فهرست مطالب جعفر اولادی قادیکلایی

Due to the increasing trend of destructive effects of land use change due to human activities, it is necessary to discover and highlight these changes in order to plan and make managerial decisions. In this study, land use changes in the metropolitan area of ​​Tehran during the years 2000 to 2015 were studied. For this purpose, ETM, OLI satellite image data of four periods of 2000, 2005, 2010 and 2015 were used. Also, 1: 25000 digital maps of the study area and information obtained from the field visit were used to supplement the collected data. After pre-processing the images and correcting them, information was extracted and extracted, and land use maps were extracted for the studied years. The overall accuracy and kappa index indicated the reliability of the output maps used. The results showed that in all periods, the highest level was related to man-made use, the trend of changes in this use from 2000 (61.8%) to 2015 (78.7%) has increased and the area of ​​this use has increased. According to the results, during the three periods under study, land use, barren and other land uses have decreased in area and the rate of this decreasing trend has been more intense in the period of 2000-2005. On the other hand, man-made land use has shown an increasing trend during three periods. This increase is due to population growth and the highest increase in this land use has been observed in the northeastern parts.

In order to determine the spatial pattern of the probability of fire in the forests of Boyerahmad city, Belife evidence function models and support vector machines were used. For this purpose, at first 145 past fire positions were reported, MODIS data and field surveys were recorded using GPS, of which 70% were used for modeling and 30% for model validation. Next, 15 factors (altitude, slope gradient, slope direction, topographic position index, topographic moisture index, surface curvature, distance from village, distance from river, distance from road, geological formations, NDVI, land use, evapotranspiration annual, annual rainfall and annual temperature) were selected to assess the fire risk and maps were prepared. After performing a linear test between the independent variables, the Belife evidence function and the support vector machine models were used to create the fire zoning map. For modeling, past fire locations were identified and 70% of the data collected were used as training data for modeling and 30% for model validation. The results of the fire map study showed that areas with very high sensitivity cover 40% of the area. The results of the validation of the performance of the Belife evidence function models showed that the area under the curve was equal to 72.2% and the support vector machine with the area below the curve was 83%.Predication. Results of the current research can be used to plan and manage future fire hazards in the study area.

Charat Watershed Basin is the one of fresh, high potential and beautiful mountainous ranges of Mazandaran province. Although it has various ecotourism, geotourism and environmental phenomena, there are no facilities and background for tourism plans. In this study, SWOT methodology has been applied to investigate the potential and limitation of effective geologic items over ecotourism and finally, the best strategic was present for developing the ecotourism development planning. The present study was performed based on a descriptive-analytic methodology and the data collection was done through fieldwork and documentary investigations. Using the SWOT method, the strengths, weaknesses, opportunities and threats of the internal and external areas of the region were identified. Then, according to the matrix of data and analysis of the matrix elements, the most important internal and external factors affecting the cherat region were identified and prioritized. Finally, the strategies for applying appropriate and applicable management were presented for the study area. The findings indicate that the ecotourism power of Cherat village is important for the development of tourism industry. The advantages of tourism opportunities and internal strengths, such as exciting ecotourism attraction and diversity of natural and virgin landscapes in the region, indicate the extremely favorable conditions for nature tourism tours, various ecotourism activities and development of adventure tourism, sports, medical, scientific, summer and winter activities in the region. The results show that the study area has a high potential for the development of ecotourism and sustainable tourism. The results also show that tourism development requires identifying, introducing and promoting advertising, developing facilities and infrastructure, as well as investing on expanding tourism attraction.

Vegetation is one of the most important components of ecosystem, which plays an important role in protecting soil and preventing its rise. So, monitoring and evaluating its changes in planning to control the dust storm is effective. The purpose of this study was to investigate the changes of vegetation in the internal and external dust storm sources of Kermanshah province and dust extension areas. For this purpose, horizontal visibility data were obtained from the meteorological administration of Kermanshah in the statistical period 2005 to 2015. In 2008 and 2009, as well as in the months of May, June and July, due to the most frequent occurrence of days with dust were the basis of study. In the next step, the most important events of dust in the years and months were selected based on two criteria of minimum horizontal visibility and maximum duration of continuity and the MODIS satellite image (MOD02) was obtained for them. The BTD method was used to detect dust storm. Then vegetation changes were evaluated using MOD09 Q1 MODIS and NDVI index in three year intervals and in good, moderate and poor vegetation categories. The results of this study showed that in 2006 the area of good vegetation class in the external and internal dust storm sources was 1.8% and 28.25%, respectively, but in 2015 the area of this class decreased in both external (0.87%) and internal (18.62%). In general, considering the important role of vegetation in soil conservation, it can be taken with the conservation, regeneration and extension of the vegetation, especially in areas where the results of this research have been reduced, have taken an effective step in controlling the dust storm.

