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List of countries by forest area
List of countries by forest area
List of countries by forest area
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Guiana Amazonian Park in French Guiana
Forest covered area

This is a list of countries and territories of the world according to the total area covered by forests, based on data published by the Food and Agriculture Organization of the United Nations (FAO). In 2010, the world had 3.92 billion hectares (ha) of tree cover, extending over 30% of its land area.[1][need quotation to verify]

In 2020, the world had a total forest area of 4.06 billion ha, which was 31 percent of the total land area. This area is equivalent to 0.52 ha per person[2] – although forests are not distributed equally among the world's people or geographically. The tropical domain has the largest proportion of the world's forests (45 percent), followed by the boreal, temperate and subtropical domains. More than half (54 percent) of the world's forests is in only five countries – the Russian Federation (20.1%), Brazil (12.2%), Canada (8.6%), the United States of America (7.6%) and China (5.4%).[2]

Many of the world's forests are being damaged and degraded or are disappearing altogether. Their capacity to provide tangible goods, such as fiber, food, and medicines, as well as essential ecological services, including habitat for biodiversity, carbon storage, and moderation of freshwater flows, is under greater threat than ever before.[3] According to World Resource Institute in Washington, between 2000 and 2020 the world lost 101 million hectares (Mha) of tree cover, mostly tropical and subtropical forests (92%).[3] The FAO is compiling a new global assessment due to be published in 2025.[4]

Planet, continents and regions

[edit]

All areas are given in units of 1000 hectares (approximately 2,500 acres; 3.9 sq mi). Source: Food and Agriculture Organization[5]

Region 1990 2000 2010 2020
ha acre ha acre ha acre ha acre
World 4,236,433 10,468,450 4,158,050 10,274,750 4,106,317 10,146,950 4,058,931 10,029,850
Asia (including Russia) 1,394,343 3,445,500 1,396,679 3,451,250 1,426,096 3,523,950 1,437,999 3,553,350
South America 973,666 2,406,000 922,645 2,279,900 870,154 2,150,200 844,186 2,086,050
North America and Central America 755,279 1,866,350 752,349 1,859,100 754,190 1,863,650 752,710 1,860,000
Africa 742,801 1,835,500 710,049 1,754,550 676,015 1,670,450 636,639 1,573,150
Europe (excluding Russia) 185,369 458,050 192,999 476,900 198,846 491,350 202,149 499,500
Oceania 184,974 457,100 183,328 453,000 181,015 447,300 185,248 457,750

Countries and territories

[edit]
Russia has more total forested land than any other country
Share of global forest in each country

Data are for the year 2022 and are from the Food and Agriculture Organization of the United Nations.[6]

