"Space data offer a wide range of possibilities for observing the state of land ecosystems and their changes. More importantly, space data offer great potential for monitoring changes in ecosystems, e. g. in response to human intervention or climate extremes. Today, these data streams are available in unprecedented spatial, spectral, and temporal resolution. One challenge is interpreting these space data in tandem with a wide range of in-situ data on various aspects of ecosystem function and structure. BACI aims to exploit the potential of existing and planned space-based Earth observation (EO) in conjunction with in-situ data to derive new essential ecosystem variables and identify changes in ecosystem function. BACI mainly works in six target regions across Europe and Africa.
The project is divided into seven scientific work packages:
1) BACI explores the potential of integrating existing data archives and new observations as they become available (i.e. from Sentinel 1 & 2). Of particular importance is the integration of optical and radar data. While optical data allow tracking the phenology of the ecosystem and responses to short term anomalies in ecosystem functioning, radar data are expected to reveal structural properties of ecosystem and their changes. BACI aims to provide a generic, scalable framework for combining data from multiple wavelength.
2) BACI works on the integration of a wide range of in-situ data from different sources. As with the EOs, the project puts much emphasis on realistic uncertainty estimates. The main components of the in-situ database are global plant traits at various aggregation level for 18 traits, ecosystem parameters derived from eddy flux measurements across ecosystems, synthesis data sets of plants and bird diversity for Europe; synthesis data sets of annual ring data sets for Europe; and finally, vegetation structure and biomass data derived from LiDAR, including stand information. We also conduct a series of experiments on integrating these data with EOs.
3) The project aims at deriving new downstream data products. For example, we seek to produce global data products on land-atmosphere fluxes of carbon dioxide, water, and energy or ecosystem scale phenologies. As these variables are can only be observed in-situ, we need to integrate EOs and in-situ observations. In BACI we realize this integration with machine learning methods. BACI focuses on the derivation of so-called ""Essential Ecosystem Variables"", i.e. variables that are essential for the monitoring of the fundamental interactions and feedback in the earth system between biosphere and atmosphere.
4) A key element of BACI project is the development of a generic index of change. This index should be able to detect a variety of different types of anomalies and extremes. This novelty index enables the detection of sudden events and abnormal changes in multivariate EO data streams. For now, BACI focuses in particular on the identification of abrupt changes relevant to essential ecosystem variables.
5) To guarantee highest quality standards, BACI performs rigorous quality assessments. Both the generated downstream data products as well as the generic index of change are validated with independent reference data. Different regional validation sites are used for validation efforts.
6) BACI embraces a component that allows us to contextualize detected changes. In particular, we consider socioeconomic changes as key drivers of change in land ecosystems.
7) BACI explores new ways to ingest space data into the study of biodiversity patterns and its changes. For instance, we explore to potential of radar data as predictors for species distribution modelling. The project also explored the value of the change index for monitoring protected areas in Europe.
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