Next Article in Journal
Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity
Next Article in Special Issue
Evaluation of MODIS Gross Primary Production across Multiple Biomes in China Using Eddy Covariance Flux Data
Previous Article in Journal
Building Earthquake Damage Information Extraction from a Single Post-Earthquake PolSAR Image
Previous Article in Special Issue
Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(3), 173; doi:10.3390/rs8030173

High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery

1
Institute of Landscape Ecology, University of Muenster, Heisenbergstr. 2, 48149 Muenster, Germany
2
Institute for Geoinformatics, University of Muenster, Heisenbergstr. 2, 48149 Muenster, Germany
3
Centro Austral de Investigaciones Científicas (CADIC-CONICET), B. Houssay 200, 9410 Ushuaia, Tierra del Fuego, Argentina
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editors: Richard L. Miller, Cheng-Chien Liu, Norman Kerle and Prasad S. Thenkabail
Received: 13 November 2015 / Revised: 2 February 2016 / Accepted: 14 February 2016 / Published: 25 February 2016
(This article belongs to the Special Issue Remote Sensing of Biogeochemical Cycles)
View Full-Text   |   Download PDF [6835 KB, uploaded 25 February 2016]   |  

Abstract

South Patagonian peat bogs are little studied sources of methane (CH4). Since CH4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH4 fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH4 fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species- and 0.83 for the microform-level classification, respectively. CH4 fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH4 closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH4 fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg·m−2·day−1 to 26.8 mg·m−2·day−1. Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH4 fluxes when plot-scale fluxes are up-scaled. View Full-Text
Keywords: closed chamber; object-based image analysis; OBIA classification; methane; peatland; RPAS; UAV closed chamber; object-based image analysis; OBIA classification; methane; peatland; RPAS; UAV
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lehmann, J.R.K.; Münchberger, W.; Knoth, C.; Blodau, C.; Nieberding, F.; Prinz, T.; Pancotto, V.A.; Kleinebecker, T. High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery. Remote Sens. 2016, 8, 173.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top