S.B. Hosseini; A. Saremi; M.H. Noury Gheydari; Hossein Sedghi; A.R. FiroozFar
Abstract
Introduction: Land use is an aggressive process applying to human activities and different uses accomplished over land. It can be argued that human actions can lead to significant changes in current state of earth’s surface. Changes in surface cover (land cover change) may in turn lead to alternations ...
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Introduction: Land use is an aggressive process applying to human activities and different uses accomplished over land. It can be argued that human actions can lead to significant changes in current state of earth’s surface. Changes in surface cover (land cover change) may in turn lead to alternations in balance of energy, water, and geochemical fluctuations at local, regional or global levels. Thus, studies on different land uses changes seem necessary in general environmental evaluation. LULC change detection include implementing multi-temporal Remote Sensing (RS) knowledge to analyze the historical LULC data (maps) and therefore helps in determining the trend of changes associated with LULC properties.
Materials and Methods: Image processing and performing supervised image classification helps to extract information from imageries. In this study, ENVI 5.3 software was used for processing two selected imageries in this project (2014 and 2017). Five LULC classes were established as forest, bare land, vegetation, mountain and water body. For each LULC class, 500 samples were collected at least and used for the supervised classification of images in ENVI. About half of these samples, which were used as “training samples” were collected from the study area through Land Surveying Geographical Positioning System GPS (ground truth data) and Google Earth images. The first step in pre-processing of LANDSAT 8 OLI data in this study referred to the collection of training samples for each class and validating the geometric accuracy of Landsat images, while the next step belonged to the conversion of DNs into At-Satellite radiance using algorithms such as FLAASH. Two dated Landsat images were compared via the supervised classification technique. In this classification technique, two or more images with different dates are independently classified. Maximum Likelihood Classification (MLC) algorithm as a supervised classification method was carried out using training areas and test data for accuracy assessment in ENVI 5.3 and accuracy assessment was done for both images using ENVI v5.3.
Results and Discussion: In order to recognize the past land use pattern of Tarom, researchers first focused on imagery of Landsat 8 ETM+ for the year 2014. Summary of supervised classification accuracy for the 2 different time frames (2014 and 2017) found from accuracy assessment showed that the highest accuracy was found for 2014 supervised classification (92.16% accuracy). Kappa value is also used to check accuracy in classification and having a Kappa value (0.81–1.00) denotes almost perfect match between the classified and referenced data. Different LULC classes had been recognized and used as the base map. From the identified LULC classes, Mountain area by 3524 km2 (62.75% of total land area) was the highest category, after which, came bare land areas with 1295 km2 (24.0%) coverage and vegetation area with 194.6 Km2 (3.7%). Forest was the next class with (2.7%) coverage whereas, water body (1.4%) and unknown pixels 8 km2 (0.15%) specified the least amount of coverage, respectively. Based on the 2017 image classification results, the highest category belonged to mountain area (3532 Km2, sharing 67.7% of total area). The remaining land uses were bare land (23.21%), forest (2.73%), vegetation (4.3%), and water body (1.75%). The unknown and uncategorized pixels were identifiable in this stage that shared 0.31% of the total area. The relative changes in land use and land cover from 2014 and 2017 images showed some irregular patterns in the study area. Land-use change from this period showed positive changes in most of the categories. About 31.4 Km2 of vegetation area had increased in 2014–2017 period which showed a positive change of (+16.14%). While a negative decrease (83 Km2, -6.4%) in bare land category. The results showed that the extraction of adequate samples from different classes of land cover/land use would increase the possibility of correct distinction of image pixels received from the satellite and accurate extraction of LULC classes. Thus, obtaining accurate results from the classification of images via the maximum likelihood method is depending on adequate and appropriate training samples. The trend of land-use changes found in this study, especially percentage increase in forest land and a decrease in bare lands will be helpful for policymakers to make appropriate decisions.
