scholarly journals Observation of Maritime Traffic Interruption in Patagonia during the COVID-19 Lockdown Using Copernicus Sentinel-1 Data and Google Earth Engine

2021 ◽  
Vol 13 (6) ◽  
pp. 1119
Author(s):  
Cristina Rodríguez-Benito ◽  
Isabel Caballero ◽  
Karen Nieto ◽  
Gabriel Navarro
Keyword(s):  
Satellite Data ◽  
Spatial Information ◽  
Radar Data ◽  
Google Earth ◽  
Tierra Del Fuego ◽  
Geographical Information ◽  
Synthetic Aperture Radar Data ◽  
Maritime Traffic ◽  
Google Earth Engine ◽  
Satellite Radar

Human mobilization during the COVID-19 lockdown has been reduced in many areas of the world. Maritime navigation has been affected in strategic connections between some regions in Patagonia, at the southern end of South America. The purpose of this research is to describe this interruption of navigation using satellite synthetic aperture radar data. For this goal, three locations are observed using geoinformatic techniques and high-resolution satellite data from the Sentinel-1 satellites of the European Commission’s Copernicus programme. The spatial information is analyzed using the Google Earth Engine (GEE) platform as a global geographical information system and the EO Browser tool, integrated with several satellite data. The results demonstrate that the total maritime traffic activity in the three geographical hotspots selected along western Patagonia, the Chacao Channel, crossing of the Reloncavi Fjord and the Strait of Magellan was totally interrupted during April–May 2020. This fact has relevant repercussions for the population living in isolated areas, such as many places in Patagonia, including Tierra del Fuego. The study also demonstrates the relevance of satellite radar observations in coastal areas with severe cloud cover, such as the one evaluated here.

scholarly journals On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards

2021 ◽  
Author(s):  
Mara S. Bernardi ◽  
Pasquale C. Africa ◽  
Carlo de Falco ◽  
Luca Formaggia ◽  
Alessandra Menafoglio ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Natural Hazards ◽  
Satellite Data ◽  
Radar Data ◽  
Ground Subsidence ◽  
Interferometric Synthetic Aperture Radar ◽  
Synthetic Aperture Radar Data ◽  
Monitoring And Forecasting ◽  
Satellite Radar ◽  
Computational Resources

AbstractRecent advances in satellite technologies, statistical and mathematical models, and computational resources have paved the way for operational use of satellite data in monitoring and forecasting natural hazards. We present a review of the use of satellite data for Earth observation in the context of geohazards preventive monitoring and disaster evaluation and assessment. We describe the techniques exploited to extract ground displacement information from satellite radar sensor images and the applicability of such data to the study of natural hazards such as landslides, earthquakes, volcanic activity, and ground subsidence. In this context, statistical techniques, ranging from time series analysis to spatial statistics, as well as continuum or discrete physics-based models, adopting deterministic or stochastic approaches, are irreplaceable tools for modeling and simulating natural hazards scenarios from a mathematical perspective. In addition to this, the huge amount of data collected nowadays and the complexity of the models and methods needed for an effective analysis set new computational challenges. The synergy among statistical methods, mathematical models, and optimized software, enriched with the assimilation of satellite data, is essential for building predictive and timely monitoring models for risk analysis.

scholarly journals Use of satellite data for detecting icebergs and evaluating the iceberg threats

2018 ◽  
Vol 58 (4) ◽  
pp. 537-551 ◽  
Author(s):  
I. A. Bychkova ◽  
V. G. Smirnov
Keyword(s):  
Satellite Data ◽  
Radar Data ◽  
Arctic Shelf ◽  
The Arctic ◽  
Satellite Monitoring ◽  
Landsat 8 ◽  
Space Resolution ◽  
Detection Techniques ◽  
Satellite Radar ◽  
High Space

Te methods of satellite monitoring of dangerous ice formations, namely icebergs in the Arctic seas, representing a threat to the safety of navigation and economic activity on the Arctic shelf are considered. Te main objective of the research is to develop methods for detecting icebergs using satellite radar data and high space resolution images in the visible spectral range. Te developed method of iceberg detection is based on statistical criteria for fnding gradient zones in the analysis of two-dimensional felds of satellite images. Te algorithms of the iceberg detection, the procedure of the false target identifcation, and determination the horizontal dimensions of the icebergs and their location are described. Examples of iceberg detection using satellite information with high space resolution obtained from Sentinel-1 and Landsat-8 satellites are given. To assess the iceberg threat, we propose to use a model of their drif, one of the input parameters of which is the size of the detected objects. Tree possible situations of observation of icebergs are identifed, namely, the «status» state of objects: icebergs on open water; icebergs in drifing ice; and icebergs in the fast ice. At the same time, in each of these situations, the iceberg can be grounded, that prevents its moving. Specifc features of the iceberg monitoring at various «status» states of them are considered. Te «status» state of the iceberg is also taken into account when assessing the degree of danger of the detected object. Te use of iceberg detection techniques based on satellite radar data and visible range images is illustrated by results of monitoring the coastal areas of the Severnaya Zemlya archipelago. Te approaches proposed to detect icebergs from satellite data allow improving the quality and efciency of service for a wide number of users with ensuring the efciency and safety of Arctic navigation and activities on the Arctic shelf.

