Global analysis of forest attribute layers for the EU observatory on deforestation and forest degradation.
Date: 2022-09-28
Institutions: Joint Research Centre
Subjects: big data, deforestation, degradation of the environment, environmental law, environmental monitoring, fire, forestry research, information analysis, research report, satellite communications, statistics, tropical forest

ISSN 1831-9424 

Global analysis of forest attribute layers 
for the EU Observatory on Deforestation 
and Forest Degradation  

Algorithm Technical Basis 
Document (ATBD) 

Vogt P., Caudullo G. 

2022 

EUR  31200 EN 

 
 
 
 
This publication is a technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims 
to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy 
position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible 
for  the  use  that  might  be  made  of  this  publication.  For  information  on  the  methodology  and  quality  underlying  the  data  used  in  this 
publication  for  which  the  source  is  neither  Eurostat  nor  other  Commission  services,  users  should  contact  the  referenced  source.  The 
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of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation 
of its frontiers or boundaries. 

Contact information  
Name: Peter Vogt 
Address: TP261, Via Fermi, 21027 Ispra (VA), Italy 
Email: peter.vogt@ec.europa.eu 
Tel.: +39 0332 78 5002 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC130494 

EUR 31200 EN 

PDF 

ISBN 978-92-76-56423-2 

ISSN 1831-9424 

doi:10.2760/41048  KJ-NA-31-200-EN-N 

Luxembourg: Publications Office of the European Union, 2022 

© European Union, 2022 

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How to cite this report: Vogt P., Caudullo G. Global analysis of forest attribute layers for the EU Observatory on Deforestation and Forest 
Degradation, Publications Office of the European Union, Luxembourg, 2022, doi:10.2760/41048, JRC130494.

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Acknowledgements .................................................................................................................................................1 

Abstract ...................................................................................................................................................................2 

1 

2 

Introduction .......................................................................................................................................................3 

Implementation summary .................................................................................................................................6 

2.1  Data source ...............................................................................................................................................6 

2.1.1 

Downloading individual tiles..........................................................................................................6 

2.1.2  Merge tiles into a global raster map .............................................................................................7 

2.2  Forest definition ........................................................................................................................................8 

2.3  Forest Attribute Layers .............................................................................................................................8 

2.3.1 

Fragmentation ...............................................................................................................................8 

2.3.2 

Accounting .....................................................................................................................................9 

2.3.3 

Pattern ........................................................................................................................................ 10 

2.3.4  Network ....................................................................................................................................... 10 

2.4  Reporting units ....................................................................................................................................... 11 

2.5  Data provision ........................................................................................................................................ 13 

3 

Implementation example ............................................................................................................................... 14 

3.1  Forest Fragmentation ............................................................................................................................ 14 

3.2  Forest Accounting .................................................................................................................................. 15 

3.3  Forest Pattern ........................................................................................................................................ 16 

3.4  Forest Network ....................................................................................................................................... 17 

3.5  Access to the FAL through the EUFO web platform .............................................................................. 17 

Conclusions .......................................................................................................................................................... 19 

References ........................................................................................................................................................... 20 

List of acronyms .................................................................................................................................................. 22 

List of figures ....................................................................................................................................................... 23 

List of tables ........................................................................................................................................................ 24 

Annex .................................................................................................................................................................... 25 

i 

 
 
Acknowledgements 

The  development  of  the  Web  Platform  of  the  EU  Observatory  on  Deforestation  and  Forest  Degradation  is 
supported by the Directorate-General for Environment of the European Commission (DG-ENV) through the Dev-
ForObs Administrative Arrangement. This report applies methodologies that have been jointly developed within 
the  context  of the  Collaborative  Research Arrangement between the Joint  Research Centre  of the European 
Commission and The United States Department of Agriculture, United States Forest Service (JRC Agreement 
Number: 33385, USDA Forest Service Agreement Number: 14-MU-11330110-001).  

Special  thanks  go  to  Luis  Bausa  Lopez,  Paula  Veronica  Figueiredo  Vilar  Pires  Correia,  Frédéric  Achard,  Kurt 
Riitters  and  Jennifer  Costanza  for  providing  suggestions  and  feedback  during  the  development  and 
implementation phase of this study. 

Authors 

Peter Vogt, Giovanni Caudullo (JRC, D.1) 

1 

 
 
Abstract 

This document summarizes the design process, definitions, and algorithmic implementation conducted by the 
Joint Research Centre to support the development and implementation of a global analysis of forest attribute 
layers. The analysis scheme and data products were designed to be hosted and visualised on the EU Observatory 
on Deforestation and Forest Degradation. 

The spatial forest coverage is derived from the Copernicus Global Land Cover dataset for the assessment year 
2019 (accessed in February 2022). We outline the procedure of pre-processing the original Copernicus data 
segments into a continuous global forest mask, which is compliant with the FAO forest definition. This forest 
mask is then analysed for various forest attribute layers summarising the spatial status of forest cover and its 
degree of forest degradation. The analysis is conducted in a dual approach: first, using the original WGS84 map 
projection for explanatory and user-friendly visualisation on web portals and second, in equal area projection 
allowing statistical evaluation of the forest attribute layers. Spatially explicit maps and statistical summaries 
are derived for a total of 278 reporting units, comprised of 255 countries, 21 global ecological zones, the EU27, 
and the full global coverage. 

2 

1 

Introduction 

In the European Union (EU) communication on stepping up EU action to protect and restore the world’s forests 
(COM/2019/352), the European Commission announced it would “build on the already existing monitoring tools, 
and establish an EU Observatory on deforestation, forest degradation, changes in the world’s forest cover, and 
associated drivers. The objective of this is to facilitate access to information on supply chains for public entities, 
consumers and businesses.” The Commission’s Joint Research Centre (JRC) develops this EU Observatory on 
deforestation, forest degradation, changes in the world’s forest cover, and associated drivers, hereafter referred 
to as “EUFO”. 

The proposal for a Regulation to prevent deforestation and forest degradation associated with commodities 
products placed on or exported from the EU market (COM(2021)706) refers to EUFO in the introduction section 
by indicating that this Observatory "will thus directly support the implementation of this Regulation by providing 
scientific evidence in regard to global deforestation and forest degradation and related trade". 

Global datasets are intended to be displayed in the Web Platform of the EUFO including a global spatial dataset 
on  forest  attributes  derived  from  the  Copernicus  global  land  cover  map  of  the  year  2019  and  JRC  pattern 
analysis tools. Forest area and its spatial configuration is a key aspect in biodiversity, ecosystem services and 
the ever-increasing pressure from anthropogenic land use. Forest degradation may lead to the isolation and 
loss of species and gene pools, degraded habitat quality, and a reduction in the forest’s ability to sustain the 
natural  processes  necessary  to  maintain  ecosystem  health.  The  term  forest  degradation  encompasses  a 
multitude  of  aspects  from  various  sources  such  as  forest  management,  landscape  planning,  and  policy 
directives. This report will focus on those aspects only that can be reliably retrieved from satellite data. In the 
following,  we  use  the  generic  term  forest  attribute  layers  (FAL)  for  a  series  of  data  layers  describing  the 
morphology, spatial contagion, and spatial arrangement of forest land cover. The FAL directly address key forest 
monitoring components. For example, forest fragmentation, or its complement forest connectivity, is used in 
many application fields and the interpretation of it strongly depends on the end-user priorities and reporting 
needs. The wood-processing sector, tourism industry, environmentalists, nature protection organisations, and 
policy makers all have their own perceptions and evaluations whether a high degree in forest connectivity is 
good or bad. For example, in biodiversity a high degree in connectivity can be good for certain bird species but 
at the same time it is very bad, because it favours the proliferation of invasive species or forest fires. Equally, 
some birds/butterflies benefit from a highly fragmented landscape while other species require unfragmented 
habitats. For this reason, we provide quantitative indicators only, which the end-user can then interpret for the 
specific application focus. 

