This study was carried out in the western catchment of the Behzisty Township in Gorgan, Golestan province in order to develop the regression MODELs of soil moisture. In this study, the desired measurements were made in 18 locations using TDR during 6 running days after a relatively intense rainfall event at different depths of soil. The DIGITAL ELEVATION MODEL was prepared from Ultra Cam images taken in 2014 with the pixel size of 1×1 m in order to estimate the terrain properties. Finally, the regression MODEL was developed with respect to these characteristics and soil moisture. The results showed that important terrain characteristics which are greatly associated with soil moisture are horizontal curvature, slope, aspect and wetness index in the regression MODEL. These results suggest that the diversion of material flow has controlled the amount of water in the soil, slope aspect and soil moisture. The results showed that the developed regression MODELs can forecast at least 44 to 60% of the total variations of soil moisture in the watershed scale.

DEM is needed for many branches of earth and environmental sciences such as control of erosion, flood planning, road construction, earth resource's recognition, and soil mapping. There are varieties of resources in generation of DEM, one of them is stereo satellite imagery. The objective of this research is the study of how to extract DEM from ASTER sensor stereo image. ASTER consists of three different subsystems: the Visible and Near-infrared (VNIR) and an additional backward telescope for stereo, the Shortwave Infrared (SWIR) and the Thermal Infrared (TIR). Each subsystem operates in a different spectral region, with its own telescopes, and is built by a different Japanese company. In addition, one more telescope is used to view backward in the near-infrared spectral band (band 3B) for stereoscopic capability. Images of Kashan area, which has been undulating topographic condition, were used. DEM extraction was based on the correlation algorithms between right image (3N) and left image (3B).The more correlation these tow images, the more precise DEM will be achieved. Finally, by correlation between tow images relative ELEVATION attained. The results show that ASTER sensor, using along track imaging will attain the more precise DEM. The result accuracy of these images will be less than one pixel. This factor will improve the extraction of another production result by DEM.

Different resolutions of DIGITAL ELEVATION MODELs (DEMs) can generate varied topographic and hydrological features. The objective of this study was determination of suitable cell size of DEMs and its effects on prediction of some soil properties. For this purpose, two study areas were selected with different topographic properties in Selin plain, East-Azerbaijan Province. A total of 31 and 37 points were selected randomly from study area (1) and (2), respectively, and then, ELEVATION, slope, clay and organic matter contents were measured by GPS, manual, hydrometer and Walkly-Black methods. The results showed that the number of cells with sink was more in smaller cell size than bigger cells, causing error in determination of hydrological characteristics. Therefore, they must be removed. Appropriate cell size of DEMs depends on the properties of the area topography, which for the study area (1) and (2) with flat and severely undulating topography, the cell size was 50 and 40 meters, respectively. Geostatistical analysis showed that, in both study areas, spatial correlation linearly decreased with increases in cell size upto 75 m; while in study area (2), it decreased with more intensity, reflecting the loss of large volumes of topographic information. Difference between R2 values for the estimation of soil organic matter and clay from DEMs with different cell sizes was less in area (1) than area (2). Generally, the results of this study showed that lower cell size (<75 m) was recommendable for areas with sever topographic changes and complex morphology, while higher cell size (200 m) was suitable for flat areas.

Flooding is one of the most devastating natural disasters occurring annually in the Philippines. A call for a solution for this malady is very challenging as well as crucial to be addressed. Mapping flood hazard is an effective tool in determining the extent and depth of floods associated with hazard level in specified areas that need to be prioritized during flood occurrences. Precedent to the production of maps is the utilization of reliable and accurate topographic data. In the present study, the performance of 3 DIGITAL ELEVATION MODELs having different resolution was evaluated with the aid of flood MODELing software such as hydrologic engineering centre-hydrologic MODELing system and hydrologic engineering centre-river analysis system. The two-dimensional MODELs were processed using three different DIGITAL ELEVATION MODELs, captured through light detection and ranging, interferometric synthetic aperture radar, and synthetic aperture radar technologies, to simulate and compare the flood inundation of 5-, 25-100-year return periods. The accuracy of the generated flood maps was carried out using statistical analysis tools-Overall accuracy, F-measure and root-mean-squareerror. Results reveal that using light detection and ranging– DIGITAL ELEVATION MODEL, the overall accuracy of the flood map is 82. 5% with a fitness of 0. 5333 to ground-truth data and an error of 0. 32 meter in simulating flood depth which implies a promising performance of the MODEL compared to other data sources. Thus, higher resolution DIGITAL ELEVATION MODEL generates more accurate flood hazard maps while coarser resolution over-predicts the flood extent.

