Reza Parhizcar isalu; Khalil Valizadeh Kamran; Bakhtiar Faizizadeh
Abstract
Extended Abstract
Introduction
Geothermal energy is one of the major sources of new and environmentally friendly energieswhich, if used correctly and based on environmental parameters, plays an important role in the energy balance of the country and the goals of sustainable development.However, detecting ...
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Extended Abstract
Introduction
Geothermal energy is one of the major sources of new and environmentally friendly energieswhich, if used correctly and based on environmental parameters, plays an important role in the energy balance of the country and the goals of sustainable development.However, detecting and exploring sources of this energy using modern and low cost methods –as a replacement for land surveying methods-can help planners and authorities working in the field of energy. In this regard, thermal remote sensing with a vast coverage of the earth’s surface, and the possibilityof calculating land surface temperature using satellite imagery plays an important role as a new economic tool.Mapping land surface temperature is a key point in achieving geothermal anomalies and different algorithms play an important role in land surface temperature estimation. Therefore, identifying potential sources of geothermal energyusingremotely sensed thermal data is a challenging and yet interesting subject.
Materials and Methods
The present study takes advantage of images received from OLI and TIRS sensors (Landsat 8) to estimate land surface temperature, analyze thermal anomalies, and identify areas with potential geothermal resources in Meshkinshahr.The images were retrieved fromUSGSin Geo TIFF format.Envi 5.3, eCognition 9.1, MATLAB and ArcMap 10.4.1 were used to prepare, process and analyze the images.Moreover, meteorological data received fromMeshkinshahr station was collected from the General Department and Meteorological Center of Ardabil Provincewith the aim of identifying the optimal algorithm for calculation ofland surface temperature. Data wascollected for a one-day period (31/08/2017), i.e. the same day Landsat 8 passed over the areaunder study.
Results and Discussion
The present study sought to identify areas with potential geothermal resources using thermal remote sensing and a combination of surface temperature and thermal anomaly models. In order to calculate thermal anomaly, an observational thermal image is required, which is in fact the same land surface temperature calculated using Split Window and Mono Window algorithmsfor the image received from the satellite thermal band at the moment of collecting images. It should be noted that the land surface temperature calculated with these algorithms was evaluated using statistical data recorded in the temperature monitoring station. Results indicated higher accuracy of Split Window algorithm (3 ° C difference). Since, temperature obtained from this algorithm was more consistent with the actual temperature, its results were used as the observational thermal image.A thermal model was also defined to model factors responsible for heat variation from one pixel to another one. These two images were calculated and subtracted to reach the thermal anomaly image.In order to identify thermal anomalies caused by undergroundfactors heating the earthsurface, other factors responsible for increasing/decreasinglandsurfacetemperature should be normalized in the image. Thus, the effect of parameters such as solar energy, environmental degradation and evaporation on land surface temperature obtained from split window algorithm was investigated and finally, areas with heat anomalies and evidences indicating the presence of geothermal resources around themwere selected as areas with potential geothermal resources.Results indicate that inthe area surroundingSabalanmountains,two regions with 5.5 and 10.05 hectares in the northern and northeastern parts of Moyelvillage, a1.4 hectares area in the southwestern part of Qutursouli Spa, and the southern part of the Qinrjah Spa with an area of 1.1 hectare had potentialgeothermal resources and a high potential for exploration of geothermal resources.
Conclusion
The presence of hot springs, a geothermal power plant and other evidences shows that Ardabil Province and especially Meshkinshahr city has the potential for geothermal energy production as one of the major sources of new and environmentally friendly energies.However, no effective studies have been performed to identify these resources using modern and low-cost methods including thermal remote sensing.Therefore, the present study for the first time took advantage ofGIS and remote sensingto identify areas appropriate for geothermal energy extraction inMeshkinshahr city and concluded that remote sensing studies on Landsat 8 satellite images have a high efficiency for identifying areas with potential geothermal resources. Thus, areas identified in the present study have a strong spatial correlation with the geothermal evidences founded in the region.
Bakhtiar Faizizafeh
Abstract
Introduction
Nowadays, detailed land cover and land use information is considered to be an important research topic in geosciences, environmental changes and natural resources. In this regard, monitoring agricultural land-use provides essential information for land use planners and decision makers. ...
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Introduction
Nowadays, detailed land cover and land use information is considered to be an important research topic in geosciences, environmental changes and natural resources. In this regard, monitoring agricultural land-use provides essential information for land use planners and decision makers. Multiple methods are used for monitoring agricultural land use from remotely sensed images. Object-oriented techniques used for processing satellite images makes high accuracy recognition of various land use patterns possible. Compared to traditional pixel-based approaches, these techniques reach a higher accuracy in extracting land-use information from satellite imageries using geometric information and features of different phenomena. In fact, object oriented methods relay on processing unit or image objects made by the integration of homogenized pixels in the segmentation process. Once segments are formed, various indices and spatial information like texture, pattern, form, content, and etc. are applied on processing units. In this way, identifying appropriate land use classes based on geometric features becomes possible. As compared to traditional pixel-based methods, these methods are more flexible and thus can apply a combination of spectral and spatial information. The main goal of the present article is to develop land-use maps and to evaluate agricultural activities in eastern basin of Urmia Lake using object-oriented processing techniques.
