iForest - Biogeosciences and Forestry


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

D Klobucar (1)   , M Subasic (2)

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

Research Articles

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.


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

Authors’ address

D Klobucar
Hrvatske sume Ltd., Croatian National Forestry Agency, Ljudevita F. Vukotinovica 2, 10000 Zagreb (Croatia)
M Subasic
Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb (Croatia)

Corresponding author



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

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List of the papers citing this article based on CrossRef Cited-by.

Annas S, Kanai T, Koyama S (2007)
PCA and SOM for visualizing and classifying fire risks in forest regions. Agricultural Information Research 16 (2): 44-51.
CrossRef | Gscholar
Bauer HU, Pawelzik KR (1992)
Quantifying the neighborhood preservation of self-organizing feature maps. IEEE Transactions on Neural Networks 3 (4): 570-579.
CrossRef | Gscholar
Boncina A, Cavlovic J (2009)
Perspectives of forest management planning: Slovenian and Croatian experience. Croatian Journal of Forest Engineering 30 (1): 77-87.
Online | Gscholar
Chon TS (2011)
SOM applied to ecological sciences. Ecological Informatics 6 (1): 50-61.
CrossRef | Gscholar
Corona P (2010)
Integration of forest mapping and inventory to support forest management. iForest 3 (1): 59-64.
CrossRef | Gscholar
Foody GM, Cutler MEJ (2006)
Mapping the species richness and composition of tropical forests from remotely sensed data with neural network. Ecological Modelling 195 (1-2): 37-42.
CrossRef | Gscholar
Foody GM (1999)
Applications of the SOFM neural network in community data analysis. Ecological Modelling 120 (2-3): 97-107.
CrossRef | Gscholar
Fujino M, Yoshida M (2006)
Development and validation of a method of forestry region classification using PCA and cluster analysis together with SOM algorithm. Journal of the Japanese Forest Society 88 (4): 221-230.
CrossRef | Gscholar
Giraudel JL, Lek S (2001)
A comparison of SOM algorithm and some conventional statistical methods for ecological community ordination. Ecological Modelling 146 (1-3): 329-339.
CrossRef | Gscholar
Hasenauer H, Merkl D (1997)
Forest tree mortality simulation in uneven-aged stands using connectionist networks. In: Proceedings of the “International conference on engineering applications of neural networks” (Liljenström H, Bulsari AB eds). Stockholm (Sweden), 16-18 June 1997, pp. 341-348.
Hsu AL, Halgamuge SK (2003)
Enhancement of topology preservation and hierarchical dynamics SOMs for data visualization. International Journal of Approximate Reasoning 32 (2-3): 259-279.
CrossRef | Gscholar
Ji CY (2000)
Land-use classification of remotely sensed data using Kohonen self-organizing feature map neural networks. Photogrammetric Engineering & Remote Sensing 66 (12): 1451-1460.
Online | Gscholar
Kaski S, Kohonen T (1996)
Exploratory data analysis by the SOM: structures of welfare and poverty in the world. In: Proceedings of the 3rd “International conference on neural networks in the capital markets” (Abu-Mostafa A-PN, Moody Y, Weigend A eds). Singapore 1996, pp. 498-507.
Kaski S (1997)
Data exploration using Self Organizing Maps. Acta Polytechnica Scandinavica, Mathematics, Computing and Management in Engineering Series No. 82, pp. 57.
Online | Gscholar
Klobucar D, Pernar R (2009)
Artificial neural networks in the estimation of stand density from cyclic aerial photographs. Sumarski list (3-4): 145-155.
Online | Gscholar
Klobucar D (2010)
Using geostatistics in forest management. Sumarski list (5-6): 249-259.
Online | Gscholar
Klobucar D, Pernar R, Loncaric S, Subasic M, Seletkovic A, Ancic M (2010)
Detecting forest damage in CIR aerial photographs using neural network. Croatian Journal of Forest Engineering 32 (2): 157-163.
Online | Gscholar
Klobucar D, Subasic M, Pernar R (2011)
Estimation of stands parameters from IKONOS satellite images using textural features. In: Proceedings of the 7th International Symposium on “Image and signal processing and analysis” (Loncaric S, Ramponi G, Sersic D eds). Dubrovnik (Croatia) 4-6 September 2011, pp. 491-496.
Kohonen T (2001)
Self-organizing maps (3rd edn). Series in Information Sciences, vol. 30. Springer, Berlin, Germany.
