The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R² = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R² = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R² = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R² = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only.

The present study adopts a game theory approach analyzing land-use planning in Zagros forests, Iran. A Static Game of Incomplete Information (SGII) was applied to the evaluation of participatory forest management in the study area. This tool allows a complete assessment of sustainable forest planning producing two modeling scenarios based on (i) high and (ii) low social acceptance. According to the SGII results, the Nash Bayesian Equilibrium (NBE) strategy suggests the importance of landscape protection in forest management. The results of the NBE analytical strategy show that landscape protection with barbed wires is the most used strategy in local forest management. The response to the local community includes cooperation in conditions of high social acceptance and noncooperation in conditions of low social acceptance. Overall, social acceptance is an adaptive goal in forest management plans.

The magnitude and duration of ongoing global warming affects tree growth, especially in semi-arid forest landscapes, which are typically dominated by a few adapted tree species. We investigated the effect of climatic control on the tree growth of Persian oak (Quercus brantii Lindl.), which is a dominant species in the Central Zagros Mountains of western Iran. A total of 48 stem discs was analyzed from trees at three sites, differing in local site and stand conditions (1326 to 1704 m a.s.l.), as well as the level and type of human impact (high human intervention for the silvopastoral site, moderate for the agroforestry site, and low for the forest site). We used principal component analysis (PCA) to investigate the common climatic signals of precipitation, air temperature, and drought (represented by SPEI 1 to 48 months) across the site chronologies. PC1 explains 83% of the total variance, indicating a dominant common growth response to regional climatic conditions that is independent of the local environmental conditions (i.e., forest stand density and land-use type). Growth–climate response analyses revealed that the radial growth of Q. brantii is positively affected by water availability during the growing season (r = 0.39, p < 0.01). Precipitation during April and May has played an ever-important role in oak growth in recent decades. Our study provides evidence that hydroclimatic conditions control tree-ring formation in this region, dominating the effects of topography and human impact. This finding highlights the great potential for combining historical oak samples and living trees from different forest stands in order to generate multi-centennial tree-ring-based hydroclimate reconstructions.

Estimation of forest trees biomass for various purposes is fundamental. One method of estimating biomass uses allometric equations that limit the normality of variables and the homogeneity of variances. In this study, artificial neural networks were used as an alternative method to increase biomass estimation accuracy. Fifty three sprout chumps of Brant's Oak (Quercus brantii Lindl) were randomly selected from the Melah¬Shabanan of Khorramabad in Iran. Diameter at knee height, diameter at breast height, crown diameter, number of sprouts, and height of trees were measured. To calculate the dry weight of the biomass, a disk 3–5 cm from the trunk and crown was separated and weighed, and with the ratio of dry weight to fresh weight, the dry weight of the crown, trunk, and aboveground biomass of the trees was calculated. Modeling the relationships between variables with regression equations and Multilayer Perceptron and Radial Basis Function neural networks showed that both neural networks could increase the coefficient of determination to R² = 0.98 and R² = 0.96 and reduce the error to RMSE% = 11.6 and RMSE% = 16.9 and thus the neural network models can increase the quality forest biomass estimates are compared with allometric equations.