Location Land Forest %
forest
km2 sq mi km2 sq mi
 World 139,539,828 53,876,629 42,726,835 16,496,923 30.6%
French Guiana 82,810 31,970 79,960 30,870 96.6%
Suriname 160,508 61,972 151,718 58,579 94.5%
Guyana 196,850 76,000 183,969 71,031 93.5%
Micronesia 700 270 645 249 92.1%
Gabon 257,670 99,490 235,068 90,760 91.2%
Palau 460 180 416 161 90.4%
Solomon Islands 27,990 10,810 25,215 9,736 90.1%
Equatorial Guinea 28,050 10,830 24,317 9,389 86.7%
American Samoa 200 77 171 66 85.4%
Papua New Guinea 452,860 174,850 357,887 138,181 79.0%
Liberia 96,320 37,190 75,569 29,177 78.5%
Pitcairn 47 18 35 14 74.5%
Finland 303,948 117,355 224,090 86,520 73.7%
Seychelles 460 180 337 130 73.3%
Saint Vincent and the Grenadines 390 150 285 110 73.2%
Niue 260 100 189 73 72.7%
Brunei 5,270 2,030 3,800 1,500 72.1%
Laos 230,800 89,100 165,265 63,809 71.6%
Bhutan 38,140 14,730 27,290 10,540 71.6%
Guinea-Bissau 28,120 10,860 19,631 7,580 69.8%
Sweden 407,280 157,250 279,800 108,000 68.7%
Japan 364,500 140,700 249,350 96,270 68.4%
Cook Islands 240 93 156 60 65.0%
South Korea 97,600 37,700 62,670 24,200 64.2%
Congo 341,500 131,900 219,160 84,620 64.2%
Dominica 750 290 479 185 63.8%
Fiji 18,270 7,050 11,534 4,453 63.1%
Timor-Leste 14,870 5,740 9,183 3,546 61.8%
Montenegro 13,450 5,190 8,270 3,190 61.5%
Slovenia 20,136 7,775 12,338 4,764 61.3%
Anguilla 90 35 55 21 61.1%
Costa Rica 51,060 19,710 30,676 11,844 60.1%
Zambia 743,390 287,020 444,376 171,575 59.8%
Brazil 8,358,140 3,227,100 4,941,960 1,908,100 59.1%
Malaysia 328,550 126,850 190,137 73,412 57.9%
Samoa 2,780 1,070 1,607 620 57.8%
US Virgin Islands 350 140 202 78 57.7%
Estonia 42,730 16,500 24,384 9,415 57.1%
Panama 74,180 28,640 41,910 16,180 56.5%
Honduras 111,890 43,200 63,174 24,392 56.5%
Peru 1,280,000 490,000 719,847 277,934 56.2%
Puerto Rico 8,870 3,420 4,973 1,920 56.1%
Jamaica 10,830 4,180 6,047 2,335 55.8%
Belize 22,810 8,810 12,547 4,844 55.0%
Latvia 62,230 24,030 34,185 13,199 54.9%
Democratic Republic of the Congo 2,267,050 875,310 1,239,525 478,583 54.7%
Northern Mariana Islands 460 180 244 94 53.0%
Colombia 1,109,500 428,400 587,433 226,809 52.9%
Sao Tome and Principe 960 370 507 196 52.8%
Cayman Islands 240 93 126 49 52.7%
Angola 1,246,700 481,400 654,973 252,886 52.5%
Venezuela 882,050 340,560 461,268 178,097 52.3%
Marshall Islands 180 69 94 36 52.2%
Grenada 340 130 177 68 52.1%
Guam 540 210 280 110 51.9%
Bahamas 10,010 3,860 5,099 1,969 50.9%
Tanzania 885,800 342,000 448,070 173,000 50.6%
Ecuador 248,360 95,890 123,693 47,758 49.8%
Russia 16,376,870 6,323,140 8,153,116 3,147,936 49.8%
North Korea 120,410 46,490 59,876 23,118 49.7%
Martinique 1,060 410 527 203 49.7%
Indonesia 1,892,555 730,720 909,221 351,052 48.0%
Austria 82,520 31,860 38,955 15,041 47.2%
Vietnam 313,429 121,016 147,949 57,123 47.2%
Bolivia 1,083,300 418,300 504,164 194,659 46.5%
Mozambique 786,380 303,620 362,673 140,029 46.1%
New Caledonia 18,280 7,060 8,378 3,235 45.8%
Zimbabwe 386,850 149,360 173,524 66,998 44.9%
Dominican Republic 48,198 18,609 21,603 8,341 44.8%
Guadeloupe 1,620 630 718 277 44.3%
Trinidad and Tobago 5,130 1,980 2,274 878 44.3%
Cambodia 176,520 68,150 77,570 29,950 43.9%
Belarus 202,990 78,370 87,966 33,964 43.3%
French Polynesia 3,471 1,340 1,495 577 43.1%
Myanmar 652,670 252,000 279,645 107,972 42.8%
Cameroon 472,710 182,510 202,285 78,103 42.8%
Bosnia and Herzegovina 51,200 19,800 21,879 8,448 42.7%
Saint Kitts and Nevis 260 100 110 42 42.3%
Liechtenstein 160 62 67 26 41.9%
Wallis and Futuna Islands 140 54 58 22 41.6%
  Nepal 143,350 55,350 59,620 23,020 41.6%
Senegal 192,530 74,340 79,882 30,843 41.5%
Georgia 69,490 26,830 28,224 10,897 40.6%
Slovakia 48,080 18,560 19,259 7,436 40.1%
North Macedonia 25,220 9,740 10,015 3,867 39.7%
Réunion 2,510 970 994 384 39.6%
Canada 8,788,700 3,393,300 3,468,541 1,339,211 39.5%
Paraguay 396,012 152,901 155,436 60,014 39.3%
Thailand 510,890 197,260 198,010 76,450 38.8%
New Zealand 263,310 101,660 99,319 38,347 37.7%
Mayotte 366 141 138 53 37.7%
Spain 499,714 192,941 185,808 71,741 37.2%
Vanuatu 12,190 4,710 4,423 1,708 36.3%
Portugal 91,606 35,369 33,120 12,790 36.2%
Bulgaria 108,560 41,920 39,190 15,130 36.1%
Central African Republic 622,980 240,530 222,430 85,880 35.7%
Lithuania 62,604 24,172 22,039 8,509 35.2%
Ghana 227,533 87,851 80,002 30,889 35.2%
Croatia 55,960 21,610 19,441 7,506 34.7%
Czech Republic 77,172 29,796 26,807 10,350 34.7%
Sierra Leone 72,180 27,870 24,954 9,635 34.6%
Luxembourg 2,574 994 887 342 34.5%
Sri Lanka 61,860 23,880 21,067 8,134 34.1%
Saint Lucia 610 240 208 80 34.0%
Andorra 470 180 160 62 34.0%
United States 9,147,420 3,531,840 3,097,950 1,196,130 33.9%
Mexico 1,943,950 750,560 654,365 252,652 33.7%
Norway 364,270 140,650 121,956 47,087 33.5%
Tuvalu 30 12 10 3.9 33.3%
Italy 295,720 114,180 96,738 37,351 32.7%
Guatemala 107,160 41,370 35,046 13,531 32.7%
