Improving the accuracy of multisource forest inventory estimates to reducing plot location error — a multicriteria approach

By M. Halme, Erkki Tomppo. 2001

75 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/5c42fa8446c2b2617b300afa00a7b870921fedc2

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In Ecology

In Environmental Engineering

In Nature and Landscape Conservation

Citations in the corpus

Improvements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification (2003, background): “Despite its widespread use, crop separation from remotely sensed data still poses problems and various techniques have been developed to improve crop identification, such as localized ground truthing and the combined use of seasonal images ( Halme and Tomppo 2001 , Rees 2001).”
Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery (2009, background): “Also, prediction errors are overestimated as a result of field plot location errors (Halme & Tomppo, 2001; Verbyla & Hammond, 1995).”
Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories (2009, methodology): “Details of this procedure are in Halme and Tomppo (2001).”
Making forestry decisions with multiple criteria: A review and an assessment (2008, methodology): “Finally, a discrete multi-criteria method to reassign the pixels of satellite images to field plot data of a Finnish forest inventory can be seen in Halme and Tomppo (2001).”
Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery (2007, methodology): “The k-NN technique has been particularly popular in Finland (Halme & Tomppo, 2001; Katila & Tomppo, 2001, 2002; Tomppo, 1991; Tomppo et al., 1999).”