This research was done to model the fire spread using Alexandridis model and cellular automata method in part of District Three of Neka Zalemroud forests. The effective factors on fire spread were elevation, slope, wind velocity and direction, vegetation type and vegetation density, based on the mentioned model (Alexandridis model). To simulate of the fire spread and evaluate its accuracy, the required data of actual fire including the fire start point, fire area, wind velocity and direction in fire time was provided. All effective parameters for actual fire confine were provided and coded in GIS. Finally the fire spread model was implemented using programming of Alexandridis model in MATLAB, regarding to Moore neighborhood and loading of digital layers of all effective parameters for actual fire confine. Some cellular situations and fire transfer rules were considered to predict the fire spread based on cellular automata method. The dynamic exhibition of the fire spread was presented by selecting the fire start point and number of burned and unburned cells was determined. Results of the model output including fire spread direction and form was compared with the actual fire confine to evaluate the accuracy of the used model qualitatively. The total precision and Kappa index were used to evaluate the accuracy of the used model quantitatively. The total precision and Kappa index were 0.88 and 0.74 respectively that it can show the precision of prediction of the fire spread model and the cellular automata method in modeling of fire spread in current research.

Nowadays, assessment of the outdoor recreation demand in forest parks is very important to prepare the successful outdoor recreation plans and to schedule the facilities development in these plans. Recognition of visitor’s economic- social needs is essential to estimate outdoor recreation demand, because visitor’s socio-economic conditions have the great effect on mall selection to use of it. In this study outdoor recreation demand of Sorkhe Hesar forest park in east of Tehran was evaluated by interviewing and completing of questionnaire in the spring. Obtained results of this study were compared with the results of another study done in Sisangan forest park to investigate visitor’s outdoor recreation demand changes during thirty years. Comparison of outdoor recreation demand and investigation of visitor’s needs in in-city forest parks (Sorkhe Hesar) and out-city forest parks (Sisangan) showed that visitor’s outdoor recreation demand has not changed during thirty years. In addition, visitor’s outdoor recreation demand is similar in in-city forest parks (Sorkhe Hesar) and out-city forest parks (Sisangan) (except demands that depend on visitor’s distance) and visitor’s major purpose is rest in the healthy environment and inspiration in the healthy air. Finally we suggest that outdoor recreation planners consider the visitor's demand and tendency to create and develop the forest parks.

Today, urban environment experts are endeavoring after new ways to increase the amount of urban green spaces and discover the best way to enter them as lively part of cities. Greenways can supply the needs of urbanization to green spaces in addition they are effective in urban green transportation.
This study is trying to offer an approach in process of urban greenways planning by AHP analytical methods and geographic information systems (GIS) on bases of gravity formula. Using this method in eleven region of Mashhad metropolitan, suggested a greenway about 11.5 km length. Using this method is totally suitable to metropolitan areas lacking natural elements among their artificial urban textures and at the same time requiring change in the urban form and creation of connections and interactions between walking man and the urban spaces.

The aim of this study was to evaluate the efficiency of three support vector machine algorithms, fuzzy decision trees and neural networks for mapping land vegetation map of Arakvaz watershed using OLI sensor of Landsat images (2014). Geometric correction and image pre-processing were utilized to determine the training samples of land vegetation classes for the classification operations. Sample resolution in the vegetation classes has been evaluated using a statistical divergence index. On the next stage, to evaluate the accuracy of algorithm's classification results, ground truth map with the dimensions of 550 m was designed using systematic approach and land vegetation types in the sampling plots were determined. Finally, the efficiency of each classification methodwas investigated bysuch criteria as overall accuracy, kappa coefficient, producer accuracy and user accuracy.Comparing the accuracy and kappa coefficient obtained for three categories with a proper band set in comparison with the ground truth map indicates that the Support Vector Machine (SVM) classifier with overall accuracy of 91.26% and kappa coefficient of 0.8731 has had more appropriate results than other algorithms. The results showed that the separation and classification of forest landswith high accuracy have beenperformedas compared to the other land use classes.

