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18. A. A. Alekseyenko et al., Genes Dev. 27, 853–858 (2013). 769–777 (2006). Z. Walton and A. Gorchakov for technical assistance.
19. I. Marín, A. Franke, G. J. Bashaw, B. S. Baker, Nature 26. Q. Zhou, D. Bachtrog, Science 337, 341–345 (2012).
20. Materials and methods are available as supplementary 1017–1018 (2011).
Our global analysis, based on Landsat data, im- related to tree cover density for global climate in 2011, is to be determined.
forest loss and gain in magenta is shown in (D), with loss and gain en- graphic grid.
centered at 0.4°S, 101.5°E; (C) the United States, centered at 33.8°N, 93.3°W; and (D) Russia, centered at 62.1°N, 123.4°E.
S3. in particular. high on the x axis (Swaziland. change. both loss and gain in its calculation. reliable spread deforestation. southeastern Brazil.1 for >50% or absolute area of change dynamic. as a spur to capacity building regrowth dynamics. Australia.jpg). and it occurred across a covery signal. Figure S2 compares forest change dynamics dis- Chile. Cote d’Ivoire. are displayed. calculated from columns b the global observational record. Cambodia. indicating forestry land uses. and forest recovery after fire [as in Euro. agricultural abandonment rates >1%. Fire is the most significant cause of forest loss loss dynamic without a corresponding forest re. and as a basis of com- of tree cover). due to forestry. To date. Thirty-one countries have an aggre- 1D). and Global-scale studies require systematic global with North America having a higher overall rate Zambia). in Amazon Basin deforestation. Argentina. whereas the formulation and implementation. (i) proportion of total aggregate forest sions from Deforestation and forest Degradation dles the temperate and subtropical domains. Russia has the most forest loss glob. (Fig. indicating intensive forestry practices. 11 have annual loss found on all continents within the subtropical period. ecozones are similar in terms of forest loss rates. the resulting aggregate change dy. and Paraguay. including South Africa. a 2005 extratropical cyclone of total aggregate forest change emphasizes coun. In fig. are found ally. shown in column q of table investment and scientific capacity to begin im- due to fire. forests to the Earth system. that is.6 for >50% of tree cover). Two measures of Convention of Climate Change Reducing Emis- high ratio of loss to gain. and of colocated loss and gain (magenta tones in Fig. South Africa. only eled extensive forest areas in southwestern proportion of loss to gain measure differentiates Brazil produces and shares spatially explicit France (10). Given slower ize the forest dynamic in order to directly compare lacking such data. countries experiencing deforestation or another information on annual forest extent and change. The temperate climatic domain has a forestry. data/fra2010/ecozones2010. central (12). and along the cover. (14). large-scale A goal of large-area land cover mapping is to vention is an example of how awareness of forest tree mortality due to mountain pine bark beetle produce globally consistent characterizations that valuation can reverse decades of previous wide- infestation. climate domain. reflecting a slower regrowth dynamic in this cli. have local relevance and utility. Portugal. Areas with loss and gain in close zilian gross forest loss is the second highest glob- subtropics in the intensity of indicated forest land proximity. use. Forest area disturbed and/or reforested/afforested are its forest cover was either lost or regrown. most evident in British Colombia. with a dramatic policy-driven reduction ratio of loss to gain (1. which strad. has a change relative to year-2000 forest area [(loss + (REDD) program (13). high on the y axis (Paraguay. Elevated [loss/(loss + gain)].sciencemag. 2D)]. For example. the ratio of boreal forest loss national-scale change regardless of country size in the establishment of national-scale forest ex- to gain is high over the study period (2. The proportion the policy is far ahead of operational capabilities areas. Uruguay. and New Zealand. Brazil’s use of Landsat data in documenting led to a historic blowdown of southern Sweden tries with likely forestry practices by including trends in deforestation was crucial to its policy temperate forests. an initial reference for a number of countries range of tree canopy densities. The maps and statistics we present can be used as in boreal forests (11). 3. 852 15 NOVEMBER 2013 VOL 342 SCIENCE www. Brazil’s policy inter- fire. loss due to storm damage is indicated for a few and c in table S3. The two ratio measures normal. in other words.org . other countries. and a 2009 windstorm lev.fao. such as the United Natons Framework dynamic with Estonia and Latvia exhibiting a capability at the national scale. southern pean Russia and the Siberia region of Russia aggregated by ecozone (http://foris. ally. and 5 have annual gain rates of >1%. including Malaysia. as is the entire range of tem. Temperate Europe has a forestry information across scales. Boreal coniferous and mountain countries that have lost forests without gain are parison in evolving national monitoring methods. Co-located gain and loss are nearly absent in Brazil is a global exception in terms of forest dominant change dynamic and a relatively low the high-latitude forests of the boreal domain. Annual forest loss totals for Brazil and Indonesia from 2000 to 2012. and (ii) proportion of total change that is loss plementation of a program that can make use of namic is fourth in intensity globally. The forest loss annual increment is the slope of the estimated trend line of change in annual forest loss. Although Bra- Oceanic ecozones. the boreal/temperate transition zone in eastern experienced a greater percentage of loss of forest perate Canada.REPORTS rainforests during the study period. Areas gain is substantial in the boreal zone. Tanzania. Countries with a large fraction of forest image acquisitions available at low or no direct Fig. International policy ini- Canada (9)]. change. logging. Mongolia. often lack the institutional complicated dynamic of forestry and forest loss gain)/2000 forest]. and disease [for example. intensive forestry. with Eur. Russia. are similar to the matic domain. asian coniferous forests having the largest area Uruguay). tent and change maps. parts of European Russia. are of gain of all global ecozones during the study gate dynamic >1% per year. over 31% of and Eurasia a higher absolute area of loss. The northwest United States is an area of within nearly the entirety of Sweden and Finland. Given consensus on the value of natural America exhibits a loss dynamic. The intermountain West of North Canada. largely due to Angara River in central Siberia. Figure S1 reflects this tiatives.org/static/ China. S1.
includ. University of Wyoming. Changing the T-junction channel di- *Corresponding author. Alternatively. Laramie. umes of Xenopus egg extract (12). M. a strong within the device to produce stable emulsions of 82071. M.. appspot. Instituto Nacional de Pesquisas Especias. 10. Forests: Past and Forthcoming Impacts (European Forest viewable online at full resolution: http://earthenginepartners. Thus. because uncoupling cell size from a developmental tation (8. rather than developmental cues or occurring during development [i. embryos. Science 309. T. 2013).. S1 to S8 protected-area networks (22). M. functions to segregate chromo. The cell size 100-fold: from the 1. Results are depicted and ciated emissions of disturbed forest areas (16–18). health. Agency for International Development (2010). F. Bioscience 52.. Acad. component(s). P.2 Paul Mooney. 21).e. San Jose dos David and Lucile Packard Foundation.com/science-2013-global-forest. L.sciencemag. process and characterize global-scale time-series 2.edu has been characterized in vivo for several differ. such as the Google cloud. U. S. 16.. E. M. 695–716 (2004). correlation emerges between spindle length and extract droplets in a continuous oil phase (Fig. USA. 1. or product ing (i) the proximate causes of the mapped forest 9.. Change Secretariat. Ecol. microfluidic droplet-generating device before rapid and reductive cell divisions that character. Hansen. T. GLAS data analysis was supported by the (Instituto Nacional de Pesquisas Especias.1 Ken Gerow. M. The issue to enter mitosis and immediately pumped into a dimensional relationships is challenged by the of scale is epitomized during Xenopus embryo. D. cell size (2). Land Use Change. firm. S. 8. fied by the Landsat program. O. Houghton et al.. Spindle size may be Sufficient to Drive Spindle Scaling directly dictated by the physical dimensions of a cell. Rural Sociol. Baccini et al. REPORTS cost and the preprocessing of geometric and ra. Food and Agricultural Organization of the United 22. Rome. D. Potapov et al. and sets. For. Science 313. Germany. names is for descriptive purposes only and does not imply disturbances (15). 2012). Terrestrial Ecology. characterization was provided by the Gordon and Betty and foster greater transparency of the development. a fluorinated cellular machine that drives these divisions.2 J. Reducing Emissions from Deforestation in Figs. Hansen. Acknowledgments: Support for Landsat data analysis and enable greater use of these data for public good 4. S. Prishchepov. 9)]. 3708–3719 (2008).1 Miroslav Tomschik. (vi) the economic Tables S1 to S5 Developing Countries: Approaches to Stimulate drivers of natural forest conversion to more in. U. 18. little is known about the direct regulation of spindle size by cell size or the under- lying mechanism(s) (2–4)..org/content/342/6160/850/suppl/DC1 natural forests of the world and threats to bio. diometric corrections of satellite imagery. methods was supported by NASA through its Land Cover and The information content of the presented data 6. M. MEaSUREs programs (grants NNH05ZDA001N. 533–552 (1998). 