A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine

Yang, Zelong; Li, Wenwen; Chen, Qi; Wu, Sheng; Liu, Shanjun; Gong, Jianya

doi:10.1080/17538947.2018.1494761

Cited by 18 publications

(5 citation statements)

References 27 publications

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“…Besides various datasets, which are already available within GEE, researchers can easily upload and share their own datasets as well as their scripts and models through URLs [9]. Other maps and products are generated on-the-fly [28], [29], once any user wants to run the code [36], [37]. Additionally, there is no need to install third-party software packages, such as ENVI and ERDAS, because almost all of the required tools are already available on GEE [38].…”

Section: A Advantages 1) Cloud Infrastructurementioning

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

651

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: A Advantages 1) Cloud Infrastructurementioning

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

651

216

View full text Add to dashboard Cite

show abstract

“…They applied the settlement data to population mapping and improved the accuracy of the output. In addition, Yang et al (2018) integrated Google Earth Engine (GEE), Fusion Tables, and Google Cloud Platform (GCP) to develop a cloud computing platform for estimating forest aboveground biomass, providing a reference for population estimation studies based on the GEE platform.…”

Section: Research Prospectmentioning

confidence: 99%

From auxiliary data to research prospects, a review of gridded population mapping

Lei

Xie

Cheng

et al. 2023

Transactions in GIS

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Population has significant application value and scientific significance in resource use, public health, public transportation, disaster assessment, and environmental management. However, traditional census data can not show the population density difference within census units. Furthermore, census data are not uniform across countries, and reconciling these differences when using data from multiple countries require considerable effort. Finally, there are scale differences between census and geospatial data (e.g., land use/cover), making data analysis and needed research difficult. These challenges significantly limit the applications of census data. The advent of gridded population mapping (GPM) technology has overcome these challenges. GPM technology has developed rapidly in recent years. The research data and models are rich and diverse, and many achievements have been made. A systematic review of the current state of GPM research will help relevant researchers and data users. This article begins by summarizing the core elements of GPM research in four aspects: auxiliary data, models, accuracy, and products. It will then go on to four problems prevalent in GPM research that have direct or indirect effects on the accuracy of GPM. Finally, the article prospects GPM research from four different aspects based on the current state of research.

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“…There are various possible algorithms to ABG estimation based on Google Earth Engine (Yang et al, 2018). Nonetheless, they utilize very complex equations, especially for small (pixel or area) scales, how Yang et al (2018) shows in their study. This even may compromise the ability for widespread use of Google Earth for forest biomass estimates.…”

Section: Google Earth Pros and Cons In Forestry Biomass Estimationsmentioning

confidence: 99%

Traditional and Artificial Intelligence based techniques to measure trees: an overview

Candeias¹

2023

Preprint

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To address the problem of climate changes, CO2 sequestration by forests should be assessed. Forests store carbon in their biomass- about half of it is carbon. The trees’ diameter, height and age are relevant parameters for forests’ biomass estimation. Various methods have been utilized to estimate forests’ biomass. Initially, field measurements using tape measures, clinometers and frequently a ruler were used. They are precise but limited to local scales. In contrast, remote sensing, like drones and satellites, can provide images at regional and global scales. They can use optical, radar or Light Detection and Ranging (LiDAR) sensors for this purpose. LiDAR is more accurate than optical or radar sensors. It is becoming incorporated in smartphones such as I-Phone 12. Mixed-reality devices also have emerged to involve people in forests mapping, in addition to existing tree measuring apps. Google Earth provides free and historical data, but does not offer hypespectral images required to properly estimate biomass. Drones perform three-dimensional terrain geometry, which is relevant to determine trees’ height. They are widely available today. Carbon captured by forests (and for other land use types) can be used to compensate forest growers and farmers, as they are contributing to mitigate climate change.

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A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine

Cited by 18 publications

References 27 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

From auxiliary data to research prospects, a review of gridded population mapping

Traditional and Artificial Intelligence based techniques to measure trees: an overview

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