In this study, we summarize a series of forest change data sets based on satellite observations in circa 1990, 2000, and 2005 “epochs” [ Sexton et al. , 2013 ; Kim et al. , 2014 ] to estimate changes in tropical forest area at high (30 m) spatial resolution in 34 tropical countries from 1990 to 2005. Using a consistent definition of forest throughout, the data enable a spatiotemporally comprehensive alternative to the FAO reports and other sample‐based satellite analyses [e.g., FAO , Joint Research Centre ( JRC ), 2012 ; Achard et al. , 2014 ]. We extend the series forward as well, from 2005 to 2010, to estimate changes in tropical forest area in the latter part of that decade and to complete the first fine‐scale satellite‐based estimates of change in humid tropical deforestation spanning the turn of the millennium. Several local and regional studies have been made of changing rates of deforestation between the 1990s and 2000s, but this is the first pan‐tropical analysis.

Recent progress in data availability and processing power have enabled national and global forest cover change assessments based on long‐term archives of satellite imagery [ Townshend et al. , 2012 ; Hansen et al. , 2013 ; Sexton et al. , 2013 ; Kim et al. , 2014 ]. Importantly, these satellite assessments are now possible at subhectare resolution, the scale at which most anthropogenic changes occur [ Townshend and Justice , 1988 ]. Landsat data offer a spatial resolution suitable to map such changes (e.g., shifting cultivation in the rainforest) with instantaneous field of view of 30 m and effective resolution element smaller than 75 m, the minimum area for which spectral properties of the center can be assigned with at least 95% confidence [ Townshend , 1981 ; Wilson , 1988 ].

However, estimates of forest area changes across the tropics prior to 2000 remain uncertain. The FRA has been criticized for inconsistencies in the definition of forest among countries and over time, as well as its dependence on national self‐reporting [ Matthews , 2001 ; Defries et al. , 2002 ; Grainger , 2008 ]. Previous studies have shown that the FRA overestimated changes in forest area [ Houghton , 1999 ; Steininger et al. , 2001 ; Achard et al. , 2002 ; Defries et al. , 2002 ] in the 1980s and the 1990s. In the tropics especially, the FRA reported a declining rate of deforestation from the 1980s to the 1990s, while studies based on satellite data observed opposite trends [ Defries et al. , 2002 ].

Tropical deforestation was among the largest anthropogenic sources of greenhouse gas emissions in the 1990s [ Gibbs et al. , 2007 ]. Based on statistics from the United Nations Food and Agriculture Organization (FAO) Forest Resource Assessment (FRA) [ Food and Agriculture Organization ( FAO ), 2010 ], the Intergovernmental Panel on Climate Change reported a 1.84 Gt CO 2 yr −1 global decline in CO 2 emissions from land use change from the 1990s to the 2000s, attributed largely to a decreasing rate of deforestation [ Stocker et al. , 2013 ].

Forest cover change statistics—including gross forest (cover) loss, gross forest gain, and net change—were generated for the periods between the four epochs. These estimates were adjusted to account for missing data due to clouds and their shadows. The forest cover change statistics in each period were adjusted using error matrices from a global accuracy assessment [ Kim et al. , 2014 ] to minimize incompatibility due to the different biases in forest cover change statistics for each period. Forest cover change statistics from 2000 to 2010 were estimated by averaging the estimates for 2000–2005 and 2005–2010 periods.

