جستجوی مقالات مرتبط با کلیدواژه « etm » در نشریات گروه « جغرافیا »

Thermal remote sensing, as a branch of remote sensing science plays an important role in environmental studies. Thermal sensing is one of the new and low cost methods against ground surveys in remote areas, which provides valuable information from the study area to researchers in the shortest time. It is possible to prepare a surface temperature map to identify areas prone to geothermal and plant stress by using an infrared thermal band. Which provides the basis for further exploratory studies. In this research, we used the ETM + and ASTER sensors to map the vegetation anomalies using the NDVI index of Khoy city. And plant seasonal variations were examined. Two datasets, one in early summer 2002 and the other in late summer and fall 2002, were derived from Landsat 7 data. By calculating the LST for the ETM + sensor, in this study, changes beyond the normal seasonal changes were considered as thermal anomalies. Also, by calculating the surface temperature by ASTER data, the surface manifestations of these anomalies under dense vegetation were revealed. MODIS sensor images were used to validate LST calculated by ASTER sensor and NDVI and ALI were calculated using it. The results of the present study showed that the identification of geothermal regions has a good spatial correlation with plant stress in Khoy city. ETM + and ASTER sensors, due to their high resolution in the infrared thermal band, 60 meters and 90 meters, respectively, are suitable sensors for calculating the surface temperature and detecting thermal anomalies. Therefore, the image difference method in this study was not a suitable method and did not show good results.

The process of identifying landforms is a subject that has been researched by many researchers. All the definitions of geomorphology emphasize the study and identification of landforms. Understanding landforms and how they are distributed are some sort of essential requirements in applied geomorphology and other environmental sciences (Shayan et al., 2012). On the other hand, remote sensing is a powerful tool for studying different ecosystems of the earth to produce valuable temporal and spatial data (Rezaei Moghaddam and Saghafi, 2006). Arekhi (2014) used ETM+ digital data to map the land use of the Abbas plain. To classify images, artificial neural network, supporting vector machine and maximum likelihood were used. Based on the results, neural network classification method has the highest accuracy of land cover mapping. Also, De laet et al. (2007), by studying the development and stability of desert pebbles in Turkey, concluded that desert pebbles in this area are formed likely (in situ) by mechanical erosion of the surface fragments and minimal Tophonomic effects, in sediment with diameters greater than 2 cm. The purpose of this study is to evaluate the efficiency of Landsat imagery in identifying and classifying desert pavement comprehensively using satellite imagery classification strategies.

The studied area with an area of ​​47645/98 ha, in Semnan city is located in 53° 28¢ to 53° 53¢ 43° east and 35° 20¢ to 35° 40¢north. ETM+ multispectral satellite data were selected for this study because of spatial, temporal and especially radiometric resolution. The data of this sensor comprises seven spectral bands, obtained from the USGS site. In order to distinguish different types of desert pebbles in terms of cover density using EDRISI Selva and ENVI 4.5 software and ETM+ sensor images of Landsat satellite, 4 methods including supervised classification methods, maximum likelihood, Mahalani distance, minimum distance from mean and Parallel surfaces were used. Each classification method was compared for classification accuracy using overall accuracy coefficients, kappa, user accuracy and producer accuracy.
The error matrix table was also presented for each method. In this matrix, the degree of compatibility of each class with the ground reality is shown, in which the degree of overlap of one class in the other classes can be observed. The error matrix diameter and the percentage of correctly classified classes and other cells show the number of assigned errors (column of each class in the error matrix) and deleted row of each class in the error matrix) (Lillesand et al. 2004, Ahmadpour et al. 2011). Finally, the spatial mapping of each method was plotted in Arc GIS 10.2.

