globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2816
论文题名:
Conservation policy and the measurement of forests
作者: Joseph O. Sexton
刊名: Nature Climate Change
ISSN: 1758-738X
EISSN: 1758-6858
出版年: 2015-10-05
卷: Volume:6, 页码:Pages:192;196 (2016)
语种: 英语
英文关键词: Forestry ; Biogeography ; Conservation biology ; Forestry ; Biogeography
英文摘要:

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 × 106km2 to 41.4 × 106km2. 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 × 106km2, some 13% of Earths 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.

Forests are the focus of efforts to mitigate harmful ecological and social impacts of land use, including agreements to reduce carbon dioxide emissions from deforestation and forest degradation (REDD+; refs 9, 10, 11). The goals are both scientific—to balance regional and global carbon budgets—as well as political, to reduce carbon emissions and stop species extinctions by defining national baselines and managing future anthropogenic change12.

The Forest Resources Assessments (FRAs) of the United Nations Food and Agriculture Organization (FAO)—the authority for national and global accounting—recorded 40.8 × 106km2 of forest in 2000, equalling 31% of Earths land area13. The FRAs rely on self-reporting by participating countries, raising concerns about subjectivity and consistency14, 15, 16. Although estimates from satellite images should provide a more objective base9, even these disagree significantly over the amount and distribution of forests worldwide. Figure 1 maps the consensus among eight global satellite data sets over the class ‘forest in or near the year 2000 (Methods). The densely canopied biomes of the tropical, temperate and boreal zones, and the treeless deserts, prairies and tundra show near-perfect agreement across all sources on the presence or absence of forests. Yet the data disagree over the planets semi-arid savannahs, shrublands and woodlands, and over the northern limits of the boreal forest. Although 102.2 × 106km2 show perfect consensus among the eight data sets on either the presence or absence of forests, 9.4 × 106km2 were identified as forest by four out of the eight sources. These sparsely forested regions are the areas of greatest remaining uncertainty.

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.
Global distribution of consensus among eight satellite-based data sets on the presence or absence of forest in or near the year 2000.

Colours represent the number of times each pixel is identified as forest among the eight data sets—that is, the number of ‘votes (out of eight possible) for forest cover. Larger values (in green) show agreement on the presence of forest. Conversely, values near zero (in red and black) show agreement on its absence. Yellow values (near four) represent areas of maximum disagreement over both the presence or absence of forest.

To generate the forest-agreement map (Fig. 1), each of eight global land cover data products4, 8, 31, 32, 33, 34, 35, 36 was translated into a binary (forest versus non-forest) map and resampled to a common projection and 1-km resolution37. Percentage tree-cover data sets4, 8, 38 were first spatially averaged to 1-km resolution and then translated to forest/non-forest by applying a 30% tree-cover threshold. We then evaluated each pixel as the number of times it was identified as forest by the eight maps, resulting in a score between zero and eight ‘votes. Larger values represent greater agreement between the products for the forest class, and smaller values represent greater agreement for the non-forest class. Values near four represent the greatest disagreement.

Maps of global forest cover (Fig. 3) and estimates of area were calculated by applying 10% and 30% thresholds to the global, Landsat-based data set8 of circa-2000, percentage tree cover at 30-m resolution (data available at http://www.nature.com/nclimate/journal/v6/n2/full/www.landcover.org). Gaps in the 2000 data were filled with Landsat-based estimates from circa 2005 (ref. 39) when available, or with estimates based on data from the MODerate-resolution Imaging Spectroradiometer (MODIS; ref. 38) otherwise. This produced two binary maps of forest cover. We subtracted the value in each pixel based on the 30% threshold from the value based on the 10% tree-cover threshold and spatially aggregated the result to a percentage for display.

To calculate affected area, biomass and carbon value, we aggregated the forest-cover data from 270-m resolution to match the 1-km resolution of the carbon density map22. We then calculated the difference in carbon stock for every 1-km grid cell by multiplying the forest-change percentage by the forest-carbon density in each cell and summed over area. The social cost of carbon (SCC) (US$23/t C, in 1995 US dollars) was adopted as the mean of Tols (2008) survey of peer-reviewed SCC estimates40 and adjusted for inflation to the year 2000 at an average rate of 2.37%/year.

The power-law model of the frequency distribution of forest-patch area:

was estimated by ordinary least squares regression (R2 = 0.9485). To minimize the effect of heteroscedasticity on model fit, the ordinate and abscissa were log-transformed, and the model was fitted based on the median patch area at each frequency level. We then evaluated the integral of equation (1) between thresholds to determine the effect of the patch-size criterion on forest area.

  1. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
  2. Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).
  3. Strategic Plan for Biodiversity 2011–2020 (Secretariat of the Convention on Biological Diversity, 2010); https://www.cbd.int/sp/targets
  4. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850853 (2013).
  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, 123132 (2005).
  6. Expert Meeting on Harmonizing Forest-Related Definitions for Use by Various Stakeholders (UNFAO, 2002).
  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. 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, 427448 (2013).
  9. Houghton, R. A. Aboveground forest biomass and the global carbon balance. Glob. Change Biol. 11, 945958 (2005).
  10. Defries, R. et al. Reducing Greenhouse Gas Emissions from Deforestation in Developing Countries: Considerations for Monitoring and Measuring GOFC-GOLD Report No. 6; 122 (Secretariat of the Global Terrestrial Observing System (GTOS), 2006).
  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).
  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).
  13. Global Forest Resources Assessment 2010, Main Report FAO Forestry Paper 163 (FAO, 2010).
  14. Matthews, E. Understanding the FRA 2000, World Resources Institute Forest Briefing No. 1; 112 (World Resources Institute, 2001).
  15. Grainger, A. Difficulties in tracking the long-term global trend in tropical forest area. Proc. Natl Acad. Sci. USA 105, 818823 (2008). URL:
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/4566
Appears in Collections:气候变化事实与影响
科学计划与规划
气候变化与战略

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Joseph O. Sexton. Conservation policy and the measurement of forests[J]. Nature Climate Change,2015-10-05,Volume:6:Pages:192;196 (2016).
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