Automatic analysis of morphometric of topography is one of the new sections and unprecedented internal studies. Due to this fact, the validation of this type of studies are also similarly important to their assigned POSITION. Hereof, the leading research, analysis of the topography POSITION INDEX ((TPI)) automatically in two regions of Sanandaj- Sirjan zone and broken Zagros zone to achieve most correct results, were studied. In this study, according to verify and adaptation with condition of observations was used of Dickson & Beier within through of the other methods. After preparing of (TPI) layer with separation to 4 classes (ridgetop, steep slope, flat-gentle slope and canyon bottom) approach by Dickson & Beier from DEM layer with a resolution of 10 m of the total Sahneh township in Kermanshah province, in the next step typically from two parts of the geomorphologic Zagros region selected two part with dimensions of 6.07 × 6.07 km. In the final part of the project as well as the results of the TOPOGRAPHIC POSITION INDEX with respect to satellite images and field visits. The results represent the perfect match the values of the (TPI)=1, with valleys and Canyon (Exist of Drainage Network), (TPI)=2, with the residential part of the crop, and the slope gentle, (TPI)=3, fitted with steep slopes and sparse vegetation and (TPI)=4, with ridgetop. In two parts areas of steep slope has been included maximum area parts of the both regions (pilot) and then the landforms of slope gentle and the ridgetop and finally also canyon bottom as well as the minimum area...

The main objective of this study was to classify landforms by TOPOGRAPHIC POSITION INDEX and assessment of the relation between landforms and lithological features. In order to classify landform, the 10 m Digital Elevation Model (DEM) and Geology map (1: 100000) was used. In this paper TOPOGRAPHIC POSITION INDEX and the deviation from mean elevation (DEV) were used for classification of landforms. The result showed that, the valley was the largest category, with 33. 37 %. The lower slopes was the lowest category, with 5. 63 %. Each of the other four categories (flat area, middle slope, upper slope and ridge) represented between 5. 8 % and 30. 79%. According the results, the most variable classes were the valley, increasing from 20. 62% (50 m) to 55. 7% (750 m), and the middle slope area, decreasing from 6. 4% (50 m) to 1. 01% (750 m). The results of ANOVA showed a significant relationship at 99% probability level for landform classification map and geology formation map. More than 60% of limestone (OMl, El, TRjm, K2, TRkh, Jgr-vc, OMas, K1) were in middle slopes, upper slopes and ridge.

Introduction system of landform classification for soil mapping has been desired by soil scientists in Canada for a long time. The Canada Soil Survey Committee (CSSC) adopted a system at a meeting held at the University of Guelph in February 1976. Many aspects of the system came from mapping schemes used by the Geological Survey of Canada for mapping surficial geology. The system also embodies concepts developed initially by R.J. Fulton and later by N.F. Alley while doing terrain mapping in British Columbia. However, the needs of the soil scientist for a terrain or landform classification system are not necessarily compatible with those of the geologist. Relief analysis is a tool to analyse a landscape based on a Digital Elevation Model (DEM). One of the simplest parameters might be the elevation itself, or slope or the exPOSITION of a given point in a landscape. Moore et al. (1991) state that the spatial distribution of TOPOGRAPHIC attributes can often be used as an indirect measure of the spatial variability of hydrological, geomorphologic and biological processes. The advantage compared to other information such as soil parameters or biomass production estimates is based on the relatively simple and fast techniques to model processes in large areas and the complex spatial patterns of environmental systems as seen by Moore et al. (1993b). Another relief parameter relevant for this work is landforms or relief units. Each of these contains certain characteristic physical, chemical, and biological processes and parameters (see Dehn et al., 2001). Milne (1936) was one of the first scientists, who recognised the catena principle of soil formation in a hilly terrain (Ruhe, 1960).Material And Methods Materials are classified according to their essential properties within a general framework of their mode of formation. Four groups (components) of materials have been recognized to facilitate further characterization of the texture and the surface expression of the materials. They are unconsolidated mineral, organic, consolidated, and ice components. These groups and the classes established within them are presented below. This research is trying to classify landforms on the basis of self-organizing neural network algorithm (SOM) in the watershed Gavkhoni pay to use the SOM algorithm is used to classify landforms of 6 parameters that includes orientation (aspect), height (elevation), tilt (slope), the longitudinal and transverse profiles (plan, profile) and curvature (curvature) is.Generally, The aim ofthisstudyis theclassificationof landformsin thebasinGavkhoni. Classification methodstohelpmajorlandformsvisitthe field, usingTOPOGRAPHIC mapsandaerial photos, which requires experience. Theautomaticmethodbased ondigital elevation model(DEM)can beusedto classifylandformsBasinGavkhoni. Result And discussion The results of the classification of landforms using SOM algorithm showed that 6 cluster (class) in the study area there as clusters 1 and 5 includes landforms that are at high altitudes and cluster 3 includes landforms that are located at the lowest height. The rest of the cluster, including the landforms that the average height of the watershed studied. So the algorithm can be used to predict the landforms of the study area. The results showed that6isthe maximumdatain SOM algorithm. Also, at leastinthishexdatais zero, which indicates thatthere areno numbersinthislocation. The results ofprincipal component analysisshowedhigh densityanddistributiondata. According to theabove resultsshow thatthelandformsinput datain Figure6 classeshave beendistributedin the study area.Conclusion In this research was used SOM (SOM) to classify landforms. In order to use algorithms for classification of landforms of 6 parameters were used in the watershed Gavkhoni, The results of the classification of landforms using SOM algorithm showed that 6 cluster (class) in the study area ther. as clusters 1 and 5 includes landforms that are at high altitudes and cluster 3 includes landforms that are located at the lowest height. While cluster 3 includes landforms that are the lowest height. The rest of the cluster, including the landforms that the average height of the watershed studied. In general, using the SOM algorithm can be 6 classes to classify landforms in the study area predicted. Using the results of the SOM algorithm to manage watershed management approaches should be considered 6.

