Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Stem Taper Estimation Using Artificial Neural Networks for Nothofagus Trees in Natural Forest
The objective of the study was to estimate the diameter at different stem heights and the tree volume of the Nothofagus obliqua (Mirb.) Oerst., Nothofagus alpine (Poepp. et Endl.) Oerst. and Nothofagus dombeyi (Mirb.) Oerst. trees using artificial neural networks (ANNs) and comparing the results with estimates obtained from six traditional taper functions. A total of 1380 trees were used. The ANN trained to estimate the stem diameter with the best performance generated RMSE values in the training phase of 7.5%, and 7.7% in the validation phase. Regarding taper functions, Kozak’s model generated better RMSE indicators, but performed not as well as that generated by the ANN. The ANN estimation of the total volume was carried out in two phases. The first used the diameter estimation to determine the volume at one-centimeter intervals along the stem (one-phase ANN), and the second used the estimation of the one-phase ANN as an additional variable in an ANN that directly estimated the tree cumulative volume (two-phase ANN). The two-phase ANN method generated the best performance for estimating the cumulative volume in relation to one-phase ANN and the Kozak taper function, generating RMSE values for N. obliqua, N. alpina and N. dombeyi of 9.7%, 8.9% and 8.8%, respectively.
Stem Taper Estimation Using Artificial Neural Networks for Nothofagus Trees in Natural Forest
The objective of the study was to estimate the diameter at different stem heights and the tree volume of the Nothofagus obliqua (Mirb.) Oerst., Nothofagus alpine (Poepp. et Endl.) Oerst. and Nothofagus dombeyi (Mirb.) Oerst. trees using artificial neural networks (ANNs) and comparing the results with estimates obtained from six traditional taper functions. A total of 1380 trees were used. The ANN trained to estimate the stem diameter with the best performance generated RMSE values in the training phase of 7.5%, and 7.7% in the validation phase. Regarding taper functions, Kozak’s model generated better RMSE indicators, but performed not as well as that generated by the ANN. The ANN estimation of the total volume was carried out in two phases. The first used the diameter estimation to determine the volume at one-centimeter intervals along the stem (one-phase ANN), and the second used the estimation of the one-phase ANN as an additional variable in an ANN that directly estimated the tree cumulative volume (two-phase ANN). The two-phase ANN method generated the best performance for estimating the cumulative volume in relation to one-phase ANN and the Kozak taper function, generating RMSE values for N. obliqua, N. alpina and N. dombeyi of 9.7%, 8.9% and 8.8%, respectively.
Stem Taper Estimation Using Artificial Neural Networks for Nothofagus Trees in Natural Forest
Simón Sandoval (Autor:in) / Eduardo Acuña (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
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