The  scope  of  the  FAL  and  its  indicators  described  in  this  document  is  to  mediate  the  various  end-user 
expectations with the provision of an objective and tangible solution: provide spatially explicit maps, detailing 
the  topographic  distribution  and  arrangement  of  forest  attributes.  Spatially  explicit  maps  allow  detecting 
hotspots  and  areas  of  changes,  which  is  important  for  making  informed  decisions  in  forest  management 
activities, landscape planning and policy directives.  In addition, the FAL indicators are normalised within the 
range of [0, 100] %, providing an intuitive and neutral reporting summary for status and trend assessments, as 
well  as  permitting  for  a  direct  comparison  between  individual  reporting  units.  The  combination  of  spatially 
explicit maps and normalised summary statistics facilitates communication and serves as a common reference 
base for all end-users, who can then further interpret the assessment results for their specific priorities and 
preferences.  

The conceptual approach of the FAL described in this document is the result of a collaborative development on 
the reporting on forest pattern between JRC D.1 and the USDA-Forest Service (USFS). For example, the reporting 
scheme on fragmentation is used by the USFS for official reporting to the Montréal Process for over twenty 
years (Forest Service 2012, 2016; Riitters et al. 2002, 2012; Wickham et al. 2008). Customised indicators on 
forest fragmentation based on the same scheme are provided by the USFS (indicator 1.03 Fragmentation of 
forests), Food and Agriculture Organization of the United Nations (FAO) (indicator 1.5), and the Mapping and 
Assessment of Ecosystems and their Services (MAES) project, (i.e., download Fact sheet 3.3.103: Fragmentation 
by  forest  cover  loss).  Fine-tuned  versions  of  the  same  approach  have  also  been  designed  by  JRC  D.1  for 
reporting  on  forest  fragmentation  in  FAO’s  State  of  the  World's  Forests  –  2020  (SOFO)  (FAO  2020)  report, 
indicator  4.7  (Forest  Fragmentation)  in  the  State  of  Europe’s  Forests  2020  report  (FOREST  EUROPE  2020), 
published by FOREST EUROPE, and measuring forest connectivity (Vogt and Caudullo 2022) contributing to the 
European Statistical Office (EUROSTAT) Regional Yearbook 2022. The computation of all FAL described in this 
report is available in the JRC software GuidosToolbox (GTB, Vogt and Riitters, 2017) for interactive analysis and 

3 

 
GuidosToolbox Workbench (GWB, Vogt et al, 2022) for automated processing. GWB is also implemented on the 
public cloud computing platforms FAO System for Earth Observations, data access, processing & analysis for 
land monitoring (SEPAL) and the JRC Big Data Analytics Platform (BDAP, Soille et al. 2018).  

In  general,  the  analysis  of  forest  attributes  depends  on  the  input  data  source,  the  definition  of  forest,  the 
processing settings, and the reporting style of the results. Each end-user must evaluate the analysis conditions 
and take appropriate decisions to best match the desired results. In this document, we followed the decisions 
taken in the Forest Europe Pilot Study on reporting on forest fragmentation, and other decisions described in 
previous Technical Reports (see EUR reports in the section References) to setup a portfolio of FAL matching the 
needs of the EUFO.  

This report outlines the design of various FAL derived from satellite data and using the Copernicus Global Land 
Cover dataset as input data. The satellite source data and derived FAL products are for evaluation purposes in 
EUFO. The final public version of EUFO may use only subsets of the FAL described here and may use a different 
satellite input dataset if deemed appropriate.  The evaluation phase with the EUFO coordination team concluded 
on the following key findings (KF): 

KF1 – Data source: the COPERNICUS Global Land Cover Map (hereafter COP) was selected to be the most 
appropriate public and operational data source within the context of the assessment at this point of time. The 
Copernicus Global Land Service provides systematic monitoring of bio-geophysical parameters at global scale. 
The monitoring activity is conducted by 20+ institutes across Europe dealing with the processing of low-to-
medium spatial resolution optical and radar data, data validation, data provision and product quality control. All 
Global Land service products are provided under the free and open access policy as defined in the European 
Union’s  Copernicus  regulation  (N°  377/2014  of  3  April  2014)  and  Commission  Delegated  Regulation  (N° 
1159/2013).  In  this  respect,  the  products  described  in  this  ATBD  contain  modified  Copernicus  Service 
information (2019). A summary of the COP data set is provided at https://lcviewer.vito.be/about with a visual 
online  representation  and  download  information  at:  https://lcviewer.vito.be/.  Further  information  on  COP  is 
summarized in Buchhorn et al (2019). The COP dataset (version 3.0.1, accessed in December 2021) provides 
global land cover maps using a harmonized method and legend. At the time of writing, COP data is available 
on an annual basis for the years 2015 to 2019. For EUFO, we use the most recent assessment year 2019. The 
global coverage of the original COP data is provided in 94 individual tiles in EPSG 4326 projection, each covering 
20 x 20. The tiles are to be reassembled into a single global thematic map. 

KF2 – Forest definition: the Discrete Classification layer of COP provides 23 individual land cover classes 
following the FAO UN Land Cover Classification System (LCCS).  The additional layer Forest Fractional Cover 
provides  the  percentage  of  a  pixel  that  is  filled  with  forest/trees  independent  of  the  discrete  classification. 
Forest is defined for pixels being either in the Discrete Land Cover class “Closed Forest” (Map code in [111-
116]) or where the Forest Fractional Cover is at least 30%. Neighbouring forest pixels are considered connected 
if they share a common pixel border or edge (8-connectivity). Besides forest, the final forest mask also includes 
pixels classified as water and missing in the Discrete Land Cover class (Map code 80 or 0, resp.).  

KF3 – Forest Attribute Layer: the following forest attribute layers (FAL) were chosen for EUFO: 

• 

Fragmentation:  the  method  “Forest  Area  Clustering”  (FAC)  at  Fixed  Observation  Scale  (FOS)  was 
selected to measure forest fragmentation. This methodology is a modified version of the methodology 
“Forest Area Density” (FAD) used by FAO, Forest Europe, and EUROSTAT. FAC is a modified version of 
FAD,  which  provides  similar  results  but  better  captures  locally  clumped  forests  leading  to  a  more 
intuitive  representation  of  fragmentation  features,  see  the  Fragmentation  product  sheet. 
Fragmentation  is  scale  dependent,  and  the  scale  is  set  by  the  window  size  of  the  moving  window 
approach. The EUFO coordination team decided that a fragmentation analysis scale of ~ 10 km2 is 
appropriate  to  capture  the  diverse  functions  of  forest  patches  and  their  role  in  sustainable  forest 
management,  for  forest  naturalness  and  the  protection  of  biodiversity  in  forests.  Considering  the 
spatial pixel resolution of 100 meters, this analysis scale corresponds to a moving window size of 31 
x 31 pixel, or 961 hectares (~ 10 km2). With this setup, the fragmentation assessment provides per-
pixel normalised FAC values considering a local neighbourhood of 10 km2 around each forest pixel. To 
facilitate  interpretation,  the  full  range  of  FAC  in  [0,  100]  %  is  divided  into  the  following  five 
fragmentation classes: Rare (FAC < 10%), Patchy (10%  FAC < 40%), Transitional (40%  FAC < 60%), 

4 

 
 
 
Dominant (60%  FAC < 90%), and Interior (90%  FAC  100%). These five classes capture meaningful 
degrees in forest fragmentation for management, habitat, and biodiversity assessment studies. They 
are complemented with FACAV, the average FAC value over all forest pixels in each reporting unit. The 
result of the FAL fragmentation is a spatially explicit map and summary statistics. 

