TheForest Landscape Integrity Index (FLII) is a global, map-based indicator offorest condition that estimates the degree of anthropogenic modification of forest ecosystems.^[1]Developed by an international research team led by theWildlife Conservation Society, the index integrates spatial data onobserved andinferred 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 ashigh integrity (FLII ≥ 9.6), 33.9% (14.6 million km²) asmedium integrity and 25.6% (11 million km²) aslow integrity (FLII ≤ 6.0).^[1]High-integrity forests were concentrated in boreal regions ofRussia andCanada and in large tropical forest blocks such as theAmazon,Central Africa andNew Guinea.^[1]

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

In ecology,ecological integrity (sometimesecosystem integrity) refers to the extent to which anecosystem's structure, speciescomposition and ecological processes fall within their natural range of variation.^[5]In forests, integrity is often discussed alongside, but distinct from,deforestation (area loss) andforest 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 inconnectivity to generate a globally consistent, continuous integrity score.^[1]

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

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 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]

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]

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]

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% experiencedobserved 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 (withRussia andCanada together containing about half of the global high-integrity forest area).^[1]Only about 27% of high-integrity forest area fell within nationally designatedprotected areas, while 56% of forests within protected areas were classed as high integrity.^[1]

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
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

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

The 2023Forest 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'sJoint Research Centre has incorporated a Forest Landscape Integrity layer in itsGlobal Forest Types 2020 map product and related "primary forest" outputs.^[9]

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

Croweet al. (2023) applied FLII to assess forest integrity within thousands ofKey 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]

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

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 asclimate change andinvasive species), and that the forest extent definition can include tree crops and plantations, which typically score as low integrity under the model.^[1]

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

The FLII was first published on 8 December 2020 inNature 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 forestconservation experts, including:^[1]

• Intact forest landscape
• List of countries by forest area
• Forest degradation
• Deforestation
• Habitat fragmentation
• Human Footprint
• Key Biodiversity Areas
• Convention on Biological Diversity

• Official site & map

• Biodiversity
• Sustainable forest management
• Environmental terminology
• Forest conservation
• Environmental indices

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