جستجوی مقالات مرتبط با کلیدواژه « نقشه کاربری اراضی » در نشریات گروه « جغرافیا »

       Satellite data is one of the fastest and the least expensive methods available to researchers to prepare land use maps (Pal and Mather, 2005). Analysis of this data can provide accurate insights into human interaction with the natural environment. In particular, the use of multispectral image analysis can help humans identify land cover (Brian and Michael, 2005). The use of different parts of the magnetic energy spectrum to record the properties of phenomena and the possibility of using hardware and software have made the use of satellite images particularly popular (Richard and Jia, 2006). In general, classification methods can be divided into supervised and unsupervised methods (Ommen, 2008). In the monitored method, we can refer to the maximum probability methods, the minimum distance from the mean, the Mahalanui distance, the parallelogram, the neural network and the support vector machine. In fact, the classification process is the conversion of data into comprehensible information (Rakis, 2011). The maximum probability method is one of the most efficient methods of classifying images (Jensen, 2005). In most research studies, this method has been introduced as the most accurate classification method (Riahi Bakhtiari, 2000; Hovang and Tonshend, 2002). In this method, the user must be careful that the classification follows the normal Gaussian distribution, and this method is more suitable for multispectral classes.

Landsat 8 was launched on an Atlas-V rocket from Vandenberg Air Force Base, California on February 11, 2013. The satellite carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments. The capabilities and advantages of the OLI sensor compared to the ETM + Landsat sensor are as follows: better spectral resolution with narrower bandwidth ranges and 2 more spectral bands (obtaining information in 9 spectral bands), quadrupling the geodetic recording accuracy absolute images, changing the geometry of the image from Whisk broom to Push Broom and thus obtaining150 more information scenes per day (400 images per day), improving radiometric resolution from 8 bits to 12 bits and the possibility of better description of the ground cover and increasing the Signal Ratio to Noise (SNR) (Biranvand and hashim, 2015). In this research, satellite images of Landsat 8 sensor on 13/03/2020 and Envy 4.5 software for processing satellite images and classification of images, and from GIS 10.3 software for creating educational map, ground reality map and layer format conversion were used. Google Earth Pro 4.2 software was used to collect land points and Excel and Office 2018 software were used to generate data tables. According to the purpose of the study, only from visible bands, near infrared, infrared, short wavelength (with cell size of 30 m) and panchromatic band (with cell size of 15 m), to extract spectral values ​​corresponding to the plot of ground samples and statistical analysis have been used. In this study, in order to control the quality of data and increase awareness of atmospheric, geometric and radiometric errors, the data were first studied (Kiani et al, 2014). In the images prepared for this study, due to the newness Landsat 8 satellite and also the non-mountainous area, no device error was observed. The results of the evaluation of the classification accuracy with four algorithms of maximum likelihood classification, minimum distance from the mean, Mahlon distance and parallel level were shown in Tables 2 and 3. In order to better show the effects and reduce the number of bands in the data and to compact the most information of the main bands in the fewest number of bands, principal component analysis was used and the appropriate band combinations were selected. According to research on Google Earth software data, the reseults showed a high confirmation of the accuracy and precision of the software data (Pakravan et al, 2012). In this research, Google Earth software has been used to determine the accuracy of the mentioned classifications. Then, in order to prepare an educational sample from the image of the main components, the three main bands are combined, after which a number of areas or levels are selected as a sample to be used to classify information (Firoozinejad et al, 2014). Also, after carefully selecting the educational samples, the classification of the classes was done using the Training Sample Manager tool and the study area was examined in the classification with 7 user classes (Gong et al, 1996). According to the sampling, 7 land uses were identified in the study area, which include agricultural land, medium rangeland, poor rangeland, barren land, rock outcrop, plains and canals, and residential areas due to errors in the study area occurred with the wasteland as a class. According to the purpose of the study, the baseline image was classified with four monitored classifications, i.e., maximum probability, minimum distance from the mean, Mahalano distance and parallel surfaces. Then it was applied in order to remove single and scattered pixels on the surface of the classified image and also to obtain the desired image of the 3 * 3 model filter. Then, using the obtained results, the accuracy and precision of the classification and further evaluations were calculated using Envy software.

