1
Estimating carbon carrying capacity in natural forest ecosystems across heterogeneous
2
landscapes: addressing sources of error
3
Heather Keith, Brendan Mackey, Sandra Berry, David Lindenmayer, Philip Gibbons
4
Supporting Information
5
Appendix 1: Estimation of tree biomass
6
Estimation of tree biomass derived from stem volume needs to include a reduction factor to
7
account for decay in large trees (see Mackowski 1987, Lindenmayer et al. 2000, Dean et al.
8
2003, Roxburgh et al. 2006). For calculations of biomass from stem volume at the sites used in
9
southeast Australia, we applied a reduction factor to tree size categories of 50 – 120 cm DBH
10
(diameter of stem at 1.3 m height) and >120 cm DBH, based on relationships with DBH
11
described in Roxburgh et al. (2006). Additionally, we used a buttress modification factor
12
developed by Dean et al. (2003) where measurements of cross-sectional areas of buttresses were
13
used to derive a fractional area deficit formula of up to 40% for trees >3.5 m DBH.
14
The range in allometric equations used to estimate biomass at sites in southeast Australia is
15
shown in Figure S1. Separate curves are shown for allometric equations that include reduction
16
factors for decay in volume equations and buttressing in large trees. Factors influencing
17
differences among these equations include height of trees, taper of stems, proportion of branches,
18
wood density and amount of decay. Calculation of biomass for individual trees using these
19
allometric equations was estimated within the size range of trees used to derive the equation
20
(maximum tree sizes were DBH of 6.4 m for E. regnans (Mountain Ash) (Dean and Roxburgh
21
2006), 2.8 m for E. obliqua (Messmate), 1.8 m for E. piluaris (Blackbutt), 1.5 m for Corymbia
22
maculata (Spotted gum), 0.9 for E. delegatensis (Alpine Ash)). At the few sites where
1
1
extrapolations were necessary on the NSW south coast, a very conservative equation with an
2
asymptote was used. From the tree biomass data available to derive allometric equations, there is
3
no evidence that the largest trees in a sequence had a relatively lower biomass or were below the
4
trend line. However, it is important to be cautious in using these logarithmic equations as
5
extrapolation beyond the size range could cause significant errors.
6
Figure A1
7
Allometric equations derived for forest trees in southeast Australia.
8
1. Applegate (1982) E. pilularis Fraser Island, 2. Montagu et al. (2005) E. pilularis, 3. Keith et
9
al. (2009) E. delegatensis Tumbarumba, 4. Keith et al. (2000) E. delegatensis Brindabella
10
Ranges, 5. Ash & Helman (1990) Corymbia maculata with adjustment for decay, 6. Adams and
11
Attiwill (1988) E. obliqua, 7. Keith et al. (2000) E. obliqua southern forests Tasmania , 8. Turner
12
et al. (1989), 9. Mackowski (1987) E. pilularis north coast NSW 10. Ximenes et al. (2004)
13
Corymbia maculata, 11. Dean and Roxburgh (2006) E. regnans with adjustment for decay.
14
Solid lines represent the size range of trees harvested for biomass measurements.
15
[Figure A1]
16
References
17
1. Applegate GB (1982) Biomass of Blackbutt (Eucalyptus pilularis Sm.) forests on Fraser
18
19
Island. unpublished Masters thesis. University of New England, Armidale.
2. Montagu KD, Düttmer K, Barton CVM, Cowie AL (2005) Developing general allometric
20
relationships for regional estimates of carbon sequestration – an example using
21
Eucalyptus pilularis from seven contrasting sites. Forest Ecology and Management, 204,
22
113-127.
2
1
3. Keith H, Leuning R, Jacobsen KL et al. (2009) Multiple measurements constrain estimates of
2
net carbon exchange by a Eucalyptus forest. Agricultural and Forest Meteorology, 149,
3
535-558.
4
4. Keith H, Barrett D, Keenan R (2000) Review of allometric relationships for woody biomass
5
for NSW, ACT, Vic., Tas., SA. Australian Greenhouse Office, NCAS Technical Report
6
5b.
7
8
9
10
11
5. Ash J, Helman C (1990) Floristics and vegetation biomass of a catchment, Kioloa, south
coastal New South Wales. Cunninghamia, 2, 167-182.
