Combining spectral and textural remote sensing variables using random forests: predicting the age of Pinus patula forests in KwaZulu-Natal, South Africa

Journal of Spatial Science

Volumn 57, Issue 2, 2012, Pages 193-211

  Dye, Michelle ^a   Mutanga, Onisimo ^a   Ismail, Riyad ^a  

^a UNIVERSITY OF KWAZULU NATAL   (South Africa)

Author keywords

backward variable selection; forest age; Pinus patula; random forests; texture

Indexed keywords

ACCURACY ASSESSMENT; CONIFEROUS TREE; FOREST ECOSYSTEM; PREDICTION; QUICKBIRD; REMOTE SENSING; SPECTRAL ANALYSIS; TEXTURE;

KWAZULU-NATAL; SOUTH AFRICA;

PINUS PATULA;

EID: 84870942138     PISSN: 14498596     EISSN: None     Source Type: Journal    
DOI: 10.1080/14498596.2012.733620     Document Type: Article

References (58)

1. Ahern, F.J., Erdle, T., Maclean, D.A. and Kneppeck, I.D. 1991. A quantitative relationship between forest growth rates and thematic mapper reflectance measurements. International Journal of Remote Sensing, 12: 387-400.
2. Archer, K.J. and Kimes, R.V. 2008. Empirical characterization of random forest variable importance measures. Computational Statistics and Data Analysis, 52: 2249-2260.
3. Bel, L., Allard, D., Laurent, J.M., Chaddadi, R. and Bar-Hen, A. 2009. CART algorithm for spatial data: Application to environmental and ecological data. Computational Statistics and Data Analysis, 53: 3082-3093.
4. Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. 1984. Classification and Regression Trees Wadsworth International Group, Belmont
5. Breiman, L. 2001. Random forests. Machine Learning, 45: 5-32.
6. 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: 2999-3011.
7. Cohen, W.B., Spies, T.A. and Bradshaw, G.A. 1990. Semivariograms of Digital Imagery for Analysis of Conifer Canopy Structure. Remote Sensing of Environment, 34: 167-178.
8. Curran, P. 1980. Multispectral remote sensing of vegetation amount. Progress in Physical Geography, 4: 315-341.
9. Cutler, R.D., Edwards, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J. and Lawler, J.J. 2007. Random forests for classification in ecology. Ecology, 88(no. 11): 2783-2792.
10. Danson, F.M. and Curran, P.J. 1993. Factors affecting the remotely sensed response of coniferous forest plantations. Remote Sensing of Environment, 43: 55-65.
11. De'ath, G. and Fabricius, K. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81(no. 11): 3178-3192.
12. DeFries, R.S., Hansen, M., Steininger, M., Dubayah, R., Sohlberg, R. and Townshend, J. 1997. Sub pixel forest cover in Central Africa from multisensor, multitemporal data. Remote Sensing of Environment, 60: 228-246.
13. Digital Globe. 2008. http://www.digitalglobe.com/index.php/85/QuickBird (accessed 10 November 2009)
14. Dye, M., Mutanga, O. and Ismail, R. 2008. Detecting the severity of woodwasp, Sirex noctilio, infestation in a pine plantation in KwaZulu-Natal, South Africa, using texture measures calculated from high spatial resolution imagery. African Entomology, 16(no. 2): 263-275.
15. Eastman, J.R. and Fulk, M. 1993. Long sequence time series evaluation using standardized principal components. Photogrammetric Engineering and Remote Sensing, 59(no. 6): 991-996.
16. Elith, J., Leathwick, J.R. and Hastie, T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77: 802-813.
17. ENVI. 2006. ENVI Version 4.3, ITT Industries, Inc Boulder, CO
18. ESRI. 2006. ArcGIS Version 9.1, ERSI Redland, CA
19. Franklin, S.E., Hall, R.J., Moskal, L.M., Maudie, A.J. and Lavigne, M.B. 2000. Incorporating texture into classification of forest species composition from airborne multispectral images. International Journal of Remote Sensing, 21(no. 1): 61-79.
