Auxiliary Material for
Parsing variability in CH4 fluxes at a spatially heterogeneous wetland: Integrating multiple eddy
covariance towers with high-resolution flux footprint analysis
Jaclyn Hatala Matthes, Cove Sturtevant, Joseph Verfaillie, Sara Knox,
and Dennis Baldocchi
(Dept. Geography, Dartmouth College, Hanover, NH)
Journal of Geophysical Research, Biogeosciences, 2014
Introduction
This file contains supporting materials (2 figures and 1 table) detailing specific processing steps
referred to within the main text of this article.
1
Figure S1. The WorldView-2 reference spectra for the four classes used in the ENVI Spectral
Angle Mapper routine were measured by high-resolution GPS plots on 13 May 2012 at the
restored wetland sites in the footprint of the permanent flux tower. Reference spectra showed
enough differences among the eight spectral bands, particularly in the infrared bands, to achieve
high classification accuracy (see Table S1).
2
Classified
cells
Photosynthetic Veg
Aquatic Veg
Senesced Veg
Water
Producer's
Accuracy (%)
Photosyn.
Veg
125
9
2
1
91.2
Reference plots
Aquatic
Senesced
User's
Veg
Veg
Water
Accuracy (%)
12
2
1
89.3
109
4
1
88.6
3
75
1
92.6
1
1
137
97.9
87.2
91.5
97.9
Table S1. Accuracy statistics for classification of WorldView-2 imagery into 4 classes. We
classified the WorldView-2 satellite imagery by the supervised spectral angle mapper (SAM)
routine in ENVI, using endmember spectra outlined in Figure S1. Classification accuracy was
determined by the collection of five high-accuracy GPS ground plots of 3-5m in diameter within
200m of the tower footprint of each classification type (separate from the data used for
endmember spectra) that were revisited every 2 weeks throughout the study period to determine
whether their classification state changed. This table represents the aggregated statistics for
classification accuracy across all four images used in this analysis.
3
Figure S2. a) In the hierarchical linear model without a temporal autocorrelation structure, there
is strong temporal autocorrelation in the modeled residuals. b) Imposing a temporal
autocorrelation structure of autoregressive order-1 (AR-1) on the within-tower random effects
removed the temporal autocorrelation pattern within the modeled residuals.
4