مجله جنگل ایران
سال دوازدهم شماره 4 (زمستان 1399)

Deterioration of Zagros forests may partly be related to changes in climatic parameters. In the present study, the trends of precipitation and temperature changes in relation to the trend of forest cover changes in Sardasht area, Northern Zagros, was studied using 30 years data (1988-2017) recorded at Sardasht Synoptic Weather Station. Mann-Kendall statistical tests, the Sen’s slope estimator and Pettitt test were used for trend detecting and finding mutation points of climatic parameters. Trends of forest cover changes in Sardasht were also investigated using NDVI and SAVI vegetation indices. The relationship between the climatic parameters and the vegetation indices was carried out by the simple linear regression analysis and stepwise multivariate regression. Mann-Kendall test showed that the annual, seasonal, and monthly temperatures of January, February, March, May, June, July, August, and September had a significant increasing trend. The trend of changes in NDVI and SAVI indices were not significant, however, these indices pointed out significant relationships with climatic parameter so that temperature of April and SAVI demonstrated the highest simple linear correlation coefficient (0.499). Stepwise multivariate regression analysis displayed that the SAVI had the highest multivariate correlation (0.810) with temperature of August, temperature of spring, precipitation of July and temperature of winter. Regarding the impact of climate change on decline of Zagros forests, the regression models presented in this study can be used to make an appropriate decision to protect these forests. Managers should think of the unexpected changes in meteorological parameters owing to global warming.

In this study, in order to investigate the effects of climatic-economic variables on the degradation of forests in Iran, the Autoregressive Distributed-lagged model (ARDL) has been applied during the period from 1991 to 2014. The results of the short-term estimation revealed that the dependent variable is significant at the 10% level and also, according to the "Bannerji statistics", the long-term relationship between the variables is confirmed. Long-term model results reveal that the per capita income has a negative sign which is significant at 1% level. The second-power variable has a positive sign which is significant at 1% level, in other words, there is U-shaped relationship between the degradation of forest and per capita income, which means that Kuznets's environmental hypothesis is not in line with the degradation of forests in Iran. Moreover, the elasticity of per capita income reveals that with a one percent increase in per capita income, the degradation of forest will decline by 4.03 percent. Considering that per capita income is derived from GDP into the population, therefore, in order to achieve the least degradation in the relationship between per capita income and deforestation, it is recommended to pay attention to environmental considerations in domestic production. In other words, considering green accounting, in addition to importing forest income, forest degradation costs are also included. In case of harvesting, the conditions of planting and restoration should be considered. It is also suggested that in the field of population, demographic policies should also be oriented towards the proportionate populations with resources. Also, the variables of rainfall to temperature ratio and the second power of precipitation to temperature ratio have negative and positive signs respectively. In other words, there is a U-shaped relationship between forest degradation and precipitation to temperature.

Forest logging is one of the most important goals of forest management system having different economic and social consequences. This research aims at identifying, weighting and prioritizing the economic and social consequences of forest logging using the points of view of the experts, specialists in this fild and rural communities on the margins of forested forests. For thispurpose, forests managed by the department of natural resources of Mazandaran province in Sari were selected. In this regard, a researcher-made questionnaire has been used to identify the effective and important consequences of forest logging from both economic and social perspectives. Entropy technique has been used to calculate their weight and TOPSIS has been applied to prioritize them. Analyzing the findings of this study by using 55 questionnaires identified 12 positive and 8 negative consequences from economic and social aspects of forest logging. The results of weighting the positive and negative consequences of forest logging using entropy technique showed that employment created positive consequence with weight (0.083560) and logging costs negative consequence with weights (0.125503) had the highest weight among other consequences. Also the results of prioritizing the positive and negative consequences of forest logging by TOPSIS technique showed that the economic viewpoint had the highest priority in both consequences. Therefore, it is suggested more attention be paid to the positive and negative economic consequences of forest logging in forest management programs and not to focus only on stopping the forest logging.