•  Accounting: is designed to provide an overview and concise summary of the location and size class 
distribution of forest patches in a given forest map. The methodology provides a map product together 
with a statistical summary for a series of forest patch size classes. Accounting can be used to describe 
the forest patch size class distribution at a given point in time or to directly compare the patch size 
distribution between the various reporting units. The EUFO coordination team agreed on the following 
six forest patch size classes: [100; 1,000; 10,000; 100,000; 1,000,000; > 1,000,000] hectares. Forest 
pixels  are  considered  connected  if  they  share  a  common  pixel  border  or  edge  (8-connectivity).  The 
same  methodology  is  also  used  by  FAO  and  Forest  Europe.  The  result  of  the  FAL  accounting  is  a 
spatially explicit map and summary statistics. 

•  Pattern: the method Morphological Spatial Pattern Analysis (MSPA) conducts a segmentation of the 
forest  pixels  into  mutually  exclusive  feature  classes  describing  the  morphology  of  and  connecting 
pathways between forest patches. We use the default MSPA processing settings (8111, more details 
in the MSPA Guide). The result of the FAL pattern is a spatially explicit map and summary statistics. 

•  Network:  we  conduct  a  forest  network  status  assessment,  resulting  in  a  list  of  key  indicators 
describing the forest network in each reporting unit, including its spatial integrity, and the degree of 
forest network coherence in [0, 100] %. The result of the FAL network are summary statistics only.  

KF4 -  Reporting units: all FAL are derived for 278 reporting units, comprised of 255 countries, 21 Global 
Ecological Zones (GEZ), the EU27, and the full global coverage.  

KF5 – Data provision: all FAL maps show the respective value at the pixel level for each forest pixel in the 
2019. EUFO provides a visual interface to display any type of spatial map product as well as derived statistical 
summary information on various thematic forest attributes. While the visualization is best addressed with the 
standard WGS84 projection (EPSG 4326 used for example by GoogleEarth and Google Earth Engine), statistical 
analysis  requires  an  equal-area  projection.  To  accommodate  both  aspects,  we  apply  the  following  two-tier 
approach in EUFO: 

• 

• 

Tier 1: Maps for visualization: spatially explicit maps in GeoTIFF format and EPSG 4326 projection 
for the three FAL fragmentation, accounting, and pattern.  

Tier 2: Maps for statistical analysis: Statistical area estimates require an equal-area projection, 
where each map pixel has the same spatial extent. This is achieved by reprojecting the original EPSG 
4326 forest map into an equal-area projection (Goode-Homolosine Land, EPSG 54052). Because area 
estimates are measured in pixels, the reprojected map is further resampled to a map with a pixel-
resolution  of  100  meters  (1  pixel  =  1  hectare)  to  facilitate  the  interpretation  of  the  resulting  area 
estimates. The equal-area global forest map is then used to derive all FAL maps and statistics in equal 
area  projection.  Finally,  Tier  2  provides  spatially  explicit  maps  in  GeoTIFF  format  and  EPSG  54052 
projection  for  the  three  FAL  fragmentation,  accounting,  and  pattern  and  in  all  278  reporting  units. 
These maps are complemented by a single spreadsheet containing all tabular summary statistics for 
all four FAL in equal-area projection and all 278 reporting units. 

The entire FAL product (KF5) is stored in a single directory.  Following the user selection, the EUFO server can 
then display any map and respective summary statistics in a dedicated map viewer display with adequate chart 
summaries.  

Please note that EUFO may not use the entire information of the four FAL described here or equally EUFO may 
add additional FAL, or derived from a different data source in the future. 

5 

 
 
 
 
2 

Implementation summary 

This section provides an overview on the implementation of the respective key finding KF 1-5 described before. 
The data source used, necessary pre-processing steps, details on the algorithms applied, details on the output 
format and other documentary information on the  individual FAL. All status maps and related statistics are 
available directly at: https://doi.org/10.6084/m9.figshare.21177175, see also section 2.5 

2.1  Data source 

This section describes the retrieval and pre-processing of the COPERNICUS source data used to setup the initial 
forest map for further FAL processing. 

2.1.1  Downloading individual tiles 

The forest map is based on two COP datasets: Land Cover Classification: Discrete Classification (hereafter DC) 
and Cover Fractions: Forest (hereafter FC). Both datasets are provided in 94 tiles, each covering 20 x 20: 

Figure 1. Copernicus Global Land Cover download interface. 

Source: https://lcviewer.vito.be/download  

The raster characteristics of the tiles are: 

●  File format: GeoTIFF 

●  Number of bands: 1 

●  Coordinate reference system: EPSG:4326 

●  Pixel type: 8 bit unsigned 

●  No Data value: 255 

●  Pixel size in decimal degrees: 0.000992063492063489 x 0.000992063492063489 

To download each tile, click on the respective cloud icon and a popup window will open (Figure 2) with the layers 
and the years. Required layers are Land Cover Classification: Discrete Classification and Cover Fractions: Forest. 
In both cases, the download starts after clicking on the desired year: 

6 

 
 
 
 
 
Figure 2. Copernicus download interface: selection of Land Cover Classification and Cover Fractions. 

Source: https://lcviewer.vito.be/download  

2.1.2  Merge tiles into a global raster map 

For both datasets, DC and FC, a single global raster must be generated by merging the tiles of each dataset, 
maintaining all original raster characteristics described in the previous section of this document. After various 
tests, we found the Geospatial Data Abstraction Library (GDAL) from the command line to be the most efficient 
and fastest software to process large GeoTIFF raster layers. To merge all downloaded tiles, use the following 
GDAL command sequence in the directory where all tiles were downloaded into the subdirectory tiles: 

> gdalbuildvrt tmp.vrt tiles/*.tif 

> gdal_translate -co "COMPRESS=LZW" -co BIGTIFF=YES -of GTIFF tmp.vrt composite.tif 

The  first  command  defines  a  temporary  virtual  raster  image,  covering  the  spatial extent  of  all  downloaded 
Copernicus tiles and linking their locations into the virtual raster image. The second command will populate, 
write out and compress the virtual raster image in GeoTIFF format with the name composite.tif. This process 
must be performed for each of the two downloaded Copernicus datasets (DC and FC). The above commands 
assume that all downloaded tiles are in a subdirectory called tiles, and the result composite.tif will be placed in 
the current folder. It is highly recommended to use the LZW compression to reduce the size of the output file. 
The GDAL command sequence outlined above completes in approximately 7 minutes resulting in a ~800 MB 
global raster file of dimension 362,880 x 141,120 pixels and covering an extent of [-180, -60: 180, 80]°. 

7 

 
 
 
 
 
 
 
 
 
 
 
2.2  Forest definition 

A forest definition is needed to create a global forest map from the two COP datasets: Land Cover Classification 
and Cover Fractions. Land cover classes and a threshold for forest fractional cover need to be selected. This 
selection is replicating on the selection made with FAO for the State of the World’s Forests (SOFO) 2020 report 
that is aimed to correspond at closest to the FAO definition of forest and to the FAO Global Forest Resources 
Assessment reporting process. 

