SECTION 6
Mapping of carbon fluxes for British
afforestation and land use change
Contents
Mapping of carbon fluxes for British afforestation and land use change ...........................1
1
Afforestation fluxes ........................................................................................................1
1.1 Introduction ..................................................................................................................1
1.2 Methods.........................................................................................................................1
1.3 Results ...........................................................................................................................5
1.4 Future work (Contract due for completion by December 2003) ..................................6
2
Agricultural land use change ........................................................................................13
2.1 Introduction ................................................................................................................13
2.2 Methods.......................................................................................................................13
2.3 Results .........................................................................................................................14
2.4 Future work (Contract due for completion by December 2003) ................................14
3
References.....................................................................................................................23
Acknowledgement ................................................................................................................24
6:i
Mapping of carbon fluxes for British afforestation and land use change
R Milne and T A W Brown
Centre for Ecology & Hydrology, Bush Estate, Penicuik
(Jointly funded by sub-contract from NETCEN under main DEFRA contract for UKGHG
Inventory)
1
Afforestation fluxes
1.1 Introduction
In Milne et al. (2002) the use of the EuroBiota and C-Flow models for estimating uptake of
carbon by afforestation in Greta Britain on a 20 km grid was described. Although the
EuroBiota model has the advantage of being process based and driven by climate data the use
of C-Flow for preparing grid-based estimates is preferable since it is used to prepare estimates
on carbon uptake by forest at the scale of the UK devolved regions for use in the various
GHG Inventories. Work in the last year has therefore focused on improving the use of C-Flow
for grid-based estimates.
The Woodlands Surveys Branch of the Forestry Commission has now provided an estimate of
variation in Yield Class across Great Britain. This report describes the results from running CFlow for broadleaf and conifer forests in each 20 km grid-cell in Great Britain including the
effect of Yield Class variation.
1.2 Methods
Forest Enterprise (the forest management agency of the Forestry Commission) maintains
information of the status of all patches (sub-compartments) of forest under its control. This
Sub-compartment Database (SCDB) contains an estimate of the Yield Class of the forest in
each patch. The SCDB does not include privately owned forest but its geographical range is
likely to include areas similar to those managed privately. To summarise the Yield Class
information of the SCDB into a form that could be used for running the 20 km grid-based CFlow the Woodland Surveys Branch used the ITE/CEH Land Class system. Each 1 km gridcell in Great Britain has been assigned to one of 32 land types based on climatological,
geological and geographical attributes of land. These Classes therefore capture the variations
that are likely to affect Yield Class. The average Yield Class of Sitka spruce and beech were
each estimated for each of the 32 Land Classes by combining the SCDB information for all
forest patches within the totality of 1 km grid-cells in each Land Class. The results of this
analysis are presented in Table 1. All beech with Yield Class less than 3 m3 ha-1 a-1 were
assumed to have negligible growth. Average Yield Class for Sitka spruce and beech in each
grid-cell was estimated by calculating a weighted average from the frequency of occurrence
of Land Class in each separate 20 km grid-cell. These average Yield Classes are mapped in
Figure 1a for Sitka spruce and Figure 1b for beech.
6:1
Table 1 Yield Class (m3 ha-1 a-1) from Sub-Compartment Database and ITE Land Classes. BE –
beech, SS – Sitka spruce.
