Forest Landscape Integrity Index

WCS — (2020)

Description

This dataset quantifies the forest landscape integrity index (FLII) at 300 m spatial resolution, with global spatial coverage for the year 2019. FLII is a score that quantifies human impact on forest integrity, where higher values indicate less human impact.

Dataset ID
03f5ee4b…
Category
Ecosystem integrity
Type
Raster
CRS
EPSG:4326
Spatial coverage
Global
Spatial resolution
300 m
Temporal coverage
2019
Temporal resolution
Single time point

Usage notes

FLII is a conservative index that may overestimate forest integrity due to data limitations. Forest is defined broadly (woody vegetation > 5 m height, > 20% canopy cover) and includes natural forests, tree plantations, agroforestry, and tree crops. Aggregation of 30 m forest cover data to 300 m resolution can reclassify sparse forest as non-forest. Impacts on forests before 2000 are not captured due to temporal coverage of input data. Impacts from human infrastructure may be underestimated due to missing input data. Impacts from climate change, invasive species, and anthropogenic fires are not included.

Variables

integrity_score

Typefloat32NoData-9999

Description

The forest landscape integrity index (FLII) score. Data is generated from multi-step calculations made on datasets of forest cover and a range of observed and diffuse human pressures.

Usage notes

Scores are 0-10 and fall into three indicative groups: high integrity (≥ 9.6), medium integrity (> 6.0 and < 9.6), and low integrity (≤ 6.0). Scores are only calculated for pixels defined as forest. NoData values indicate a pixel was non-forest in 2019, either because it did not meet the forest definition or was permanently deforested between 2001 and 2018. Forest cover in 2019 is derived from the Hansen Global Forest Change dataset.

Pricing

This dataset has no data acquisition cost.

Resources

Provider

WCS

www.wcs.org

The Wildlife Conservation Society (WCS) is a global conservation organisation working to save wildlife through science, conservation, education, and inspiring people to value nature. This dataset was developed by WCS in collaboration with the University of Queensland and a team of international researchers to provide a globally consistent measure of forest landscape integrity.