Introduction: Climate change, driven by the increasing concentration of greenhouse gases—particularly carbon dioxide—in the atmosphere, leads to significant alterations in rainfall patterns, runoff volumes, wind speed, solar radiation reaching the Earth's surface, and air temperature. In recent years, the economic and social consequences of climate-related events have heightened the importance of addressing climate change. To study the impacts of climate change on future trends in rainfall, temperature, and other parameters, projected data from climate MODELs, known as General Circulation MODELs (GCMs) or Atmospheric and Oceanic General Circulation MODELs (AOGCMs), are widely used. A critical step in utilizing GCM data for projecting climate parameters (e.g., rainfall) is the downscaling process, which converts data from large-scale grid cells to smaller-scale cells or specific points. In this research, rainfall data from CMIP6 climate MODELs (such as TaiESM1, ACCESS-CM2, and CanESM5) were employed to project future rainfall patterns in Yazd Province, located in central Iran. As an arid region, Yazd heavily relies on rainfall as a fundamental component of the hydrological cycle, making it a critical factor in groundwater resource management.   Materials and methods: the study area is Yazd Province, covering an area of 73,000 square kilometers. It is geographically located between latitudes 29.5° to 33.5° N and longitudes 52.5° to 56.5° E. The data used in this research include: Annual rainfall records from 53 rain gauge stations across Yazd Province. A DIGITAL ELEVATION MODEL (DEM) map with a spatial resolution of 90 meters. Rainfall data from several climate MODELs included in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), specifically the TaiESM1, ACCESS-CM2, and CanESM5 MODELs. These MODELs have spatial resolutions of 0.942° × 1.25°, 1.875° × 1.25°, and 2.813° × 2.791°, respectively, corresponding to approximate cell dimensions of 105 × 118 km, 175 × 138 km, and 270 × 310 km. The area covered by each cell of the TaiESM1, ACCESS-CM2, and CanESM5 MODELs is approximately 12,400 km², 23,600 km², and 83,700 km², respectively. Each MODEL incorporates four Shared Socioeconomic Pathway (SSP) scenarios for carbon dioxide emissions: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. These scenarios were used to simulate monthly and annual rainfall for future periods. Monthly rainfall data were extracted for two time periods: the base period (1850–2014) and the future period (2015–2100), under the aforementioned emission scenarios. Additionally, a regression-based relationship between annual rainfall data and the altitudes of the rain gauge stations was established to derive the rainfall-altitude gradient for the study area. This relationship was then used to generate a rainfall map and, subsequently, a dimensionless rainfall map with a spatial resolution of 90 × 90 meters. The dimensionless rainfall map represents the ratio of each pixel's annual rainfall to the long-term average rainfall of the region. This map was instrumental in downscaling the coarse-resolution rainfall data from the CMIP6 MODELs into high-resolution rainfall maps with a pixel size of 90 × 90 meters.   Results: the results of this study indicate that the proposed method for downscaling CMIP6 rainfall data exhibits varying levels of efficiency across the three climate MODELs with different spatial resolutions. Specifically, the method demonstrated higher accuracy with the TaiESM1 MODEL compared to the ACCESS-CM2 and CanESM5 MODELs. As previously mentioned, the area covered by each cell of the TaiESM1, ACCESS-CM2, and CanESM5 MODELs is relatively large, resulting in low spatial resolution for these MODELs. It is concluded that the accuracy of the proposed downscaling method is generally higher for climate MODELs with finer spatial resolutions. Among the CMIP6 climate MODELs examined, the following MODELs have smaller cell sizes and are therefore recommended for applying the proposed downscaling method: BCC-CSM2-MR, CAMS-CSM1-0, CESM2-WACCM, CIESM, CMCC-CM2-SR5, FIO-ESM-2-0, GFDL-ESM4, MRI-ESM2-0, INM-CM4-8, Additionally, the findings of this research suggest that the average annual rainfall in Yazd Province is projected to increase in the coming decades. The MODELs predict a rainfall increase ranging from 1% to 23%, with an average increase of 13% compared to the base period. Among the three climate MODELs analyzed, the ACCESS-CM2 MODEL predicted the highest increase in rainfall, particularly under the SSP3-7.0 scenario. Discussion and Conclusion: In this research, a method for downscaling large-scale rainfall data from CMIP6 climate MODELs was introduced, utilizing the rainfall-altitude gradient and a DIGITAL ELEVATION MODEL (DEM) of the region. This method was successfully applied to downscale data from three CMIP6 MODELs: TaiESM1, ACCESS-CM2, and CanESM5. Although these MODELs have relatively large cell sizes, the proposed method enabled the generation of high-resolution rainfall maps for the study area with a spatial resolution of 90 × 90 meters. The results indicate that the accuracy of the proposed downscaling approach is higher for CMIP6 climate MODELs with finer spatial resolutions compared to those with coarser resolutions. This method is recommended for downscaling CMIP6 rainfall data in regions with similar land surface topography. However, for regions with different topographical characteristics, further investigations may be required to adapt and validate the method.

