The concept of Essential Biodiversity Variables (EBVs)initiated by GEO BON have been proposed as a layer between biodiversity observation and biodiversity indicators used in the policy. However, the biodiversity community still lacks a global observing system that revolves around the monitoring of a set of agreed variables essential to the tracking of changes in biological diversity on Earth. Therefore, there is an urgent need for remote sensing-enabled EBVs (RS-EBVs) to fill the spatial and temporal gaps between in situ observation data of biodiversity from the field. In other words, without remotely sensed systematic and continuous observations, a global framework for monitoring biodiversity cannot exist.
The NextGEOSS' subtask 6.2.1 Remote sensing-enabled Essential Biodiversity Variables (RS-EBVs) aims to demonstrate the value of a European data hub for the creation of RS-EBVs, which facilitates the creation of a GEO hub for EBVs by linking the key policy/user network groups(GEO BON, CBD and IPBES). The subtask will particularly focus on creating the EBVs data-hub by identifying and populating available RS-EBVs products as well as generating a number of RS-EBVs (at this stage leaf area index (LAI)) from high-resolution satellite data (e.g., Sentinel-2). The products of this subtask will be used in subtask 6.2.2 to demonstrate the use of the European data hub in terms of high-resolution RS-EBVs for habitat monitoring to further support the European Environment Agency (EEA) and its Topic Centre for Biological Diversity (ETC-BD).
The community portal facilitates the generation of a number of RS-EBVs from high-resolution Sentinel-2 images. At this stage this is limited to the generation of the LAI
Leaf area index (LAI) as a crucial plant biophysical trait provides valuable information on vegetation structure and functioning. It plays a key role in climate modelling, and biodiversity monitoring; hence is recognised as an essential climate variable while proposed as an essential biodiversity variable. The strong relationship that exists between LAI and other plant structural and functional parameters such as the fraction of photosynthetically active radiation (FPAR), specific leaf area (SLA), yield, biomass, aboveground net primary productivity (NPP), and canopy cover fraction highlight its key contribution towards monitoring vegetation growth, productivity and generation of relevant biodiversity information.
The Enhanced vegetation index (EVI) which utilize the information from the blue, red and near-infrared spectral regions is used for LAI generation. The index designed to enhance the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences. Studies have shown that the EVI is mainly responsive to canopy structural variables such as leaf area index (LAI), canopy type and architecture. A similar procedure as it is described in Boeg et al. (2002) has been used for calculation of the LAI products using the Sentinel-2 data obtained from the Copernicus data hub.
Data sources: Copernicus data hub
About: Web application developed for NextGEOSS by consortium partner ITC (Faculty of Geo-information Sciences & Earth Observation, Department of Natural Resources, UTwente). For more info contact firstname.lastname@example.org
The NextGEOSS project has received funding from the EU H2020 program under grant agreement No. 730329.