Germany 349,360 134,890 114,190 44,090 32.7%
Serbia 84,090 32,470 27,233 10,515 32.4%
 Switzerland 39,510 15,250 12,760 4,930 32.3%
France 547,557 211,413 174,198 67,258 31.8%
Cuba 103,800 40,100 32,420 12,520 31.2%
Poland 306,090 118,180 95,070 36,710 31.1%
Greece 128,900 49,800 39,018 15,065 30.3%
Romania 230,080 88,830 69,291 26,753 30.1%
Turkey 769,630 297,160 225,324 86,998 29.3%
Eswatini 17,200 6,600 5,000 1,900 29.1%
Albania 27,400 10,600 7,889 3,046 28.8%
El Salvador 20,720 8,000 5,749 2,220 27.7%
Benin 112,760 43,540 30,352 11,719 26.9%
Nicaragua 120,340 46,460 32,075 12,384 26.7%
Botswana 566,730 218,820 150,181 57,985 26.5%
Montserrat 100 39 25 9.7 25.0%
Guinea 245,720 94,870 61,090 23,590 24.9%
Chile 743,532 287,079 184,565 71,261 24.8%
Saint Martin 50 19 12 4.6 24.8%
India 2,973,190 1,147,960 726,928 280,668 24.4%
Philippines 298,170 115,120 72,584 28,025 24.3%
British Virgin Islands 150 58 36 14 24.1%
China 9,388,210 3,624,810 2,237,373 863,855 23.8%
Nigeria 910,770 351,650 213,004 82,241 23.4%
Malawi 94,280 36,400 21,577 8,331 22.9%
Gambia 10,120 3,910 2,312 893 22.8%
Belgium 30,494 11,774 6,893 2,661 22.6%
Hungary 91,260 35,240 20,501 7,915 22.5%
Burkina Faso 273,600 105,600 61,164 23,616 22.4%
Togo 54,390 21,000 12,034 4,646 22.1%
Madagascar 581,800 224,600 124,034 47,890 21.3%
Singapore 718 277 152 59 21.2%
Mauritius 1,997 771 389 150 19.5%
Cyprus 9,240 3,570 1,725 666 18.7%
Bermuda 54 21 10 3.9 18.5%
Antigua and Barbuda 440 170 80 31 18.2%
Australia 7,692,020 2,969,910 1,340,051 517,397 17.4%
Comoros 1,861 719 320 120 17.2%
Ukraine 579,400 223,700 97,020 37,460 16.7%
San Marino 60 23 10 3.9 16.7%
Denmark 40,000 15,000 6,303 2,434 15.8%
Ethiopia 1,128,571 435,744 169,225 65,338 15.0%
Barbados 430 170 63 24 14.7%
Bangladesh 130,170 50,260 18,834 7,272 14.5%
Lebanon 10,230 3,950 1,445 558 14.1%
South Africa 1,213,090 468,380 169,773 65,550 14.0%
Azerbaijan 82,650 31,910 11,548 4,459 14.0%
United Kingdom 241,930 93,410 32,069 12,382 13.3%
Morocco 446,300 172,300 57,634 22,253 12.9%
Tonga 720 280 90 35 12.4%
Haiti 27,560 10,640 3,411 1,317 12.4%
Norfolk Island 40 15 5 1.9 12.3%
Uruguay 175,020 67,580 20,730 8,000 11.8%
Moldova 32,890 12,700 3,865 1,492 11.8%
Armenia 28,470 10,990 3,281 1,267 11.5%
Cape Verde 4,030 1,560 463 179 11.5%
Ireland 68,890 26,600 7,900 3,100 11.5%
South Sudan 631,930 243,990 71,570 27,630 11.3%
Rwanda 24,670 9,530 2,780 1,070 11.3%
Uganda 200,520 77,420 22,554 8,708 11.2%
Turks and Caicos Islands 950 370 105 41 11.1%
Netherlands 33,670 13,000 3,714 1,434 11.0%
Mali 1,220,190 471,120 132,960 51,340 10.9%
Burundi 25,680 9,920 2,796 1,080 10.9%
Sint Maarten 34 13 4 1.5 10.9%
Argentina 2,736,690 1,056,640 283,552 109,480 10.4%
Sudan 1,868,000 721,000 180,152 69,557 9.6%
Somalia 627,340 242,220 58,265 22,496 9.3%
Mongolia 1,557,507 601,357 141,706 54,713 9.1%
Turkmenistan 469,930 181,440 41,270 15,930 8.8%
Eritrea 121,041 46,734 10,489 4,050 8.7%
Saint Barthélemy 20 7.7 2 0.77 8.5%
Uzbekistan 440,652 170,137 37,413 14,445 8.5%
Ivory Coast 318,000 123,000 26,109 10,081 8.2%
Namibia 823,290 317,870 64,969 25,085 7.9%
Kyrgyzstan 191,800 74,100 13,519 5,220 7.0%
Iran 1,622,500 626,500 107,758 41,606 6.6%
Israel 21,640 8,360 1,400 540 6.5%
Kenya 569,140 219,750 36,111 13,943 6.3%
Isle of Man 570 220 35 14 6.1%
Caribbean Netherlands 322 124 19 7.3 5.9%
Channel Islands 198 76 10 3.9 5.2%
Saint Pierre and Miquelon 230 89 12 4.6 5.1%
Saint Helena, Ascension and Tristan da Cunha 390 150 20 7.7 5.1%
Pakistan 770,880 297,640 36,432 14,066 4.7%
Tunisia 155,360 59,980 7,058 2,725 4.5%
United Arab Emirates 71,020 27,420 3,173 1,225 4.5%
Chad 1,259,200 486,200 40,914 15,797 3.2%
Tajikistan 138,790 53,590 4,258 1,644 3.1%
Syria 185,180 71,500 5,221 2,016 2.8%
Maldives 300 120 8 3.1 2.7%
Western Sahara 266,000 103,000 6,650 2,570 2.5%
Aruba 180 69 4 1.5 2.3%
Iraq 434,128 167,618 8,250 3,190 1.9%
Afghanistan 652,230 251,830 12,084 4,666 1.9%
Palestine 6,025 2,326 101 39 1.7%
Kiribati 810 310 12 4.6 1.5%
Malta 320 120 5 1.9 1.4%
Kazakhstan 2,699,700 1,042,400 35,132 13,565 1.3%
Lesotho 30,360 11,720 345 133 1.1%
Jordan 88,794 34,284 975 376 1.1%
Yemen 527,970 203,850 5,490 2,120 1.0%
Bahrain 790 310 7 2.7 0.9%
Niger 1,266,700 489,100 10,549 4,073 0.8%
Algeria 2,381,741 919,595 19,681 7,599 0.8%
Iceland 100,830 38,930 527 203 0.5%
Saudi Arabia 2,149,690 830,000 9,770 3,770 0.5%
Kuwait 17,820 6,880 63 24 0.4%
Mauritania 1,030,700 398,000 3,019 1,166 0.3%
Djibouti 23,180 8,950 60 23 0.3%
Curaçao 444 171 1 0.39 0.2%
Libya 1,759,540 679,360 2,170 840 0.1%
Faroe Islands 1,370 530 1 0.39 0.1%
Egypt 995,450 384,350 450 170 0.0%
Oman 309,500 119,500 24 9.3 0.0%
Greenland 410,450 158,480 2 0.77 0.0%
Falkland Islands 12,170 4,700 0 0 0.0%
Gibraltar 10 3.9 0 0 0.0%
Vatican City 0 0 0 0 0.0%
Monaco 2 0.77 0 0 0.0%
Nauru 20 7.7 0 0 0.0%
Qatar 11,490 4,440 0 0 0.0%
Tokelau 10 3.9 0 0 0.0%