Introduction: A DIGITAL ELEVATION MODEL or DEM is a physical representation of terrain and topography that is MODELed by a DIGITAL 3D MODEL. DEMs have various applications in many fields. Today, with respect to improvements in technology and importance of generating DEM from every region in our country, the importance of satellite remote sensing is more sensible. One of the main topics in satellite remote sensing is radar remote sensing. In recent years, a number of satellites have been launched to capture SAR information from the surface of the Earth. The last project is Sentinel, and Sentinel-1generates SAR data. It generates images with medium spatial resolution from the Earth every 12 days. DEMs are generated through multiple methods, one of which is SAR interferometry. Material and Methods: The area under study in this research for conducting experiments and generating the DEM is Iran and the city of Tehran. Tehran is located in the north of the country and south of the Alborz Mountains, 112 kilometers south of the Caspian Sea. Its ELEVATION ranges from 2000 meters in the highest points of the north to1200 meters in the center and 1050 meters in the south. In this paper, the Sentinel-1 stereo images are used to generate DEM. Tehran is located on part of these images. These images are shown in Figure (1). In order to evaluate the DIGITAL MODEL generated by these images, a reference DIGITAL MODEL which has been prepared from the city of Tehran with an accuracy of 1 meter is used. This ELEVATION data was collected using terrestrial surveying and aerial photogrammetry. In this paper, radar interferometry was used to generate DIGITAL ELEVATION MODEL from the Sentinel-1 images. In SAR interferometry, the phase of images taken from various imaging positions or various imaging times is compared pixel by pixel. The new image is produced by differentiating between these values which is called interferogram. Interferogram is a Fringe interference pattern. Fringes are lines with the equal phase differences similar to contours in topographic maps. The phase difference obtained from SAR interferometry is affected by several components. Some of the most important components are orbital paths, topographic, displacement and atmospheric components. By eliminating the major part of the orbital component (and calculating the effect of other components or assuming their insignificance effects comparing with orbital and topographic components), since the topographic radar observes the Earth from two different points, the stereoscopic effect is revealed. This topographic component leads to fringes which encompasses the topography like contours. These patterns are called topographic fringes. Results and Discussion: In order to conduct the experiments considered in this paper, two mountainous and flat areas in Tehran are picked out and separated from the main image. The mountainous area is selected from the north and the flat one from the south of Tehran. The aforementioned technique is implemented and executed on these images. The generated DEM in these two areas is shown in Figure (2). After generating the Earth DEM using the Sentinel-1 images, and comparing it with the reference DEM having an ELEVATION accuracy of 1 meter, the accuracy of the generated DEM was determined. As expected, the results in the flat area were more desirable compared to the mountainous area. The accuracy of the generated DEM was evaluated by creating a network with the dimensions of 138761 points from the flat area and a network with the dimensions of 78196 points from the mountainous area, from both generated and reference DEMs and comparing the corresponding ELEVATIONs of the network points. DIGITAL numbers of images represent the magnitude of error occurring in the generation of DEM. After testing the 3 error (blunder detection) and eliminating large errors occurred in DEM, a standard deviation error of 1. 26 meters for the flat area (South of Tehran), and 10. 32 meters for the mountainous area (North of Tehran) were obtained. Conclusion: Considering the development of technology and the launch of new satellite imagery projects from the Earth and the importance of the existence of a DIGITAL ELEVATION MODEL from the country, it is possible to recognize the importance of studying these images more and more. One of the latest satellite remote sensing projects is the Sentinel project. The Sentinel-1 radar images with medium spatial resolution capabilities provide the possibility of generating a DIGITAL ELEVATION MODEL (DEM) from the country. This research is the first study on the accuracy of DIGITAL ELEVATION MODEL resulted from the Sentinel-1 radar images in Iran. An ELEVATION accuracy of 10. 32 meters in the mountainous area, and 1. 26 meters in the flat area were obtained. The results show that these satellite images have the capability of generating a relatively optimal DEM, particularly in non-mountainous area.