987–990 (2008). geometrically defined systematic changes in cytoplasmic composition volumes of cytoplasm. T. 286–298 and by the U. forest dynamics associated with governance and 15. Proc. In large blastomeres. and consistent basis on which 7. L. Materials and Methods Supplementary Text diversity (20. Science 336. A. data policies and image processing capabilities. Harris et al. Potapov. data sets in quantifying land change. Maintenance of these adapting to rapid changes in cell size. D. R. 182–185 (2012). 20. 8650–8655 (2010). and reactions to policy initia. and implementation. S. C. E-mail: jgatlin@uwyo. (United Nations Framework Convention on Climate 14 August 2013. USA. transparent. stock gains for both managed and unmanaged K. V. Reductions in cytoplasmic volume. USA. Inc. R. Lambin. Loveland. (viii) 14. 11)]. R. Nature Clim. cell size could constrain spin- John Oakey. mechanisms intrinsic to the or cellular context has proven experimentally challenging. 21. Radeloff. Development of all tives that affect the world’s forests. 63. and other relevant human dimensions data. Proc. Rodrigues et al. Bioscience 60. 51 (2008). several satellite systems in place or planned for 3. 1573–1576 (2012). Any use of trade.. (iii) the rates of growth and associated carbon Institute. Saatchi et al. Pittman.A. W. T. Supplementary Materials forests (19).. spindle assembly. Applied Sciences. P. component limi- mechanism responsible for this scaling is unclear. sound. These two discrete. through its CARPE program. the United States Geological Survey. Baumann. 112. (vii) the relationships be. 2010). G.S. Dubinin. DeFries. 5. Remote Sens. Change 2. Stehman. (iv) the status of remaining intact 12. FAO Forestry Paper No. WY cell size.3 (5–7)]. Although this scaling relationship A and C). and its dimensions such as cytoplasmic spindle assembly or length- scale with the rapid. Germany. 107. Edwards. mensions and relative flow rates of the two phases www. 143–150 (2002). a limiting pool of resources The mitotic spindle must function in cell types that vary greatly in size. Sci. Waterworth. M. Stehman. as cell size decreases. Ecol. provides a Environ. W. Brack. mechanisms extrinsic to the spindle. A. Similar free and open data policies would Organization of the United Nations. Brazil. 33 (2011). A. Soc. Carbon Manage. were sufficient to recapitulate spindle scaling observed in Xenopus tal cues (10. Muller. where a rapid series of divisions reduces nuclear envelope breakdown and the onset of ize early embryogenesis in many organisms. G. which are publicly available. 169 (Food and Agricultural sat. Gardiner et al. Hansen. Gatlin1* dle size by providing a fixed and finite cytoplasmic volume and. A. C. S. Bonn. WY 82071. P. boundary sensing James Hazel. 873–884 (2013). Geist. At the same time. H. (ii) the carbon stocks and asso. (v) the effectiveness of existing 13. Natl. G. therefore. However. mitotic spindle. accepted 15 October 2013 tween forest dynamics and social welfare. Nature 452.1 Kaspars Krutkramelis. Freiburg. Laramie. P.e. Department of Molecular Biology. J. Given such progressive policy actions—and many other regional-to-global– 9899–9904 (2011). We combined microfluidic technology spindle could be actively tuned in response to with Xenopus egg extracts to characterize spindle assembly within discrete. Ecosystems 7. 13. government.1126/science. Sci. University of Wyoming. 253–259. V. P. N. specifically a limiting pool of cytoplasmic To elucidate the responsible scaling mechanism(s). Global Forest Land-Use Change 1990-2005. to efficiently References and Notes 19.org SCIENCE VOL 342 15 NOVEMBER 2013 853 . is now possible to use advanced computing sys. Berndes. collecting data with similar capabilities to Land. Nature 428.A. Monitoring of Moore Foundation. WY 82071. NNX12AB43G. A. it scale applications.. and NNG06GD95G) to quantify critical environmental issues. 1.S. 17. Foley et al. exempli. Richards. 2000-2012 Google. NNX12AC78G. 231–243 (2007). Barretto. 65–72 (2012). United Nations Framework Convention on Climate Change. 1. 23. NNX08AP33A. tems. 11. perhaps through microtubule-mediated in- teraction with the cell cortex [i. 238. 3Department of Statistics. an upper limit that is uncoupled from changes in immiscible phases merged at a T-shaped junction Laramie. Klug.1244693 Changes in Cytoplasmic Volume Are ent organisms. Kurz et al. 2005). Evans. I. Action – Draft Conclusions Proposed by the President References (24–40) tensive land uses (23).e. reductive cell divisions that accompany early stages of development. G. Last.. University of Wyoming. A. B. F. 58–61 (2006). Loveland. Sparovek. V. Interphase extract tures must scale with cell size in order to somes in cells that vary greatly in size while also containing Xenopus sperm nuclei was induced function properly. www. Manage. Potapov. NNH07ZDA001N..2-mm-diameter fer. Rudel. developmen- changes in cell shape. Environ. C. Drummond.S. Nations. play a major role in determining spindle size. Natl. The determining components [i. Campos. 2Department of Chemical and Pe- troleum Engineering. Brooks et al. the Brazilian Amazonian Forest by Satellite.sciencemag. Laurance. the tilized egg to ~12-mm-diameter cells in the adult oil/surfactant mixture was pumped into the de- 1 frog (1). Destructive Storms in European endorsement by the U. Policy 16. There are Acad. 108. 10. spindle length reaches vice through a second inlet. 640–643 (2004). Nature 477. 570–574 (2005). we developed a microfluidic-based platform to con- fine spindle assembly in geometrically defined vol- O rganelles and other intracellular struc.S. Sci. Land Use Policy 30.. genesis.
Tyukavina. R. J. Loveland.edu Published 15 November 2013.* P. V. L. Turubanova. A. Goetz. C. D. Moore. T. E-mail: mhansen@umd. S. A. Kommareddy. A. M. O. Science 342. C. Townshend *Corresponding author.1126/science. Justice.1244693 This PDF file includes: Materials and Methods Supplementary Text Figs. www. Egorov. J. 850 (2013) DOI: 10. R. G.sciencemag. R.org/content/342/6160/850/suppl/DC1 Supplementary Materials for High-Resolution Global Maps of 21st-Century Forest Cover Change M. Hancher. A. Hansen. S. S1 to S8 Tables S1 to S5 References (24–40) . Potapov. Chini. Stehman. S. Thau. V.
existing percent tree cover layers derived from Landsat data (29).28).8Mkm2. 50. Gain was defined as the inverse of loss. Google Earth Engine contains a nearly complete set of imagery from the Landsat 4. 10-90%. Image interpretation on-screen was used to delineate change and no change training data for forest cover loss and gain. Forest degradation (24).3 million available at the time of the study.178 growing season Landsat 7 Enhanced Thematic Mapper Plus (ETM+) scenes from a total of 1. and 8 satellites downloaded from the USGS Earth Resources Observation and Science archive (25). longer-lived regrowing stands of tree cover that did not begin as non-forest within the study period were not mapped as forest gain. The global Landsat analysis was performed using Google Earth Engine. Three groups of per-band metrics were employed over the study interval: (i) reflectance values representing maximum. rescaled using the higher spatial resolution percent tree cover data sets. trees were defined as all vegetation taller than 5m in height. or the equivalent of 143 billion 30m Landsat pixels. or a non-forest to forest change. 25-50%. (ii) mean reflectance values for observations between selected percentiles (for the max-10%. 50-75%. For this study. and 25-75% intervals). The stack of QA layers was used to create a per- pixel set of cloud-free image observations which in turn was employed to calculate time- series spectral metrics. 7. totaling 128. 5. 75-90%. For this study.g. Growing season data are more appropriate for land cover mapping than imagery captured during senescence or dormant seasonal periods (26). Metrics represent a generic feature space that facilitates regional- scale mapping and have been used extensively with MODIS and AVHRR data (2. minimum and selected percentile values (10. >50% crown cover to ~0% crown cover) and by year. a cloud platform for earth observation data analysis that combines a public data catalog with a large-scale computational facility optimized for parallel processing of geospatial data. and global MODIS percent tree cover (30). 10-25%. Forest loss was defined as a stand-replacement disturbance. and (iv) image normalization.4) and more recently with Landsat data in characterizing forest cover loss (27. including mapping of crown/no crown categories using very high spatial resolution data such as Quickbird imagery. we analyzed 654. forest loss and forest gain training data were related to the time- series metrics using a decision tree. Gain was related to percent tree crown cover densities >50% and reported as a twelve year total. Decision trees are hierarchical classifiers that predict class membership by recursively partitioning a data set into more homogeneous or less 2 . “forest land use”. 25. Automated Landsat pre-processing steps included: (i) image resampling.g. 90%-max. In this study. e. min-max. All pre-processing steps were tested at national scales around the globe using a method prototyped for the Democratic Republic of Congo (27). Results were disaggregated by reference percent tree cover stratum (e. and (iii) slope of linear regression of band reflectance value versus image date. (ii) conversion of raw digital values (DN) to top of atmosphere (TOA) reflectance.Materials and Methods The study area included all global land except for Antarctica and a number of Arctic islands. 75 and 90% percentiles). Training data to relate to the Landsat metrics were derived from image interpretation methods. was not included in the change characterization. the term “forest” refers to tree cover and not land use unless explicitly stated. Percent tree cover. (iii) cloud/shadow/water screening and quality assessment (QA). for example selective removals from within forested stands that do not lead to a non-forest state.
