Рассмотрен один из вариантов решения задачи детектирования водных объектов и определения их площади на основе данных со спутников серии «Sentinel-2», обработанных с помощью программного обеспечения SNAP. По материалам спутникового мониторинга выполнены исследования площади зеркала 14 гипергалинных озер Крыма, большинство из которых относятся к категории рыбохозяйственного значения. В результате было установлено, что наибольшие сезонные изменения претерпевала площадь зеркала оз. Малое Ялы-Майнакское Евпаторийской группы, оз. Акташское и Киркояшское Керченской группы; наименьшие -оз. Кирлеутское и Круглое Перекопской группы, оз. Ярылгач Тарханкутской группы, оз. Ойбурское и Мойнакское Евпаторийской группы. Среднемноголетние показатели исследуемых площадей относительно показателей 2004 г. были ниже, за исключением оз. Ярылгач. Наибольшие отклонения в сторону уменьшения прослеживались в оз. Акташское. Ключевые слова: методика расчета, дистанционное зондирование, детектирования водных объектов, площадь зеркала, спутниковый мониторинг, гипергалинные озера Крыма, рыбохозяйственное значение озер. Поступила в редакцию: 13.04.2020.
Knowledge of the spatio-temporal distribution of salinity provides valuable information for understanding different processes between biota and environment, especially in hypersaline lakes. Remote sensing techniques have been used for monitoring different components of the environment. Currently, one of the biggest challenges is the spatio-temporal monitoring of the salinity level in water bodies. Due to some limitations, such as the inability to be located there permanently, it is difficult to obtain these data directly. In this study, machine learning techniques were used to evaluate the salinity level in hypersaline East Sivash Bay. In total, 93 in situ data samples and 6 Sentinel-2 datasets were used, according to field measurements. Using linear regression, random forest and AdaBoost models, eight water salinity evaluation models were built (six with simple, one with random forest and one with AdaBoost). The accuracy of the best-fitted simple linear regression model was 0.8797; for random forest, it was equal, at 0.808, and for AdaBoost, it was −0.72. Furthermore, it was found that with an increase in salinity, the absorbing light shifts from the ultraviolet part of the spectrum to the infrared and short-wave infrared parts, which makes it possible to produce continuous monitoring of hypersaline water bodies using remote sensing data.
Climatic changes that have occurred over the past decades, with an acceleration of urbanization of territories and technological development, leads to the significant changes not only in the atmosphere, but also in the Earth’s surface. Surface water bodies are one of these components. Today there are 3 main methods of monitoring water bodies -field, remote and combined. In this paper, we show the possibility of automating remote monitoring of water bodies using QGIS, Python and Sentinel-2 data of the main and largest lakes of the Kerch Peninsula. Having analysed both the available satellite data and the features of the study area, we came to the conclusion that it is advisable to use the NDWI index instead of the mNDWI. After processing and analysing the Sentinel-2 data for 2018 using the data processing model presented in the work, we obtained time series of changes in the areas of the studied lakes of the Kerch Peninsula.
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