20. Franklin, S.E., Wulder, M.A. and Gerylo, G.R. 2001. Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia. International Journal of Remote Sensing, 22(no. 13): 2627-2632.
21. Franklin, S.E., Hall, R.J., Smith, L. and Gerylo, G.R. 2003. Discrimination of conifer height, age and crown closure classes using Landsat-5 imagery in the Canadian Northwest Territories. International Journal of Remote Sensing, 24(no. 9): 1823-1834.
22. Gebreslasie, M.T., Ahmed, F.B. and van Aardt, J. 2008. Estimating plot-level forest structural attributes using high spectral resolution ASTER satellite data in even-aged Eucalyptus plantations in southern KwaZulu-Natal, South Africa. Southern Forests, 70(no. 3): 227-236.
23. Gerylo, G.R., Hall, R.J., Franklin, S.E. and Smith, L. 2002. Empirical relations between Landsat TM spectral response and forest stands near Fort Simpson, Northwest Territories, Canada. Canadian Journal of Remote Sensing, 28(no. 1): 68-79.
24. Grimm, R., Behrens, T., Märker, M. and Elsenbeer, H. 2008. Soil organic carbon concentrations and stocks on Barro Colorado Island-Digital soil mapping using Random Forests analysis. Geoderma, 146: 102-113.
25. Hall-Beyer, M. 2010. The GLCM Tutorial Home Page, University of Calgary, Canada http://www.ucalgary.ca (accessed 12 March 2010)
26. Hansen, M.C., DeFries, R.S., Townshend, J.R.G., Sohlberg, R., Dimiceli, C. and Carroll, M. 2002. Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sensing of Environment, 83: 303-319.
27. Haralick, R.M., Shanmugan, K. and Dinstein, I. 1973. Texture features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3(no. 6): 610-621.
28. Ismail, R. 2009. Remote sensing of forest health: the detection and mapping of Pinus patula trees infested by Sirex noctilio, School of Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa. Dissertation (PhD)
29. Ismail, R. and Mutanga, O. 2010. A comparison of regression tree ensembles: predicating Sirex noctilio induced water stress in Pinus patula forest of KwaZulu-Natal, South Africa. International Journal of Applied Earth Observation and Geoinformation, 12: 45-51.
30. Jakubauskas, M.E. and Price, K.P. 2000. Regression-based estimation of lodgepole pine forest age from Landsat Thematic Mapper Data. Geocarto International, 15(no. 1): 1-6.
31. Jensen, J.R., Qui, F. and Ji, M. 1999. Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data. International Journal of Remote Sensing, 20(no. 14): 2805-2822.
32. Jobanputra, R. and Clausi, D.A. 2006. Preserving boundaries for image texture segmentation using grey level co-occurring probabilities. Pattern Recognition, 9: 234-245.
33. Johansen, K., Coops, N.C., Gergel, S.E. and Stange, Y. 2007. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sensing of Environment, 110: 29-44.
34. Kayitakire, F., Giot, P. and Defourny, P. 2002. Automated delineation of the forest stands using digital color orthophotos: Case study in Belgium. Canadian Journal of Remote Sensing, 28(no. 5): 629-640.
35. Kayitakire, C., Hamel, C. and Defourny, P. 2006. Retrieving forest structure based on image texture analysis and IKONOS-2 imagery. Remote Sensing of Environment, 102: 390-401.
36. Kuhn, K. 2009. Variable selection using the caret package http://ftp.udc.es/public/CRAN/web/packages/caret/vignettes/caretSelection.pdf (accessed 21 November 2009)
37. Lawrence, R.L., Wood, S.D. and Sheley, R.L. 2006. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sensing of Environment, 100: 356-362.
38. Liaw, A. and Wiener, M. 2002. Classification and regression by randomForest. R News, 2/3: 18-22.
39. Lobell, D.B., Oritiz-Monasterio, J.I., Asner, G.P., Naylor, R. and Falcon, W. 2007. Combining field surveys, remote sensing and regression trees to understand yield variations in an irrigated wheat landscape. Agronomy Journal, 97: 241-249.
