Background and Objectives:The global corona outbreak has had a direct impact on many navigation activities around the world. Maritime navigation is one of the main commercial and economic activities. The purpose of this study is to monitor maritime traffic in the Strategic and International Strait of Hormuz and the port of Pol during the Corona outbreak. In line with the objectives of this research, VV polarization of Sentinel-1 radar images in C band was used. These images were processed in the Google Earth Engine cloud environment to identify and extract marine vehicles, including ships. The results of this study showed that traffic and maritime traffic in the two study areas of the Strait of Hormuz and Bandar Pol in April and May 2020 compared to the same month in 2018 and 2019 has a significant decrease, which has a direct impact on the shipping industry. But with the normalization of the corona situation and the resumption of maritime activities again in 2021, maritime traffic and traffic has increased as in the pre-corona period. In general, this study demonstrates the role of using satellite data, especially artificial aperture radar images, in marine applications and global crisis management, as well as the rapid processing of high-volume radar data in the Google Earth Engine cloud. The outbreak of coronary heart disease (Covid-19) began in late 2019 in Wuhan, China, and later spread worldwide. To prevent the spread of Covid disease, many countries imposed restrictions on communities and travel. In Europe, the European Space Agency has also considered the Covid-19 epidemic in reducing air pollution and its indirect effects on the environment. In Europe, the European Space Agency has also paid attention to the Covid-19 epidemic in reducing air pollution and its indirect effects on the environment. Sea transportation is one of the oldest methods of transportation that is used today to transport goods and passengers to other parts of the country, regionally and internationally. For this reason, the monitoring of maritime traffic in any country is of great importance. Iran's geographical location has also been surrounded by the busiest seas from the north and south, as about 30% of the world's maritime transport takes place in the Persian Gulf. During the quarantine period, many countries imposed restrictions. These restrictions also included ports, shipping and sea voyages, disrupting the movement of passengers, crew and preventing ships from entering ports. Ultimately, all of these actions have disrupted maritime traffic. The maritime transport industry has been affected since the outbreak of Covid-19 because many countries closed maritime ports during the quarantine period and banned export and import activities. Today, Syntactic aperture radar (SAR) radar waves are used to detect ships because of their advantages over optical data. Radar waves have the ability to detect objects at sea. Google Earth Engine is one of the most popular geographic data processing systems that is used with free access to users ...

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Generally, the term biomass is used for all materials originating from photosynthesis. However, biomass can equally apply to animals. Conservation and management of biomass is very important. There are various ways and methods for biomass evaluation. One of these methods is remote sensing. Remote sensing provides information about biomass, but also about biodiversity and environmental factors estimation over a wide area. The great potential of remote sensing has received considerable attention over the last few decades in many different areas in biological sciences including nutrient status assessment, weed abundance, deforestation, glacial features in Arctic and Antarctic regions, depth sounding of coastal and ocean depths, and density mapping.

Mangrove forests are considered one of the most complex and dynamic ecosystems facing various challenges due to anthropogenic disturbance and climate change. The excessive harvesting and land-use change in areas covered by mangrove ecosystems are critical threats for these forests. Therefore, the continuous and regular monitoring of these forests is essential. Fortunately, remote sensing data has made it possible to regularly and frequently monitor this type of forest. This study has two goals. Firstly, it combines optical data of Landsat- 8 and Sentinel-2 with Sentinel-1 radar data to improve land cover mapping accuracy. Secondly, it aims to evaluate the SVM machine learning algorithms and random forest to detection and differentiate forest cover from other land types in the Google Earth Engine system. The results show that the support vector machine (SVM) algorithm in the S2 + S1 dataset with a kappa coefficient of 0.94 performs significantly better than when used in the L8 + S1 combination dataset with a kappa coefficient of 0.88. On the other hand, the kappa coefficients of 0.89 and 0.85 were estimated for the random forest algorithm in S2 + S1 and L8 + S1 datasets. This again indicates the superiority of Sentinel-2 and Sentinel-1 datasets over Landsat- 8 and Sentinel-1 datasets. In general, the support vector machine (SVM) algorithm yielded better results than the RF random forest algorithm in optical and radar datasets. The results showed that the use of the Google Earth engine system and machine learning algorithms accelerates the process of mapping mangrove forests and even change detection.