Five thousand four hundred forty four Landsat scenes were collected from the 1990, 2000, 2005, and 2010 epochs of the Global Land Survey (GLS) collection of Landsat images. The GLS is intended to provide full, multitemporal coverage of Earth's terrestrial surface in service of land cover mapping and change detection [ Gutman et al. , 2008 ]. The original GLS data were augmented with additional images to improve radiometric calibration, reduce cloud cover, and maximize spectral discrimination of forests [ Kim et al. , 2011 ]. Each image of this augmented GLS data set was atmospherically corrected to estimate surface reflectance using the LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) [ Masek et al. , 2006 ]. Forest cover in the 2000 and 2005 epochs was estimated by translation of percent‐tree cover to categorical forest cover and change [ Sexton et al. , 2013 , 2015 ], using probability thresholds of 0.5 to detect forest loss and 0.7 to detect forest gain to account for their different detectabilities. Stable pixels identified in the 2000 and 2005 epochs were then used to extend the classification and change estimate of forest cover to the 1990 and 2010 epochs [ Kim et al. , 2014 ]. Each GLS epoch spans a range of years focused on the nominal year [ Gutman et al. , 2008 ], so the forest/nonforest layer in each year was accompanied by the year of image acquisition to estimate changes over time as rates. Forest cover data in 1990, 2000, and 2005 epochs are publicly available from the Global Land Cover Facility ( www.landcover.org ).

Consistent with the United Nations Framework Convention on Climate Change [ UNFCCC , 2002 ], United Nations Food and Agriculture Organization [ UNFAO , 2002 ], and the International Geosphere‐Biosphere Programme (IGBP) [ Belward , 1996 ], we defined forest cover (as opposed to forest use [ Belward , 1996 ; Hansen et al. , 2010 ]) as parcels >1 ha in area and comprising pixels with >30% tree cover. Our definition corresponds with the definitions of IGBP classes for forest (>60% tree cover) and woody savannas (>30% tree cover) combined. Table 1 shows the differences in forest definition for each set of estimates compared in this study. It is notable that among the sources, only the FAO definition relies on dominant land use [ Stibig et al. , 2014 ].

The study area comprises 34 countries spanning the humid tropics, each of which is covered at least 50% by forest biomes [ Olson et al. , 2001 ]. These countries' forests comprise over 80% of forest area in the tropics [ Hansen et al. , 2013 ] and dominate the forest area of the humid tropics.

3 Results and Discussion

Satellite analysis revealed forest cover totals of 1340 × 106 ha in 1990, 1300 × 106 ha in 2000, and 1240 × 106 ha in 2010 across the 34 countries. These estimates are broken down by continent and by country in Table 2. During the 1990–2000 period, the annual net change across all the countries was −4 × 106 ha yr−1, the gross rate of loss was 4.9 × 106 ha yr−1, and the gross rate of gain was 0.9 × 106 ha yr−1. During the 2000–2010 period, the rate of loss was 7.8 × 106 ha yr−1, and the rate of gain was 1.3 × 106 ha yr−1, resulting in a −6.5 × 106 ha yr−1 net rate of change. Our estimates indicate a dramatic 62% (2.5 × 106 ha yr−1) acceleration of net forest loss from the 1990s to the 2000s. This acceleration is corroborated by estimates adjusted for classification errors (supporting information). Forest area change rates by continent and country in each period area shown in Table 3.

Table 2. Landsat‐Based Estimates of Forest Area (106ha) in 1990, 2000, and 2010 by Continent and Country 1990 2000 2010 1990 2000 2010 Belize 1.93 1.85 1.79 Gabon 23.38 22.92 22.99 Bolivia 57.95 56.05 53.06 Liberia 7.46 7.27 7.23 Brazil 431.47 412.12 386.4 Madagascar 8.93 8.55 7.58 Colombia 74.75 73.45 69.82 Sierra Leone 3.79 3.7 3.53 Costa Rica 3.99 3.9 3.69 Tropical Africa 243.58 241.06 235.12 Ecuador 15.22 14.95 14.63 Bangladesh 2.03 1.99 1.88 Guatemala 7.53 7.14 6.65 Brunei Darussalam 0.52 0.52 0.51 Guyana 18.39 18.23 18.16 Cambodia 7.81 7.5 6.32 Honduras 7.44 7.19 6.73 Indonesia 154.82 148.29 139.87 Nicaragua 6.27 5.98 4.91 Laos 19.22 18.79 18.14 Panama 4.6 4.44 4.01 Malaysia 30.12 28.81 27.18 Peru 74.31 73.79 73.04 Myanmar 40.12 39.29 37.5 Suriname 14.01 13.95 13.88 Papua New Guinea 41.81 41.21 40.54 Venezuela 51.22 50.33 47.07 Philippines 16.86 16.11 14.46 Tropical Latin America 769.08 743.37 703.84 Sri Lanka 2.91 2.8 2.45 Cameroon 20.32 20.21 19.88 Thailand 17.81 17.16 15.46 Congo 23.88 23.66 23.43 Vietnam 16.39 15.79 14.07 Democratic Republic Congo 153.23 152.2 147.93 Tropical Asia 350.43 338.24 318.37 Equatorial Guinea 2.59 2.56 2.54 Pan‐Tropics 1363.08 1322.68 1257.33

