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Conservation policy and the measurement of forests


Deforestation is a major driver of climate change1 and the major driver of biodiversity loss1,2. Yet the essential baseline for monitoring forest cover—the global area of forests—remains uncertain despite rapid technological advances and international consensus on conserving target extents of ecosystems3. Previous satellite-based estimates4,5 of global forest area range from 32.1 × 106 km2 to 41.4 × 106 km2. Here, we show that the major reason underlying this discrepancy is ambiguity in the term ‘forest’. Each of the >800 official definitions6 that are capable of satellite measurement relies on a criterion of percentage tree cover. This criterion may range from >10% to >30% cover under the United Nations Framework Convention on Climate Change7. Applying the range to the first global, high-resolution map of percentage tree cover8 reveals a discrepancy of 19.3 × 106 km2, some 13% of Earth’s land area. The discrepancy within the tropics alone involves a difference of 45.2 Gt C of biomass, valued at US$1 trillion. To more effectively link science and policy to ecosystems, we must now refine forest monitoring, reporting and verification to focus on ecological measurements that are more directly relevant to ecosystem function, to biomass and carbon, and to climate and biodiversity.

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Figure 1: Global distribution of consensus among eight satellite-based data sets4,8,31,32,33,34,35,36 on the presence or absence of forest in or near the year 2000.
Figure 2: Global area of forest cover as a function of the tree-cover criterion.
Figure 3: Global distribution and discrepancy of forest cover based on United Nations Framework Convention on Climate Change (UNFCCC) definitions.
Figure 4: Global frequency distribution of forest-patch size, assuming the 30% tree-cover criterion.


  1. 1

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

    Google Scholar 

  2. 2

    Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).

    CAS  Google Scholar 

  3. 3

    Strategic Plan for Biodiversity 2011–2020 (Secretariat of the Convention on Biological Diversity, 2010);

  4. 4

    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    CAS  Article  Google Scholar 

  5. 5

    Giri, C., Zhu, Z. & Reed, B. A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sens. Environ. 94, 123–132 (2005).

    Article  Google Scholar 

  6. 6

    Expert Meeting on Harmonizing Forest-Related Definitions for Use by Various Stakeholders (UNFAO, 2002).

  7. 7

    Report of the Conference of the Parties on its Seventh Session, held at Marrakesh from 29 October to 10 November 2001 Addendum Part two (UNFCCC, 2002).

  8. 8

    Sexton, J. O. et al. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int. J. Digit. Earth 6, 427–448 (2013).

    Article  Google Scholar 

  9. 9

    Houghton, R. A. Aboveground forest biomass and the global carbon balance. Glob. Change Biol. 11, 945–958 (2005).

    Article  Google Scholar 

  10. 10

    Defries, R. et al. Reducing Greenhouse Gas Emissions from Deforestation in Developing Countries: Considerations for Monitoring and Measuring GOFC-GOLD Report No. 6; 1–22 (Secretariat of the Global Terrestrial Observing System (GTOS), 2006).

    Google Scholar 

  11. 11

    GOFC-GOLD A Sourcebook of Methods and Procedures for Monitoring and Reporting Anthropogenic Greenhouse Gas Emissions and Removals Associated with Deforestation, Gains, and Losses of Carbon Stocks in Forests Remaining Forests, and Forestation Report Version COP-19 (Wageningen University, 2013).

    Google Scholar 

  12. 12

    Olander, L. P., Gibbs, H. K., Steininger, M., Swenson, J. J. & Murray, B. C. Reference scenarios for deforestation and forest degradation in support of REDD: A review of data and methods. Environ. Res. Lett. 3, 025011 (2008).

    Article  Google Scholar 

  13. 13

    Global Forest Resources Assessment 2010, Main Report FAO Forestry Paper 163 (FAO, 2010).

  14. 14

    Matthews, E. Understanding the FRA 2000, World Resources Institute Forest Briefing No. 1; 1–12 (World Resources Institute, 2001).

    Google Scholar 

  15. 15

    Grainger, A. Difficulties in tracking the long-term global trend in tropical forest area. Proc. Natl Acad. Sci. USA 105, 818–823 (2008).

    CAS  Article  Google Scholar 

  16. 16

    Mather, A. S. Asessing the world’s forests. Glob. Environ. Change 15, 267–280 (2005).

    Article  Google Scholar 

  17. 17

    Sexton, J. O., Bax, T., Siqueira, P., Swenson, J. J. & Hensley, S. A comparison of lidar, radar, and field measurements of canopy height in pine and hardwood forests of southeastern North America. Forest Ecol. Manage. 257, 1136–1147 (2009).

    Article  Google Scholar 

  18. 18

    Townshend, J. R. et al. Global Characterizationand monitoring of forest cover using Landsat data: Opportunities and challenges. Int. J. Digit. Earth 5, 373–397 (2012).

    Article  Google Scholar 

  19. 19

    Lund, H. G. Definitions of Forest, Deforestation, Afforestation, and Reforestation (Forest Information Services, 2014);̃yde/DEFpaper.htm

    Google Scholar 

  20. 20

    Belward, A. S. The IGBP-DIS Global 1 km Land Cover Data Set “DISCover”: Proposal and Implementation Plans (IGBP-DIS Office, 1996).

