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Knowledge-based classification of remote sensing data for the estimation of below- and above-ground organic carbon stocks in riparian forests

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Abstract

Floodplain forests play a crucial role in the storage of organic carbon (Corg). However, modeling of carbon stocks in these dynamic ecosystems remains inherently difficult. Here, we present the spatial estimation of Corg stocks in riparian woody vegetation and soils (to a depth of 1 m) in a Central European floodplain using very high spatial resolution remote sensing data and auxiliary geodata. The research area is the Danube Floodplain National Park in Austria, one of the last remaining wetlands with near-natural vegetation in Central Europe. Different vegetation types within the floodplain show distinct capacities to store Corg. We used remote sensing to distinguish the following vegetation types: meadow, reed bed and hardwood, softwood, and cottonwood forests. Spectral and knowledge-based classification was performed with object-based image analysis. Additional knowledge rules included distances to the river, object area, and slope information. Five different classification schemes based on spectral values and additional knowledge rules were compared and validated. Validation data for the classification accuracy were derived from forest inventories and topographical maps. Overall accuracy for vegetation types was higher for a combination of spectral- and knowledge-based classification than for spectral values alone. While water, reed beds and meadows were clearly detectable, it remained challenging to distinguish the different forest types. The total carbon storage of soils and vegetation was quantified using a Monte Carlo simulation for all classified vegetation types, and the spatial distribution was mapped. The average storage of the study site is 428.9 Mg C ha−1. Despite certain difficulties in vegetation classification this method allows an indirect estimation of Corg stocks in Central European floodplains.

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Acknowledgments

We would like to thank the administration of the Danube Floodplain National Park, especially Christian Baumgärtner and Christian Fraissl, as well as the Austrian Federal Forests Agency (ÖBf) for the support and for supplying us with geographic data, especially with data for the validation of results. We would like to thank Tobias Schmidt for the data processing and statistical support, and Kelaine Vargas for improving our English. We would also like to thank two anonymous reviewers for their helpful and detailed comments on our manuscript. The German Research Foundation (DFG): project-number 2215/2-1, provided support for this project.

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Authors and Affiliations

  1. Department of Geoinformation in Environmental Planning, Berlin Institute of Technology, Office EB 5, Straße des 17. Juni 145, 10623, Berlin, Germany

    L. Suchenwirth, M. Förster & B. Kleinschmit

  2. Department of Ecology, Ecosystem Science/Plant Ecology, Berlin Institute of Technology, Berlin, Germany

    A. Cierjacks

  3. Department of Soil Science, Berlin Institute of Technology, Berlin, Germany

    F. Lang

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  1. L. Suchenwirth
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  2. M. Förster
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  3. A. Cierjacks
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  4. F. Lang
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Correspondence to L. Suchenwirth.

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Suchenwirth, L., Förster, M., Cierjacks, A. et al. Knowledge-based classification of remote sensing data for the estimation of below- and above-ground organic carbon stocks in riparian forests. Wetlands Ecol Manage 20, 151–163 (2012). https://doi.org/10.1007/s11273-012-9252-8

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