Mr Gerhard Weis1, Dr. Siddeswara Guru1 , Tiancheng Lan 1, Enzhen Luo 1
1University of Queensland, Indooroopilly, Australia
Biography:
Siddeswara Guru is the program lead for the TERN Data Services and Analytics Platform.
Gerhard Weis is the solution architect and technical lead in the TERN Data Services and Analytics Platform.
Tiancheng Lan is a geospatial Data Engineer in the TERN Data Services and Analytics Platform.
Enzhen Luo is a senior Software Engineer in the TERN Data Services and Analytics Platform.
Abstract:
The Terrestrial Ecosystem Research Network (TERN) is an NCRIS-funded land-based observatory that measures the environment from continental to site scales using various observation methods, including remote sensing, in situ, and human observations.
A substantial part of TERN’s geospatial data holdings are regional and continental-scale raster datasets, complemented by high-resolution drone imagery and LiDAR datasets. Often, these datasets form a time series over multiple decades. The large volume and size of these datasets provide significant challenges in data movement, processing, publication, and visualization. The solution should be robust enough to support effective management and enable users to discover, visualise, and access data easily.
The presentation will showcase how TERN is developing spatial data infrastructure to support scalable and interoperable data services. The geospatial infrastructure utilises the GridFTP service provided by QRISCloud to transfer data from data providers to the Network File System (NFS) and subsequently to ARDC NeCTAR Swift storage in cloud-native file format, Cloud-optimised Geo Tiffs. After additional data validation, raster data will be published as an OGC-compliant Web Mapping Service and Web Coverage Service, accompanied by ISO 19115-3 compliant metadata that includes descriptions, citation information, platform and instrument details, methods, and additional information. In addition, the STAC catalog is created to enable users to discover, interrogate, visualise and easily access data on demand.
The cloud-native nature of this approach aims to accelerate scientific data discovery, foster interdisciplinary research, and drive innovation in ecosystem management and conservation efforts.