High-Resolution Global Maps of 21st-Century Forest Cover Change
Forests in Flux
Forests worldwide are in a state of flux, with accelerating losses in some regions and gains in others. Hansen et al. (p. 850) examined global Landsat data at a 30-meter spatial resolution to characterize forest extent, loss, and gain from 2000 to 2012. Globally, 2.3 million square kilometers of forest were lost during the 12-year study period and 0.8 million square kilometers of new forest were gained. The tropics exhibited both the greatest losses and the greatest gains (through regrowth and plantation), with losses outstripping gains.
Abstract
Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil’s well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.
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Supplementary Material
Summary
Materials and Methods
Supplementary Text
Figs. S1 to S8
Tables S1 to S5
Resources
File (hansen.sm.pdf)
References and Notes
1
Foley J. A., Defries R., Asner G. P., Barford C., Bonan G., Carpenter S. R., Chapin F. S., Coe M. T., Daily G. C., Gibbs H. K., Helkowski J. H., Holloway T., Howard E. A., Kucharik C. J., Monfreda C., Patz J. A., Prentice I. C., Ramankutty N., Snyder P. K., Global consequences of land use. Science 309, 570–574 (2005).
2
Hansen M. C., Stehman S. V., Potapov P. V., Quantification of global gross forest cover loss. Proc. Natl. Acad. Sci. U.S.A. 107, 8650–8655 (2010).
3
Food and Agricultural Organization of the United Nations, Global Forest Land-Use Change 1990-2005, FAO Forestry Paper No. 169 (Food and Agricultural Organization of the United Nations, Rome, 2012).
4
Hansen M., DeFries R., Detecting long-term global forest change using continuous fields of tree-cover maps from 8-km Advanced Very High Resolution Radiometer (AVHRR) data for the years 1982-1999. Ecosystems 7, 695–716 (2004).
5
Instituto Nacional de Pesquisas Especias, Monitoring of the Brazilian Amazonian Forest by Satellite, 2000-2012 (Instituto Nacional de Pesquisas Especias, San Jose dos Campos, Brazil, 2013).
6
Sparovek G., Berndes G., Barretto A. G. O. P., Klug I. L. F., The revision of the Brazilian Forest Act: Increased deforestation or a historic leap towards balancing agricultural development and nature conservation. Environ. Sci. Policy 16, 65–72 (2012).
7
Edwards D. P., Laurance W. F., Carbon emissions: Loophole in forest plan for Indonesia. Nature 477, 33 (2011).
8
Drummond M., Loveland T., Land-use pressure and a transition to forest-cover loss in the Eastern United States. Bioscience 60, 286–298 (2010).
9
Kurz W. A., Dymond C. C., Stinson G., Rampley G. J., Neilson E. T., Carroll A. L., Ebata T., Safranyik L., Mountain pine beetle and forest carbon feedback to climate change. Nature 452, 987–990 (2008).
10
B. Gardiner et al., Destructive Storms in European Forests: Past and Forthcoming Impacts (European Forest Institute, Freiburg, Germany, 2010).
11
Potapov P., Hansen M., Stehman S., Loveland T., Pittman K., Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss. Remote Sens. Environ. 112, 3708–3719 (2008).
12
Prishchepov A., Muller D., Dubinin M., Baumann M., Radeloff V., Determinants of agricultural land abandonment in post-Soviet European Russia. Land Use Policy 30, 873–884 (2013).
13
United Nations Framework Convention on Climate Change, Reducing Emissions from Deforestation in Developing Countries: Approaches to Stimulate Action – Draft Conclusions Proposed by the President (United Nations Framework Convention on Climate Change Secretariat, Bonn, Germany, 2005).
14
R. Houghton et al., The role of science in REDD. Carbon Manage. 1, 253–259.
15
Geist H., Lambin E., Proximate causes and underlying driving forces of tropical deforestation. Bioscience 52, 143–150 (2002).
16
Saatchi S. S., Harris N. L., Brown S., Lefsky M., Mitchard E. T., Salas W., Zutta B. R., Buermann W., Lewis S. L., Hagen S., Petrova S., White L., Silman M., Morel A., Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci. U.S.A. 108, 9899–9904 (2011).
17
Baccini A., Goetz S. J., Walker W. S., Laporte N. T., Sun M., Sulla-Menashe D., Hackler J., Beck P. S. A., Dubayah R., Friedl M. A., Samanta S., Houghton R. A., Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Clim. Change 2, 182–185 (2012).
18
Harris N. L., Brown S., Hagen S. C., Saatchi S. S., Petrova S., Salas W., Hansen M. C., Potapov P. V., Lotsch A., Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573–1576 (2012).
19
Waterworth R., Richards G., Brack C., Evans D., A generalized hybrid process-empirical model for predicting plantation forest growth. For. Ecol. Manage. 238, 231–243 (2007).
20
Potapov P., et al., Mapping the world’s intact forest landscapes by remote sensing. Ecol. Soc. 13, 51 (2008).
21
Brooks T. M., Mittermeier R. A., da Fonseca G. A., Gerlach J., Hoffmann M., Lamoreux J. F., Mittermeier C. G., Pilgrim J. D., Rodrigues A. S., Global biodiversity conservation priorities. Science 313, 58–61 (2006).
