جستجوی مقالات مرتبط با کلیدواژه "حداکثر احتمال" در نشریات گروه "محیط زیست"

The aim of this study was to determine the accuracy of Landsat 8 and Sentinel 2 satellite data sets based on Maximum Likelihood, Support Vector Machine, Neural Network algorithms for mapping the LU/LC of Kobgian watershed in Kohgiluyeh and Boyer-Ahmad Province. For this purpose, corrections, data preparation, data set creation, classification and analysis, mapping and verification were done using ENVI® 5.3, ArcGIS® 10.5, Google Earth Pro and Excel 2016 software. The results showed that the highest total accuracy and kappa coefficient for Landsat 8 and Sentinel 2 satellites belongs to the maximum likelihood algorithm with a value of 74.18% and 0.69 and neural network algorithm with a value of 72.84% and 0.67, respectively. The overall accuracy order of the algorithms for mapping LU/LC the watershed using Landsat 8 and sentinel 2 data was as maximum likelihood > support vector machine > neural network and using data was as neural network> maximum likelihood > support vector machine, respectively. The accuracy of the algorithms indicated that if a specific LU/LC is the main goal such as basin rangelands, the support vector machine algorithm should be used. The area of eight classes of Kabgian watershed is: agriculture 1342, residential 1356, rock 3579, forest 23289, water body 407, abandoned lands 9571, garden 3139 and pasture 54125 ha. Therefore, depending on the type of LU/LC, the type of satellite data available, and the purpose of study, the priority of using algorithms will be different and based on the desired factor, suitable algorithm should be selected.

Knowledge of the characteristics of morphodynamic river systems, Landform and its evolution as one of the most vital components of the Earth's surface that In many studies and projects, including the flood control, watershed studies, and environmental hydrology of the basic requirements for environmental planners. This study aimed to evaluate the changes of landforms and the KalShour riverbed in protected area ShirAhmad is Sabzevar emphasis on environmental considerations.

in the Research using topographic map and satellite images of Landsat, years 1988, 2000 and 2015. For this purpose, was determined changes kaleshor of riverbed and morphodinamic landform changes occurred in the study period of 27 years and Using maximum likelihood map landforms and account for the years of research and changes in any areas identified and analyzed and assessed.

Calculations show that the rivers in the region of 8.1 km2 kalshour shorter and less meandering, and become more the radius of the circle is tangent to any show that has become curved meander pattern to direct arterial. The map Landforms has changed was produced with maximum likelihood and overall accuracy0.78 and kappa coefficient 0.84.

 The results show a large changes of sand dunes and vegetation especially .Based on the results, because of having high groundwater levels and also enter wastewater Sabzevar and.31.5 km2 of sand dunes volume reduction and conversion to fields Tamarix hispida and forest Tamarix. This issue is for the benefit of the natural landscape and on the other hand depletes water resources and soil and adverse impact on animal life protected area's Shirahmd that require special strategic programs in order to preserve natural heritage and sustainable development.

In many studies concerning monitoring the water surface changes, multi-temporal images are sepa-rately analyzed and after extracting water boundaries in each image, these boundaries are compared and the changes are determined. Nevertheless, there is a demand for methods that can perform accurately as well as facilitating the identification of changes. Therefore, to this end, in this research synergy of multi-temporal image fusion and classifi-cation methods were investigated to detect surface water changes in Maharlu Lake between 2013 and 2018.

After performing the necessary pre-processing, the Gram-Schmidt (GS) and Principal Component Analysis )PCA( methods were applied to fuse images and, then, changed and unchanged areas were extracted through applying classification methods to the fused images. Support Vector Machine )SVM) and Max-imum Likelihood )ML( were used to classify fused images. In the next step, combinations of these methods were compared to each other and the best pair was extracted. Finally, the selected pair was compared with conventional change detection methods.

The results showed that based on the GS-SVM methods, the Maharlu Lake retreated about 163.3 km2 from 2013 to 2018. For accuracy assessment of the methods, the overall accuracy and Kappa coefficient were calculated. The GS-SVM method had an overall accuracy of 99.33%, Kappa coefficient of 0.99 and a relative error of 3.92 km2. This pair detected changes more accurately and the results were closer to reality. In the next step, the water surface was extracted from the images using conventional change detection methods, such as image differencing, band rationing, and NDVI differencing, and their results were compared to that of the GS-SVM. According to the results, the GS-SVM had higher overall accuracy and Kappa coefficient compared with other methods, and simultaneously, the least relative error.

The results of this study showed that a combination of the GS image fusion method and SVM clas-sifier provides satisfactory results to extract changes from multi-temporal images. This synergy can be used as an effective tool for detecting changes, particularly since fusing images can also be effective in improving classifica-tion accuracy by enhancing the spatial resolution of images.

The knowledge of urban land usage and the trend of its variations is one of the major challenges in Land Management and Sustainable Urban Development. Today by attention to the fast development of cities, knowing the status of land cover is of great importance. Remote sensing techniques is the best means to extract land use maps. In the past few decades, Tehran, with growth and urban development and expansion of many surrounding towns, had changed in urban land and surrounding areas. In this paper we use the method of supervised classification and maximum likelihood algorithm in order to prepare the land use map.
The study area is Tehran and its surrounding areas including the north and east of Tehran. In this research we have used the satellite images of Landsat 5 images (June) of the TM sensor in 2010 and after geometric correction metric radio, the maximum likelihood method was used for image classification.
The research shows that respectively pasture areas (range and pasture), urban areas, farming and gardening zones, forest cover and tree planting, land and water zones forms the largest area of use.
The overall accuracy and kappa coefficient was determined in order to evaluate the accuracy of classifications and we received to %91.235 and %0.874 respectively.