Forest is defined for pixels being either in the Discrete Land Cover class “Closed Forest” (DC map code in [111-
116]) or where the Forest Fractional Cover is at least 30% (FC map code > 29). On this forest mask we assign 
pixels to Missing where the Discrete Land Cover class is either Missing (DC map code 0) or Ocean (DC map code 
200). In the last step, inland waters (DC map code 80) are overplotted resulting in the final forest mask with 
the following assignment: 

Value 

Land cover class 

RGB – Colour Code 

Description 

Table 1. Class names and description of the forest mask. 

0 

1 

2 

3 

Missing 

0/0/0 

Ocean, no data, or outside reporting unit 

Background 

255/255/210 

Forest 

Water 

50/150/85 

0/100/255 

Non-forest 

Forest 

Inland waters 

Source: JRC, 2022. 

This process results in a continuous global forest mask in the original projection for Tier 1 analysis with the 
name: fm_2019_4326.tif. 

Tier 2 analysis requires a reprojected forest mask in equal-area projection. The Goode-Homolosine Land (EPSG: 
54052) projection is designed to minimize distortion for the entire world. It is an interrupted pseudocylindrical 
equal-area projection. Statistical per-pixel analysis is facilitated by assigning a pixel resolution of 100 meters, 
which is achieved with the following GDAL command sequence: 

> gdalwarp -s_srs EPSG:4326 -t_srs ESRI:54052 -tr 100 100 -te -20037400 -6539900 20037400 
8190700 -r near -ot Byte -of GTIFF -co COMPRESS=LZW fm_2019_4326.tif fm_2019_54052.tif 

>  gdal_edit.py  -mo  TIFFTAG_IMAGEDESCRIPTION="Global  forest  mask  2019  3-class",  ESRI:54052 
fm_2019_54052.tif 

The first and second option (-s_srs and -t_srs) of the gdalwarp command specify the source and target 
projection,  followed  by  the  target  pixel  resolution  (-tr),  target  map  extent  (-te),  resampling  method  (-r), 
output  data  type,  file  format,  and  compression.  The  gdal_edit  command  simply  adds  a  further  image  data 
description to the existing metadata for the Tier 2 forest mask: fm_2019_54052.tif 

2.3  Forest Attribute Layers 

This section describes the processing steps applied to the forest map in Tier 1 and Tier 2 and resulting in the 
individual Forest Attribute Layers (FAL) outlined under KF3. 

2.3.1  Fragmentation 

Forest  fragmentation  is  conducted  via  the  GWB-module  GWB_FRAG  and  using  the  methodology  FAC  (Forest 
Area  Clustering).  Forest  pixels  are  considered  connected  if  they  share  a  common  pixel  border  or  edge  (8-
connectivity). As agreed under KF3, fragmentation is analysed at a Fixed Observation Scale (FOS) of ~ 10 km2, 
corresponding to moving window size of 31 x 31 pixel, or 961 hectares. We use the five-class reporting scheme, 

8 

 
 
 
 
 
 
 
 
 
 
named  FAC5, further  detailed in  the  Fragmentation product  sheet.  This  analysis  results  in  two  global maps 
fm_2019_<EPSG>_FAC5_31.tif and tabular summary statistics. In Tear 2, the global equal-area maps are cut 
and summarised by reporting unit.  

Note: the same analysis is also conducted for the additional fixed observation scale of ~100 km2 (99 x 99 pixel) 
resulting  in  an  alternative  parallel  dataset  of  fragmentation  (fm_2019_<EPSG>_fac5_99.tif)  derived  for  a 
larger observation scale. The alternative observation scale of ~100 km2 may be used by other end-users or in 
conjunction with default scale of ~ 10 km2 to study fragmentation across varying scales. 

Table 2. Class names and description of the FAL: Fragmentation. 

Colour 

Forest fragmentation 

Forest connectivity 

Forest cover (1) 

Forest Area Clustering (FAC) 
(1) 

Very high 

Very low 

High 

Low 

Rare 

Patchy 

[0 FAC < 10] % 

[10  FAC < 40] % 

Intermediate 

Intermediate 

Transitional 

[40  FAC < 60] % 

Low 

Very low 

High 

Dominant 

[60  FAC < 90] % 

Very high 

Interior 

[90  FAC  100] % 

(1) Fragmentation product sheet: https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Fragmentation.pdf 

Source: JRC, 2022. 

2.3.2  Accounting 

The  analysis  of  accounting  is  conducted  using  the  GWB-module  GWB_ACC.  Forest  pixels  are  considered 
connected if they share a common pixel border or edge (8-connectivity). Accounting is setup for six forest patch 
size classes: [100; 1,000; 10,000; 100,000; 1,000,000; > 1,000,000] hectares. The analysis is applied to both 
tiers and results in two global maps fm_2019_<EPSG>_acc.tif and tabular summary statistics. In Tear 2, the 
global equal-area maps are cut and summarised by reporting unit.  

Table 3. Class names and description of the FAL: Accounting. 

Colour 

Accounting class 

Forest patch size (1) [hectare] 

ACC 1 

ACC 2 

ACC 3 

ACC 4 

ACC 5 

ACC 6 

[1  100] 

[101  1,000]  

[1,001  10,000] 

[10,001  100,000] 

[100,001  1,000,000] 

[  1,000,001] 

(1) Accounting product sheet: https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Objects-Accounting.pdf  

Source: JRC, 2022. 

9 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2.3.3  Pattern 

Pattern of forest patches is described with Morphological Spatial Pattern Analysis (MSPA) and the analysis of 
pattern is conducted using the GWB-module GWB_MSPA. MSPA requires an input map encoded with 0 byte - 
missing (optional), 1 byte - background, and 2 byte – foreground. This requirement is accomplished by recoding 
inland waters, 3 byte in the original forest map, to background, and in this way setting up the MSPA-compatible 
binary input forest maps: fm_2019_<EPSG>_2.tif. The pattern analysis is then conducted with the default MSPA 
settings 8111: 8-connectivity, a forest edge width of one (1) pixel, show (1) Transition pixels, and distinguish 
(1) between internal and external features. Further details are available in the MSPA Guide and the Morphology 
two  global  maps 
product 
fm_2019_<EPSG>_2_8_1_1_1.tif and tabular summary statistics. In Tear 2, the global equal-area maps are 
cut and summarised by reporting unit.  

The  analysis 

is  applied 

to  both 

resulting 

sheet. 

tiers, 

in 

Figure 3. Class names and description of the FAL: Pattern (MSPA1). 

(1) Pattern product sheet: https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Pattern-Morphology.pdf  

Source: JRC, 2022. 

2.3.4  Network 

The network analysis is conducted using the GWB-module GWB_RSS, where the status of the forest network is 
described via a set of indicators (see Table 4). The network indicators are outlined in the GTB Restoration Planner 
product  sheet  and  they  are  complemented  with  statistics  from  the  Accounting  module.  The  network  status 
assessment is the only FAL that does not provide a spatially explicit map. Consequently, the network assessment 
by reporting unit cannot be cut from the global map assessment but must be conducted on the forest map of 
each reporting unit. This implies that the forest network of a given reporting unit is seen and treated like an 
isolated  and  confined  area,  where  forest  patches  in  the  neighbourhood  of  the  reporting  unit  are  neglected. 
Moreover, the network status assessment is based on the area of the forest patches. Consequently, the network 
analysis can only be derived for equal-area Tier 2 dataset and each of the 278 reporting units. The network 
indicators are summarised in the following table: 

10 

 
 
 
 
 
 
Table 4. Class names and description of the FAL: Network. 

