Experimenting the design-based k-NN approach for mapping and estimation under forest management planning

doi:10.3832/ifor0604-009

Estimation and mapping of forest attributes are a fundamental support for forest management planning. This study describes a practical experimentation concerning the use of design-based k-Nearest Neighbors (k-NN) approach to estimate and map selected attributes in the framework of inventories at forest management level. The study area was the Chiarino forest within the Gran Sasso and Monti della Laga National Park (central Italy). Aboveground biomass and current annual increment of tree volume were selected as the attributes of interest for the test. Field data were acquired within 28 sample plots selected by stratified random sampling. Satellite data were acquired by a Landsat 5 TM multispectral image. Attributes from field surveys and Landsat image processing were coupled by k-NN to predict the attributes of interest for each pixel of the Landsat image. Achieved results demonstrate the effectiveness of the k-NN approach for statistical estimation, that is compatible with the produced forest attribute raster maps and also proves to be characterized, in the considered study case, by a precision double than that obtained by conventional inventory based on field sample plots only.

Keywords

Forest management planning, k-Nearest Neighbors, Landsat, Estimation, Mapping

Authors’ address

(1)

Department for Innovation in Biological, Agro-food and Forest systems (DIBAF), University of Tuscia, v. San Camillo de Lellis snc, I-01100 Viterbo (Italy)

(2)

Gran Sasso and Monti della Laga National Park, Assergi, AQ (Italy)

Corresponding author

W Mattioli
walter.mattioli@unitus.it

Citation

Mattioli W, Quatrini V, Di Paolo S, Di Santo D, Giuliarelli D, Angelini A, Portoghesi L, Corona P (2012). Experimenting the design-based k-NN approach for mapping and estimation under forest management planning. iForest 5: 26-30. - doi: 10.3832/ifor0604-009

Academic Editor

Agostino Ferrara

Paper history

Received: Oct 14, 2011
Accepted: Jan 19, 2012

First online: Feb 27, 2012
Publication Date: Feb 27, 2012
Publication Time: 1.30 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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