6. Adams MA and Attiwill PM (1988) Nutrient cycling in forests of north-east Tasmania.
Research Report 1 Tasmanian Forest Research Council Inc., Hobart.
7. Keith H, Barrett D, Keenan R (2000) Review of allometric relationships for woody biomass
12
for NSW, ACT, Vic., Tas., SA. Australian Greenhouse Office, NCAS Technical Report
13
5b.
14
15
16
17
18
8. Turner J, Lambert M J, Kelly J (1989) Nutrient cycling in a New South Wales subtropical
rainforest: Organic matter and phosphorus. Annals of Botany, 63,635-642.
9. Mackowski CM (1987) Wildlife hollows and timber management in Blackbutt forest:
University of New England, Masters thesis.
10. Ximenes F deA, Gardner WD, Marchant JF (2004) Total biomass measurement and recovery
19
of biomass in log products in spotted gum (Corymbia maculata) forests of SE NSW.
20
Australian Greenhouse Office, NCAS Technical Report 47.
21
22
11. Dean C, Roxburgh SH (2006) Improving visualisation of mature, high-carbon-sequestering
forests. Forest Biometry, Modelling and Information Sciences, 1, 48-69.
3
1
Dean C, Roxburgh S, Mackey BG (2003) Growth modelling of Eucalyptus regnans for carbon
2
accounting at the landscape scale. In: Modelling Forest Systems. Eds. A Amaro, D Reed & P
3
Soares, CABI, Oxon. pp. 27-39.
4
Lindenmayer DB, Cunningham RB, Pope ML, Gibbons P, Donnelly CF (2000) Cavity sizes and
5
types in Australian eucalypts from wet and dry forest types – a simple rule of thumb for
6
estimating size and number of cavities. Forest Ecology and Management, 137, 139-150.
7
Roxburgh SH, Wood SW, Mackey BG, Woldendorp G, Gibbons P (2006) Assessing the carbon
8
sequestration potential of managed forests: a case study from temperate Australia. Journal
9
of. Applied Ecology, 43, 1149-1159.
4
1
Appendix 2: Site carbon stock database for southeast Australia.
2
Table A2: Summary of key site attributes and modelled variables used in the analysis on a regional basis, with mean, ±SD and range.
Living
biomass
(tC ha-1)
Dead
biomass
(tC ha-1)
Total
biomass
(tC ha-1)
SE
Queensland
397 (± 377)
67 (± 59)
465 (± 435)
(99 - 821)
(29 - 135)
(129 - 956)
North coast
NSW
334 (± 125)
98 (± 66)
(223 - 505)
(30 - 181)
South coast
NSW
382 (± 191)
(78 - 818)
Southern
tablelands
NSW
299
East
Gippsland,
Victoria
406 (± 186)
Region
(± 2)
(298 - 300)
Central
Highlands,
Victoria
(21 - 520)
(± 38)
(104 - 179)
29 (± 7)
15 (± 4)
19 (± 2)
1523 (± 67)
214 (± 6)
(21 - 34)
(11 - 18)
(17 - 21)
(1448 - 1574)
(208 - 221)
432 (± 190)
152 (± 47)
30 (± 2)
15 (± 1)
16 (± 2)
1412 (± 158)
212 (± 1)
(252 - 686)
(94 - 192)
(27 - 31)
(13 - 15)
(14 - 19)
(1192 - 1548)
(212 - 213)
82 (± 47)
464 (± 227)
293 (± 134)
27 (± 2)
13 (± 1)
12 (± 2)
1024 (± 84)
186 (± 2)
(17 - 222)
(95 - 980)
(100 - 652)
(20 - 30)
(9 - 15)
(9 - 16)
(841 - 1243)
(183 - 189)
105 (± 35)
404 (± 33)
180 (± 74)
27 (± 1)
12 (± 1)
11 (± 0)
916 (± 62)
(80 - 129)
(381 - 427)
(128 - 232)
(26 - 28)
(12 - 13)
(11 - 11)
(872 - 959)
(± 52)
(9 - 266)
510 (± 228)
373 (± 99)
27 (± 2)
12 (± 1)
11 (± 2)
1075 (± 223)
183 (± 1)
(55 - 1116)
(174 - 631)
(22 - 30)
(10 - 14)
(7 - 14)
(671 - 1682)
(181 - 185)
(± 37)
(4 - 173)
240 (± 140)
323 (± 140)
25 (± 3)
11 (± 1)
1173 (± 297)
189 (± 4)
(24 - 635)
(113 - 547)
(17 - 28)
(8 - 13)
(6 - 13)
(640 - 1650)
(182 - 194)
(± 42)
(310 - 513)
27 (± 1)
13 (± 1)
10 (± 1)
1644 (± 179)
178 (± 1)
(25 - 28)
(12 - 13)
(7 - 13)
(790 - 1845)
(177 - 181)
977 (± 315)
432 (± 145)
25 (± 0)
12 (± 0)
9 (± 1)
1572 (± 130)
148 (± 2)
(673 - 1302)
(303 - 589)
(25 - 25)
(12 - 12)
(9 - 10)
(1490 - 1722)
(146 - 149)
105
50
501 (± 451)
1102 (± 656)
(120 - 2092)
(113 - 1865)
(262 - 2884)
(554 - 1100)
Mean
annual
radiation
(W m-2)
NPP
581 (± 479)
810 (± 274)
Tasmania
Mean
annual
precipitation
(mm)
GPP
(46 - 913)
190 (± 113)
Montane
Soil
(tC ha-1)
Mean
annual
temperature
(C)
166
(± 43)
(119 - 202)
144
421
9 (± 2)
No.