Supervised Maximum Likelihood (MXL) classification method According to the results, the mentioned method has less interference than the other classification methods and except for the class with 40-70% pavement density which has 22.41% interference with the class with 70-90% density, the rest of the applications have less than 8% interference. According to the results, desert pavement with 20-40% density had the highest percentage of 56.62%.  Minimum Distance to Average (MinDis) supervised classification method The overall kappa coefficient for the minimum distance from the mean is 75.54% and the overall accuracy is 81.61%. According to the results, this method has less interference in two classes of 20-40 and 70-90% of desert pavement density than the other two classes. According to the results, desert pavement with a density of 40-70% had the highest percentage of area with 41.88% (9935.79 ha) and the exposed rock with 537.7 ha (1.12%) compose the lowest area in the region.Mahalani Distance Monitoring Classification Method (MahD)In this method, there is a high degree of overlap between the classes, with the highest interference being between 40-70% and 70-90% of the desert pavement, which accounts for 25.86% of pixels for the 70-90 class. According to the zoning, the desert pavement with a density of 20-40% with an area percentage of 53.55 have the widest class in this method. Also, the lowest percentage of area with 13.39% (6376.43 ha) of the whole area is related to the rock outcrop.Supervised Classification Method of Parallel SurfacesThe overall kappa coefficient for this method was 21.06% and the overall accuracy was 41.25%. The estimated coefficients indicate the inefficiency of the model in separating the pavement classes. The lack of recognition and separation of the two rock outcrops and pavement classes with density of 20-40% has increased the area belonging to the two classes of pavement and the mismatch with real conditions and ground reality, so that, with pavement with 70-90% density and the area covers of 34/99,995 ha (73.52%) of the region.

Given that for optimal use of multispectral data, it is necessary to identify the best band composition. Choosing the best bonding combination is difficult and time consuming visual comparison. Therefore, a technique called Optimal Determination Factor (OIF) can be used to determine the most appropriate band composition to produce the best false color image and to determine the most appropriate bands for digital classification (Alavi Panah, 2000). The results of applying the optimum index in the present study showed that the best band composition for the detection and separation of the desert pavement in the south of Semnan is a combination of 6-4-3 with the optimum index value equal to 71.45 that are located in visible and infrared thermal band (VNIR +TIR). Climatic and geographical differences, satellite harvest time, physicochemical and biological properties of landforms and other effects in the area can produce different results. On the other hand, the classification methods used in this study differ in terms of the structure and complexity of the algorithm, which were calculated to evaluate the performance of the methods, kappa coefficients and overall accuracy. The classification results show that the coefficients of the classification results accuracy obtained from the used methods are considerable. Since the classes, bands, and other conditions used for all methods are the same, the difference in accuracy depends only on the computational algorithms of the methods. In all the investigated methods (except for the least distance from the mean), the class with desert pavement of 20-40% has the lowest kappa coefficients and accuracy, indicating the low ability of the investigated methods in spectral resolution of this class. In general, spectrally separable classes show pretty high accuracy in all methods, and vice versa. The use of other object-based classification methods as well as better spatial and spectral resolution sensors images and the incorporation of properties such as particle diameter can be effective in mapping desert pavement classes.

In recent years, the issue of improving the spatial resolution of thermal images in urban areas has been introduced as a new challenge. The purpose of this study is to use the impervious surfaces indices and vegetation indices to improve the spatial resolution of Landsat ETM thermal band over Tehran as a part of the study area. After the initial pre-processing, images obtained using the mean filter was simulated at spatial resolutions 120, 240, 480, 720 and 960 m. The relationships between these simulated imaged with the image simulated at the resolution of 960 m were calculated by the use of regression models.
These derived models, containing vegetation and impervious surface indices, were then used to simulation of surface temperatures in different pixel sizes. The accuracy of each output, has been evaluated using the thermal images of ETM and MODIS sensors.
The results showed that by increasing the spatial resolution, the errors increases while the gradient of error is not fixed. So that in all indices, there are more increasing in gradient of error when the pixel size goes to smaller than 240 meters.
Moreover, the best performance was obtained by combination of impervious surfaces indices and vegetation indices due to the enhancement of spatial resolution of thermal images in Tehran city.
Using the combination of these indices, the spatial resolution of the MODIS sensor can be reached to about 240 meters, while the absolute error value is less than 1 K Kelvin.