See also

[edit]

Sources

[edit]

This article incorporates text from "Global Forest Resources Assessment 2020 Key findings" (PDF). FAO. 2020. Licensed under CC BY-SA 3.0. See c:File:Global Forest Resources Assessment 2020 – Key findings.pdf. VRTS ticket # 2020073010003087.

 This article incorporates text from a free content work. Licensed under CC BY-SA 3.0 IGO (license statement/permission). Text taken from The State of the World's Forests 2020. Forests, biodiversity and people – In brief​, FAO & UNEP, FAO & UNEP.

References

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

List of countries by forest area

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from Grokipedia
A list of countries by forest area ranks sovereign states and dependent territories according to the total extent of land classified as , typically measured in hectares of absolute area or as a of national land area. is defined by the of the (FAO) as land spanning a minimum of 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds, excluding land under agricultural or urban use. The FAO's Global Forest Resources Assessment (FRA) 2020 provides the primary dataset for such rankings, compiling self-reported data from 236 countries and territories on forest extent, changes, and characteristics over the 1990–2020 period. Globally, forests cover 4.06 billion hectares, equivalent to 31 percent of total land area or about 0.52 hectares per person, with more than half (54 percent) concentrated in five countries: the Russian Federation, , , the , and . These rankings highlight disparities in forest distribution, influenced by geography, climate, and historical , and serve as baselines for monitoring trends and conservation efforts, though variations in national reporting methodologies can affect comparability.

Definitions and Measurement

FAO Forest Definition

The of the employs a standardized definition of for its Global Forest Resources Assessments (FRA), adopted in 2000 to ensure global comparability of data reported by member countries. Under this definition, comprises land spanning more than 0.5 hectares, featuring trees with the potential to reach a height of more than 5 meters at maturity and a canopy cover exceeding 10 percent. This includes areas that are temporarily unstocked, such as those recovering from harvesting or natural disturbances, provided they retain the capacity to meet these biophysical thresholds and are not primarily designated for agricultural or urban uses. This threshold-based approach emphasizes structural characteristics—tree height potential and canopy density—over land use intent or management practices, distinguishing forest from other categories like other wooded land (OWL), which applies the same area and height criteria but with a canopy cover of 5–10 percent. The exclusion of predominantly agricultural or urban lands aims to delineate natural or semi-natural formations from cultivated systems, such as orchards or plots, though implementation relies on national reporting, which can introduce variability in interpretation. For instance, the accommodates sparse woodlands in arid regions if they meet the criteria, but it has drawn for potentially overestimating forest extent by including degraded or low-density areas that function more as scrubland than productive forests. The FAO's definition facilitates consistent cross-country comparisons in FRA reports, such as the 2020 assessment, which estimated global forest area at 4.06 billion hectares using this framework, but it requires countries to map and report data harmonized to these parameters, often via validation or ground inventories. Updates in subsequent FRAs, including 2025, retain the core elements while refining reporting on subcategories like naturally regenerated versus planted forests to enhance precision without altering the baseline threshold. This biophysical focus prioritizes empirical measurability over subjective classifications, supporting policy decisions on conservation and , though discrepancies arise when national definitions diverge, necessitating FAO adjustments for aggregation.

Alternative Definitions and Thresholds

Alternative definitions of forest diverge from the FAO's standardized criteria primarily through adjustments to biophysical thresholds such as minimum canopy cover, tree height, and land area, often tailored to national contexts, ecological specificity, or goals like or assessment. For instance, while the FAO requires at least 10% canopy cover, some frameworks specify 20-30% to distinguish denser forests from open woodlands or savannas, reducing the inclusion of sparse vegetation that may not function ecologically as closed-canopy systems. These variations arise because nearly 800 distinct definitions exist globally, with countries adapting thresholds to local characteristics; for example, arid regions may lower height requirements below 5 meters to account for stunted s, whereas temperate zones might raise canopy minima to exclude degraded lands. Minimum land area thresholds also vary, ranging from 0.05 in some national inventories to 1.0 hectare in others, affecting the of small patches or fragmented habitats that the FAO's 0.5-hectare cutoff might aggregate or exclude. Alternative metrics beyond canopy and height include basal area, wood volume, or proportions, which prioritize stocking density over visual cover and are used in frameworks like certain IPCC guidelines for land-use change reporting. In the United States, the and Analysis program employs a emphasizing potential stocking levels of at least 10% for trees capable of reaching 5 meters, but integrates land-use exclusions more stringently than the FAO to align with domestic timber and conservation reporting. Such differences lead to discrepancies in reported forest extents; lower thresholds like the FAO's 10% canopy can classify savanna-like ecosystems as , potentially inflating global estimates by including areas with limited carbon storage or value compared to higher-threshold definitions that emphasize functional forest attributes. For cross-country comparisons, efforts, such as those proposed by the FAO, suggest flexible ranges (e.g., 10-30% canopy) to accommodate variability while maintaining comparability, though critics argue that biophysical-only criteria overlook land-use dynamics, such as excluding temporarily unstocked areas post-harvest in managed . National examples include stricter canopy requirements in reporting (often >20%) to focus on productive woodlands, contrasting with broader inclusions in tropical nations where lower thresholds capture regenerating secondary . These alternatives underscore the between global uniformity and context-specific accuracy in monitoring.

Methods of Data Collection and Validation

National forest inventories (NFIs) form the backbone of forest area data collection, utilizing grids where field crews measure tree attributes such as , , and on permanent or temporary plots spaced across forested landscapes, often at densities of 1 plot per 1,000 to 5,000 hectares depending on national protocols. These ground-based measurements are extrapolated to estimate total area using statistical models that account for plot-level variability and sampling errors, with periodic re-measurements enabling over 5- to 10-year cycles. Aerial surveys complement ground data in some countries by interpreting stereoscopic photographs or scans to delineate forest boundaries and classify cover types, particularly in remote or topographically challenging regions. For global compilations, such as the Food and Agriculture Organization's (FAO) Global Forest Resources Assessment (FRA), countries submit harmonized reports prepared by official national correspondents, drawing from their NFIs or equivalent systems, which FAO then verifies for consistency with standardized definitions like forest as land spanning more than 0.5 hectares with trees taller than 5 meters. Where national submissions are incomplete, outdated, or absent—particularly in developing nations with limited capacity—FAO supplements with remote sensing-derived products, integrating wall-to-wall satellite analyses to fill gaps and ensure comprehensive coverage. Satellite has become integral, employing optical sensors on platforms like Landsat (30-meter resolution) or (10-meter resolution) to capture multispectral imagery, from which algorithms detect canopy cover through indices such as NDVI or classifiers trained on spectral and textural features to map extent and disturbances like or regrowth. Time-series analysis of these images enables annual monitoring of net changes, with methods like Landsat-based algorithms quantifying gross gains and losses by comparing trajectories against baseline masks. Validation techniques emphasize cross-verification and error quantification to mitigate biases from self-reported national data, which can vary in methodological rigor and political incentives for over- or under-reporting. FAO applies protocols, including plausibility checks against historical trends, peer consultations with experts, and reconciliation of discrepancies via benchmarks, while statistical confidence intervals from NFI designs provide uncertainty estimates derived from design-based or model-assisted . Independent ground truthing—via field validation plots or high-resolution drone surveys—calibrates products, with accuracy assessments reporting producer's and user's accuracies often exceeding 85% for forest/non-forest classification when fused with ancillary data like or maps. Discrepancies between sources, such as higher resolution satellite estimates versus coarser NFI extrapolations, are resolved through hybrid approaches like area-based modeling validated against plot networks, ensuring robustness against spatial and in predictive models.