Network indicator 

Description (1) 

RAC 

NR_OBJ 

Reference Area Coverage: forest area/reporting unit area [%] 

Total number of forest patches   

LARG_OBJ 

Size of the largest forest patch [hectare] 

APS 

COH 

Average forest patch size [hectare] 

Coherence: degree of spatial network intactness [%] 

REST_POT 

Restoration Potential: 100 – COH [%] 

(1)  Restoration Planner product sheet: https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-RestorationPlanner.pdf  

Source: JRC, 2022. 

2.4  Reporting units 

The reporting units are 278 comprising 255 countries, 21 Global Ecological Zones, the European Union of 27 
member states (EU27), and the full global coverage (see Table 5). 

The  255  country  boundaries  were  derived  from  Eurostat  GISCO  (Geographic  Information  System  of  the 
COmmission). This spatial data set  of world countries (GISCO2020) is a vector file  in ESRI shapefile format 
providing  the  boundaries  of  257  countries  with  a  precision  scale  of  1:100,000  projected  in  the  European 
Terrestrial Reference System 1989 ETRS89 (EPSG: 4258). From this data set the Antarctic was removed, as 
outside of the COP’s extent, and the British Virgin Islands and the Anguilla (both British Overseas Territories in 
the Caribbean) were merged in order to count a maximum of 255 values necessary to create a GeoTIFF raster 
layers in byte data type. 

The 21 Global Ecological Zones (GEZ) were derived from the Global Forest Resources Assessment (FRA) of the 
Food  and  Agriculture  Organization  of  the  United  Nations  (FAO).  The  GEZ  spatial  dataset  version  2010  (2nd 
edition) is a vector file in ESRI shapefile format with a precision scale of 1:100,000 projected in World Geodetic 
System 1984 WGS84 (EPSG: 4326). 

Both  vector  layers  were  first  re-projected  from  their  original  projections  to  Goode-Homolosine  Land  (EPSG: 
54052), then rasterized with a pixel resolution of 100 x 100 m, having the same extent, pixel resolution and 
spatial  reference  system  as  the  COP.  The  following  GDAL  command  sequence  provides  an  example  of 
rasterization parameters used for the layer GISCO2020: 

> gdal_rasterize -at -tr 100 100 -a OBJECTID -tap -co "COMPRESS=LZW" -ot Byte -a_nodata 0 -te 
-20037400 -6539900 20037400 8190700 CNTR_RG_100K_2020_54052.shp GISCO2020.tif 

The GISCO2020 and the GEZ are vector layers with a precision scale of 1:100,000. At this scale, the boundary 
shapes are simplified and small-scale features might be not well-defined or even detected. As the two layers 
were  created  from  different  sources,  they  provide  the  same  features  with  different  levels  of  detail  and 
simplification, consequently their coastlines do not overlap precisely.  

In order to harmonize all datasets, a binary inland mask was used derived from the COP. The COP land cover 
layer has a higher precision resulting from remote sensing with a pixel resolution of 100 m. The binary inland 
mask was created re-classifying the land cover classes to 1, except the class “open sea” to 0. Successively, the 
GISCO2020 and GEZ raster layers were expanded towards the waters by 100 pixels (10 km), and finally masked 
with the COP’s binary inland mask. For the raster expansion with different values, the function Expand provided 
in ESRI ArcGIS Pro 2.8 with Spatial Analyst extension was adopted. 

11 

 
 
 
 
Table 5. List of 278 reporting units extracted from EUROSTAT GISCO and FAO GEZ. 

REPORTING UNIT 
Andorra 
United Arab Emirates 
Afghanistan 
Antigua and Barbuda 
Albania 
Armenia 
Angola 
Argentina 
American Samoa 
Austria 
Australia 
Aruba 
Azerbaijan 
Bosnia and Herzegovina 
Barbados 
Bangladesh 
Belgium 
Burkina Faso 
Bulgaria 
Bahrain 
Burundi 
Benin 
Saint Barthélemy 
Bermuda 
Brunei 
Bolivia 
Bonaire, Sint Eustatius, Saba 
Brazil 
Bahamas 
Bhutan 
Bouvet Island 
Botswana 
Belarus 
Belize 
Canada 
Cocos (Keeling) Islands 
DR Congo 
Central African Republic 
Congo 
Switzerland 
Côte d'Ivoire 
Cook Islands 
Chile 
Cameroon 
China 
Colombia 
Clipperton Island 
Costa Rica 
Cuba 
Cape Verde 
Curaçao 
Christmas Island 
Cyprus 
Czechia 
Germany 
Djibouti 
Denmark 
Dominica 
Dominican Republic 
Algeria 
Ecuador 
Estonia 
Egypt 

ID 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
15 
16 
17 
18 
19 
20 
21 
22 
23 
24 
25 
26 
27 
28 
29 
30 
31 
32 
33 
34 
35 
36 
37 
38 
39 
40 
41 
42 
43 
44 
45 
46 
47 
48 
49 
50 
51 
52 
53 
54 
55 
56 
57 
58 
59 
60 
61 
62 
63 
64  Western Sahara 
65 
66 
67 
68 
69 
70 

Greece 
Eritrea 
Spain 
Ethiopia 
Finland 
Fiji 

REPORTING UNIT 

Northern Mariana Islands 

REPORTING UNIT 
Falkland Islands 
Micronesia 
Faroes 
France 
Gabon 
Grenada 
Georgia 
Guernsey 
Ghana 
Gibraltar 
Greenland 
Gambia 
Guinea 
Equatorial Guinea 
South Georgia & South Sandwich Islands 
Guatemala 
Guam 
Guinea-Bissau 
Guyana 
Hong Kong 
Heard Island & McDonald Islands 
Honduras 
Croatia 
Haiti 
Hungary 
Indonesia 
Ireland 
Israel 
Isle of Man 
India 
British Indian Ocean Territory 
Iraq 
Iran 
Iceland 
Italy 
Jersey 
Jamaica 
Jordan 
Japan 
Kenya 
Kyrgyzstan 
Cambodia 
Kiribati 
Comoros 
Saint Kitts and Nevis 
North Korea 
South Korea 
Kuwait 
Cayman Islands 
Kazakhstan 
Laos 
Lebanon 
Saint Lucia 
Liechtenstein 
Sri Lanka 
Liberia 
Lesotho 
Lithuania 
Luxembourg 
Latvia 
Libya 

ID 
71 
72 
73 
74 
75 
76 
77 
78 
79 
80 
81 
82 
83 
84 
85 
86 
87 
88 
89 
90 
91 
92 
93 
94 
95 
96 
97 
98 
99 
100 
101 
102 
103 
104 
105 
106 
107 
108 
109 
110 
111 
112 
113 
114 
115 
116 
117 
118 
119 
120 
121 
122 
123 
124 
125 
126 
127 
128 
129 
130 
131 
132  Morocco 
133  Monaco 
134  Moldova 
135  Montenegro 
136  Madagascar 
137  Marshall Islands 
138 
North Macedonia 
139  Mali 
140  Myanmar/Burma 

