Earth:Forest Landscape Integrity Index

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Short description: Global index of forest condition

The Forest Landscape Integrity Index (FLII) is a global, map-based indicator of forest condition that estimates the degree of anthropogenic modification of forest ecosystems.[1] Developed by an international research team led by the Wildlife Conservation Society, the index integrates spatial data on observed and inferred human pressures and loss of forest connectivity to produce a continuous score from 0 (most modified) to 10 (least modified) for each ~300 m forest pixel.[1][2]

In the study's global map for early 2019, 40.5% of forest area (about 17.4 million km2) was classified as high integrity (FLII ≥ 9.6), 33.9% (14.6 million km2) as medium integrity and 25.6% (11 million km2) as low integrity (FLII ≤ 6.0).[1] High-integrity forests were concentrated in boreal regions of Russia and Canada and in large tropical forest blocks such as the Amazon, Central Africa and New Guinea.[1]

FLII has been used in forest-condition monitoring and referenced in policy and research contexts, including discussions of ecosystem integrity indicators under the Kunming-Montreal Global Biodiversity Framework.[3][4]

Forest integrity

In ecology, ecological integrity (sometimes ecosystem integrity) refers to the extent to which an ecosystem's structure, species composition and ecological processes fall within their natural range of variation.[5] In forests, integrity is often discussed alongside, but distinct from, deforestation (area loss) and forest degradation (declines in condition or function without complete land-cover conversion).[1]

The FLII operationalizes forest integrity as the inverse of cumulative human modification at the landscape scale, combining mapped pressures, modeled indirect effects and changes in connectivity to generate a globally consistent, continuous integrity score.[1]

Methodology

Grantham et al. calculated FLII by integrating four main spatial inputs—forest extent, observed pressures, inferred pressures and loss of forest connectivity—with processing carried out in Google Earth Engine.[1]

Forest extent and temporal baseline

The forest extent layer was designed to represent forest at the start of 2019. Forest was defined as woody vegetation taller than 5 m with at least 20% canopy cover. To map extent, the authors used global tree-cover estimates for 2000 and subtracted mapped tree-cover loss from 2001–2018 (excluding temporary canopy loss).[1]

Observed and inferred pressures

Observed pressures represent human activities that can be mapped directly at global scale (e.g. built infrastructure, agriculture and recent deforestation). Inferred pressures represent additional impacts that tend to occur around observed pressures but are harder to map directly (e.g. access-related extraction such as selective logging, fuelwood collection and hunting), modeled as a decay function of proximity to observed pressure sources and access networks.[1]

Connectivity loss

The connectivity component estimates reductions in forest connectivity caused by forest loss and fragmentation, capturing the influence of surrounding forest cover on a given pixel's integrity.[1]

Scores and classes

The continuous FLII ranges from 0 to 10. For communication and reporting, the authors also provided an illustrative three-class map: low (≤6.0), medium (>6.0 and <9.6) and high (≥9.6), with thresholds benchmarked against reference locations of known ecological integrity.[1] The study noted the approach could be updated as new global datasets become available, including potentially on an annual basis.[1]

Global results

In the 2019 map, the authors estimated that 91.2% of the world's remaining forests were affected by some degree of human pressure, and 31.2% experienced observed pressures.[1] They reported a global mean FLII score of 7.76; 18 countries had mean scores greater than 9, and no biome or biogeographic realm contained more than half of its forest area in the high-integrity class.[1][3]

High-integrity forests were disproportionately concentrated in a small number of countries (with Russia and Canada together containing about half of the global high-integrity forest area).[1] Only about 27% of high-integrity forest area fell within nationally designated protected areas, while 56% of forests within protected areas were classed as high integrity.[1]

Country rankings

172 countries have been ranked: [6]

