Training Data Complete Image
Overall Overall
Accuracy Kappa Accuracy Kappa
83 0.65 83 0.67
Table 1. Summary of the accuracy achieved for the forest/non-
forest classification
Features in the spectral importance curve include a sharp peak
at 1720nm, which sits between two broad liquid water
absorption bands. This wavelength region is known to be
sensitive to absorption by cellulose and lignin content (Fourty
et al., 1996; Gao and Goetz, 1995). Secondary peaks appear in
the visible green at 539nm and on the red edge at 701nm. There
is also a smaller peak at 640nm in the strongly chlorophyll
absorbing red wavelength region.
Importance
500 1000
1500 2000
Wavelength (nm)
Figure 1. Variable importance (solid line) for the Random
Forests classification model and a Eucalyptus leaf reflectance
spectrum measured in the laboratory (dashed line).
4. CONCLUSIONS
The accuracy of forest and non-forest classification achieved
here was comparable with those reported in previous studies
using Random Forests for the classification of vegetation using
hyperspectral imagery (Chan and Paelinckx, 2008; Sluiter and
Pebesma, 2010). Variable importance highlighted spectrally
broad features that have previously been associated with
biochemical absorption. For example, the importance feature at
1720nm, which is thought to be associated with cellulose and
lignin absorption is the dominant feature. Since the image was
collected just after the summer season, this importance may be
due to the presence of dead material within non-forest
(grassland and pasture) areas of the image.
While importance measures are an interesting diagnostic which
may help us to understand the key biophysical characteristics of
forests that allow their discrimination within a satellite image
scene, they also allow the investigation of appropriate broad
band data types for operational monitoring of forests as an
ongoing exercise. This is a key focus for our further research in
this area.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
REFERENCES
Bajcsy, P. and Groves, P., 2004. Methodology for hyperspectral
band selection. Photogrammetric Engineering and Remote
Sensing, 70(7): 793-802.
Breiman, L., 2001. Random forests. Machine Learning, 45(1):
5-32.
Brown, M., Elliott, H. and Hickey, J., 2001. An overview of the
Warra long-term ecological research site. Tasforests, 13: 1-8.
Chan, J.C.W. and Paelinckx, D., 2008. Evaluation of Random
Forest and Adaboost tree-based ensemble classification and
spectral band selection for ecotope mapping using airborne
hyperspectral imagery. Remote Sensing of Environment,
112(6): 2999-3011.
Cohen, J., 1960. A Coefficient of Agreement for Nominal
Scales. Educational and Psychological Measurement, 20(1): 37-
46.
Datt, B., McVicar, T.R., Van Niel, T.G., Jupp, D.L.B. and
Pearlman, J.S., 2003. Preprocessing EO-1 Hyperion
hyperspectral data to support the application of agricultural
indexes. IEEE Transactions on Geoscience and Remote
Sensing, 41(6): 1246-1259.
Everingham, Y., Lowe, K.H., Donald, D., Coomans, D. and
Markley, J., 2007. Advanced satellite imagery to classify
sugarcane crop characteristics. Agronomy for Sustainable
Development, 27: 111-117.
Fourty, T., Baret, F., Jacquemoud, S., Schmuck, G. and
Verdebout, J., 1996. Leaf optical properties with explicit
description of its biochemical composition: direct and inverse
problems. Remote Sensing of Environment, 56(2): 104-117.
Furby, S., 2002. Land cover change: specification for remote
sensing analysis - technical report no. 9. CSIRO Mathematical
and Information Sciences, Australian Greenhouse Office.
Gao, B.C. and Goetz, A.F.H., 1995. Retrieval of Equivalent
Water Thickness and Information Related to Biochemical-
Components of Vegetation Canopies from Aviris Data. Remote
Sensing of Environment, 52(3): 155-162.
Harris, S. and Kitchener, A., 2005. From Forest to Fjaeldmark:
Descriptions of Tasmania's Vegetation. Department of Primary
Industries, Water and Environment. Australian Government
Publishing Service, Canberra.
Hughes, G., 1968. On the mean accuracy of statistical pattern
recognizers. IEEE Transactions on Information Theory, 14(1):
55-63.
Ismail, R. and Mutanga, O., 2010. A comparison of regression
tree ensembles: Predicting Sirex noctilio induced water stress in
Pinus patula forests of KwaZulu-Natal, South Africa.
International Journal of Applied Earth Observation and
Geoinformation, 12: S45-S51.
Liaw, A. and Wiener, M., 2002. Classification and Regression
by randomForest. R news, 2(3): 18-22.
