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Forest Baseline

Forest, as defined by the FAO is “land spanning more than 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 in situ. It does not include land that is predominantly under agricultural or urban land use.” 

Satelligence applies machine learning-based forest classification models, trained to enable consistent application of these parameters. We also customize forest classifications following national legal definitions. This is relevant as some countries apply slightly different parameters in area (e.g. 0.05 – 2 hectares), canopy cover, and height. 

Within the forest domain, our forest baseline identifies the following classes in order to distinguish high risk events from lower risk events: 

  1. Primary forest – We implement the widely adopted concept of Intact Forest Landscapes (IFL) to define primary forest. IFL are defined as large, unfragmented forests with minimal human influence, i.e. not altered by logging, forest management or conversion: “unbroken expanse of natural ecosystems within the zone of current forest extent, showing no signs of significant human activity and large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained.” Primary forests have a minimum area of 50,000 hectares, a minimum patch width of 10 km, and a minimum corridor/appendage width of 2 km. Our primary forest class includes High Conservation Value (HCV I) and High Carbon Stock (HCS) High Density Forest. RSPO, FSC, RTRS, some national legislations and NDPE policies forbid clearing of primary forests.

  2. Disturbed forest – Disturbed forest is moist or dry tropical forest. We define this class as intact forest  with some human influence, i.e. disturbed due to logging, forest management, including smaller patches than IFL. We use the primary forest layer developed by the University of Maryland (UMD) for 2001, in combination with the JRC Tropical Moist Forest layers and National Forest maps. These are subsequently corrected for any clearing up to the desired cutoff date. All corrected UMD primary forest outside of the IFL that has never been cleared, qualifies as disturbed forest. This layer includes many logging concessions that have been actively logged in the recent past, and as such, does not meet the qualification of primary forest. Our time-series analysis, based on the full historical satellite dataset from 1984 to  now,  has not detected any significant disturbance. Our disturbed forest class is predominantly HCV II and HCS  medium- and low density forest. In some cases, although legally allowed, clearing of this disturbed forest is not accepted under NDPE policies. 

  3. Regrowth – This includes all remaining forest that is not primary or disturbed, or areas afforested or reforested after other dominant land use. This is all forest under the FAO definition that does not fit the other 2 forest classifications. We define heavily degraded areas as <10% canopy cover, with disturbance evident from the satellite time-series signal over a short time period (less than 2.5 years). Our time-series analysis can detect such change over the full historical satellite dataset from 1984 to now. Regrowth areas remain forested land covered by existing or regrowing trees. Apart from heavy logging, drivers include fires, and other damage to canopy from hurricanes, droughts, and blowdowns. Our regrowth forest class includes Young Regenerating Forest, whether it is HCV needs to be assessed locally. Clearing is often legally allowed, and can be accepted under NDPE policies if not HCS and/or HCV. A growing number of grievances are found within this class.

References

  1. C. Vancutsem, F. Achard, J.-F. Pekel, G. Vieilledent, S. Carboni, D. Simonetti, J. Gallego, L.E.O.C. Aragão, R. Nasi. 2021. Long-term (1990-2019) monitoring of forest cover changes in the humid tropics. Science Advances

  2. Turubanova S., Potapov P., Tyukavina, A., and Hansen M. 2018. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environmental Research Letters 

  3. Potapov P., Yaroshenko A., Turubanova S., Dubinin M., Laestadius L., Thies C., Aksenov D., Egorov A., Yesipova Y., Glushkov I., Karpachevskiy M., Kostikova A., Manisha A., Tsybikova E., Zhuravleva I. 2008. Mapping the World's Intact Forest Landscapes by Remote Sensing. Ecology and Society, 13 (2)

  4. HCSA Toolkit 2.0