Land - Tree Cover Loss

Tree Cover Loss tracks changes in forest cover over time since the year 2000. Based on satellite imagery, it identifies areas where tree cover has been lost.

Summary

Data Type	Multispectral analysis of satellite imagery
Source	Collaboration between Global Land Analysis & Discovery lab Google, USGS, and NASA
Spatial Resolution	30 x 30 meters
Temporal Resolution	Annual updates
Interpretation	The area within the site boundary (or a 300m default radius around site coordinates) is screened for the occurrence of tree cover loss.
Concern Levels	Very Low = No Tree Cover Loss detected Low = Tree Cover Loss between 2001 and 2010 Moderate = Tree Cover Loss between 2011 and 2020 High = Tree Cover Loss between 2021 and 2023 Very High = Tree Cover Loss in 2024
Framework Relevance	Tree cover loss is relevant under CSRD (ESRS E4) as well as under TNFD.

Detailed Specifications

Explanation

Tree Cover Loss is an annually updated global-scale forest loss dataset, derived by analyzing multispectral Landsat satellite images over time. Tree cover is defined as all vegetation taller than 5 meters in height. This can be natural forests or plantations with high enough canopy density. Loss of tree cover is defined as the complete removal of tree cover canopy (so-called stand-replacement disturbance) at the Landsat pixel scale (30mx30m). Detected tree cover loss may be the result of human activities, including forestry practices or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease, fires or storm damage.

Transformation & Interpretation

The area within the site boundary (or a 300m default radius around site coordinates) is screened for the occurrence of tree cover loss during the years 2001 and 2022. Tree cover loss within the analyzed area is shown visually and summarized in a table in absolute terms (ha) as well as relative (in %) to the initial extent of tree cover within the area in the year 2000. SBTN guidelines and the European Deforestation Regulation (EUDR) lay particular importance to land degradation and deforestation after the cut-off date of 31.12.2020. Considering this the tree cover loss indicator is translated into State of Nature concern levels based on the following logic:

Detected Tree Cover Loss	Concern Level
No Tree Cover Loss detected	Very Low
Tree Cover Loss between 2001 and 2010	Low
Tree Cover Loss between 2011 and 2020	Moderate
Tree Cover Loss between 2021 and 2023	High
Tree Cover Loss in 2024	Very High

Source

The Tree Cover Loss data is a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA and based on Hansen et al. (2013)

Citation

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53.

Date of publication

2025

Confidence & Accuracy

High. Tree Cover Loss data is based on satellite imagery, allowing for global coverage and timely updates. The model accuracy is reported to be reasonably high with the overall prevalence of false positives at 13%, and the prevalence of false negatives at 12%. The authors are 75% confident that the loss occurred within the stated year, and 97% confident that it occurred within a year before or after. Note, that tree cover does not necessarily equate to deforestation.

Relevance for CSRD

CSRD requires companies to assess site-specific level of land degradation. The tree cover loss indicator specifically is relevant to the following ESRS requirements:

ESRS Article	Disclosure or application requirement
ESRS E4 DR 16 (b)	negative impacts w.r.t. land degradation
ESRS E4 DR 38 (a)	conversion of land cover (e.g. deforestation)

CSRD requires companies to consider and describe the data used in their analysis (ESRS E4 AR 27). For convenience, here a description of for the relevant criteria:

ESRS E4 AR 27 Criteria	Description
(a) methodology, reason for selection, assumptions, limitations & uncertainties	Methodology: The tree cover loss indicator identifies tree cover by analyzing multispectral Landsat satellite images over time. Reason for selection: The indicator has been selected as a metric to assess land degradation and conversion of land cover over time. The selected metric is science-based, peer-reviewed and widely applied. Assumptions: Tree cover is defined as all vegetation taller than 5 meters in height. This can be natural forests or plantations with high enough canopy density. Loss of tree cover is defined as the complete removal of tree cover canopy (so-called stand-replacement disturbance) at the Landsat pixel scale (30mx30m). Limitations & uncertainties:The model accuracy is reported to be reasonably high with the overall prevalence of false positives at 13%, and the prevalence of false negatives at 12%. The authors are 75% confident that the loss occurred within the stated year, and 97% confident that it occurred within a year before or after. Detected tree cover loss may be the result of human activities, including forestry practices or deforestation, as well as natural causes such as disease, fires or storm damage.
(b) scope of metric	The tree cover loss indicator is applied at site level. Screening the area within the site boundary or a 300m default radius around site coordinates.
(c) biodiversity component of metric	Tree cover loss is an ecosystem-level nature indicator. It assesses ecosystem condition by providing an indicator for ecosystem structure.
(d) covered geographies	Tree cover loss has global coverage.
(e) integration of ecological thresholds	The tree cover loss indicator relates to the planetary boundary of land-system change.
(f) frequency of monitoring, baseline condition and period	The tree cover loss indicator is updated annually. The baseline is tree cover in the year 2000.
(g) data type (primary, secondary, modelled, expert judgement	Tree cover loss relies on secondary data, specifically on multispectral satellite imagery.

Relevance for EUDR

The Tree cover loss indicator can be helpful to screen for the occurrence of deforestation as required under the European Deforestation Regulation. However, additional steps will be required for full compliance as tree cover loss does not necessarily identify deforestation linked to changes in land-use but also tree cover loss due to forestry activities or natural causes.

Relevance for TNFD

Tree Cover Loss is a suitable indicator to identify areas of rapid decline in ecosystem integrity under the criteria of Ecosystem Integrity within TNFD’s LOCATE step.

Relevance for SBTN

Tree cover loss is not directly relevant as a State of Nature indicator for Step1: Assess and Step 2: Interpret & Prioritize. Nevertheless, the data set can support the screening for land use and land use change pressures and provides additional context complementing other biodiversity State of Nature indicators.

SBTN Data & Tool Criteria	Evaluation	Comment
Relevance	❌	Tree Cover Loss is not directly relevant to SBTN methodology for State of Nature Assessments.
Representative	❌	Tree Cover Loss is not directly applicable to SBTN’s quantification of the State of Nature Assessments.
Spatial and Temporal Resolution	✅	Tree Cover Loss data is available at a 30 × 30 meters resolution for the years 2001-2024.
Stability and Preservation	✅	Tree Cover Loss data is likely to be maintained and preserved long-term as is is provided by a collaboration between the GLAD lab at the University of Maryland, Google, USGS, and NASA and has been regularly updated in the past.
Accessibility	✅	The Tree Cover Loss data is readily accessible online.
Interpretability	✅	The Tree Cover Loss indicator is sufficiently interpretable.
Coverage	✅	Tree Cover Loss data has global coverage (excluding Antarctica and Arctic islands).
Authoritative and Accurate	✅	Tree Cover Loss data is based on a science-based and peer-reviewed methodology.