ID 
141  Mongolia 
142  Macau 
143 
144  Mauritania 
145  Montserrat 
146  Malta 
147  Mauritius 
148  Maldives 
149  Malawi 
150  Mexico 
151  Malaysia 
152  Mozambique 
Namibia 
153 
New Caledonia 
154 
Niger 
155 
Norfolk Islands 
156 
Nigeria 
157 
Nicaragua 
158 
Netherlands 
159 
Norway 
160 
Nepal 
161 
Nauru 
162 
Niue 
163 
New Zealand 
164 
Oman 
165 
Panama 
166 
Peru 
167 
French Polynesia 
168 
Papua New Guinea 
169 
Philippines 
170 
Pakistan 
171 
Poland 
172 
Saint Pierre and Miquelon 
173 
Pitcairn Islands 
174 
Puerto Rico 
175 
Palestine 
176 
Portugal 
177 
Palau 
178 
Paraguay 
179 
Qatar 
180 
Romania 
181 
Serbia 
182 
Russian Federation 
183 
Rwanda 
184 
Saudi Arabia 
185 
Solomon Islands 
186 
Seychelles 
187 
Sudan 
188 
Sweden 
189 
Singapore 
190 
Saint Helena, Ascension, Tristan Da Cunha 
191 
Slovenia 
192 
Svalbard and Jan Mayen 
193 
Slovakia 
194 
Sierra Leone 
195 
San Marino 
196 
Senegal 
197 
Somalia 
198 
Suriname 
199 
South Sudan 
200 
São Tomé and Príncipe 
201 
El Salvador 
202 
Sint-Maarten 
203 
Syria 
204 
Eswatini 
205 
Turks and Caicos Islands 
206 
Chad 
207 
French Southern and Antarctic Lands 
208 
Togo 
209 
210 
Thailand 
Source: JRC, 2022. 

REPORTING UNIT 
ID 
Tajikistan 
211 
Tokelau 
212 
Timor-Leste 
213 
Turkmenistan 
214 
Tunisia 
215 
Tonga 
216 
Turkey 
217 
Trinidad and Tobago 
218 
Tuvalu 
219 
United Republic of Tanzania 
220 
Ukraine 
221 
Uganda 
222 
United Kingdom 
223 
United States Minor Outlying Islands 
224 
United States 
225 
Uruguay 
226 
Uzbekistan 
227 
Vatican City 
228 
Saint Vincent and The Grenadines 
229 
Venezuela 
230 
British Virgin Islands and Anguilla 
231 
US Virgin Islands 
232 
Vietnam 
233 
234 
Vanuatu 
235  Wallis and Futuna 
236 
237 
238 
239 
240 
241 
242 
243 
244 
245 
246 
247 
248 
249 
250 
251 
252 
253 
254 
255 
256 
257 
258 
259 
260 
261 
262 
263 
264 
265 
266 
267 
268 
269 
270 
271 
272 
273 
274 
275 
276 
277 
278 

Samoa 
Paracel Islands 
Spratly Islands 
Aksai Chin 
Arunachal Pradesh 
China/India 
Halaib Triangle 
Ilemi Triangle 
Jammu Kashmir 
Kuril Islands 
Navassa Island 
Scarborough Reef 
Senkaku Islands 
Bassas Da India 
Abyei 
Bir Tawil 
Yemen 
South Africa 
Zambia 
Zimbabwe 
European Union (EU27) 
Global 
GEZ_1: Boreal coniferous forest 
GEZ_2: Boreal mountain forest 
GEZ_3: Boreal tundra woodland 
GEZ_4: Polar 
GEZ_5: Subtropical desert 
GEZ_6: Subtropical dry forest 
GEZ_7: Subtropical humid forest 
GEZ_8: Subtropical mountain system 
GEZ_9: Subtropical steppe 
GEZ_10: Temperate continental forest 
GEZ_11: Temperate desert 
GEZ_12: Temperate mountain system 
GEZ_13: Temperate oceanic forest 
GEZ_14: Temperate steppe 
GEZ_15: Tropical desert 
GEZ_16: Tropical dry forest 
GEZ_17: Tropical moist forest 
GEZ_18: Tropical mountain forest 
GEZ_19: Tropical rainforest 
GEZ_20: Tropical shrubland 
GEZ_21: Water 

12 

 
 
 
 
 
 
 
 
 
 
 
 
2.5  Data provision 

The IDL source code (eufo.pro) provides all processing steps. It comes bundled with all input/output maps and 
statistics in a single directory EUFO together with this ATBD, available on FigShare at: 
https://doi.org/10.6084/m9.figshare.21177175.  

Further details on the individual processing steps can be found in the Annex or directly in the script eufo.pro. As 
outlined in KF5, the final dataset is setup in two tiers. Figure 4 provides an overview of the first processing 
chain to derive the global FAL maps for accounting, fragmentation, and pattern in both tiers. 

Figure 4. Processing chain to derive the global FAL maps for Tier 1 and 2. 

Source: JRC, 2022. 

Figure 5 provides an overview of the second processing chain to derive Tier 2 equal-area maps and summary 
statistics for the 278 reporting units and the FAL accounting, fragmentation, and pattern, which are masked 
out from the respective global FAL maps. A third processing chain derives forest network statistics from equal-
area forest maps of each reporting unit. Summary statistics for all four FAL are finally assembled into a single 
spreadsheet, which can then be filtered for the entry of interest. 

Figure 5. Processing chain to derive maps and statistics for the Tier 2 reporting units. 

Source: JRC, 2022. 

13 

 
 
 
 
3 

Implementation example 

This section uses the reporting unit Luxembourg to exemplify the individual Tier 2 FAL products with details on 
the resulting maps and statistics.  We choose a small reporting unit to visualise local features that would not 
be visible in this report for large reporting units. 

3.1  Forest Fragmentation 

Figure 6 shows the forest map of Luxembourg (top left), the five degrees of forest fragmentation (top right), 
and the statistical summary (bottom panel). For example, red colour highlights areas where forest cover is rare, 
equivalent to the degree in forest fragmentation being very high. All forest pixels in Luxembourg with FAC < 10 
% are colour-coded in red. The bottom panel of the figure shows the statistical summary for the same reporting 
unit: the total reporting unit area (derived from the rasterised COP map with a pixel resolution of 1 hectare) is 
261,491 hectares, of which 116,979 hectares are forest, or 44.74 % of the reporting unit is forest. FACAV, or 
the average degree in forest connectivity across all forest pixels in Luxembourg is 51.07 %. 0.96 % of all forest 
pixels in Luxembourg are rare forest cover (red). Here, the degree of forest connectivity is very low (less than 
10 %), equivalent to the degree of forest fragmentation is very high. Similarly, 35.32 % of the forest cover in 
Luxembourg shows low fragmentation (light green). Low fragmentation is equivalent to high connectivity and 
covering areas where forest cover is dominant within a local neighbourhood of 961 hectares.   

The  FAL  Fragmentation  allows  locating  hotspots  of  fragmentation  and  provides  an  overview  of  the  spatial 
arrangement of the forest cover and its geographic variability in forest fragmentation. Please note that each 
reporting unit is masked out from the global map, which ensures correct fragmentation assessments at the 
boundary of the reporting unit. 

Figure 6. Forest map and the FAL Fragmentation in Luxembourg.  

Source: JRC, 2022. 

14 

 
 
 
 
3.2  Forest Accounting 

Figure 7 shows the FAL Accounting and summary statistics for the reporting unit Luxembourg. The legend shows 
the colour codes for the six forest patch size class categories. The bottom panel shows the proportion of each 
accounting class with respect to the total forest area in the reporting unit. For example, 9.61 % of the forest 
area of Luxembourg is found in the forest patch size category 2, colour-coded in red and having patch sizes 
between 101 and 1,000 hectares.  

The FAL Accounting provides information on the location and distribution for all forest patches within six forest 
patch  size  classes.  Please  note  that each reporting unit is masked out from the  global map,  which  ensures 
correct Accounting assessments at the boundary of the reporting unit. 

Figure 7. FAL Accounting in Luxembourg.  

Source: JRC, 2022. 