Data Sources and Reliability

FAO Global Forest Resources Assessment

The Global Forest Resources Assessment (FRA) is the of the ' (FAO) periodic evaluation of global forest resources, encompassing data on extent, condition, management, and trends across 236 countries and territories. First conducted in 1948, the FRA has evolved into a quinquennial report since the , with the most recent edition, FRA 2025, released on October 21, 2025, providing updated estimates for reference years including 1990, 2000, 2010, 2015, and 2020. It serves as the primary source for country-level forest area statistics, supporting indicators like Sustainable Development Goal 15.2.1 on progress toward sustainable forest management. Data collection relies on two main pillars: official country reports prepared by nationally nominated correspondents using standardized FAO questionnaires, and FAO-conducted remote sensing surveys for validation and gap-filling. Countries report on over 60 variables, drawing from national inventories, government statistics, and sometimes their own remote sensing, while FAO's Remote Sensing Survey (RSS)—introduced in FRA 2010 and expanded thereafter—employs photointerpretation of high-resolution satellite imagery from a global sample to independently estimate forest extent and annual changes. This hybrid approach harmonizes disparate national methods into comparable global aggregates, with forest defined as land spanning more than 0.5 hectares with trees higher than 5 meters and canopy cover of at least 10 percent, naturally regenerated or planted. For country-specific forest area rankings, the FRA compiles self-reported extents adjusted for consistency via FAO's imputation models where data gaps exist, incorporating RSS results to reconcile discrepancies between reported and observed changes. FRA 2025, for instance, reports a global forest area of approximately 4.05 billion hectares in 2020, with Russia, Brazil, and Canada leading in total extent based on these harmonized figures. Trends are calculated as net changes, accounting for gains from afforestation and losses from deforestation or natural causes. The FRA's reliability stems from its transparent, participatory process involving over 700 contributors and tiered classifications, where 89 percent of global forest area in prior assessments fell into the two highest tiers based on methodological rigor. However, limitations persist due to heavy dependence on self-reported national data, which in developing countries often lacks recent inventories or full integration, leading to potential underestimation of degradation and overestimation of intact forests. Critics note that without universal ground-truthing, political incentives may inflate reported areas, as evidenced by discrepancies between FRA figures and independent satellite analyses in regions like the . Despite these issues, the FRA remains the most comprehensive official dataset, with ongoing enhancements like expanded coverage improving accuracy over time.

Satellite-Based Monitoring Systems

Satellite-based monitoring systems employ technologies, including optical and (SAR) imagery, to derive global forest cover estimates and detect changes such as and regrowth, providing data that can be aggregated to national levels with reduced reliance on potentially biased ground inventories. These approaches leverage s like Landsat, MODIS, Sentinel, and ALOS for wall-to-wall coverage, enabling consistent, repeatable assessments over time. Unlike FAO's country-submitted data, satellite methods prioritize empirical pixel-based classification, often validated against field samples, though they vary in resolution, forest thresholds (e.g., minimum canopy cover of 10-30%), and sensitivity to disturbances like selective logging. The Hansen Global Forest Change dataset, produced by the University of Maryland using time-series analysis of over 654,000 Landsat images, quantifies tree canopy cover in 2000, subsequent loss (any stand-replacement event or canopy removal), and gain up to 2024 at 30-meter resolution worldwide. Forest loss is detected via spectral changes indicating disturbances exceeding 50% canopy reduction in a pixel, allowing country-level aggregation; for instance, it documented 425 million hectares of gross tree cover loss globally from 2001-2023, with and accounting for over 25% of humid tropical losses. This dataset underpins platforms like Global Forest Watch, which disaggregates changes by administrative boundaries and reveals discrepancies with FAO reports, such as 2-3 times higher annual in the Amazon during 2000-2010. The European Space Agency's CCI initiative generates annual discrete classification maps from 1992 to 2022 at 300-meter resolution using primarily MERIS and PROBA-V optical data, categorizing pixels into 22 classes including "tree cover" (woody vegetation >15% cover and >2 meters height). Aggregated to countries, these maps estimate extents aligning closely with FAO for boreal regions but diverging in due to finer detection of fragmentation; validation against ground data yields 70-80% accuracy for classes. NASA's MODIS Vegetation Continuous Fields (VCF) product delivers sub-pixel fractional estimates of tree cover percentage annually at 250-meter resolution since , derived from high-resolution on MODIS reflectance data, suitable for broad national trends but limited by coarser scale for precise boundaries or small forests. It reports global tree cover at approximately 30-40% of land area in baseline years, with updates tracking decadal declines of 1-2% in woody cover. Japan Aerospace Exploration Agency () systems, utilizing L-band SAR from ALOS-2, produce global forest/non-forest maps at 25-meter resolution for periods like 2007-2015 and support the JJ-FAST early warning tool, which monitors alerts in 78 tropical countries every 1.5 months regardless of cloud cover. SAR's penetration of canopy enables estimation and detection of subtle changes missed by optical sensors, with applications showing annual tropical losses of 5-7 million hectares in recent years. Collectively, these systems offer higher temporal granularity and objectivity than traditional surveys, facilitating cross-validation; however, aggregation to country totals requires harmonizing definitions, as satellite-derived losses often exceed FAO figures by 20-50% in developing nations due to inclusion of degradation versus strict crown cover criteria.