Introduction The increasing use of satellite remote sensing data for civilian use has proved to be the most cost-effective means of mapping and monitoring for environmental changes. Satellite remote sensing has played a pivotal role in finding forest cover, vegetation type and land use changes in urban areas. One of the most complete of these methods is classification. The conventional per-pixel image classification techniques have proven ineffective due to disregarding spatial information of the images in digitally classifying urban land-use and land-cover features in high-resolution images. From all classification approaches, texture is believed to be more advantageous for high-resolution images. This is because texture not only utilizes the spectral information but also takes into account the spatial configuration of pixels. The aim of this study is evaluation on the ability of GeoEye-1 data and image texture features and boosted tree classifiers & regression method (BRT) to delineate the urban land cover and urban land use. Materials and methods GeoEye-1 images have been employed in the land-use classification. We applied geometric rectification using a road network map. The number of training pixels should at least be equal to ten times the number of variables used in the classification model for a parametric classification approach. However, several studies have shown that non parametric machine learning algorithms require larger number of training data to attain optimal results. To create an exhaustive database with an optimal size for the training and accuracy assessment, 873 sampling points were taken in field surveys using Global Position System (GPS) in the region 3, Tehran City. The ground reference dataset was divided randomly into 7.10 and 3.10 for training and testing, respectively. Then, image texture features including mean and variance of first order, entropy, dissimilarity and homogeneity of second order was processed in ENVI. The Boosted Tree Classifier and Regression (BRT) were used in land use classification. The error matrix of the classification results was formed. The BRT is a combination of statistical and machine learning techniques and an extension of CART, a promising technique used in ecological modeling. Over the past few years, this technique has emerged as one of the most powerful methods for predictive data mining. The BRT combine the strengths of two algorithms: regression trees, models that relate a response to their predictors by recursive binary splits, and boosting, an adaptive method for combining many simple models to give improved predictive performance. It is one of the several techniques that aim to improve the performance of single models by fitting many models and combining them for prediction. The good performance of BRT is depending on regularizing the boosted trees options and stopping tree growing parameters. For boosted tree options, the shrinkage rate as specific weight for single tree and number of boosted trees are two important parameters. Choosing the best shrinkage rate is important to prevent over fitting the predictions. Empirical studies have shown that shrinkage rate of 0.1 or less usually lead to better models. In addition, for small data sets (n=500), the shrinkage rate can be set as 0.005 and for the larger ones (n=5000) it can be set to 0.05. Therefore, regarding the data, the shrinkage rate of 0.05 was used in the present study. The number of boosted trees is effective to produce unbiased results. Thus, to find the optimal tree, initial 300 additive terms trees were set as the number of simple classification trees to be computed in successive boosting steps. For applying the bootstrap training learning, we used 90 percent of training samples. The stopping parameters control the complexity of the individual trees that will be built at each consecutive boosting step. These parameters are including minimum five in child node, which control the smallest permissible number in a child node, for a split to be applied, and maximum fifteen nodes in each tree, which will split. Result and conclusion Our results indicated that the overall accuracy and Kappa coefficient for the best compositions of features and main bands were 92% and 90%, respectively. Texture analysis in classification, in fact, the spectral and spatial pattern of pixels were applied simultaneously to obtain better results. As mentioned previously, the texture analysis is capable to increase the accuracy of classification in the especially heterogeneous urban areas. This can be concluded that the strengths of the GeoEye imagery data and the potentials of the image texture features and BRT method can help the urban planners monitor and interpret complex urban characteristics.

In this study، suitable areas in the Shourab forest park were identified for intensive and extensive outdoor recreation using five ecological parameters (slope، aspect، height، soil and vegetative cover). The result showed that 46. 447، 14. 244، and 515. 401 hectares of park area have the ability of class 1 extensive outdoor recreation potential، class 2 extensive outdoor recreation potential، and class 2 intensive outdoor recreation potential respectively. Besides it was determined that one of the problems caused increasing rudeness in park nature was absence of attention to all effective factors (water resources and recreation facilities) in assigning to outdoor recreation potential of the park. Therefore questionnaire and oral interview were used for evaluation of visitor''s suitable usage of outdoor recreation sites. In order to preparing of water resources and park facilities'' map، all area related to water resources and park facilities were identified and recorded using GPS. Result showed people usage of the park have not conformed to classification plan. It was also showed that outdoor recreation has a direct relation to water resources، facilities، and tourism charisma of the park. Therefore، these factors were intersected as positive informational layers with primary recreation capability map and a final recreation capability map was obtained based on those factors. The final results showed that despite the high efficiency of conventional method، due to the lack of consideration of all the factors affecting on outdoor recreation، other information layers (for example water recourses and recreation facilities) are also required in addition to ecological factors. In the last stage according to plan of final outing power، the map of development possibility was prepared in the park. The results of these maps showed that based on spatial extent for promoting regional outing based on spaciousness there are seven and three properties for first and second degree focused classes respectively.

