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

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

Research output: Contribution to journalArticle

Abstract

Earth observation (EO) data, such as high-resolution satellite imagery or LiDAR, has become one primary source for forests Aboveground Biomass (AGB) mapping and estimation. However, managing and analyzing the large amount of globally or locally available EO data remains a great challenge. The Google Earth Engine (GEE), which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data, has appeared as an inestimable tool to address this challenge. In this paper, we present a scalable cyberinfrastructure for on-the-fly AGB estimation, statistics, and visualization over a large spatial extent. This cyberinfrastructure integrates state-of-the-art cloud computing applications, including GEE, Fusion Tables, and the Google Cloud Platform (GCP), to establish a scalable, highly extendable, and high-performance analysis environment. Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows. In addition, a web portal was developed to integrate the cyberinfrastructure with some visualization tools (e.g. Google Maps, Highcharts) to provide a Graphical User Interfaces (GUI) and online visualization for both general public and geospatial researchers.

Original languageEnglish (US)
JournalInternational Journal of Digital Earth
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

aboveground biomass
Cloud computing
engine
Biomass
Earth (planet)
Engines
visualization
Visualization
Satellite imagery
Graphical user interfaces
satellite imagery
Scalability
Fusion reactions
Statistics
Processing
experiment
Experiments
analysis

Keywords

  • Above ground biomass
  • cloud computing
  • Google Earth Engine
  • visualization

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Earth and Planetary Sciences(all)

Cite this

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.

In: International Journal of Digital Earth, 01.01.2018.

Research output: Contribution to journalArticle

@article{8d43209abde742788a485c29faa0d449,
title = "A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine",
abstract = "Earth observation (EO) data, such as high-resolution satellite imagery or LiDAR, has become one primary source for forests Aboveground Biomass (AGB) mapping and estimation. However, managing and analyzing the large amount of globally or locally available EO data remains a great challenge. The Google Earth Engine (GEE), which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data, has appeared as an inestimable tool to address this challenge. In this paper, we present a scalable cyberinfrastructure for on-the-fly AGB estimation, statistics, and visualization over a large spatial extent. This cyberinfrastructure integrates state-of-the-art cloud computing applications, including GEE, Fusion Tables, and the Google Cloud Platform (GCP), to establish a scalable, highly extendable, and high-performance analysis environment. Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows. In addition, a web portal was developed to integrate the cyberinfrastructure with some visualization tools (e.g. Google Maps, Highcharts) to provide a Graphical User Interfaces (GUI) and online visualization for both general public and geospatial researchers.",
keywords = "Above ground biomass, cloud computing, Google Earth Engine, visualization",
author = "Zelong Yang and WenWen Li and Qi Chen and Sheng Wu and Shanjun Liu and Jianya Gong",
year = "2018",
month = "1",
day = "1",
doi = "10.1080/17538947.2018.1494761",
language = "English (US)",
journal = "International Journal of Digital Earth",
issn = "1753-8947",
publisher = "Taylor and Francis Ltd.",

}

TY - JOUR

T1 - A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine

AU - Yang, Zelong

AU - Li, WenWen

AU - Chen, Qi

AU - Wu, Sheng

AU - Liu, Shanjun

AU - Gong, Jianya

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Earth observation (EO) data, such as high-resolution satellite imagery or LiDAR, has become one primary source for forests Aboveground Biomass (AGB) mapping and estimation. However, managing and analyzing the large amount of globally or locally available EO data remains a great challenge. The Google Earth Engine (GEE), which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data, has appeared as an inestimable tool to address this challenge. In this paper, we present a scalable cyberinfrastructure for on-the-fly AGB estimation, statistics, and visualization over a large spatial extent. This cyberinfrastructure integrates state-of-the-art cloud computing applications, including GEE, Fusion Tables, and the Google Cloud Platform (GCP), to establish a scalable, highly extendable, and high-performance analysis environment. Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows. In addition, a web portal was developed to integrate the cyberinfrastructure with some visualization tools (e.g. Google Maps, Highcharts) to provide a Graphical User Interfaces (GUI) and online visualization for both general public and geospatial researchers.

AB - Earth observation (EO) data, such as high-resolution satellite imagery or LiDAR, has become one primary source for forests Aboveground Biomass (AGB) mapping and estimation. However, managing and analyzing the large amount of globally or locally available EO data remains a great challenge. The Google Earth Engine (GEE), which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data, has appeared as an inestimable tool to address this challenge. In this paper, we present a scalable cyberinfrastructure for on-the-fly AGB estimation, statistics, and visualization over a large spatial extent. This cyberinfrastructure integrates state-of-the-art cloud computing applications, including GEE, Fusion Tables, and the Google Cloud Platform (GCP), to establish a scalable, highly extendable, and high-performance analysis environment. Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows. In addition, a web portal was developed to integrate the cyberinfrastructure with some visualization tools (e.g. Google Maps, Highcharts) to provide a Graphical User Interfaces (GUI) and online visualization for both general public and geospatial researchers.

KW - Above ground biomass

KW - cloud computing

KW - Google Earth Engine

KW - visualization

UR - http://www.scopus.com/inward/record.url?scp=85052150301&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052150301&partnerID=8YFLogxK

U2 - 10.1080/17538947.2018.1494761

DO - 10.1080/17538947.2018.1494761

M3 - Article

JO - International Journal of Digital Earth

JF - International Journal of Digital Earth

SN - 1753-8947

ER -