        Preparing land use maps in the study of surface and basement resources and information on current conditions and planning for sustainable management in the future are among the basic principles. Today, the use of remote sensing data and quantitative statistical methods are very common to prepare land use maps (Arkhi, 2014). Easy-to-reach, access to remote and mountainous areas, low cost of data extraction in a short time, wide coverage and reproducibility are some of the benefits of remote sensing data that has been widely used since the last decade (Sofali and Khodarahmi, 2011). According to Tables 2 and 3, the highest producer and land use accuracy is related to agricultural lands, poor rangeland, medium rangeland, barren lands and rock outcrop related to the maximum probability method, which indicates a high percentage of pixels related to the mentioned land uses. In all methods of land use classification, medium rangeland and barren lands had the highest producer accuracy. Mahalanui classification and minimum distance from the average in all land uses were close to the percentage of producer accuracy and only in agricultural lands there was a big difference, which shows the similarity of the two methods mentioned. Table 4 does not classify the parallel surface classification method for 10.2% of pixels. The reason for not classifying some pixels is that the parallel surface method uses the minimum and maximum pixel values ​​for classification. Therefore, the numerical value of the pixels may not be in the range of minimum and maximum classes and may not be known in the range of classes. Lack of classification of pixels means that this method is not used much in research (Yousefi et al, 2014). According to the results of this study, it can be concluded that the maximum likelihood method with a strong statistical basis distinguishes the boundary between classes better than other classification methods (Ahmad et al, 2013). Due to the high spectral resolution of the OLI sensor, the maximum likelihood method provides the best results for the supervised classification of OLI sensor data and the results of this study are consistent with the results of other researchers (Yousefi et al, 2014; Ahmadpour et al, 2014; Ahmad et al, 2013; Nazari et al 2013; Firouzinejad et al, 2014; Kiani et al, 2014).

     One of the most essential information needed by natural resource managers and decision makers is land use maps. Today, remote sensing technology provides a good opportunity to prepare user maps. The value and efficiency of land use maps depend on their accuracy and precision. The purpose of this study was to investigate the efficiency of supervised classification algorithms in preparing land use maps. For this purpose, Landsat 8 satellite OLI images were taken from Alam Rudasht basin on 12/23/1398 and after geometric, radiometric and atmospheric corrections, principal component analysis was performed and appropriate band compositions were selected. The four monitored classification algorithms of maximum probability, minimum distance from mean, Mahalano distance and parallel levels were compared to prepare land use map. Then, in order to remove single and scattered pixels on the surface of the classified image and also to obtain the desired image, a 3 * 3 model filter was applied. The ground reality map was prepared using satellite images to determine the accuracy of the classification. Results of maximum probability algorithms, minimum distance from mean, Mahalano distance, parallel levels with overall accuracy of 88.32, 72, 76.65, 53.3 and kappa coefficient of 0.87, 0.68, 0.73 and 0.45. Finally, the maximum likelihood method was calculated with an overall accuracy of 88.32 and a kappa coefficient of 0.87. The most accurate method is to prepare a land use map.

