Using self-organizing maps in the visualization and analysis of forest inventory

doi:10.3832/ifor0629-005

Using self-organizing maps in the visualization and analysis of forest inventory

iForest - Biogeosciences and Forestry, Volume 5, Issue 5, Pages 216-223 (2012)
doi: https://doi.org/10.3832/ifor0629-005
Published: Oct 02, 2012 - Copyright © 2012 SISEF

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A lot of useful data on forest condition can be gathered from the Forest Inventory (FI). Without the help of data analysis tools, human experts cannot manually interpret information in such a large data set. Conventional multivariate statistical analyses provide results that are difficult to interpret and often do not represent the information in a satisfactory way. Our goal is to identify an alternative approach that will enable fast and efficient interpretation and analysis of the FI data. Such interpretation and analysis can be performed automatically with a clustering method, but all clustering methods have some shortcomings. Therefore, our aim was also to provide information in a form suitable for fast and intuitive visualization. Kohonen’s Self Organizing Map (SOM) is an alternative approach to data visualization and analysis of large multidimensional data sets. SOM provides different possibilities and our experiments are presented with component matrices of individual stand parameters and label matrices. In forming data clusters, we experimented with hierarchical and non hierarchical clustering methods. Our experiments showed that SOM provides useful information in a form suitable for data clustering and data visualization. This enables an efficient analysis of large FI data sets at different analysis scales. Clustering results obtained with SOM and two clustering algorithms are in accordance with ground truth. We have also considered the efficiency of SOM component matrices by visual comparison and correlation among structural parameters and by determining contributions of individual stand parameters to clustering input data. SOM application in visualization and analysis of stand structural parameters enables gathering quickly and efficiently holistic information on the current condition of forest stands and forest ecosystem development. Therefore we recommend the application of Kohonen’s SOM for visualization and analysis of FI data.

Keywords

Forest Inventory, Stand Structural Parameters, Self-organizing Maps, Forest Data Visualization, Neural Networks

Authors’ address

(1)

Hrvatske sume Ltd., Croatian National Forestry Agency, Ljudevita F. Vukotinovica 2, 10000 Zagreb (Croatia)

(2)

Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb (Croatia)

Corresponding author

D Klobucar
damir.klobucar@hrsume.hr

Citation

Klobucar D, Subasic M (2012). Using self-organizing maps in the visualization and analysis of forest inventory. iForest 5: 216-223. - doi: 10.3832/ifor0629-005

Academic Editor

Marco Borghetti

Paper history

Received: May 15, 2012
Accepted: Sep 14, 2012

First online: Oct 02, 2012
Publication Date: Oct 30, 2012
Publication Time: 0.60 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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