warming below 2 ℃. Nat. Clim. Change 3: 4–6. https://doi.org/10.1038/nclimate1783. View in Article CrossRef Google Scholar
[11]	Le Quéré, C., Andrew, R.M., Canadell, J.G., et al. (2016). Global Carbon Budget 2016. Earth Syst. Sci. Data 8: 605–649. https://doi.org/10.5194/essd-8-605-2016. View in Article CrossRef Google Scholar
[12]	Field, C.B., Barros, V.R., Dokken, D.J., et al. (2014). Intergovernmental Panel on Climate Change. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. View in Article Google Scholar
[13]	Achard, F., Beuchle, R., Mayaux, P., et al. (2014). Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Global Change Biol. 20: 2540–2554. https://doi.org/10.1111/gcb.12605. View in Article CrossRef Google Scholar
[14]	DeFries, R.S., Houghton, R.A., Hansen, M.C., et al. (2002). Carbon emissions from tropical deforestation and regrowth based on satellite observations for the 1980s and 1990s. Proc. Natl. Acad. Sci. USA 99: 14256–14261. https://doi.org/10.1073/pnas.182560099. View in Article CrossRef Google Scholar
[15]	Houghton, and Robert. (2013). Carbon Flux to the Atmosphere from Land-Use Changes 1850-2005. https://doi.org/10.3334/CDIAC/LUE.NDP050.2008. View in Article Google Scholar
[16]	van der Werf, G.R., Morton, D.C., DeFries, R.S., et al. (2009). CO₂ emissions from forest loss. Nat. Geosci. 2: 737–738. https://doi.org/10.1038/ngeo671. View in Article CrossRef Google Scholar
[17]	Pan, Y., Birdsey, R.A., Fang, J., et al. (2011). A Large and Persistent Carbon Sink in the World’s Forests. Science *333*: 988–993. https://doi.org/10.1126/science.1201609. View in Article CrossRef Google Scholar
[18]	Baccini, A., Goetz, S.J., Walker, W.S., et al. (2012). Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2: 182–185. https://doi.org/10.1038/nclimate1354. View in Article CrossRef Google Scholar
[19]	Tyukavina, A., Baccini, A., Hansen, M.C., et al. (2015). Aboveground carbon loss in natural and managed tropical forests from 2000 to 2012. Environ. Res. Lett. 10: 074002. https://doi.org/10.1088/1748-9326/10/7/074002. View in Article CrossRef Google Scholar
[20]	Song, X.P., Huang, C., Saatchi, S.S., et al. (2015). Annual Carbon Emissions from Deforestation in the Amazon Basin between 2000 and 2010. PLoS One 10: e0126754. https://doi.org/10.1371/journal.pone.0126754. View in Article CrossRef Google Scholar
[21]	Harris, N.L., Gibbs, D.A., Baccini, A., et al. (2021). Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change 11: 234–240. https://doi.org/10.1038/s41558-020-00976-6. View in Article CrossRef Google Scholar
[22]	Popkin, G. (2015). The hunt for the world’s missing carbon. Nature *523*: 20–22. https://doi.org/10.1038/523020a. View in Article CrossRef Google Scholar
[23]	Gong, P., Wang, J., Yu, L., et al. (2013). Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. Int. J. Rem. Sens. 34: 2607–2654. https://doi.org/10.1080/01431161.2012.748992. View in Article CrossRef Google Scholar
[24]	Saatchi, S.S., Harris, N.L., Brown, S., et al. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci. USA *108*: 9899–9904. https://doi.org/10.1073/pnas.1019576108. View in Article CrossRef Google Scholar
[25]	Zarin, D.J., Harris, N.L., Baccini, A., et al. (2016). Can carbon emissions from tropical deforestation drop by 50% in 5years? Global Change Biol. 22: 1336–1347. https://doi.org/10.1111/gcb.13153. View in Article CrossRef Google Scholar
[26]	Avitabile, V., Herold, M., Heuvelink, G.B.M., et al. (2016). An integrated pan-tropical biomass map using multiple reference datasets. Global Change Biol. 22: 1406–1420. https://doi.org/10.1111/gcb.13139. View in Article CrossRef Google Scholar