plots
References
3
1, 2, 3
5
4, 5
62
6, 7, 8, 9, 10
2
11
114
9, 12, 13, 14
41
9, 11
54
15, 16, 17
3
11, 18
190 (± 1)
(190 - 191)
5
1
References
2
1. Westman WE, Rogers RW (1977) Biomass and structure of a subtropical eucalypt forest,
3
4
5
6
7
8
9
10
11
North Stradbroke Island. Australian Journal of Botany, 25,171-191.
2. Applegate GB (1982) Biomass of Blackbutt (Eucalyptus pilularis Sm.) forests on Fraser
Island. unpublished Masters thesis. University of New England, Armidale.
3. Hegarty EE (1991) Leaf litter production by lianas and trees in a sub-tropical Australian rain
forest. Journal of Tropical Ecology, 7,201-214.
4. Mackowski CM (1987) Wildlife hollows and timber management in Blackbutt forest:
University of New England, Masters thesis.
5. Turner J, Lambert M J, Kelly J (1989) Nutrient cycling in a New South Wales subtropical
rainforest: Organic matter and phosphorus. Annals of Botany, 63,635-642.
12
6. Woldendorp G (2000) Estimating carbon in mature Eucalypt forests. BSc. Hons. Thesis, ANU.
13
7. Mackey BG (unpublished data)
14
8. Turner J, Lambert MJ (1986) Effects of forest harvesting nutrient removals on soil nutrient
15
reserves. Oecologia, 70,140-148.
16
9. Claridge A. (unpublished data)
17
10. Ximenes F deA, Gardner WD, Marchant JF (2004) Total biomass measurement and recovery
18
of biomass in log products in spotted gum (Corymbia maculata) forests of SE NSW.
19
Australian Greenhouse Office, NCAS Technical Report 47.
20
11. Keith H (unpublished data) In: Keith H, Barrett D, Keenan R (2000) Review of allometric
21
relationships for woody biomass for NSW, ACT, Vic., Tas., SA. Australian Greenhouse
22
Office, NCAS Technical Report 5b.
6
1
12. Jacobsen KL (unpublished data)
2
13. Stewart HTL, Flinn DW, Aeberli BC (1979) Above-ground biomass of a mixed Eucalypt
3
forest in eastern Victoria. Australian Journal of Botany, 27,725-740.
4
14. Gibbons P (unpublished data)
5
15. Ashton DH (1976) Phosphorus in forest ecosystems at Beenak, Victoria. Journal of Ecology,
6
7
64, 171 -186.
16. Van Pelt R, Sillett SC, Nadkarni NM (2004) Quantifying and visualizing canopy structure in
8
tall forests: methods and a case study. In: Forest Canopies, eds. MD Lowman, HB Rinker,
9
Elsevier Academic Press. pp 49-72
10
17. Lindenmayer DB (unpublished data)
11
18. Dean C, Roxburgh S, Mackey BG (2003) Growth modelling of Eucalyptus regnans for
12
carbon accounting at the landscape scale. In: Modelling Forest Systems. Eds. A Amaro, D
13
Reed, P Soares, CABI, Oxon. pp. 27-39.