15 

 
 
3.3  Forest Pattern 

Figure 8 shows the FAL Pattern and summary statistics for the reporting unit Luxembourg. The legend shows 
the colour codes for the various feature classes. The bottom panel shows the proportion of seven main classes 
with respect to the total forest area in the reporting unit. For example, 56.87 % of the forest area of Luxembourg 
is Core forest, colour-coded in green. Porosity accounts for the number and size of holes inside  Core forest 
areas and hence measures the deviation from spatially intact Core area, which in this case amounts to ~ 5 %. 
Further details on pattern and its reporting are available in the MSPA Guide and the Morphology product sheet.  

The FAL Pattern provides information on the location and morphological attributes of all forest patches. Please 
note that each reporting unit is masked out from the global map, which ensures correct Pattern assessments 
at the boundary of the reporting unit. 

Figure 8. FAL Pattern in Luxembourg.  

Source: JRC, 2022. 

16 

 
 
3.4  Forest Network 

Table 6 shows the indicators of the FAL Network for the reporting unit Luxembourg in 2019. The FAL Network 
evaluates the forest network of a given reporting unit and summarises its network attributes in a series of 
indicators. In this example, we find that 44.74 % (RAC) of the reporting unit is covered by forests, there is a 
total of 1,052 individual forest patches, the largest forest patch within the limits of the reporting unit is 68,749 
hectares, the average patch size inside Luxembourg is 111.20 hectares, the degree of spatial network coherence 
is 59.77 %, and its complement, the restoration potential, is 40.23 %. 

Please  note  that  all  network  indicators  are  strictly  confined  to  the  area  covered  by  the  reporting  unit.  For 
example,  the  largest  forest  patch within  the limits  of  this  reporting  unit  is  68,749  hectares.  However,  from 
Figure 7 it is evident that the large forest patches of Luxembourg are part of even larger continuous forest 
patches extending beyond the boundaries of Luxembourg.  

Table 6. FAL Network in Luxembourg. 

Source: JRC, 2022. 

3.5  Access to the FAL through the EUFO web platform 

Figure 9 shows a developer snapshot of the EUFO web interface consisting of a map and an interface to select 
a FAL and the reporting unit of interest.   

Figure 9. List of FAL in EUFO.  

Source: JRC, 2022. 

17 

 
 
 
 
Figure 10 shows the EUFO developer snapshot for the FAL Fragmentation and the reporting unit Luxembourg 
in 2019. After selecting a reporting unit, a respective statistics panel provides summary and speedometer charts 
of the various FAL for the selected reporting unit.  

Figure 10. FAL Fragmentation for Luxembourg 2019 in EUFO.  

Source: JRC, 2022. 

Please note that these figures are early developer snapshots as of September 2022, which may look different 
in the final version of EUFO website. 

18 

 
 
Conclusions 

This document describes a methodology to map and quantify four forest attribute layers (FAL) at global scale. 
The approach is based on  building a forest map from an acknowledged  land cover classification. For visual 
inspection  in  the  EUFO,  the  forest  map,  fragmentation,  accounting  and  pattern  are  derived  in  the  original 
projection  WGS84  projection  EPSG:4326.  Area  estimates  and  statistical  summaries  are  derived  from  forest 
attribute maps in equal-area projection Goode-Homolosine Land EPSG:54052. Maps and statistics are derived 
for a total of 279 reporting units and the assessment year 2019.  

The  four  FAL  Fragmentation,  Accounting,  Pattern  and  Network  address  a  wide  range  of  morphological  and 
geographic  forest  features.  The  FAL  concept  assures  to  simultaneously  consider  key  aspects  of  forest 
connectivity including, the amount of forest, the degree of spatially continuous forest cover, perforations inside 
forest  patches,  patch  shape  and  linear  features,  and  the  distance  between  individual  forest  patches.  The 
accuracy of the derived status maps strictly depends on the accuracy of the COP input classification and may 
differ from official statistics on the forest area obtained by other means. In this context, the results of this 
study are indicative but consistent and comparable across the mapping space. 

The  purpose  of  the  map  product  is  to  exploit  the  spatial  information,  which  cannot  be  retrieved  from 
summary indices and statistics: a geographic map showing the varying degree in forest attributes is not only 
visually  appealing  but  permits  localizing  hotspots  of  fragmentation,  which  may  support  planning  and  risk 
assessment. The map product is useful to address spatially explicit assessment questions (where) such as:  

•  Where are forests patches mostly contiguous or fragmented?  

•  Where in the reporting unit are the small (large) forest patches?  

•  Where are forest patches close to each other (important for restoration questions)? 

-  Where are, or which forests patches are in high-risk areas (comparing/intersecting the  FAL 

- 

map product with other environmental data layers in a GIS)? 
Trend  analysis:  a  change  map  allows  to  delineate  areas  of  improvement/degradation  or 
locating areas where the situation is stable. Such geographic detail is essential in landscape 
management, biodiversity assessments and policy planning. It may also be used to control 
and measure progress of locally applied political directives.  

The purpose of the summary statistics is to provide a concise and comparative overview allowing to answer 
essential quantitative assessment questions (how much) such as:  

•  Which reporting unit has the highest overall degree of forest connectivity (sort column L)? 

•  Which reporting unit has the highest forest network coherence (sort column AK)? 

•  Which reporting unit has the lowest degree in porosity/highest proportion of spatially intact core forest 

(sort column AF)? 

The above, and other similar questions can be easily addressed by using the filter function in the summary 
spreadsheet  (forest_stats2019_*.xlsx).  Definitions  of  each  field  code  in  the  spreadsheet  are  provided  in  the 
second sheet “field_code_definition”.  

19 

 
 
 
 
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Service: Land Cover 100m: Collection 2: epoch 2015. Dataset of the global component of the Copernicus Land 
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FOREST EUROPE, 2020. State of Europe’s Forests 2020, https://foresteurope.org/wp-
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21 

 
 
 
 
 
List of acronyms 

APS 

ATBD 

BDAP 

COP 

DC 

EU 

Average Patch Size 

Algorithmic Technical Basis Document 

Big Data Analytics Platform (https://jeodpp.jrc.ec.europa.eu/bdap/) 

COPERNICUS Land Cover 

COPERNICUS Discrete Classification 

European Union 

EUFO 

EU Observatory on deforestation, forest degradation, changes in the world’s forest cover, and  

associated drivers 

EUROSTAT 

ESTAT (European Statistical Office), Directorate-General of the European Commission 

FAO 

FAC 

Food and Agriculture Organization of the United Nations 

Forest Area Clustering 

FACAV 

Average FAC value of all forest pixels 

FAD 

FAL 

FC 

FOS 

GEZ 

GDAL 

GIS 

GISCO 

GTB 

GWB 

JRC 

KF 

MAES 

MSPA 

SEPAL 

Forest Area Density 

Forest Attribute Layer 

COPERNICUS Cover Fractions: Forest 

Fixed Observation Scale 

Global Ecological Zone 

Geospatial Data Abstraction Library (https://gdal.org/) 

Geographic Information System 

Geographic Information System of the Commission 

GuidosToolbox (https://forest.jrc.ec.europa.eu/en/activities/lpa/gtb/) 

GuidosToolbox Workbench (https://forest.jrc.ec.europa.eu/en/activities/lpa/gwb/) 

Joint Research Centre of the European Commission 

Key findings 

Mapping and Assessment of Ecosystems and their Services 

(Morphological Spatial Pattern Analysis (https://forest.jrc.ec.europa.eu/en/activities/lpa/mspa/) 

System for Earth Observations, data access, processing & analysis for land monitoring  

(https://sepal.io/) 

