جستجوی مقالات مرتبط با کلیدواژه "مدل رقومی ارتفاع" در نشریات گروه "آبخیزداری، بیابان، محیط زیست، مرتع"

Soil resource management is essential to maintain community production and the environment. Soil is usually used to produce agricultural products and livestock fodder. As a result, the mapping of high-resolution digital maps is crucial for the distribution of soil and soil properties and land management. The decision tree model is a widely used method for predicting soil class in digital soil mapping studies. This study aimed to provide a digital soil mapping in four levels of taxonomy using a decision tree with Boost-reinforced C5.0 algorithm using satellite data and digital Elevation Model and geological maps as environmental variables in 41,000 hectares of Abyek Area. This area was identified using randomized gridding of the geographic location of 128 soil profiles and then described, sampled, and classified. In this research, using the principal component analysis method on environmental variables, 20 environmental variables were selected as the representative of stacking factors for modeling. Multiresolution Valley Flatness Index is the most important environmental variable that was selected as input for the model. The results of the overall accuracy of the integrated model for predicting taxonomic levels of the Order, Suborder, great group, and subgroup were shown to be 89%, 85%, 58%, and 58%, respectively. The study also examined the effect of the boosting technique on the tree model, which showed that all taxonomic levels were better predicted by using the boost model than when no boosting was used and boosting resulted in an increase in overall accuracy and kappa coefficient It turned out.

Landslide is one of the natural hazards in watersheds that cause a lot of damage every year. Neotectonic activity plays an important role in increasing the occurrence of landslides. The purpose of this study is to investigate the relationship between active tectonics and landslide inventory using morphotectonic indices in the ​​Fahlian River Basin. For this purpose, landslides distribution in the region were first mapped out and stored with the help of Google Earth images, LANDSAT-8 images, aerial photographs and field visits. Next to the extraction and calculation of four morphotectonic indices; volume to basin surface index (V/A), bifurcation index (BR), surface canal density index (P) and relative relief index (Bh). Morphotectonic indices were measured in each sub-basin. Preparation of these indicators has been done using topographic and geological maps, satellite images, field visits and using geometric and mathematical principles with the help of the features available in ArcGIS10/4. Each index was divided into three categories in terms of tectonic activity, and finally, based on the segmentation of each index, the level of tectonic activity of the region was determined on the basis of the index of relative tectonic activity index (Iat). Finally, by cross ovrlaying of the landslide inventory map to the relatively active tectonic zonation map, the percentage of landslides occurred in each tectonic zone was determined. The results showed that 67.34% (428.34 ha) of landslides occurred in the area with high tectonic activity, which covers only 45.73% of the total area of ​​the study basin. This result indicates the role of active tectonics in increasing landslide occurrence.

Determining the sediment delivery rate and watershed delineation is among the first stage in environmental research, especially in water erosion estimation. The determining the accurate boundary of watershed is important for the hydrological and morphologic characteristics of watersheds. The study aims to present an automatic extraction model of watershed delineation and calculate sediment delivery rate. The traditional method of determining the watershed delineation, and subsequently, calculating sediment delivery rate is performed manually that using a topographic map, the boundary of the watershed is determined and main flow length is calculated. However, nowadays due to the advancement of digital analytical spatial-based methods in GIS software, automatic delineation of watershed is feasible. For this purpose, the errors of digital elevation model are removed. After calculating the flow direction, and flow accumulation, watershed boundaries can be determined having pourpoints. Then, sediment delivery rate is calculated by local height of watershed, its perimeter, and flow length. In this research, the digital elevation models of ASTER-DEM and SRTM-DEM are utilized to design watershed delineation model and to evaluate the overall accuracy and the correspondence of them, so the border of watershed is delineated using Google map, which is used as a ground truth. The findings of this study show the automatic extraction of watershed boundary and calculation of sediment delivery rate do not have significant differences with traditional mothed. So that, the overall accuracy and Kappa index of the watershed boundary based on ASTER-DEM are 93 percent and 0.92, respectively, and their values for the watershed boundary based on SRTM-DEM are 94.3 percent and 0.94, respectively. The correlation coefficient (r2) of calculated sediment delivery rate based on SRTM-DEM is 0.98 and its value based on ASTER-DEM is 0.95.