Introduction: Topographic maps show natural and artificial features. natural features such as rivers, lakes, mountains, etc., Man-made features such as cities, roads and bridges. Using the satellite images is a way to extract DIGITAL ELEVATION MODELs. In general, there are two types of resolution in DIGITAL ground ELEVATION MODELs. ü Area resolution: The dimensions of the length and width of each cell in the pixel grid is a DIGITAL ELEVATION MODEL that shows the minimum dimensions of the topographic features taken on the ground. ü Height resolution: represents the minimum ELEVATION dimensions that the DIGITAL ELEVATION MODEL is able to display. For example, in the DIGITAL MODEL of ground ELEVATION with a resolution of 30 meters, ELEVATION features less than 30 meters are not visible. The DIGITAL ELEVATION MODEL can be prepared for a region with different accuracy. The high accuracy of the DIGITAL ELEVATION map provides more accurate estimates of the physiographic characteristics of the basin, but the preparation of such maps is very costly. PRISM sensor from ALOS satellite with three cameras: 1-Forward 2-Vertical 3-Forward, which is captured earth surface with the characteristics of the earth (low and high). Therefore, an object that is high above the ground is shown with other points on a flat surface. As a result, by imaging points from different angles, the ELEVATION of those points can be obtained through adaptive mathematical calculations. The purpose of this study is to evaluate the accuracy of the DIGITAL ELEVATION MODEL generated by the PRISM sensor of ALOS satellite in comparison with the DIGITAL ELEVATION MODEL of ASTER and SRTM for Sarakhs border region (between Iran and Turkmenistan). Method: The study area is located in north-eastern Iran in the range of 35 to 38 degrees north latitude and 56 to 60 degrees east longitude and on the border between Iran and Turkmenistan in the border region of Sarakhs. The research method in this research has an exploratory aspect that the production and extraction of DIGITAL ELEVATION MODEL from PRISM sensor stereo images from Alves satellite and its evaluation is with DIGITAL MODEL extracted from ASTER image. The DIGITAL SRTM MODEL has a spatial resolution of 90meters, the DIGITAL ASTER MODEL has a spatial resolution of 15 meters and the DIGITAL ELEVATION MODEL obtained from the PRISM sensor from the ALOS satellite is 5 meters. In this study, ELEVATION control points using Google Earth and GPS have been examined. The algorithms used in this method to extract ELEVATION information are the same as the algorithms used in the photogrammetric method. ELEVATION DIGITAL MODELs are made from satellite images taken in pairs. The accuracy of DIGITAL ELEVATION MODELs of this method is perfectly proportional to the scale or resolution of satellite images. Results & Discussion: In this study, we evaluated the DIGITAL ELEVATION MODEL from stereo satellite images of ALOS/PRISM satellite and compared it with the DIGITAL MODEL of ASTER ELEVATION and ground observations in the Sarakhs border region located on the border between Iran and Turkmenistan. In this study, the ability to generate a DIGITAL ELEVATION MODEL prepared from stereo images extracted from a PRISM sensor with a file of rational polynomial coefficients has been investigated, and we compared it with DIGITAL MODELs extracted from stereo ASTER satellite and DIGITAL MODELs extracted from SRTM. The results obtained from the DIGITAL ELEVATION MODEL are the accuracy of the DIGITAL ELEVATION MODEL produced by the pair of ASTER satellite images using a correlation between the two images of 0. 47 pixels. Due to the spatial accuracy of the image pixels, which is about 15 meters, the accuracy of the DIGITAL MODEL is less than the size of pixels, i. e. less than 15 meters, 6 meters horizontally and 7 meters vertically, which is a total of 13 meters. The results show that RMSE as error index for DIGITAL MODEL of ELEVATION extracted from ASTER and PRISM and ground observations are 7. 46, 8. 77, 3. 66 and 6. 8 meters, respectively. The results obtained from the stereo images of the PRISM sensor are the standard deviation of the pixels in the longitudinal direction of 1. 9 meters and in the transverse direction of 2. 3 meters and the distance between the pixels of the DIGITAL MODEL is 3 meters high. Therefore, the accuracy of the DIGITAL MODEL extracted from PRISM sensor images is higher than SRTM and ASTER. It is recommended to use a high-precision DIGITAL ELEVATION MODEL in all borders of the country, which uses a DIGITAL ELEVATION MODEL produced from stereo PRISM images from ALOS satellite, which is accompanied by polynomial logical coefficient (RPC) files for geometric correction of images. Conclusion: The higher the accuracy of the DEM, the more efficient it will be and give border commanders the ability to make better decisions in different situations. The ELEVATION accuracy obtained from the stereo images of the PRISM sensor is 3 meters. The accuracy of the DIGITAL MODEL of SRTM ELEVATION in the plains is about 30 meters, which can be used for studies of phase zero and one of the projects, as well as reducing the huge costs of studies. The results of this paper, shows that the accuracy of the DIGITAL ELEVATION MODEL produced from the stereo images of the PRISM sensor is higher than the DIGITAL ELEVATION and SRTM DIGITAL MODELs, i. e. the RMSE error and standard deviation are relatively lower. As a result, it is recommended for border studies that require higher accuracy, and the entire borders of the country, to use the DIGITAL ELEVATION MODEL with accuracy.