Forest Landscape Integrity 2019
Country Mean FLII Low integrity (km2) Medium integrity (km2) High integrity (km2) Total forest area (km2) Map
 Seychelles 10 0 0 68 68
 Sudan 9.8 1 72 495 569
 Guyana 9.58 4,162 40,817 147,413 192,391
 South Sudan 9.45 5,083 59,389 146,218 210,691
 Suriname 9.39 6,796 25,031 107,954 139,781
 Mongolia 9.36 520 11,915 27,407 39,841
 Central African Republic 9.28 30,161 139,350 379,097 548,608
 Botswana 9.13 13 187 372 572
 Gabon 9.07 11,780 118,348 120,852 250,979
 Russia 9.02 739,484 2,245,281 5,137,079 8,121,843
 Canada 8.99 480,206 1,027,386 2,968,268 4,475,860
 Congo 8.89 24,512 124,215 158,184 306,911
 Kyrgyzstan 8.86 329 2,819 2,761 5,909
 Peru 8.86 85,793 190,547 509,720 786,061
 Afghanistan 8.85 90 1,475 977 2,542
 Bhutan 8.85 1,620 16,769 10,140 28,529
 Papua New Guinea 8.84 37,294 183,415 216,355 437,064
 Vanuatu 8.82 734 5,322 4,448 10,504
 Venezuela 8.78 64,650 170,792 351,112 586,554
 Tajikistan 8.65 34 137 130 301
 Bolivia 8.47 78,745 280,532 272,007 631,284
 Namibia 8.43 5 13 17 36
 Angola 8.35 105,487 284,054 315,895 705,436
 Fiji 8.35 1,753 10,802 3,594 16,148
 Colombia 8.26 150,737 272,442 428,320 851,499
 Kazakhstan 8.23 6,068 18,926 15,294 40,288
 Palau 8.09 45 333 9 387
 North Korea 8.02 8,374 40,156 8,410 56,939
 Cameroon 8 66,191 181,336 119,263 366,789
 Equatorial Guinea 7.99 3,982 17,595 5,007 26,585
 Georgia 7.79 6,982 17,803 9,784 34,570
 Brunei Darussalam 7.71 1,102 2,842 1,498 5,442
 Comoros 7.69 284 1,149 82 1,515
 Iran 7.67 3,361 12,930 2,162 18,453
 Ecuador 7.66 48,822 77,585 73,492 199,900
Template:Country data DRC 7.56 533,118 935,508 727,983 2,196,608
 Micronesia 7.55 8 35 0 43
 Brazil 7.52 1,374,902 1,354,961 2,338,101 5,067,963
 Zambia 7.5 96,969 164,376 110,822 372,167
 North Macedonia 7.42 2,034 7,090 459 9,583
 Pakistan 7.42 2,090 7,859 1,139 11,088
 Lesotho 7.4 1 4 0 5
 Chile 7.37 56,849 41,971 93,537 192,357
 Bahamas 7.35 741 1,935 399 3,075
   Nepal 7.23 13,785 41,992 3,760 59,538
 Australia 7.22 117,672 239,624 103,852 461,148
 Argentina 7.21 98,249 189,966 72,557 360,772
 Solomon Islands 7.19 6,871 15,310 3,149 25,329
 Myanmar 7.18 129,745 220,188 96,924 446,857
 Ethiopia 7.16 52,652 84,430 44,397 181,479
 Mali 7.16 451 996 140 1,586
 Somalia 7.16 347 1,384 46 1,777
 China 7.14 533,800 974,431 301,051 1,809,282
 Tanzania 7.13 123,997 159,712 122,812 406,521
 New Zealand 7.12 34,503 44,155 35,334 113,992
 Senegal 7.11 847 2,456 162 3,465
 Timor-Leste 7.11 1,783 7,008 47 8,838
 India 7.09 117,992 254,792 54,428 427,211
 Cyprus 7.06 388 1,026 18 1,432
 Norway 6.98 39,343 67,383 16,627 123,352
 Saint Vincent and the Grenadines 6.95 91 221 0 312
 Mozambique 6.93 150,665 189,362 115,379 455,406
 Mexico 6.82 193,908 280,445 121,842 596,195
 Albania 6.77 2,426 5,256 122 7,805
 Uzbekistan 6.77 214 227 199 640
 Morocco 6.74 2,260 4,076 451 6,787
 United States 6.65 1,328,079 1,144,693 658,645 3,131,417
 Sao Tome and Principe 6.64 31 140 0 171
 Trinidad and Tobago 6.62 1,478 2,176 418 4,072
 Greece 6.6 14,548 27,833 1,078 43,459
 Indonesia 6.6 535,370 509,018 431,973 1,476,361
 Azerbaijan 6.55 4,820 7,189 1,534 13,543
 Montenegro 6.41 2,949 4,778 82 7,809
 Paraguay 6.39 78,538 102,626 29,877 211,041
 Turkey 6.39 43,043 68,243 3,516 114,801
 Taiwan 6.38 8,786 14,547 1,453 24,786
 Cabo Verde 6.37 27 38 0 65
 Panama 6.37 25,420 21,310 14,605 61,336
 Cambodia 6.31 30,143 31,939 16,349 78,431
 Turkmenistan 6.31 5 33 0 37
 Zimbabwe 6.31 9,450 14,417 1,644 25,511
