جستجوی مقالات مرتبط با کلیدواژه "آستانه گذاری" در نشریات گروه "جغرافیا"

Remote Sensing (RS), as one of the most efficient mapping technologies, is employed in wide areas due to its speed, cost-effectiveness, monitoring over wide areas and using time series data. So far, several data and methods are used for this purpose. In general, RS active and passive sensors provide useful information in various applications such as building extraction, natural resource management, agricultural monitoring, etc. The extraction of accurate information about the location, density and distribution of buildings in the urban areas is one of the major challenges in the urban study which is used in various applications. In this framework, the monitoring of the urban parameters, such as urban green space, public health, and environmental justice, urban density and so on has been accomplished by radar and optical image processing, in the last three decades. So far, various methods, including Artificial Intelligence (AI), Deep Learning (DL), object-based methods, etc. have been proposed to extract information in the urban areas. However, an important issue is access to the powerful computer hardware to process the time-series images. In such a situation, the use of the Google Earth Engine (GEE) as a web-based RS platform and its ability to perform spatial and temporal aggregations on a set of satellite images has been considered by many researchers. In this research, a semi-automatic method was developed building extraction in Tabriz, northwest of Iran, based on the satellite images using the GEE cloud computing platform. Since accessible data is one of the most important challenges in the use of space RS, in this study, the free Sentinel-1 and sentinel-2 data, which belongs to the European Space Agency (ESA), has been utilized.
 

2-1- Study Area The study area is central part of the city of Tabriz East Azerbaijan Province, which is located in northwestern of Iran. 2-2- Data Various data sources have been used in this study, including Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). In addition, 400 training samples were created using High-Resolution Google Earth Imagery (GEI) in two classes: urban-residential (buildings) and non-residential areas (vegetation, soil, road, water and etc.).
2-3-

The goal of this research is to develop a method for identifying the buildings in an urban area. For this purpose, after importing images and pre-processing them in the GEE Platform, a map of the Primary Urban Areas (PUA) and High-Potential Buildings (HPB) was produced from Sentinel-1 images according to the sensitivity of the radar images to the target physical parameters. Then, in order to remove the annoying features and extract the Secondary Urban Areas (SUA), spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Renormalized Difference Vegetation Index (RDVI), Normalized Difference Water Index (NDWI), Soil Extraction Index (SOEI), Normalized Difference Built-up Index (NDBI), and Build-up Extraction Index (BUEI) were extracted from Sentinel-2 images. Also, the high slope of the area and the mountainous areas was extracted from the SRTM DEM data and used as a mask in the final results. Afterwards, the unimodal histogram thresholding method was used in order to determine the threshold value for each index. Finally, by merging the map of HPB and the map of SUA, the final map was produced and evaluated by other methods. In this research, the proposed method used images from GEI with a very high spatial resolution to validate the generated map. As a result, sampling was carried out using a visual interpretation of GEI in two classes: residential areas (buildings) and non-residential areas. The samples were selected randomly and 400 points were collected for each residential and non-residential class. In the study area, a total of 800 test points were used to evaluate the results of the proposed method. To evaluate the accuracy of the results, the criteria of overall accuracy (OA), kappa coefficient (KC), user accuracy (UA) and producer accuracy (PA) were used.

According to the visual interpretation, all buildings in urban areas with a length and width greater than 10 meters (spatial resolution of the four major bands of Sentinel2) can be extracted using the proposed method in this study, and the results are acceptable in various features. According to the proposed method, annoying features such as vegetation and water body areas were removed from the building identification process with high accuracy, and the accuracy in the study area was improved. The results showed that the OA and KC were 90.11 % and 0.803, respectively. Based on the quantitative and qualitative comparisons, the proposed method had a very satisfying performance.

Due to the spectral diversity and the presence of various features in urban environments, preparing a map related to it in a large area is extremely difficult. In this regard, the current study presented a very fast semi-automatic method for preparing the urban area map and extracting buildings in Tabriz using Sentinel-1 and Sentinel-2 satellite images as a time series in the GEE platform. One of the most significant benefits of the proposed method is that the data and processing system used in our study is free. Thus, in addition to not having to download large amounts of data, the method presented in the current study has the ability to eliminate many of the limitations of traditional methods, such as classification methods and their requirement for large training samples. The proposed method did not extract the map of buildings using heavy and complex algorithms, which was an important consideration in the discussion of computational cost. Therefore, it can be concluded that the simultaneous use of Radar and optical RS data in the GEE Web-Based platform has a very high potential in distinguishing features and building mapping.

