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. Landsat data reveals details of forest losses and gains across the globe on an annual basis from 2000 to 2012. 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.

[1]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[2]  G. Klein The role of science. , 1988, Journal of acquired immune deficiency syndromes.

[3]  Organización de las Naciones Unidas United Nations framework convention on climate change , 1992 .

[4]  T. Rudel Is There a Forest Transition? Deforestation, Reforestation, and Development1 , 1998 .

[5]  F. Achard,et al.  Determination of Deforestation Rates of the World's Humid Tropical Forests , 2002, Science.

[6]  E. Lambin,et al.  Proximate Causes and Underlying Driving Forces of Tropical Deforestation , 2002 .

[7]  J. Townshend,et al.  Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm , 2003 .

[8]  C. Tucker,et al.  NASA’s Global Orthorectified Landsat Data Set , 2004 .

[9]  R. DeFries,et al.  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 1982–99 , 2004, Ecosystems.

[10]  Matthew E. Watts,et al.  Effectiveness of the global protected area network in representing species diversity , 2004, Nature.

[11]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[12]  M. Keller,et al.  Selective Logging in the Brazilian Amazon , 2005, Science.

[13]  T. Brooks,et al.  Global Biodiversity Conservation Priorities , 2006, Science.

[14]  Cristopher Brack,et al.  A generalised hybrid process-empirical model for predicting plantation forest growth , 2007 .

[15]  A. Grainger Difficulties in tracking the long-term global trend in tropical forest area , 2008, Proceedings of the National Academy of Sciences.

[16]  W. Kurz,et al.  Mountain pine beetle and forest carbon feedback to climate change , 2008, Nature.

[17]  Stephen V. Stehman,et al.  Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss , 2008 .

[18]  Martha C. Anderson,et al.  Free Access to Landsat Imagery , 2008, Science.

[19]  P. Potapov,et al.  Mapping the World's Intact Forest Landscapes by Remote Sensing , 2008 .

[20]  Barry Gardiner,et al.  Destructive storms in European forests: past and forthcoming impacts. , 2010 .

[21]  Craig Chambers,et al.  FlumeJava: easy, efficient data-parallel pipelines , 2010, PLDI '10.

[22]  Thomas R. Loveland,et al.  Land-use Pressure and a Transition to Forest-cover Loss in the Eastern United States , 2010 .

[23]  M. Hansen,et al.  Quantification of global gross forest cover loss , 2010, Proceedings of the National Academy of Sciences.

[24]  Stephen V. Stehman,et al.  International Journal of Applied Earth Observation and Geoinformation: Time-Series Analysis of Multi-Resolution Optical Imagery for Quantifying Forest Cover Loss in Sumatra and Kalimantan, Indonesia , 2011 .

[25]  Government cuts: Call to save science institute in Turkey , 2011, Nature.

[26]  S. Goetz,et al.  Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change , 2011 .

[27]  David P. Roy,et al.  Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project , 2011 .

[28]  W. Salas,et al.  Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.

[29]  Carbon emissions: Loophole in forest plan for Indonesia , 2011, Nature.

[30]  Volker C. Radeloff,et al.  Determinants of agricultural land abandonment in post-Soviet European Russia , 2011 .

[31]  W. Salas,et al.  Baseline Map of Carbon Emissions from Deforestation in Tropical Regions , 2012, Science.

[32]  S. Goetz,et al.  Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .

[33]  C. Justice,et al.  Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM + data , 2012 .

[34]  G. Berndes,et al.  The revision of the Brazilian Forest Act: increased deforestation or a historic step towards balancing agricultural development and nature conservation? , 2012 .

[35]  Frédéric Achard,et al.  Global Forest Monitoring from Earth Observation , 2012 .

[36]  G. Chander,et al.  Assessment of the NASA–USGS Global Land Survey (GLS) datasets , 2013 .

[37]  Hankui K. Zhang,et al.  Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .

[38]  Chengquan Huang,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 , 2013, Int. J. Digit. Earth.