 Nigeria 6.2 64,621 65,355 24,307 154,283
 Chad 6.18 5,261 6,016 1,910 13,187
 Saint Lucia 6.17 235 316 0 551
 Belize 6.15 7,004 7,957 2,744 17,705
 Bulgaria 6.09 18,884 26,325 847 46,057
 South Korea 6.02 25,060 32,009 888 57,956
 Thailand 6 86,276 89,326 33,612 209,214
 Bosnia and Herzegovina 5.99 13,387 17,031 574 30,993
 Romania 5.95 38,395 48,394 607 87,395
 Philippines 5.91 91,820 100,831 8,393 201,044
 Togo 5.88 5,064 4,522 1,076 10,662
 Benin 5.86 4,724 3,698 1,769 10,191
 Sri Lanka 5.83 20,.541 22,390 1,613 44,544
 Japan 5.8 135,783 133,480 16,005 285,268
 Malawi 5.74 12,514 12,167 2,396 27,078
 Guinea-Bissau 5.7 9,274 8,702 855 18,831
 Laos 5.59 92,986 80,564 19,252 192,801
 Armenia 5.46 1,894 1,681 3 3,577
 Mauritius 5.46 567 478 0 1,045
 Bangladesh 5.45 10,013 7,251 1,947 19,211
 Cuba 5.4 22,605 18,460 1,632 42,697
 Sweden 5.35 174,415 109,779 23,494 307,687
 Vietnam 5.35 82,551 75,353 9,588 167,492
 Serbia 5.29 17,513 14,112 516 32,141
 Algeria 5.22 7,418 6,044 81 13,543
 Kosovo 5.19 2,628 1,775 47 4,450
 Tunisia 5.14 1,354 987 0 2,340
 Finland 5.08 144,310 83,572 9,294 237,176
 Jamaica 5.01 5,362 3,249 158 8,770
 Malaysia 5.01 130,825 91,957 21,499 244,281
 South Africa 4.94 45,489 34,968 3,196 83,653
 Croatia 4.92 15,732 10,522 379 26,633
 Guinea 4.9 81,702 54,877 2,895 139,475
 Libya 4.85 15 2 0 17
 Liberia 4.79 51,975 31,162 11,025 94,163
 Antigua and Barbuda 4.72 114 92 0 206
 Costa Rica 4.65 27,164 12,838 4,164 44,167
 Madagascar 4.63 120,340 66,584 11,922 198,846
 Gambia 4.56 181 85 0 266
 Saint Kitts and Nevis 4.55 95 50 0 145
 Ghana 4.53 57,519 28,901 2,160 88,580
 France 4.52 161,987 49,496 74,121 285,604
 Burundi 4.5 6,882 3,841 46 10,769
 Liechtenstein 4.5 59 42 0 101
 Honduras 4.48 57,899 23,802 3,692 85,392
 Andorra 4.45 170 49 0 219
 Uganda 4.36 77,303 36,381 7,507 121,190
 Slovakia 4.34 17,615 8,165 0 25,781
 Spain 4.23 82,770 46,013 133 128,916
 Grenada 4.22 221 86 0 308
 Eswatini 4.21 5,054 2,501 14 7,569
 Kenya 4.2 28,427 13,558 4,702 46,686
 Dominican Republic 4.19 19,890 9,364 518 29,772
 Israel 4.14 170 85 0 255
 El Salvador 4.05 8,837 2,947 0 11,784
 Haiti 4.01 7,116 2,831 12 9,959
 Guatemala 3.85 58,572 18,764 5,592 82,928
 Rwanda 3.85 5,665 2,170 619 8,454
 Slovenia 3.78 11,065 3,791 0 14,856
 Lebanon 3.76 541 115 0 656
 Italy 3.65 79,403 26,858 25 106,286
 Cote d'Ivoire 3.64 158,010 41,005 7,288 206,303
 Syria 3.64 841 282 0 1,123
 Belarus 3.63 77,870 20,847 91 98,808
 Nicaragua 3.63 65,356 17,646 4,858 87,860
 Uruguay 3.61 11,793 3,998 0 15,791
 Iraq 3.59 104 9 0 113
 Austria 3.55 36,666 12,422 21 49,109
 Switzerland 3.53 13,636 4,412 10 18,058
 Ukraine 3.3 89,540 20,183 176 109,900
 Estonia 3.05 24,473 4,832 52 29,358
 Jordan 2.79 12 0 0 12
 Sierra Leone 2.76 52,512 11,858 640 65,010
 Germany 2.28 122,168 11,307 0 133,475
 Hungary 2.25 18,729 2,047 0 20,776
 Poland 2.24 101,886 7,103 0 108,989
 Moldova 2.2 3,113 202 0 3,315
 Latvia 2.09 38,164 2,137 0 40,301
 Czechia 1.71 32,161 1,611 0 33,772
 United Kingdom 1.65 29,149 2,917 35 32,101
 Lithuania 1.62 24,554 930 0 25,484
 Belgium 1.36 8,803 297 0 9,099
 Luxembourg 1.12 1,170 0 0 1,170
 Singapore 1.11 170 2 0 172
 Dominica 1.06 531 2 0 533
 Ireland 0.92 5,283 96 0 5,378
 Portugal 0.82 25,966 553 0 26,519
 Netherlands 0.6 5,250 72 0 5,322
 Egypt 0.56 4,772 218 69 5,059
 Denmark 0.5 5,756 31 0 5,787
 San Marino 0.01 7 0 0 7
  Countries with high mean FLII (8-10)
  Countries with medium mean FLII (5-7.99)
  Countries with low mean FLII (0-4.99)


