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ŠUMARSKI LIST 3-4/2009 str. 41     <-- 41 -->        PDF

D. Klobučar, R. Pernar: UMJETNE NEURONSKE MREŽE U PROCJENI SASTOJINSKIH OBRASTA...Šumarski list br. 3–4, CXXXIII (2009), 145-155
There are numerous variations of error back propagation algorithms. As
for the early stopping method, it is not advisable to use an algorithm which
converges too rapidly (Xiangcheng et al. 2005, Demuth et al. 2006). Consequently,
two algorithms were used: resilient back-propagation and scaled conjugate
gradient algorithm.


Prior to training the neural network itself, the data were preprocessed. In this
sense, two operations were performed using MATLAB functions: normalization
of input-output values and analysis of the main components of input values.


Training encompassed a total of seven algorithm models with error back
propagation with one or two hidden layers containing a different number of
hidden neurons. Different activation functions were also applied in hidden
and output layers.


Self-organizing neural network was used to control densities according to
their distribution into three categories (normal, less than normal, poor). To
study the applicability of this neural network, 80 compartments/subcompartments
were divided into two sets: training set and testing set, each consisting of
40 compartments/subcompartments. The data were preprocessed before the neural
network was trained, just as was the case with the multilayer perceptron.


Textural features of first order histograms (arithmetic means, standard deviation,
smoothness, third moment, evenness and entropy) and second order
histograms (absolute value of difference, inertia, covariance, entropy and
energy) were used as input data for the neural network, whereas output density
values were taken from the Management plan.


Output values may also be represented as the number of trees, basal area or
volume per hectare or as some other quantitative and qualitative stand values.
Stand density was used as an output value for two reasons: a) poorer spectral
features of the applied photographs, and b) the fact that, from the aspect of the
forestry profession, the photographs were obtained in the unfavorable period
(time of the year in which the ground is the least covered with vegetation).


To test the difference in stand density values between the data from the Management
plan and the optimal model of artificial neural network, the analysis
of variance for repeated measurements was used.


Research confirmed good generalization characteristics of a multilayer
perceptron in density estimation, as well as the fact that a self-organizing neural
network can be used to control and distribute stand densities. The applied
procedure of density estimation achieves an acceptable accuracy and a high
degree of automatism, which removes the subjective nature of classical remote
sensing methods.


This research confirmed the advantages and disadvantages of artificial neural
networks. The advantages are as follows: it is not necessary to know data
models, the networks can be used to analyze new conditions, and they tolerate
imperfect data. The disadvantages are: the need to determine optimal architecture
and the impossibility of estimation outside the scope of learning data values.
However, despite their numerous advantages, artificial neural networks
will not completely replace classical statistical methods. Instead, a dual approach
and integration of these two techniques in decision making processes
will be a very useful tool in forest resource management of the 21st century.
They are currently broadly applied, so we could say that this is a time of transition
to the technology of artificial neural networks. Consequently, forestry of
the Republic of Croatia should make broader use of this new technology.


Key words:artificial neural networks, remote sensing, cyclic aerial
photographs, density, texture