Forest road construction is one of the activities that not only cause a lot of changes in the plant and animal species of forest ecosystem, but also, due to its widespread use in forest management, it is one of the most important projects that should be assesed. The aim of this research was to evaluate the environmental, social, and economical impact of Dohezar road of Tonekabon in the three stages including; before, during and after the road construction. In this study, the Geocybernetic Assessment Matrix (GAM) method, which is the latest method of evaluating effects, was used. In GAM, the parameters selected or determined by the evaluation criteria were examined according to the three aspects of environmental, social and economic. The results showed that the total geocybernetic score before road construction, during road construction, and after road construction was similar (before road construction: -13.33; during road construction: -80.78, and after road construction: -36.47), indicating that the construction of the Dohezar road had negative effects, especially during construction, but social and economic services, especially in the after road construction, have kept the total geocybernetic score constant. By implementing the GAM evaluation method, a suitable tool is obtained to ensure the proper implementation of the project or to stop it, which can be considered as a way to determine, predict and interpret the environmental, social and economic effects of the road construction project on the environment of the study area.

The aim of this study was to determine, analyze and predict changes in LC/LU and the destruction trend of natural resources due to the growth of urbanization around Yasouj city. For this purpose, first, Landsat 5 and 8 satellite data were downloaded in August 1989, 1999, 2009 and 2009 from the Geological Survey of the United States. With performing the required radiometric and atmospheric corrections, the data layers were prepared. After, by creating a data set, the land cover/land use classification maps were prepared. Then, the coverage / user map in 1408 was predicted and prepared using the Markov automatic cell model. The results showed that the area of the range and forest in 1989 and 2019 has decreased from 22087 to 12381 and 16095 to 15332 hectares, respectively. The highest destruction of ranges and forests occurred between 1999 and 2009, and in contrast, the area of follow, residential, and construction areas has increased. The kappa coefficient and overall accuracy percentage values of the likelihood classification algorithm (0.77, 0.91, 0.89 and 0.9, and 84.4, 93.9, 91.9 and 92.5 percent, in 1989, 1999, 2009, and 2019, respectively) show that the classification model is appropriate to determine the classes. Based on the prediction map in 2029 using Markov’s Cellular Automata algorithm, the process of destroying and altering range and forest will continue over the next 10 years and agricultural and construction areas will be increasing in future.

The goal of this study is to evaluate the quality of numerical methods (Phi fidelity and indicator value indices) and non-statistical (constancy ratio and total cover ratio indices) in determining the indicator species of Beech plant communities in the eastern Hyrcanian forests. For this purpose, six ecological groups were first classified using two-way indicator analysis or TWINSPAN. Then, by using sum of the indicator value/association indices or TFVI, as a similarity index for assignment of the plot to the plant communities, with emphasizing the result of species and plant communities association (based on 10 algorithms), the groups were reclassified. Evaluating the compatibility of the results of each classification algorithms with the initial plant communities which have been classified by Braun-Blanquet synoptic table showed that reclassification of sample sites by using TFVI when is derived by group-equalized and incidence-based phi index (84%) and constancy ratio method (82%) showed the highest adaptability with the initial plant communities respectively. Totally, the results of this research revealed that group-equalized and incidence-based phi fidelity and constancy ratio indices have the most priority in assessing the association between species and groups of sites or in determining the diagnostic species of plant communities than the other association indices.

Coarse woody debris (CWD) play important structural and biogeochemical roles in forest ecosystem processes. The aim of this study was to determine the wood density and biomass of beech and hornbeam and its relationship with different degrees of decay and diameter classes in Khairud educational and research forest, Nowshahr city, Mazandaran province. First, in an area of 50 hectares, CWD (with a median diameter of more than 7.5 cm) were fully survived and inventoried.  Then, to determine the wood density of CWD, three cubic samples (close to the pith, close to the bark, and between the pith and bark) with dimensions of 2 × 2 × 2 cm were taken from each CWD and transferred to the laboratory. After determining the types of wood density (wet, dry, and critical density) in the laboratory, the samples were grinded, and using standard laboratory methods, the carbon and nitrogen elements of CWD were measured based on different decay grades and diameter classes. The results showed that the wood density types decrease with an increasing degree of CWD decay grade. Also, the dry wood density of Beech CWD and downed Beech CWD decreases significantly with increasing diameter. The results related to the amount of biomass showed that this value increases with an increasing degree of decay. Based on diameter classes, with an increasing diameter of CWD, the amount of biomass showed an increasing trend. The results also showed that with increasing degree of decay and diameter of CWD, their carbon storage increases. But in the case of nitrogen storage, due to the lack of significant differences, no specific pattern was observed. In general, it can be said that the presence of large CWD in the forest is very important ecologically, because stores more carbon. On the other hand, the preservation of CWD in the forest is in line with close to nature management.