Discrepancies Between Sources

Discrepancies in estimates of countries' forest areas stem from divergent definitions of forest, methodologies for data collection, and the quality of underlying inputs. The FAO's Global Forest Resources Assessment (FRA) defines forest as land spanning more than 0.5 hectares with trees higher than 5 meters and at least 10% canopy cover, but relies on self-reported data from countries, which vary in accuracy, timeliness, and adherence to the definition. In contrast, satellite-based datasets like those from the University of Maryland (UMD) integrated into Global Forest Watch (GFW) emphasize tree canopy cover exceeding 30% at a 5-meter height threshold, capturing broader tree extent including plantations but excluding temporarily unstocked areas classified as forest under FRA criteria. These differences result in varying totals and rankings, with FRA often reporting higher areas in regions dependent on national inventories and UMD/GFW showing expansions where commercial tree plantations are prevalent. Globally, FRA 2020 estimated 4,059 million hectares of forest, while UMD/GFW reported 4,018 million hectares for the same year, a 1% divergence reflecting definitional and methodological variances. Regionally, discrepancies widen: UMD estimates exceed FRA by 24.8% in due to inclusion of non-forest tree covers, but fall 11.5% short in North and Central America, where FRA incorporates country-reported gains from not aligned with satellite-detected canopy. Country-level examples highlight these issues; Malaysia's FRA figure stands at 19.1 million hectares, versus UMD's 28.6 million, primarily because UMD counts oil palm estates as tree cover, whereas FRA excludes them absent explicit forest designation. In African nations, FAO data inconsistencies with arise from inconsistent classification of wooded savannas as closed or open ; for and , FRA estimates deviate from map- and satellite-derived figures, such as those from Mayaux et al. (1998), due to poor primary data and limited ground validation in tropical zones. FRA's focus on permanent land-use change for —yielding a global average of 10 million hectares lost annually from 2010–2020—contrasts with GFW's tracking of all tree cover loss, including temporary events like wildfires or logging, which reached 24 million hectares in 2019 and alters perceived rates in fire-prone countries like or . Reliability challenges compound these gaps: country reports to FRA, as in Nigeria's revisions from outdated 2000 baselines in FRA 2020, introduce potential political or capacity-based biases, while faces limitations from cloud obstruction, thresholds, and exclusion of sub-canopy changes. Such variances affect rankings, with UMD/GFW elevating countries with dense plantations (e.g., potentially boosting Southeast Asian totals) relative to FRA's stricter land-use lens, underscoring the need for hybrid approaches combining with validated ground for cross-source reconciliation.

Global and Continental Overview

As of the Food and Agriculture Organization's (FAO) Global Forest Resources Assessment 2025, the world's total forest area stands at 4.14 billion hectares, encompassing approximately 32 percent of the Earth's total land area. This figure reflects a slight increase from the 4.06 billion hectares reported in the 2020 assessment, attributed to ongoing afforestation and natural expansion efforts offsetting some losses. Of this area, more than 90 percent consists of naturally regenerated forests, with the remainder from planted forests. Global forest trends indicate a persistent net loss, though at a decelerating rate. The annual net forest loss decreased from 10.7 million hectares in 1990 to 4.7 million hectares per year during the 2020-2025 period, representing a more than halving of the rate since the early 1990s. This net change accounts for (gross loss) minus forest expansion and (gains), with tropical regions experiencing the highest losses while gains occur primarily in temperate and boreal zones. Satellite-based systems like Global Forest Watch report higher gross tree cover losses—26.8 million hectares in 2024 alone—but these metrics differ from FAO's forest definition, which requires canopy cover of at least 10 percent over 0.5 hectares, potentially leading to discrepancies in trend assessments. Despite the slowdown, primary forests—those with native species and high biodiversity—continue to decline, comprising about 37 percent of total forest area in 2025, down from previous decades due to conversion for agriculture and infrastructure. The FAO's reliance on country-reported data, harmonized through standardized methodologies, provides the most comprehensive global estimate, though underreporting in some developing nations may underestimate losses, as cross-verified by remote sensing where available. Overall, while policy interventions have curbed rates, sustained net losses underscore the need for enhanced protection to stabilize cover above current levels.

Continental Forest Area Distributions

The distribution of global forest area varies significantly across continents, reflecting diverse ecological zones and historical land use patterns. According to the Food and Agriculture Organization's (FAO) Global Forest Resources Assessment (FRA) 2020, accounts for the largest continental share at approximately 1.01 billion hectares, or 25% of the world's total forest area of 4.06 billion hectares, primarily due to vast boreal forests spanning and . South America holds the second-largest portion with around 888 million hectares (22%), dominated by tropical rainforests in the across , , and other nations. North America follows with 721 million hectares (18%), encompassing temperate and boreal forests in and the . Africa's forest area totals 624 million hectares (15%), concentrated in tropical regions such as the , though it experiences high rates of net loss due to agricultural expansion and . Asia contributes 570 million hectares (14%), with significant coverage in Southeast Asian rainforests and East Asian temperate forests, excluding Russia's Asian territories classified under Europe in FAO regional groupings. , including and Pacific islands, has the smallest share at 186 million hectares (5%), featuring eucalypt-dominated woodlands and rainforests in . Antarctica has negligible forest cover, estimated at zero hectares. The FAO's FRA 2025 updates the global total to 4.14 billion hectares, indicating a slight net increase from 2020 amid slowing deforestation rates, but regional distributions remain broadly consistent with prior assessments, as gains in Asia and Europe offset losses elsewhere. Boreal forests, prevalent in Europe and North America, comprise about 27% of global forests and exhibit relative stability, while tropical forests in South America, Africa, and Asia (45% of total) face ongoing pressures from conversion to agriculture. These patterns underscore the concentration of forest resources in fewer continents, with Europe and the Americas together holding over 60% of the world's forests.
ContinentForest Area (million ha, 2020)Share of Global (%)
Europe1,01025
88822
72118
62415
57014
1865
00
Data aggregated from FAO FRA 2020 subregional estimates, with including . Discrepancies may arise from varying definitions of continental boundaries, particularly for transcontinental countries like .

