جستجوی مقالات مرتبط با کلیدواژه « sentinel-3 » در نشریات گروه « فیزیک »

Coastal zones are one of the most sensitive areas with the constant changes due to their dynamic nature. They are also precious zones in terms of fisheries, transportation, recreational activities, and other rich resources. Thus, any changes in such areas can influence all such activities. Natural disasters are one of the main concerns in sensitive areas such as coastal zones. Due to its sensitivities, these areas may be affected significantly by such disasters. Coastal zone management (CZM) mechanism is a proper choice, which is applied and implemented with the aim of ensuring the sustainability of resources and these environment. In the operational and research framework of CZM, natural phenomenon and disasters are considered and their affects are surveyed for best managements of the area. Large-scale natural phenomena such as earthquakes or groundwater depletion due to climate change in coastal low-lying areas cause various types of instability. On the 2nd of July 2022, three strong and consecutive earthquakes occurred in the coastal area of Sayekhosh, Located 123 km from Bandar Abbas city and 70 km from Bandar Lengeh city (South of Iran). As a result of these earthquakes, the Sayekhosh Shrimp farm center was seriously damaged. In this study, using RS and GIS techniques, the impact of these earthquakes on the coastal area has been investigated. Using the data of the SAR images from Sentinel-1 satellite and applying the DInSAR technique, a coherence map, differential interferograms and vertical changes map of the region have been produced, which are used to determine the uplift or subsidence of the land's surface. By using the data of Sentinel-2, Landsat-5, and Landsat-8 satellites and using the band ratio technique, the conditions of the farm ponds and the shoreline of the region were determined. Using the Normalized Difference Water Index (NDWI) (with Green and NIR bands) and applying unsupervised K-means classification, two water and land features have been separated. These were done using ENVI, SNAP and ArcMap software. The surface of the area in the northwestern part of the farm has changed in the form of uplift (up to 0.2 m) and in the eastern part of the farm along the coast in the form of subsidence (up to -0.1 m). Band ratio results showed that after five days of the earthquakes, 189 ponds were discharged. Besides; we detected about -30 m shoreline transformation in the areas close to the farm canals and river mouths due to the earthquakes. The results showed that the location and/or the establishment of traditional shrimp farming system should be reconsider, to avoid such damages in upcoming disasters. Since natural disasters strongly affects man-made structures especially in the coastal area, the use of satellite data and RS and GIS techniques can be useful to precisely monitor and manage the changes in the coastal area.

In inland water bodies, the water level obtained from the Level-2 data of the altimetry missions is not often correct. Therefore, to correct the water level measured in these areas, it is necessary to retrack the return waveforms. In this study, data from level-2 and level-1 SRAL altimeter of Sentinel-3A mission, measured in SAR mode, in the period from March 2016 to November 2019 to monitor the water level of Doroudzan Dam, has been used. The threshold retracking algorithm with different thresholds has also been used to retrack the waveforms in the level one data. The results showed that the OCOG retracker in L-2 data with an RMSE value of 38.23 cm and a correlation of 99.23% with in situ gauge data compared to other retrackers in L-2 data from Doroudzan dam has higher accuracy in estimating the time series of the water level. The Ocean retracker also has results close to those of the OCOG retracker, indicating that these two retrackers perform well in restoring water levels. After obtaining the water level time series from the retrackers in the L-2 data and selecting the optimal level two retracker, the return waveforms from the L-1 data were first retracked using the threshold algorithm. Then the time series of the water level for different thresholds were obtained and compared with in situ gauge data, which showed that the threshold of 60% with a value of RMSE 37.73 cm and a correlation of 99.30% improved %1.3 in accuracies and increase of %0.07 correlation with in situ gauge data has been optimized for the time series of water level obtained from L-2 retracker. Also, the results showed that, especially in the period from 2017 to 2018, the difference in water levels results from the retracking of the return waveforms with the optimal threshold algorithm (60%) with in situ gauge data less than the optimal L-2 retracker (OCOG). The average water level of Doroudzan Dam from the threshold of 60% was analyzed. Results showed the highest growth in water level with 4.09 m from March 6 to April 2, 2019, which corresponds to usually rainy months. The most significant decrease in the water level with 2.80 meters occurred from April 29, 2019, to May 26, 2019, which are usually low rainfall months. The results also showed that during the study period a slight increase in the water level of Doroudzan Dam was observed. Due to the hard, challenging shape, and topography of Doroudzan Dam and its confused waveforms, therefore, in the above study area, it is not possible to expect high accuracy from both the retrackers in the L-2 data and the results of the waveform retracking. Therefore, the proximity of RMSE results and correlation goes back to the shape and topography of the Doroudzan Dam reservoir. The results of this study show high suitability of the Sentinel-3 mission in monitoring the water level from inland water bodies, which is still a challenging area for satellite altimetry to monitor. Indeed, for a better understanding of the performance of this mission, more samples need to be analyzed.