However. and the platform automatically handled data management tasks such as data format conversion. Supplementary Text Comparison with FAO data The standard reference for global scale forest resource information is the UNFAO’s Forest Resource Assessment (FRA) (33). the main criterion used in the FRA report. Deforestation is the dominant dynamic. the large amount of tree cover change observed in satellite imagery in Canada and the USA does not conform to the land use definitions applied in the FRA for these countries. Trends in annual forest loss were derived using an ordinary least squares slope of the regression of y=annual loss versus x=year.000 computers in order to characterize year 2000 percent tree cover and subsequent tree cover loss and gain through 2012.. a bagged decision tree methodology was employed. There is much less agreement for African countries. Forest loss was disaggregated to annual time scales using a set of heuristics derived from the maximum annual decline in percent tree cover and the maximum annual decline in minimum growing season Normalized Vegetation Difference Index (NDVI). regional differences in strength of agreement exist. each continent was characterized individually: North America. S4. including this analysis (Fig.e. including Brazil. and a number of countries. employ earth observation data in estimating forest area change for official reporting. S3). For the tree cover and change products. and (iv) forest definitions used in successive reports have changed over time (34). The region with the highest correlation between FAO and Landsat net change is Latin America. and associating image metadata with pixel data. report significant forest gains that are not readily observable in time-series satellite imagery. and the latter mode during the computation of the final data products. South America. Eurasia. including (i) inconsistent methods between countries. Earth Engine uses a lazy computation model in which a sequence of operations may be executed either interactively on-the-fly or in bulk over a complete data set. Africa. and forest gain from 2000 to 2012. China. (ii) defining “forest” based on land use instead of land cover thereby obscuring the biophysical reality of whether tree cover is present. S3 and Tab. Several discrepancies exist between FAO and earth observation-derived forest area change data. annual forest loss from 2000 to 2012. and Australia. For example. Large country change area discrepancies such as these preclude a significant correlation between FAO and Landsat-based country data at the global scale. and to a lesser extent India. produced at decadal intervals. While there is significant forest change from a biophysical perspective (i. there is little or no land use change. Additionally. and examples are illustrated in Fig. To facilitate processing. referred to as nodes (31). We used the former mode during development and debugging. There are several limitations of the FRA reports that diminish their utility for global change assessments. reprojection and resampling. Large- scale computations were managed using the FlumeJava framework (32). In both cases all image processing operations were performed in parallel across a large number of computers. Outputs per pixel include annual percent tree cover. forest cover).varying subsets. (iii) forest area changes reported only as net values. A total of 20 terapixels of data were processed using one million CPU-core hours on 10. though the correlation improves when lowering the tree cover threshold to 3 .
While Africa and Southeast Asia have extremely poor correlations. European data have the least correlation of the regions examined. S4). i. The lack of agreement in Africa reflects the difficult nature of mapping change in environments with a range of tree cover as well as the lack of systematic forest inventories and mapping capabilities for many African countries. Such methods result in tabular aggregated estimates for areas having sufficient sampling densities. The FRA roundwood production data correlate well with satellite-based tree cover change area estimation for forestry land use- dominated countries. the FAO FRA. with comparatively little net area change reported in either our Landsat analysis or in the FAO FRA. to provide a more consistent depiction of global forest change.2. Understanding where such changes occur is impossible given the current state of knowledge.fao. Landsat-derived forest area gain for Latin America and both forest gain and loss for Europe exhibit strong correlations. specifically Landsat imagery. Gross forest area gain and loss for >50% tree cover were also compared to FAO roundwood production data summed by country from 2000 to 2011 (Fig.225MgC/m^3 and 0. but do not allow for local-scale area estimation or spatially explicit representation of extent and change. Our estimate of Canada’s net change from the Landsat-based study doubled when including forest loss across all tree cover strata. Countries such as Australia. The national coniferous and non-coniferous "total roundwood" production data (in cubic meters) were multiplied by 0.include more change. Sample-based methods have enabled national to global scale estimation of forest extent and change (35. Recent global forest mapping research The FAO and others have turned to earth observation data. While exhaustive land cover mapping using Landsat 4 . Indonesia reports over 5.org/. the clearing of the same natural forests followed by natural recovery or managed forestry is not deforestation and often goes undocumented. largely due to extensive burning in open boreal woodlands.3). transparent and spatio- temporally explicit quantification of natural and managed forest change is required to fully understand forest change from a biophysical and not solely forest land use perspective. Southeast Asian countries exhibit changes primarily in dense canopy forests. However. Consistent.000km2 per year of forest area loss in the FRA while Canada reports no change. Deforestation is the conversion of natural forests to non-forest land uses. there is little correlation between Landsat-based change estimates and FAO data. woodland and parkland environments. Paraguay and Mozambique have similar outcomes related to disturbances occurring within a range of tropical forest. and then added together to give national total roundwood production in Megatons of carbon.325 MgC/m^2 respectively. S4 illustrates the strength of the relationship between FRA roundwood production and Landsat-derived gross forest area gain and loss for selected regions.e. While countries such as Canada and Indonesia both clear natural forests without conversion to non-forest land uses. whether in the tropical or boreal domains. The FAO comparison reflects the confusion that results when comparing tabular data that apply differing criteria in defining forest change. The forestry dynamics and differing governance and development contexts within this region may lead to inconsistencies between countries. Tab. The importance of forest definition and its impact on change area estimation is seen for countries located in boreal and dry tropical climates. FAO data are available at http://faostat.
MODIS and very high spatial imagery from GoogleEarth. 0. where available.7% of the loss events occurred within one year before or after the estimated year of disturbance.500 blocks total). All validation sample blocks were interpreted and if a single.9Mkm2 (SE of 0. unambiguous drop in NDVI was observed in the MODIS NDVI time series. Comparable map and reference loss and gain results were achieved at the global and climate domain scales. was performed in estimating reference change for each sample block.50.2% of the forest loss events and 96.35%. Areas of forest loss and gain were validated using a probability-based stratified random sample of 120m blocks per biome. loss and gain. and were taken from our previous study on global forest cover loss (2).data has been prototyped using single best-date image methods (36. The sample blocks interpreted represented 46% of the total forest loss mapped with the Landsat imagery.01% to 0.8Mkm2. Results for forest gain indicate a possible underestimate of tropical forest gain with a user’s accuracy of 82% and a producer’s accuracy of 48%. data mining of the Landsat archive to quantify global forest cover change has not been implemented until this study. Fig. indicating high uncertainty in the validation estimate. or 1). 0. The sample allocation for each biome was 150 blocks for no change. the proportion of gain or loss was interpreted as 0. A second evaluation of forest change was made using LiDAR (light detection and ranging) data from NASA’s GLAS (Geoscience Laser Altimetry System) instrument onboard the IceSat-1 satellite.3Mkm2. user’s and producer’s accuracies are balanced and greater than 80% per climate domain and the globe as a whole.29 years. Global GLAS release 28 (L1A Global Altimetry Data and 5 . However. temperate forest. For loss.2 Mkm2) compared to the map total of 0. Fig.. 0. S5 shows the results as mean map and validation change per block for the globe and per FAO climate domain. the 95% confidence interval for the bias of tropical forest gain (expressed as a % of land area) is 0. Forest gain estimated from the validation sample totaled 0. Overall. the mean deviation of the loss date was 0. humid tropical forest and dry tropical forest biomes and other land constituted the five major strata. A possible overestimate of boreal forest gain is also indicated.37) based on the National Aeronautics and Space Administration (NASA)-United States Geological Survey (USGS) Global Land Survey data set (38). Image interpretation of time-series Landsat. The map product was used to create three sub-strata per biome: no change. the comparison of individually interpreted sample sites with the algorithm output illustrates a robust product at the 120m pixel scale.06 years and the mean absolute deviation was 0.2Mkm2 (SE of 0.3Mkm2) compared to the map total of 2. The year of disturbance matched for 75. a year of disturbance was assigned. 90 for change and 60 for gain (1. where reference change was obtained as follows. Forest loss estimated from the validation reference data set totaled 2.75. Each 120m sample block was interpreted into quartiles of reference change as gain or loss (i. a fraction similar to the 50% ratio of MODIS to Landsat- detected change in a previous global forest cover loss study (39).25. S5. Validation The validation exercise was performed independently of the mapping exercise. S6 illustrates the mean per block difference of the map and reference loss and gain estimates. Estimated error matrices and accuracy summary statistics are shown in Tab. The annual allocation of change was validated using annual growing season NDVI imagery from the MODIS sensor. Only 56% of the validation sample blocks were thus assigned. Boreal forest.e. For the interpreted blocks.
