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

Suchenwirth, Leonhard; Stümer, Wolfgang; Schmidt, Tobias; Förster, Michael; Kleinschmit, Birgit

doi:10.3390/f5071635

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessArticle

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

^1,2,*,

³,

¹,

¹ and

Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Office EB 5, Straße des 17. Juni 145, 10623 Berlin, Germany

GeoVille Information Systems GmbH, Sparkassenplatz 2, Innsbruck 6020, Austria

Thünen Institute of Forest Ecosystems, Alfred-Möller-Straße 1, Eberswalde 16225, Germany

Author to whom correspondence should be addressed.

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

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)

View Full-Text Download PDF

Browse Figures

Citation Export

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 (C_org) 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 C_org 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 C_org 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 C_org stocks. While vegetation C_org has very high RMSEs, outcomes for soil and total C_org 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

▼ Show Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License.

Share and Cite

MDPI and ACS Style

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. https://doi.org/10.3390/f5071635

AMA Style

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(7):1635-1652. https://doi.org/10.3390/f5071635

Chicago/Turabian Style

Suchenwirth, Leonhard, Wolfgang Stümer, Tobias Schmidt, Michael Förster, and Birgit Kleinschmit. 2014. "Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm" Forests 5, no. 7: 1635-1652. https://doi.org/10.3390/f5071635

Find Other Styles

Article Metrics

Abstract views Pdf views Html views

Article Access Map by Country/Region

Only visits after 24 November 2015 are recorded.

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI