hamed adab

Access paths to natural attractions in protected areas must be designed and developed considering their impacts on the environment. Visitors' movement in the areas where are highly sensitive to soil erosion may cause destructive impacts on trails such as widening, increasing susceptibility to erosion, and damaging surrounding vegetation. This research aims to suggest a sustainable trail network (off-road vehicles and hiking trails) in Sarigol National Park and Protected Area,

 The study has been carried out based on the least-cost path algorithm, and comparing the results with existing recreation trails. The required field information including the width of 431 trails and 15 environmental factors affecting the trail width was obtained through the study area. Analysis of Covariance has been used for estimating the potential of pathwidth expansion. The accuracy of the model was assessed by root mean square error which is 29cm for hiking trails and 126cm for off-road vehicles trails.

One optimized off-road vehicles trail and one optimized hiking trails in the study are were suggested using degradation map and least cost patch model. The findings of the present study indicated that existing paths are located in areas with high susceptibility to widening because of crowding.

Today, the use of raster data from climate databases such as WorldClim is one of the reliable data sources that have been used instead of the point data of weather stations. However, the use of these climate databases with low spatial resolution has generated limitations for many studies related to biological and ecological studies. This study aims to investigate the relationship between air temperature and land surface temperature and then reproduce the land surface temperature with the high spatial resolution for downscaling of annual average air temperature using two widely used products, namely,  annual average air temperature from the WorldClim database and annual average day and night temperature from MOD11A2 v061 MODIS sensor. In this study, firstly, the performance of machine learning models including random forest, artificial neural network, elastic network regression, and support vector machine for downscaling of MOD11A2 v061 product from 1 km to 250 meters was assessed. For this purpose, continuous and discrete predictor variables including height above sea level, latitude, vegetation cover, soil texture, slope direction, and land cover were used. Then, WorldClim's annual average air temperature was downscaled from 1 km to 250 meters using the land surface temperature with a 3rd-degree polynomial regression model.  Also, the weather data of seven synoptic stations were used to check the validity of the downscaled product. The results of the Taylor diagram represented that the random forest model has the best performance for the downscaled land surface temperatures with a root mean square error of 0.54 degrees Celsius. Also, the 3rd-degree polynomial regression model has a lower relative error rate in producing downscaled air temperature. The value of the root mean square error of the results for the uncorrected and corrected downscaled air temperature was 1.32 and 1.21 degrees Celsius, respectively, which did not show a significant difference at the 0.05 level according to the paired t-test. The findings of this research show that the downscaling of the mean annual air temperature of WorldClim has the required validity.

Surface temperature is one of the main indices of Earth's energy balance and has an impact on human life because all human activities, direct and indirect, are related to the temperature of the air, which is affected by the temperature of the earth's surface. Therefore, in this study, using Landsat satellite images dating from 19/05/1394, the relationship between vegetation and surface temperature in the northern part of Ardebil province was studied. To this purpose, vegetation indices (NDVI, SAVI, and LAI) and LST index using the Mono Window method were used in ENVI 4.8 software. The results showed that there is a direct correlation between vegetation indices and surface temperature, and in urban areas and Wasteland, the highest LST is evident, with the expansion of urbanization and the increase of desertification over the years, it will be added. Therefore, the northern and southeastern regions of the study area (including Moghan plain and Fandoghlo forests), which are rich in vegetation, have LST less (LST values in this region are around 290 to 300 Kelvin), and the central regions of the study area Which is poor in terms of vegetation and includes Wasteland with a higher LST (LST in this area is between 312 and 324 degrees Kelvin). The results of this study are very useful in environmental and natural resource conservation studies and could be the basis for environmental protection planning.

Rivers react to subsidence at their baseline by cutting and digging topographic features. The development of an upstream incision is often accompanied by a steep fracture called a river break (Loget & Van Den Driessche, 2009). The presence of river breaks in a geographical landscape is an indication of a steady-state in river systems. Therefore, the presence of knickpoints shows the system instability. The study of knickpoints can be used in the field of studies related to the evolution of valleys, identification of tectonic active areas and rock outcrops, river surface changes, erosion and sedimentation, and geomorphological changes in river systems. The basin studied in this study is located in the Qaleh Shahrokh-Chelgard area in the northeastern part of Chaharmahal and Bakhtiari province, Iran. The reason for selecting this basin is the extensive activities of the Zagros fault along the northwest-southeast and the existence of a hydrographic network affected by the trend of faults and the potential for knickpoints.  

In this study, the locations of knickpoints were detected from the Radiometrically Terrain-Corrected (RTC) model which is extracted from the active microwave sensor ALOS PALSAR with a spatial resolution of 12 meters (Logan et al., 2014) as input data to the MATLAB executive toolbox called Tec DEM. Tec DEM is an executable toolbox in MATLAB software and uses a Digital Elevation Model (DEM) as input for morphotectonics in the basin. Tec DEM tool can be used in a variety of fields in the analysis of surface anomalies, drainage network and surface dynamics of basins, production of base maps, incisions (local roughness), vertical dissection and drainage density of basins and sub-basins, determination of turning points or knickpoints, hypsometric analysis and slope and concavity index of canal profiles (Shahzad & Gloaguen, 2011). The determination of knickpoints according to the shape of the longitudinal profile of the river is done semi-automatically. In this study, these points in the study areas were investigated according to field observations. In this study, geological variables and geomorphic variables related to knickpoints were used to identify the knickpoints. Information layers including geology, distance from the fault, distance from the boundary of geological formations, surface roughness index, fractal dimension, base surfaces, local roughness, and the vertical dissection as predictor variables and the layers of knickpoints as the prediction variables were used for modeling. For geological and tectonic studies of the region, geological maps of 100,000 sheets of Chadegan and Fereydunshahr and 250,000 sheets of Shahrekord were used. A total of 8 raster layers were used to analyze and predict the possibility of the presence of a knickpoint in the study area. Since 8 layers have different units and are not suitable as direct input for logistic regression, the input parameters were normalized in the range of 0 to 1. Nominal layers, such as geological data, became sequential variables between 0 and 1. All of these layers were then re-sampled as a network format with a cell size of 195*195 m using the nearest neighbor method, to allow all layers to be combined. Then, a matrix of square cell structure was prepared for the study area. It consisted of a matrix of 273 rows and 273 columns representing a total of 39,650 cells. Of these, 74 cells were identified as knickpoint points. These areas were identified with code 1 (presence of knickpoint) and the rest of the cells that did not have knickpoints were recorded with code 0 (absence of knickpoint).  

The probabilistic relationship of the presence of a knickpoint as one of the important results of the research was obtained by the logistic regression method. This relationship predicted the probability of the presence of knickpoints based on geological and geomorphic variables. The probability map of the knickpoints in the study area was obtained based on the statistical relationship. According to the results, there is a possibility of river knickpoints in the southwestern regions and parts of the northeastern Basin. The results of the probability ratio test to determine the statistical significance of each of the independent variables in the proposed model showed that the geology and the distance from the boundaries of the geological formations in the model were significant. The results of the Yuden index for the training dataset, validation, and test data were equal to 0.72, 0.76, and 0.66, respectively, which indicated the accurate information on the probability status of knickpoint points, especially for the test data of the model. The results of the Kappa agreement coefficient for training, validation, and test data were also equal to 0.62, 0.73, and 0.60, respectively, which indicated the agreement of both methods with the observed values ​​of knickpoints.  

The results of this study showed that at the boundary of lithology, because of the presence of joints and cracks due to differences in the type of rocks, the probability of the presence of river break was more than other parts of the region. Although the presence of some relatively high slope knickpoints indicated active tectonics in that area, in the present study, the effect of the fault system or active tectonics in the formation of knickpoints was not statistically significant. Particularly, the reduction of local roughness index and baselines was associated with less tectonic activity, but in this study, the appearance of knickpoints has been associated with a decrease in these two factors.