Table 3. Changes in Forest Area (1000 ha yr−1) From Landsat‐Based Estimates Versus FRA Reports [FAO, ] for 1990–2000 and 2000–2010 in Tropical Latin America, Asia, and Africa This Study FRA [UNFAO, 2010] 1990–2000 2000–2010 1990–2000 2000–2010 Belize −8 −7 −10 −10 Bolivia −191 −298.5 −270 −289.5 Brazil −1936 −2571 −2890 −2642 Colombia −130 −363 −101 −101 Costa Rica −9 −21 −19 23 Ecuador −27 −33 −198 −198 Guatemala −39 −49 −54 −55 Guyana −16 −6.5 0 0 Honduras −25 −46.5 −174 −120 Nicaragua −29 −107.5 −70 −70 Panama −15 −43.5 −42 −12 Peru −52 −75 −94 −122 Suriname −5 −7.5 0 −2 Venezuela −89 −326.5 −288 −288 Tropical Latin America −2570 −3954 −4210 −3887 Cameroon −11 −33.5 −220 −220 Congo −22 −22.5 −17 −14.5 Democratic Republic Congo −104 −426.5 −311 −311 Equatorial Guinea −3 −2 −12 −12 Gabon −46 7 0 0 Liberia −19 −3.5 −30 −30 Madagascar −38 −97 −57 −57 Sierra Leone −9 −16.5 −20 −20 Tropical Africa −251 −594 −667 −664.5 Bangladesh −4 −11 −3 −3 Brunei Darussalam 0 −1 −2 −2 Cambodia −31 −117 −140 −145 Indonesia −653 −842 −1914 −497.5 Laos −43 −65 −78 −78 Malaysia −130 −163.5 −79 −113.5 Myanmar −83 −179.5 −435 −309.5 Papua New Guinea −60 −66.5 −139 −140.5 Philippines −75 −165.5 55 55 Sri Lanka −12 −34.5 −27 −22.5 Thailand −66 −170 −55 −3 Vietnam −60 −172 236 207 Tropical Asia −1218 −1988 −2581 −1052.5 Pan‐Tropics −4040 −6535 −7458 −5604

Among the continents, Tropical Latin America showed the largest acceleration of annual net forest area loss from the 1990s to the 2000s. The trend was dominated by Brazil, where net forest area loss accelerated by 33%. Tropical Asia showed the second largest acceleration of net loss from the 1990s to the 2000s (Table 3), with similar trends across the individual countries of Indonesia, Malaysia, Cambodia, Thailand, and the Philippines. Tropical Africa showed the least amount of annual net forest area loss, whereas it showed the largest increasing rate. The steady increase of net forest loss in this area is mainly dominated by Democratic Republic of Congo and Madagascar.

Figure 1 depicts the acceleration or deceleration of annual net forest area change from the 1990s to the 2000s as a percentage of each country's land area. Overall, this shows an acceleration of forest loss from the 1990s to the 2000s, which was due to the imbalance of strong acceleration in forest loss and small acceleration in forest gains (Table 4).

Figure 1 Open in figure viewer PowerPoint Acceleration and deceleration of net forest loss for the humid tropics between 1990–2000 and 2000–2010 periods. The values represent the difference in annual net forest area loss between the periods as a percent of land area.

