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Original Articles

K-NN FOREST: a software for the non-parametric prediction and mapping of environmental variables by the k-Nearest Neighbors algorithm

, , , , , & show all
Pages 433-442 | Received 01 Sep 2012, Accepted 18 Oct 2012, Published online: 17 Feb 2017

References

  • Anderson T.W. (1984)—An Introduction to Multivariate Statistical Analysis. New York, John Wiley & Sons.
  • Baffetta F., Fattorini L., Franceschi S., Corona P. (2009)—Design-based approach to k-nearest neighbours technique for coupling field and remotely sensed data in forest surveys. Remote Sensing of Environment, 113 (3): 463–475. doi: http://dx.doi.org/10.1016/j.rse.2008.06.014.
  • Baffetta F., Corona P., Fattorini L. (2011)—Design-based diagnostics for k-NN estimators of forest resources. Canadian Journal of Forest Research, 41: 59–72. doi: http://dx.doi.org/10.1139/X10-157.
  • Chirici G., Barbati A., Corona P., Marchetti M., Travaglini D., Maselli F., Bertini R. (2008)—Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and mediterranean forest ecosystems. Remote Sensing of Environment, 112 (5): 2686–2700. doi: http://dx.doi.org/10.1016/j.rse.2008.01.002.
  • Chirici G., Corona P., Marchetti M., Tonti D., Travaglini D. (2010)—Biomass estimation by satellite data and ground measurements. In: Miranda D., Suarez J., Rafael C. (Eds.), Forestsat2010, Lugo (Spain), pp. 42–45.
  • Corona P. (2010)—Integration of forest inventory and mapping to supportforest management. iForest—Biogeosciences and Forestry 3: 59–64.
  • Corona P., Chirici G., McRoberts R.E., Winter S., Barbati A. (2011)—Contribution of large-scale forest inventories to biodiversity assessment and monitoring. Forest Ecology and Management, 262 (11): 2061–2069. doi: http://dx.doi.org/10.1016/j.foreco.2011.08.044.
  • Clark Labs (2012)—IDRISI Selva GIS and Image Processing Software. Available at: http://www.clarklabs.org (last accessed 14/04/2012).
  • Crookston N.L., Finley A.O. (2008)—yaImpute: an R package for kNN imputation. Journal of Statistical Software, 23: 62–72.
  • Daelemans W., Zavrel J., Van der Sloot K., Van den Bosch A. (2010)—TiMBL: Tilburg Memory Based Learner, version 6.3, Reference Guide. ILK Research Group Technical Report Series no. 10–01.
  • Franco-Lopez H., Ek A.R., Bauer M.E. (2001)—Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sensing of Environment, 77: 251–274. doi: http://dx.doi.org/10.1016/S0034-4257(01)00209-7.
  • Ferreira L.N. (2012)—K-Nearest Neighbor (KNN) Classifier. Avaiable at: http://www.leonardonascimento.com/en/knn.html.s.
  • Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.H. (2009)—The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11 (1). doi: http://dx.doi.org/10.1145/1656274.1656278.
  • Halme M., Tomppo E. (2001)—Improving the accuracy of multisource forest inventory estimates to reducing plot location error, a multicriteria approach. Remote Sensing of Environment, 78: 321–327. doi: http://dx.doi.org/10.1016/S0034-4257(01)00227-9.
  • Gulli A. (2012)—Nearest Neighbour on KD-Tree in C++ and Boost. Available at: http://codingplayground.blogspot.it/2010/01/nearest-neighbour-on-kd-tree-in-c-and.html (last accessed 10/04/2012).
  • Holmstrom H., Nilsson M., Ståhl G. (2001)—Simultaneous estimations of forest parameters using aerial photograph-interpreted data and the k nearest neighbor method. Scandinavian Journal of Foresr Research, 16: 67–78. doi: http://dx.doi.org/10.1080/028275801300004424.
  • Katila M., Tomppo E. (2001)—Selecting estimation parameters for the Finnish multisource National Forest Inventory. Remote Sensing of Environment, 76: 16–32. doi: http://dx.doi.org/10.1016/S0034-4257(00)00188-7.
  • Kim H.-J., Tomppo E. (2006)—Model-based prediction error uncertainty estimation for k-nn method. Remote Sensing of Environment, 104: 257–263. doi: http://dx.doi.org/10.1016/j.rse.2006.04.009.
