FOREST ROADs are constructed to facilitate FOREST protection, reFORESTation, logging operations and maximizing the value of FOREST products. Therefore FOREST ROADs are key infrastructures in the development of the region. This study aims to plan FOREST ROAD NETWORK using artificial neural NETWORK and GIS regarding FOREST ROAD technical principles. First the criteria were chosen using Delphi method and then they were weighted regarding their importance in ROAD planning. After that the criteria were combined with corresponding weighs to achieve suitability MAP based on the degree of suitability for ROAD allocation. Value and coordinates of each pixel were extracted by ENVI software and were normalized in the range of 0-1 for modeling by MATLAB software. In this study two neural NETWORKs were used for modeling, including multilayer perceptron (MLP) and radial-bases functions (RBF). The neural NETWORKs estimated suitability of different pixels in other districts based on the Patom district results. Using an ArcView GIS extension, PEGGER, two FOREST ROAD NETWORKs were planned. The results showed that MLP provides better ability for estimating suitability of pixels for ROAD passage in comparison with RBF with the R2 of 0. 994. A linear regression was also used to compare the results of the proposed neural NETWORKs. The results revealed that neural NETWORK improves the results in comparison with the linear regression and results showed that the second ROAD alternative was optimum NETWORK with regard to the unit cost.

FOREST ROAD NETWORKs are essential structures to achieve the FORESTry aims, but these structures include most implemental and environmental costs. To minimize these costs, the effective factors have to be considered in FOREST ROAD design which landslide susceptibility is one of the major factors. ROAD construction on susceptible terrain increase ROAD maintenance cost and environmental impacts. Landslide hazard zonation is a method that can be applied to recognize and avoid the unstable terrain. In this research, the landslide hazard zonation MAP of Baharbon district in Kheyroud Research FOREST was developed using a combined model, AHP and Density Area methods. First, Landslides distribution MAP of study area was prepared in ArcGIS using field observations and then the MAP of effective factors in landslide occurrence were prepared. To determine the weight of effective factors AHP was used. Afterward a bivariate statistical model, Density Area, was used for calculating the weight of classes of effective factors MAPs. By combining the effective factors MAPs and classifying the combined MAP with regard to breakpoints of frequency curve, the landslide hazard zonation MAP was developed. According to the results, 14.7%, 26.9%, 38.9%, 15% and 4.5% of the district were classified as very low, low, moderate, high and very high hazard, respectively. Finally due to lack of landslide occurrence in slope class of 0-15%, as well as it is suitable slope for ROAD design, this class was suggested as positive control points. The other slope classes that were in low and very low hazard were defined as second priority for ROAD design. ROAD building in high and very high hazard is possible, if increasing the number of culverts, also biological and mechanical reinforcements of cut and fill slopes.

Background and objectives: In preventing fire and reducing their effects, FOREST fire management is important. The purpose of this study was to provide a MAP of fire hazard potential and determine the effective factors on FOREST fires using GIS and artificial neural NETWORK for the cities of Ali-Abad, Ramayan, Azadshahr, Minoodasht and Kalaleh in Golestan province. Materials and methods: The criteria used in this research included FOREST and rangeland lands, temperature, rainfall and evapotranspiration, slope, aspect, elevation of the sea level, distance from urban areas, distance from rural areas, distance from agricultural land and distance from the ROAD. Vegetation MAP, digital elevation MAP and land use MAP provided from the FORESTs of the rangelands and watersheds of the country, and the MAPs of temperature, rainfall, and evapotranspiration obtained from the organization of the meteorological for the whole of Iran. The influence of each parameter on fire ignition was determined by collecting of 37 samples from burned area and 37 sample from not burned area. 15 fire points that were not used in the modeling process was used For Validation the potential fire hazard MAP. For formation NETWORK between criteria and fire occurrence used of Multilayer perception (MLP) with Hyperbolic Pattern Algorithms. To this end, 70% of the data was used to train the NETWORK, 15% of the tests were tested and 15% of the data were used to validate the results. Results: The results were shown raining and distance from the ROAD had must be influenced on FOREST fire ignition. Validation test showed that the best NETWORK was obtained in run 4 and epoch 450 with 0. 0038 Final Mean Square Error (FMSE) in training steps. Furthermore, about 95 percent of area burned and 84 of unburned areas has been properly classified. Finally, FOREST fire hazard MAPs was obtained based on each criteria weight. Results showed this NETWORK with 2 hidden layers and 12 neurons in each of them has best accuracy, and correlation coefficient (R) was 0. 80. Furthermore, after determining the location of the 15 FOREST fire points on the MAP of potential fire hazard, the results showed that 7 points located in the area were very high fire risk, and 6 points in the area with a high fire risk, and 2 points located in the area with a low fire hazard. Conclusion: According to the findings of this research, ROADs and rain reduction have a growing impact on the development of fire and natural FOREST fire managers need to adoption the necessary measures to better manage the FOREST and rangeland areas in these situations to prevent the occurrence or spread of fire.