Strategic decisions about the construction of engineering structures along the river which are essential for the management of sediment entering the reservoir will be facilitated by understanding the behavior and characteristics of the sedimentation of rivers leading to large dam reservoirs. Multi-temporal and spectral remote sensing technology has been applicable for detecting of the rivers morphological changes. However, the specific nature of the narrow and shallow rivers is responsible for increasing the complexity of morphology with available data. This study was undertaken in order to detect narrow and shallow rivers by assessing the ability of six famous water indices, including: Normalised Difference Water Index, Modified Normalised Difference Water Index, Automated Water Extraction Index no shadow, Automated Water Extraction Index shadow, Enhanced Water Index and Water Index 2015 which were derived from two Landsat ETM and OLI sensors. The optimal threshold for each of these indices was determined using ROC curves and validation process was carried out using Google Earth images captured in August 2013. The accuracy of results was evaluated by using different statistics including combined error, producer’s accuracy, user accuracy and omission and commission errors. Consequently, the results of this study have shown that the ETM sensor was generally more accurate than OLI sensor. All in all the Modified Normalised Difference Water Index and the Automated Water Extraction Index shadow was the most accurate indices. Also Automated Water Extraction Index no shadow index had the lowest accuracy for the river’s detecting process.

The coastal areas are always physically and ecologically changing that depends to natural and human factors. Natural factors of coastline changes are evaluated through three

short term changes including the effects of increasing and decreasing of flow; long term changes including climate variation, temporal storms and waves; and random changes including natural unexpected events. These happened changes would effect on coastline changes and coastal areas, and in future would negatively effect on human and human activities and sea communications. Monitoring of coastal areas is an important fact in steady development and environment maintenance. To monitor a coastal area, coastline extraction in various times is an essential task. Coastline is one of the most important linear features on the Earth’s surface showing a dynamic nature. Coastlines are introduced as one of the most important geographical features on the Earth’s surface by International Geographical Data Committee (IGDC), geographically are the intersection of coastal area and the sea water. Accordingly, it is important to produce coastline map and to determine the changes for a secure shipping, resource management, environment maintenance, planning and coastal steady development. Case Study Dair is the one town of Bushehr province in southern Iran and the Persian Gulf coast area. Faults, fractures range of mountains and sea shells on the tracks, show the lowing sea level, the movements of the earth and the organic material being buried with lime flowers at the bottom of the sea.

Remote sensing is one of the best and most reliable methods in monitoring and management of environment and resources. Various methods have been created to extract coastline from satellite images. Since the reflection of water in IR bands are almost Zero and most of vegetation have a bigger reflection versus water, coastline can be extracted from even one band of the image. This extraction can be achieved from Thresholding on one IR band of TM and ETM+ images. Thus, in this study we have used satellite images of TM, ETM and ETM+ Landsat in the years 1987, 2000 and 2006 to monitor the coastline changes of Dair town. Based on results from amplifying methods, contrast enhancement by saturated linear expansion method is more effective in image amplifying, and band 5 is also effective than band 7 in border separation; but since this method only covers the spectral amplifying, in order to extract a distinct border between water and land, it was used along other completing methods, one common of them is temporal amplifying. Thus, various filtering methods were used that edge detection filter on images of band 5 showed better results versus the others. Because the mentioned filter has less effect on existing features in image, extraction of water-land border and even the flat wet lands and water, especially for band 5, was more convenient.

In this paper, using temporal detection of band 5 of Landsat satellite images in 1987, 2000 and 2006, coastline changes of Dair town were extracted. To investigate and determine the spatial changes of coastline, spatial changes from images of 1987, 2000 and 2006 in 50 points with equal distances of 3000 m were measured. Spatial changes of coastline in these points are in such a way that from 1987 to 2000 and 2000 to 2006, the area has encountered a raise in sea level toward the land and a decrease of the area. Minimum and maximum values of spatial change of coastline during 1987 and 2000 are 17 and 2829 m, respectively. Spatial changes of coastline during 2000 and 2006 have been lessened versus the former period, that minimum and maximum values of changes are 2 and 800 m, respectively. Average spatial coastline changes in first and second periods are obtained 502 and 138 m respectively.