Table 4. Forest Loss and Gain (1000 ha yr−1) by Countries for 1990–2000, 2000–2005, and 2005–2010 Country 1990–2000 Loss 1990–2000 Gain 2000–2005 Loss 2000–2005 Gain 2005–2010 Loss 2005–2010 Gain Bangladesh 6.80 2.58 8.20 2.07 22.16 6.01 Belize 7.94 0.37 9.24 2.52 9.11 2.34 Bolivia 214.86 23.99 304.27 45.43 380.60 42.24 Brazil 2191.39 255.59 3001.27 281.01 2787.45 365.45 Brunei Darussalam 1.04 1.05 1.29 0.19 0.99 0.40 Cambodia 38.33 7.32 141.71 10.33 107.17 4.34 Cameroon 20.66 9.80 37.91 4.09 48.02 15.42 Colombia 170.41 40.88 324.68 44.08 498.87 54.00 Congo 26.10 3.71 41.34 20.41 39.88 16.09 Costa Rica 10.81 1.39 24.81 3.10 26.19 6.41 Democratic Republic Congo 227.97 124.40 388.50 47.21 600.46 88.35 Ecuador 31.24 4.53 70.58 18.84 55.06 41.45 Equatorial Guinea 3.10 0.45 2.29 2.30 5.37 1.11 Gabon 49.53 3.39 39.88 29.73 13.07 36.86 Guatemala 43.68 4.89 47.54 2.20 59.10 6.48 Guyana 18.23 2.04 18.16 10.52 14.03 9.23 Honduras 26.95 1.65 41.96 3.41 61.19 7.14 Indonesia 789.11 135.99 1384.35 190.19 808.61 319.47 Laos 61.24 18.06 88.92 18.80 95.09 34.97 Liberia 19.13 0.17 3.35 0.62 4.64 0.43 Madagascar 54.48 16.72 88.60 19.83 141.28 16.77 Malaysia 174.84 44.35 304.51 36.02 144.40 85.40 Myanmar 127.61 44.19 213.81 48.08 254.41 61.24 Nicaragua 31.04 1.68 102.80 19.30 142.84 12.30 Panama 16.83 1.61 46.49 4.13 54.66 9.39 Papua New Guinea 64.98 4.73 149.89 31.39 64.96 49.89 Peru 67.08 15.44 133.18 23.82 84.69 43.99 Philippines 87.57 12.59 224.85 16.26 169.48 47.44 Sierra Leone 9.55 0.83 10.17 0.59 25.44 2.07 Sri Lanka 13.97 2.31 30.73 2.15 49.72 9.60 Suriname 6.41 1.21 16.11 2.90 8.53 6.73 Thailand 90.78 25.26 244.44 19.08 151.81 37.17 Venezuela 109.59 20.79 288.52 43.94 461.29 53.38 Vietnam 113.03 52.67 183.37 34.76 234.62 39.29 Total 4926.27 886.62 8017.75 1039.31 7625.20 1532.85

Separate estimates of forest cover change statistics for 2000–2005 and 2005–2010 reveal a small deceleration of 7.5% (0.9 × 106 ha yr−1) in net forest loss in the later periods, due to the imbalance between small deceleration in forest loss and accelerated forest gain. The deceleration of net forest loss between 2000–2005 and 2005–2010 was mainly driven by Brazil and Tropical Asian countries (Table 4).

These national and continental trends confirm other satellite‐based studies. Ernst et al. [2013] showed a 100% acceleration of net forest loss in the Democratic Republic Congo and an 89% acceleration in the Congo Basin from the 1990s to the 2000s, driven by increased population density, small‐scale agriculture, fuel‐wood collection, and forest accessibility. Eva et al. [2012] corroborated the trends we observed in Tropical Latin America and Brazil, showing 25% and 23% acceleration of net forest loss between the 1990s to the 2000s, changes which Defries et al. [2013] attributed to forest clearing for cattle pasture and soybean cultivation. Stibig et al. [2014] showed a 124% acceleration in forest loss in continental Southeast Asia in the 1990–2000 period. Rapid growth of agribusinesses (cattle ranching, soybean farming, and plantation agriculture) after decline of smallholder farmer‐driven deforestation has been identified as a major driver of acceleration of net deforestation in this area [Rudel et al., 2009]. Our post‐2000, national estimates of forest change were significantly correlated with those of Hansen et al. [2013] (r2 > 0.95), who also found an overall acceleration of tropical forest loss after 2000, with an exception of Brazil. The Brazilian exception was explained by enforcement of policy, interventions in soy and beef supply chains, and expansion of protected areas [Nepstad et al., 2014]. Accelerated annual loss in Tropical Africa and Asia we observed was also identified by Hansen et al. [2013]. Our estimates complement sample‐based estimates for the 1990s [e.g., Ernst et al., 2013; Eva et al., 2012; Stibig et al., 2014; Achard et al., 2014] and the estimates limited to the post‐2000 period [e.g., Hansen et al., 2013].