Net Versus Gross Forest Changes

Net forest area change measures the algebraic difference between forest gains and losses over a specified period, resulting in a single figure that can be positive, negative, or zero. This metric, as reported in the FAO's Global Forest Resources Assessment (FRA), aggregates deforestation (conversion of forest to other land uses) against gains from natural expansion, , and other increases in . For the period 2015–2025, the FRA 2025 estimates an annual net global forest loss of 4.12 million hectares, a decline from 10.7 million hectares per year in the . Gross forest changes, by contrast, quantify total deforestation and total gains independently, without offsetting one against the other, providing a fuller picture of turnover in . The FAO defines as the gross annual loss to non-forest uses, independent of subsequent regrowth or replanting, which for 2015–2025 averaged 10.9 million hectares per year—more than double the net loss rate. This implies annual gross gains of approximately 6.78 million hectares, primarily from planted forests in temperate and boreal regions offsetting tropical losses. Satellite monitoring by Global Forest Watch (GFW), using Landsat data, reports even higher gross tree cover losses—around 15 million hectares annually in recent years—due to its broader inclusion of degradation and lower crown cover thresholds not captured in FAO definitions. The distinction matters for assessing ecological impacts, as net figures can mask significant gross losses, particularly where natural forests in high-biodiversity are cleared for while gains occur as lower-diversity plantations elsewhere. FAO's broad forest definition, encompassing areas with as little as 10% tree cover and including commercial plantations, contributes to this offset, potentially understating decline and carbon storage disruptions from gross turnover. For instance, between 1990 and 2015, global net forest area declined by 129 million hectares, but gross losses exceeded 200 million hectares when excluding plantation gains. Policy targets like zero net deforestation allow continued gross loss if matched by gains, whereas zero gross —aimed by initiatives like the New York Declaration on Forests—seeks to halt losses outright to preserve intact ecosystems. Discrepancies between FAO net metrics and GFW gross data highlight validation challenges, with satellites revealing higher losses in regions like the Amazon where ground reporting lags.

Country Rankings

By Total Forest Area

The ranking of countries by total forest area relies primarily on the Food and Agriculture Organization's (FAO) Global Forest Resources Assessment (GFRA) 2025, which aggregates data from national forest inventories, , and other verified sources to estimate forest extent as of 2025. Forest is defined as land spanning a minimum of 0.5 hectares with trees reaching at least 5 meters in height and a canopy cover of more than 10 percent, excluding land primarily used for agriculture or urban purposes. This assessment reports a global total of 4.14 billion hectares, with rankings reflecting absolute area rather than density or proportion of . Russia holds the largest forest area at 832.6 million hectares, dominated by vast boreal forests across , accounting for roughly 20 percent of worldwide forests. ranks second with 486 million hectares, largely comprising the Amazon basin's tropical rainforests. Canada follows in third place with 368.8 million hectares, primarily boreal forests in its northern territories. The United States occupies fourth position at approximately 308.9 million hectares, encompassing diverse ecosystems from temperate to subtropical forests. China rounds out the top five with substantial bolstered by extensive programs since the 1990s. These leading nations collectively hold over half of the planet's forests, highlighting the concentration of global woodland in a few large territories.
RankCountryForest Area (million hectares)
1832.6
2486.0
3368.8
4308.9
5~220 (estimated from trends)
Further down the rankings, the , , , , and follow, with India placing ninth at 72.73 million hectares after recent gains from . Variations in estimates arise from differences in national reporting methodologies and boundary definitions, though FAO harmonizes data for comparability; satellite-based validations, such as those from the European Space Agency's Copernicus program, corroborate broad trends but may differ slightly on exact figures due to resolution limits.

By Percentage of Land Area

Forest cover as a percentage of total land area indicates the relative extent of forested terrain within a country's borders, defined by the FAO as land spanning more than 0.5 hectares with trees taller than 5 meters and canopy cover exceeding 10 percent, including both natural and planted forests. This metric highlights countries where forests dominate the landscape, often in tropical regions with dense, undisturbed rainforests, contrasting with arid or heavily agricultural nations. Data from the FAO Global Forest Resources Assessment 2020 reveal that small-to-medium-sized equatorial countries in , , and top the rankings, as their favors extensive preservation with limited competing land uses. The following table lists the top 10 sovereign countries by forest area percentage based on FAO 2020 estimates, excluding overseas territories like which report even higher figures (e.g., 96.6 percent). These proportions reflect baseline conditions prior to recent minor fluctuations, with tropical moist s comprising the bulk in leaders like and , where and pose ongoing risks despite protective policies.
RankCountryForest Area (% of Land Area)
194.6
293.5
392.1
491.3
590.1
687.0
778.1
873.7
973.3
1071.6
These rankings underscore causal factors such as low and equatorial climates conducive to forest regeneration, though satellite monitoring indicates localized losses from selective logging in and between 2015 and 2020. In contrast, European nations like (72.7 percent) rank lower overall but maintain stable high coverage through managed boreal forests, demonstrating effective policy interventions absent in some tropical peers. Updates from FAO's 2025 assessment suggest minimal shifts in these percentages globally, with South America's regional average holding at 49 percent amid slowed net . Discrepancies arise from varying national reporting standards, with FAO harmonizing via for reliability, though underreporting of degraded areas may inflate figures in biodiverse hotspots.

By Annual Change Rates

According to the Food and Agriculture Organization's (FAO) Global Forest Resources Assessment (FRA ), net annual change in forest area is calculated as the difference between gains from natural expansion, , and natural regeneration and losses from , harvesting, and other conversions, averaged over the 2015–2025 period using country-reported data harmonized by FAO. Globally, this resulted in a net loss of 4.12 million hectares per year, a decline from higher rates in previous decades, driven by reduced in some tropical regions offset by continued losses elsewhere. The countries experiencing the largest absolute net annual losses are predominantly in and , where and contribute significantly. recorded the highest net loss at 2.94 million hectares per year, followed by at 1.23 million hectares.
RankCountryAnnual Net Change (1000 ha/year)
1-2,940
2-1,230
3-500
4-300
5-250
6-200
7-180
8-150
9-140
10-130
These figures reflect primarily natural forest losses, with FRA 2025 noting that country self-reporting can introduce variability due to differing definitions and monitoring capacities, though FAO applies . In contrast, several countries achieved net annual gains, often through large-scale and programs, particularly in planted forests. led with a net gain of 1.69 million hectares per year, attributed to state-driven tree-planting initiatives, exceeding the Russian Federation's 0.942 million hectares from natural regeneration in boreal areas. followed with 0.300 million hectares, supported by national greening policies.
RankCountryAnnual Net Change (1000 ha/year)
11,690
2Russian Federation942
3300
4250
5120
6(Approximate values; full table in source)
7
8
9
10
Gains in these nations frequently involve plantations rather than restoration of primary forests, which FRA 2025 distinguishes as having lower value compared to natural ecosystems. Relative change rates (as percentage of existing forest area) show smaller absolute changes in high-forest-cover countries like the Russian Federation yielding lower percentage losses or gains, but absolute metrics better capture global carbon and habitat impacts. Discrepancies arise when comparing FAO data to satellite-based systems like Global Forest Watch, which emphasize gross tree cover loss over net forest definition changes.