Climate is one of the most important geographical factors affecting defense and military affairs, which influences the designers of this field in choosing the type of equipment, maintenance, construction of facilities, etc; Therefore, accurate knowledge of the region's climate and its role in establishing security and strong defense are considered the most important military issues and the main axes of informational and operational activities. In this research for evaluation meteorology in north khorasan province in terms of military activities , Bojnourd station which had data and the length of the statistical period was 25 years was selected , then data related to climatic parameters affecting military operations including temperature , relative humidity , precipitation ( liquid and solid ) , visibility , speed and direction of wind were received from meteorological organization . Then, the climatic elements influencing the military operation were determined and the Hierarchical Model (AHP) was used to weight and rank the parameters, and as a result, the probabilities of the occurrence of the parameters affecting the military operation were calculated.  The results of this study show that the desirability of the military climate in the warm months of the year is more than the cold months of the year, so that the months of June, July, August and September are suitable for military operations, May has an acceptable value in terms of military climate compared to the cold months of the year, and the rest of the months are unfavorable in terms of climate and military activities

Using Landsat satellite images taken in 2015/08/10 and also SEBAL and metric methods, surface albedo amounts for various land uses in the northern half of the Ardabil province was estimated. ENVI4.8 and ArcGIS10.3 softwares were also used. To determine the type of usage of different levels, the maximum likelihood algorithm classification method was used with Kappa coefficient of 86.14% and overall accuracy of 92.63%. The results indicated that the water levels with the mean value of 0.93 and 0.414, respectively, had the least amount of albedo in SEBAL and METRIC methods. Also, based on the results obtained from SEBAL and METRIC methods the city albedo is about 0.313 and 0.278 respectively.  These values are the highest levels of albedo among Land use levels. In this study, the amount of albedo in rangelands was determined to be between 0.183 to 0.266 in the SEBAL method and between 0.237 and 0.265 in METRIC method. The amount of albedo was also examined in agricultural (0.240 based on SEBAL method and 0.247 based on METRIC method) and forest lands (0.149 based on SEBAL method and 0.225 based on METRIC method). Finally, according to the results of Albedo values based on SEBAL and METRIC methods, it was concluded that due to the difference in net energy received at different levels, it is possible to estimate the level of albedo levels, which is very effective in estimating evapotranspiration by remote sensing methods.

Evapotranspiration monitoring has important implications for climate modeling and water resource management at the global and regional levels. Considering that the semi-northern half of Ardabil province is one of the important agricultural poles of the country, evapotranspiration was, therefore, estimated in this research using the Landsat 8 images (dated 2018/7/1) and the SEBAL and mountain SEBAL methods were applied and compared with the Penman-Monteith method. Then, different levels of land use were extracted in the region using the object-oriented image classification with a kappa coefficient of 0.945 and a general accuracy of 0.956. Based on the obtained results, the water levels (9.61 and 9.5 mm/day) had the highest evapotranspiration and urban and bare lands with mean values of 2.845 and 2.08 mm/day in the SEBAL and Mountain SEBAL methods, respectively, had the lowest 24-hour evapotranspiration. Moreover, amounts of 7.14 and 6.70 mm/day were estimated for water requirement of pea crop for SEBAL and mountain SEBAL methods, respectively. These values were compared with that of Penman-Monteith method (6.32 mm/day) with a mean absolute difference (MAD) of 0.60. Subsequently, the extracted area of each land revealed the lowest amount (an area of 1202.62 hectares) of cultivated pea land, which was about 4.6 percent different from the area  (1147.25 hectares) previously declared by the Agricultural Jihad in Ardabil province; though, it seems to be acceptable. In addition to precipitation and runoff, evapotranspiration is one of the main components of the hydrological cycle, which is affected by two biophysical and environmental processes among the soil, plant, and atmosphere. Monitoring evapotranspiration has important implications for both global and regional climate modeling; it is also helpful in the understanding of the hydrological cycle and assessment of the environmental stresses affecting the forests and agricultural ecosystems. This is because evapotranspiration is one of the processes influenced by the climatic elements such as precipitation, cloudiness, humidity, distribution of wind, and concentration of atmospheric gases. Constant precipitation and reduced rainfall in some cases, and, in contrast, increased evapotranspiration have faced most of human, agricultural, and natural needs with available water shortages. As a result, hydrologic equilibrium methods, such as evapotranspiration modeling, are widely used to assess the impacts of climate change on the water requirements of crops and plants. For this purpose, many studies have been carried out around the world and Iran to estimate the amounts of evapotranspiration.