Average Yield Class per Land Class of Beech and Sitka Spruce
6:2
Species
Land Class
Average YC
Species
Land Class
Average YC
BE
0
0
SS
0
15
BE
1
6
SS
1
16
BE
2
6
SS
2
16
BE
3
6
SS
3
15
BE
4
3
SS
4
13
BE
5
7
SS
5
14
BE
6
6
SS
6
15
BE
7
6
SS
7
13
BE
8
6
SS
8
12
BE
9
7
SS
9
14
BE
10
6
SS
10
13
BE
11
6
SS
11
7
BE
12
6
SS
12
5
BE
13
5
SS
13
15
BE
14
2
SS
14
16
BE
15
6
SS
15
15
BE
16
6
SS
16
13
BE
17
6
SS
17
14
BE
18
5
SS
18
12
BE
19
5
SS
19
13
BE
20
6
SS
20
14
BE
21
6
SS
21
12
BE
22
4
SS
22
13
BE
23
Not present
SS
23
11
BE
24
5
SS
24
11
BE
25
5
SS
25
15
BE
26
3
SS
26
14
BE
27
5
SS
27
15
BE
28
3
SS
28
13
BE
29
1
SS
29
13
BE
30
1
SS
30
13
BE
31
Not present
SS
31
12
BE
32
1
SS
32
14
b) Beech Yield Class
Figure 1: Yield class in each 20 km grid-cell in Great Britain based on Forest Enterprise Sub-Compartment Database and ITE Land Classes
a) Sitka spruce Yield Class
6:3
The C-Flow model was run in three ways:
•
National (Great Britain)
o Uniform YC,
o Planting history by regions (Scotland, England, Wales)
•
Grid based (20 km)
o Uniform YC
o Planting history per grid-cell (NIWT)
•
Grid based (20 km)
o YC per grid-cell
o Planting history per grid-cell (NIWT)
The model was run separately for conifer and broadleaves for each grid-cell using these time
series and the uptake per grid-cell, and nationally, extracted for 1990.
1.3 Results
General agreement between the three approaches to estimating uptake of carbon by British
forests can be seen from Table 2, Table 3 and Table 4. Table 2 presents the national run
results for 1990 for Great Britain. Note the usual assumption is made that uptake by conifer
soils is excluded. The data in Table 3 shows the sum of the equivalent outputs from the CFlow run for each 20 km grid-cell. Table 4 shows the estimates when Yield Class varies from
cell to cell. The forest areas appropriate to each calculation are also included and go some
way to explaining the differences in the results of the different ways of running C-Flow. The
two sources agree reasonably between the national run and the grid-cell approach with
uniform Yield Class for conifer woodlands but the national scale run of C-Flow (Table 2)
included much less broadleaf forest. This is unexpected as the 20 km scale woodland data was
limited to planting for a similar time span to the national data i.e. starting in 1920. It is likely
therefore that the NIWT includes many broadleaved woodlands that fall outside of those
reported in Forestry Commission planting statistics, upon which the national C-Flow runs are
based. Figure 2 shows these planting differences as time series for Great Britain. The run with
different Yield Class for each grid-cell shows an even greater difference from the national
approach. This suggests that there is an interaction between Yield Class and age since
planting, which is as expected, but the difference is greater than would be expected from
previous sensitivity testing of C-Flow. Further comparison of the NIWT data and planting
statistics is required. A first step will be to compare these for Scotland, England and Wales
separately.
The main purpose of the study reported here is to map the national data and although the use
of C-Flow at the 20 km scale appears promising it is not yet possible to carry out calculations
for just those woodlands in the national scale planting data. The total difference in area
between this and the NIWT data can be calculated but the geographical distributions for
conifer and broadleaf planting is not known. A national run using the range of Yield Classes
from the SCDB may also help to identify the reasons for the differences between the results of
the three approaches reported here
Having calculated carbon fluxes at the 20 km scale maps can be drawn of their distribution.
Figure 3 shows the distribution of fluxes from C-Flow for the approaches with fixed and
variable Yield Classes. Figure 4 shows the equivalent broadleaf data.
6:5
Table 2 Results from national scale run of C-Flow showing total carbon uptake for woodlands in
Great Britain.
C-Flow National 1990
Trees Products Litter Soil
(MtC)
Total
Area
uptake
(kha)
Conifer
1.24
0.41
0.38
-
2.03
1,185
Broadleaf
0.21
0.01
0.04
0.14
0.40
144
ALL
1.45
0.41
0.41
0.14
2.42
1,328
Table 3 Results from 20 km scale run of C-Flow showing total carbon uptake for woodlands in
Great Britain.
C-Flow Sum of 20 km grid-cells 1990
Trees Products Litter Soil
(MtC)
Total
Area
uptake
(kha)
Conifer
1.98
0.19
0.32
-
2.49
1,268
Broadleaf
0.90
0.00
0.14
0.57
1.62
578
ALL
2.88
0.19
0.46
0.57
4.10
1,846
Table 4 Results from 20 km scale run of C-Flow with different Yield Class per cell showing
total carbon uptake for woodlands in Great Britain.
