Biodiversity and Climate Change

It is necessary to gain a better understanding of the mutual interaction between climate change and biodiversity dynamics through joint actions aimed at integrating data at a global scale with the involvement of experts on both sides of the Atlantic. Current lack of knowledge means that key parameters on the impact of the biodiversity system on the climate system (and reverse) are missing and currently entered as assumptions in climate models. Another approach would require a holistic methodology to identify and unravel patterns and processes in the system-system interactions. This methodological approach assumes the availability of large-scale data sets with observations and measurements of the systems components, together with advanced analytical and modelling software. It also requires computational capacity to run the demanding workflows on huge data sets.

The co-operation from Brazilian and European centres is key, as expertise in biodiversity modelling (e.g. openModeller) and data (e.g. speciesLink) comes from the Brazilian side (CRIA), while the expertise on climate change data analysis, together with the access to the ESGF federated data archive and additional remote sensing data sources, comes from both the European (CMCC) and Brazilian (UFCG) side.

The Scientific Gateway where to access, analyse and visualise data for climate change & biodversity research activity will be soon accessible from this page.

Two Brazilian areas objects of this case study

The Semiarid region

The semiarid region of Brazil, object of the case study, is a large drought and desertification prone region, with 1 million square kms and highly populated. It is one of the main targets of the considerable Brazilian efforts on poverty reduction. Water resource scarcity, land degradation and desertification are driven by climate variability and poverty (overgrazing and deforestation for energy use), which put major pressures on natural resources. Early warning systems (EWS) have been developed and applied for both environmental forecasting and monitoring in the past two decades, which helped to cope with the recurrent droughts and mitigate their effects. However, those information systems still make limited use of remote sensing. Appropriate use of satellite images in their full potential would substantially help a best understanding about the main land use changes drivers: human activities and climate changes

Satellite images derived from several sensors will be processed by specific algorithms to produce estimates of energy balance and evapotranspiration of water to the atmosphere. These estimates, combined with the analysis of historical time-series, allow the detection of changes in the terrestrial plant systems and they will be used to discriminate the influences from human occupation and those from climate variability and/or change on energy fluxes and land cover. The algorithms have to be calibrated and validated using ground-based data. Thus, a large multiple source set of satellite and ground data has to be processed and comparatively analyzed. Differences in temporal and spatial resolution among the satellites’ images and between them and ground data, as well as differences in the time series extension and algorithms features and parameterization, introduce further processing challenges. Identification of a basic set of climate change indicators will be cross-related with biodiversity indicators data and plant species occurrences data, to improve understanding climate change and biodiversity dynamics of terrestrial plant systems through multi-level and multi-dimensional analysis.

The Adolfo Ducke Forest

The Adolfo Ducke Forest is a 10,000 a protected area on the outskirts of  Manaus (AM). The reserve sits at the intersection of two major drainage areas, the Amazon River and the Negro River. The reserve is made up of research plots designed to study the biota of the regions, which may serve as a basis for biodiversity surveys in other areas of the Amazon region, and to study the impacts of fragmentation. Biodiversity and its interaction with climate change is only sparsely monitored, mostly on the basis of field observations at a local scale.

Remote sensing technologies can be applied at scales ranging from local to global extent. Various technologies, such as LiDAR have shown to provide spectral signals that offer great opportunities to describe the 3D structure of the vegetation and the patterns that underly the earth surface. Hyper spectral images provide information on the physical (soil water) and chemical (nutrients) quality of the land surface and biochemical properties The methods used in this use case intend to extract 3D vegetation patterns from LiDAR elevation data and hyperspectral imagery (Remote sensing data). The combination of 3D-structure and quality of the vegetation can thereupon be used as a proxy for biodiversity and habitat suitability. The inversion of remote sensing data to 3D vegetation structures is still at its infancy because of the high throughput computation needed. Also Object Based Image Analysis (OBIA) and classification, including segmentation and machine learning, generates the need for extensive computational power.

The Negro River flowing through the eastern edge of Brazil’s Jau National Park. Source: Wikimedia Commons

These approaches in both areas has the potential to help identify forest patch systems and vegetation structures (and the associated biodiversity) that has an important role in mitigating the negative impacts of climate change. The workflow for this use case requires the processing of data from various origins, computing of models and structures, and the visualization of results. EUBrazil Cloud Connect is well-placed to implement a combined workflow for the study of climate change that gathers experience from LifeWatch (UvA) and ENES (CMCC) and Ecological Niche Modelling tools and data (CRIA).

Driving cloud adoption to tackle biodiversity & climate change

In EUBrazil Cloud Connect applications and services will be adapted to provide a framework for the study of the impact of biodiversity and climate change. The cooperation from Brazilian and European centres is key, as expertise in biodiversity modelling (e.g. OpenModeller) and data (such as SpeciesLink) come from the Brazilian side (CRIA). The interaction and data from the climate change analysis come from the European side (CMCC) and Brazilian side (UFCG). EUBrazil Cloud Connect Use Case 3 uses the tools and frameworks provided to run these services on the federated resources. It is important to remark that the small scale of the project requires the selection of two study areas with available data.  The climate datasets will be used to infer a set of climate indicators for the target areas (the parallel data analysis service will do the computation of the climate indicators and will store the results into a clearinghouse system). The biodiversity workflow will include several steps like (i) filtering out the areas with human induced changes, (ii) analyzing vegetation structure and physical/chemical quality of soil surface, (iii) modelling ecological niche to compute (potential) species distributions/abundance, (iv) modelling and identifying key forest systems to buffer climate change.


Data Sources

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 (  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 ( 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 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.



Potential Users

The LiDAR geo-community consists of hundreds of research institutions and companies and many thousands of individual and/or group users.  The user community for LiDAR and GIS technologies in general is rapidly growing upon increased availability of data. Examples of users are governmental agencies (both research & education) and commercial companies. Non-scientific users come from the field of forestry, earth science, biology, environmental conservation, agriculture,food industry, pharmaceutical industry, environmental engineering companies  and others.

The gateway will be also useful for other professionals of many fields, such as hydrologists, agriculturalists, climatologists, environmentalists and remote sensing professionals, who will use the services provided in the gateway for their labour.

What we offer them

A scientific gateway that integrates tools to understand how biodiversity (and its ecosystems) is buffering climate change and may reduce negative and/or negative effects of climate change. Workflows will combine the analysis of data acquired with different technologies, such as LiDAR, hyper-spectral imagery, satellite images and ground level sensors, with meteorological and biodiversity data to study the impact of climate change in regions with high interest for biodiversity conservation, such as the Brazilian Amazon and the semi-arid & Caatinga regions in Brazil. It will include 3D information concerning the structure of the vegetation, such as the biomass distribution within the forest canopy and forest gap density patterns, which should improve biodiversity indicators such as the energy balance and evapotranspiration.

What we expect from them

We expect environment and climate professionals to become frequent users of the application. We expect informing them on how they can do interventions in the biodiversity and ecosystem in order to mitigate negative effects of climate change (for example by preferential seeding of target plant species), to define policies to mitigate land degradation and desertification and to develop conservation projects.


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