Use and applications

Policy and reporting

FLII has been referenced as a complementary indicator for monitoring ecological integrity, connectivity and ecosystem restoration under Target 2 of the Kunming-Montreal Global Biodiversity Framework.[4] The World Resources Institute's Global Forest Review uses FLII as a measure of forest degradation in its synthesis of global forest change and condition.[3]

The 2023 Forest Declaration Assessment includes "FLII units lost per year" as an indicator of forest degradation for tracking progress toward international forest goals.[7][8]

The European Commission's Joint Research Centre has incorporated a Forest Landscape Integrity layer in its Global Forest Types 2020 map product and related "primary forest" outputs.[9]

Research

Researchers have used FLII to compare forest condition across governance and conservation regimes. For example, Sze et al. (2022) used FLII in a pan-tropical analysis of forest integrity across overlaps of protected areas and Indigenous peoples' lands.[10]

Crowe et al. (2023) applied FLII to assess forest integrity within thousands of Key Biodiversity Areas (KBAs), highlighting its potential role in monitoring the condition of biodiversity-important sites.[11] BirdLife International has used FLII-based analyses to communicate trends in forest integrity within KBAs identified for forest species.[12]

FLII has also been used as an input or comparison layer in composite integrity metrics and validation studies, including an ecosystem integrity index integrating multiple global datasets[13] and field-based evaluation of how FLII corresponds to ecological indicators in boreal forests.[14]

Standards and finance

The High Integrity Forest (HIFOR) methodology incorporates FLII thresholds in eligibility criteria for "high integrity" forest areas in its monitoring framework.[15]

Limitations

The original study described FLII as a conservative estimate and noted several limitations. Some pressures are difficult to map consistently at global scale (e.g. finer-scale infrastructure or small-scale extraction), and forest modification prior to 2000 may not be reflected in the underlying global datasets.[1] The authors also noted that the index does not explicitly account for all drivers of integrity loss (such as climate change and invasive species), and that the forest extent definition can include tree crops and plantations, which typically score as low integrity under the model.[1]

Data and availability

An interactive map and downloadable data products (including continuous and classified layers) are provided via the project website.[16][17]

Background

The FLII was first published on 8 December 2020 in Nature Communications.[1] An author correction published in 2021 corrected an error in a protected-area table in the original article.[18]

The index was authored by a global team of forest conservation experts, including:[1]


Institution Author(s)
Wildlife Conservation Society, The Bronx H.S. Grantham; A. Duncan; T. D. Evans; K. R. Jones; J. Walston; J. G. Robinson; M. Callow; T. Clements; H. M. Costa; A. DeGemmis; P. R. Elsen; P. Franco; S. Jupiter; A. Kang; S. Lieberman; M. Linkie; M. Mendez; C. Samper; J. Silverman; T. Stevens; E. Stokes; T. Tear; R. Tizard; S. Wang; J. E. M. Watson
University of Queensland, Brisbane H. L. Beyer; S. Maxwell; H. Possingham; J. E. M. Watson
Carleton University, Ottawa R. Schuster
Wildlife Conservation Society Canada, Toronto J. C. Ray
United Nations Development Program, Manhattan J. Ervin
World Resources Institute, Washington, DC E. Goldman; R. Taylor
Northern Arizona University, Flagstaff S. Goetz; P. Jantz
Montana State University, Bozeman A. Hansen
Rainforest Foundation Norway, Oslo E. Hofsvang
Global Wildlife Conservation, Austin, Texas P. Langhammer; R. Mittermeier
Arizona State University, Tempe, Arizona P. Langhammer
James Cook University, Cairns W. F. Laurance; N. J. Murray
University of Oxford, Oxford Y. Malhi
The Nature Conservancy, Arlington, Virginia H. Possingham
Jet Propulsion Laboratory, Pasadena, California S. Saatchi
World Wide Fund for Nature Germany, Berlin A. Shapiro
International Institute of Sustainability, Rio de Janeiro B. Strassburg
University of Northern British Columbia, Prince George, British Columbia O. Venter
International Institute for Applied Systems Analysis, Laxenburg, Austria P. Visconti

