Data Sources for Biodiversity impact analysis

EUBrazil Cloud Connect Infrastructure will integrate different data sources to be used for the analysis of biodiversity impacts of climate change.

  • Meteorological data from land surface monitoring stations.The ground data are acquired from government agencies such as the Brazilian National Institute of Meteorology (INMET), Brazilian National Water Agency (ANA) and National Environmental Data Systems (Sinda)
  • Satellite images. Major international agencies that provide orbital data are the United States Geological Survey (USGS) and National Aeronautics and Space Administration (NASA). These data are freely available on the web but, for download, user registration is required. In particular, the infrastructure will allow the processing of LANDSAT (http://landsat.usgs.gov/)  data coming from the Brazilian Semiarid region, the integration of meteorological/climate data sources and the processing of large-scale datasets of satellite images series. Landsat represents the world's longest continuously acquired collection of space-based moderate-resolution land remote sensing data. Four decades of imagery provides a unique resource for those who work in agriculture, geology, forestry, regional planning, education, mapping, and global change research. Landsat images are also invaluable for emergency response and disaster relief. On May 30, 2013, data from the Landsat 8 satellite (launched as the Landsat Data Continuity Mission - LDCM- on February 11, 2013) became available. The Landsat project is an integral part of the Remote Sensing Missions component of the USGS Land Remote Sensing (LRS) Program.
  • LIDAR data and hyperspectral data. For the areas near Manaus in Brazil, where hyper-spectral imagery is apparently absent, EUBrazil Cloud Connect will leverage of the available LiDAR data (provided by EMBRAPA – Brazilian Agricultural and Livestock Research Corporation), which is the most important data source for the extraction of 3D vegetation information. LiDAR data has recently received more attention because it can overcome the data saturation shortcoming of Landsat providing more robust biomass estimations. The confidence of these measures has long been recognized as an important part in forest biomass estimation; however, research on biomass uncertainty analysis has only recently obtained sufficient attention due to the difficulty in collecting reference data (Lu et al, 2012). Synergy of hyper-spectral imagery and LiDAR data can substantially improve 3D vegetation structure information, especially for environmental parameter extraction and biodiversity mapping / species definition. Therefore, imaging spectroscopy sensor data such as AVIRIS, Hyperspectral Mapper (HyMap) or hyper-spectral air-photos will be taken into account. In the absence of hyper-spectral data, alternative air-born orthophoto imagery can potentially be used to better locate tree tops, to delineate tree crowns, and visually inspect image versus LiDAR data. The AVIRIS sensor (http://aviris.jpl.nasa.gov/data/index.html) collects data that can be used for characterization of the Earth's surface and atmosphere from geometrically coherent spectroradiometric measurements. This data can be applied to studies in the fields of oceanography, environmental science, snow hydrology, geology, volcanology, soil and land management, atmospheric and aerosol studies, agriculture, and limnology. Applications under development include the assessment and monitoring of environmental hazards such as toxic waste, oil spills, and land/air/water pollution. With proper calibration and correction for atmospheric effects, the measurements can be converted to ground reflectance data which can then be used for quantitative characterization of surface features.

 

 

“Data were acquired by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, and USAID, and the US Department of State.”

 

  • Biodiversity data sources. Provided by CRIA , the Reference Center on Environmental Information, it will also support the implementation of interoperability required for carrying out the biodiversity analysis with the deployment of cloud computing. CRIA´s participation is based on the expertise on data infrastructure and software development to insure data quality and fitness of use. The CRIA´s suite of tools for data cleaning will provide efficient “scans” of data sets, detecting a broad suite of errors, inconsistencies, and potential problems.
  • Climate data from the CMIP5 Federated Data Archive (ESGF). The Coupled Model Intercomparison Project (CMIP http://cmip-pcmdi.llnl.gov/) provides a community-based infrastructure in support of climate model diagnosis, validation, intercomparison, documentation and data access and the current phase of the project is CMIP5. The CMIP5 federated data archive collects 61 global climate models from 29 different modelling groups (e.g. NCAR, MPI-M, CMCC) with a total amount of about 2 petabytes of datasets (June 2013), that can be accessed via any one of distributed data nodes of the Earth System Grid Federation, the official site for CMIP5 outputs. Specifically, CMIP5 promotes a standard set of model simulations in order to (i) evaluate how realistic the models are in simulating the recent past, (ii) provide projections of future climate change (iii) understand some of the factors responsible for differences in model projections including quantifying some key feedbacks such as those involving clouds and the carbon cycle. It is worth of mentioning that the CMIP5 strategy includes two types of climate change experiments: long-term (century time scale) integrations and near-term integrations (10–30 years), also called decadal prediction experiments. CMIP5 also provides a large number of complex models running at high resolution, with a complete representations of external forcings and different types of scenario. In the EUBrazilCC project, climate change indicators on the targeted areas will be evaluated starting from the data available through this federated data archive.