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The objective of this study was to investigate the influence of the development of road quality (surfacing condition and road type) and road quantity (road density and distance to main road) on forest extent changes in a region in Zagros forests of Iran over the three time periods of 1960s, 2000s and 2010s. Ranks of 1 to 5 were assigned to road quality. Forest extent changes were analysed on digital aerial photo mosaic. The Spearman and Pearson correlation coefficients were used to test the relationship between the road parameters and forest extent changes. Results showed that rural-forest road network has more influence on settlement development. Forest extent was correlated negatively with the road quality. Moreover, there were more disturbances to native forests closer to roads. It was concluded that forest cover decrease was associated with physical parameters including access to roads, cultivation practices, road quality and settlement development.

The last two decades consisted of a steady decrease in timber production levels in Iran in order to preserve the Hyrcanian forest as the only source of industrial wood. Consequently, under the so-called “the Forests’ Breathing Plan” in 2017 the Islamic Parliament of Iran stipulated that any wood exploitation from Iranian forests will be prohibited beginning in 2020. In order to respond to increasing wood demand and the cessation of the domestic production of natural-forest wood, the future prospect for the production and trade of wood commodities must be assessed. The main objective of this study was to predict the monetary values of domestic production, imports, exports, and prices of wood products in Iran through 2030 under the Forests’ Breathing Plan. The assessment was performed using the Global Forest Products Model (GFPM) and for three categories of wood products: unprocessed wood, semi-processed wood products and pulp and paper. The GFPM is a partial equilibrium model that forecasts wood demand based on the growth rate of gross domestic product in each country and predicts price by establishing equilibrium between demand and supply. The results indicate that the growth of domestic production will be larger for the second category of wood products than for pulp and paper. The import of unprocessed wood will reach nearly USD 200 million in 2030 and the production and import values of semi-processed wood products will increase 2.2 and 2.6 times, respectively, compared to 2015. Nevertheless, the total trade balance of wood products will deteriorate by 91% for Iran. Compared to pulp and paper, the increase in the price of the unprocessed and semi-processed wood will be greater. Overall, the global position of Iran’s trade and production of wood products will not improve significantly except for the import of unprocessed and semi-processed wood during the period examined.

International trade has a significant share of the global economy; the issue of regulating foreign economic relations is a major contributor to political talks in the countries. Social, political and cultural events, including political and economic agreements, have a tremendous impact on the trade of countries. Forests around the world produce large amounts of wood and trade in products throughout the world. Since Iran has been trading in wood with Russia and other European countries about 100 years ago, this study was aimed at investigating the effects of political events on the bilateral exports of timber in Iran and Russia, as well as the effect of the Warsaw Treaty and its collapse on The wood trade of member countries with Iran was done using the gravity pattern. The results of the study show that the Warsaw Pact as a political treaty affects the trade relations of the countries of the covenant with Iran, and the per capita income of two trade partners, exchange rate changes and geographical distances on the volume of wood trade and Wood products have been effective. Results for both Iran and Russia show the impact of wood trade on political events as well as domestic laws and bills approved by the two countries.

Today, concern about environmental degradation and conservation is a priority. In this regard, the impact of trade on the environment is an important and growing issue in current politics. International trade affects the quality of the environment with respect to the three dimensions of scale, composition and technology. The carbon dioxide emission index is used to demonstrate environmental quality. The purpose of this study was to investigate the effects of the liberalization of the wood industry on Iran's environmental quality in relation to the 16 main wood trade partners within 1995-2015. In this study, the data panel method and the Stata software were used to estimate the model. The results of the model showed that the effect of the scale of 0.08 units and the effect of the combination of 0.427 units were overcome by the technical effect. Also, the results showed that due to the liberalization of the wood industry of Iran in relation to its wood partners, the combination of the three mentioned factors amounting to 0.424 units would reduce the quality of the environment.

Using remote sensing data is an applied method to estimate above ground biomass. In this study, satellite radar data of ALOS-2, with the full polarization and the optical data of Sentinel-2, has been used to estimate the aboveground biomass in the Nav-e Asalem forests, Gilan province. The backscattering coefficients at different polarization, the texture measures and target decomposition features of SAR images, obtained original and synthetic bands from optical images in three different combinations of radar images, optical images and the composition of radar and optical images, as inputs to the Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models were used. In order to measure aboveground biomass, 149 sample plots were laid out. Evaluation of ANN and MLR models using R2 and RMSE statistics showed that in all cases the ANN was better performance to estimate the aboveground biomass than MLR. The best results showed that the ANN from combined optical and radar data with R2 and RMSE, 0.86 and 31.62 Mg/ha (15.34%), respectively, can be the best applied method to estimate the aboveground biomass. The results of radar images and optical separately, with the R2 and RMSE for the modeling of aboveground biomass have been shown, respectively, 0.57 and 49.17 Mg/ha (23.85%) by radar images and 0.7 and 39.53 Mg/ha (19.17%) by the optical images, superior modeling to estimate aboveground biomass represents by optical imaging. The overall and more accurate results to estimate of aboveground biomass have been shown when we used combined radar and optical images with the ANN model.

Background: Around 2000 plant species occur naturally in Lorestan Province of which 250 species are medicinal and myrtle is one of them. Myrtle is a shrub whose leaves and fruits have medicinal value and thus, if managed and harvested properly, could produce sustained economic benefits. In recent years, however, over half of the myrtle site areas was destroyed, due to inappropriate management and excessive harvesting practices. Thus, coming up with a practical harvesting approach along with identifying those factors damaging the sites, seems to be very crucial. Methods: In our investigation, we calculated the conservation value per hectare of myrtle in the Dooreh forest area in Lorestan Province. Using the Contingent Valuation (CV) and Double Bounded Dichotomous Choice (DBDC) methods, we determined the willingness to pay (WTP) for myrtle conservation. The WTP was estimated with a logit model for which indices were obtained based on a maximum precision criterion. Results: The results showed that 86.67 per cent of people were willing to pay for the conservation of these myrtle sites. Average monthly WTP per family was calculated as $0.79. The annual conservation value in terms of WTP for the preservation of the myrtle sites in Dooreh was estimated as $102,525. Among the variables of the model presented, education had a positive impact, while the amount proposed for payment and family size had a negative impact on the WTP. Conclusions: Our estimate of the value of myrtle conservation should provide justification for policy makers and decision making bodies of natural resources to implement policies in order to conserve the natural sites of this species more effectively. Keywords: Conservation value, Myrtle, Contingent valuation method, Double Bounded Dichotomous method, Logit model

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.

In this paper the financial maturity and optimal rotation age of poplar (Popolus deltoides Marsh.) was studied in a study site in in Kurdistan province. First, the growth information of poplar was obtained from poplar plantation plans of Agricultural and Natural Resources Research Center in Sanandaj. Then data concerning stumpage price, fix and variable cost per hectare were collected by using questionnaires and conducting interviews with local people and poplar farmers. Finally, the age in which farmers achieve the maximum net present value was determined using the expected value of land model, i.e. Faustmann model. The findings showed that optimal rotation period occurs at the age of 12 years, where the marginal revenue and marginal cost curves intersect. However, the local farmers cut poplar trees at the premature ages of 6-7 years, thereby ignoring higher profits associated with the further growth. The obtained results can be attributed to the risks in regional poplar market, which is attempted to be avoided by the farmers.