Study area and martial
The study area was chosen based on a mixture of agricultural land use, human settlements, and salt marshes in Urmia lake eastern margins. The main goal of the present study was to produce accurate maps of agricultural systems in Urmia lake eastern margins. Thus, various agricultural land uses were extracted from satellite imageries with an emphasis on orchard land use classes. Training data were collected through field operation using GPS. Moreover, 1:25000 scale topographic maps were used for geometric correction and rectifying of the satellite images.
Methods and techniques
Various agricultural and orchard land use were extracted from Aster satellite imageries received in 2016. In pre-processing stage, geometric correction including geo-referencing, orthorectification and atmospheric corrections were performed on imageries. In processing stage, detection functions were applied and images were then classified according to the research goals based on pixel-based and object-oriented algorithms. In this regard, maximum likelihood, parallelepiped, and minimum distance algorithms were used to classify images. Segmentation process was performed based on homogeneity, shape and compactness parameters. Accordingly, the geometric and spectral algorithms were used for modeling each class in object-oriented environment and object-oriented classification was applied based on nearest neighbor algorithm.
Results
Using object oriented and pixel based processing techniques, four land use maps were extracted. In order to evaluate and compare final results, overall accuracy and Kappa coefficients were extracted for each algorithm. Results indicate that among pixel-based classification algorithms, maximum likelihood algorithm with overall accuracy of 87.67 percent and kappa coefficient of 0.86 is more accurate than other methods. However, with a Kappa coefficient of about 0.93 and overall accuracy of 94.20 percent, this algorithm has a lower accuracy level as compared to object-oriented methods.
Discussion and conclusion
Results indicate that compared to pixel based techniques, object oriented processing techniques possesses a higher potentiality for extracting agricultural land use. The main advantage of object oriented methods is that they employ a combination of spatial information, spectral information and integrate them with GIS and remote sensing datasets. Moreover, using texture and shape algorithms in object based classification leads to improved accuracy of land use maps. Besides, it is possible to improve the accuracy of results using effective techniques in object oriented classification. According to research findings, object oriented techniques provide an effective method for classification of satellite imageries and extraction of land use maps. It is possible to use these techniques in landscape planning, natural resources, regional land-use and land-cover changes, sustainability of land cover, and etc.
Bakhtiar Feizizadeh; Khalil Didehban; Khalil Gholamnia
Abstract
Abstract
Land Surface Temperature (LST) is one of important criteria in regional planning and management. LST can be used in many practical programs of environment, agriculture, meteorology and relevant surveys. Due to the limitations of meteorological stations, remote sensing can be used as the basis ...
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Abstract
Land Surface Temperature (LST) is one of important criteria in regional planning and management. LST can be used in many practical programs of environment, agriculture, meteorology and relevant surveys. Due to the limitations of meteorological stations, remote sensing can be used as the basis of many meteorological data. One of the most important practical aspects of remote sensing in climate studies is the estimation of surface temperature. In this regard, the split window algorithm is considered as an effective method for extracting surface temperature, which provides the highest accuracy based on scientific resources. In this research, Landsat 8 satellite’s multi-spectral and thermal images have been used to estimate the land temperature in Mahabad catchment. To accomplish the goal, modeling and analyzing of the images were performed after radiometric corrections. The vegetation index, the vegetation shortage, the temperature of the satellite illumination, the emissivity of the land surface, the column water vapor (CWV) are of effective criteria for estimating the land surface temperature by the method of split window algorithm. The values necessary to calculate the land surface temperature were obtained by performing mathematical relation computation. Eventually, the land surface temperature was accurately estimated with an error of 1.4 degrees Centigrade. Areas with high vegetation cover and covered with water show low temperatures and, areas with low vegetation cover and bare soil show a high temperature, all of which are effective in temperature variations in the studied area. The results of the research indicate that the method of split window algorithm provides exact and reliable results in the estimation of land surface temperature, which can be used in environmental studies and geosciences.
Bakhtiar Feizizadeh; Mojtaba Pirnazar; Arash Zand karimi; Hassan Abedi Gheshlaghi
Abstract
In line with the goal of rapid extraction of land use maps, remote sensing technology has been recognized as an efficient technology which provides the possibility for extraction of land use maps by presenting satellite imagery.By providing different satellite images with various temporal power, remote ...
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In line with the goal of rapid extraction of land use maps, remote sensing technology has been recognized as an efficient technology which provides the possibility for extraction of land use maps by presenting satellite imagery.By providing different satellite images with various temporal power, remote sensing has made the modeling and monitoring of the environmental changes possible, which is an important step in the management of natural resources.The object-oriented classification method based on knowledge-based algorithms is one of the effective methods for classification of satellite imagery which, in addition to the use of satellite imageryspectral information, provides the necessary facilities for using environmental information and physical and geometric properties of the land surface phenomena.The present researchwas conducted with the aim of evaluating the increase rate in the accuracy resulted from the application of knowledge-basedfuzzyalgorithms in the classification of land use / land cover maps.In this research, the AVNIR2 sensor images of the ALOS satellite have been used to compare the object-oriented methods of satellite imagery classification without using fuzzy algorithms and object-oriented methods based on fuzzy algorithms and the land use map for the city of Maragheh has been extracted by both of the aforementioned methods. The results of the accuracy assessment show that the land use map produced by knowledge-based fuzzy methods with a general accuracy of 93.38 is more reliable compared with the land use map produced by the object-oriented method without using fuzzy algorithms with an accuracy of 88.66%. Given the comparative nature of this research, its results have been of great importance in identifying the optimal methods for production and preparation of land use maps, and the produced maps have also a high applied value for the executive organizations (such as agricultural Jihad, natural resources, etc.).