The growth-limiting climate of the boreal domain would preclude the observation of regrowth over such a short period. all of which passed Wilcoxon-Mann- Whitney significance tests (non-parametric alternative of t-test) for pairs of +1\-1 and +2\-2 years. All climate domains except for the boreal passed Wilcoxon-Mann-Whitney significance tests for 2004 and 2008.the L2 Global Land Surface Altimetry Data) data were screened for quality and viable GLAS shots used to calculate canopy height (40). Fig. 6 . but over the entire study period. gain-identified pixels with no tree cover for year 2000 and co-located with GLAS data were analyzed. GLAS shots co-located with Landsat forest loss by pixel were identified. The Landsat-estimated year of disturbance was subtracted from the year of the GLAS shots and populations of ‘year since disturbance’ created. To compare GLAS-derived change in height with Landsat-derived gain. Forest gain was not allocated annually. Additionally. only clustered gain was analyzed. specifically sites where six out of nine pixels within a 3x3 kernel were labeled as forest gain. Significant differences in height before and after Landsat- derived forest loss indicate both a reasonable approximation of forest loss and year of disturbance. S8 illustrates the results. S7 shows the results by ecozone. For forest loss. the beginning and end years for GLAS data collection. Fig.
0 7.8 5.8 Temperate 273390 155989 20938580 676500 1195036 4080868 7856 11588 25881 228064 1.3 7.4 1.1 5.0 5.4 8.3 16.5 Total 2291851 804425 86958989 8610500 9330573 23512797 182544 281261 385982 1442065 1. Climate domain tree cover extent.3 5.8 9.5 .Table S1.3 13.0 7. loss and gain summary statistics (km2).0 7.7 5.9 Subtropical 305835 194103 19087918 769954 829023 1830148 37924 28761 32363 206787 1.0 4.4 7.6 3.5 8.8 7.3 Boreal 606841 207100 11066215 2988449 3782283 4360312 51285 114990 147631 292935 2.1 2.0 11.8 5.5 4.5 6.7 2. Climate Total Total Treecover 2000 Loss within treecover Total loss >25% tree >50% tree >75% tree Total gain >50% loss Previous Domain Loss Gain <25% 26-50% 51-75% 76-100% <25% 26-50% 51-75% 76-100% / total cover loss cover loss cover loss / year + total column less land area / year / year / year 2000 gain / double (excluding 2000 2000 2000 >50% tree 2000 counting water) (%) >25% tree >50% tree >75% tree cover (%) >50% tree pixels with cover (%) cover (%) cover (%) cover (%) both loss and gain (%) a) b) c) d) e) f) g) h) i) j) k) l) m) r) n) o) p) q) Tropical 1105786 247233 35866276 4175597 3524230 13241470 85479 125921 180106 714278 1. .9 4.6 6.9 5. ranked by total loss.4 6.8 6.
8 3.2 7.0 5.6 3.5 8.5 8.9 9.7 9.3 0.1 2.6 NAM Boreal mountain system 39485 1332 568948 180517 189178 233314 1145 8887 13879 15573 3.3 4.4 EAS Temperate oceanic forest 19089 13471 929909 40517 84314 216456 379 640 2583 15488 1.0 20.5 5.2 12.9 3.3 3.8 6.7 5.1 4.1 NAM Subtropical humid forest 122915 103420 399595 55264 65639 522260 854 1582 6486 113993 11.5 5.4 7.4 3.3 9.2 4.2 7.6 7.7 EAS Tropical mountain system 14459 5388 301136 34772 93240 350957 304 495 2473 11189 1.7 2.9 4.6 6.2 2.4 0.9 8.1 8.2 4.Table S2.7 9.0 10.0 7. Ecozones by vegetation realm Total Total Treecover 2000 Loss within treecover Total loss >25% tree >50% tree >75% tree Total gain >50% loss Previous AFR – Africa Loss Gain <25% 26-50% 51-75% 76-100% <25% 26-50% 51-75% 76-100% / total cover loss cover loss cover loss / year + total column less AUS – Australia/Oceania land area / year / year / year 2000 gain / double EAS –Eurasia (excluding 2000 2000 2000 >50% tree 2000 counting water) (%) >25% tree >50% tree >75% tree cover (%) >50% tree pixels with NAM – North America cover (%) cover (%) cover (%) cover (%) both loss SAM – South America and gain (%) a) b) c) d) e) f) g) h) i) j) k) l) m) r) n) o) p) q) SAM Tropical rainforest 253435 33042 678685 100431 131817 5607324 2125 3847 10919 236544 3.1 9.0 EAS Tropical rainforest 228011 104488 563048 116976 275092 1661696 2666 2856 15050 207438 8.3 4.1 6.0 0.9 10.0 3.1 14.0 5.6 38.8 3.2 forest 162095 33615 2456904 379932 404571 1003315 17675 25214 36530 82676 EAS Boreal mountain system 143573 36555 2266444 756678 1000381 1048086 23000 30952 38751 50869 2.9 3.4 6.2 EAS Temperate continental forest 63068 48939 3126680 210065 500447 1003265 1118 2023 9041 50886 1.1 6.6 6.5 AFR Tropical rainforest 96848 24575 511549 752270 742393 1941290 2272 10590 28693 55293 2.7 11.2 0.8 4.8 0.5 5.5 AFR Tropical mountain system 12236 4813 1098346 176193 80558 107352 1040 2161 3400 5635 0.8 EAS Subtropical dry forest 10987 5120 734840 62992 48342 87437 1515 1729 2677 5066 1.8 3.8 2.6 23.3 7.8 13.2 4.5 7.8 5.0 2.9 2.7 .3 7.2 2.1 4.1 7.3 3.7 11.6 4.2 13.3 4.4 11.4 16.5 SAM Subtropical humid forest 17149 25269 971549 38429 40670 132183 491 648 1350 14660 1.8 9.3 6.1 2.9 9.9 1.2 4.2 3.6 8.1 3.4 10.2 0.3 8.2 6.2 2.5 10.8 4.2 6.5 5.1 NAM Temperate continental 2. loss and gain summary statistics (km2).4 forest 25169 8174 284373 55814 73719 247384 737 1413 3932 19087 NAM Tropical rainforest 22777 2641 97950 23299 36653 253265 202 470 1770 20334 5.5 SAM Tropical mountain system 15624 4700 1154706 64730 90321 559185 561 799 1303 12961 0.7 4.8 17.2 NAM Boreal coniferous forest 120804 39978 314668 264683 474734 917683 3223 21444 22386 73751 6.9 EAS Tropical dry forest 13952 1755 1181479 74787 75701 67938 1575 1586 3699 7093 1.5 9.7 SAM Tropical dry forest 88784 3032 898296 382134 231661 150647 10581 30683 29104 18416 5.0 11.3 11.2 1.2 8.9 14.5 12.5 8.5 10.8 8.5 10.4 6. ranked by total loss.7 7.0 EAS Temperate mountain system 19037 8892 4100378 101904 174080 568263 995 1449 2534 14058 0.7 3.9 4.7 8.9 10.6 8.4 7.5 1.3 5.7 EAS Boreal coniferous forest 229331 124488 1774827 904244 1577094 1831760 15280 29602 50285 134164 3.9 20.5 21.1 31.9 4.5 forest 84719 3649 2370104 1331743 845218 40070 27852 28032 25474 3361 NAM Temperate mountain system 82998 39105 791649 107937 153970 891664 2385 4274 6475 69864 4.5 12.9 5.3 3.6 2.8 5.2 forest 28166 7837 739252 91874 189904 298608 1829 1938 6724 17674 NAM Tropical moist deciduous 3.4 1.8 5.2 7.7 2.0 5.6 6.2 6.7 NAM Boreal tundra woodland 63370 4342 924788 659389 405281 284991 3266 21744 20580 17779 2.6 4.8 6.4 3.9 7.0 11.1 7.2 7.8 9.4 12.9 2.1 3.9 1.2 3.2 0.9 8.6 6.3 8.5 2.7 0.2 2.8 2.1 2.3 7.9 forest 56749 26139 731687 80291 131018 1000282 216 549 2045 53939 EAS Subtropical humid forest 44693 17066 989723 193841 339619 469305 1208 1962 10323 31200 2.8 19. Ecozone tree cover extent.4 NAM Subtropical mountain system 18861 5631 370255 56439 45414 117601 2137 2207 2500 12018 3.0 5.9 4.3 7.4 SAM Tropical moist deciduous 3.4 2.3 10.1 AFR Tropical moist deciduous 1.3 7.4 2.4 4.8 3.3 7.5 7.0 3.2 EAS Tropical moist deciduous 2.0 7.0 6.5 6.3 AFR Tropical dry forest 33259 3298 3076095 412938 125125 19520 11240 11866 7820 2332 0.4 7.