Areas under study in this research were the semi-northern half of Ardabil province, including Ardabil, Bilesvar, Germi, Meshkinshahr, Namin, and Parsabad, with an area of 12571.33 square kilometers located in the northwest of Iran. The province is geographically located between 37° 45׳ to 39° 42׳ N, and 47° and 30׳ to 48° and 55׳ E latitudes. Maximum and minimum altitudes in this area are 4732 and 36 m above sea level.  Ardabil is the most important city in this area. In this study, Landsat 8 image of 2018/7/1 and meteorological data of the same day were used to compare the evapotranspiration of different land uses by SEBAL and mountain SEBAL with Penman-Monteith methods. ENVI 4.8, ERDAS and RefET were used to process images and implement the algorithms of SEBAL, Mountain SEBAL, and Penman-Monteith. The eCognition software was also employed for object-based land use classification. The SEBAL algorithm (Surface Energy Balance Algorithm for Land) and the mountain SEBAL algorithm are both determined based on the energy balance at the surface. In other words, the energy used for evapotranspiration is calculated as the remaining equation of the flux of the surface flow energy.

To estimate the actual evapotranspiration of different land uses and the pea crop, the required images were prepared and processed, followed by calculations related to the SEBAL and mountain SEBAL algorithms. Then, the land use map was classified into nine classes (agriculture, bare lands, grade 1 range, grade 2 range, grade 3 range, forest, pea, residential areas, and water levels) using the object-based image classification method with a Kappa coefficient of 0.945 and an overall accuracy of 0.956 for the studied area. Then the area of each application was calculated in hectare, indicating the bare and pea-cultivated lands as the largest (847985.10 ha) and the smallest (1202.62 ha) ones among the studied locations. Also, the area of pea-cultivated land was compared with that (1147.25 ha) declared by the Agricultural Jihad in Ardabil Province, which were 4.6% different that seems to be acceptable.

The SEBAL model has high accuracy in estimating the evapotranspiration of flat agricultural areas. However, due to the influence of some factors such as slope, elevation, and direction of gradient on evapotranspiration, a new model, called mountain SEBAL, was presented in order to correct these factors while interfering with the factors mentioned in the calculation process. In this research, therefore, the SEBAL and mountain SEBAL methods were used to estimate the evapotranspiration of different lands. Also, amounts of 7.14 and 6.70 were estimated for water requirement of the cultivated pea crop using the SEBAL and mountain SEBAL, respectively. These values were compared with the Penman-Monteith FAO method (6.32 mm/day) due to non-availability of a lysimeter and the wide range of the study area. Accordingly, the Penman-Monteith method used to estimate the water demand of chickpea had an absolute difference (AD) of 0.82 and 0.38, and a mean absolute difference (MAD) of 0.66, respectively.

Fractal geometry is a method for describing a self-similar or a self-affine property in complex landforms and explanation of surface complexities and roughness. In the present study, the surface fractal dimensions (SFDs) were investigated by a cellular model by covering the divider method. Results indicated that geological and geomorphological processes change the character of the fractal dimension of the landforms. Changes in lithologic boundaries and faults influence changes in the fractal dimension and their mode of influence vary according to the topographic characters such as frequency, amplitude, and  types of formations. In lithologic units with hard limestone formations, the fractal dimension is low, while in alluvial formations, the fractal dimension increases. The drainage network density and tributaries margins affect the fractal dimension. Moreover, homogeneity of the lithologic units decreases the fractal dimension. In this study, the lowest fractal dimension is associated with the integrated units of Mesozoic orbitolina limestones on the border of the two structural zones of Sanandaj-Sirjan and High Zagros belt. However, friable and sensitive to erosion formations of the quaternary increase the fractal dimension. The succession of the hard and friable layers is effective on the local scale on the fractal dimension. Furthermore, mountains have lower fractal dimensions than lowlands. Generally, there is an inverse relationship between the fractal dimension and elevation and this relationship there is about the roughness index in the basin. The results illustrated that changes in the surface fractal dimension were dependent on a set of lithologic, tectonic, and geomorphologic factors. Also in complex topographic zones investigation of changes in the fractal dimension can be a useful and effective instrument for detecting and surveying of the surface anomalies.

Nowadays, people’s life is at risk because of various pollutants into the atmosphere by human action and biological activities. One of the major air pollutants are particulate matter. The aim of this study was to evaluate spatial interpolation methods to determine the concentration PM2.5 and PM10 in Sabzevar city and select the most suitable interpolation method for preparation of zoning maps particulate matter in GIS.