[27]	Mitchard, E.T.A., Feldpausch, T.R., Brienen, R.J.W., et al. (2014). Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Global Ecol. Biogeogr. 23: 935–946. https://doi.org/10.1111/geb.12168. View in Article CrossRef Google Scholar
[28]	Liu, Y.Y., van Dijk, A.I.J.M., de Jeu, R.A.M., et al. (2015). Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5: 470–474. https://doi.org/10.1038/NCLIMATE2581. View in Article CrossRef Google Scholar
[29]	Xiao, J., Chevallier, F., Gomez, C., et al. (2019). Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Rem. Sens. Environ. *233*: 111383. https://doi.org/10.1016/j.rse.2019.111383. View in Article CrossRef Google Scholar
[30]	Chang, Z., Fan, L., Wigneron, J.-P., et al. (2023). Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019. J. Remote Sens. 3: 0005. https://doi.org/10.34133/remotesensing.0005. View in Article CrossRef Google Scholar
[31]	Hall, F.G., Bergen, K., Blair, J.B., et al. (2011). Characterizing 3D vegetation structure from space: Mission requirements. Rem. Sens. Environ. *115*: 2753–2775. https://doi.org/10.1016/j.rse.2011.01.024. View in Article CrossRef Google Scholar
[32]	Hansen, M.C., Potapov, P., and Tyukavina, A. (2019). Comment on "Tropical forests are a net carbon source based on aboveground measurements of gain and loss". Science *363*: eaar3629. https://doi.org/10.1126/science.aar3629. View in Article CrossRef Google Scholar
[33]	Quegan, S., Le Toan, T., Chave, J., et al. (2019). The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space. Rem. Sens. Environ. *227*: 44–60. https://doi.org/10.1016/j.rse.2019.03.032. View in Article CrossRef Google Scholar
[34]	Qi, W., and Dubayah, R.O. (2016). Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping. Rem. Sens. Environ. *187*: 253–266. https://doi.org/10.1016/j.rse.2016.10.018. View in Article CrossRef Google Scholar
[35]	Rosen, P.A., Hensley, S., Shaffer, S., et al. (2015). The NASA-ISRO SAR Mission - an International Space Partnership for Science and Societal Benefit (IEEE International Radar Conference (RadarCon)). View in Article Google Scholar
[36]	Rodríguez, E., Morris, C.S., and Belz, J.E. (2006). A global assessment of the SRTM performance. Photogramm. Eng. Rem. Sens. 72: 249–260. https://doi.org/10.14358/pers.72.3.249. View in Article CrossRef Google Scholar
[37]	Farr, T.G., Rosen, P.A., Caro, E., et al. (2007). The shuttle radar topography mission. Rev. Geophys. 45: 33. https://doi.org/10.1029/2005rg000183. View in Article CrossRef Google Scholar
[38]	Rossi, C., Rodriguez Gonzalez, F., Fritz, T., et al. (2012). TanDEM-X calibrated Raw DEM generation. ISPRS J. Photogrammetry Remote Sens. 73: 12–20. https://doi.org/10.1016/j.isprsjprs.2012.05.014. View in Article CrossRef Google Scholar
[39]	Ni, W., Sun, G., Zhang, Z., et al. (2014). Co-Registration of Two DEMs: Impacts on Forest Height Estimation From SRTM and NED at Mountainous Areas. Geosci. Rem. Sens. Lett. IEEE 11: 273–277. https://doi.org/10.1109/lgrs.2013.2255580. View in Article CrossRef Google Scholar
[40]	Kellndorfer, J.M., Walker, W.S., LaPoint, E., et al. (2010). Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: A regional-scale method based on LVIS, SRTM, Landsat ETM plus, and ancillary data sets. J. Geophys. Res. *115*: G00e08. https://doi.org/10.1029/2009jg000997. View in Article CrossRef Google Scholar