Natural phenomena could be recognize through the selection and then produce the appropriate scale. The critical subject in this way that have been less studied in Iran is identifying the multi-scale algorithms and distinguish them from simplification algorithms. In this research, operation of each multi-scale algorithms at certain scales have supposed, then implementation of structural simplifying algorithms- low pass filter and simple average- in order to identify the flood process using three Digital Elevation Models at 1:25000, 1:50000, and 1:1000000 scales in ArcMap software and HEC-HMS model have evaluated. Results demonstrated that different approaches of map production make different results as the generalization and the best approximation of spatial data. Comparing the result of flood simulation proved sensitivity of such algorithms with respect to scale of base map. It has been specified that the effect of generalization algorithms do not necessarily conform the hierarchical structure of scale of input data. Finally, we suggested to investigate the flood phenomena of Jamash watershed, we should use low pass filter and simple average algorithms to simplify DEMs at 1:25000 and 1:1000000 scales but using these algorithms at 1:50000 scale have not suggested.

GIS users usually use the contour line maps for construction of Digital Elevation Models (DEMs) but the accuracy of these data is not completely obvious in terrain and hydrologic analysis. The aim of this paper is determination of quality and accuracy of drainage network analysis resulted from a DEM which is derived from contour lines map with 1:50000 scale. Study area with 443 squares Km is located in the northeastern of Iran and combined of mountain، hills، pediment، alluvial fans and floodplain. ArcGIS package have been used for manual digitizing of contours and extraction of stream network respectively. We have compared the Extracted networks from TOPODEM (DEM layer) with the one derived from high resolution satellite Images (IRSpan) for assessment the DEM accuracy. For determination of TOPODEM accuracy we took the extracted networks from satellite images as a real stream channels. This is partly because more detailed scale of the satellite images guarantees a good reference map with which to compare the network obtained from the DEM. The comparison process has been done in both raster and vector formats. These comparisons are included morph metric characteristics as river frequency، stream length، stream density and drainage ratio as well as the spatial pattern of the drainage lines، which was evaluated by visual analysis and calculating the differences rate between two networks. Visual study and numerical results showed only a network map which is derived in 50 threshold value is near to the network extracted from satellite images. River order and cell frequencies is been decreasing in higher threshold values. The comparison of the cell and line frequencies presented good agreement for all stream orders but there are large differences for 4، 5 and sixth- order streams. The differences were measured more than 80 percents for 4، 5 and sixth-order Rivers in the study area. it means the distribution of first- order rivers have a big difference with natural drainage patterns caused we were done the extra analyzing in the separate geomorphologic units. the results showed better agreement in the mountainous and hilly parts only for first and second-order streams. The differences for above areas are ranged between 20 to 40 percentages at the first and two river orders. The highest differences were found in the alluvial fan and plain area which is ranged between 60 to more than 80 percents in different orders. The results for river lengths shows satisfied agreement degree for pediment and mountain units especially for second-order Rivers. We found the lower agreement degrees for alluvial fans and plain sectors. The Drainage density calculated for whole basin area shows good agreement between rivers، which extracted by different methods. Obviously، differences appear in separate Geomorphologic units as we can see completely incorrect results for alluvial fan and plain sectors for rivers delineated from DEM. The best agreement of drainage density is been recognized for the pediment where the surface slop is moderate. In the mountain area drainage density for the layer extracted from satellite image is higher than the layer extracted from TOPODEM while the results are inversely for alluvial fans and plain areas. Then it should be better to use the results only for mountain and pediment units. results for alluvial fan and plain sectors for rivers delineated from The best agreement of drainage density is been recognized for pediment where the surface slop is moderate

Snow depth is the most common parameter used for the assessment of water resources in the mountainous areas. Therefore، knowledge about spatial distribution of snow depth is the substantial knowledge of watershed characteristics. At present research، it was tried to estimate the spatial distribution of snow depth using regression kriging based on M5 model tree. Therefore، location of 216 points was selected systematically، and then snow depth was measured with a Monte - Rose sampler in Yazd-Sakhvid region. Then، 30 terrain parameters were derived from a digital elevation model using SAGA software. Our results indicated that channel network base level، stream power and wetness index were the most important parameters in decision-tree model. The correlation coefficient of 90% confirmed the strong performance of regression kriging model. Moreover، this method is very simple، so it is recommended the regression kriging model is being used to estimate spatial distribution of snow depth in other regions.

