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Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imageryAuthor(s): Erkki O. Tomppo; Caterina Gagliano; Flora De Natale; Matti Katila; Ronald E. McRoberts
Source: Remote Sensing of Environment
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionThe k-Nearest Neighbour (k-NN) estimation and prediction technique is widely used to produce pixel-level predictions and areal estimates of continuous forest variables such as area and volume, often by subcategories such as species. An advantage of k-NN is that the same parameters (e.g., k-value, distance metric, weight vector for the feature space variables) can be used for all variables, whether continuous or categorical. An obvious question is the degree to which accuracy can be improved if the k-NN estimation parameters are tailored for specific variable groups such as volumes by tree species or categorical variables. We investigated prediction of categorical forest attribute variables from satellite image spectral data using k-NN with optimisation of the weight vector for the ancillary variables obtained using a genetic algorithm. We tested several genetic algorithm fitness functions, all derived from well-known accuracy measures. For a Finnish test site, the categorical forest attribute variables were site fertility and tree species dominance, and for an Italian test site, the variables were forest type and conifer/broad-leaved dominance. The results for both test sites were validated using independent data sets. Our results indicate that use of the genetic algorithm to optimize the weight vector for prediction of a single forest attribute variable had a slight positive effect on the prediction accuracies for other variables. Errors can be further decreased if the optimisation is done by variable groups.
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CitationTomppo, Erkki O.; Gagliano, Caterina; De Natale, Flora; Katila, Matti; McRoberts, Ronald E. 2009. Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery. Remote Sensing of Environment. 113(3): 500-517. https://doi.org/10.1016/j.rse.2008.05.021.
KeywordsNational Forest Inventory, ik-NN, Categorical variables, Genetic algorithm
- Using genetic algorithms to optimize k-Nearest Neighbors configurations for use with airborne laser scanning data
- Applying an efficient K-nearest neighbor search to forest attribute imputation
- Multivariate inference for forest inventories using auxiliary airborne laser scanning data
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