1- Playa saline zones as ecological environment has been regarded by researchers and many researchers have been seeking new methods for studying this phenomenon, because access to these areas due to special circumstances, it has always been difficult and limited (Metternicht et al, 2010: 324). Monitoring of saline arid zones in sustainable development and environmental protection is an important parameter. Monitoring of this phenomenon requires extraction and thematic maps at different times. Remote sensing technology is an effective method for obtaining the required data. This procedure avoids the usual constraints of time and place (Rezaie Moghadam, 2005: 221). Limitations on the use of satellite data to produce maps of salt affected areas of the spectral behavior of salt types, spatial distribution of salts on the surface, the changes in salinity, vegetation as barrier and spectral mixing with other levels of the ground depends (Alavipanah, 2002: 451). The main objective of this study was to assess methods saline mining zones and provide a new method for monitoring the spatial extent of this phenomenon with the satellite images of Playa. With regard to the above and the saline playa zones, monitoring area of saline cover in this study using IRS-P6 satellite images LISSIII sensor can be studied in 2010. The study area for this research is the watershed cover at 35 degrees 53 minutes north latitude and 36 degrees 17 minutes and 54 degrees 35 minutes east longitude and 55 degrees 18 minutes East province is located in the southeastern city of Damghan which has a stretch east - west with an area of 2474700 hectares. Cover an area of ​​239,100 hectares where the desert playa Haji Aliguli (Chjam) with an area of ​​46,600 hectares is located. 2- The data used in this study, IRS-P6 satellite image sensor LISSIII 2010. This sensor is equivalent bands TM2, TM3, TM4 and TM5 and ground resolution 5/23 meter bands visible light and near- infrared and mid- latitude and their shooting is 140 kilometers (Alavipanah, 2006: 53). According to a study that evaluates conventional separation zones cover and thematic mapping of the salt processing satellite imagery, the methods of research proposed in this color eye pseudo seizure interpretation, use of the combine the gangs respect on the threshold Histogram images. After applying this correction, the entire image area of ​​study was attempting to cut a small part of the full frame range used in satellite images. Thus, the next operation was performed on the cropped area. Next, in order to separate the saline from the area of ​​the image with the proposed method, the images were ILWIS3.3 software. Due to the complex interaction of a range saline and other phenomena in the area of ​​Playa cover and requires high precision in the final output efficiency of each of the proposed methods mentioned were analyzed. 3– Powerful tool in the field of remote sensing to study different ecosystems, such as Playa environments in order to produce valuable data and useful. Threshold on the histogram of the common methods that can be used to blank the saline extracted from satellite images. Due to the fact that the spectral reflectance of salt compared to other phenomena in the mid-infrared bands is very small allotment and close to zero, so the blank extract saline from satellite imagery, the action threshold are one of the mid-infrared bands. It should be noted that the choice of the threshold value is difficult in practice because the same underlying removing salt from saline zones is not possible or careless. Although this method has high accuracy, but it can be easily extracted automatically and quickly raised. Ratio between the bands used to extract saline zones is also difficult because of the different coatings than in places where the ground does not have the results. 4– Due to the variable nature of the salt zone boundary lines are part of its natural features, its continuous monitoring does not appear very good based on visual interpretation. Due to the complex nature of these problems and Damghan playa new method presented in this study and were analyzed. This method is based on combining the method of threshold and ratio between bands. This study aimed to investigate the feasibility of monitoring area Saline Damghan playa LISSIII sensor image processing was done in 2010 found that between two methods of spectral bands and threshold rationing on the image histogram for monitoring and mapping arid zones saline is a good way. The results also showed that measures of Damghan playa salt RSCI and NDSCI the resolution satellite imagery mapping and monitoring their performance.

Coast is a unique environment in which atmosphere, hydrosphere and lithosphere contact each other. Coastline is one of the most important linear features on the earth’s surface, which display a dynamic nature. Coastal zone, and its environmental management requires the information about coastlines and their changes. This paper examines the current methods of coastline change detection using satellite images. Based on the advantages and drawbacks of the methods, a new procedure has been developed. The proposed procedure is based on a combination of histogram thresholding and band ratio techniques. The study area of the project is Urmia Lake; the 20th largest, and the second hyper saline lake in the world. In order to assess the accuracy of the results, they have been compared with ground truth observations. The accuracy of the extracted coastline has been estimated as 1.3 pixels (pixel size=30meters). Based on this investigation, the area of the lake has been decreased approximately 1040 square kilometers from Aug-1998 to Aug-2001. This result has been verified through TOPEX/Posidon satellite information that indicates a height variation of three meters.