TOW_RELEASE_V1_NCEA_Report.pdf
Source: environment.data.gov.uk (2026-01-05)

National Trees Outside Woodland Map. The National Trees Outside Woodland (TOW) V1 map is a vector product funded by DEFRA’s Natural Capital and Ecosystem Assessment (NCEA) programme produced under Forest Research’s Earth Observation for Trees and Woodlands (EOTW) project.

The TOW map identifies canopy cover over 3m tall and 5m2 area which exists outside the National Forest Inventory (National Forest Inventory - Forest Research). Canopy cover is categorised into the following woodland types - lone trees, groups of trees and small woodlands.

The data set was derived from the Vegetation Object Model (VOM) (Environment Agency, EA), the National Lidar Survey (EA), and Sentinel-2 (European Space Agency) imagery using spatial algorithms. The method is fully automated with no manual manipulation or editing. The map and its production method has been quality assured by DEFRA science assurance protocols and assessed for accuracy using ground truth data.

Because the process classifies objects based on proximity to features within OS mapping, there could be some misclassifications of those objects not included in the OS (specifically: static caravans, shipping containers, large tents, marquees, coastal cliffs and solar farms).

This is a first release of this dataset, the quality of the production methods will be reviewed over the next year, and improvements will be made where possible.

The TOW map is available under open government licence and free to download from the Forestry Commission open data download website (Forestry Commission) and view online on the NCEA ArcGIS Online web portal (Trees Outside Woodland). A full report containing details on methodology, accuracy and user guide is available.

TOW map web portal link : ncea.maps.arcgis.com/apps/instant/sidebar/index.html?appid=cf571f455b444e588aa94bbd22021cd3

FR TOW map web page :  https://www.forestresearch.gov.uk/tools-and-resources/fthr/trees-outside-woodland-map/  Attribution statement: © Forestry Commission copyright and/or database right 2025. All rights reserved.

The Research Agency of the
Forestry Commission

NCEA: National Trees
Outside Woodland Map

Method and User Guide Report

Freddie Hunter, Ben Ditchburn,
Nancy Burns, Conor Strong

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Forest Research is the Research Agency of the Forestry Commission and is the leading
UK organisation engaged in forestry and tree related research.
The Agency aims to support and enhance forestry and its role in sustainable
development by providing innovative, high quality scientific research, data, technical
support and consultancy services.
Acknowledgement: Funded by the UK Government through Defra’s Natural Capital
and Ecosystem Assessment programme (NCEA) Natural Capital and Ecosystem
Assessment Programme - GOV.UK

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Table of Contents
1
Introduction ............................................................................................ 4
1.1
Outcomes ............................................................................................... 5
2
Remote Sensing Data Sources .................................................................. 6
3
Methodology............................................................................................ 7
3.1
Tree Identification.................................................................................... 7
3.2
NFI Removal ........................................................................................... 7
3.3
Feature Classification ............................................................................... 8
4
Map Assessment and Usage .................................................................... 10
4.1
Map Summary Statistics ......................................................................... 10
4.2
Accuracy Assessment ............................................................................. 11
4.2.1
TOW classification confusion matrix ...................................................... 13
4.4
User Guide ............................................................................................ 15
4.5
References ............................................................................................ 18

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1 Introduction
The Trees Outside Woodland (TOW) map is a project funded by the UK Government
through Defra’s Natural Capital and Ecosystem Assessment programme (NCEA) to
map all trees above 3m outside of woodland, including small woodlands, groups of
trees and lone trees in England. Approximately 30% of tree cover in England exists
outside of the National Forest Inventory (NFI) woodland map (Forestry Commission,
2017). The NFI contains only woodlands greater than 0.5ha in size and therefore,
until now TOW have been unmapped by a government agency. TOW provide valuable
ecosystem services such as habitat, reduction of soil erosion, carbon sequestration,
connectivity, and access to urban green space.
This map represents the first version of the NCEA TOW spatial dataset and provides
an initial representation of lone trees, groups of trees, and small woodlands based
on LiDAR and satellite imagery. However, it is important to note that this version
(V1) will not fully differentiate between linear groups of trees (and other similarly
shaped features) and hedgerows, leading to some misclassification. In 2025, we aim
to update the TOW map to include hedges over 0.5m in height and a clear separation
between linear groups of trees and hedges. This layer will also be made available
separately in the first instance. The hedgerow dataset will provide a more accurate
distinction between hedgerows and trees. As a result, there may be discrepancies
between the findings of this map and the forthcoming hedgerow layer, including areas
where the new dataset detects features not represented in V1. Users should consider
this when producing statistics or making landscape level assumptions based on V1 of
the map.
TOW are continually assessed as a natural capital asset through the NCEA programme
via field work surveys. This map will support TOW surveying by providing extent and
location information for baselining and change across time, thereby enabling tracking
of the influence of government policy on TOW.