22
Rodrigues A. S., Andelman S. J., Bakarr M. I., Boitani L., Brooks T. M., Cowling R. M., Fishpool L. D., Da Fonseca G. A., Gaston K. J., Hoffmann M., Long J. S., Marquet P. A., Pilgrim J. D., Pressey R. L., Schipper J., Sechrest W., Stuart S. N., Underhill L. G., Waller R. W., Watts M. E., Yan X., Effectiveness of the global protected area network in representing species diversity. Nature 428, 640–643 (2004).
23
Rudel T., Is there a forest transition? Deforestation, reforestation and development. Rural Sociol. 63, 533–552 (1998).
24
Asner G. P., Knapp D. E., Broadbent E. N., Oliveira P. J., Keller M., Silva J. N., Selective logging in the Brazilian Amazon. Science 310, 480–482 (2005).
25
Woodcock C. E., Allen R., Anderson M., Belward A., Bindschadler R., Cohen W., Gao F., Goward S. N., Helder D., Helmer E., Nemani R., Oreopoulos L., Schott J., Thenkabail P. S., Vermote E. F., Vogelmann J., Wulder M. A., Wynne R., Free access to Landsat imagery. Science 320, 1011 (2008).
26
Tucker C., Grant D., Dykstra J., NASA’s orthorectified Landsat data set. Photogramm. Eng. Remote Sensing 70, 313–322 (2004).
27
Potapov P., Turubanova S. A., Hansen M. C., Adusei B., Broich M., Altstatt A., Mane L., Justice C. O., Quantifying forest cover loss in Democratic Republic of the Congo, 2000-2010. Remote Sens. Environ. 122, 106–116 (2012).
28
Broich M., Hansen M. C., Potapov P., Adusei B., Lindquist E., Stehman S. V., Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia. Int. J. Appl. Earth Obs 13, 277–291 (2011).
29
Hansen M., Egorov A., Roy D. P., Potapov P., Ju J., Turubanova S., Kommareddy I., Loveland T. R., Continuous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project. Remote Sens. Letters 2, 279–288 (2011).
30
Hansen M., DeFries R. S., Townshend J. R. G., Carroll M., Dimiceli C., Sohlberg R. A., Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS vegetation continuous fields algorithm. Earth Interact. 7, 1–15 (2003).
31
L. Breiman, J. Friedman, R. Olsen, C. Stone, Classification and Regression Trees (Wadsworth and Brooks/Cole, Monterey, CA, 1984).
32
Chambers C., Raniwala A., Perry F., Adams S., Henry R. R., Bradshaw R., Weizenbaum N., FlumeJava: Easy, efficient data-parallel pipelines. ACM SIGPLAN Notices 45, 363–375 (2010).
33
Food and Agricultural Organization of the United Nations, Global Forest Resources Assessment (Food and Agricultural Organization of the United Nations, Rome, 2010).
34
Grainger A., Difficulties in tracking the long-term global trend in tropical forest area. Proc. Natl. Acad. Sci. U.S.A. 105, 818–823 (2008).
35
Achard F., Eva H. D., Stibig H. J., Mayaux P., Gallego J., Richards T., Malingreau J. P., Determination of deforestation rates of the world’s humid tropical forests. Science 297, 999–1002 (2002).
36
Gong P., Wang J., Yu L., Zhao Y., Zhao Y., Liang L., Niu Z., Huang X., Fu H., Liu S., Li C., Li X., Fu W., Liu C., Xu Y., Wang X., Cheng Q., Hu L., Yao W., Zhang H., Zhu P., Zhao Z., Zhang H., Zheng Y., Ji L., Zhang Y., Chen H., Yan A., Guo J., Yu L., Wang L., Liu X., Shi T., Zhu M., Chen Y., Yang G., Tang P., Xu B., Giri C., Clinton N., Zhu Z., Chen J., Chen J., Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens. 34, 2607–2654 (2013).
37
J. Sexton 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).
38
Gutman G., Huang C., Chander G., Noojipady P., Masek J., Assessment of the NASA-USGS Global Land Survey (GLS) datasets. Remote Sens. Environ. 134, 249–265 (2013).
39
M. Hansen, P. Potapov, S. Turubanova, in Global Forest Monitoring from Earth Observation, F. Achard, M. Hansen, Eds. (CRC Press, Boca Raton, FL, 2012), 93–109.
40
Goetz S., Dubayah R., Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change. Carbon Manage. 2, 231–244 (2011).
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Published In
Science
Volume 342 | Issue 6160
15 November 2013
15 November 2013
Copyright
Copyright © 2013, American Association for the Advancement of Science.
Submission history
Received: 14 August 2013
Accepted: 15 October 2013
Published in print: 15 November 2013
Acknowledgments
Support for Landsat data analysis and characterization was provided by the Gordon and Betty Moore Foundation, the United States Geological Survey, and Google, Inc. GLAS data analysis was supported by the David and Lucile Packard Foundation. Development of all methods was supported by NASA through its Land Cover and Land Use Change, Terrestrial Ecology, Applied Sciences, and MEaSUREs programs (grants NNH05ZDA001N, NNH07ZDA001N, NNX12AB43G, NNX12AC78G, NNX08AP33A, and NNG06GD95G) and by the U.S. Agency for International Development through its CARPE program. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. government. Results are depicted and viewable online at full resolution: http://earthenginepartners.appspot.com/science-2013-global-forest.
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