USFS 

United States Department of Agriculture - Forest Service 

22 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of figures 

Figure 1. Copernicus Global Land Cover download interface. ..................................................... 6 

Figure 2. Copernicus download interface: selection of Land Cover Classification and Cover Fractions. ........ 7 

Figure 3. Class names and description of the FAL: Pattern (MSPA1). ............................................ 10 

Figure 4. Processing chain to derive the global FAL maps for Tier 1 and 2. .................................... 13 

Figure 5. Processing chain to derive maps and statistics for the Tier 2 reporting units. ....................... 13 

Figure 6. Forest map and the FAL Fragmentation in Luxembourg. .............................................. 14 

Figure 7. FAL Accounting in Luxembourg. ......................................................................... 15 

Figure 8. FAL Pattern in Luxembourg. ............................................................................. 16 

Figure 9. List of FAL in EUFO. ...................................................................................... 17 

Figure 10. FAL Fragmentation for Luxembourg 2019 in EUFO. ................................................. 18 

23 

 
 
List of tables 

Table 1. Class names and description of the forest mask. ......................................................... 8 

Table 2. Class names and description of the FAL: Fragmentation. ................................................. 9 

Table 3. Class names and description of the FAL: Accounting. ..................................................... 9 

Table 4. Class names and description of the FAL: Network. ...................................................... 11 

Table 5. List of 278 reporting units extracted from EUROSTAT GISCO and FAO GEZ. ........................... 12 

Table 6. FAL Network in Luxembourg. ............................................................................. 17 

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Annex 

This section describes the file hierarchy and input and output formats in the various sub-directories of the base-
directory  EUFO  available  at:  https://doi.org/10.6084/m9.figshare.21177175.  Files  are  shown  in  italics  and 
directories in bold. 

•  2019: directory with input/output data for FAL analysis: 

-  4326: input/output data in projection EPSG:4326 
-  54052: input/output data in projection EPSG:54052 

•  docs: directory with general documentation: 

- 
- 
- 

CGLOPS1_ATBD_LC100V3_I3.4.pdf: COP manual, downloaded December 2021. 
forest_stats2019.xslx: searchable summary statistics with field code definitions. 
ru_list.csv: list of reporting units. 

•  gez: directory with map and list of Global Ecological Zones boundaries. 
•  GISCO2020: directory with map and list of country boundaries. 
• 

eufo.pro, eu27q.sav, fmapcolors.sav: IDL source code and saved settings necessary to process the four 
FAL layers from the Copernicus dataset. 

Note: to keep the file size of the base-directory EUFO to the minimum, intermediate data sets and the original 
COPERNICUS land cover data set are not included in the results. 

All  map  products  are  provided  in  GeoTIFF  format  and  in  the  data  type  byte.  Details  of  the  geo-header 
information can be retrieved with the command gdalinfo or from your preferred GIS application. Details on 
the input and output pixel values are summarized in the following figures: 

Copernicus global maps: 

COP maps: directory: 2019/4326/composite/    

File name: CF_forest_global_2019.tif and LCC_discrete_global_2019.tif 

Value 

Class 

RGB – Colour Code 

Description (see section 2.1.2) 

See: https://lcviewer.vito.be and https://lcviewer.vito.be/about  

Forest maps: global and per reporting unit: 

Forest masks: directory: 2019/4326/ or 2019/54052/ 

File name: fm_2019_<proj>.tif or fm_2019_<proj>_2.tif 

Value 

0 

1 

2 

3 

Class 

Missing 

RGB – Colour Code 

Description (see section 2.2) 

0/0/0 

Ocean, No data or outside reporting unit 

Background 

255/255/210 

Forest 

Water 

50/150/85 

0/100/255 

Non-forest 

Forest cover 

Inland waters 

Note: the fm_2019_<proj>_2.tif maps are recoded forest maps where inland water (3 byte) was remapped to 
Background (1 byte) to generate MSPA-compatible input maps. 

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
FAL – Fragmentation, global and per reporting unit: 

Fragmentation: directory: 2019/4326/fm_2019_4326_frag/ or 2019/54052/fm_2019_54052_frag/   

Value 

0 - 9 

Class 

Rare 

214/49/39 

10 - 39 

Patchy 

249/139/89 

File name: <reporting-unit>_fac5_<31/99>.tif 

RGB – Colour Code 

Description (see section 2.3.1 and Table 2) 

Forest patches in class: Rare 

Forest patches in class: Patchy 

40 - 59 

Transitional 

254/199/0 

Forest patches in class: Transitional 

60 - 89 

Dominant 

139/199/99 

Forest patches in class: Dominant 

90 - 100 

Interior 

0/174/0 

Forest patches in class: Interior 

101 

102 

105 

Background 

175/175/175 

Non-forest land cover 

Missing 

Water 

255/255/255 

0/100/255 

No data or outside reporting unit 

Inland waters 

FAL – Accounting, global and per reporting unit: 

Accounting: directory: 2019/4326/fm_2019_4326_acc/ or 2019/54052/fm_2019_54052_acc/  

File name: <reporting-unit>_acc.tif 

Value 

Class 

RGB – Colour Code 

Description (see section 2.3.2) 

0 

Background 

220/220/220 

103 

Size class 1 

Size class 2 

0/0/0 

255/0/0 

Non-forest land cover 

Forest patches: 1  100 ha 

Forest patches: 101  1,000 ha 

Forest patches: 1,001  10,000 ha 

Forest patches: 10,001  100,000 ha 

Forest patches: 100,001  1,000,000 ha 

Forest patches:  1,000,001 ha 

No data or outside reporting unit 

Inland waters 

33 

65 

1 

9 

17 

129 

105 

Size class 3 

255/255/0 

Size class 4 

255/140/0 

Size class 5 

Size class 6 

Missing 

Water 

160/60/0 

0/200/0 

255/255/255 

0/0/255 

26 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
FAL – Pattern, global and per reporting unit: 

Pattern: directory: 2019/4326/fm_2019_4326_2_mspa/ or 2019/54052/fm_2019_54052_2_mspa/  

File name: <reporting-unit>_mspa.tif 

RGB – Colour Code 

Description (see section 2.3.3) 

0/200/0 

160/60/0 

0/0/255 

0/0/0 

255/255/0 

255/255/0 

255/255/0 

255/0/0 

255/0/0 

255/0/0 

Interior area excluding Perforation and Edge 

Disjoint and too small to contain Core 

Internal patch perimeter 

External patch perimeter 

Connected to the same Core area 

Loop in Edge 

Loop in Perforation 

Connected to different Core areas 

Bridge in Edge 

Bridge in Perforation 

255/140/0 

Connected at one end to Edge. Perforation, Bridge or Loop 

Background 

220/220/220 

Border-Opening 

194/194/194 

Non-forest land cover 

Opening along Edge 

Core-Opening 

136/136/136 

Opening within Perforation 

Missing 

255/255/255 

No data or outside reporting unit 

Value 

17/117 

9/109 

Class 

Core 

Islet 

105 

Perforation 

Edge 

Loop 

Loop 

Loop 

Bridge 

Bridge 

Bridge  

Branch 

3/103 

65/165 

67/167 

69/169 

33/133 

35/135 

37/137 

1/101 

0 

220 

100 

129 

FAL – Network, global and per reporting unit: 

The  FAL  Network  provides  forest  network  statistics  only,  which  are  derived  from  the  forest  map  of  each 
reporting unit using the GWB-module GWB_RSS. Network statistics for all reporting units are finally added to 
the summary statistic table forest_stats2019.xlsx stored in the directory docs. 

27 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
28 

 
 
 
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