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.

1The remote sensing science and applications communities have developed increasingly reliable, 2 consistent, and robust approaches for capturing land dynamics to meet a range of information 3 needs. Statistically robust and transparent approaches for assessing accuracy and estimating area 4 of change are critical to ensure the integrity of land change information. We provide 5 practitioners with a set of "good practice" recommendations for designing and implementing an 6 accuracy assessment of a change map and estimating area based on the reference sample data. 7The good practice recommendations address the three major components: of the process 8 including the sampling design, response design and analysis. The primary good practice 9 recommendations for assessing accuracy and estimating area are: (i) implement a probability 10 sampling design that is chosen to achieve the priority objectives of accuracy and area estimation 11 while also satisfying practical constraints such as cost and available sources of reference data; 12(ii) implement a response design protocol that is based on reference data sources that provide 13 sufficient spatial and temporal representation to accurately label each unit in the sample (i.e., the 14 "reference classification" will be considerably more accurate than the map classification being 15 evaluated); (iii) implement an analysis that is consistent with the sampling design and response 16 design protocols; (iv) summarize the accuracy assessment by reporting the estimated error matrix 17 in terms of proportion of area and estimates of overall accuracy, user's accuracy (or commission 18 error), and producer's accuracy (or omission error); (v) estimate area of classes (e.g., types of 19 change such as wetland loss or types of no changepersistence such as stable forest) based on the 20 reference classification of the sample units; (vi) quantify uncertainty by reporting confidence 21 intervals for accuracy and area parameters; (vii) evaluate variability and potential error in the 22 3 reference classification; and (viii) document deviations from good practice that may substantially 23 affect the results. An example application is provided to illustrate the recommended process. 24 4

Land change is a cause and consequence of global environmental change. Changes in land use and land cover considerably alter the Earth's energy balance and biogeochemical cycles, which contributes to climate change and-in turn-affects land surface properties and the provision of ecosystem services. However, quantification of global land change is lacking. Here we analyse 35 years' worth of satellite data and provide a comprehensive record of global land-change dynamics during the period 1982-2016. We show that-contrary to the prevailing view that forest area has declined globally-tree cover has increased by 2.24 million km (+7.1% relative to the 1982 level). This overall net gain is the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16 million km (-3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover. The mapped land changes and the driver attributions reflect a human-dominated Earth system. The dataset we developed may be used to improve the modelling of land-use changes, biogeochemical cycles and vegetation-climate interactions to advance our understanding of global environmental change.

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A globally consistent methodology using satellite imagery was implemented to quantify gross forest cover loss (GFCL) from 2000 to 2005 and to compare GFCL among biomes, continents, and countries. GFCL is defined as the area of forest cover removed because of any disturbance, including both natural and human-induced causes. GFCL was estimated to be 1,011,000 km 2 from 2000 to 2005, representing 3.1% (0.6% per year) of the year 2000 estimated total forest area of 32,688,000 km 2 . The boreal biome experienced the largest area of GFCL, followed by the humid tropical, dry tropical, and temperate biomes. GFCL expressed as the proportion of year 2000 forest cover was highest in the boreal biome and lowest in the humid tropics. Among continents, North America had the largest total area and largest proportion of year 2000 GFCL. At national scales, Brazil experienced the largest area of GFCL over the study period, 165,000 km 2 , followed by Canada at 160,000 km 2 . Of the countries with >1,000,000 km 2 of forest cover, the United States exhibited the greatest proportional GFCL and the Democratic Republic of Congo the least. Our results illustrate a pervasive global GFCL dynamic. However, GFCL represents only one component of net change, and the processes driving GFCL and rates of recovery from GFCL differ regionally. For example, the majority of estimated GFCL for the boreal biome is due to a naturally induced fire dynamic. To fully characterize global forest change dynamics, remote sensing efforts must extend beyond estimating GFCL to identify proximate causes of forest cover loss and to estimate recovery rates from GFCL.change detection | global change | monitoring | remote sensing | probability sampling T he synoptic nature of satellite-based earth observation data enables the consistent characterization of forest cover across space and over time. Information on forest cover and forest cover change is necessary for carbon accounting efforts as well as for parameterizing global-scale biogeochemical, hydrological, biodiversity, and climate models. Because of the vast area that must be examined, earth observation data offer one of the few viable information sources suitable for global-scale monitoring of forest cover dynamics. Such monitoring has been hindered by data access policies (costs of imagery), inadequate imagery acquisition protocols (few systematic global acquisition strategies), and data processing limitations (methods for processing global data for change monitoring). However, new data streams, freely available imagery, and improved methods now allow operational monitoring of global forest cover change. We present estimates of gross forest cover loss (GFCL) from 2000 to 2005 by using data from two sensor systems appropriate for global-scale inquiry. The global consistency of the methodology allows for comparisons of GFCL among biomes, continents, and countries. A GFCL map is also produced to provide a spatial depiction of primary areas ("hotspots") of GFCL.Over the past three decades, methods for monitoring ...

Forest cover is an important input variable for assessing changes to carbon stocks, climate and hydrological systems, biodiversity richness, and other sustainability science disciplines. Despite incremental improvements in our ability to quantify rates of forest clearing, there is still no definitive understanding on global trends. Without timely and accurate forest monitoring methods, policy responses will be uninformed concerning the most basic facts of forest cover change. Results of a feasible and cost-effective monitoring strategy are presented that enable timely, precise, and internally consistent estimates of forest clearing within the humid tropics. A probabilitybased sampling approach that synergistically employs low and high spatial resolution satellite datasets was used to quantify humid tropical forest clearing from 2000 to 2005. Forest clearing is estimated to be 1.39% (SE 0.084%) of the total biome area. This translates to an estimated forest area cleared of 27.2 million hectares (SE 2.28 million hectares), and represents a 2.36% reduction in area of humid tropical forest. Fifty-five percent of total biome clearing occurs within only 6% of the biome area, emphasizing the presence of forest clearing ''hotspots.'' Forest loss in Brazil accounts for 47.8% of total biome clearing, nearly four times that of the next highest country, Indonesia, which accounts for 12.8%. Over three-fifths of clearing occurs in Latin America and over one-third in Asia. Africa contributes 5.4% to the estimated loss of humid tropical forest cover, reflecting the absence of current agro-industrial scale clearing in humid tropical Africa.deforestation ͉ humid tropics ͉ remote sensing ͉ change detection ͉ monitoring Q uantifying rates of humid tropical forest cover clearing is critical for many areas of earth system and sustainability science, including improved carbon accounting, biogeochemical cycle and climate change modeling, management of forestry and agricultural resources, and biodiversity monitoring. Concerning land cover dynamics, humid tropical forest clearing results in a large loss of carbon stock when compared with most other change scenarios. The humid tropical forests are also the site of considerable economic development through direct forestry exploitation and frequent subsequent planned agro-industrial activities. The result is that tropical forests and their removal feature prominently in the global carbon budget (1). In addition, the humid tropics include the most biodiverse of terrestrial ecosystems (2), and the loss of humid tropical forest cover results in a concomitant loss in biodiversity richness.Assessing the dynamics of this biome is difficult because of its sheer size and varying level of development within and between countries. To date, there is no clear consensus on the trends in forest cover within the humid tropics. Grainger (3) illustrated this point mainly through the use of data from the Food and Agriculture Organization of the United Nations Forest Resource Assessments (4-6) and consequentl...

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