C-Flow Sum of 20 km grid-cells 1990
Trees Products Litter Soil
(MtC)
Conifer
2.40
0.20
0.41
Broadleaf
0.90
0.00
0.14
ALL
3.30
0.20
0.55
Total
Area
uptake
(kha)
3.01
1,268
0.57
1.62
578
0.57
4.62
1,846
1.4 Future work (Contract due for completion by December 2003)
6:6
•
Investigation of difference between age data of NIWT and planting data from Forestry
Commission for Scotland, England and Wales separately.
•
Summarise Yield Class data per 20 km grid-cell as frequency histogram.
•
Run C-Flow at regional scale with FC planting data but distribution of Yield Class
from histogram
•
Decide on method to distribute devolved region afforestation fluxes across 20 km
grids
•
Produce maps.
Figure 2: Comparison of woodland planting in Great Britain as indicated by FC new planting
statistics and NIWT age data from samples.
6:7
Fixed Yield Class (YC12)
Variable Yield Class
Figure 3: Sitka spruce carbon uptake (ktC/year/grid-cell)
6:9
Fixed Yield Class (YC 6)
Variable Yield Class
Figure 4: Beech carbon uptake (ktC/year/grid-cell)
6:11
2
Agricultural land use change
2.1 Introduction
Maps at the 20 km grid-cell scale of emissions and removals of atmospheric carbon from/to
soils due to non-forest land use change are also required within this contract. The base for
such estimates is land use change matrices. These are used for the national/regional estimates
for the GHG Inventory and the challenge for mapping is to produce the matrices at a finer
scale. This is also a requirement of ongoing work led by Rothamstead Research to investigate
the use of the RothC soil carbon model for fluxes due to changes in soil carbon from land use
change. Earlier work on deriving land use change matrices based on a mapped version of
Agricultural Census data is still in progress but here we describe work on preparing matrices
from Land Cover maps derived from remotely sensed data.
2.2 Methods
Land cover maps of Great Britain have been prepared by ITE/CEH (with DOE/DETR and
other funding) using remotely sensed data for 1990 (Land Cover Map 1990, Barr et al. 1993)
and 1998 (Land Cover Map 2000, Haines-Young et al. 2000). However different methods
were used to classify land cover in the two maps.
In 1990 a “per pixel” classification was used. Each 25 m pixel of spectral data from the
satellite sensor was classified into one of 25 “Target Classes”. For the 1998 map a “per
parcel” classification was used. Land area was divided into parcels (fields) using line mapping
from Ordnance Survey. Then the 25 m pixel spectral data for each parcel were classified
“together”, i.e. parcel boundaries helped with classification. For 1998 “Broad Habitats” were
used for land cover classes in contrast to the “Target Classes” of 1990.
The “per pixel” classification in 1990 (LCM 1990) tends to cause noise on data and hence
spurious land change when compared to 1998 (LCM 2000). There are also many cover types
and therefore a simple matrix cannot be produced.
A method to minimise the effect of these differences on land use changes matrices is therefore
required. The approach described here was as follows:
•
Use Countryside Survey data as “ground truth”. In the Countryside Surveys sample 1
km grid-cells were visited in both 1990 and 1998 and maps drawn of land cover and
use. Comparison of the two maps for a grid-cell allows a “true” LUC matrix to be
prepared.
•
Use broad cover/use types i.e. (Semi-) Natural, Arable, Pasture, Woods, Urban, Zero
carbon.
•
Block together 25 m pixels into groups, i.e. 2x2, 4x4, 5x5, 8x8, 10x10, 20x20, before
preparing LUC matrix.
•
Decide “best” block size by comparison with the field data for LUC from the 501
Countryside Survey sample grid-cells
•
“Best” block size to vary with ITE Land Class
•
Work at 20 km rather than 1 km if possible
6:13
2.3 Results
Figure 5 shows the Land Cover maps for 1990 (LCM 1990) and 1998 (LCM 2000)
reclassified into six broad based types. The effect of the different classifications methods of
LCM 1990 and LCM 2000 on apparent land use change is illustrated in Figure 6 and Figure 7.
