Abstract
Land monitoring is a key issue in Earth system sciences analyzing environmental changes. To generate knowledge about changes, e.g., by decreasing uncertainties in the products and to build confidence in land change monitoring, multiple information sources are needed. Earth observation (EO) satellites and in situ measurements are available for operational monitoring of the land surface. As the availability of well-prepared geospatial time-series data for environmental research is limited, user-dependent processing steps with respect to the data source and formats pose additional challenges. Further steps are necessary for the analysis of time-series data. In most cases, it is possible to support science with spatial data infrastructures (SDI) and web services to provide data in a processed format and to provide time-series plots for further interpretation. Data processing middleware is proposed as a technical solution to improve interdisciplinary research using multi-source time-series data and standardized data acquisition, pre-processing, updating and analyses. This solution is being implemented within the Siberian Earth System Science Cluster (SIB-ESS-C), which combines various sources of EO data and climate data with a focus on vegetation and temperature data. Products from the Moderate Resolution Imaging Spectroradiometer (MODIS), in situ data from meteorological stations and high spatial resolution Landsat data are available in the processing middleware that is connected to different data providers. Analytical tools have been integrated and can be used for time-series plotting, phenological dates, trend calculations, break point detection, and data comparison using existing open-source software packages. The development of this SDI is based on the definition of automated and on-demand tools for data searching, ordering and processing, implemented along with standard-compliant web services. Therefore, open-source software is used to build up this system. The tools developed, consisting of a user-friendly data access, download, analysis and interpretation infrastructure, are available within SIB-ESS-C for operational use.
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Acknowledgments
The authors would like to thank the Friedrich Schiller University, Jena, for funding the development of the Siberian Earth System Science Cluster. We also thank the NASA, USGS, and NOAA for the provision of satellite data and data from climate stations.
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Eberle, J., Urban, M., Homolka, A., Hüttich, C., Schmullius, C. (2016). Multi-Source Data Integration and Analysis for Land Monitoring in Siberia. In: Mueller, L., Sheudshen, A., Eulenstein, F. (eds) Novel Methods for Monitoring and Managing Land and Water Resources in Siberia. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-319-24409-9_20
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