Conclusion: Land cover is the physical material at the earth’s surface and an essential variable which links the physical environment by human activities, and land use is the description of how the land has been utilized for the socio-economic activities purposes. Population growth increases the demand for food, water, and energy, which causes a prompt change in land cover and pattern of land-use. The mentioned process depends on the social and economic development of the nation. In order to have appropriate and unrestrictive management of natural resources (water and soil), it is necessary to have complete information about the pattern of land use and its alteration pattern over time. Thus, it can be concluded that remote sensing is a proper technique to investigate the land-use changes using satellite imagery. Spatiotemporal analyses of LULC help us to manage the environmental changes, which are an appropriate tool for decision-makers on water resources’ to enhance their decisions. In the presented study, LULC map for Tarom basin, Iran, acquired from OLI sensor data sets (Landsat-8) by applying a pixel-based classification method (MLC) with the aid of remote sensing technology. The results that are presented in this study proved the usefulness, effectiveness and also convenience of the MLC technique for generating land-use maps by using a free archive of Landsat data and processing the digital images through the ENVI software. Accuracy assessment using overall accuracy and kappa coefficient for 2014 and 2017, shows the performance of the used algorithm. What matters most in this regard is the accuracy, speed, and quality of land-use maps. In the present study, it was shown that due to high speed and accuracy in generating land-use maps of Tarom, MLC method, would act as the best classification method in this area. However, it is suggested to classify the data by using other methods and compare the results with image outputs provided by Landsat 8 satellite.
Farhang Azarang; Abdolrasoul Telvari; Hossein Sedghi; Mahmoud Shafai Bajestan
Abstract
Introduction: The critical role of the rivers in supplying water for various needs of life has led to engineering identification of the hydraulic regime and flow condition of the rivers. Hydraulic structures such dams have inevitable effects on their downstream that should be well investigated. The ...
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Introduction: The critical role of the rivers in supplying water for various needs of life has led to engineering identification of the hydraulic regime and flow condition of the rivers. Hydraulic structures such dams have inevitable effects on their downstream that should be well investigated. The reservoir dams are the most important hydraulic structures which are the cause of great changes in river flow conditions.
Materials and Methods: In this research, an accurate assessment was performed to study the flow regime of Karkheh river at downstream of Karkheh Reservoir Dam as the largest dam in Middle East. Karkheh River is the third waterful river of Iran after Karun and Dez and the third longest river after the Karun and Sefidrud. The Karkheh Dam is a large reservoir dam built in Iran on the Karkheh River in 2000. The Karkheh Reservoir Dam is on the Karkheh River in the Northwestern Khouzestan Province, the closest city being Andimeshk to the east. The part of Karkheh River, which was studied in this research is located at downstream of Karkheh Reservoir Dam. This interval is approximately 94 km, which is located between PayePol and Abdolkhan hydrometric stations. In this research, 138 cross sections were used along Karkheh River. Distance of cross sections from each other was 680m in average. The efficient model of HEC-RAS has been utilized to simulate the Karkheh flow conditions before and after the reservoir dam construction using of hydrometric stations data included annually and monthly mean discharges, instantaneous maximum discharges, water surface profiles and etc. Three defined discharges had been chosen to simulate the Karkheh River flow; maximum defined discharge, mean defined discharge and minimum defined discharge. For each of these discharges values, HEC-RAS model was implemented as a steady flow of the Karkheh River at river reach of study. Water surface profiles of flow, hydraulic parameters and other results of flow regime in HEC-RAS model were obtained for the conditions before and after the construction of the Karkheh Reservoir Dam and then it was reviewed and analyzed.
Results and Discussion: By exploiting the Karkheh Reservoir Dam, the river flow was changed from the natural condition to the regulatory situation. The results indicate that the river flow was considerably declined because the regulatory effect of the reservoir dam which has contributed to the great alternations at hydraulic parameters of the river. For example, the mean annual discharge of the Karkheh River shows 44pecent reduction during the time period of simulating (after the dam construction in comparison with the natural river flow before construction of reservoir dam) in PayePol hydrometric station. Flow velocity of Karkheh River is influenced by discharge, slope of the river channel and geometry of cross section. By increasing the river flow, the flow velocity has increased and there is a significant difference between pre and post-dam condition at the mean velocity of river flow in different sections. The flow area is directly influenced by river discharge and there is a significant difference in the maximum defined discharge before and after dam construction. The width of water surface is a parameter of the geometric situation of the river cross section that also shows the maximum width of the cross sections, passing discharge through the desired cross section. Since Karkheh River has a relatively large water surface width, it has a high wetted perimeter. For this reason, the Karkheh river hydraulic radius is usually low. The significant reduction of all these quantities is for reduction of flow rate by construction of Karkheh Reservoir Dam. Studying the water surface profiles represents reduction of water level in the longitudinal profile of Karkheh River and water level of hydrometric stations by construction of the Karkheh Reservoir Dam. Also, due to the reduction of the discharge in the downstream of Karkheh Dam, all hydraulic parameters of the river such as flow velocity, flow area, width of surface water, hydraulic depth, shear stress and the hydraulic radius have been changed. In general, it can be concluded that the construction of a large dam such as Karkheh Reservoir Dam has a significant effects on the flow regime conditions at river downstream. Our survey would be helpful for environmental, geological and ecological experiments on effects of dam construction and for engineering next hydraulic structures on such rivers.
