جستجوی مقالات مرتبط با کلیدواژه « Golestan National Park » در نشریات گروه « جنگلداری »

Golestan National Park is the oldest registered national park in Iran and a unique sanctuary for wildlife. This area was registered as one of Iran's Biosphere Reserves by UNESCO in 1957. Different non-governmental sectors play a role in the protection of this park, and the purpose of this study is to prioritize the importance of their role in its protection. For this purpose, participatory indicators of preventing forest destruction, educating local communities, extinguishing fire, dealing with predators, preventing the entry of livestock, preventing fire, cleaning the environment and performance scores for non-governmental groups, non-governmental organizations, nature partners and local communities with The use of the questionnaire was investigated, and the weight of each index was calculated using Shannon's entropy, and finally, the importance of each influential community in park management was prioritized using the TOPSIS model. In this study, the high importance of participatory indicators of forest destruction prevention, cooperation with environmental department, dealing with illegal hunters and environmental cleaning in park management were obtained with weights of 0.337, 0.187, 0.145 and 0.112, respectively. The results also show the high importance of non-governmental organizations with a weight of 0.87 in the participatory management of the park, so that local communities and nature partners were placed in the next priorities with weights of 0.36 and 0.06. Since the park, with its large area and special regional conditions, is affected by environmental and human tensions, according to the results obtained from this study, it can be said that non-governmental organizations and communities

Hyrcanian forests are one of the most valuable forests in the world, dating from about 25 to 50 million years old. Climate variation, unique plant and animal species accompany with habitat diversity have made these forests one of the most unique ecosystems on Earth and worthy of being listed on the UNESCOchr('39')s World Heritage List. Therefore, the main purpose of this study is to use both Delphi and Ideal Point methods to identify effective criteria and prioritize proposed habitats for inscription on the UNESCOchr('39')s World Heritage List. For this purpose, five habitats from east to west of the Hyrcanian forests including Golestan National Park, Afra Takhteh Yew Forest (Golestan Province), Zarbin Hassanabad Chalus Forest, Kojoor Forest (Mazandaran Province) and Lomer Forest (Gilan Province) were selected. The results of this study showed that Golestan and Afratakhte with 0.8 and 0.5 points have the highest potential for listing on the UNESCOchr('39')s World Heritage List. In contrast, habitat access, animal species dispersal and disasters such as floods and fires have a negative impact on inscription of the sites. According to the results of this study, prioritization of habitats for inscription is very consistent with criterion X؛ and finally criteria such as endemic species and IUCN endangered species can have a significant impact on the selection of a habitat as a proposed habitat for inscription.

The aim of this study is to determine the status of forest spatial disturbance in Golestan National Park using remote sensing and landscape metrics. Detailed forest/non-forest map was prepared by on-screen digitizing method using Landsat 8 data. After determining the optimal area of gridding, the studied area was divided into 550 hexagonal zones with the area of 100 hectares each one. Spatial distribution, connectivity and composition of non-forest patches in each zone were measured using 12 spatial metrics (MESH, NP, PD, ENN-MN, LPI, FRAC-MN, CONTIG_MN, SPLIT, AREA-MN, DIVISION, PLAND and SHAPE-MN). After standardization of metrics values, principal component analysis was also performed in order to determine the effective metrics in disturbance and their associate weight in the spatial disturbance model. Afterwards, based on the prepared model, the disturbance index map was prepared and classified in four classes of disturbance including 1) without disturbance, 2) low disturbance 3) medium disturbance and 4) high disturbance. The results showed that the mentioned classes cover 48.53, 16.15, 19.90 and 15.41 percentage of the studied area, respectively. With the approach that used in this study, the disturbances map of the under management forest areas can be prepared periodically and used as one of its monitoring tools.

  The presence of roads plays an important role in the occurrence and prevention of forest fires, which causes damage to the ecosystem of the soil and seriously endanger the ecosystem's performance and service, and threat the stability of forest eco-environment. The rising incidence of this event has increased the need to develop appropriate management plans for areas that are susceptible to fire. Therefore, the present study investigates the role of the transit road known as the Asian Shahrah, which passes through the Golestan National Park and has direct and indirect effects on the park fires. In this study, first the human factors (distance of transit road, side roads, villages, tourist areas and the presence hunters and farmers) affecting the fire in this area were determined. After that, according to the way of influencing, these layers were fuzzy and weight factors were obtained in two cases of transit road and transit road transport using the Analytic Hierarchy Process. After determining the weight and prepare invoices using the weighted average method arranged in six scenarios and modeling was performed both with and without transit road and best-case scenario was found with comparison with the real situations fire map and use of the Relative Operating Characteristic statistics. Finally, zoning with regard to the risk of using Run macro modules, and where the zones were fitted. Results showed the role of transit roads in the presence of 0/296 weight and transfer road with 0/077 weight. The Scenario with the average level of risk and decompensation was recognized as the best scenario with ROC = 0/87. The results also showed that if transported, the high risk areas would from 5647 ha would reduce to 2986 ha which is in fact 47% reduction of the whole high potential risk area. Road transit plays a direct role in 46 percent of fires in the area. Considering the high weight of the road presence role in the fires of Golestan National Park, it can be said that Because of the presence of Turkmen Sahra region, and also deserts and plains of Semnan province in the north and south of Golestan National Park, warm winds from these areas blowing to the Golestan National Park, so there are favorable conditions for fire occurrence in the park, which will increase the chance of fire in the park by high traffic on the transit road. Therefore, with due attention to role of the Asian highway in wildlife road accidents and the loss of countless numbers of them and probability of fire occurrence in the region, the need to transfer this road to the outside of the park is felt more and more.