Land use maps are the most essential tools and information in the hand of natural resources managers. Over the last years، many applications of neural network classifiers for land use classification have been reported in the literature، but few studies have assessed the use of decision tree classifiers and their comparison. In this study، first، geometric and radiometric corrections were performed on ETM+ data. Then، with field surveying، the various land cover classes were defined and training areas were selected. The main objective of this study is to compare three artificial neural network methods for land cover classification in Daresher catchment of Ilam province. Meanwhile، the performance of these algorithms has been compared with that obtained using decision tree (with three siplitting methods). The obtained results of accuracy assessment of the classified images showed that neural network classification methods (except Kohonen method) outperformed with overall mean accuracy of 92 and Kappa coefficient of 0. 90 than by decision tree with mean overall mean accuracy of 90 and Kappa coefficient of 0. 88. In addition، when different neural networks classifiers were analysed، fuzzy artmap approach outperformed than by perceptron multi-layer and Konohon classifiers in terms of overall Kappa coefficient accuracies (with more overall accuracies of %2، %22 and Kappa coefficients of %3 and %24). The highest accuracy of artificial neural network was with fuzzy artmap method. So، this study confirms efficiency and capability of artificial neural network methods for land cover classification.

In order to classify satellite image of the ETM+, classes of land uses were classified in six groups as agricultural lands, rangeland, forest, land barren, garden, lake and then training samples were collected from study area using Google Earth satellite image and the field visits. At the next stage, by using the images characteristics, According to results, the tree classification with three splitting methods(gain ratio, entropy, and gini) produced the overall accuracy of 87 and kappa coefficient 0.84, respectively, while, methods of fuzzy Artmap and maximum likelihood were classified with overall accuracies of 84, 81 and Kappa coefficients of 0.81, 0.78, respectively. Thus, the splitting methods of tree classification (average overall accuracy of 3% and Kappa coefficient of 3% in comparing with to methods of fuzzy Artmap artificial neural network, and average overall accuracy 6% and kappa coefficient of 6%) than likelihood maximum classification for the data series used in this study were of higher accuracy. The efficiency of the tree classification with gain ratio splitting to be roughly comparable to the fuzzy Artmap ANN method and this reflects the high efficiency of fuzzy Artmap neural network. Finally, we can say that among three splitting methods used in this study, the gini splitting method with overall accuracies of 6% and 2% and kappa coefficients of 7% and 2% higher than the entropy method respectively has better performance. This study shows tree classification methods have many advantages over the other methods of classification such as fuzzy Artmap artificial neural network and maximum likelihood methods. They are computationally fast (Unlike artificial neural network methods) and make no statistical assumptions regarding the distribution of data (Unlike the maximum likelihood method). As the result, it can be said that, tree classification is a good alternative for other methods of classification.

The study of distribution of temperature patterns associated with different land uses and their influences on the temperature range is of great importance in understanding the urban microclimate. Nowadays it can be said that human activities and changes in the natural landscape of the city، lead to variance of temperature between the downtown and its suburbs. Satellite imagery can be used for measuring and monitoring these changes. The purpose of the current research is to study the temporal and spatial changes of urban thermal patterns in Shiraz، through satellite images and determine locations with high temperatures and based on point in order to map and analyze city land use. The used Method is based on Satellite imagery LANDSAT TM and ETM+ sensor to extract the thermal pattern of the user as well as the mapping to land uses monitor changes in the city for 24 years has time frame. The results of this study showed that surfaces soil barren and devoid of vegetation the highest temperature in the city suburb of user''s texture than other compact urban areas is higher than the temperature، thermal and rings with urban traffic areas and are adaptable. Well as for the coldest area of vegetation is adapted to the period also witnessed a decline trend of user changes to User Account in favor of the city are barren.