H. Khedmati; M. Manshouri; M. Heydarizadeh; H. Sedghi
Abstract
Abstract
South-east basins of Iran which is including Sistan and Baluchistan, Kerman, Yazd and Hormozgan provinces has an extensive desert called " Loot " and this region is one of the hottest and driest parts of Iran. Few numbers and the lack of uniformity in scattering of hydrometric stations are ...
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Abstract
South-east basins of Iran which is including Sistan and Baluchistan, Kerman, Yazd and Hormozgan provinces has an extensive desert called " Loot " and this region is one of the hottest and driest parts of Iran. Few numbers and the lack of uniformity in scattering of hydrometric stations are main characteristics of this region.
Another problem in this region is that data are short-term, so it leads to have unguaged sites.Data generation helped us to have at most 43 hydrometric stations with 20 years data plus 10 stations with 30 years data.On the other hand, in hydrology for fitting statistical distribution on rainfall and runoff, at least we need to have 30 years data and even more. Thus for analyzing different methods of flood estimation and presenting logical relationships in sub-basins, first of all we gathered meteorology, hydrology and ecological features and also morphometric characteristics were measured.Homogeneity test was done for data and they have been completed in a 20 years data.The group of homogeneous sub-basins has been determined using some methods like: Index Flood, cluster analysis and, multi-variable regression while some physiographic properties and ordinary and linear moments were used.Common statistical distributions have been tested and dominant statistical distribution of region was finally determined Log Pearson type III and based on that peak discharge with different return periods have been estimated to present mathematical models.Then, generated models have been tested using three other sub-basins which hadn't participated in presenting mathematical models.At last, the most appropriate mathematical relationships for flood discharge estimation in different return periods have been achieved in unguaged sites of south-east basins of Iran.
Keywords: Regional flood analysis, Unguaged sites, Flood index, Mathematical model, Cluster analysis, Multi-variable regression
F. Khamchin Moghadam; H. Sedghi; F. Kaveh; M. Manshouri
Abstract
Abstract
Most heavy storms result in destructive floods. One of the basic elements in analyzing floods in watersheds without data is hourly storms. The Determination of the storm of the watershed needs regional analysis of storms and transferring them to the gravity center of the watershed. Maximum ...
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Abstract
Most heavy storms result in destructive floods. One of the basic elements in analyzing floods in watersheds without data is hourly storms. The Determination of the storm of the watershed needs regional analysis of storms and transferring them to the gravity center of the watershed. Maximum daily precipitation ( ), is the most accessible storm in any region, which can be converted to hourly precipitation. The analysis of the point and regional is one of climate studies requirement. Regionalization of , can be an influential step toward analyzing storms and floods. In order to accomplish such a task, two approaches are possible, one is using the old methods of geographical regionalization and the other one is using the new methods like "Cluster Analysis" and "L-Moments Homogenous Tests". In this paper second approach was employed. All existing rain-gauge stations (N=396) were considered and their available data were collected in this study. Basic tests were applied and 266 stations were removed due to the lack of the required conditions and only 130 stations were used in analysis. "Principal Components" method was used to omit the uninfluential variables (only 6 variables out of 21 were proved as basic and important). "Hierarchical Clustering" was used in the process of regionalization of the stations indicated of seven different regions. These regions were distributed in different locations throughout the country and the regionalization map is presented. The "L-Moments Homogenous Tests" were also employed for further indication. According to the final results, the regionalization of of Iran's rain-gauge stations can be defined as 7 homogenous regions.
Keywords: Regionalization, Maximum daily precipitation, Principal Components, Cluster Analysis, L-Moment