Remote Sensing Enabled EBVs

For Understanding Terrestrial Ecosystem Dynamics

Biodiversity Monitoring and Mapping

The concept of Essential Biodiversity Variables (EBVs) initiated by GEO BON has 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 monitoring a set of agreed variables essential to tracking changes in biological diversity on Earth. Therefore, there is an urgent need for remote sensing-enabled EBVs (RS-enabled 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.

Considered RS-EBVs

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 recognized as an essential climate variable while proposed as EBV. The strong relationship that exists between LAI and other plants' 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.

Chlorophyll can be used for vegetation health monitoring, forage quality assessment, input variables for key EU (and global) ecological and ecosystem models, ecosystem classification, biomass estimation, productivity measures and indices (NPP, GPP etc), habitat extent and condition and restoration potential. The RS-enabled EBV Chlorophyll is highly correlated with leaf nitrogen content.


The enhanced vegetation index (EVI), which utilizes the information from the blue, red and near-infrared spectral regions, is used for LAI generation. The index is designed to enhance the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring by de-coupling the canopy background signal and reducing atmosphere influences. Studies have shown that the EVI is mainly responsive to canopy structural variables such as LAI, canopy type and architecture. A similar procedure as described in Boeg et al. (2002) has been used to calculate the LAI products using the Sentinel-2 data obtained from the Copernicus data hub.


Boegh, E., S√łgaard, H., Broge, N., Hasager, C. B., Jensen, N. O., Schelde, K., & Thomsen, A. (2002). Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote sensing of Environment, 81(2-3), 179-193.