6 17.9 83.8 7.0 58.8 0.0 AFR Subtropical humid forest 1556 1229 58127 14101 8704 3540 74 160 517 805 1.4 13.3 11.7 0.2 0.5 1.5 2.3 1.0 20.9 3.7 12.8 SAM Temperate oceanic forest 3292 3510 107543 8332 23900 90484 41 15 45 3192 1.0 0.3 5.0 1.3 1.5 1.7 42.1 1.6 .5 AUS Temperate oceanic forest 5439 5786 108801 6052 13996 82774 24 54 216 5146 2.3 38.0 2.5 1.4 AUS Temperate mountain system 4220 2269 94608 14214 21282 60561 35 68 257 3860 2.8 12.1 1.9 2.2 0.3 1.6 AFR Subtropical mountain system 5180 5137 390362 9522 6115 5316 295 536 1898 2451 1.3 23.5 9.1 8.2 forest 375 94 10919 7706 10154 25954 4 7 46 317 SAM Temperate mountain system 248 142 52497 1603 3662 17420 15 3 8 221 0.5 51.5 3.5 EAS Boreal tundra woodland 8300 286 1081882 150336 86494 15996 4393 1951 1432 524 0.6 2.2 SAM Subtropical dry forest 8256 10797 57044 5787 6298 30265 49 84 269 7854 8.9 2.7 0.0 46.0 2.5 6.9 NAM Subtropical dry forest 1717 723 67075 3801 3009 12153 128 170 238 1181 2.8 1.4 22.1 NAM Tropical mountain system 2832 522 123244 30126 31947 72471 113 236 448 2035 1.1 AUS Tropical moist deciduous 0.5 1.9 2.AUS Tropical rainforest 9972 3374 48615 20176 30783 687847 65 97 345 9465 1.1 AUS Subtropical dry forest 4234 3902 81757 17375 10024 12111 417 1037 886 1894 3.0 AUS Tropical mountain system 751 317 7778 3977 7622 101354 4 9 46 691 0.4 3.8 22.8 1.8 6.1 44.0 29.7 4.7 0.4 2.6 15.9 NAM Tropical dry forest 3177 870 142817 31827 25026 22140 283 616 1151 1127 1.1 13.6 NAM Temperate oceanic forest 2854 2317 14521 1511 1900 20962 7 12 35 2801 7.4 5.4 5.7 6.8 5.8 1.4 1.1 1.4 0.4 10.9 4.5 AUS Subtropical humid forest 6488 5983 130692 22580 30269 90635 82 206 394 5806 2.5 20.2 6.7 SAM Subtropical mountain system 147 119 226487 2143 1706 7185 8 3 7 129 0.7 12.8 6.3 1.3 0.7 EAS Subtropical mountain system 9695 4213 3012481 116266 177782 285763 726 1155 2571 5243 0.6 0.3 1.1 1.0 11.5 9.4 4.3 0.0 8.2 2.3 1.6 6.0 6.1 5.2 8.4 2.7 1.4 3.7 0.4 2.2 25.3 2.1 2.3 2.8 14.2 26.7 10.2 4.8 1.0 1.8 15.9 AUS Tropical dry forest 707 379 414034 31011 7067 8741 312 222 82 91 0.8 AFR Subtropical dry forest 1864 880 308429 10588 7605 8179 224 341 526 772 0.3 1.8 5.3 19.2 9.6 13.1 22.6 30.6 1.3 1.8 3.6 5.4 9.9 10.8 1.5 2.7 1.7 4.6 10.8 0.4 4.6 2.4 9.5 5.1 6.
6 Argentina 46958 6430 2352414 170266 125227 105950 5039 14574 14378 12966 1.5 10.4 11.7 4.9 16.8 10.8 6.9 Cote d'Ivoire 14889 2298 139172 116351 58468 5111 2893 3830 6953 1213 4.7 2.2 4. Country Total Total Treecover 2000 Loss within treecover Total loss >25% tree >50% tree >75% tree Total gain >50% loss Previous loss gain <25% 26-50% 51-75% 76-100% <25% 26-50% 51-75% 76-100% / total cover loss cover loss cover loss / year + total column less land area / year / year / year 2000 gain / double (excluding 2000 2000 2000 >50% tree 2000 counting water) (%) >25% tree >50% tree >75% tree cover (%) >50% tree pixels with cover (%) cover (%) cover (%) cover (%) both loss and gain (%) a) b) c) d) e) f) g) h) i) j) k) l) m) n) o) p) q) r) Russia 365015 162292 8414687 1846554 2707260 3304608 43907 62789 88346 169972 2.6 Colombia 25193 5516 302224 41544 61240 721695 315 619 1943 22315 2.7 Chile 11879 14611 546019 17728 36107 142031 112 107 331 11329 1.5 7.8 9.2 16.5 15.9 1.5 4.6 8.2 6.4 5.5 7.1 1.3 5.4 China 61130 22387 7565646 387994 694764 618901 2250 3955 17457 37469 0.8 6.7 3.7 12.9 10.4 2.2 4.0 7.1 Vietnam 12289 5643 152816 22284 40869 106971 660 637 2247 8744 3.1 8.0 Brazil 360277 75866 3118579 509317 464530 4292417 23699 30816 43577 262185 4.4 17.2 1.2 9.1 Malaysia 47278 25798 32061 5538 17263 272365 289 309 1818 44862 14.1 13.4 2.8 5.7 Mexico 23862 6333 1391412 131003 126317 294988 1919 2013 4042 15887 1.5 16.5 0.2 .9 16.1 1.4 1.1 3.1 3.1 0.2 6.9 25.0 3.0 8.4 16.1 2.4 0.2 1.1 4.3 6.0 7.5 1.7 3.2 2.3 3.8 8.3 9.7 3.6 4.0 6.9 5.4 9.0 2.5 2.7 10.8 Australia 58736 14142 7209820 209287 79484 177890 28168 17318 2335 10915 0.2 3.5 United States 263944 138082 6294153 424662 462709 1982786 9274 17956 30306 206408 2.8 14.8 11.8 3.3 13.3 3.0 6.2 5.1 Indonesia 157850 69701 252964 68334 141996 1411892 1558 1611 8887 145795 8.7 10.8 0.0 0.5 4.1 10.7 5.6 14.2 Thailand 12049 4992 301598 25899 69216 110454 1402 1050 2985 6612 2. Country tree cover extent.1 4.8 4.2 3.0 7.5 Madagascar 14659 4051 402077 64144 63773 58637 1382 2490 4900 5888 2.3 10.7 Venezuela 12958 1910 328461 33724 43779 493663 695 993 2389 8881 1.2 3.0 4.5 16.9 8.1 1.8 5.8 3. ranked by total loss.6 16.3 Angola 19320 638 612241 314210 281472 37883 4423 6556 6579 1762 1.8 Peru 15288 1910 498646 14200 23555 744591 80 101 245 14863 1.3 12.1 7.2 5.Table S3.2 15.2 4.1 5.4 Canada 263943 91071 4141357 1068282 1074635 2167262 6860 44780 46295 166007 3.1 9.6 2.9 Paraguay 37958 510 146165 110139 64482 75473 1230 12592 11751 12385 9.6 13.8 23.5 Laos 12084 3379 34908 11904 36244 144908 384 422 1861 9417 5.7 3.2 6.6 15.2 4.6 10.5 6.5 8.7 7.0 Mozambique 21552 1446 403453 266480 101514 3414 3078 10181 7818 476 2.8 4.3 8.2 2.0 10.4 16.7 5.5 14. loss and gain summary statistics (km2).4 9.9 13.1 Sweden 25533 15281 130774 42494 85855 153996 95 302 1790 23346 6.6 5.6 Bolivia 29867 1736 424460 75142 96512 478387 464 1340 4295 23768 2.2 14.4 Cambodia 12595 1096 86064 16785 16401 58431 484 748 2478 8884 7.7 17.4 3.6 6.7 12.1 10.1 16.1 6.7 0.0 2.9 8.8 7.3 6.0 0.5 3.9 2.0 0.4 0.2 2.6 16.4 15.8 4.4 8.4 5.1 2.1 6.0 20.2 6.5 8.3 7.9 16.7 6.5 15.1 6.6 2.9 9.4 6.8 2.8 Tanzania 19903 3041 544450 243832 84708 12320 5111 7880 5740 1173 2.5 52.6 17.4 6.4 2.2 2.1 7.3 6.2 15.4 9.4 8.9 5.3 5.6 9.2 3.1 Myanmar 14958 3149 227580 37863 123465 274434 556 896 3991 9514 2.3 4.8 Zambia 13163 181 419962 224188 91539 429 3460 5562 3918 223 1.0 3.8 12.5 Finland 19516 10849 85929 36353 78775 104264 74 263 1684 17496 6.5 DRCongo 58963 13926 175163 469228 453121 1190506 917 4863 12362 40821 2.4 12.0 10.