7
1
Appendix 3: Global site data for NPP:GPP.
2
Table A3. Field site data for forest trees with measured GPP and NPP. Climate data was obtained from www.cru.uea.ac.uk/cru/data/tmc.htm if site data
3
was not provided in the reference. Climate data was extracted for the site latitude and longitude from the 10 minute grid cell and mean annual
4
temperature and precipitation calculated from the mean monthly values.
Modelled
Species
Biome
Tree type
Latitude
Longitude
Pglobal
Tglobal
W
Qs_global
(mm yr-1)
(°C)
(mm yr-1)
(MJ m-2 yr-1)
GPP
(tCha-1yr-1)
NPP:GPP
NPP:GPP
Ref.

Picea mariana
(north)
Pinus banksiana
(north)
Populus tremuloides
(north)
Picea mariana
(south)
Pinus banksiana
(south)
Populus tremuloides
(south)
boreal
conifer
55.8833º N
98.3333º W
519
-3.3
-1026
3784
8.6
0.29
boreal
conifer
55.8833º N
98.3333º W
519
-3.3
-1026
3784
6.8
0.34
boreal
deciduous
55.8833º N
98.3333º W
519
-3.3
-1026
3784
9.0
0.46
boreal
conifer
55.8833º N
104.8833º W
469
-0.3
-1268
4257
7.9
0.39
boreal
conifer
55.8833º N
104.8833º W
469
-0.3
-1268
4257
5.5
0.43
boreal
deciduous
55.5666º N
106.2333º W
484
-0.4
-1060
3784
10.4
0.42
Pinus sylvestris
Picea sitchensis,
Tsuga heterophylla
boreal
conifer
62.8666º N
30.8167º E
633
1.8
-397
2523
10.2
0.54
temperate
conifer
45.0500º N
123.9500º W
2023
9.5
92
4730
14.0
0.49
Alnus rubra
Pseudotsuga
menziesii, Quercus
garryana
Tsuga heterophylla,
Pseudotsuga
menziesii
Tsuga mertensiana,
Abies, Picea
temperate
deciduous
45.0500º N
123.9500º W
2023
9.5
92
4730
15.6
0.52
temperate
conifer
44.6000º N
123.2667º W
1151
11.0
-780
4730
16.7
0.46
temperate
conifer
44.6667º N
122.6000º W
1871
8.3
-60
4730
24.0
0.47
temperate
conifer
44.4167º N
121.8333º W
1241
6.3
-690
4730
8.8
0.42
Pinus ponderosa
temperate
conifer
44.4167º N
121.6667º W
1241
6.3
-690
4730
3.6
0.44
0.39
1
0.39
1
0.42
1
0.40
1
0.40
1
0.45
1
0.50
2
0.51
3
0.55
3
0.50
3
0.50
3
0.46
3
0.46
3
8
Juniperus
occidentalis
Pinus radiata –
control
Pinus radiata –
irrigated
Pinus radiata –
irrigated and
fertilized
Quercus rubra, Acer
rubrum
Q. alba, Q. pinus,
Carya ovata
Pinus ponderosa
E. delegatensis
2001-02
E. delegatensis
2002-03
Pinus sylvestris,
Quercus robur
E. pauciflora –
control
E. pauciflora - P
fertilizer
Pseudotsuga
menziesii
17 yr
Pseudotsuga
menziesii
56 yr
tropical forest old
growth
temperate
conifer
44.2833º N
121.3333º W
359
8.3
-1572
4730
3.0
0.40
temperate
conifer
35.3500º S
148.9333º E
873
12.6
-1702
6307
24.2
0.46
temperate
conifer
35.3500º S
148.9333º E
873
12.6
-1702
6307
25.3
0.50
temperate
conifer
35.3500º S
148.9333º E
873
12.6
-1702
6307
34.4
0.48
temperate
deciduous
42.5333º N
72.1833º W
1698
8.0
-233
4730
12.5
0.53
temperate
deciduous
35.9500º N
84.2833º W
1323
14.1
-994
5676
17.3
0.55
temperate
conifer
44.0500º N
121.6167º W
1078
3.8
-1111
5361
9.0
0.45
temperate
evergreen
35.6434ºS
148.1456ºE
1104
12.7
-1470
6307
22.1
0.51
temperate
evergreen
35.6434ºS
148.1456ºE
1104
12.7
-1470
6307
17.0
0.49
temperate
conifer
51.3092º N
4.5206ºE
792
10.0
-495
3154
10.4
0.56
temperate
evergreen
35.3897ºS
148.8039ºE
1088
10.9
-1487
6307
17.0
0.43
temperate
evergreen
35.3897ºS
148.8039ºE
1088
10.9
-1487
6307
16.8
0.48
temperate
conifer
49.5217º N
124.9031ºW
1572
8.6
-101
4100
13.4
0.56
temperate
conifer
49.8706º N
125.3367ºW
1986
8.0
313
4100
20.8
0.61
tropical
evergreen
2.5833º S
50.1000ºW
2374
27.0
-458
6938
30.4
0.51
0.45
3
0.45
3
0.45
3
0.45
3
0.52
3
0.53
4
0.41
5
0.46
6
0.46
6
0.54
7
0.45
8
0.45
8
0.51
9
0.53
9
0.60
4
9
1
Figure A3
2
Global data for NPP:GPP at forest sites, distinguished by tree type: () evergreen, () deciduous,
3
() conifer forests. The dashed line represents a constant ratio of 0.47 that is commonly used in
4
carbon models.