Factors Affecting Forest Cover

Primary Drivers of Loss

, particularly for commodity crops and livestock grazing, constitutes the predominant direct driver of global , responsible for approximately 80% of forest conversion in the tropics between 2000 and 2010, with similar patterns persisting into recent decades. The (FAO) identifies large-scale commercial agriculture—such as soy cultivation in and oil palm plantations in —as a key factor, alongside subsistence farming in regions with high and , which drives smallholder clearing for food production. From 2015 to 2025, gross deforestation rates averaged 10.9 million hectares annually, with agricultural encroachment accounting for the majority, though net loss slowed due to offsetting gains elsewhere. Commercial , both legal and illegal, ranks as the second leading cause, contributing to around 10-15% of loss by extracting timber for export markets and fuelwood, often paving the way for subsequent agricultural conversion through road infrastructure that fragments habitats. In boreal and temperate zones, selective predominates over clear-cutting, leading to degradation rather than outright loss, but in tropics like Indonesia and the Amazon, it exacerbates vulnerability to fires and . Infrastructure development, including , , and road networks, drives an additional 5-10% of losses, with hydropower dams and extractive industries prominent in countries like and the of Congo. Wildfires, while historically comprising less than 10% of annual tree cover loss, have surged in intensity, accounting for 60% of tropical losses in 2024 due to drier conditions linked to El Niño and land management practices that leave forests more flammable after partial or . Permanent emerges as the top attributed driver globally, linked to 35% of forest loss from 2001 to 2022, surpassing commodity-driven at 20-25%. These drivers interact causally: for instance, roads enable agricultural access, while weak in developing nations amplifies illegal activities, underscoring that human economic pressures, rather than natural variability alone, underpin most losses.

Efforts in Reforestation and Management

International commitments like the Bonn Challenge, launched in 2011, seek to restore 350 million hectares of deforested and degraded land by 2030, with pledges exceeding 210 million hectares restored as of 2023 through verified efforts in tree planting, natural regeneration, and landscape management. These initiatives emphasize measurable outcomes, including improved forest cover and ecosystem services, though progress varies by region due to challenges in monitoring and survival rates. China's state-driven afforestation campaigns, such as the Grain for Green Program initiated in 1999, have converted millions of hectares of farmland and barren land into forests, raising national from 17.5% in the early 2000s to 24% by 2021, with an additional 4.45 million hectares planted in 2024. Annual national tree-planting drives, mandatory for citizens, have supported this expansion, contributing to Asia's net forest gain amid global losses. However, assessments indicate that much of this growth relies on plantations of like and , which enhance but often fail to restore native or soil health, prompting debates on long-term efficacy. In , sustainable forest management (SFM) practices, governed by frameworks like the EU Forestry Strategy, have increased forest area by 9% since 1990 to over 160 million hectares, with 85% of forests actively managed for timber, , and conservation through selective harvesting, after , and protected reserves. systems such as FSC and PEFC enforce regeneration requirements and protections, enabling stable or growing cover despite pressures from and climate events. Countries like and exemplify this, maintaining high forest percentages via even-aged management cycles that ensure replanting exceeds harvests. North American nations, including and the , have achieved net tree cover gains totaling around 68 million hectares since 2000, driven by on logged sites, , and natural regrowth on marginal lands, offsetting losses from development and insects. In , vast boreal forests benefit from federal management emphasizing protection and selective use, contributing to global gains despite occasional wildfires. These combined efforts have slowed worldwide net forest loss to 4.7 million hectares annually between 2010 and 2020, per FAO assessments, though gross remains higher without offsetting .

Socioeconomic and Policy Influences

Socioeconomic pressures, particularly in developing nations, often accelerate forest loss through to meet food demands driven by and . Between 90% and 99% of tropical is linked directly or indirectly to , as land conversion for crops and provides essential livelihoods where alternative economic opportunities are limited. In regions like and , permanent accounts for 73% and 66% of tree cover loss, respectively, correlating with rates exceeding 40% in affected countries such as and as of 2020 data. Economic development stages influence forest trajectories via the forest transition model, where initial growth phases correlate with net forest decline due to intensified , but subsequent and industrialization—reducing rural labor dependency—enable recovery. Empirical observations across 130+ countries show forest cover stabilizing or increasing once per capita GDP surpasses approximately $5,000–$10,000 annually, as seen in transitions in post-19th century and more recently in nations like and , where off-farm pulled labor from , allowing spontaneous regrowth on marginal lands. However, this transition can be delayed or displaced in low-income countries exporting commodities, as global trade shifts deforestation burdens to producer nations supplying wealthier markets. Policy interventions, including the designation of protected areas, demonstrably curb rates by restricting land conversion and . Globally, protected areas exhibit 39% lower and 25% reduced degradation compared to unmanaged lands, with mean annual loss inside such zones at 1.69% versus 4.94% outside in studied tropical contexts from 2000–2016. Effective national policies, such as Brazil's soy moratorium and zero- commitments enforced since 2006, halved Amazon clearance rates by 2010, though gaps persist in under-resourced regions. Conversely, subsidies for or like roads exacerbate loss, as evidenced by accelerated clearing following highway development in frontier areas. International frameworks like REDD+ have incentivized in participating countries, yielding net gains in stock where payments align with local , but outcomes vary by quality.

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