ESTIMATION OF THE ICE-FREE PERIOD LENGTH FOR THE RUSSIAN ARCTIC SEAS BASED ON SATELLITE DATA FOR 2018-2020

2021 ◽  
pp. 48-56
Author(s):  
V. G. SMIRNOV ◽  
◽  
I. A. BYCHKOVA ◽  
N. YU. ZAKHVATKINA ◽  
S. V. MIKHAL’TSEVA ◽  
...  
Keyword(s):  
Neural Network ◽  
Satellite Data ◽  
Radar Data ◽  
Russian Arctic ◽  
Arctic Seas ◽  
Neural Network Method ◽  
Northern Sea Route ◽  
Network Method ◽  
Satellite Radar

The paper describes the experience of using routine satellite radar data to estimate the length of the ice-free period in the Northern Sea Route using a neural network method for the ice cover classification. An earlier onset of melt and a later freezing of ice in the Russian Arctic seas as compared to long-term dates is confirmed.

scholarly journals Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression

2021 ◽  
Vol 14 (1) ◽  
pp. 146
Author(s):  
Matías Salinero-Delgado ◽  
José Estévez ◽  
Luca Pipia ◽  
Santiago Belda ◽  
Katja Berger ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Satellite Data ◽  
Gaussian Process Regression ◽  
Google Earth ◽  
Gap Filling ◽  
Area Index ◽  
Filling Time ◽  
Google Earth Engine ◽  
Mean Square Errors

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production.

scholarly journals INVESTIGATING WATER STORAGE CHANGES IN IRAN USING GRACE AND CHIRPS DATA IN THE GOOGLE EARTH ENGINE SYSTEM

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 981-984
Author(s):  
S. Shami ◽  
Z. Ghorbani
Keyword(s):  
Satellite Data ◽  
Water Storage ◽  
Google Earth ◽  
Annual Precipitation ◽  
Resources Management ◽  
Total Annual Precipitation ◽  
The World ◽  
Grace Satellite ◽  
Google Earth Engine ◽  
Engine System

Abstract. Water storage in regions with the weather hot and arid or semi-arid such as Iran have many uses. Including these water storage, can be referred to groundwater. Groundwater is one of the sources of sweet waters in the world, and one of the factors is economical and social development. Hence, monitoring its changes in water resources management is of great importance. On the other hand, precipitation is one of the factors directly affecting the water storage level and groundwater level changes. In this study, water storage changes with GRACE satellite data and total annual precipitation with CHIRPS data in the Google Earth Engine system investigated for Iran during 2003–2017. The results obtained from the GRACE satellite data indicate over 10 cm reducing of the water storage levels in Iran during the period between 2008 to 2017. Also, the chart obtained from the CHIRPS data for the total annual precipitation shows that the amount of rainfall since 2008 has decreased in this region.

scholarly journals BUILDING OF TEMPERATURE MAPS OF NOVOSIBIRSK CITY BASED ON LANDSAT 8 DATA

2021 ◽  
Vol 4 (1) ◽  
pp. 52-59
Author(s):  
Elena A. Mamash ◽  
Igor A. Pestunov ◽  
Dmitrii L. Chubarov
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Google Earth ◽  
Underlying Surface ◽  
Landsat 8 ◽  
Google Earth Engine ◽  
Engine System ◽  
Temperature Maps

An algorithm for constructing temperature maps of the underlying surface based on a multi-time series of atmospheric corrected satellite data from Landsat 8, implemented in the Google Earth Engine system, is presented. The results of the construction of temperature maps of Novosibirsk using this algorithm are discussed.

scholarly journals SATELLITE DATA BASED LANDUSE AND LANDCOVER CHANGE ANALYSIS IN SOUTHEASTERN PART OF PANCHKULA CITY, HARYANA

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Anup Kumar ◽  
Shishupal Singh ◽  
V.S. Arya
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Earth Satellite ◽  
Google Earth ◽  
Geographical Information ◽  
Human Beings ◽  
Land Area ◽  
Change Analysis ◽  
Southeastern Part

Landuse refers to the use of land by human beings while the land cover refers to the natural cover on land. Landuse and land cover mapping is important for better developmental planning purpose. In the present time remote sensing satellite data, geographical information system (GIS) and global positioning system (GPS) are widely used in mapping of land use and land cover. In the present study landuse and land cover change analysis of southeastern part of Panchkula city have been done using Google Earth satellite data of 2002 and 2018. Satellite data downloaded from Google Earth and geo-referenced in ArcGIS 10.4 software. Landuse and landcover classes had been interpreted and field visit was done at selected location to check the interpreted data. Final maps were prepared and area of landuse and land cover classes were calculated. The study shows that during the year 2002 to 2018 built-up land area increased 95.01Hect, agriculture land area increased 1.24Hect., river course area decreased 20.35 Hect., vacant land area decreased 119.43Hect., park area increased 14.64 Hect., open scrub area decreased 7.82 Hect., road area increased 7.21 Hect.,water body area increased 0.02 Hect. and forest area increased 30.48 Hect. The study can be used for monitoring land use and land cover for planning purpose in the study area.

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