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The TOW map aims to provide geospatial data for environmental monitoring,
planning, and canopy expansion. This report describes the method used to map TOW
(version release V1) canopy cover across England from remote sensing technologies
and data science techniques. An accuracy assessment and a brief user guide are also
included.
1.1 Outcomes
1. Map the spatial location and extent of TOW canopy cover across England. This
output is presented as a vector dataset where each polygon delineates a TOW
feature.
2. Classification of TOW features into woodland type (lone trees, groups of trees,
small woodlands) according to characteristics, with additional attributes based on
area and height.
Table 1: TOW feature classification criteria by size only.

* Note the overlap between lone tree and group of trees, details of the criteria for
separation in the methods section 2.3 explain their separation.
3. Generation of accuracy statistics for the map product based on TOW hand mapping
(digitisation) and on the ground surveys of 1km2 areas.
4. Publication of the TOW map, making it available for public access with Open
Government Licence (OGL).
TOW Feature
Min Height
Min Area
Max Area
Lone Tree
3m
5m2
350m2
Group of Trees
3m
5m2
1000m2
Small Woodland
3m
1000m2
NA

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2 Remote Sensing Data Sources
All TOW features were detected and mapped directly from the Environment Agency’s
(EA) Vegetation Object Model (VOM) using advanced spatial algorithms and high-
performance computing architectures. The entire VOM dataset was acquired directly
from the EA.
The VOM was derived from the National LIDAR Programme (NLP). The NLP data was
captured using an airborne laser which accurately measures the height of terrain and
surface objects on the ground. The NLP data is of medium point density quality
(approx. 4ppm) and processed at high resolution (1m) to derive the Canopy Height
Model (CHM). The VOM is a raster product derived directly from the CHM where each
pixel represents the height value of vegetation above 2.5m. The VOM vegetation
classification was aided by Sentinel-2 imagery and spatial queries against Ordnance
Survey (OS) MasterMap. Infrastructure (e.g. buildings and bridges) in the OS
MasterMap were removed and remaining pixels above 2.5m were filtered for
vegetation using the Normalised Difference Vegetation Index (NDVI) from Sentinel-
2 imagery.
Table 2: Data sources used to create VOM which the TOW map is derived from.
Data Source
Date
Description
National LIDAR Programme
2017-2023
Point cloud data
Sentinel-2
2020
Satellite raster data
Ordnance Survey
NA
Infrastructure vector data

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3 Methodology
TOW release version V1 was generated using the method documented below.
All processing was completed using R version 4.2.1 (R Core Team 2023) and GDAL
Version 3.6 (GDAL/OGR 2024). The method relies heavily on the LidR package
(Roussel et al. 2021) and Simple Features package (Pebesma et al. 2018).
3.1 Tree Identification
The first stage is to filter the VOM to identify individual tree locations.
1. Modal filter binary (0 = <2.5m, 1 = 2.5m) VOM tile (1km2 raster) to remove
gaps in canopy.
2. Mean smooth each tile using a moving window to improve tree detection.
3. Run a watershed-based tree detection algorithm over each smoothed tile
(Roussel et al. 2021).
4. This results in a point per detected treetop (maxima) with height attributed.
5. Delineate (polygonise) each feature with area greater than 5m2 in the
smoothed tiles and count the number of trees detected in each.
6. Smooth polygon delineation using buffering and vertex simplification.
7. Remove any feature with a max height <3m or area < 5m2. These thresholds
were chosen to reduce inaccurate TOW detections.
3.2 NFI Removal
The TOW map strictly represents tree and woodland features outside the NFI. As a
result, any NFI woodland must be removed from the TOW map.
1. Acquire the NFI woodland map for 2022.

