Showing 10 results for Classification
Dr Shahrivar Rostaei, Dr Rahim Heydari Chyaneh, Mr Ayoub Zoghi,
Volume 0, Issue 0 (3-1921)
Abstract
As cities become more physically in structure and demographically wider, the rate of crises is also increasing consequently, and cities become more vulnerable to natural disasters for a variety of reasons, including economic and cultural poverty, fault alignment and non-compliance to regulations of earthquake-related issues. What matters is the level of community's preparedness and the degree of vulnerability of the city and its residents, which can be reduced to the lowest level if properly planned and principle policies are adopted. In this research, considering the high potential of Sanandaj in terms of seismicity and the existence of many faults in the vicinity and around of the city, it is tried to gain an authentic understanding of the subject with identifying the factors affecting the earthquake and combining indicators using the Classification Tree Analysis (CTA) model. The results indicate that a large area of the city is in the category of moderate to high vulnerability.52% is in the middle vulnerability category, 16.5% is in the high vulnerability category and 1% of the city is considered to be in the very high vulnerability category, which exactly matches the same marginal, old and densely populated neighborhoods. This situation does not render a proper structure and needs more consideration in prospective development plans.
Bakhtiar Feizizadeh, Ali Khedmat Zadeh, Mohammad Reza Nikjoo,,
Volume 18, Issue 48 (4-2018)
Abstract
Remote sensing technology is one of the most efficient and innovative technologies for agricultural land use/cover mapping. In this regard, the object-based Image Analysis (OBIA) is known as a new method of satellite image processing which integrates spatial and spectral information for satellite image process. This approach make use of spectral, environmental, physical and geometrical characteristics (e.g. texture, shape) together images contexts for modeling of land use/cover classes. The main objective of this study is to classify micro land use/cover of Meyandoab County by applying appropriate and effective algorithms and parameters in the object based approach. For this goal, Quick Bird and Aster satellite images were used within the integrated approach for processing and land use modeling. Accordingly, the land use map was classified in 9 class based on spectral and spatial characteristics. In order to perform OBIA, the segmentation was applied in the scale of 10, shape parameter of 0.7 as well as the compactness of 0.3. In terms of the classification task, 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, brightness algorithms based on fuzzy operators and assign class algorithm. In order to applying the validation of results, the accuracy assessment step was performed and the finally overall accuracy of 93.6 was obtained for the 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.
Dr Sayyad Asghari, Hadi Emami,
Volume 19, Issue 53 (7-2019)
Abstract
Earth surface temperature is an important indicator in the study of energy equilibrium models at the ground level on a regional and global scale. Due to the limitation of meteorological stations, remote sensing can be an appropriate alternative to the Earth's surface temperature. The main objective of this study is to monitor the surface temperature and its relationship with land use, which is monitored using satellite imagery. For this purpose, the images were first obtained and the necessary pre-processing was applied to each one. Then it was compared to modeling and classification of images. Firstly, in order to investigate the changes in user-orientation, a user-defined classification map for each object was extracted using the object-oriented method. Then, to investigate the land use change, a map of user-landing changes map was extracted in an 18-year time period (2000-2017). Finally, in order to monitor the surface temperature, the surface temperature map of Ardebil was extracted. The results showed that there is a strong relationship between land use and surface temperature. As a user, urban users have a temperature of about 41 ° C (2017), which is also due to heat-absorbing urban temperatures. This is despite the fact that the use of hydrocarbons is due to a lower heat absorption of 34 ° C (2017). This shows the role of different uses in determining surface temperatures. Also, the relationship between surface temperature and vegetation cover was investigated in this study. The results showed that areas such as soil and urban areas with a lower coverage than areas such as agriculture and pasture, have a higher temperature. Because the coating is always an obstacle to the entry of heat, it has an inverse relationship with superficial heat.
Mehdi Shafaghati, Zahra Hejazizadeh, Hasan Afrakhteh,
Volume 20, Issue 56 (3-2020)
Abstract
Each geographical location, topography, landscape, flora and fauna, air and climate natural resources for tourism and recreation form. Given that every business needs a bed a place in the geography of this place, defined geographical space.This geographical space supplier of tourist activities. Many factors affect the tourism industry, one of the most important climates. Along with geographic location, topography, landscape, flora and fauna, water and air as one of the most important local resource base plays a role in the development of tourism industry. Gilan province is one of the countries northern even with Mesa 14711 square kilometers .The province has two different morphology of the southern part of the province of North Alborz heights shown and the foothills and plains in coastal areas. The province because of the special circumstances of the geographical, exquisite natural scenery and abundant water resources in the row is one of the most tourist areas of the country. In this study, with the presentation of applied research, analytical and application software, Excel, Google Earth, ArcGIS10 to check the status of existing and potential climate in Gilan province was one of the country's Northern provinces. Which has convenient facilities in the field of tourism is also significant to analyze the specific situation of the province and also to discuss tourism and its development will conform to discuss climate. The result of climatic classification methods Domarten temperature and precipitation maps also will be show that there are good conditions for tourism development in the province and Finally, using the climate index TCI zoning province, and the results were presented in the field of tourism.
