Izvorni znanstveni članak
Artificial Neural Networks in the Estimation of Stand Density from Cyclic Recordings
APA 6th Edition
Klobučar, D. i Pernar, R. (2009). Artificial Neural Networks in the Estimation of Stand Density from Cyclic Recordings. Šumarski list, 133 (3-4), 154-155. Preuzeto s https://hrcak.srce.hr/36398
MLA 8th Edition
Klobučar, Damir i Renata Pernar. "Artificial Neural Networks in the Estimation of Stand Density from Cyclic Recordings." Šumarski list, vol. 133, br. 3-4, 2009, str. 154-155. https://hrcak.srce.hr/36398. Citirano 30.06.2022.
Chicago 17th Edition
Klobučar, Damir i Renata Pernar. "Artificial Neural Networks in the Estimation of Stand Density from Cyclic Recordings." Šumarski list 133, br. 3-4 (2009): 154-155. https://hrcak.srce.hr/36398
Klobučar, D., i Pernar, R. (2009). 'Artificial Neural Networks in the Estimation of Stand Density from Cyclic Recordings', Šumarski list, 133(3-4), str. 154-155. Preuzeto s: https://hrcak.srce.hr/36398 (Datum pristupa: 30.06.2022.)
Klobučar D, Pernar R. Artificial Neural Networks in the Estimation of Stand Density from Cyclic Recordings. Šumarski list [Internet]. 2009 [pristupljeno 30.06.2022.];133(3-4):154-155. Dostupno na: https://hrcak.srce.hr/36398
D. Klobučar i R. Pernar, "Artificial Neural Networks in the Estimation of Stand Density from Cyclic Recordings", Šumarski list, vol.133, br. 3-4, str. 154-155, 2009. [Online]. Dostupno na: https://hrcak.srce.hr/36398. [Citirano: 30.06.2022.]
In the field of remote sensing the results of research undertaken with the purpose of determining quantitative and qualitative stand parameters showed the usefulness of artificial neural networks (Ardö et al. 1997, Skidmore et al. 1997, Wang & Dong 1997, Moisen & Frescino 2002, Ingram et al. 2005, Joshi et al. 2006, Kuplich 2006, Verbeke et al. 2006, Klobučar et al. 2008) as an alternative approach to classical statistical methods.
This paper explores the possibility of estimating and distributing stand density using methods of artificial neural networks. These methods involve particular textural features of first and second order histograms on a digital ortophoto compiled from black and white aerial photographs at an approximate scale of 1:20,000. The paper is also aimed at collecting data with an acceptable accuracy, which will reduce material investments. Research encompassed the area of the MU “Jamaričko Brdo”, Lipovljani forest administration. Cyclic surveying was conducted in 2000.
In order to determine textural features of first and second order histograms, a sample was cut out from a digital ortophoto for 80 stand scenes (compartments/subcompartments) in management classes of pedunculate oak, sessile oak and common beech of the fourth (the most common), fifth and sixth age class.
A multi-layer perceptron was used to solve the problem of stand density estimation. A multi-layer perceptron is a neural network without feedback connections, where supervised learning is carried out with the error back propagation algorithm.
An early stopping method was applied to improve generalization. The early stopping method is a statistical cross-validation method in which the available data are divided into three sets: training, validation and testing set. Of the overall dataset, 50 % (or 40 compartments/subcompartments) relates to the training set, whereas the two remaining datasets were divided equally: 25 % (20 compartments/subcompartments) relate to the validation set and 25 % (20 compartments/ subcompartments) to the testing set.
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.
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