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2. Clip all NFI woodland from the delineation in step 4 of the previous, tree
identification procedure.
3. To clean the output dataset, remove any slivers remaining that touch NFI
woodland features with an area < 10m2.
3.3 Feature Classification
To aid the utility of the TOW map, features (polygons) were classified into woodland
types, see table 1 for basic definitions. The feature classification processing steps
were performed at the 10km2 tile level. This allowed for accurate classification of TOW
features that straddle 1km2 tile borders, while also processing at scale.
1. Gather all 1km2 tiles in each 10km2 tile into separate directories.
2. Load and combine all files in each 10km2 directory into a single spatial data
frame.
3. Merge all touching polygons.
4. Run Minimum Bounding Circle (MBC) algorithm and calculate MBC area.
5. Classify woodland type according to the criteria in table 3.
Table 3. Feature classification criteria. *MBCAP means MBC area percentage.
Woodland Type
Min Area
Max Area
Min Height
Tree Count
MBCAP
Lone Tree
5m2
350m2
3m
1
NA
Lone Tree
5m2
350m2
3m
NA
>0.6
Lone Tree
5m2
350m2
3m
2
>0.5
Group of Trees
5m2
1000m2
3m
=>2
<0.5
Small Woodland
1000m2
NA
3m
NA
NA

6. Features touching NFI woodland and within 10m buffered area of the NFI are
labelled NFI Over Hanging Canopy (OHC)*1.

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7. Assign each feature a unique ID.
8. Extract height (min, mean, max and standard deviation) per merged and
classified feature using the raw VOM.
9. Split 10km2 spatial data frame back to 1km2 tiles to improve tile downloading
and querying.
a. Recalculate area*2
10. Merge all 1km2 tiles per region of England.
*1 NFI OHC: NFI woodlands are delineated to the outer facing trunks, not to the
dripline (e.g. total canopy extent). The TOW mapping method delineates features to
the dripline. The TOW method therefore also delineates OHC associated with NFI
woodland. These features are not strictly TOW but have not been removed - they are
retained in the map for aesthetic purposes and to provide complete information on
tree canopy cover in England when used alongside the NFI.
*2 As a result, features can have an area < 1000m2 but have woodland type of small
woodland because all the parts which straddle multiple 1km2 tile add up to > 1000m2.
TOW ID is consistent for all parts of a feature that straddle 1km2 multiple tiles so the
total areas of the individual feature (falling with 10km2 tile) can be computed. The
IDs differ when crossing a 10km2 tile boundary.

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4 Map Assessment and Usage
4.1 Map Summary Statistics

Table 4: Canopy cover area in hectares for woodland types per region.
Region
Group of
Trees
Lone Tree
NFI OHC
Small
Woodland
East Midlands
19663
10785
6593
28342
Eastern
26823
13077
10955
45052
London
5868
3638
1252
5951
Northeast
6745
3897
4019
8276
Northwest
20125
11369
7305
22549
Southeast
31927
15005
22027
68059
Southwest
35464
xx-xxxx-xxxx
21099
74058
West Midlands
23678
12612
9671
37183
Yorkshire and the
Humber
xx-xxxx-xxxx
9320
6667
20973
England Total
186787
96578
89588
310443

Table 5: Feature count for woodland types per region.
Region
Group of
Trees
Lone Tree
NFI OHC
Small
Woodland
East Midlands
911825
2383111
349144
134499
Eastern
1220321
2996235
523857
201280
London
294944
1022011
71888
29297
Northeast
335417
947077
248852
40623
Northwest
961337
2629998
450647
113161
Southeast
1485276
3552456
849799
270209
Southwest
1633546
3860126
791757
298257
West Midlands
1041619
2620069
423698
167175
Yorkshire and the
Humber
800078
2162108
xx-xxxx-xxxx
99128
England Total
8684363
22173191
4092520
1353629

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Figure 1: Distribution of woodland type by area across regions in England and nationally.

