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Forest road planning with the available tools, e.g. PEGGER and GIS, still requires a lot of time of an expert, and the designed roads are not guaranteed to be efficient in terms of the cost or suitability of the road. In this article, we propose a novel Genetic Algorithm (GA) based method for forest road planning. To do so, each road is represented as a sequence of fixed and variable (control) points. A novel objective (fitness) function is defined based on the length, gradient, and suitability of the roads (individuals). The proposed algorithm is applied to the Arasbaran forest area and the resulted roads are compared with PEGGER-designed roads regarding length, Bachmund index, accessibility, and suitability. The results clearly show the power of the proposed GA algorithm in reducing computation time, road construction costs, and environmental impacts compared to the common road planning approaches. [ABSTRACT FROM AUTHOR]

Forest roads alter the biotic and abiotic components of ecosystems, modifying temperature, humidity, wind speed, and light availability that, in turn, cause changes in plant community composition and diversity. We aim at investigating and comparing the diversity of herbaceous species along main and secondary forest roads in a temperate-managed hornbeam-beech forest, north of Iran. Sixteen transects along main and secondary forest roads were established (eight transects along main roads and eight along secondary roads). To eliminate the effect of forest type, all transects were located in Carpinetum- Fagetum forests, the dominant forest type in the study area. The total length of each transect was 200 m (100 m toward up slope and 100 m toward down slope), and plots were established along it at different distances from road edge. The diversity of herbaceous plant species was calculated in each plot using Shannon-Wiener index, species richness, and Pielou's index. The results showed that diversity index decreased when distance from road edge increases. This decreasing trend continued up to 60 m from forest road margin, and after this threshold, the index slightly increased. Depending on the type of road (main or secondary) as well as cut or fill slopes, the area showing a statistical different plant composition and diversity measured through Shannon-Wiener, species richness, and Pielou's index is up to 10 m. The length depth of the road edge effect found in main and secondary forest roads was small, but it could have cumulative effects on forest microclimate and forest-associated biota at the island scale. Forest managers should account for the effect of road buildings on plant communities. [ABSTRACT FROM AUTHOR]

Forest roads are important for adequate management of natural resources, but they are also a dominant contributor of sediment to forest streams and water pollution. This study measured road sediment concentration from forest roads to determine the impact of rainfall intensity and cutslope types on sediment concentration in the Patom district of northern Iran. Two 110 m road segments with variable soil and rock fragment cover exposed at the cutslopes were studied. Seven rainfall and corresponding runoff events were measured in 1-liter bottle samplers every 10 minutes at the outlet of cross sectional culverts, and from two 3 m² plots on the roadbeds adjacent to each cutslope. A statistically significant difference at the 95% confidence level between two cutslope types and rainfall intensity on sediment concentration was determined from the field data based on nonparametric tests, though no statistically significant difference in the concentration of sediment between the two roadbeds plots were found. The average of sediment concentration of soil trench and the rock fragment cover cutslopes were 60.3 and 46.8 g/l, respectively. The results reported here should help forest road managements to improve the understanding of cutslopes erosion and sediment production from forest roads and to employ suitable methods to reduce sediment production. [ABSTRACT FROM AUTHOR]

Forest roads are important for adequate management of natural resources, but they are also a dominant contributor of sediment to forest streams and water pollution. This study measured road sediment concentration from forest roads to determine the impact of rainfall intensity and cutslope types on sediment concentration in the Patom district of northern Iran. Two 110 m road segments with variable soil and rock fragment cover exposed at the cutslopes were studied. Seven rainfall and corresponding runoff events were measured in 1-liter bottle samplers every 10 minutes at the outlet of cross sectional culverts, and from two 3 m² plots on the roadbeds adjacent to each cutslope. A statistically significant difference at the 95% confidence level between two cutslope types and rainfall intensity on sediment concentration was determined from the field data based on nonparametric tests, though no statistically significant difference in the concentration of sediment between the two roadbeds plots were found. The average of sediment concentration of soil trench and the rock fragment cover cutslopes were 60.3 and 46.8 g/l, respectively. The results reported here should help forest road managements to improve the understanding of cutslopes erosion and sediment production from forest roads and to employ suitable methods to reduce sediment production. [ABSTRACT FROM AUTHOR]

In this research, we used the integration of frequency ratio and adaptive neuro-fuzzy modeling (ANFIS) to predict landslide susceptibility along forest road networks in the Hyrcanian Forest, northern Iran. We began our study by first mapping landslide locations during an extensive field survey. In addition, we then selected landslide-conditioning factors, such as slope, aspect, altitude, rainfall, geology, soil, road age, and slip position from the available Geographic Information System (GIS) data. Following this, we developed Adaptive Neuro-Fuzzy Inference System (ANFIS) models with two different membership functions (MFs) in order to generate landslide susceptibility maps. We applied a frequency ratio model to the landslide susceptibility mapping and compared the results with the probabilistic ANFIS model. Finally, we calculated map accuracy by evaluating receiver-operating characteristics (ROC). The validation results yielded 70.7% accuracy using the triangular MF model, 67.8% accuracy using the Gaussian MF model, and 68.8% accuracy using the frequency ratio model. Our results indicated that the ANFIS is an effective tool for regional landslide susceptibility assessment, and the maps produced in the study area can be used for natural hazard management in the landslide-prone area of the Hyrcanian region. [ABSTRACT FROM AUTHOR]