.2 3.9 .4 5.5 4.2 1.7 Papua New Guinea 6337 2308 27933 11647 23295 396660 29 35 197 6076 1.4 3.6 1.3 6.0 4.3 7.5 13.3 4.7 19.1 3.0 1.6 5.3 6.7 5.2 Ethiopia 2821 625 968731 100537 33866 20399 375 947 828 671 0. - Sierra Leone 1967 451 11320 24844 33463 2424 24 240 1231 472 2.0 25.9 0.4 2.Nigeria 10239 603 772371 82016 44023 3125 5987 2670 1435 147 1.0 5.8 4.3 Ecuador 5246 1027 62024 12832 20737 158764 58 118 373 4697 2.2 2.1 8.3 1.6 17.3 15.4 6.3 1.4 0.4 Nicaragua 8225 662 39402 8527 12384 58289 109 230 650 7236 6.8 5.0 7.4 5.5 2.5 1.4 6.0 11.5 3.8 45.3 8.1 10.2 Uganda 3654 685 105539 67065 24230 8489 154 703 1118 1679 1.5 13.2 Romania 2307 1530 154828 7088 10939 62836 20 25 103 2158 1.6 7.7 4.2 1.0 77.9 9.3 Turkey 3426 1783 664081 24543 23040 59104 535 488 706 1697 0.6 5.7 Poland 5829 5041 201808 11013 30655 64857 79 163 814 4773 1.3 26.8 United Kingdom 2689 2111 203651 8793 14394 15185 84 167 478 1960 1.5 5.8 0.7 1.8 7.7 Ukraine 5657 3529 470946 19510 28312 68474 150 234 900 4372 1.4 6.0 8.2 4.2 11.5 0.7 5.5 13.2 6.3 8.4 15.8 Republic of Congo 2993 467 52860 46265 25236 214202 101 272 803 1819 0.7 3.0 1.6 Spain 6908 4482 386553 36472 28533 52468 676 982 1698 3552 1.5 4.5 1.8 3.3 100.1 0.9 10.2 5.1 5.3 9.4 5.8 7.9 49.7 26.1 Germany 4890 2585 226231 10219 25175 91960 61 79 457 4294 1.3 14.6 Liberia 3955 1084 1221 3783 54435 36117 7 96 2305 1547 4.0 Estonia 2179 894 16601 2052 5085 19569 5 11 126 2036 5.4 6.6 1.3 5.9 29.1 5.5 1.2 12.6 19.6 2.3 4.8 10.1 Latvia 4120 1857 27713 2742 7086 26126 10 32 253 3825 6.5 12.1 3.9 7.3 35.8 75.0 4.1 Zimbabwe 3869 486 362829 21175 2132 799 2284 1018 353 214 1.4 3.9 2.1 8.4 12.2 3.6 Dominican Republic 1929 393 21365 4064 4619 17744 72 120 276 1462 4.5 1.5 9.4 5.9 3.0 0.3 50.2 4.2 12.5 Central African Republic 4719 395 101812 209547 238922 68765 226 910 1869 1714 0.1 2.5 8.8 3.8 Ghana 5406 1345 153157 36659 40464 2074 911 1099 2863 533 2.8 5.8 3.6 9.0 2.4 24.2 1.8 3.0 1.2 3.6 4.6 6.8 12.2 Austria 2015 658 39299 2743 7205 33933 11 22 130 1853 2.3 1.3 14.7 1.4 4.0 2.1 4.4 3.0 4.4 3. 1.6 12.8 36.3 1.5 11.8 8.8 9.9 1.9 7.3 Uruguay 2027 4985 157077 3568 5359 8522 26 73 282 1646 1.1 0.9 7.1 Honduras 4860 582 32713 11870 14297 52664 84 238 560 3978 4.4 4.4 New Zealand 6883 7102 149606 5653 14375 93838 24 22 126 6711 2.6 40.6 5.9 19.4 12.3 Portugal 4987 2866 64476 8726 6753 9147 459 784 1246 2497 5.1 4.9 29.3 18.1 13.0 41.1 1.2 1.6 2.6 12.6 0.6 18.2 India 8971 2549 2703530 112692 136677 167827 810 855 2117 5189 0.6 4.7 Benin 3307 69 108992 5962 81 8 2835 405 59 8 2.2 5.1 15.5 152.4 3.7 2.5 1.3 1.3 Burkina Faso 1993 0 274158 11 0 0 1987 5 0 0 0.2 6.6 6.6 13.1 10.3 4.6 Mongolia 4779 103 1508028 26031 15552 3080 857 1514 1485 922 0.4 23.2 11.1 0.0 6.2 11.7 2.4 South Africa 9526 8313 1145626 39602 20818 10826 678 1074 3365 4411 0.6 4.9 12.6 2.5 27.6 36.7 13.1 12.5 6.7 45.5 0.9 10. .1 2.8 16.1 6.2 3.8 Chad 3306 1 1257866 9549 72 0 2914 382 10 0 0.3 11.8 8.0 7.0 7.3 5.5 19.5 9.6 13.2 Norway 3520 1729 187652 21453 35282 62297 18 37 214 3252 1.3 Kenya 3059 1005 530692 20815 9271 9636 732 587 612 1129 0.2 12.7 5.4 .9 Panama 2675 323 16563 3089 4854 49687 35 69 262 2308 3.2 12.0 6.9 3.9 5.6 4.3 2.8 143.6 3.1 10.5 Belarus 4167 3755 112240 8835 25820 57913 26 89 547 3504 2.8 5.9 17.4 1.1 4. .2 11.2 1.6 0.9 1.3 6.6 10.6 5.7 3.4 1.3 1.3 5.9 Guinea 3933 296 130230 91912 19736 1821 1251 1604 923 155 1.8 1.2 11.1 6.3 Japan 4303 2570 102902 9616 22119 233863 71 57 253 3924 1.3 4.8 3.4 6.4 2.8 6.8 0.6 35.6 Cameroon 4816 651 120385 90110 77877 174702 548 691 1595 1981 1.9 Philippines 6227 2726 102570 15788 33051 141108 84 90 485 5569 2.0 France 7664 5062 373705 19842 37021 115684 209 330 1149 5977 1.0 8.7 1.9 2.7 8.8 12.4 4.8 2.5 .1 3.7 2.3 3.0 2.0 Guatemala 8883 1094 29734 8709 11952 57571 105 323 1097 7357 8.4 14.9 1.1 4.
7 2.0 7.7 Denmark 533 322 35730 1281 2506 3105 8 13 68 444 1.7 7.2 26.6 4.5 0.5 2.0 Costa Rica 1653 382 11327 2752 5663 31183 28 68 200 1356 3.2 1.2 5.1 3.1 5.7 1.8 31. 0.4 Mali 1694 0 1247103 1007 3 0 1650 40 3 0 0.7 7.0 5.8 Malawi 1290 103 71949 19030 2967 163 399 540 309 43 1.3 2.7 0.1 1.5 23.5 10.7 0.1 6.3 2.8 1.7 1.1 4.6 9.5 2.6 2.1 7.5 0.7 0.0 .8 Belgium 601 373 21609 1139 2011 5756 7 11 59 524 2.1 4.1 2.5 29.4 1.1 40.8 1.5 0.0 1.5 0.6 3.0 4.1 Czech Republic 1646 1331 46934 2445 6429 22264 14 25 197 1410 2.3 0.0 1.0 13.8 Kazakhstan 828 239 2628744 13973 13954 17598 329 196 125 178 0.3 5.4 2.5 Guinea-Bissau 676 65 18081 11875 3106 35 164 315 186 11 2.2 2.7 3.4 Taiwan 267 61 12174 1158 2368 20098 6 7 27 227 0.8 7.7 2.4 61.3 4.6 1.3 6.1 1.4 3.4 Switzerland 227 104 24221 1255 3120 11363 3 3 19 201 0.5 13.7 Nepal 434 134 94352 9930 21761 21318 67 65 106 195 0.0 Haiti 286 48 17810 2494 2238 4334 19 42 76 148 1.7 0.1 69.2 6.3 5.3 5.1 4.4 3.2 5.0 South Sudan 1635 38 460581 128358 39278 1773 567 629 356 83 0.0 Jamaica 329 68 3185 479 753 6545 7 9 28 285 3.9 El Salvador 567 86 9961 2231 3309 4710 25 56 206 280 2.0 Sri Lanka 985 264 24684 4952 9285 26177 64 77 294 551 1.3 0.9 8.6 11.0 7.3 6.9 6.0 5.9 5.6 0.1 4.3 7.3 14.0 5.4 2.9 14.7 9.9 2.3 2.3 0.9 0.6 4.7 7.6 1.6 6.7 4.4 Ireland 778 1238 60220 2358 3492 2899 26 38 122 592 1.9 1.8 1.8 1.1 8.8 3.1 0.0 1.5 13.0 5.0 6.5 0.6 0.2 4.6 1.3 5.8 1.1 7.5 Suriname 724 70 4791 420 567 138564 2 4 9 708 0.0 1.3 10.6 6.4 1.8 8.0 3.8 12.3 0.9 7.1 5.4 Belize 1206 128 4073 437 675 16434 5 10 35 1155 5.0 30.3 1.0 1.8 Slovakia 1237 523 24608 1245 2814 20170 6 11 67 1153 2.1 5.2 1.7 Cuba 1725 2271 68008 4982 7388 28775 90 126 295 1214 1.7 14.1 4.3 0.9 0.3 31.4 1.1 0.2 2.3 1.0 5.4 19.5 8.1 4.8 Swaziland 747 603 11310 3962 1386 597 48 84 204 412 4.1 South Korea 1463 271 43787 9776 28496 16694 230 215 560 458 1.1 2.5 0.7 1.0 0.6 9.7 0.4 Italy 1603 898 201331 13199 19020 64805 113 105 244 1142 0.5 0.5 1.4 5.5 9.9 Algeria 743 325 2294657 4212 3989 4967 67 136 211 329 0.1 5.1 1.6 0.1 0.0 9.2 5.5 0.0 100.7 Bulgaria 779 678 68662 3910 6653 32070 18 20 71 670 0.8 1.6 14.8 5.5 0.7 1.4 6.3 Macedonia 296 104 16148 1234 1600 5359 11 11 31 244 1.1 4.1 Serbia 267 356 49076 3338 5852 19132 5 4 13 245 0.2 4.4 4.5 Senegal 832 2 192538 1723 20 1 806 23 3 0 0.5 4.4 6.1 0.4 5.8 23.3 7.0 6.2 0.7 3.3 5.8 French Guiana 441 42 928 110 216 81317 1 1 4 435 0.2 Morocco 315 196 405884 2870 2251 2110 58 65 85 107 0.2 4.5 5.5 0.6 1.7 3.0 4.8 4.9 Solomon Islands 630 203 528 122 441 26825 3 1 5 621 2.4 6.0 11.6 10.5 Croatia 454 265 31487 2890 3396 18892 53 35 40 326 0.5 1.3 7.5 4.0 Togo 768 24 48707 6809 1270 5 383 333 50 2 1.5 14.9 2.4 0.5 13.1 8.3 11.1 14.0 100.9 1.5 Greece 1566 356 91341 10219 8443 21132 188 181 311 886 1.5 3.6 0.5 5.2 5.5 0.4 2.2 1.4 4.8 2.5 2.0 4.9 3.7 0.6 1.1 4.2 3.4 2.3 2.4 0.6 1.6 Equatorial Guinea 439 56 199 251 1864 24513 2 12 76 349 1.0 2.6 1.3 100.3 4.5 0.0 0.2 2.1 Bangladesh 543 70 108675 5560 8449 6965 15 23 116 389 0.5 41.8 Albania 311 74 21128 1719 1711 3642 20 25 55 212 1.4 0.6 1.1 0.4 30.0 4.3 2.Gabon 1891 391 11898 8112 10885 230775 27 110 285 1469 0.5 3.3 2.2 3.2 10.3 1.5 0.2 20.7 0.0 4.4 3.0 Guyana 915 114 18733 1096 1319 187681 4 6 14 890 0.8 3.4 0.1 North Korea 1605 137 67695 12808 31773 9164 96 262 837 411 1.7 2.7 3.3 3.1 1.3 10.5 Hungary 1107 1350 71070 2658 3705 14263 12 19 84 992 1.3 1.8 0.5 1.7 1.3 11.1 3.2 .1 1.6 7.8 Lithuania 1845 1226 40296 1889 5303 16472 9 20 160 1655 2.0 7.
