1. “Multi-temporal data driven analyses to support Biodiversity and Climate Change research” Harry Seijmonsbergen - a.c.seijmonsbergen@uva.nla.c.seijmonsbergen@uva.nl Universiteit van Amsterdam (UvA) - The Netherlands

2. Use case 3 Goals / Big picture: - Combine data and expertise between Brazilian and European researchers to fill current gaps in biodiversity and climate change research - Provide a computational framework for studying the impact of biodiversity and climate change in Brazil using cloud computing services in Europe - Integrate multiple data sources for extracting key biodiversity and climate change Indicators - Cases: develop transparent, representative science-based information of two selected vulnerable areas in Brazil The semi-arid area: climate change indicators are cross-related with biodiversity indicators data and plant species occurrences data The Adolfo Ducke Forest near Manaus: study of 3D forest structure, biomass and biodiversity 20/10/14Cloudscape, Rio de Janeiro, Brazil 2

3. Data driven use case 20/10/14 3 Data format: raster ascii Data volume: 10 MB > 100TB > 2PBs Las/lazxml formattedNetCDF Landsat Imagery (USGS) Modis Imagery (Terra/Aqua) Imagery (USGS) Meteorological data (INMET-Brazil) Meteorological data (CPTEC) - Brazil World 90m-DEM (CGIAR/CSI) Meteorological data (ANA – Brazil) Species occurrence data speciesLink network Multi/hyper-spectral imagery (Rapideye/Aviris) Air-born LiDAR point cloud data (EMBRAPA-Brazil) Climate Change data ( CMIP5 – Federated Data Archive) Meteorological data (SINDA – Brazil) Cloudscape, Rio de Janeiro, Brazil

4. First results 20/10/14 4 Colors = tree height class LiDAR point cloud data Scientific Gateway – output 30 years time series analysis Using Sebal algorithm Trends in surface Temperature Based on satellite sensor proxies Forest and Terrain Indicator Products Processing workflows Scientific Gateway – output Cloudscape, Rio de Janeiro, Brazil