40. Michaelson, J., Schimel, D.S., Friedl, M.A., Davis, F.W. and Dubayah, R.O. 1994. Regression tree analysis of satellite and terrain data to guide vegetation sampling and surveys. Journal of Vegetation Science, 5: 673-696.
41. Moskal, L.M. and Franklin, S.E. 2001. Classifying multilayer forest structure and composition using high resolution, compact airborne spectrographic imager image texture. American Society of Remote Sensing and Photogrammetry Annual Conference, St. Louis, April, 2001
42. Mucina, L. and Rutherford, M.C. 2006. The Vegetation of South Africa, Lesotho and Swaziland. Strelitzia 19 South African National Biodiversity Institute, Pretoria
43. Nieman, K.O. 1995. Remote sensing of forest stand age using airborne spectrometer data. Photographic Engineering and Remote Sensing, 61: 1119-1127.
44. Owen, D.L. 2000. Southern African Forestry Handbook Volume 1, The Southern African Institute of Forestry
45. Prasad, A.M., Iverson, L.R. and Liaw, A. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9: 181-199.
46. Puissant, A., Hirsch, J. and Weber, C. 2005. The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery. International Journal of Remote Sensing, 26(no. 4): 733-745.
47. R Development Core Team. 2008. R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing, Vienna
48. Ricotta, C., Avena, G.C. and Volpe, F. 1999. The influence of principal component analysis on the spatial structure of a multispectral dataset. International Journal of Remote Sensing, 20(no. 17): 3367-3376.
49. Schmidt, K.S. 2003. Hyperspectral Remote Sensing of Vegetation Species Distribution in Saltmarsh International Institute for Geo-Information Science and Earth Observation, Enschede
50. Spatial Ecology. 2010. Hawth's Analysis Tools for ArcGIS www.spatialecology.com/htools/overview.php (accessed 2 April 2010)
51. St-Louis, V., Pidgeon, A.M., Radeloff, V.C., Hawbaker, T.J. and Clayton, M.K. 2006. High resolution image texture as a predictor of bird species richness. Remote Sensing of Environment, 105: 299-312.
52. Strobl, C. and Zeileis, A. 2008. Danger: high power!-exploring the statistical properties of a test for random forest variable importance. Proceedings in Computational Statistics, 2: 59-66.
53. Tomppo, E.O., Gagliano, C., De, Natale, F., Katila, M. and McRoberts, R.E. 2009. Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery. Remote Sensing of Environment, 113: 500-517.
54. Tuttle, E.M., Jensen, R.R., Formica, V.A. and Gonser, R.A. 2006. Using remote sensing image texture to study habitat use patterns: a case study using the polymorphic white-throated sparrow (Zonotrichiaalbicollis). Global Ecology and Biogeography, 15: 349-357.
55. van Aardt, J.A.N. and Norris-Rogers, M. 2008. Spectral-age interactions in managed, even-aged Eucalyptus plantations: application of discriminant analysis and classification and regression trees approaches to hyperspectral data. International Journal of Remote Sensing, 29: 1841-1845.
56. Wunderle, A. L., Franklin, S.E. and Guo, X.G. 2007. Regenerating boreal forest structure estimation using SPOT-5 pan-sharpened imagery. International Journal of Remote Sensing, 28(no. 19): 4351-4364.
57. Yuan, X., King, D. and Vleck, J. 1991. Sugar maple decline assessment based on spectral and textural analysis of multispectral aerial videography. Remote Sensing of Environment, 37: 47-54.
58. Zhang, C., Franklin, S.E. and Wulder, M.A. 2004. Geostatistical and texture analysis of airborne-acquired images used in forest classification. International Journal of Remote Sensing, 4: 859-865.