4 1.0 8.6 8.4 6.8 Bhutan 129 22 13772 2471 7040 16652 9 13 35 73 0.2 4.3 0.6 3.6 0.0 50.9 1.3 1.7 Fiji 194 119 2205 1834 1857 12339 1 2 17 174 1.9 3.4 5. - Tunisia 103 115 152233 568 712 1074 13 12 20 58 0.1 1.2 1.5 Trinidad and Tobago 154 16 1188 111 168 3664 2 2 9 140 3.8 10.3 1.4 0.7 Iran 44 11 1600192 2553 5459 9996 10 5 13 15 0.2 3.2 0.4 4.2 3.2 2.1 3.4 0.0 0.8 3.3 2.8 5.1 Guadeloupe 20 12 673 59 84 818 2 1 3 14 1.8 2.1 8.7 4.1 0.4 8.6 0.2 1.6 2.4 1.7 3.5 0.9 10.8 7.3 0.9 8.5 0.7 Bosnia and Herzegovina 198 265 23549 2532 4225 20546 26 15 38 120 0.8 0.2 0.2 1.6 1.3 0.3 0.7 4.1 1.9 7.9 1.6 0.2 0.8 16.9 1.8 0.4 0.8 2.1 0.0 2.9 3.2 1.3 0.0 Cyprus 24 2 8057 572 385 231 8 5 5 7 0.4 1.0 2.3 .0 3.8 0.9 3.0 0.1 East Timor 185 61 7265 1467 1871 4296 4 5 20 156 1.5 .4 4.5 1.5 1.5 Moldova 41 63 29783 625 1033 2099 4 4 7 26 0.1 Egypt 24 50 974071 3726 140 6 9 8 6 1 0.3 0.3 Botswana 56 1 577110 529 13 0 47 9 0 0 0.1 1.3 Bahamas 95 8 8628 571 575 1995 3 6 10 75 0.6 0.3 1.3 0.1 1.3 2.0 2.3 3.8 11.4 0.0 3.7 3.7 0.4 5.6 10.3 4.7 0.9 3. .5 0.4 Kosovo 90 56 7064 494 959 2388 3 3 7 76 0.0 1.8 1.2 1.1 1.4 10.7 Rwanda 178 71 16805 4966 1171 889 22 65 63 27 0.5 1.3 0.3 3.2 3.4 0.3 Sudan 69 0 1868187 2471 12 0 55 13 1 0 0.6 0.0 0.5 0.8 3.4 4.7 10.8 7.0 0.5 0.1 8.3 7.2 4. .4 0.1 1.2 Puerto Rico 141 64 3591 397 539 4381 10 7 19 105 1.2 39.6 3.4 4.1 5.8 50.2 2.5 3.5 19.3 5.7 0.9 5.7 3.3 Martinique 22 5 351 45 81 629 1 1 2 19 2.8 4.5 2.9 3.9 9.2 2.1 0.1 8.6 9.1 0.0 3.0 New Caledonia 125 57 3883 4303 3261 7254 7 24 37 57 0.5 Azerbaijan 76 9 71829 2175 2938 8193 9 8 17 43 0.5 0.1 0.0 1.7 7.6 0.5 0.4 0.1 3.2 1.1 1.5 Lebanon 32 18 9695 268 249 191 8 5 8 11 0.0 1.6 Pakistan 100 8 861788 4077 3320 3349 9 11 35 46 0.0 Montenegro 77 70 6683 899 1309 4194 5 6 16 51 0.5 Namibia 128 0 822966 122 5 1 104 21 3 0 0.5 1.0 3.2 4.0 0.5 4.3 4.3 Mauritius 25 30 1020 326 216 292 2 5 8 11 1.4 3.7 Slovenia 162 35 6851 612 1304 11166 3 3 16 140 0.3 2.0 0.4 1.1 6.0 0.3 8.5 Armenia 21 13 24882 519 698 2252 2 1 3 15 0.3 19.5 2.6 Somalia 76 3 631245 1191 146 11 44 22 9 1 0.9 0.9 4.3 2.1 Uzbekistan 15 5 433865 712 309 205 5 3 4 4 0.2 1.8 1.5 0.2 .3 3.4 Brunei 171 88 434 68 157 5066 2 1 7 161 3.8 0. .6 2.1 0.2 2.4 1.2 Reunion 31 31 673 489 573 742 1 3 15 12 1.0 1.1 1.1 1.5 2.1 1.6 7.4 6.2 1.0 Kyrgyzstan 35 5 185261 2969 2220 2085 12 4 6 12 0.3 8.7 2.3 3.0 0.5 Comoros 7 4 236 350 369 690 0 1 3 4 0.8 2.0 .0 18.4 9.0 0.7 34.0 0.1 0.5 2.6 2.3 0.5 0.4 Gambia 111 0 10221 213 0 0 99 11 0 0 1.3 1.9 1. 0.0 1.7 4.6 1.0 2.7 7.3 Luxembourg 44 27 1543 59 149 808 1 1 4 39 1.0 Tajikistan 7 1 140238 507 131 63 4 1 1 1 0.9 1.7 2.5 4.0 3.2 0.7 0.4 4.1 Israel 29 19 21626 149 104 77 9 4 7 9 0.0 50.7 0.4 7.3 0.8 2.5 Georgia 90 48 37436 2601 4558 25006 5 3 11 71 0.2 1.1 0.3 3.0 0.1 Vanuatu 41 14 227 211 630 10997 0 0 2 38 0.2 1.9 0.7 1.6 Syria 91 16 184360 319 366 455 10 10 17 54 0.9 3.4 2.4 1.1 Afghanistan 20 3 641182 1064 729 755 2 4 8 6 0.6 0.7 1.Burundi 204 36 16600 6956 1189 222 43 86 65 11 0.3 0.8 4.6 0.3 1.6 1.3 Netherlands 166 71 28258 1503 1896 2866 6 8 32 121 0.8 3.0 3.3 1.5 2. 7.8 0.0 39.0 7.0 0.7 4.6 11.