Land use reflects the interactive characteristics of humans and the environment and describes how human exploitation works for one or more targets on the ground. Land use is usually defined on the basis of human use of the land, with an emphasis on the functional role of land in economic activities. In this research, a pixel-based method and object-oriented method for mapping the map of Maragheh city using ASTER sensor images in a 17-year time series were compared between 2000 and 2017. Also the effect of land use changes on erosion was studied. In order to compare the results, both methods used the same educational data for classification. Then, the most important methods for assessing accuracy including precision and kappa coefficient of classification were extracted, and it was determined that the result of the object-oriented classification was better than the 3% Offers. The increase in accuracy in a method based on object-oriented classification largely depends on choosing the appropriate parameters for classification, defining the rules, and applying the appropriate algorithm to obtain the degree of membership. After classifying and comparing the extracted maps, we attempted to reveal the changes that occurred during this period and it was determined, that the rangeland and Bayer classes have a decreasing trend and dense garden classes and water with increasing trend. According to the user-mapped maps of the two methods of maximum-likeness and object-oriented classification and comparison and adaptation of these maps with ground-based properties, the results of the object-oriented classification method were confirmed. According to the results of the study, with object-oriented method, in the Maragheh area, densely populated gardens, gardens, housing, housing, agriculture, industry, and communication in the object-oriented method have increased, and agronomic, pasture, dryland and Bayer uses Reduced area. This indicates the importance of agriculture and horticulture in this city. In 2000, 9.08 percent and 15.88 percent of study area are located in very high-risk and high-risk erodibility and these values for 2017 are 13.66 and 29.76 percent respectively. Also results showed that dense and low dense agricultural land, residential and industrial land use have been increased. The increase in accuracy in a method based on object-oriented classification largely depends on choosing the appropriate parameters for classification, defining the rules, and applying the appropriate algorithm to obtain the degree of membership. After classifying and comparing the extracted maps, we attempted to reveal the changes that occurred during this period and it was determined, that the rangeland and Bayer classes have a decreasing trend and dense garden classes and water with increasing trend. According to the user-mapped maps of the two methods of maximum-likeness and object-oriented classification and comparison and adaptation of these maps with ground-based properties, the results of the object-oriented classification method were confirmed. According to the results of the study, with object-oriented method, in the Maragheh area, densely populated gardens, gardens, housing, housing, agriculture, industry, and communication in the object-oriented method have increased, and agronomic, pasture, dryland and Bayer uses Reduced area. This indicates the importance of agriculture and horticulture in this city. In 2000, 9.08 percent and 15.88 percent of study area are located in very high-risk and high-risk erodibility and these values for 2017 are 13.66 and 29.76 percent respectively. Also results showed that dense and low dense agricultural land, residential and industrial land use have been increased. The increase in accuracy in a method based on object-oriented classification largely depends on choosing the appropriate parameters for classification, defining the rules, and applying the appropriate algorithm to obtain the degree of membership. After classifying and comparing the extracted maps, we attempted to reveal the changes that occurred during this period and it was determined, that the rangeland and Bayer classes have a decreasing trend and dense garden classes and water with increasing trend. According to the user-mapped maps of the two methods of maximum-likeness and object-oriented classification and comparison and adaptation of these maps with ground-based properties, the results of the object-oriented classification method were confirmed. According to the results of the study, with object-oriented method, in the Maragheh area, densely populated gardens, gardens, housing, housing, agriculture, industry, and communication in the object-oriented method have increased, and agronomic, pasture, dryland and Bayer uses Reduced area. This indicates the importance of agriculture and horticulture in this city. In 2000, 9.08 percent and 15.88 percent of study area are located in very high-risk and high-risk erodibility and these values for 2017 are 13.66 and 29.76 percent respectively. Also results showed that dense and low dense agricultural land, residential and industrial land use have been increased.

Remote sensing technology is one of the efficient and innovative technologies for agricultural land use/cover mapping. In this regard, the object-based Image Analysis (OBIA) is new method of satellite image processing which integrates spatial and spectral information in satellite image process domain. This approach make use of spectral, environmental, physical and geometrical characteristics (texture, shape) context of images for modeling of land use/cover classes. Current study, aims to classify micro land use/cover of Meyandoab County by applying appropriate algorithms and parameters in the object based approach. For this goal, Quick Bird and Aster satellite images were used for processing and land use modeling. Accordingly, land use map was classified in 9 class based on spectral and spatial characteristic. The segmentation was performed in the scale of 10, shape parameter of 0.7 as well as the compactness of 0.3. In order to apply classification, fuzzy based algorithm and operators (AND, OR) was applied to detriment the membership functionality of segments for each class as well as classifying the related objects. We also applied textures, geometric, NDVI, GLCM, Braitnese algortims based on fuzzy operators. In order to validate results, the accuracy assessment step was performed and the finally overall accuracy of 93.6 was obtained for derived map. The Kappa coefficient was also detriment to be 0.92. The area under cultivation included respectively for lands of wheat and barley, prunes and plums, apples, vineyards and alfalfa hay2622.42, 4505, 4354.55, 4457.85, 14110.58 hectares.