5
[Figure A3]
6
References
7
1. Ryan MG, Lavigne MB, Gower ST (1997) Annual carbon cost of autotrophic respiration in
8
boreal forest ecosystems in relation to species and climate. Journal of Geophysical Research
9
102:28871-28883.
10
2. Zha T, Xing Z, Wang K-Y, Kellomäki S, Barr AG (2007) Total and component carbon fluxes
11
of a Scots pine ecosystem from chamber measurements and eddy covariance. Annals of Botany
12
99:345-353.
13
3. Waring RH, Landsberg JJ, Williams M (1998) Net primary production of forests: a constant
14
fraction of gross primary production? Tree Physiology 18: 129-134.
15
4. Malhi Y, Baldocchi DD and Jarvis PG (1999) The carbon balance of tropical, temperate and
16
boreal forests. Plant, Cell and Environment 22: 715-740.
17
5. Law BE, Ryan MG, Anthoni PM (1999) Seasonal and annual respiration of a ponderosa pine
18
ecosystem. Global Change Biology 5: 169-182.
19
6. Keith , Leuning R, Jacobsen KL, Cleugh HA, van Gorsel E, Raison RJ, Medlyn BE, Winters
20
A, Keitel C (2009) Multiple measurements constrain estimates of net carbon exchange by a
21
Eucalyptus forest. Agricultural and Forest Meteorology 149: 535 – 558.
10
1
7. Nagy MT, Janssens IA, Yuste JC, Carrara A, Ceulemans R (2006) Footprint-adjusted net
2
ecosystem CO2 exchange and carbon balance components of a temperate forest. Agricultural and
3
Forest Meteorology 139:344-360.
4
8. Keith H, Raison RJ, Jacobsen KL (1996) Allocation of carbon in a mature eucalypt forest and
5
some effects of soil phosphorus availability. Plant and Soil 196: 81-99.
6
9. Schwalm CR, Black TA, Morgenstern K, Humphries ER (2007) A method for deriving net
7
primary productivity and component respiratory fluxes from tower-based eddy covariance data: a
8
case study using a 17-year data record from a Douglas-fir chronosequence. Global Change
9
Biology 13:370-385.
10
11
1
Appendix 4: Method for calculation of Gross Primary Productivity over the southeast
2
Australia case study region
3
A spatial data layer of GPP was calculated from climate and satellite imagery of forest cover
4
using the simplified light-use efficiency model of GPP as formulated by Roderick et al. (2001).
5
PG  e' f *C '
6
where, e (mol CO2 mol-1 PAR) is the efficiency of the canopy modified to account for diffuse
7
light as described below, f is the fraction of photosynthetically active radiation (PAR, = 400 to
8
700 nm) absorbed by the canopy, Rs is solar irradiance (MJ m-2 d-1) received at the surface, Ro is
9
the global solar irradiance at the top of the atmosphere, and C is a conversion factor to convert
*
Rs '
Ro '
Ro '
(3)
10
MJ to mol PAR (2.3 mol PAR MJ-1). The superscripts denote that the input variables are: ′
11
monthly (long term) averages (m2 d-1), or * values specific to a particular month and year. This
12
formulation takes into account the effect of diffuse solar radiance on the carbon assimilation rate
13
of the canopy (Roderick et al. 2001).