Dr Sayyad Asghari, Roholah Jalilyan, Dr Noshin Pirozineghad, Dr Aghil Madadi, Milad Yadeghari,
Volume 20, Issue 58 (10-2020)
Abstract
Water is one of most important human needs for life. According to importance of subject, discussion of management and utilization of water resources has become one of the most important global issues. Remote sensing data are often used in water body extraction studies and type of remote sensing data used plays an important role in water body extraction. In this study, ability of Landsat satellite with application of water indices, to extraction of Gamasiab River in Kermanshah and comparing these indices have been investigated. Specific feature of Low width and shallow rivers has increased the complexity of studies of such rivers using available data. Water body extraction from remote sensing images has been over the past two decades. Water indices were first developed using Landsat TM and Landsat ETM. But its better performance in Landsat 8 is well documented by the researchers. In this study, NDWI, MNDWI, AWEI_nsh, AWEI_sh and WRI indices were used. With extracting optimal threshold from histogram of indices and applying this threshold, the study area was classified into two classes of water and non-water. Then overall accuracy and kappa coefficient values were taken from each of the indices. Finally, AWEI index with overall accuracy of 99.09% and a Kappa coefficient of 0.98 was the best response among the indices in the study area. The results this study showed that approach can easily extract water from satellite imagery.
Saideh Eiyni, Dr Saeide Eini,
Volume 21, Issue 60 (3-2021)
Abstract
The aim of this research is to investigate drought stress in rangeland rangelands in Ardabil province. According to the monthly rainfall data, 4 synoptic stations of Ardebil province (Ardebil, Khalkhal, Meshgin Shahr and Parsabad Moghan) during the statistical period of 2016-1996 were used to calculate drought index (SEPI) index for 4 periods of 1, 3, 6 and 9 months. Landsat TM and OLI satellite imagery was also used to prepare landslide classification maps based on the maximum probability model and calculation of vegetation indices NDVI, EVI, SAVI and LAI. In order to investigate the relationship between the studied indices, Pearson correlation coefficient (R) and root mean square error (RMSE) have been used. The results of the classification showed that the extent of the rangelands of Ardebil province in 1394 in the year 1377, both in the rangeland and in the rangelands, is a significant decrease. According to the results of SPI, the drought condition during 2011-2015 is more than the other periods studied. Vegetation dispersal maps were based on decision tree tree classification algorithm and according to NDVI index for the studied months. Also, according to the results of the evaluation, the highest correlation was observed between the NDVI index and the 6-month SEPI index, and the lowest mean squared error was found between the SAVI index and the 6-month SEPI index, but in general, the most suitable indicator for Drought monitoring in Ardebil province pastures is a 6-month NDVI and SEPI indicator.
Khadijeh Haji, Abazar Esmali-Ouri, Raoof Mostafazadeh, Dr Habib Nazarnejad,
Volume 22, Issue 66 (10-2022)
Abstract
Also, because of human activities and natural phenomena, the face of the earth has always undergone a change. Therefore, for optimal management of natural areas, awareness of the ratio of land cover/land use changes is a necessity. Therefore, extraction of land use maps as the most important goal in the management of the natural resource can be considered. The purpose of the present study was to evaluate land cover/ land use changes at the Rozechai Watershed during the period of 30-years 1985-2015 using Landsat 5 and Landsat 7 satellite imageries such as TM and ETM+ sensors; plus, land use maps were prepared using TerrSet software and object-oriented classification in 1985 and 2000 years. As well as the land use map of procurement by the geographical organization in 2015 has been used. The results show that rangelands level has the highest percentage among all land use types during the period of 30 years, but between 1985 and 2000, and 2000 to 2015, the level of rangelands has a decreasing trend indicating the destruction trend in the region of the replacement of moderate- poor rangelands and good rangeland by dry farming. Also, the tables of obtained from the error matrix indicate that the observed values in the diameter of the error matrix are much larger than the values outside the diameters. Thus, the overall accuracy for the years 1985, 2000, and 2015 were 97, 90 and 96 percent, and The values of Kappa index were 91%, 84% and 94% respectively, indicating a high degree of accuracy in the object-oriented approach to classification.