Kuzmanovski I, Trpkovska M, Soptrajanov B (2005)
Optimization of supervised SOM with genetic algorithms for classification of urinary calculi. Journal of Molecular Structure 744-747: 833-838 .
CrossRef | Gscholar
Legendre P, Legendre L (1998)
Numerical ecology. Elsevier Science BV, Amsterdam, The Netherlands, pp. 870.
Lippitt CD, Rogan J, Li Z, Eastman R, Jones TG (2008)
Mapping selective logging in mixed deciduous forest: a comparison of machine learning algorithms. Photogrammetric Engineering & Remote Sensing 74 (10): 1201-1211.
Online | Gscholar
Liu C, Zhang L, Davis CJ, Solomon DS, Brann TB, Caldwell LE (2003)
Comparison of neural networks and statistical methods in classification of ecological habitats using FIA data. Forest Science 49 (4): 619-631.
Online | Gscholar
Minowa Y (2001)
Analyzing a combination of factors for thinning trees with a neural network. Journal of Forest Research 6 (2): 95-100.
CrossRef | Gscholar
Minowa Y (2008)
Verification for generalizability and accuracy of a thinning-trees selection model with the ensemble algorithm and the cross-validation method. Journal of Forest Research 13 (5): 275-285.
CrossRef | Gscholar
Pardé J (1961)
Dendrométrie. Ecole Nationale des Eaux et Forêts, Louis-Jean GAP, Paris, France, pp. 350.
Park YS, Céréghino R, Compin A, Lek S (2003)
Applications of artificial neural networks for pattering and predicting aquatic insects species richness in running waters. Ecological Modeling 160 (3): 265-280.
CrossRef | Gscholar
Park YS, Chon TS, Kwak IS, Lek S (2004)
Hierarchical community classification and assessment of aquatic ecosystems using artificial neural networks. Science of the Total Environment 327 (1-3): 105-122.
CrossRef | Gscholar
Park YS, Chung YJ (2006)
Hazard rating of pine trees from a forest insect pest using artificial neural networks. Forest Ecology and Management 222 (1-3): 222-233.
CrossRef | Gscholar
Peng C, Wen X (1999)
Recent application of artificial neural networks in forest resource management: an overview. In: Proceedings of the Meeting “Environmental decision support systems and artificial intelligence” (Corté U, Srnchez-Marrc M eds). Orlando (FL - USA) 18 July 1999. AAAI Press, Palo Alto, CA, USA, pp. 15-22.
Pölzlbauer G (2004)
Survey and comparison of quality measures for SOM. In: Proceedings of the 5th “Workshop on data analysis” (Paralic J, Pölzlbauer G, Rauber A eds). Vysoké Tatry (Slovakia) 24-27 June 2004. Elfa Academic Press, pp. 67-82.
Shanmuganathan S, Sallis P, Buckeridge J (2003)
Ecological modelling with self-organising maps. In: Proceedings of the International Congress on “Modelling and simulation; integrative modelling of biophysical, social and economic systems for resource management solutions” (Post DA ed). Townsville (Australia) 14-17 July 2003, pp. 759-764.
Spiranec M (1975)
Prirasno prihodne tablice. Sumarski Institut Jastrebarsko, Zagreb, Croatia, pp. 103. [in Croatian]
Spitz F, Lek S (1999)
Environmental impact prediction using neural network modelling. An example in wildlife damage. Journal of Applied Ecology 36 (2): 317-326.
CrossRef | Gscholar
Stümer W, Kenter B, Köhl M (2010)
Spatial interpolation of in situ data by SOM algorithm (neural networks) for the assessment of carbon stock in European forests. Forest Ecology and Management 260 (3): 287-293.
CrossRef | Gscholar
Sulkava M, Hollmén J (2003)
Finding profiles of forest nutrition by clustering of the SOM. In: Proceedings of the Workshop on “Self-organizing maps”. Kitakyushu (Japan) 11-14 September 2003, pp. 243-248.
Venna J, Kaski S (2001)
Neighborhood preservation in nonlinear projection methods: An experimental study. Lecture Notes in Computer Science 2130: 485-491.
CrossRef | Gscholar
Vesanto J (1999)
SOM-based data visualization methods. Intelligent Data Analysis 3 (2): 111-126.
CrossRef | Gscholar
Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (2000)
SOM Toolbox for Matlab 5 Documentation. Helsinki University of Technology, Helsinki, Finland.
Wilppu R (1997)
The visualization capability of SOMs to detect deviation in distribution control. TUCS Technical Report 153, Centre for Computer Science, Turku, Finland.
Zhang J, Yang H (2008)
Application of self-organizing neural networks to classification of plant communities in Pangquagou Nature Reserve, North China. Frontiers in Biology in China 3 (4): 512-517.
CrossRef | Gscholar

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