Discover, Visualize, and Deliver Geospatial Data through OGC Standards-based WebGIS System

Geospatial data are important to understand the Earth ecosystem dynamics, land cover changes, resource management, and human interactions with the Earth to name a few. One of the biggest difficulties users face is to discover, access, and assemble distributed, large volume, heterogeneous geospatial data to conduct geo-analysis. Traditional methods of geospatial data discovery, visualization, and delivery lack the capabilities of resource sharing and automation across systems or organizational boundaries. They require users to download the data "as-is" in their original file format, projection, and extent. Also, discovering data served by traditional methods requires prior knowledge of data location, and processing requires specialized expertise. These drawbacks of traditional methods create additional burden to users, introduce too much overhead to research, and also reduce the potential usage of the data. At the Oak Ridge National Laboratory (ORNL), researchers working on NASA-sponsored projects: Distributed Active Archive Center (DAAC) and Modeling and Synthesis Thematic Data Center (MAST-DC) have tapped into the benefits of Open Geospatial Consortium (OGC) standards to overcome the drawbacks of traditional methods of geospatial data discovery, visualization, and delivery. The OGC standards-based approach facilitates data sharing and interoperability across network, organizational, and geopolitical boundaries. Tools and services based on OGC standards deliver the data in many user defined formats and allow users to visualize the data prior to download. This paper introduces an approach taken to visualize and deliver ORNL DAAC, MAST-DC, and other relevant geospatial data through OGC standards-based Web Services, including Web Map Service (WMS), Web Coverage Service (WCS), and Web Feature Service (WFS). It also introduces a WebGIS system built on top of OGC services that helps users discover, visualize, and access geospatial data.