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The SOPPHY vLab is currently used in Oostvaardersplassen (OVP), a large wetland area and an important Natura2000 nature reserve in the Netherlands. The OVP is an important bird breeding area and acts as a winter-feeding site for large numbers of the Barnacle and Greylag goose. Several species of large herbivores (including Red Deer and Konik Ponies) have been introduced to regulate the habitat structure and biomass via grazing. The introduction of the alien invasive plant Jacobaea vulgaris in the OVP has changed the habitat structure and food availability for geese because the plant is toxic for mammalian herbivores, who therefore avoid it in their food. To analyse the invasion of Jacobaea vulgaris, LifeWatch ERIC – VLIC currently explores how physiological traits of Jacobaea vulgaris (e.g., chlorophyll, nitrogen and leaf area index) can be derived from high resolution and multi-spectral satellite imagery, potentially improving the monitoring of this invasive species from space.

• The monitoring of the Jacobaea vulgaris invasion is so far mostly performed using airborne observations. Because such imagery is only acquired once per year, the temporal dynamics of spreading cannot be assessed properly. Mapping the distribution of this species using phenological patterns of plant physiological traits (acquired from frequent multi-spectral observations) promises to solve this limitation and allows us to investigate the effect of environmental drivers on the spreading of this invasive species. The objective is to utilise the plant species trait products from the SOPPHY vLab to identify and map Jacobaea vulgaris over the whole OVP area (56 km²).
• The showcase focuses first on classifying Jacobaea vulgaris using spectral classification (from the Ailanthus species detection workflow) to identify their fractional cover in the study area. We will then use previously measured ground-truth data on foliar chlorophyll, nitrogen and leaf area index (available from the literature) with these cover maps to simulate different multi-spectral remote sensing observations using radiative transfer models. After comparing actual satellite imagery against the simulations, we will perform a radiative transfer inversion (using the MULTIPLY retrieval framework) to detect Jacobaea vulgaris by using species physiological traits from satellite observations (chlorophyll, nitrogen and leaf area index).

For the development of this virtual research lab, we are documenting its progress through technical reports, scientific papers, and background information.
Scientific manuscripts

• Timmermans, J., Kissling, W.D.: Scientific opportunities for developing terrestrial essential biodiversity variables from satellite remote sensing in the context of the post-2020 global biodiversity framework, submitted in 2022 to Ecological Indicators, preprint https://www.biorxiv.org/content/10.1101/2022.04.25.489356v1

Technical reports

• Timmermans, J. (2021): Deployment tool for the MULTIPLY Multi sensor framework.  Internal technical report available from LifeWatch ERIC – VLIC.
• Timmermans J. (2021): Initial design of the SRS enabled Species Trait EBV Workflow. Internal technical report available from LifeWatch ERIC – VLIC.
• Timmermans, J. (2021): Analysing remote sensing approaches and models to retrieve traits for species trait essential biodiversity variables. Internal technical report available from LifeWatch ERIC – VLIC.
• Timmermans, J. (2020): Reviewing existing essential biodiversity variables and remote sensing global data products. Internal technical report available from LifeWatch ERIC – VLIC.

Software packages

• DART radiative transfer model, for simulating satellite imagery using full 3D representations of the study area. https://dart.omp.eu/#/
• SAIL radiative transfer model, for simulating satellite imagery using 1D homogeneous representations of the study area. https://github.com/jbferet/prosail
• Deployment tool for installing the MULTIPLY framework on (cloud) computing systems. https://github.com/JorisTimmermans/Deploy_MULTIPLY
• The MULTIPLY framework for retrieving vegetation traits with multiple satellite remote sensing observations. https://github.com/multiply-org/
• The DeepForest tool available as a Python package for training and predicting individual tree crowns from very high resolution RGB imagery. https://github.com/weecology/DeepForest

Demos

• Notebook as a VRE, EGI conference 2021:
  https://www.youtube.com/watch?v=RR5bHTigTSc;
  https://indico.egi.eu/event/5464/contributions/15646/

• Enabling “LiDAR data processing” as a service in a Jupyter environment, presentation at EGU conference 2021:
  https://drive.google.com/file/d/1KCFpEWX7eK95qVfM1-D4QV9Y-RIeaOBI/view?usp=sharing

Storymaps

• Generating ecosystem structural products using “Laserfarm”:
  https://arcg.is/11qKDi

• Eliminating powerlines from LiDAR-derived products:
  https://arcg.is/1Kb8SC

Zhao, Z., Koulouzis, S., Bianchi, R., Farshidi, S., Shi, Z., Xin, R., Wang, Y., Li, N., Shi, Y., Timmermans, J., & Kissling, W.D. (2022) Notebook-as-a-VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment. Software: Practice and Experience. https://doi.org/10.1002/spe.3098

Wang, Y., Koulouzis, S., Bianchi, R., Li, N., Shi, Y., Timmermans, J., Kissling, W.D., & Zhao, Z. (2022). Scaling Notebooks as Re-configurable Cloud Workflows. Data Intelligence, 4, 409-425. https://doi.org/10.1162/dint_a_00140

Meijer, C., Grootes, M.W., Koma, Z., Dzigan, Y., Gonçalves, R., Andela, B., van den Oord, G., Ranguelova, E., Renaud, N., Kissling, W.D. (2020). Laserchicken — A tool for distributed feature calculation from massive LiDAR point cloud datasets. SoftwareX 12, 100626. https://doi.org/10.1016/j.softx.2020.100626