Particulate matter were measured by a monitoring device, environmental dust model Haz-Dust EPAM 5000 at 48 stations in the city, then in ARC GIS software three well-known spatial interpolation techniques, namely Kriging, Inverse Distance Weighting (IDW) were applied for generating the prediction maps. Finally the best interpolation method was chosen according to the values of each algorithm error.

The results indicated that the RMS was lower between geostatistical and deterministic methods, and the MAPE in deterministic methods was lower.

The best interpolation method for the particulate matter (PM2.5 and PM10) was deterministic method by IDW function.

Khorasan Razavi province has the potential for growing and producing rapeseed because of favorable environmental conditions, so that the northern and central cities of province have high potential for cultivation of rapeseed. Modeling the correct relationship between environmental conditions and yields is a critical step to find how crop-planting choices in different regions of Iran.Spatial modeling in GIS is one of the most important strategies that can provide a basis for measuring environmental factors and land suitability for the cultivation of a particular product by combining statistical methods and spatial data. In this research, the link between water, soil and meteorological factors and yields modeled during the growing season in sample farms.

In this research, the position of 24 sample fields of rapeseed farming was recorded by Global Positioning System (GPS) and then actual yield was calculated. To explore how the environmental conditions and yields relationship has changed over space, we used ten environmental parameters influencing rapeseed productions yield, including elevation, slope, aspect, EC and pH groundwater resources, mean air temperature, incoming solar radiation, potential evapotranspiration, wind exposition index, Soil texture during the growing season. The values of each independent variables were extracted into samples by nearest neighbor method. Then, after normalizing the variables and taking into account the range of numbers, the samples were divided into two subsets: training (60%, 14 farms) and the testing dataset (40%, 10 farms) randomly. Two methods of nonparametric K of the nearest neighbor and random forest were then used to estimate rapeseed yield over the study area.

The results of mean absolute error percentage in the methods used showed that K is the nearest neighbor with 26% error and random forest with 11% error. The results of Nash–Sutcliffe efficiency index for validation data set represent the value of 0.65 for K nearest neighbor and 0.82 for random forest method. In general, the results indicate that the random forest method has a lesser error than the K nearest neighbor method in estimating the yield of rapeseed productions for the study area.

Based on the results of this research, it can be concluded that among the variables used, two variables of wind supply index and average temperature had the most effect on the yield of rapeseed in comparison with other variables. Also, according to the final map, it was determined that suitable areas for rapeseed cultivation over Sabzevar region are located in the northern and northwestern regions. Low yield in the central regions of this part is mainly due to the excessive salinity of water and gypsum formations. Crop yield is a result of combination of genetic factors and also environmental conditions of the cultivation, which we emphasized on the environmental factors in this study.

Fires in natural areas are one of the factors decreasing forested area of northern Iran. In this study, forest and rangelands susceptibility to fire were analyzed using data-driven methods over Mazandaran Province. Fourteen important environmental and anthropogenic parameters influencing forest and rangelands susceptibility to fire were used to model probability of fire susceptibility. Binary logistic regression and artificial neural network, as two well-known data driven methods was then used to evaluate environmental and anthropogenic performance on landfire and map of forest fire susceptibility estimates were prepared in GIS environment. The area under the successive rate curve (AUSC) showed that ANN method modeled forest fire susceptibility with an accuracy of around 88% and BLR with 85%. 21.6% of the total area of Mazandaran province is located in areas with high and very high susceptibility levels of forest and rangeland fire. Overall, ANN method showed promising results to estimate landfire susceptibility. The forestry and rangelands fire susceptibility map presented in this study can be used as a basic map of the strategic planing in Mazandaran province to reduce probability fire damages.

Many varied attitudes exist about how the changes occur in the land-surface from the time of William Davis’s research to the latest researchers in the history of geomorphologic philosophy and many different terms are used by scientists in order to observe their ideas related to geomorphic phenomena. The phenomenon of duality has been seen less in the geomorphology field. There exist some contradictory phenomena in nature, but further investigation can show their correlation clearly. Durability can be considered as a better dynamic entropy. In this research, radar interferometry technique has been used in Yazd-Bahadoran area and the amount of subsidence and uplifting has been investigated Through field and library studies and the results have been compared with the other researchers' studies, which is a new concept in the literature of geomorphology under geo-duality. The study results indicate that the main reason for the subsidence was not to cut down the level of groundwater, and in this regard the tectonic movements play a significant role. Also, the study shows a significant relationship between subsidence in the Bahadoran plain and the collapse in adjacent heights in terms of duality or dichotomy.