14
Roderick et al. (2001) derived the following general equations to estimate e for Australia;
15
e'  0.024
16
Rd '
R'
 1.11  1.31 s
Rs '
Ro '
Rd '
 0.012
Rs '
(4)
(5)
17
where Rd ' Rs ' is the estimated fraction of the solar irradiance received at the surface that is
18
diffuse. Solar irradiance received at the surface (Rs′) was computed using monthly averages from
19
the BIOCLIM database. Solar irradiance received at the top of the atmosphere (Ro′) was
20
computed for monthly time steps using the formulae given in Roderick (1999).
12
1
The fraction of photosynthetically active radiation absorbed by the canopy (f*) was estimated
2
from a time series of MODIS 16-Day L3 Global 250m (MOD13Q1) satellite imagery (Land
3
Processes Distributed Active Archive Center and the National Aeronautic and Space
4
Administration with final MODIS data products processed/re-formatted by CSIRO Marine and
5
Atmospheric Research using the MODIS Reprojection Tool ('mrtmosaic', 'resample')). The
6
MODIS product used was Normalized Difference Vegetation Index (NDVI) for the 5 year period
7
from July 2000 to July 2005 covering the Australian continent. A time series of f* at a monthly
8
time step was produced from the NDVI data using a linear function, following Roderick et al.
9
(1999) and Berry & Roderick (2002), but with NDVI endpoint values specific to the MODIS
10
11
imagery calibrated for bare soil.
f *  1.118 NDVI *  0.168 where NDVI *  0.15
(6)
12
and f *  0 where NDVI *  0.15
13
Following the calculation of PG* (mol CO2 m-2 day-1) for each 250 m grid cell for the 60 month
14
time-series of f, we calculated GPP for each calendar month and then computed annual sums of
15
GPP (mol CO2 m2 yr-1) for the five years commencing in July 2000.
16
17
References
18
Berry SL, Roderick ML (2002) Estimating mixtures of leaf functional types using continental-
19
scale satellite and climatic data. Global Ecology and Biogeography, 11, 23-40.
20
21
Roderick ML (1999) Estimating the diffuse component from daily and monthly measurements of
global radiation. Agricultural and Forest Meteorology, 95, 169-185.
13
1
Roderick ML, Noble IR, Cridland SW (1999) Estimating woody and herbaceous vegetation cover
2
from time series satellite observations. Global Ecology and Biogeography Letters, 8, 501-
3
508.
4
Roderick ML, Farquhar GD, Berry SL, Noble IR (2001) On the direct effect of clouds and
5
atmospheric particles on the productivity and structure of the vegetation. Oecologia, 129,
6
21-31.
7
14
1
Appendix 5: Biomass carbon model coefficients
2
Coefficients for factorial combinations of vegetation, topography and geology in Table 4.
3
Factor classes are described in Table 2.
4
1. Values of the regression constant a:
geol
1
2
3
4
veg
topo
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
Total Biomass
2
3
9.10
6.78
9.08
8.61
7.64
9.09
6.77
9.07
8.60
7.63
8.90
6.58
8.88
8.41
7.44
9.05
6.73
9.03
8.56
7.59
9.76
7.43
9.74
9.26
8.30
9.75
7.43
9.73
9.26
8.29
9.56
7.23
9.54
9.06
8.10
9.71
7.39
9.69
9.22
8.25
Living Biomass
2
3
8.28
7.14
9.30
8.99
8.03
8.25
7.11
9.27
8.96
8.00
8.06
6.92
9.08
8.77
7.81
8.19
7.05
9.22
8.91
7.94
9.33
8.19
10.36
10.05
9.08
9.30
8.17
10.33
10.02
9.06
9.11
7.98
10.14
9.83
8.86
9.25
8.11
10.27
9.96
9.00
5
6
2. Values of the coefficient b2:
Total Biomass
Living Biomass
veg
2
3
2
3
topo
1
0.0022
0.0028
0.0014
0.0024
2
0.0003
0.0010
0.0004
0.0014
3
0.0022
0.0028
0.0022
0.0032
4
0.0018
0.0025
0.0019
0.0029
5
0.0015
0.0021
0.0016
0.0026
7
8
15