2 0. . . - Falkland Islands 0 0 11977 0 0 0 0 0 0 0 0. - Kuwait 0 0 17384 0 0 0 0 0 0 0 0. - Niger 1 0 1183525 0 0 0 1 0 0 0 0. . . .1 1.Turkmenistan 7 3 466423 69 33 25 2 2 1 2 0. .4 0.1 21.7 4. .2 10. .0 21.0 0.0 0.9 5. . . . . . .2 8. . - Saudi Arabia 0 0 1908357 0 0 0 0 0 0 0 0.0 0. 40.0 . .0 0. .3 0.0 .0 0. - Qatar 0 0 11214 0 0 0 0 0 0 0 0.3 10. .3 2. . . . . . . . . . .0 . .0 .2 54.0 Palestina 1 1 6019 5 2 1 1 0 0 0 0.3 Oman 1 0 309101 0 0 0 1 0 0 0 0. .6 1.0 . .0 0. .0 .3 33. . .0 40.0 .4 0. - Iceland 0 0 99299 0 0 0 0 0 0 0 0. .0 0. . .1 Cape Verde 4 20 3862 41 13 24 3 0 0 1 0. .0 .0 1. - Yemen 1 0 452043 2 0 0 1 0 0 0 0. - Djibouti 0 0 21514 0 0 0 0 0 0 0 0.0 0.0 33.0 . . .0 3.0 0.8 Iraq 3 3 442709 133 58 8 2 0 0 0 0. .0 1. .0 . - Eritrea 0 0 119719 0 0 0 0 0 0 0 0. - Jordan 0 0 88670 9 11 5 0 0 0 0 0.5 4.5 4.0 0.0 .0 4. . .0 0.0 5. .0 . . . - . - Mauritania 1 0 1040803 0 0 0 0 0 0 0 0.0 0.0 United Arab Emirates 0 0 79190 0 0 0 0 0 0 0 0.0 0.4 0.1 56.5 Hong Kong 2 3 436 117 242 280 0 0 1 1 0. - Western Sahara 0 0 267282 0 0 0 0 0 0 0 0.3 33.0 Lesotho 2 2 30302 110 5 0 1 0 0 0 0.0 . .0 0. . . .0 0.1 21.0 .8 56.0 0.0 40.3 Libya 7 4 1615869 55 16 3 5 1 0 0 0.
with magenta= USA and Canada. National and climate-domain scale intercomparisons using ratio measures of aggregate forest change ((loss+gain)/2000 forest) versus percent of aggregate forest change that is forest loss (loss/(loss+gain)).05). and cyan=Australia and Oceania. forest is defined as tree cover >50%. Only countries with >1000km2 of year 2000 >50% tree cover are shown.Fig. green=Latin America. Countries exhibiting a statistically significant trend in forest loss during the study period are indicated (e. S1 and S3 for values. *** for p<0. orange=East Asia. red=Africa. blue=Europe. S1. For this figure. 7 . brown=South Asia. purple=Southeast Asia. Regional groupings are highlighted. Refer to Tab.g.
AUS=Australia and Oceania.Fig.g. AFR=Africa. 8 . forest is defined as tree cover >50%. For this figure. EAS=Eurasia. Ecozones exhibiting a statistically significant trend in forest loss during the study period are indicated (e. Colors refer to climate domains. S1 and S2 for values.05). SAM=South America. NAM=North America. S2 Ecozone and climate-domain scale intercomparisons using ratio measures of aggregate forest change ((loss+gain)/2000 forest) versus percent of aggregate forest change that is forest loss (loss/(loss+gain)). Refer to Tab. *** for p<0.
Colors denote regional groupings of Fig. S2. versus Landsat-derived net change. 2000 to 2012. 2000 to 2010. 9 .Fig. S3 FAO FRA net forest area change.
Fig. 10 . S2. S4 FAO FRA roundwood production in megatons of carbon totaled per country from 2000 through 2011 versus total Landsat-derived forest area loss and gain from 2000 to 2012. Colors denote regional groupings of Fig.
including all tree cover strata in loss estimation. S5 Sample-based estimation of forest cover loss and gain. Map is from the Landsat-derived map product. Reference is from validation data derived from multi-source image interpretation. 11 . Mean and two standard error range are shown at global and climate domain scales.Fig.
Reference is from validation data derived from multi-source image interpretation. Mean and two standard error range are shown at global and climate domain scales.Fig. including all tree cover strata in loss estimation. S6 Sample-based difference of map minus reference forest loss and gain per block. 12 . Map is from the Landsat-derived map product.
S7 GLAS-derived vegetation heights for Landsat-derived forest loss pixels. 13 . GLAS median and quartiles are displayed by number of years from Landsat-estimated year of disturbance.Fig.
S8 Median and quartile GLAS-derived vegetation heights for areas of Landsat-derived zero percent tree cover in 2000 that were mapped as forest gain within the 2000 to 2012 study period. 14 .Fig.
gain area FRA vs.68 15 .37 0.68 0.09 Roundwood production FRA vs.15 0. >50% r2 slope r2 slope Latin America (excluding Brazil) 0.11 Southeast Asia (excluding Indonesia) 0.69 0. Regression results for selected regions comparing 2000-2010 FAO FRA net change and 2000-2011 FAO roundwood production versus 2000-2012 global Landsat-derived gross forest area gain minus gross forest area loss for two tree cover thresholds (>1% and >50%).Table S4.70 0.45 0.19 0.70 0.06 -0.16 0.91 Africa (excluding DRC) 0.02 -0.03 Europe (excluding Russia) 0.03 0.17 0. loss area r2 r2 Latin America (excluding Brazil) 0.28 0.18 Europe (excluding Russia) 0.26 Africa (excluding DRC) 0.26 0. Net area change FRA vs.23 Southeast Asia (excluding Indonesia) 0.63 0. >1% FRA vs.
8 (0.45 Producer’s 73.0) Overall accuracy = 99.70 87.6%) 16 .32 Producer’s 87.14 99.7% (0.55 99.8) 99.9 (0.68 98.30 99.10 98. Global (n=1500) Loss error matrix expressed as percent of area (selected standard errors are shown in parentheses) Reference Loss No Loss Total User’s (SE) Map Loss 1.8) No Loss 0.6% (0. Accuracy assessment of 2000 to 2012 forest loss and gain at global and climate domain scales.4 (0.41 0.8 (2.0 (2.7) 99.48 0.7%) Gain error matrix expressed as percent of area (selected standard errors are shown in parentheses) Reference Gain No Gain Total User’s (SE) Map Gain 0.1) Total 1.1%) Overall accuracy = 99.13 0.9 (0.Table S5.32 99.54 76.20 98.0) Total 0.46 99.6) No Gain 0.9 (0.8 (0.22 1.
7) No Loss 0.9 (0.1) Overall accuracy = 99.7 (0.1) Gain error matrix expressed as percent of area (selected standard errors are shown in parentheses) Reference Gain No Gain Total User’s (SE) Map Gain 0.1) 17 .0 (8.9 (8.72 87.7 (0.30 97.50 0.3) 99.04 0.77 99.8 (0.80 98.Climate domains Tropical (n=628) Loss error matrix expressed as percent of area (selected standard errors are shown in parentheses) Reference Loss No Loss Total User’s (SE) Map Loss 1.56 99.98 98.5 (0.20 Producer’s 83.1) Overall accuracy = 99.21 99.22 1.0 (4.1) Total 1.6) 99.60 Producer’s 48.8) No Gain 0.8 (0.23 81.19 0.28 99.1) Total 0.40 99.1 (5.
71 0.5 (8.7 (0.12 0.Subtropical (n=295) Loss error matrix expressed as percent of area (selected standard errors are shown in parentheses) Reference Loss No Loss Total User’s Map Loss 0.70 99.30 Producer’s 79.8 (0.4 (7.70 79.8 (0.02 99.8 (0.17 99.1) Total 0.1) Overall accuracy = 99.7 (0.9) No Gain 0.16 99.56 0.4) 99.15 0.83 85.6) No Loss 0.4 (5.1) Overall accuracy = 99.14 99.1) Total 0.30 99.15 99.9 (0.3 (8.1) 18 .1) 99.14 Producer’s 82.1) Gain error matrix expressed as percent of area (selected standard errors are shown in parentheses) Reference Gain No Gain Total User’s (SE) Map Gain 0.86 99.
9 (4.5 (14.22 0.15 88.01 0.31 99.1) Total 1.1) 19 .1) Overall accuracy = 99.8 (0.5) 99.79 98.8 (0.14 1.1) Gain error matrix expressed as percent of area (selected standard errors are shown in parentheses) Reference Gain No Gain Total User’s (SE) Map Gain 0.0) No Gain 0.11 99.36 0.Temperate (n=298) Loss error matrix expressed as percent of area (selected standard errors are shown in parentheses) Reference Loss No Loss Total User’s (SE) Map Loss 1.1) 99.9 (0.9 (0.4) No Loss 0.1) Total 0.58 62.47 99.07 98.9 (0.42 99.53 Producer’s 76.7 (0.2 (5.08 98.0 (15.85 99.92 Producer’s 93.1) Overall accuracy = 99.
11 Producer’s 98.7 (0.1) Overall accuracy = 99.9 (1.83 96.9 (0.1) Total 3.1) 99.88 99.7 (11.7) No Loss 0.06 99.94 88.13 76.2) Gain error matrix expressed as percent of area (selected standard errors are shown in parentheses) Reference Gain No Gain Total User’s (SE) Map Gain 0.7) 99.23 95.1) Overall accuracy = 99.01 98.8) No Gain 0.47 0.85 98.3 (0.7 (0.8 (0.70 96.87 0.0 (4.1) Total 0.47 3.Boreal (n=258) Loss error matrix expressed as percent of area (selected standard errors are shown in parentheses) Reference Loss No Loss Total User’s (SE) Map Loss 3.4 (1.5 (0.30 Producer’s 93.26 1.86 99.1) 20 .
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