Comparison of different machine learning and regression methods for estimation and mapping of forest stand attributes using ALOS/PALSAR data in complex Hyrcanian forests

Abstract

The forest structural attributes are required information for sustainable forest management. The use of different remote sensing sources has been investigated intensively as a new potential and an alternative for the forest stand characteristics estimation during the last few years. This research purpose was to examine the phased array type L-band synthetic aperture radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) data ability in order to estimate stand volume, basal area, and tree density in the Hyrcanian forests of Iran with high composition and structure variations. The required preprocessing and processing steps were performed on the ALOS/PALSAR raw data, and the corresponding values of circular plots were extracted on all SAR data. The modeling of forest structure attributes was performed using field-collected attributes by the k-nearest neighbor (kNN), support vector machine (SVM), artificial neural network (ANN), and multiple linear regression (MLR) algorithms. The modeling validity was performed by unemployed plots and by the absolute and relative root mean square error (RMSE) and bias measures. The results of this study have shown that although the results of ANN, SVM, and kNN algorithm were not very different but compared to MLR algorithm, they had better performance. In addition, based on the results of this study, the ANN algorithm showed slightly better performance in forest attribute prediction than the other used algorithms. The results were 34.56%, 27.65%, and 31.16% in relative RMSE for stem volume, basal area, and tree density prediction.