A comparative analysis of kNN and decision tree methods for the Irish National Forest Inventory

International Journal of Remote Sensing

Volumn 30, Issue 19, 2009, Pages 4937-4955

  Mcinerney, Daniel O ^a   Nieuwenhuis, Maarten ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; FORESTRY; MEAN SQUARE ERROR; NEAREST NEIGHBOR SEARCH;

COMPARATIVE ANALYSIS; DECISION TREE METHOD; DIGITAL ELEVATION MODEL; K NEAREST NEIGHBOURS (K-NN); NATIONAL FOREST INVENTORIES; NON-PARAMETRIC ESTIMATIONS; RANDOM FOREST ALGORITHM; ROOT MEAN SQUARE ERRORS;

SATELLITE IMAGERY;

ALGORITHM; COMPARATIVE STUDY; DIGITAL ELEVATION MODEL; ESTIMATION METHOD; FOREST INVENTORY; SATELLITE IMAGERY;

DECISION MAKING; ERRORS; ESTIMATION; FORESTRY; IMAGE ANALYSIS; PARAMETERS; SATELLITES; TREES;

EURASIA; EUROPE; IRELAND; WESTERN EUROPE;

EID: 70449347128     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431160903022936     Document Type: Article

References (46)

1. Altman, N.S., 1992, An introduction to kernel and nearest-neighbour nonparametric regression. American Statistician, 46, pp. 175-185.
2. Bivand, R., 2008, SPGRASS6: Interface between GRASS 6 geographical information system and R. R package version 0.5-2. Available online at: http://grass.osgeo.org/.
3. Breiman, L., 1996, Bagging predictors. Machine Learning, 24, pp. 123-140.
4. Breiman, L., 2001, Random forests. Machine Learning, 45, pp. 5-32.
5. Breiman, L., Freidman, J.H., Olshen, R.A. and Stone, C.J., 1984, Classification and Regression Trees (California: Wadsworth).
6. Cressie, N.A., 1993, Statistics for Spatial Data (New York: John Wiley).
7. Finley, A.O. and McRoberts, R.E., 2008, Efficient k-nearest neighbor searches for multi-source forest attribute mapping. Remote Sensing of Environment, 112, pp. 2203-2211.
8. Fix, E. and Hodges, J.L., 1951, Discriminatory analysis - Nonparametric discrimination consistency properties. Project 41-49-004, Report 4, pp. 261-279 (Randolph Field, Texas: School of Aviation Medicine).
9. Franco-Lopez, H., Ek, A.R. and Bauer, M.E., 2001, Estimation and mapping of forest stand density, volume and cover type using the k-nearest neighbour method. Remote Sensing of Environment, 77, pp. 251-274.
10. th International Conference on Machine Learning, Bari, Italy, pp. 148-156.
11. Furlanello, C., Neteler, M., Merler, S., Menegon, S., Fontanri, S., Donini, A., Rizzoli, A. and Chemini, C., 2003, GIS and the random forest predictor: Integration in R for tickborne disease risk assessment. Third International Workshop on Distributed Statistical Computing (DSC 2003), Vienna, Austria, 20-22 March 2003.
12. Ghosh, A.K., 2006, An optimum chose of the k in nearest neighbor classification. Computational Statistics and Data Analysis, 50, pp. 3113-3123.
13. Gjertsen, A.K., 2007, Accuracy of forest mapping based on Landsat TM data and a kNN-based method. Remote Sensing of Environment, 110, pp. 420-430.
14. Haapanen, R., Ek, A., Bauer, M. and Finley, A., 2004, Delineation of forest nonforest land use classes using nearest neighbor methods. Remote Sensing of Environment, 89, pp. 265-271.
15. Hamilton, G.J., 1986, Forest Mensuration Handbook, Vol. 16 (London: HMSO).
16. Hastie, T., Tibishirani, R. and Friedman, J., 2001, The Elements of Statistical Learning - Data Mining, Inference and Prediction (New York: Springer).
17. Holmgren, J., Joyce, S., Nilsson, M. and Olsson, H., 2000, Estimation and mapping of forest stand density, volume and cover type using the k-nearest neighbour method. Scandinavian Journal of Forest Research, 15, pp. 103-111.
18. Holmstrom, H. and Fransson, J., 2003, Combining remotely sensed optical and radar data in kNN estimation of forest variables. Forest Science, 49, pp. 409-418.
19. Hudak, A.T., Cookston, N., Evans, J., Hall, D. and Falkowski, M., 2008, Nearest neighbor imputation of species-level, plot scale forest structure attributes from LiDAR data. Remote Sensing of Environment, 112, pp. 2232-2245.
20. Katila, M. and Tomppo, E., 2001, Selecting estimation parameters for the Finnish multisource national forest inventory. Remote Sensing of Environment, 76, pp. 16-32.
21. Liaw, A. and Wiener, M., 2002, Classification and regression by randomForest. R News, 2, pp. 18-22. Available online at: http://CRAN.R-project.org/doc/Rnews/.
