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

1 Scopus citations


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)
Pages (from-to)995-1012
Number of pages18
JournalInternational Journal of Digital Earth
Issue number9
StatePublished - Sep 2 2019



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

ASJC Scopus subject areas

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

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