Earth surface temperature has been used in a wide variety of scientific studies including climatology, astronomy, meteorology,hydrology, ecology, geology, and medical sciences. The design and optimization of transportation network and site selection of conflagration and particularly in the calculation of the actual evapotranspiration we require such data. Considering the earth's surface temperature monitoring in a limited number of meteorological stations to the distribution point we need to place and estimate the surface temperature in a wide area, and at the same time the surface temperature. To access the earth's surface temperature and classification the SEBAL and decision tree algorithm was used.Using ETM+ images dated 31 August 2000 and pre-process, files were ready for implementation of SEBAL method. Processing of the above mentioned software was through Envi4.5 and ArcGIS 9.3.This paper estimates the temperature differences if less than 5.57°C between a satisfactory level through remote sensing and statistics,while we estimated temperature measured from ground level for a period of 12 years (1993-2005) at Maragheh meteorological station.Results indicate that temperature estimates through remote sensing and such studies are applicable for earth science research and the environment.

In a wide variety of scientific climatology studies earth surface temperature, is important, Astronomy, meteorology hydrology,ecology, geology, medical science, design and optimization of transportation network and site selection of fire extinction and particularly cases required. In the calculation of the actual evapotranspiration also we consider these.. Considering the earth's surface temperature monitoring in a limited number of meteorological stations to the distribution point and the need to place the surface temperature in a wide area and at the same time the surface temperature were estimated. To access the earth's surface temperature and classification SEBAL algorithm and decision tree were used. Using ETM + image dated 31 August 2000 and pre- process, files became ready for implementation. For processing of SEBAL method. the above mentioned software Envi4.5 and ArcGIS9.3 were used. This paper estimates the difference less than 5.57° C, temperature difference between a satisfactory level was estimated through remote sensing and statistics. Temperature measured from ground level 12 years (1993 -2005) in Maragheh meteorological station was achieved.Temperature was estimated through remote sensing and studies applicable in earth sciences research and the environment.

In this research, the method of extraction and identification of hot and cold water springs using thermal images of TM and ETM+ is investigated. The process is as follows. After taking images from the TM and + ETM sensors on different dates and implementing geometric and radiometric corrections on the images, surface temperature (LST) for the images is extracted using existing relations. Of course, this requires NDVI and emission extraction for the studied areas due to the certain effect of vegetation on surface temperature. This was carried out in TM and + ETM images using bands 3 and 4. After the extraction of surface temperature by using limits and surface profiles in different directions and by using high pass filters, and finally matching vector layers related to the location of the springs on available images prepared by GPS during the fieldwork, the ability of TM and ETM+ images to identify the springs was evaluated. The results show that the use of images of cooler seasons has a higher priority and importance in comparison with the warmer seasons for the identification and extraction of thermal points and thermal anomalies of the surface of the earth on the images. The reverse is true for the identification of cold springs. Furthermore, the size and temperature of springs regarding their geographical location can be effective factors in identifying and separating these resources.

The thermal remote sensing is a branch of remote sensing which discusses about processes and interpretation of data in thermal infrared (TIR) of electromagnetic spectrum. The main of the research is to present a method measuring the surface temperature of Ahvaz city using thermal images. To achieve the aim, after pre-process operations on images and geometric correction, the supervise classification method was used to determine the land use types. The sixth band of ETM+ was used to determine the surface temperature. After exerting the relevant relationships and corrections to the kind of land use, the thermal map was produced dividing into five qualitative temperature levels. The results of the research showed that the 8th municipality area in Ahvaz by 39 o C was distinguished with the highest surface temperature because of the existence of magnificent steel factories of khoozestan. Accordingly, the 5th area where are known with most green space among the total municipality areas, by 32.7 o C was considered with the lowest temperature. Applying land use temperature, it is distinguished that the water and green land classes with 24.3 o C and 30.1 o C have the lowest and the industrial and unutilized land classes with 39.6 o C and 38.1 o C have the highest surface temperature, respectively. This research indicated a direct relationship between land use and surface temperature.