22. McGovern, E.A., Holden, N.M., Ward, S.M. and Collins, J.F., 2002, The radiometric normalization of multitemporal Thematic Mapper imagery of the midlands of Ireland - A case study. International Journal of Remote Sensing, 23, pp. 751-766.
23. McRoberts, R.E. and Tomppo, E., 2007, Remote sensing and national forest inventories. Remote Sensing of Environment, 111, pp. 412-419.
24. McRoberts, R.E., Tomppo, E., Finley, A. and Heikkinen, J., 2007, Estimating areal means and variances of forest attributes using the k-nearest neighbors technique and satellite imagery. Remote Sensing of Environment, 111, pp. 466-480.
25. McRoberts, R.E., 2009, Diagnostic tools for nearest neighbors techniques when used with satellite imagery. Remote Sensing of Environment, 113, pp. 489-499.
26. Mäkelä, H. and Pekkarinen, A., 2001, Estimation of timber volume at the sample plot level by means of image segmentation and Landsat TM imagery. Remote Sensing of Environment, 77, pp. 66-75.
27. Maselli, F., Chirici, G., Bottai, L., Corona, P. and Marchetti, M., 2005, Accuracy of forest mapping based on Landsat TM data anda kNN-based method. International Journal of Remote Sensing, 26, pp. 3781-3796.
28. Matthews, R.W. and Mackie, E.D., 2006, Forest Mensuration - A Handbook for Practitioners, Vol. 16 (London: British Forestry Commission).
29. National Forest Inventory, 2007a, Methodology (Co. Wexford, Ireland: Department of Agriculture, Fisheries and Food).
30. National Forest Inventory, 2007b, Results (Co. Wexford, Ireland: Department of Agriculture, Fisheries and Food).
31. Neteler, M. and Mitasova, H., 2002, Open Source GIS: A GRASS GIS Approach, 2nd edn (Boston: Kluwer Academic).
32. Pal, M., 2005, Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26, pp. 217-222.
33. Pal, M. and Mather, P.M., 2003, An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86, pp. 554-565.
34. Quinlan, J.R., 1993, C4.5: Programs For Machine Learning (Los Altos: Morgan Kaufman).
35. Quinlan, J.R., 1996, Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research, 4, pp. 77-90.
36. R Development Core Team, 2007, R: A Language and Environment for Statistical Computing (Vienna, Austria: R Foundation for Statistical Computing). Available online at: http://www.R-project.org.
37. Segal, M., 2004, Machine learning benchmarks and random forest regression. eScholarship Repository, University of California. Available online at: http://repositories.cblib.org/cbmd/bench_rf_regn.
38. Shapire, R., Freund, Y., Bartlett, P. and Lee, W., 1998, Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, 26, pp. 1651-1686.
39. Teillet, P.M., Guindon, B. and Goodenough, D.G., 1982, On the slope aspect correction of multi spectral scanner data. Canadian Journal of Remote Sensing, 8, pp. 84-106.
40. Tokola, T., Pitkanen, J., Partinen, S. and Muinonen, E., 1996, Point accuracy of a non-parametric method in estimation of forest characteristics with different satellite materials. International Journal of Remote Sensing, 17, pp. 2333-2351.
41. Tomppo, E., 1991, Satellite image based national forest inventory of Finland. International Archives of Photogrammetry and Remote Sensing, 28, pp. 419-424.
42. Tomppo, E., 1996, Application of remote sensing in Finnish national forest inventory. In Proceedings of International Workshop on the Application of Remote Sensing in European Forest Monitoring, 14-16 October 1996, Institute for Surveying and Remote Sensing, University of Agriculture, Vienna, Austria, pp. 147-156.
43. Tomppo, E. and Halme, M., 2004, Using coarse scale forest variables as ancillary information and weighting of variables in kNN estimation: A genetic algorithm approach. Remote Sensing of Environment, 92, pp. 1-20.
44. Tomppo, E., Olsson, H., Ståhl, G., Nilsson, M., Hagner, O. and Katila, M., 2008, Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sensing of Environment, 112, pp. 1982-1999.
45. Tomppo, E., Goulding, C. and Katila, M., 1999, Adapting Finnish multi-source forest inventory techniques to the New Zealand preharvest inventory. Scandinavian Journal of Forest Research, 14, pp. 182-192.
46. Tuominen, S. and Poso, S., 2001, Improving multi-source forest inventory by weighting auxiliary data sources. Silva Fennica, 35, pp. 203-214.