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Forests 2014, 5(7), 1635-1652; doi:10.3390/f5071635

Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

1
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Office EB 5, Straße des 17. Juni 145, 10623 Berlin, Germany
2
GeoVille Information Systems GmbH, Sparkassenplatz 2, Innsbruck 6020, Austria
3
Thünen Institute of Forest Ecosystems, Alfred-Möller-Straße 1, Eberswalde 16225, Germany
*
Author to whom correspondence should be addressed.
Received: 1 April 2014 / Revised: 15 June 2014 / Accepted: 2 July 2014 / Published: 11 July 2014
(This article belongs to the Special Issue Applications of Remote Sensing to Forestry)
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Abstract

Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm have been used successfully. We applied both methods for the mapping of organic carbon (Corg) in riparian forests due to their considerably high carbon storage capacity. Despite the importance of floodplains for carbon sequestration, a sufficient scientific foundation for creating large-scale maps showing the spatial Corg distribution is still missing. We estimated organic carbon in a test site in the Danube Floodplain based on RapidEye remote sensing data and additional geodata. Accordingly, carbon distribution maps of vegetation, soil, and total Corg stocks were derived. Results were compared and statistically evaluated with terrestrial survey data for outcomes with pure remote sensing data and for the combination with additional geodata using bias and the Root Mean Square Error (RMSE). Results show that SOM and kNN approaches enable us to reproduce spatial patterns of riparian forest Corg stocks. While vegetation Corg has very high RMSEs, outcomes for soil and total Corg stocks are less biased with a lower RMSE, especially when remote sensing and additional geodata are conjointly applied. SOMs show similar percentages of RMSE to kNN estimations. View Full-Text
Keywords: organic matter; machine learning algorithm; neural network; Danube Floodplain; RapidEye; additional geodata; Austria organic matter; machine learning algorithm; neural network; Danube Floodplain; RapidEye; additional geodata; Austria
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Suchenwirth, L.; Stümer, W.; Schmidt, T.; Förster, M.; Kleinschmit, B. Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm. Forests 2014, 5, 1635-1652.

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