Darvishzadeh, R.

Hennekens, Stephan

Meijninger, Wouter

Mucher, Sander

Neinavaz, E.

Nieuwenhuis, W.

Skidmore, A.K.

English

In NextGEOSS Biodiversity Pilot WP 6.2.1, we focus on creating the NextGEOSS European remote sensing-enabled EBVs (RS enabled-EBVs) data-hub by identifying and populating available RS-enabled EBVs products. 123 variables were compiled as EBV candidates for five out of six EBV classes, as the genetic composition cannot be measured using remote sensing data. All EBV candidates were prioritized based on different criteria and observation requirements including relevancy to Aichi biodiversity targets, availability through remote sensing data (i.e., feasibility), and a measure of accuracy and maturity of remote sensing technologies and techniques. The 30 highest-prioritized RS-enabled EBVs were selected, and from these available RS-enabled EBVs products were identified with special consideration to their spatial resolution and scales. Metadata was created for each considered RS- enabled EBVs products with respect to the data provider and inserted in the NextGEOSS data-hu

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For more informationAndrew K. Skidmore a, b, Sander Mucher c, Elnaz Neinavaz a, Roshanak Darvishzadeh a, Stephan Hennekens c, Willem Nieuwenhuis a, Wouter Meijninger c a Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente,   Enschede, the Netherlands.b Department of Environmental Science, Macquarie University, NSW 2109, Sydney, Australiac Wageningen Environmental Research, Wageningen University and Research, the NetherlandsA	 Creating the NextGEOSS European RS- enabled EBVs Data-hubThe NextGEOSS European RS-enabled EBVs data-hub was created.Fig. 1. NextGEOSS data-hubB  Generating RS-enabled EBVsFrom the RS-enabled EBVs, which were initially proposed to be derived from high-resolution satellite data (Sentinel-2), leaf area index (LAI) was selected.Fig. 2. The integration process for data processing for the prediction of the RS-enabled EBVs (e.g., LAI) over the Netherlands on Terradue cloud platformFig. 3. Leaf area index predicted over the Netherlands using Sentinel-2 data, on 6 May 2018 in NextGEOSS biodiversity pilot.C  Remote Sensing-enabled EBV’s for European Habitat MappingUsing 1,5 M vegetation plot records as input (Derived from the European Vegetation Archive) covering ~200 EUNIS habitats for modelling.Selection of a maximum of 30 predictors (Comprising 7 climate parameters, 10 soil and terrain parameters,and 13 RS-EBVs).Using open source software Maxent, version 3.4.1 for the habitat modelling, by applying a machine-learning technique called maximum entropy modelling.Running the modelling process in the cloud which  is controlled by a WPS client.Fig. 4. The procedure followed to modelling the spatial distribution of European habitats using in situ data, environmental layers as RS-EBVs products.Opening web application Habitat selectionSelect predictors & runEuropean habitat suitability map for selected habitat type and downloadable outputNextGEOSS Biodiversity Pilot: Remote Sensing-enabled Essential Biodiversity Variables Data-hub for European Habitat Mapping

NextGEOSS Biodiversity Pilot: Remote Sensing-enabled Essential Biodiversity Variables Data-hub for European Habitat Mapping:poster

https://ris.utwente.nl/ws/files/168412854/Poster_NextGEOSS_Canbera_2019_Final_e_poster.pdf

NextGEOSS Biodiversity Pilot: Remote Sensing-enabled Essential Biodiversity Variables Data-hub for European Habitat Mapping:poster

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