Table 5 shows the difference between satellite‐based estimates of forest change in each time period from studies at tropical biome level. Estimates of forest change differ among satellite‐based studies. The major sources of difference include differences in the definition of forest, resolution of input data, classification accuracy, and sensitivity of algorithms to detect change. Sample‐based estimates vary widely, especially in estimating differences in rates of change over time. Due to similarities in spatial and temporal scales, Hansen et al. [2013] provide the only estimates directly comparable to ours. Our estimates for the 34 countries show strong correlation to those of Hansen et al. [2013], but ours are consistently higher (Table S2 in the supporting information) due in large part to different sensitivities to forest gain.

Table 5. Recent Satellite‐Based Estimates of Biome‐Level Forest Change (1000 ha yr−1) in the 1990s and 2000s Area 1990s 2000s ΔRate Method Data FAO, JRC [ 2012 Tropics −5648 −9111 61.3% Sampling Landsat FAO, JRC [2014] Tropics −6000 −7000 16.7% Sampling Landsat Achard et al. [ 2002 Humid tropics −5800 ‐ Sampling Advanced very high resolution radiometer (AVHRR) Achard et al. [ 2014 Tropics −6050 −5930 −2% Sampling Landsat Humid tropics −3960 −3170 −20% Sampling Landsat Defries et al. [ 2002 Tropics −5563 ‐ ‐ Wall‐to‐wall AVHRR Hansen et al. [ 2008 2010 Humid tropics ‐ −5400 (gross loss) ‐ Sampling Landsat Hansen et al. [ 2013 Tropics ‐ −7100 ‐ Wall‐to‐wall Landsat Humid tropics (34 countries) ‐ −5500 Wall‐to‐wall Landsat This study Humid tropics (34 countries) −4040 −6535 61.8% Wall‐to‐wall Landsat

Large differences are evident between the FRA 2010 report and our estimates of forest area and change. Our long‐term results contradict the FAO [2010] report of a 25% reduction in the rate of forest loss. Also contrary to our results, 16 out of 34 countries in the FRA main report were estimated to have a constant net rate of forest change through the 1990–2000 and 2000–2010 periods [FAO, 2010]. The discrepancies are likely due to differences in survey methods and definition of forest. The FRA 2010 reports forest area defined by “forest use,” and it compiles country‐level estimates from national reports, which have been criticized for inaccuracy and inconsistency [Mayaux et al., 1998; Defries et al., 2002; Hansen et al., 2008; Grainger, 2008; Hansen et al., 2013; Achard et al., 2014]. The difference is likely partly due to changes in the area of commodity forest plantations, which are included in most current satellite estimates as forest cover26 but are variably reported as “forest” in the FRA report [FAO, 2010]. Errors from backward and forward projection based on previous FRA reports may also contribute to overestimated net forest loss for the 1990s, thus resulted in muting the effect of acceleration of forest loss during the 2000s [Grainger, 2008]. The difference might arise partly from a statistical bias from the satellite data gaps from clouds, especially for countries such as Indonesia (gap~30%). This may be resolved as other satellite images become available.

These findings highlight the importance of a consistent definition and method to track forest area changes. Our findings provide a consistent, spatially explicit basis for the inference of the drivers of forest cover change in various geographical and socioeconomical contexts, especially where the relationship between long‐term trends in forest cover change and its drivers are hindered by inaccurate estimates of forest cover change resulting from semantic and methodological inconsistencies.