Generating geospatial data products of ecosystem structure from LiDAR using Notebook-as-a-VRE (NaaVRE)

Quantifying ecosystem structure is of great importance for forest management, ecology, biodiversity monitoring, and climate change modeling. Advances in remote sensing — specifically Light Detection And Ranging (LiDAR) — have enabled the mapping of vegetation structure with unprecedented detail. However, considerable effort and advanced technical skills are required for researchers to process massive amounts of LiDAR data, giving the challenges in handling big data and high computational costs. Different requirements from end users also indicate that the FAIRness (i.e. Findability, Accessibility, Interoperability, and Reusability) of the processing workflow is needed for a broad user community. In this context, we developed a virtual research environment (VRE) solution for the Jupyter environment named Notebook-as-a-VRE, which allows users to search research assets (e.g. data, algorithms), compose workflows, manage the lifecycle of an experiment, and share the results among the user community. Functional components, including the component containerizer, the experiment manager, the VRE knowledge base, and the semantic search engine were deployed as Jupyter extensions on the user environment. In this way, users can encapsulate and containerize selected cells from Jupyter Notebook as standardized RESTful API services, use them for their customized workflows and publish the containerized cells or workflows as reusable components via community repositories. A high-throughput workflow called ‘Laserfarm’ was implemented in the NaaVRE for deriving geospatial data products of ecosystem structure at high resolution across the Netherlands. Geospatial data products containing 25 LiDAR-derived metrics were generated at 10 m resolution covering the whole Netherlands, representing open data on ecosystem height, ecosystem cover, and ecosystem structural complexity. The demonstrated NaaVRE solution can be flexibly expanded to other use cases in ecology, biodiversity, and the Earth science domain, with potential contributions to newly emerging national and regional biodiversity observation networks.