Climate change and human interventions as a whole have had negative and remarkable effects on the quantity and quality of forests. Beside the change in the forests extent, which has always been considered and monitored, its phenological changes have also been investigated in the last decade. The NDVI Vegetation Index derived from satellite data is an appropriate proxy for quantifying and expressing forest status including phenological changes. This study aimed to characterize the trend of start, end, and length of growing season using NDVI satellite time series dataset over 18-year time periods and then assess their relationship with precipitation and temperature parameters. This study was carried out over the southern Zagros forests using MODIS-NDVI time series with temporal and spatial resolution of 16-day and 250 meters, respectively. The precipitation and temperature datasets were also collected from regional synoptic meteorological stations. After preprocessing steps, 414 NDVI images during 2000-2017 were analyzed pixel by pixel to extract the start, end, and length of the growing season using Midpoint method, considering 50% and 35% thresholds of NDVI annual amplitude for start and end of the growing season, respectively. Then, the statistical significance of phenological metrics was assessed. Based on the results, the mean dates for the start and the end of growing season were 16th March and 15th August, respectively, with the mean length of growing season of 151 days in the study period from 2000 to 2017. Considering 35% of NDVI annual amplitude for the end of growing season, the mean date for the end of growing season was 8th September and the mean length of growing season was 190 days. The seasonal trend showed that the start and the end of growing season has occurred respectively 0.02 and 1.04 days earlier per year in the southern Zagros forests during 2000-2017. The length of growing season has been shortened 1.02 day per year. However, variation in the start and the end dates and the length of growing season have not been significant in 93%, 81% and 81% of the region, respectively, at 90 % confident level. Generally, the change in the occurrence of the end of growing season was greater than the start of growing season. A weak correlation was observed between phenological changes and climate parameters like temperature and precipitation in the study area.

In order to understand the distribution models of corticolous mosses (epiphytes growing on trees barks) and variations in the their abundance related to altitudinal gradients, southern forests of nowshahr were selected along a longitudinal transect from 0-2500 m.a.s.l. These forests consisted of three dominant forest types and within each type, 100 trees were selected as mosses substrata. By using parametercdiversity indices and a variety of abundance distribution models, the extent of species abundance variation and diversity of the species have been studied along the altitudinal gradient. Results showed that, in the middle layers of Hyrcanian forests, which has more precipitation, Fagus orientalis-Carpinus betulus forest types were significantly more diverse than other types including Parrotia persica-Carpinus betulus in the lower parts and Quercus macranthera-Carpinus orientalis in upper parts. The distribution model of two lower types were fitted in broken-stick model but Quercus macranthera-Carpinus orientalis which was in the upper parts was fitted in the logarithmic model.

Tree density as one of the most importantfeatures of forest structuralis necessary for management, conservation and reforestation of northern Iran forest. In this research, tree densities were estimated using physiographic, soil and human factors using artificial neural network supervised self-organized, multi-layer perceptron and multiple linear regression model and compared according to their performance evaluation criteria. For this purpose, homogeneous units in GIS environment were prepared. Sampling was performed by random-systematic method with 150 200 m network dimensions and a total of 779 0.1 ha circular shape plots were implemented. By measuring the diameter at breast height of all trees above 7.5 cm, tree density was calculated for each sample plot and homogeneous units. The results showed that SSOM 5 neural network (R2 = 0.9117, R2adj = 0.9909, RMSE% = 9.16, Bias% = 4.26) compared to MLP 4 neural network (R2 = 0.8321, R2adj = 0.8760, RMSE% = 15.14, Bias% = 10.96) and multiple linear regression model (R2 = 0.6812, R2adj = 0.6910, RMSE% = 28.71, Bias% = 24.26) had more accuracy and less error. To select the best model, T- test was performed and the results showed that the neural network, of the competitive and supervisory type, had values similar to the actual values. This is due to Gaussian functions, which are not seen in MLP neural networks with sigmoid functions. Therefore, SSOM neural network can be a suitable alternative to multilayer perceptron neural network in estimating the density of trees in the northern forests of Iran.