The aim of this study was to identify similar areas of physiography and climatic potential of walnut in Baft city. Logistic regression and discriminant analysis were used for the purpose of classifying of study area using a set of predictor variables to find the suitable areas to grow walnut. For this purpose, environmental conditions affecting walnut trees used included Annual Mean Temperature, Annual Precipitation, Precipitation of Driest Month, Precipitation of Wettest Month, Elevation, Slope Gradient and Aspect. 70% of the data, as training data and the remaining 30%, are used as model validation data. Using probit logistic regression and discriminant analysis, the effect of variables used on walnut in Baft County was calculated and then map of walnut was calculated. The results showed effect of physiographic and climatic variables on walnut. The results of validation data set in this study showed that probit logistic regression with 75% and discriminant analysis with 86% able to accurately explain the favorable areas for walnut cultivation. Suitable physiographic areas and climatic potentials for planting walnut trees are located in the central, northern, and northwestern regions of Baft area.

A limited number of agricultural weather stations measure moisture in the soil surface. Furthermore, soil moisture information may be required in areas where there is no weather station. The aim of the present study was to use Landsat 8 satellite images to estimate soil surface moisture in an area without agricultural meteorological stations. Gravimetric soil moisture for a total of 14 samples was calculated in the cold season in depths of 0-10 cm when Landsat 8 satellite was overpassing poor rangeland of North of Sabzevar. Furthermore, the first four principal components were extracted from seven Landsat-derived vegetation indices and bio-physical factors affecting soil moisture. Afterwards, the first four components were used to estimate soil surface moisture at the moment of the satellite passing the region using a multivariate linear regression and neural networks. The obtained results of instantaneous soil surface moisture showed that the neural networks had mean absolute percentage error of while classical regression analysis had mean absolute percentage error of 40%. The results also showed the benefits of using both in-situ soil moisture data and Landsat 8 satellite images to model instantaneous soil surface moisture content for areas lacking meteorological networks.

Studying the variations of energy balance and surface temperature, vegetation and spectral reflectance of geomorphological features during the catastrophic processes such as storms and environmental hazards on a large scale requires powerful tools such as thermal remote sensing. This research has considered the effective use of remote sensing methods by such practical approach. The study area is located in the southeast area of Isfahan province and Gavkhuni Playa Lake that is remarkable from the viewpoint of diversity of geomorphological features in arid and desert regions. In this study, variations of surface albedo, emissivity, and land surface temperature, water vapor, vegetation index and reflectance of geomorphological features and land use before and after the storm was investigated by MODIS satellite images. The result showed that the dust storm, has increased land surface temperature and has intensified dry condition of soil, also in a short time has had a negative impact on the vegetation index. T-test rejects assuming equal means in condition before and after the storm therefore the storm has had significant influences on the parameters that have been studied. The severity of these changes has been distinct according to land use and geomorphological features and devoid of vegetation areas such as Playa, desert and sand dunes have had the most variations. Variations in spectral reflectance of land surface were considered by standardized differences map and results showed that Gavkhuuni Playa Lake has had the most variations and has been the source of deflation by strong winds. In addition, investigation of the aspect of slope map and topography map showed the eastern and northeastern slopes have had more variations compared to the western and south-western slopes.

The most important indicator of climate in a region is vegetation community. Climate can be different over large areas، because of changes in vegetation community. Vegetation influences weather and climate in its surrounding areas mostly by way of evapotranspiration and albedo so they play a role in the earth’s energy balance. These effects on the earth’s energy balance are taken through air temperature، relative humidity، rainfall، solar radiation and cloud cover on their own micro-weather (Neilson، 1986، Small and Kurc، 2003 and Weiss، et al 2004). Disasters such as drought، flood، forest fire and among them can occur whenever the global energy balance become outside of normal range. Remote sensing data provide valuable information for vegetation studies because it presents a quick look evaluation of the vegetation conditions. Normalized Difference Vegetation Index (NDVI)، Land Surface Temperature (LST) and land surface albedo are key biophysical variables for studying land surface processes and surface-atmosphere interactions. These variabels can be calculated by transforming raw satellite data. The aim of this paper was to study relationship between NDVI، LST and land surface albedo due to the impact of vegetation on surface temperature and albedo and identify dryness status in the northern Iran.