4.2 Accuracy Assessment
TOW map detection accuracy has been assessed by comparison with features
mapped by hand from aerial photography in a sample of 271 1km squares, including
a representative sample of non-woodland area across 14 English regions. These
include upland, lowland, urban, and rural areas. Overall, the sample area represents
only a fraction of a percent of the total area of England, thus any statistics derived
from this data should be viewed with caution.
The TOW methodology outlined above using remote sensing technology and data
science techniques differs from the protocol used to hand map trees outside
woodlands in the sample squares mapped for calibration. Features are effectively
detected/mapped on a different area basis; in the remote sensing approach features
are detected to the canopy edge (dripline), whilst the mapping protocol dictates that
features are mapped to the line of the outermost trunks, excluding overhanging
canopy. Whilst the remote sensing methodology is less resource intensive and

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reflects a more inclusive definition of canopy cover and is therefore the most
appropriate for producing a map for the whole of England in a timely manner, the
more intensive approach used to hand map the sample squares provides a more
accurate classification of features.
Due to differences between the mapping protocol and the TOW detection
methodology described above, it is not possible to directly compare the total area of
tree features detected to those mapped by hand. Feature detection accuracy has
instead been assessed on a ‘hit rate’ basis, i.e. whether or not a feature detected by
remote sensing coincides with a feature mapped by hand, regardless of feature size.
Accuracy is comprised of sensitivity (what proportion of true features are detected)
and precision (what proportion of detected features are true), and can be
summarised as:
- Balanced accuracy = (sensitivity + precision) / 2
- Detection accuracy describes how effectively the algorithm has detected
TOW, regardless of feature type (lone tree, group of trees or small
woodland).
- Classification accuracy describes how effectively the algorithm has
classified TOW features as the correct feature type (i.e. small woodland,
group of trees, lone tree).

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Table 6: TOW map accuracy (detection accuracy and classification accuracy) by feature
type.

Overall, the TOW map accurately detects the location of TOW features (95%) and
around two thirds (66%) of TOW features are correctly classified. Larger features
(small woodlands) are more accurately detected and classified than smaller features
(lone trees).
4.2.1
TOW classification confusion matrix
Table 6 gives more detail about the performance of the algorithm in classifying
features as lone trees, groups of trees, small woodland or NFI extended canopy,
showing the total area of features classified as each type by the detection algorithm
(“detected”) and by hand mapping (“mapped”).
It should be noted again that direct comparison of areas is not appropriate, due to
differences in the survey mapping protocol and TOW methodology. The areas given
in table 6 relate to the total area of detected features coinciding with one or more
hand-mapped feature types.

Detection accuracy
Classification accuracy
Sensitivity
Precision
Balanced
accuracy
Sensitivity
Precision
Balanced
accuracy
Lone Tree
81%
84%
83%
54%
35%
45%
Group of
Trees
97%
91%
94%
59%
56%
58%
Small
Woodland
99%
99%
99%
81%
74%
78%
TOW total
96%
94%
95%
71%
62%
66%

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The confusion matrix shows that just over half of the area detected as small woodland
was hand-mapped as a combination of multiple TOW classes, just over a third was
hand-mapped as only small woodland and 11 per cent of the area was hand-mapped
as only group(s) of trees. Smaller detected TOW feature types (lone trees and groups
of trees) were less likely to be mapped to multiple feature types. More than half (51
percent) of the detected area of groups of trees was confirmed in hand mapping as
groups of trees, and 22 percent was mapped as small woodland and 9 percent as
lone trees.
Overall, the TOW detection algorithm has tended to classify features as a smaller
feature type than hand mapping.
Table 7: Confusion matrix showing total area (hectares) of detected TOW features detected
by feature type and mapped feature type, in 271 1km2 tiles. *cells are highlighted in green
where directly related.

Mapped as:
Detected
as:
Lone
Tree
Group of
Trees
Small
Woodland
Multiple
TOW
classes
Not
Mapped
Total Area
and Percent
Lone Tree
70.6
74.7
21.3
1.6
32.1
T = 200.3
% = 12

Group of
Trees
32.0
188.0
79.7
34.2
35.9
T = 369.8
% = 23
Small
Woodland
4.9
71.0
220.7
318.9
8.5
T = 624.0
% = 39

NFI
Extended
Canopy
1.9
12.8
9.6
34.6
54.1
T = 113.0
% = 7
Not
Detected
33.3
111.8
158.0
n/a
n/a
T = 303.1
% = 19
Total And
Percent
T = 142.7
% = 9
T = 458.3
% = 28
T = 489.3
% = 31
T = 389.3
% = 24
T = 130.6
% = 8
n/a