Continues occurrence of fire in northern forests of Iran during past years, necessitates conducting research, in order to predict future fire occurrence in these forests. For this reason a 1:25000 topographical map of Zarrin Abad Forests of Neka Township of Mazandaran province of I.R. Iran. was geo-referenced in GIS, firstly. Then digital model of elevation (DEM) with 30-meter pixel size was obtained from the geo-referenced map and slope, aspect and elevation maps were developed from the DEM. The vegetation cover density and residential areas (villages) maps were obtained from Nekachoob Corporation Company and digitized in GIS. All roads in the study area were routed using GPS and their map was developed. Then 200 and 500 meter buffers were considered on the maps, around the roads and the villages. All of the digital layers were classified according to Dong model. The fire potential map with five classes was developed by overlaying all of the maps, in GIS environment and allocating weight to each factor (map), regarding the Dong model. The past forest fires map was developed, using actual forest data and applying ground sampling with GPS. Then this map was overlaid on the forest fire potential map. Results showed that about 40 percent of the past forest fire areas are located on sites with very high or high fire risk which indicates the medium validity of the used model. Thus, Dong model was modified and weight of the layers was changed, based on their importance at the studied area and finally a new model was developed. A new fire potential map was generated, based on the modified model and was overlaid on the past forest fires map. Results showed that about 80 percent of the past forest fire areas are located on sites with very high or high fire risk which indicates a high validity of the used model for the studied area. It seems that the model might be applied for other forest sites at Caspian region of Iran, based on spatial locations of their watershed basins.

The aim of the study was to predict future fires at District Three of Neka-Zalemroud Forests، using fire risk modeling. Fuzzy Analytical Hierarchy (AHP) method was used to weight the effective fire factors، then map of each factor was developed for modeling sites with fire risk. The fire factors consisted of four major criteria and 17 sub-criteria. The major criteria consisted of topographical، biological، climatical and human factors. The maps of these factors were developed، using DEM and existing data in Neka Corporation Company. In addition، the past fires map was generated، using existing data in the Company and mapping fire patches perimeter by GPS. Verbal scales and results of distributed questionnaires among forest fire experts were used to weight the effective fire factors، using fuzzy AHP. Then relation consistency of each criterion was calculated، in order to investigate accuracy of criteria weight. Results showed that final weight of human، biological، climatical and topographical factors in forest fire risk potential were 0. 301، 0. 2595، 0. 2315 and 0. 208، respectively. Local fire risk model was presented، using weight of each criterion and sub-criterion، based on fuzzy AHP. Then map of effective fire factors (17 sub-criteria) was standardized، using fuzzy membership command in GIS and the pixel values of each map were scaled between 0 and 1. Finally، at first major criteria map (using weighted overlay of sub-criteria maps of each criterion) then fire risk map (using weighted overlay of four major criteria maps) was developed، using fire risk model equation. Overall، fire risk map was presented at five classes. Results showed that the sites with high fire risk on the developed map of fire risk potential are highly in accordance with the sites with past fire records which shows the high validation and accuracy of the developed model.

Since forest parks have high importance in urban environment, evaluation of outdoor recreation potential and identification of the suitable regions for outdoor recreation usage in forest parks is necessary. In this research, the suitable areas for intensive and extensive outdoor recreation, their various degrees and outdoor recreation potential of Sorkhehesar forest park was identified using systemic analysis method and Makhdoum tourism ecological model using GIS. Five ecological parameters (slope, aspect, elevation, soil and vegetation cover density) were used to perform this research and to evaluate outdoor recreation potential of Sorkhehesar forest park regarding to Makhdoum tourism ecological model. Results showed that whole of Sorkhehesar forest park have outdoor recreation usage capability and tourist attract potential. Therefore it is suggested that role of other effective factors on outdoor recreation such as accessibility, water resources and recreation facilities also investigate to identify recreation potential of outdoor areas.