Spatial prediction of fire risk and preparing the forest fire risk map across the natural areas are among the ways that can be used to prevent and to manage fire. The aim of this research was zonation of forest fire risk in Golestan National Park using non-parametric algorithms, namely Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF).
About 100 occurred fire points were considered for modeling the fire risk. The effective factors on fire occurring including vegetation types, physiographic, climatic, and human factors were identified and their relevant maps were prepared from different sources. To modeling purposes, initially the zone was divided into 1-ha levels of decision-making and modeling and then the pixel values of the effective factors on classes of fire occurring, across the 1-ha levels, were extracted and standardized. Based on non-parametric algorithms, fire risk was modeled with 70 percent of the fire points, as training samples. The prepared forest fire risk map was zoned in terms of four classes of low-risk, medium-risk, high-risk and dangerous. The classification accuracy of the maps, resulted from this modeling, was assessed through the overall classification accuracy given 30 percent of the remained fire points.
Results & The results indicated that RF algorithm, with the overall accuracy of 75%, was the best algorithm in predicting the fire risk compare to other ones. Likewise, after matching the fire risk occurring with the results gained from algorithms, it turned out that all algorithms were able to classify the area properly in terms of the fire risk, as more than 80 percent of fire points were placed in the high-risk and dangerous classes

Temporal and spatial variations of fire behavior can be predicted using one of the semi-physical or empirical models developed over recent years. This article investigates the effect of different fuel moisture conditions on fire spread and behavior in National Golestan Park in July 2011 using FARSITE, FlamMap simulation systems, and previous ignition history. Landscape data contains the topography and vegetation spatial information along with weather data are used as simulation input. Fuel moisture conditions representing moderate, moderate-dry, dry, extreme fuel moisture scenarios were used to generate the fire behavior characteristics. These characteristics includes the fire perimeter and area for each time step provided by FARSITE and rate of spread, surface flame length, and fireline intensity as calculated parameters by FlamMap. Fire Area Simulator was validated with the real mapped fire scar by GPS mapping. Based on the results, the simulated fire spread covered 90% of the observed fire scar which was almost high, whilst overestimation was 30%. The results of simulations show that the fuel moisture reduction causes a remarkable growth on the fire perimeter and area, with an increase on both maximum and average fire spread rate, flame length and fireline intensity. The results revealed spatial variation in fire behavior parameters that is useful in prioritizing fuel treatments and guiding other wildfire risk management activities.

Forest fires in the Iran and particularly in the northern forests had destructive effects on the physiognomy of these forests. Recognition، prevention and controlling the Scio-economic destroys caused by natural hazard is one of the main objectives of applied and educational organizations. One of the methods for prevention on forest fires is mapping the probability risk zones. In this study، map of fire probability risk for Golestan national park was prepared using regression logistic method and GIS. The effective factors on fires including climate، topography، vegetation and human factors were prepared in the GIS environment by different methods and sources. The occurred forest fires map was gathered and generated as a Boolean map. The logistic regression modelling was done using effective factors as independent variables and the occurred forest fire map as dependent variable. The obtained PseudoR²= 0. 3121 and ROC = 0. 9132 from model indicate that regression logistic could modeled forest fire probabilities on the study area. The probability fire map was classified to four low، medium، high and sever dangerous classes. The obtained forest fire probability map was assessed using the some unused occurred fire points. The assessment results showed that more of occurred forest fire points were in the medium and high dangerous classes.

Forest fires in Iran and particularly in the northern forests had destructive effects on the physiognomy of these forests. Forest fire risk zone mapping in forest lands is one of the main approaches of recognition and prevention by educational organizations. In this study, the Golestan national park was zoned based on fire risk probability using weighted linear combination (WLC) method and analytical hierarchical process (AHP) in the GIS environment. The various effective factors on fire including climate, topography, vegetation and human elements were prepared using different methods and sources in GIS. The occurred forest fires map was gathered and generated and the fire occurrences rates in each category of four elements were computed. Regards to the fire occurrences rates in each category and dual comparative analysis, the weight of each element was determined using AHP method. The different layers were combined using weighted linear combination method to generate the fire risk probability map of study area. The obtained map for study area was divided to four fire risk categories including low, medium, dangerous and highly dangerous. The obtained forest fire probability map was assessed using the some unused occurred fire points. The results showed that more of occurred forest fire points were in the dangerous and highly dangerous classes. This results show that WLC method could zone the study area based on fire occurrence probability.