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4.4 User Guide
Web Portal
The TOW map can be accessed via the Forest Research web portal at Trees Outside
Woodland Public Map. The portal currently enables basic soft analytics (attribute pop
ups), download options are in development.
Geospatial Product
The map data itself is available in geodatabase file format which is readable by both
open-source GIS software (e.g. QGIS) and commercial GIS software (e.g. ESRI). The
product comes with a metadata file containing information relating to coordinate
reference system and attribute units.
Table 8: TOW data attributes, units, and descriptions.
Attribute Name
Units
Description
TOW_ID
Alphanumeric characters
Unique identifier
Woodland_Type
Four distinct classes
Woodland feature type
MEANHT
Integer metres
Mean height of polygon
MINHT
Integer metres
Minimum height of polygon
MAXHT
Integer metres
Maximum height of polygon
STDVHT
Integer metres
Standard Deviation height of polygon
KM1_TILE
Alphanumeric characters
1km2 Square ID
KM10_TILE
Alphanumeric characters
10km2 Square ID
TOW_Area_M
Integer metres
Area of polygon
LiDAR_Survey_Year
Calendar year
LiDAR survey year of polygon
When conducting analysis using the dataset it is necessary to use the TOW feature
IDs in conjunction with woodland type counts and area estimates. TOW IDs are
duplicated when features straddle 1km2 tile boundaries, however each feature

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polygon contains its own area calculation. The areas can therefore be added together
using the feature ID.
Data Quality and Limitations
The project strived to map TOW across England exhaustively. However, due to the
data sources, methodological requirements and for particular geographical contexts
there are known limitations to the accuracy of the data and these are explained
below.
Sources of error include:
Feature misclassification
Several false positives are known to be contained within the map. Objects that are
>= 3m and mobile – such as static caravans – can be found erroneously delineated
in the TOW map. The false positive rate however is extremely low overall but can be
high in localised areas (e.g. static caravan parks, large pylons, solar farms).
Shrubs that look like trees and that take the morphology of a tree – Rhododendrons,
Laurel, Buddleia, etc and reach the minimum height area thresholds are included in
the map, but do not technically qualify as TOW so would be removed from any official
TOW estimates using a calibration factor.
Small lone trees near the qualifying height threshold and due to the precision of the
base data are erroneously excluded from the map.
Source data specification
The locations of features may be +/- 8m out on the ground and feature boundaries
may be +/- 8m from their actual boundary. This arises from the inbuilt accuracy of
LiDAR used to derive the product.
Please submit any errors or issues to xxx@xxx.xx

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Official Statistics
Forest Research publishes official statistics for TOW. The statistics published on TOW
are calculated based on a geospatial map such as the one produced here for the
NCEA and are calibrated using hand survey and ground surveying. The protocols and
methods used for field surveying of TOW differ from those used to produce the TOW
feature delineation released in this publication. As a result, the official calibrated TOW
statistics for England, and the statistics shown in tables 1 and 2 are not in complete
agreement. For official TOW statistics please consult the reports found at this
webpage – Trees Outside Woodland (TOW) - Forest Research.
Updates
This publication represents the first release of the TOW map (V1). Future release and
improvements, such as potential addition of hedges delineation, are planned subject
to ongoing research and development and government funding.

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4.5 References
• Forestry Commission (2017). Tree cover outside woodland in Great Britain.
National Forest Inventory.
• GDAL/OGR contributors (2024). GDAL/OGR Geospatial Data Abstraction software
Library. Open Source Geospatial Foundation. URL https://gdal.org DOI:
10.5281/zenodo.5884351
• NLP - National LIDAR Programme - data.gov.uk
• Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial
Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-
009
• R Core Team (2022). R: A language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria.URL https://www.R-
project.org/.
• Roussel, J.R., Auty, D., Coops, N. C., Tompalski, P., Goodbody, T. R. H.,
Sánchez Meador, A., Bourdon, J.F., De Boissieu, F., Achim, A. (2021). lidR: An R
package for analysis of Airborne Laser Scanning (ALS) data. Remote Sensing of
Environment, 251 (August), 112061. .
• TOW Report - Trees Outside Woodland (TOW) - Forest Research
• VOM - LIDAR Vegetation Object Model (VOM) (data.gov.uk)

End of document

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content of our documents available in alternative formats.
Please send any such requests to: xxx@xxx.xx
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National Trees Outside Woodlands Map
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Tel: xx-xxxx-xxxx

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