This research aims to analyze vegetation changes in desert landforms in the northern region of Isfahan province using Landsat images 5 and 8, specifically TM5 and OLI-TIRS sensors, from 1987 to 2020. For this purpose, 200 images were extracted during the 27 warm months, from June 1 to the end of August. After performing the necessary pre-processing on the images that cover the study region, the landforms were classified using the TOPOGRAPHIC POSITION INDEX ((TPI)). Subsequently, the NDVI INDEX was analyzed. The results showed that the pattern of vegetation changes from mountainous areas to lowlands is downward. The analysis of the vegetation INDEX on different landforms, as extracted from the (TPI), revealed that the unit of mountains and heights exhibited the highest values, while flat plains such as playas, mud, and clay pans had the lowest values. In general, NDVI values have a decreasing trend from mountain landforms to playas, mud, and clay pans. A significant decrease in NDVI is evident at an altitude of 1400 m (middle pediments). NDVI values increase in mountainous landforms (elevations above 1400 m). On slopes greater than 65° (high stony and rocky lands), the decreasing trend of NDVI has intensified. The NDVI INDEX illustrates four phases of change. The fourth phase, which spanned from 2014 to 2020, exhibited a decreasing trend. During this period, the area covered by this INDEX decreased from 34,380 km² to 34,200 km². The spatial changes of the NDVI INDEX in 2030 indicate that the elevations from Karkas to Marshenan and the areas between Kashan and Ardestan will experience critical conditions. This issue requires the special attention of relevant officials and executives in this field.

The aim of the study is the classification of landform based on elevation, slope, relief and curvature inputs (old method) and TOPOGRAPHIC POSITION INDEX ((TPI)) (new method) in the south of Bojnoord. The input data for the two methods is a digital elevation model (DEM). The results of TOPOGRAPHIC POSITION INDEX ((TPI)) model showed that most area of landform were covered by class 5 (plains small) and the lowest area of landform was covered with open slope (class 6) (<0.1%). The results of landform classification using elevation, slope, relief and curvature showed that the upper terraces (shoulder) were located in the many parts of the study area (green color). Plateau (back slope) landform was located in center, some parts of the west and south of the study area. In general, with increasing slope and elevation different types of landforms occur. Thus slope, elevation, relief and curvature are effective in preparing the landform classification map. The comparison of the two methods showed that the (TPI) method was more accurate because the method revealed more details.

The most important subject in quantitative geomorphology is increasing of spatial resolution to increase the information in the digital elevation model (DEM). Different models have been used to improve the spatial resolution. Among the different models, sub-pixel/pixel spatial attraction model as the newest model is very high accuracy. In the study area was used the sub-pixel/pixel spatial attraction model for the first time to improve the spatial resolution DEM in the southern city of Darab (Qalatuyeh). The sub- pixel attraction models convert the pixel towards sub- pixels based on the fraction values in neighboring pixels that can be attracted only by central pixel. Based on this approach only a maximum of eight neighboring pixels can be selected for the attraction. In the model other pixels are supposed to be far from the central pixel to have any attraction. In this study by using sub- pixel attraction model the spatial resolution of digital elevation models (DEM) was increased. The design of the algorithm is accomplished by using digital elevation model (DEM) with spatial resolution of 30 m (Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER)) and 90 m (Shuttle Radar Topography Mission (SRTM)) in the north of Darab, Fars province, Iran…