See also

References

  1. ↑ 1.00 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 1.11 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 Grantham, H. S. (2020). "Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity". Nature Communications 11 (1). doi:10.1038/s41467-020-19493-3. PMID 33293507. Bibcode: 2020NatCo..11.5978G. 
  2. ↑ "Forest Landscape Integrity Index". https://earthmap.org/forestintegrity/index.html. 
  3. ↑ 3.0 3.1 3.2 "Forest degradation". World Resources Institute. https://research.wri.org/gfr/forest-degradation. 
  4. ↑ 4.0 4.1 "Target 2". Convention on Biological Diversity. https://www.cbd.int/gbf/targets/2. 
  5. ↑ Parrish, Jeffrey D.; Braun, David P.; Unnasch, Robert S. (2003). "Are We Conserving What We Say We Are? Measuring Ecological Integrity within Protected Areas". BioScience 53 (9): 851–860. doi:10.1641/0006-3568(2003)053[0851:AWCWWS2.0.CO;2]. 
  6. ↑ Grantham, H. S.; Duncan, A.; Evans, T. D.; Jones, K. R.; Beyer, H. L.; Schuster, R.; Walston, J.; Ray, J. C. et al. (2020). "Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity - Supplementary Material". Nature Communications 11 (1): 5978. doi:10.1038/s41467-020-19493-3. ISSN 2041-1723. PMID 33293507. Bibcode: 2020NatCo..11.5978G. 
  7. ↑ "Overarching forest goals: Theme 1 Assessment". 2023. https://forestdeclaration.org/resources/overarching-forest-goals-2023/. 
  8. ↑ 2023 Forest Declaration Assessment report: Technical Annexes (Report). Forest Declaration Assessment. 2023. https://forestdeclaration.org/wp-content/uploads/2023/10/2023_FDA_Report_Technical_Annexes.pdf. Retrieved 15 December 2025. 
  9. ↑ "Global Forest Types 2020". https://forobs.jrc.ec.europa.eu/GFT2020/. 
  10. ↑ Sze, Jocelyne S. (2022). "Indigenous lands in protected areas have high forest integrity across the tropics". Current Biology 32 (22): 4949–4956.e3. doi:10.1016/j.cub.2022.09.040. Bibcode: 2022CBio...32E4949S. 
  11. ↑ Crowe, O. (2023). "A global assessment of forest integrity within Key Biodiversity Areas". Biological Conservation 286. doi:10.1016/j.biocon.2023.110293. Bibcode: 2023BCons.28610293C. https://www.sciencedirect.com/science/article/abs/pii/S0006320723003944. Retrieved 15 December 2025. 
  12. ↑ "Over half of forest within Key Biodiversity Areas identified for forest species no longer has high integrity". 31 January 2024. https://datazone.birdlife.org/articles/over-half-of-forest-within-key-biodiversity-areas-identified-for-forest-species-no-longer-has-high-integrity. 
  13. ↑ Dias, F. (2023). "An ecosystem integrity index for the global assessment of human impacts on ecosystems". Frontiers in Ecology and Evolution 11. doi:10.3389/fevo.2023.1111947. 
  14. ↑ Malcangi, Francesca (2024). "Correlation between mammal track abundance and Forest Landscape Integrity Index validates actual forest ecological integrity". Oecologia 206 (1–2): 61–72. doi:10.1007/s00442-024-05613-z. PMID 39230725. Bibcode: 2024Oecol.206...61M. 
  15. ↑ HIFOR Methodology (English) (Report). HIFOR. 2025. https://hifor.org/wp-content/uploads/2025/04/HIFOR_methodology_English.pdf. Retrieved 15 December 2025. 
  16. ↑ "Forest Landscape Integrity Index". https://www.forestlandscapeintegrity.com/. 
  17. ↑ "Forest Landscape Integrity Index – Further Information". https://www.forestlandscapeintegrity.com/further-information. 
  18. ↑ Grantham, H. S. (2021). "Author Correction: Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity". Nature Communications 12. doi:10.1038/s41467-021-20999-7. PMID 33473136. Bibcode: 2021NatCo..12..592G. 



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