In this study the land surface in western half of hyper-arid Lut desert، in south east of Iran، which is covered by Yardangs، a worldwide typical landform for Aeolian erosion، were classified by Self Organizing Maps (SOM) method. In the first step by using Digital Elevation Model with 10 m resolution and Matlab software، 22 morphometric parameters were calculated based on derivative of the surface elevation with first، second and third orders. In the second step most affective parameters for classification and the optimum number of classes were found through utilizing Optimum Index Factor and Davies Bouldin Index. Finally SOM classification was performed on seven morphometric parameters to result in seven classes. The results showed that most appropriate parameters in classification of area are plan curvature، rotor، hypsometric Integral، total accumulation curvature، slope steepness، extreme curvature and mean curvature. The study area were divided to seven classes including saddle valley، Concave ellipsoid، Gentle slope corridor، shoulder with concave slope، shoulder with convex slope، ridge، corridor channels. Sensitivity analysis results revealed that the most sensitive parameters are rotor، mean curvature and hypsometric Integral. Also the results of Jeffreys-Matusita Distance illustrated that parameter pair hypsometric integral / extreme curvature has the most ability in separation of classes in this area. Comparison of the separated classes with the landforms on aerial photographs confirms our classification results.

In this study the performance of the Leakiness Index (LI) for assessing rangeland landscape function has been analyzed using TM 2009 satellite image in semi-steppe rangelands of Boin- Miandasht town, Isfahan province. LI values range from 0 to 1 which indicate non-leaky and leaky landscapes, respectively. In this research, the study area was divided into 16 sub-catchments using DEM map extracted from a 1:25000 topographic map and LI index was then calculated based on the PD54 vegetation index in LI V.4 software environment for each sub-catchment. Results showed that LI value for the whole area was 0.08 which indicates that rangeland functionality in this area is high. LI index varied across the sub-catchments from about 0.04 to more than 0.2 in the region. According to the regression analysis results, the relationships between LI and PD54 index were significant at 99% confidence level. As a result, high performance of LI index in mountainous rangelands of the study area would mean that this index has capability for rangeland function analysis and it can provide useful information for land managers in rehabilitation practices in broad-scale rangeland landscapes.

Soil salinity is one of the main increasing problems of the world. In the recent years application of remote sensing and GIS techniques in order to assess saline soils is used because they bring about vast uniform coverage of ground phenomena in a short time. The images of LISS III sensor of the Indian satellite (IRS) were used in this research. The Brightness index (BI) could discriminate highly saline soils from non –saline and the salinity index (SI) show high potential to separate very high, high and non- saline soils. Results of supervised classification without combination DEM and remote sensing data have overall accuracy of 76%, producer accuracy of 78% and user's accuracy of 82%. While in supervised classification combination of remotely sensed data and DEM have overall accuracy of 98.1%, producer accuracy of 98.28% and user's accuracy of 98.4%. The reason for low accuracy of the classification, before combination of remote sensing and topographic data, can be explained by highly moist saline soils spectral interference with non-saline soils (soil with 25 to 65% gravels), but these two soils had different topographic condition with 200 meters elevation difference, thus, with combination of the DEM, this kind of area and other areas with similar conditions have been separated from each other.

Salinity is the major factors of soil degradation in semi arid and arid regions. The mainaim of this study was to evaluate the capability of Landsat ETM+ data for soil Salinitymapping in the selected part of the Qazvin plain, an area of arid environment. In thisstudy spectral classes carried out on remotely sensed data and with the help of fieldobservation and soil analysis were regrouped to soil salinity classes to prepare soilsalinity map.. Soil sampling was implemented using stratified random samplingmethod, depending on landscape complexity and homogeneity as well as on therepresentativeness of Landsat ETM+ data. Also in each soil map unit at least one profilewas studied for subsoil salinity variations. Field samples taken by using augur andprofiles were analyzed in laboratory for Na+, Ca2+, Mg2+ cations, as well as soil texture,ECe and pH. We have analyzed the effectiveness of additional data such as digitalelevation model to improve the accuracy of classification. Also NDVI, SRVI, PVI,SAVI, SI, BI and NDSI indices, PCA and Tasseled cap were analyzed. Soil salinity mapof each selected bands produced and with ground truth map crossed. The resultsindicated that combination of DEM with ETM+ bands has highest accuracy. This studyaddressed that thermal band of ETM+ can increase the classification accuracy whichillustrated its effective role to classify the soil salinity. Tasseled cap and other indiceshad almost high accuracy among studied image processing techniques. The SI and BIindices had the highest correlation with EC and could distinguish the saline and nonsaline soils while the optimum index factor had overall low accuracy.