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.
The semi-Mediterranean Zagros forests in western Iran are a crucial source of environmental services, but are severely threatened by climatic and anthropological constraints. Thus, an adequate inventory of existing tree cover is essential for conservation purposes. We combined ground samples and Quickbird imagery for mapping the canopy cover in a portion of unmanaged Quercus brantii stands. Orthorectified Quickbird imagery was preprocessed to derive a set of features to enhance the vegetation signal by minimizing solar irradiance effects. A recursive feature elimination was conducted to screen the predictor feature space. The random forest (RF) and support vector machines (SVMs) were applied for modeling. The input datasets were composed of four sets of predictors including the full set of predictors, the four original Quickbird bands, selected vegetation indices, and the soil line-based vegetation indices. The highest r2 and lowest relative root mean square error (RMSE) were observed in modeling with total indices and the full data set in both modeling methods. Regardless of the input dataset used, the RF models outperformed the SVM by returning higher r2 and lower relative RMSEs. It can be concluded that applying these methods and vegetation indices can provide useful information for the retrieval of canopy cover in mountainous, semiarid stands which is crucial for conservation practices in such areas.
Estimation of structural forest attributes, such as volume, basal area, and tree density using a combination of remote sensing and field data, is currently considered a favored option compared to only using field survey data. In a comparative study, multiple linear regression (MLR) and classification and regression tree (CART) models were used to estimate volume, basal area, and tree density using advanced space-borne thermal emission and reflection radiometer (ASTER) and satellite poure I’observation de la terre (SPOT)-high resolution grounding (HRG) imagery in the Darabkola forests, located at the Hyrcanian region of Iran. Results showed that the CART model using SPOT-HRG data achieved the best overall performance for all three forest structural attributes, with adjusted R2=0.746 and RMSE=67.9 m3 ha−1 for volume, adjusted R2=0.771 and RMSE=3.94 m2 ha−1 for basal area, and adjusted R2=0.871 and RMSE=34.71 nha−1 for tree density. In general, the CART model, using both ASTER and SPOT-HRG data, produced better estimates of forest attributes compared to the MLR model. In addition, results showed that forest attribute estimations using SPOT-HRG were better than those obtained from ASTER data.
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