  • Lachenbruch P.A., Mickey M.R. (1986)—Estimation of error rates in discriminant analysis. Technometrics, 10: 1–11. doi: http://dx.doi.org/10.1080/00401706.1968.10490530.
  • Lammertsma P. (2004)—K-nearest-neighbor algorithm. Available at: http://paul.luminos.nl/documents/show_document.php?d=197 (last accessed 6/05/2012).
  • Lasserre B., Chirici G., Chiavetta U., Garfí V., Tognetti R., Drigo R., DiMartino P., Marchetti M. (2011)—Assessment of potential bioenergy from coppice forests trough the integration ofremote sensing andfield surveys. Biomass and Bioenergy, 35(1): 716–724. doi: http://dx.doi.org/10.1016/j.biombioe.2010.10.013.
  • Li L. (2003)—Variable selection and sample classification using a genetic algorithm and k-nearest neighbors (GA/KNN) method. Available at: http://www.niehs.nih.gov/research/resources/software/gaknn (last accessed 1/05/2012).
  • Magnussen S., McRoberts R.E., Tomppo E.O. (2009)—Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories. Remote Sensing of Environment, 113: 476–488. doi: http://dx.doi.org/10.1016/j.rse.2008.04.018.
  • Maselli F. (2001)—Extension of environmental parameters over the land surface by improved fuzzy classification of remotely sensed data. International Journal of Remote Sensing, 22: 3597–3610. doi: http://dx.doi.org/10.1080/01431160010006458.
  • Maselli F., Chirici G., Bottai L., Corona P., Marchetti M. (2005)—Estimation of Mediterranean forest attributes by the application of k-NN procedures to multitemporal Landsat ETM+ images. International Journal of Remote Sensing, 17: 3781–3796. doi: http://dx.doi.org/10.1080/01431160500166433.
  • Mattioli W., Quatrini V., Di Paolo S., Di Santo D., Giuliarelli D., Angelini A., Portoghesi P., Corona P. (2012)—Experimenting the design-based k-NN approach for mapping and estimation under forest management planning. iForest—Biogeosciences and Forestry, 5: 26–30.
  • McRoberts R.E. (2008)—Using satellite imagery and the k-nearest neighbors technique as a bridge between strategic and management forest inventories. Remote Sensing of Environment, 112: 2212–2221. doi: http://dx.doi.org/10.1016/j.rse.2007.07.025.
  • McRoberts R.E. (2009)—Diagnostic tools for nearest neighbors techniques when used with satellite imagery. Remote Sensing of Environment, 113: 489–499. doi: http://dx.doi.org/10.1016/j.rse.2008.06.015.
  • McRoberts R.E., Tomppo E.O. (2007)—Remote sensing support for national forest inventories. Remote Sensing of Environment, 110: 412–419. doi: http://dx.doi.org/10.1016/j.rse.2006.09.034.
  • McRoberts R.E., Magnussen S., Tomppo E.O., Chirici G. (2011)—Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data. Remote Sensing of Environment, 115: 3165–3174. doi: http://dx.doi.org/10.1016/j.rse.2011.07.002.
  • Richards J.A. (1993)—Remote sensing digital image analysis: An introduction (2nd ed.). Heilderberg: Springer-Verlag, 340 pp.
  • SPSS (2007)—SPSS Statistics Base 17.0. User's Guide. SPSS Inc. Available at: www.spss.com (last accessed 31/03/2012).
  • Teknomo K. (2012)—K Nearest Neighbors Tutorial. Available at: http://people.revoledu.com/kardi/tutorial/KNN/index.html (last accessed 28/03/2012).
  • Thisara F. (2012)—Optimized Nearest Neighbor Framework. Available at: http://code.google.com/p/thisara/people/list (last accessed 1/05/2012).
  • Tokola T.J., Pitkänen S., Muinonen E. (1996)—Point accuracy of a nonparametric method n estimation of forest characteristics with different satellite materials. International Journal of Remote Sensing, 17 (12): 2333–2351. http://dx.doi.org/10.1080/01431169608948776.
  • Tomppo E. (1991)—Satellite image-based nationalforest inventory of Finland. International Archives of Photogrammetry and Remote Sensing, 28 (7–1): 419–424.
  • Wang F. (1990)—Fuzzy supervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 28: 194–201. doi: http://dx.doi.org/10.1109/36.46698.