Estimation of forest structural parameters is one of major basic information in sustainable management and planning in forest stands. In this study، relationship between ASTER satellite data and three forest structure factors including stand volume، basal area and number of trees per hectare were investigated in Darabkola forest، northern Iran. A multivariate linear regression approach was used to analyze and evaluate relationship between mentioned characteristics and ASTER satellite data. Relevant preprocessing and methods were conducted on spectral data. After gathering terrestrial information، stand volume، basal area and number per hectare were calculated for sample plots. Using some plots، performance of the best models examined by relevant evaluation criterions. The results showed that a combination of MSAVI2، NDVI and Green bands could predict stand volume characteristics better with R2adj=59. 2; and RMSE=116. 5 m3/h-1 in comparison with other indices and band combinations. For basal area، the best results were obtained using combination of MSAVI2، NDVI and simple ratio of SWIR12 with R2adj=73. 5 and RMSE=5. 14 m2/h-1. In addition، combination of MSAVI2، SWIR1 and SWIR2، was a better predictor for number per hectare rather than the other combinations by R2adj equal to 0. 85 and RMSE about 50. 95 number per hectare. Generally، this research showed that using linear regression approach by the ASTER data presents only general status of forest structure attributes in the study area and having more precise estimation of these attribute needs investigating other approaches such as nonlinear or nonparametric and learning machines approaches.

Evaluation of outdoor recreation potential is meaning the identification of land capability for outdoor recreation and ecotourism land use. First we identified the suitable areas for intensive and extensive outdoor recreation using five ecological parameters (slope, aspect, height, soil and vegetative cover) and usual method in Sorkhe Hesar forest park. Results showed that 297.223 hectares of park area have class 1 extensive outdoor recreation potential, 86.465223 hectares of park area class 2 extensive outdoor recreations potential, 1.126 hectares of park area class 1 intensive outdoor recreation potential and 137.417 hectares of park area class 2 intensive outdoor recreation potential and 16.212 hectares of park area is protection region. with field surveys to become characterized that one of problems that caused to increase rudeness in park nature is absence of attention to all effective factors (water resources and recreation facilities) in assigning to outdoor recreation potential of park. For this purpose and evaluation of visitor's suitable use from outdoor recreation places, used of questionnaire and verbal interview technique. Coincident with this evaluation, recognized all points concern to water resources and facilitie's park and recorded for provision of the water resources and facilitie's map using GPS. In addition to recorded situation all of points that questionnaires was dispensed in them at outdoor recreation classes. Result showed people usage of park, dont conforms to classification plan and outdoor recreation have direct relation with to water resources, facilities and tourism charisma of the park. Therefore these factors as positive informational layers intersected with primary recreation capability map and obtained final recreation capability map based on this factors. Final results showed that usual method despite very efficiency, because dont consideration all effective factors in outdoor recreation; need to enter other informational layers (for example water recourses and recreation facilities) in addition to ecological factors.

To evaluate the capability of SPOT5 HRG and IRS-P6 LISS-III in separating the pollarding areas of northern Zagros forests and preparing the map of this area, some parts of pollarded forests located at Baneh city were selected. The pollarding areas (Shan, Kurpe, Khert and Koor) were determined as ground truth in a 3 year alternation period using a global positioning system (GPS). Assessing the radiometric quality, no radiometric error was observed in these data. SPOT5 data which had been already geometrically corrected was used as basis for geometric correction of IRS-P6 color image and IRS-1C panchromatic image. Nonparametric method was used to do geometric correction with Root Mean Square Error (RMSE) of 0.54 and 0.75 pixels for these tow images, respectively. Principal component analysis (PCA) and various spectral rationing methods were used to prepare artificial bands used in data analysis. Likewise, for more image enhancement, HRG and LISS-III multi-spectral bands were fused with IRS-1C pan image. The data was classified using a maximum likelihood (ML) algorithm. This classification was applied using 4 and 6 classes for the studying area and a classification with 3 classes was used for each northern and southern aspects. The separability of classes was studied using Bhuttacharrya Distance Criteria. Result showed that separability of different classes of northern aspect was better than southern aspect. Likewise, northern Khert and agriculture classes were completely separated from other classes. The vegetation indices showed lower results compare to the original bands. Fused bands of SPOT 5 images showed the highest overall accuracy is equal to 65.3% and the highest Kappa coefficient is equal to 0.63. The highest overall accuracy (70%) and Kappa coefficient (0.60) was obtained using the first component analysis resulted from PCA in combination to bands 1 and 4 IRS-P6 data. According to the results of classifying of these two images, the data obtained before pollarding and during of vegetation growth season, showed better results. Regardless of spectral interference between soil and trees crown cover, the results showed the high capability of above mentioned images to separate the pollarding areas and to prepare the map of the area.