Khadijeh Mikaeli Hajikandi, Behrooz Sobhani, Saeid Varamesh,
Volume 23, Issue 68 (4-2023)
Abstract
Study of land use/cover changes is widely used in environmental planning. During the last decade, growing increase of aridity in Uromiyah Basin has become a major regional and even national problem. The purpose of this study is to reveal the changes in land use/cover in the southern and southeastern parts of the basin with using 2 images for month of July of 2000 to 2017. Landsat TM and OLI data and NDVI were used for classification this study. Land use/cover maps in the two studied years were provided using Maximum Likelihood Classifier (MLC) algorithm applied on two series data including spectral bands (data series 1) also spectral bands and filter texture layer (data series 2) and six categories of land use/cover containing Irrigated Farmland, Dry Farmland, garden, rangeland, bare land and water bodies were distinguished.. The accuracy of the produced maps were assessed and compared with the training samples derived from Google Earth images and Kappa Index, overral accuracy, producer accuracy and user accuracy. The results demonstrated that the maps produced using the data series 1 have higher accuracy and the overall accuracy of the maps of 2000 and 2017 using the data series 2 are 98.93 and 98.29 and these values for data series 1 were gained 99.28 and 91.45, respectively. In additional, texture filtering decreased amount of mixing between classes of rangeland, Irrigated Farmland and garden. The results of change detection showed considerable increase in the area of Irrigated Farmland (13.44) and garden 1.85 (27.24) an also at the studied period, the area of the water bodies and rangeland were decreased to 1.58 and 22.94%.
Sara Kaviani Ahangar, Rasool Mahdavi, Gholamreza Zehtabian, Hamid Gholami, Ashok K Chapagain,
Volume 24, Issue 72 (6-2024)
Abstract
Desertification is a serious environmental and socio-economic threat to the planet. The aim of this study is to use a scientific, reasonable and repeatable method to evaluate the process of vegetation and land use as two important factors in the process of desertification on different scales (local-regional and global). In this study, Sarvestan plain in Fars province was selected as the study area. For this purpose, Landsat images were used for TM (1993), ETM + (2001 and 2006) and OLI / TIRS (2016). Image monitoring was performed using image differentiation, NDVI index difference and land use maps. In 1993, 2001, and 1993, and 2016 difference maps, the decrease in the amount of water in the mouth of Lake Maharloo can be clearly seen as increasing changes in the infrared band. The results of the difference between the vegetation index and the increase in vegetation in the form of agricultural lands in 2016 compared to 2006 and 1993. According to the results of the monitoring classification, from 1993 to 2016, irrigated areas decreased from 7.11 hectares to 0.7575 hectares, on the other hand, the level of saline lands increased from 143.99 hectares to 223.83 hectares and the level of cultivated lands increased. (Agricultural and horticultural) has increased from 113.28 hectares to 14/2014 hectares, which due to the importance of saline lands and land use change indicators in the studies of the desertification assessment process, it can be concluded that the desertification process in the study area is growing.
Hamid Bagheri, Rahime Rostami,
Volume 24, Issue 74 (12-2024)
Abstract
Wetland cover classification is of special importance in order to identify the type of plant species inside the wetland and also to distinguish it from the wetland margin vegetation and to study their ecosystem changes. Due to the spectral similarity between different plant species of wetlands and plants along the wetlands and agricultural lands, this is faced with problems using multispectral data and hyperspectral data can be very useful in this regard. in this study power of hyperspectral and multispectral sensors in identifying the characteristics of the wetland and the ability of ETM + (2011), Hyperion (2011) and ALI (2011) sensors to study the characteristics of Shadegan wetland during 1390 and different spectral indices with a suitable combination of The satellite imagery bands of these sensors were compared as input to a variety of classification methods including maximum likelihood, minimum distance, neural network and support vector machine. The results showed that the support vector machine and neural network methods with closer classification accuracy of 85% in all three images show closer results to reality. The classification accuracy for all three images was at its highest for the backup vector machine method, with a total accuracy of 95.73 for the Hyperion image, 88.03 for the ALI and 89.34 for the ETM +. Therefore, the characteristics considered for the wetland, in the three images obtained from the SVM algorithm showed that showing the differentiation of wetland vegetation use from irrigated agricultural land use is more ambiguous than other wetland features. Studies have shown that this part is less recognizable in ALI and ETM + images than Hyperion images, or in some areas these parts are not separable from aquaculture land at all, while Hyperion due to having 220 bands and having a higher level of Spectral details have the ability to distinguish between the two classes.