ROAD is one of the most important and obvious extractable feature in satellite imagery. Automatic ROAD extraction from satellite imagery has many advantages such as updating data bases by spending less time and cost. The aim of present research is the automatic extraction of FOREST ROADs MAP using Liss_IV sensor imagery of IRS_P6 satellite. Because of frequent irregular objects in FOREST, ROADs are very complicated for extracting automatically. Therefore, the designed methodology for this research was in a way that can deal with this problem. For this aim, image of the study area was classified into two ROAD and non ROAD areas by a fuzzy logic. Then, morphological mathematic algorithm was used to extract the existed ROADs. By this method, FOREST ROADs MAP was extracted automatically with 88% overall accuracy. Also, morphological mathematic algorithm showed a great ability for recovering ROAD line that was hidden or was cut off under FOREST canopy.

A study was conducted to determine the most appropriate FOREST ROAD NETWORK using GIS software at series7, district 38 of Sardabrood and Chalus in 2013. To investigate and design FOREST ROADs, a land digitalized model in TIN format was prepared by 3D MAP of FOREST organization in a scale of 1:25000. Then, basic MAPs such as MAPs of slope, aspect and contour line distance of 10-m were prepared using this model. In the next step, the MAP of geology, penology, typology, proposed paths and parcel borders were digitalized based on the booklet of FOREST management plan. Eight effective factors in designing of FOREST ROAD NETWORKs were investigated such as FOREST type, geology, pedology, distance from ROAD, hydrography NETWORK, geographical aspects of the area, slope, and landscapes. The effect of each factor was weighed using hierarchical analysis. Then in the GIS environment considering the overlapping of aforementioned eight factors, capability ROAD pass MAP of the area was created. Three variants of various paths in the ROAD quickly predicted and evaluated by inserting the desired longitudinal slope (regarding to the kind of ROADs) into PEGGER program. The evaluation of the path proposed by the organization and the designed paths was done using Backmond regulations and capability ROAD pass MAPs. The results showed that the proposed path with the density of 9.39 m per ha and coverage of 85.33% had the most suitable FOREST ROAD NETWORK.

FORESTs as a renewable source has effective and useful role in economic and social activities of society. NETWORK of FOREST ROADs as infrastructure division’s FOREST planning units (compartments) of FOREST management plans, management, logging, exit of Wood and plays a major role FOREST protection. Aim of this study, designed FOREST ROAD NETWORK according to multi-purpose FORESTry using the NETWORK analysis model in the one series of Darabkla FOREST. For this purpose, initially, the effective criteria and alternatives in the design of FOREST ROAD NETWORK in the area Determined, and then the NETWORK between criteria was designed. In the next step, using the expertise and ANP method, determined the final weight of effective alternatives in designing FOREST ROAD NETWORK and MAPs required for this evaluation were provided. Then using weighted linear combination (WLC) potential final MAP was provided for ROAD design. Then using the software Pegger was designed FOREST ROAD NETWORK. Comparison of existing ROADs and proposed ROADs done by selecting 30 points in place of the two ROADs and Chi-square test. The results showed that the five classes of cross ROADs making plans for construction ROAD has significant difference at the level of 95%. In overall, the results of this study showed that the use ANP model with acceptable performance and has made a significant difference to the existing NETWORK.