Rapid multispectral data sampling using Google Earth Engine

Brooke, Sam; D’Arcy, Mitch; Mason, Philippa J.; Whittaker, Alexander C.

doi:10.1016/j.cageo.2019.104366

Cited by 9 publications

(9 citation statements)

References 42 publications

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“…The availability of RS datasets in GEE makes it an appealing platform for the Pedosphere studies at diverse scales. GEE was utilized for digital soil mapping [50], [161], geology and mining [162], [163], geomorphology studies [164], soil topography mapping [165], soil moisture derivation [166], and soil carbon and salinity estimation [167], [168]. For example, [168] applied Landsat-5 and Landsat-8 datasets within GEE to produce a global soil salinity map based on the thermal anomaly.…”

Section: Pedospherementioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

601

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: Pedospherementioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

601

216

View full text Add to dashboard Cite

show abstract

“…; the systems that support online data processing and analysis and provide SaaS (Software as a Service) include ArcGIS online and NASA EOSDIS. Among them, the cloud computing platforms corresponding to GEE (Google Earth Engine) [100,101], Data Cube, ESE (ENVI Services Engine) and ERDAS Apollo can provide both DaaS and SaaS service modes.…”

Section: Development Of Rs Application Systemmentioning

confidence: 99%

Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

Liu

Dang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the rapid development of high-resolution earth observation system, the data processing, algorithm design and system development of Remote Sensing Spatial-Temporal Big Data (RS-STBD) have gradually become the bottleneck problems in the application and development of earth observation system. The research on the model, algorithm and system of RS-STBD processing involves complex scientific problems, technical bottlenecks and inconstant requirements of engineering applications. This paper summarizes the data type and processing theory model of RS-STBD, the high performance algorithm design based on cloud service and intelligent computing, and the architecture design and engineering development methods of the complex remote sensing application system. Furthermore, the existing problems in the current research are analyzed, and the related solutions are given. Finally, the future development trend of scientific exploration, technical research and application development of RS-STBD is prospected.

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“…The GEE online platform was useful for working with many images, both for classification and for the calculation of spectral indices. NDVI and EVI were calculated for each month over the six years of study [42][43][44]. This methodology is easily replicable in similar landscapes affected by forest fires.…”

Section: Contributions To the Evaluation Of Ecosystem Services In Similar Territoriesmentioning

confidence: 99%

The Effects of a Megafire on Ecosystem Services and the Pace of Landscape Recovery

Mancilla-Ruiz

Barrera

González

et al. 2021

Land

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(1) Background: Megafires have affected several regions in the world (e.g., Australia, California), including, in 2017, the central and south-central zones of Chile. These areas represent real laboratories to monitor the impacts on the sustainability of landscapes and their recovery after fires. The present research examines the modification of dynamics and the provision of ecosystem services by a megafire in a Mediterranean landscape in central Chile, combining remote sensing technologies and ecosystem service assessments. (2) Methods: Land cover and spectral indices (NBRI, BAIS-2, NDVI, and EVI) were measured using Sentinel-2 imagery, while the provision of ecosystem services was evaluated using an expert-based matrix. (3) Results: The megafire affected forest plantations, formerly the dominant land cover, as well as other ecosystems, e.g., native forests. After five years, the landscape is dominated by exotic shrublands and grasslands. (4) Conclusions: The megafire caused a loss of 50% of the landscape’s capacity to supply ecosystem services. Given that native forests are the best provider of ecosystem services in this landscape, restoration is a key to recovering landscape sustainability.

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Rapid multispectral data sampling using Google Earth Engine

Cited by 9 publications

References 42 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

The Effects of a Megafire on Ecosystem Services and the Pace of Landscape Recovery

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