In order the rapid extraction, land use maps, remote sensing technology has been recognized as an efficient technology to provide satellite images enabling the extraction provides maps land use. Satellite remote sensing images in different temporal power modeling and monitoring changing environmental conditions that made it possible is an important step in the management of natural resources. The basic knowledge object-oriented classification the method based algorithm, an efficient the method for classification of satellite images, in addition of spectral data, satellite images, facilities for the use of environmental information and the physical and geometric characteristics of the land surface phenomena provides The present study aimed to evaluate the increase accuracy of classification utilize algorithms and fuzzy The basic knowledge maps use / land cover done. In this Study To evaluate and compare methods for objectoriented classification of satellite images using fuzzy algorithms and object-oriented methods based on the fuzzy algorithm, Alos Landsat satellite images were used to AVNIR2 sensor and Maragheh city maps and land with both of these methods have been used. The accuracy of the results of the evaluate the show that land use map produced by the the method of fuzzy The basic knowledge With an overall accuracy of 93.28 in comparison with land use map produced by the object-oriented the method using fuzzy algorithm with accuracy 88.06% of the credit is more according to the nature of this study was to compare the results to identify optimal methods are important in the production of land-use mapping And maps produced for performance agencies (such as agriculture, natural resources, etc.) are of high practical value.

The population growth and urban development are the effective factors of increasing the air temperature for urban areas، which may cause formation of heat island، which itself influences air quality and consequently، the public health. The heat island is one of the phenomena which effects the human beings’ living environment in urban areas on a large scale. The heat island occurs when an extra percentage of surface vegetation is wiped out and replaced with buildings، roads and other urban constructions. This problem causes the trammel of the ripe solar radiation into the urban structures during the day and its reflection at night. Thus، the natural process of earth surface getting cold during the night happens more slowly. Consequently، the air temperature of cities will be naturally higher than the temperature of suburb regions. Because of its important effects on environment and health، urban heat island was evaluated for Mashhad، as a major city in Iran، using satellite images and the fractal theory. Mashhad is located at latitude 36ْ 17 ''45 «-N and longitude 59ْ 36 ''43» –E. The population is 2410800، and it is one of the largest cities in Iran. An extra percentage of the surface natural covers is wiped out and replaced with urban constructions and many landscapes have changed into residential areas. Surface radiation emittance، as recorded by thermal infrared sensors، includes both topographically and non-topographically induced high frequency variations such as roads and edges which are caused by different spectral characteristics of different neighboring land covers. The use of fractals for analyzing thermal infrared images would improve our understanding of thermal behavior of different land-cover types as well as the effects of landscape pattern on thermal environmental processes. In this research، TM images of LANDSAT for June، 25th، 1992 and ETM+ of LANDSAT 7 for Aug، 6th، 2002 were used to study the urban heat island in Mashhad and also to obtain temperature and Land-use maps by using them. In addition، for better understanding of this phenomenon، the profiles in that direction of North-South، East-west and Northwest-Southeast were considered. Moreover، the fractal dimensions of these profiles were computed using the divider method، to as to have better understanding of thermal behavior of different coverings and the effects of land-space pattern on ambient temperature. The results showed that the surface radiant temperature of Mashhad during the decade 1992 to 2002 increased and this increase was remarkable in the residential areas. Land-use maps demonstrated developing of the residential areas for 2002 rather 1992، and many plant covers were destroyed and this subject was approved by calculation of the fractal dimensions. The relatively low values of fractal dimension suggested that the texture was less spatially complex. It means that the spectral responses to the thermal band along the line tend not to vary drastically. In urban areas due to the unsteady vegetation and roughness variability، the fractal dimension had high value. In northwest – southeast profile where urban or built-up cover had occupied the majority of the surface، the fractal dimension and temperature in both images were higher than other profiles. Because in east-west profile، urban area was developed more severely during 1992 to 2002، the fractal dimension increased more than other profiles. So، we concluded that، in Mashhad، the urban development resulted in increase of spatial variability، temperature and the fractal dimensions.