Wetlands comprise roughly 6–9 percent of the Earth’s surface. The role of wetlands in maintaining environmental quality includes the storage of global terrestrial carbon. In addition, they influence many aspects of ecology, economy and human welfare. Furthermore, wetlands act as an oasis in an urban area which is important in the reduction of surrounding surface air temperature. Changes in the spatial distribution of wetlands (croplands, forests, water bodies and rivers), either by natural factors or anthropogenic activities could significantly affect the ecosystem. Satellite imagery enables us to monitor short-and long-term changes in wetlands and its vegetation density. Due to the fact that the electromagnetic waves reaching the satellites pass through the Earth's atmosphere, the reflections recorded by the sensor of these satellites do not really reflect the phenomena of the Earth's surface. By applying some corrections on the images, it is possible to convert them to Top Of Atmosphere reflectance values (TOA) and the Earth's surface reflections (BOA) ones. In this paper, we have reviewed and compared the results of extraction of wetlands and water body using raw and atmospheric corrected images of Sentinel 2 in south of Lake Urmia. The study area includes wetland and agricultural lands of southern Urmia Lake. Due to the existence of two large Zarrineh and Simineh rivers in the region as well as its very fertile soil, agriculture has expanded rapidly. The main data of this study is satellite images of Sentinel 2 (spring 2019). The dark object subtraction (DOS) is one of the methods of atmospheric correction of satellite images, which with a partial fraction of the dark object's reflection of the whole image, it makes an atmospheric correction of the satellite image. In this paper, this method has been used for atmospheric correction. In fact, a copy of the raw satellite image of the study area was made and the atmospheric corrections were applied. Then the results were compared with the raw images. In order to compare two raw and corrected images, it was attempted to separate the wetlands (rivers, ponds, wetlands) from non-moisturizing lands, so that the effect of atmospheric correction on the ground reflection could be observed. For this purpose, NDWI2 MNDWI, NDTI and SAVI indexes have been used in this paper. To compare the effect of atmospheric correction on Sentinel satellite images, the image of the study area was first provided and entered into the QGIS software for atmospheric correction. Then, using a combination of short infrared, near-infrared and green bands, the extraction and classification of wetlands, water bodies and vegetation cover density was made by SNAP software. Due to the lack of ground control points, the images were sampled by the ArcMap software and verified by using google earth images. Three precision coefficients were used to check and compare the accuracy of raw data with the atmospheric corrected data. In order to compare the accuracy of the outputs, Kappa coefficients, users’ accuracy and producers’ accuracy were calculated using the ArcMap software. The MNDWI and NDTI indices are the best indicators for raw images and for corrected images, to extract wetlands and water bodies. Kappa values of these indicators are above 0.9 and also users’ accuracy and producers’ accuracy are above 96%. Among the four above-mentioned indicators, the NDWI2 index has the lowest accuracy as well as the minimum Kappa coefficient. The results show that corrected images have high accuracy in extracting and displaying wetlands and water bodies. The area of the wetlands and water bodies to be redirected from corrected images is closer to actual areas. The actual area in the SAVI index is 25.15 square kilometers and the redistributed area of raw and corrected images are 25.71 and 25.38 km2, respectively. The actual area in the NDWI2 index is 180 km2, with a corrected area of 178.93 square kilometers. For other indicators, NDTI and MNDWI, the actual areas are 10.37 and 29.5 km2, respectively. In general, it can be concluded that atmospheric corrected images using the DOS method show better results in showing wetlands and water bodies areas. The results of this paper show that the application of atmospheric corrections to the Sentinel 2 images can increase the accuracy of the extraction of wetlands and water bodies areas and even other landcovers. Considering the four indicators and extracting the zones from raw and atmospheric corrected images, it was determined that, firstly, MNDWI and NDTI indices are the best indicators for extracting wetlands and water bodies in the south of Lake Urmia. Secondly, among these two indicators, the data from the corrected atmospheric images have high precision coefficients than raw images. Therefore, it can be said that in estimating the wetlands and water bodies using Sentinel images 2, these images must be corrected using different methods to minimize their error of representations.