Study area is located in the northern part of Iran which covers with dense vegetation such as Hyrcanian forests in the north and sparse vegetation in the southern part of the Alborz Mountain range. This area is an alteration bio-climate region lying between the humid climate in the north and arid and semi-arid climate in the south. The methodology used in this study consisted of remotely sensed data processes. The remotely sensed data processes involved data acquisition of Moderate Resolution Imaging Spectroradiometer (MODIS)، data pre-processing (i. e. atmospheric correction، geometric correction، and data masking)، and data processing (i. e. derivation of vegetation، biophysical indices such as NDVI، LST and land surface albedo variables). In this study، MODIS data was selected because the MODIS sensor has several benefits compared to other instruments for example MODIS has a wide swath of 2330 km that covered the entire study area. The MODIS dataset had a variety of products that each contained different levels of data processing. In this study، Level 1B dated July، 2010 (MOD021KM and MYD021KM calibrated and geolocated) was used to derive NDVI and surface albedo، and retrieving LST. This level 1B collection contains calibrated and geolocated radiances at-aperture for all 36 MODIS spectral bands at 1km resolution. In this study، Simplified Model for Atmospheric Correction (SMAC) was used for atmospheric correction of MODIS data. Absorbtion in visible light (solar radiation) occurs in live green vegetation because of photosynthesis. Scattering (reflectance) of solar energy in the near infrared occurs at the same time. This difference in absorption and reflectance lead us to NDVI. NDVI is an vegitation index which measures this difference to show vegetation density and condition. NDVI value ranges between -1 and +1. The values close to zero means no green vegetation and close to +1 (0. 8 - 0. 9) represents the highest density of green vegetation. LST is an important parameter in determining the earth radiation budget and heat and moisture flow between the surface and the atmosphere and temperature strongly influence vegetation processes. Thermal bands of MODIS data (band 31 and 32) converted to radiance then converted to brightness temperature using plank law for calculating surface temperature. Albedo is also an important bio-physical indicator of reflecting land surface energy distribution and balance. In the process of broadband albedo retrieval، an empirical regression was used for MODIS data (Liang et al.، 2002). Finally، regression and geostatistical approach (e. g. CoKriging) was used in this study to estimate LST and surface albedo using NDVI of MODIS data.

In this study three criteria such as mean absolute error، root mean square error and mean absolute percentage error have been used to measure the differences between the values estimated for LST and Albedo using regression and CoKriging geostatistical method. The results obtained in this research indicate that the geostatistical method of cokriging has good potential to estimate LST and surface albedo using normalized difference vegetation index. The results of the study show that changes in vegetation cover alter the LST and surface albedo، leading to a local temperature change. Plants and forest have a very low albedo and absorb a large amount of energy. The relationship between normalized difference vegetation index and LST and surface albedo equations were then used to found the surface dryness condition in the study area. The result of 3D feature space of Albedo-NDVI-LST spectral shows that it has a suitable index for extracting drought information. The study revealed that coastal and forested northern slopes of the Alborz Mountain are identified with high normalized difference vegetation value (0/85)، minimum surface temperature (23° C) and albedo (7%). The southern part of Alborz Mountain and the central Iran experiences low normalized difference vegetation value (-0/09)، high surface temperature of 45 ° C and high surface albedo (38%).

In this study، the relationship between NDVI and LST and surface albedo was analyzed to estimate LST and surface albedo derived by regression and CoKriging methods. It has been recognized that there is a strong correlation between albedo، LST and NDVI. By comparison of the regression relationship among LST، Albedo and NDVI، results exhibit that LST and Albedo are negative correlations with NDVI. In this paper، a 3D feature space of Albedo-NDVI-LST spectral is analyzed for monitoring surface dryness condition. The result represents that surface albedo nad LST is affected by the change of vegetation. This 3D feature space is reliable index to show surface dryness status for soil and plant cover. it is recommended that 3D feature space of Albedo-NDVI-LST spectral can monitor to the surface dryness condition and also it is easy to operate for quick surface dryness assessment. Studies are underway to incorporate other variables in surface dryness condition.