The analysis of the relationship between spatial distribution of environmental factors and vegetation types is crucial for understanding mountainous ecosystems. In this research a GIS based approach was used to produce a vegetation map for Sabzkouh protected area in the Chaharmahal- Va-Bakhtiari province. To identify environmental parameters affecting the vegetation cover, 6 primary and secondary environmental parameters including hypsometric, slope steepness, slope direction, annual precipitation, temperature and sun radiation maps were derived from the study area DEM. To investigate the relationship between these factors and the spatial distribution of vegetation cover, quantitative analyses using statistical techniques like Principal Components Analysis(PCA) were undertaken. Then, the spatial distribution of vegetation types was predicted using a multi-logistic regression. Results showed that topographic variables derived from the OEM were very useful for indicating habitats of range and forest types. Although lack of information on the anthropogenic effects led to some uncertainties in the interpretation of spatial pattern of vegetation types, the topographic and climatic variables, derived from the OEM, were considerably effective in MODELling the spatial distribution of vegetation types.

Limitations of traditional (conventional) soil surveys and improvement of information technology have lead soil surveyors to invent new methods which are generally called DIGITAL soil mapping (DSM). The aim of these methods is the prediction of soil classes or soil properties based on easily-available or measuring environmental variables. The objective of this investigation is to study the efficiency of DIGITAL ELEVATION MODEL and its derivates for soil mapping using Soli-Land Inference MODEL (SoLIM) and credibility of its results in the Borujen area, Chaharmahal-va-Bakhtiari province. Eighteen terrain attributes including height, slope (angle), aspect, curvature, minimum curvature, maximum curvature, tangent curvature, profile curvature, planform curvature, flow direction, flow accumulation, direct radiation, diffuse duration, diffuse radiation, area solar radiation, power index, sediment index and wetness index, were derived from the DEM. These derivates as well as three dominant soil subgroups and seven soil families of the region were used to construct the input data matrix of the MODEL. Results showed an accuracy of 65% and 40% for interpolation and extrapolation of the soils at subgroup level, respectively. The accuracy decreased to half when soil families were considered for credibility of the MODEL. Because of using crisp limitations in American Soil Taxonomy system, assessing soil survey results can be miss-leading partially, whereas using SoLIM MODEL shows well the reality of the soils in the field.

Introduction: Planting date plays main role in crop performance. Planting date through correspondence with the climatic elements affect vegetative and reproductive growth and ultimately affect the quality and quantity of crops. Among the climatic elements, temperature and day length are more important under irrigated condition. It is necessary to mention that the majority of crops cultivated in Iran are indifferent to day length. The temperature is the most important element in controlling their growth period. By using long-term weather data and related software such as Arc map we can determine the suitable planting dates for a wide area. Therefore, by eliminating field experiment and avoiding large amount of time and cost, much can be saved. The purpose of this study is to determine the best planting dates for spring safflower in different parts of Esfahan province in order to gain the maximum performance in any climatic zone.