ABSTRACT:In this study, the pine tree (Pinus Eldarica Medw.) needles were evaluated as the biomonitors of heavy metal contamination in Tehran, Iran. The pine needle samples supplied from the old trees according to the main wind direction (highest wind speed) were obtained from each parts of tree and then were homogeneously mixed. The samples were taken from different locations with different degrees of metal pollution (urban, industrial, highway and control sites). Then, the concentrations of lead, zinc, copper, nickel and chromium were measured using a flame atomic absorption spectrophotometer. The result of this study showed that the highest and the lowest metal concentrations were found in the heavy traffic sites and the control site, respectively. However, samples taken from highway sites contained the high concentrations of nickel, copper and lead. Moreover, industrial areas were found to have high contents of zinc and chromium. The variation in heavy metal concentrations between the studied locations is due to changes in traffic density and anthropogenic activities. This research proved significant correlations between the heavy metal concentrations in pine needle samples. Finally, it is concluded that Pinus Eldarica Medw. needles can be applied to monitor polluted sites.

ABSTRACT:A better understanding of the carbon biomass from forests is needed to improve both models and mitigation efforts related to the global C cycle and greenhouse gas mitigation. Despite the importance of Hyrcanian forests for biodiversity conservation, no study with biomass destruction has been done to predict biomass and carbon pools from this forest. Mixed-specific regression equations with 45 sample trees using different input variables such as diameter, height and wood density were developed to estimate the aboveground biomass of beech-hornbeam stands. All the sample trees were harvested and the diameter at breast height (DBH) spanned from 31 to 104 cm so as to represent the diameter distribution reported in the beech-hornbeam stand management. Using only diameter as an input variable, the stands regression model estimates the aboveground biomass of the stand with an average deviation of 19% (R

No abstract

Species diversity is one of the most important indices used for evaluating the sustainability of forest communities. This study aims to characterize the forest communities and to identify and compare the plant species diversity in the study area. For this purpose, 152 relevés were sampled by a randomized-systematic method, using the Braun-Blanquet scale. Classification of the vegetation was conducted by the twinspan algorithm. Four communities, including Querco-Carpinetum betulii, Carpineto-Fagetum Oriental, Rusco-Fagetum Oriental and Fagetum Oriental were recognized. Species richness, Shannon, and Simpson indices were applied to quantify diversity of the different communities. Turkey test was used to investigate the differences in the species richness, diversity and evenness indices among the different communities. The results illustrate that Querco-Carpinetum betulii and Carpineto-Fagetum Oriental communities are significantly more diverse than Rusco-Fagetum Oriental and Fagetum Oriental communities. The spatial structure of the releves becomes more 'homogenous' and the dominance structure changes: the proportion of beech-forest species is gradually increasing. At the same time, the number of species per unit area decreases constantly, reaching eventually the value comparable to that recorded for hornbeam forest. Generally, species diversity is inversely correlated with the dominance of shade tolerant climax species.

No abstract

Hyrcanian forests of North of Iran are of great importance in terms of various economic and environmental aspects. In this study, Spot-6 satellite images and regression models were applied to estimate above-ground biomass in these forests. This research was carried out in six compartments in three climatic (semi-arid to humid) types and two altitude classes. In the first step, ground sampling methods at the compartment level were used to estimate aboveground biomass (Mg/ha). Then, by reviewing the results of other studies, the most appropriate vegetation indices were selected. In this study, three indices of NDVI, RVI, and TVI were calculated. We investigated the relationship between the vegetation indices and aboveground biomass measured at sample-plot level. Based on the results, the relationship between aboveground biomass values and vegetation indices was a linear regression with the highest level of significance for NDVI in all compartments. Since at the compartment level the correlation coefficient between NDVI and aboveground biomass was the highest, NDVI was used for mapping aboveground biomass. According to the results of this study, biomass values were highly different in various climatic and altitudinal classes with the highest biomass value observed in humid climate and high-altitude class.

No abstract

No abstract