In Figure 6 a 20 x 20 km grid-cell is compared between the two dates. Changes in land use
can be seen, e.g. some pasture appears to have become more “Natural” and there may be some
urbanisation, but there is significant “noise” on the 1990 map. This effect can be seen more
clearly in Figure 7, which is for a 1 km x 1 km grid-cell within the area of Figure 6. The
central part of the maps show some pixels classed as mixed water/urban/woods in 1990 (LCM
1990) but the map of 1998 (LCM 2000) reflects the reality of a water area with some
woodland and a small built up area.
In Figure 8 the effect of block averaging the 25 m pixel data of each land cover map, before
calculating a change matrix, is illustrated for two specific 1 km sample grid-cells. It can be
seen for block sizes around 5 pixels the best agreement is obtained between the matrices
based on remote sensed data and from using the Countryside Survey ground data. The mean
square error between the two forms of matrix is calculated by averaging across all of the field
sample grid-cells from the Land Class in question. There are 10-12 sample grid-cells in each
Land Class.
“Best” block size has been estimated in this way for each Land Class and the results are
presented in Table 5.
2.4 Future work (Contract due for completion by December 2003)
•
Use best block size to build LUC matrices for
o RothC modelling project for each 1 km grid-cells
o Flux mapping contract for each 20 km grid-cells (use sum of 1 km matrices in
the cell)
•
Compare LUC matrices from land cover maps with those from Agricultural Census
data
•
Use the better approach for mapping soil fluxes at 20 km scale
6:14
1990
Figure 5: National Land Cover maps reclassified into six broad types
Arable
Pasture
Natural
Urban
Woods
Zero
2000
6:15
6 land use classes for 1998
CEH Land Cover Map 2000
Figure 6: Comparison of 20 x 20 km area from the two land cover maps
6 land use classes for 1990
ITE Land Cover Map 1990
Arable
Pasture
Natural
Urban
Woods
Zero
6:17
2000
Figure 7: Comparison of land cover of1 km x 1km grid-cell in 1990 and 1998(2000)
1990
6:19
0.0
200.0
400.0
600.0
800.0
1000.0
1200.0
1400.0
1
2
Block size
5
8
Land Class 9 grid-cell
4
10
20
0.0
500.0
1000.0
1500.0
2000.0
2500.0
3000.0
1
4
5
Block size
8
Land class 25 grid-cell
2
10
20
6:21
Figure 8: Effect of pixel block averaging on mean square error difference between RS based estimate and CS based estimate using all sample grid-cells
in the Land Class (~10)
Mean square error
1600.0
Mean square error
Table 5: “Best” pixel block averaging size for producing land use change matrices from LCM
1990 and LCM 2000 for each of the 32 ITE Land Classes of CS 1990.
Land
Class
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
3
Block
Size
5
5
5
1
5
1
1
1
5
1
10
5
5
5
2
10
1
5
8
4
1
10
10
10
1
5
1
10
2
1
8
5
References
Barr, CJ, Bunce, RGH, Clarke, RT, Fuller, RM., Furse, MT, Gillespie, MK., Groom, GB,
Hallam, C.J, Hornung, M, Howard, DC, Ness, MJ, (1993) Countryside Survey 1990,
Main Report. Department of the Environment, London.
R.H. Haines-Young, C.J. Barr, H.I.J. Black, D.J. Briggs, R.G.H. Bunce, R.T. Clarke, A.
Cooper, F.H. Dawson, L.G. Firbank, R.M. Fuller, M.T. Furse, M.K. Gillespie, R. Hill,
M. Hornung, D.C. Howard, T. McCann, M.D. Morecroft, S. Petit, A.R.J. Sier, S.M.
6:23
Smart, G.M. Smith, A.P. Stott, R.C. Stuart and J.W. Watkins (2000) Accounting for
nature assessing habitats in the UK countryside. DETR, London
R. Milne and T. A. W. Brown (2002) Mapping of carbon uptake in British woodlands and
forests using EuroBiota and C-Flow. In: UK Emissions by Sources and Removals by
Sinks due to Land Use, Land Use Change and Forestry Activities. Annual report for
DEFRA Contract EPG1/1/160 (Ed. by R. Milne).
Acknowledgement
The contribution of Steve Smith and Justin Gilbert at Woodland Surveys Branch of Forestry
Commission, Pete Smith at Aberdeen University and Pete Falloon at Rothamstead Research is
gratefully noted.
6:24