[41]	Ni, W.J., Sun, G.Q., Ranson, K.J., et al. (2014). Model-Based Analysis of the Influence of Forest Structures on the Scattering Phase Center at L-Band. IEEE Trans. Geosci. Rem. Sens. 52: 3937–3946. https://doi.org/10.1109/tgrs.2013.2278171. View in Article CrossRef Google Scholar
[42]	Ni, W., Zhang, Z., Sun, G., et al. (2018). Modeling Interferometric SAR Features of Forest Canopies Over Mountainous Area at Landscape Scales. IEEE Trans. Geosci. Rem. Sens. 56: 2958–2967. https://doi.org/10.1109/tgrs.2017.2787704. View in Article CrossRef Google Scholar
[43]	Simard, M., Zhang, K., Rivera-Monroy, V.H., et al. (2006). Mapping height and biomass of mangrove forests in Everglades National Park with SRTM elevation data. Photogramm. Eng. Rem. Sens. 72: 299–311. https://doi.org/10.14358/PERS.72.3.299. View in Article CrossRef Google Scholar
[44]	Kellndorfer, J., Walker, W., Kirsch, K., et al. (2013). NACP Aboveground Biomass and Carbon Baseline Data, V. 2 (NBCD 2000) (ORNL DAAC Oak Ridge). https://doi.org/10.3334/ORNLDAAC/1161. View in Article Google Scholar
[45]	Wessel, B., Huber, M., Wohlfart, C., et al. (2018). Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data. ISPRS J. Photogrammetry Remote Sens. *139*: 171–182. https://doi.org/10.1016/j.isprsjprs.2018.02.017. View in Article CrossRef Google Scholar
[46]	Lefsky, M.A. (2010). A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System. Geophys. Res. Lett. 37: L15401. https://doi.org/10.1029/2010gl043622. View in Article CrossRef Google Scholar
[47]	Longo, M., Keller, M., dos-Santos, M.N., et al. (2016). Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Global Biogeochem. Cycles 30(11): 1639–1660. https://doi.org/10.1002/2016gb005465. View in Article CrossRef Google Scholar
[48]	Mokany, K., Raison, R.J., and Prokushkin, A.S. (2006). Critical analysis of root: shoot ratios in terrestrial biomes. Global Change Biol. 12: 84–96. https://doi.org/10.1111/j.1365-2486.2005.001043.x. View in Article CrossRef Google Scholar
[49]	Baccini, M., Walker, W., Farina, M., et al. (2016). CMS: Estimated Deforested Area Biomass, Tropical America, Africa, and Asia, 2000 (ORNL DAAC). https://doi.org/10.3334/ORNLDAAC/1337. View in Article Google Scholar
[50]	Poorter, L., Bongers, F., Aide, T.M., et al. (2016). Biomass resilience of Neotropical secondary forests. Nature *530*: 211–214. https://doi.org/10.1038/nature16512. View in Article CrossRef Google Scholar
[51]	Friedlingstein, P., O’Sullivan, M., Jones, M.W., et al. (2022). Global Carbon Budget 2022. Earth Syst. Sci. Data 14: 4811–4900. https://doi.org/10.5194/essd-14-4811-2022. View in Article CrossRef Google Scholar
[52]	Shang, R., Chen, J.M., Xu, M., et al. (2023). China’s current forest age structure will lead to weakened carbon sinks in the near future. Innovation 4: 100515. https://doi.org/10.1016/j.xinn.2023.100515. View in Article CrossRef Google Scholar
[53]	Wang, L., Zuo, B., Le, Y., et al. (2023). Penetrating remote sensing: Next-generation remote sensing for transparent earth. Innovation 4: 100519. https://doi.org/10.1016/j.xinn.2023.100519. View in Article CrossRef Google Scholar

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Zhang Z., Ni W., Quegan S., et al., (2024). Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests. The Innovation 5(3), 100610. https://doi.org/10.1016/j.xinn.2024.100610

Zhang Z., Ni W., Quegan S., et al., (2024). Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests. The Innovation 5(3), 100610. https://doi.org/10.1016/j.xinn.2024.100610

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Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests

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