The reason for this study was the lack of a coherent work on the role of quantitative characteristics of hydrology and topography in determining the spatial distribution pattern of nomad camps in Iran. In this investigation, Kermanshah Province, in west of Iran, was studied. Quantitative hydrology and Topography indices of the province including Heterogeneity INDEX (TRI), TOPOGRAPHIC Wetness INDEX (TWI), Altitude, slope, slope direction, stream distance, ridge distance, spring distance, formation type, TOPOGRAPHIC POSITION INDEX ((TPI)), Surface Relief Ratio (SRR), and Compound TOPOGRAPHIC INDEX (CTI) were calculated. To determine the results, Pearson correlation and linear regression (for parametric data) and LOWESS regression (for non-parametric data) were used between hydrology and topography data and the camps’ area. Then, the type of spatial distribution pattern and spatial pattern type radius of the camps were determined for each one of the above-mentioned factors using Moran’ s Autocorrelation INDEX and Ripleys’ K Function, respectively. There was a significant relationship between the (TPI) INDEX (the steep slope landform) and the camps’ area. In sum, the first priority in determining the regular pattern of nomads in the Kermanshah Province considers two heterogeneity and slope indices, and the second priority is among the rest of hydrology and topography indices. The nomads’ almost identical choices in selecting location of their camps are dependent on access to non-jagged lands, flat lands, the places with more than 600 m distance from the ridges and less than 500 m from the streams and 2 km distance from the springs, special ranges of TWI, CTI and SRR indices, the altitude range of 1, 400 to 2, 000 m above sea level, and establishment in the Landform 3 range of the (TPI) INDEX and limestone formation.

Abstract Intuition is one of the characteristics of entrepreneurs and an important factor in the decision-making processes of entrepreneurs. In recent years, at the same time as the development of entrepreneurship as an interdisciplinary science, intuition and providing different definitions of it have not been noticed by researchers and researchers in this field, and many researches have not been written in this field. This is despite the fact that in other fields of humanities, especially management (with which entrepreneurship is closely related), basic sciences and even medical sciences, there is a suitable background and theoretical literature about this. On the other hand, it seems that the theoretical and epistemological foundations of this concept have not been researched as much as necessary in the few researches that have been conducted, and very little research has been done in this field. In this article, firstly, the definitions and researches about intuition are reviewed, and secondly, the little researches that have been done in entrepreneurship and its relationship with other sciences are discussed.

Remote sensing data provide a high ability to represent habitat characteristics and use in species distribution models. The purpose of this study is to determine the most important of remote sensing predictors, including climatic indices (precipitation and temperature), primary and secondary TOPOGRAPHIC indices (elevation, slope, direction, TOPOGRAPHIC POSITION INDEX ((TPI)) and Terrain Roughness INDEX (TRI) and remote sensing indices (Global Environmental Monitoring INDEX (GEMI), Leaf Area INDEX (LAI), Modified Normalized Difference Water INDEX (MNDWI), Modified Simple Ratio INDEX (MSR), Normalized Burn Ratio INDEX (NBR) and Visible Atmospherically Resistant INDEX (VARI)) using two species distribution models (Boosted Regression Tree and Random Forest) to predict the presence of J. excelsa in Khalkhal County of Ardabil province and northern part of Zanjan province using SAHM software. The evaluation of prediction models using AUC chart (Area under curve) showed that it is at an excellent level for both the BRT model (0.991) and the RF model (0.974). The most important affecting habitat desirability based on the BRT method include annual precipitation, slope, digital elevation model, temperature, GEMI INDEX and TRI INDEX variables respectively. The most important variables affecting habitat desirability based on the RF method, respectively, include annual precipitation, digital elevation model, GEMI INDEX, slope, VARI INDEX, temperature, MSR INDEX, TRI INDEX, (TPI) INDEX, NBR INDEX, MNDWI INDEX, LAI INDEX and aspect. The region mapped in the study as suitable habitats’ for the species could be used in the planning strategies with the aim of evaluating the susceptible habitats, the possibility of conservation, reproduction and breeding. Considering that the modeling method choice is the main source of variability in predictions and choosing the best prediction model is not simple, therefore, it is suggested to use a combination of these models instead of relying on the outputs of a single model.