Online supplementary material
Appendix 1. Testing the accuracy of age prediction of wooded subcompartments using
reflectance bands.
The age of each subcompartment was modelled by regressing the recorded number of years
since planting against the linear and quadratic terms of the reflectance values of each of the
six bandwidths. Each quadratic term was divided by 100 for ease of interpretation. Because
of potential non-linear relationships between age and reflectance values, subcompartments
with an age greater than 50 were constrained to 50 (Pearce-Higgins et al. 2007). All possible
combinations of these twelve variables were modelled, and model averaging across these
models used to produce the best model fit to the data (Table A1).
Examination of the fit of these models suggested there was still non-linearity between
observed and predicted age with an asymptote beyond about 35 years of age. Therefore, to
examine the fit of the data, we look only at the strength of the correlation (r 2) between the
predictions from these models and the observed ages in the test subcompartments for
subcompartments up to 35 years old, which ranged from 36 to 56% accuracy in age
prediction, equivalent to that achieved by Pearce-Higgins et al.. (2007). The slope of the
relationship between observed and predicted age was then used to correct for any over- or
under-prediction in age in each image, to ensure that the three images yielded comparable
estimates of forest age.
Pearce-Higgins, J.W., Grant, M.C., Robinson, M.C. & Haysom, S.I. 2007. The role of forest
maturation in causing the decline of Black Grouse Tetrao tetrix. Ibis 149: 143–155.
Table A1. Model averaged coefficients describing forest subcompartment age as a function
of Landsat 5 reflectance values for the three satellite images. The accuracy of each model is
derived from the correlation between observed and predicted age for the 10% of test data.
Image
Intercept
Band 1
Band 2
Band 3
Band 4
Band 5
Band 7
Band 12
Band 22
Band 32
Band 42
Band 52
Band 72
Accuracy (r2)
206/21
Slope
SE
-12.9684
-0.0081
0.2702
-0.2288
-0.0281
0.0238
0.3252
-0.0029
-0.5685
0.5002
0.0153
-0.0393
-0.1578
2.7589
0.0121
0.0459
0.0195
0.0070
0.0031
0.0508
0.0115
0.0986
0.0366
0.0060
0.0026
0.0238
0.44
206/20
Slope
204/21
SE
0.1082 0.9980
0.1595 0.0379
0.1076 0.0775
-0.1741 0.0248
0.0101 0.0050
0.0225 0.0029
0.0006 0.0224
-0.1386 0.0337
-0.2148 0.1786
0.3955 0.0493
-0.0260 0.0060
-0.0365 0.0026
-0.0083 0.0080
0.36
Slope
SE
8.5192 7.3541
0.0947 0.0739
0.3798 0.1504
-0.4449 0.0661
-0.0253 0.0109
0.0586 0.0088
-0.1120 0.1122
-0.0392 0.0756
-0.9315 0.3310
0.9809 0.1207
0.0083 0.0095
-0.0597 0.0078
0.0377 0.0425
0.56
Appendix 2. Testing the accuracy of predicted cover of non-woodland habitat types.
Linear Discriminate Analysis was used to conduct a supervised classification of habitats from
colour-composite images produced from bands 2, 3 and 4 in Idrisi Kilimanjaro 14.0 (Clark
Labs 2003). After the exclusion of known areas of woodland using existing GIS layers (see
main text), polygons delineating ten habitat types (dry heath, wet heath, improved
grassland, arable, rough grassland / bog, water, urban, rock, cloud, snow) were
distinguished by eye from the remainder of the image. Based on previous relationships with
Black Grouse (Pearce-Higgins et al. 2007), only three were of potential interest as
explanatory variables (dry heath, improved grassland / arable combined, and rough
grassland / bog). Whilst improved grassland / arable habitats can easily be distinguished by
this process (Pearce-Higgins et al. 2007), the separation of unenclosed upland habitats
might prove difficult. Therefore to test our predictions for dry heath and rough grassland /
bog, we correlated predicted cover of these two types against independent habitat cover
data derived in the field, from twenty unenclosed upland sites of 2-9.3 km2 in area, collected
for the purposes of a separate study (see Pearce-Higgins et al. 2009 for field methods).
Observed percentage cover of heather from these 20 sites was used as a measure of dry
heath, whilst the observed percentage cover of grasses, sedges and rushes combined was
used to provide a measure of rough grassland / bog cover (Pearce-Higgins et al. 2007).
Predicted dry heath (x) was strongly correlated with observed heather (y) cover (r = 0.94, n =
20, P < 0.001, y = 5.40 + 0.72x) and predicted rough grassland / bog (x) strongly correlated
with observed grass, sedge and rush (y) cover (r = 0.95, n = 20, P < 0.001, y = 27 + 0.63x).
The supervised classification therefore provided an accurate map of variation in key
vegetation variables across Scotland.
Clarke Laboratories. 2003. Idrisi Kilimanjaro 14.0. Clark University, WA, USA.
Pearce-Higgins, J.W., Grant, M.C., Robinson, M.C. & Haysom, S.I. 2007. The role of forest
maturation in causing the decline of Black Grouse Tetrao tetrix. Ibis 149: 143–155.