    Google Scholar 

  21. 21

    Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on Earth. BioScience 51, 933–938 (2001).

    Article  Google Scholar 

  22. 22

    Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).

    CAS  Article  Google Scholar 

  23. 23

    Estimating Biomass and Biomass Change in Tropical Forests, FAO Forestry Paper 163 (UNFAO, 1997).

  24. 24

    Zomer, R. J., Trabucco, A., Verchot, L. V. & Muys, B. Land area eligible for afforestation and reforestation within the Clean Development Mechanism: A global analysis of the impact of forest definition. Mitig. Adapt. Strateg. Glob. Change 13, 219–239 (2008).

    Article  Google Scholar 

  25. 25

    Kellndorfer, J. et al. Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets. Remote Sens. Environ. 93, 339–358 (2004).

    Article  Google Scholar 

  26. 26

    Lefsky, M. A. A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System. Geophys. Res. Lett. 37, L15401 (2010).

    Article  Google Scholar 

  27. 27

    Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Clim. Change 2, 182–185 (2012).

    CAS  Article  Google Scholar 

  28. 28

    Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. 116, G04021 (2011).

    Article  Google Scholar 

  29. 29

    Sexton, J. O. et al. A model for the propagation of uncertainty from continuous estimates of tree cover to categorical forest cover and change. Remote Sens. Environ. 156, 418–425 (2015).

    Article  Google Scholar 

  30. 30

    Romijn, E. et al. Exploring different forest definitions and their impact on developing REDD+ reference emission levels: A case study for Indonesia. Environ. Sci. Policy 33, 246–259 (2013).

    Article  Google Scholar 

  31. 31

    Loveland, T. R. et al. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens. 21, 1303–1330 (2000).

    Article  Google Scholar 

  32. 32

    Hansen, M. C., DeFries, R. S., Townshend, J. R. G. & Sohlberg, R. A. Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens. 21, 1331–1364 (2000).

    Article  Google Scholar 

  33. 33

    Bartholomé, E. & Belward, A. S. GLC2000: A new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26, 1959–1977 (2005).

    Article  Google Scholar 

  34. 34

    Hansen, M. C., Townshend, J. R. G., DeFries, R. S. & Carroll, M. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Remote Sens. 26, 4359–4380 (2005).

    Article  Google Scholar 

  35. 35

    Bicheron, P. et al. GlobCover: Products Description and Validation Report (Toulouse Cedex, 2008).

    Google Scholar 

  36. 36

    Friedl, M. et al. Global land cover mapping from MODIS: Algorithms and early results. Remote Sens. Environ. 83, 287–302 (2002).

    Article  Google Scholar 

  37. 37

    Song, X.-P. et al. Integrating global land-cover products for forest cover characterization: An application in North America. Int. J. Digit. Earth 7, 709–724 (2014).

    Article  Google Scholar 

  38. 38

    DiMiceli, C. M. et al. Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000–2010 Collection 5 Percent Tree Cover (Univ. Maryland, 2011);

    Google Scholar 

  39. 39

    Channan, S. et al. The GLS+ : An enhancement of the Global Land Survey datasets. Photogramm. Eng. Remote Sens. 81, 521–525 (2015).

    Article  Google Scholar 

  40. 40

    Tol, R. S. J. The social cost of carbon: Trends, outliers, and catastrophes. Economics 2, 2008-25 (2008).

    Google Scholar 

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Funding was provided by the following NASA programmes: Making Earth System Data Records for Use in Research Environments (NNX08AP33A-MEASURES), Land Cover and Land Use Change (NNX08AN72G-LCLUC), Carbon Cycle Science (NNH13ZDA001N-CARBON), and Earth System Science Research Using Data and Products from Terra, Aqua, and Acrimsat Satellites (NNH06ZDA001N-EOS). X.-P.S. was also supported by NASA’s Earth and Space Science Fellowship (NESSF) Program (NNX12AN92H). P.N. was also supported by the Norwegian Agency for Development Cooperation’s Department for Civil Society under the Norwegian Forest and Climate Initiative. The opinions expressed do not represent those of the Global Environmental Facility or the World Bank Group. Data processing and analysis were performed at the Global Land Cover Facility ( in the Department of Geographical Sciences at the University of Maryland in service of the Global Forest Cover Change Project (, a partnership of the University of Maryland Global Land Cover Facility and NASA Goddard Space Flight Center. We thank A. Whitehurst, C. Jenkins and N. Aguilar-Amuchastegui for comments and T. B. Murphy for political insights.

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J.O.S., P.N., X.-P.S., S.C., C.H. and J.R.T. conceived the study. P.N., D.-X.S., X.-P.S., M.F., A.A. and J.O.S. carried out the analyses. J.O.S. and S.L.P. wrote the manuscript with contributions from all authors.

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Correspondence to Joseph O. Sexton.

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Sexton, J., Noojipady, P., Song, XP. et al. Conservation policy and the measurement of forests. Nature Clim Change 6, 192–196 (2016).

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