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Remote Sens. 2016, 8(1), 20; doi:10.3390/rs8010020

Remote Sensing Based Simple Models of GPP in Both Disturbed and Undisturbed Piñon-Juniper Woodlands in the Southwestern U.S.

1
Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA
2
Geospatial Laboratory for Environmental Dynamics, University of Idaho, Moscow, ID 83844, USA
3
McCall Outdoor Science School, University of Idaho, McCall, ID 83638, USA
4
Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA
5
CIMMYT-Bangladesh, House 10/B, Road 53, Gulshan-2, Dhaka 1213, Bangladesh
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Randolph H. Wynne and Prasad S. Thenkabail
Received: 2 November 2015 / Revised: 3 December 2015 / Accepted: 17 December 2015 / Published: 30 December 2015
View Full-Text   |   Download PDF [1829 KB, uploaded 30 December 2015]   |  

Abstract

Remote sensing is a key technology that enables us to scale up our empirical, in situ measurements of carbon uptake made at the site level. In low leaf area index ecosystems typical of semi-arid regions however, many assumptions of these remote sensing approaches fall short, given the complexities of the heterogeneous landscape and frequent disturbance. Here, we investigated the utility of remote sensing data for predicting gross primary production (GPP) in piñon-juniper woodlands in New Mexico (USA). We developed a simple model hierarchy using climate drivers and satellite vegetation indices (VIs) to predict GPP, which we validated against in situ estimates of GPP from eddy-covariance. We tested the influence of pixel size on model fit by comparing model performance when using VIs from RapidEye (5 m) and the VIs from Landsat ETM+ (30 m). We also tested the ability of the normalized difference wetness index (NDWI) and normalized difference red edge (NDRE) to improve model fits. The best predictor of GPP at the undisturbed PJ woodland was Landsat ETM+ derived NDVI (normalized difference vegetation index), whereas at the disturbed site, the red-edge VI performed best (R2adj of 0.92 and 0.90 respectively). The RapidEye data did improve model performance, but only after we controlled for the variability in sensor view angle, which had a significant impact on the apparent cover of vegetation in our low fractional cover experimental woodland. At both sites, model performance was best either during non-stressful growth conditions, where NDVI performed best, or during severe ecosystem stress conditions (e.g., during the girdling process), where NDRE and NDWI improved model fit, suggesting the inclusion of red-edge leveraging and moisture sensitive VI in simple, data driven models can constrain GPP estimate uncertainty during periods of high ecosystem stress or disturbance. View Full-Text
Keywords: semi-arid; red-edge; NDWI; woody mortality semi-arid; red-edge; NDWI; woody mortality
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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).

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MDPI and ACS Style

Krofcheck, D.J.; Eitel, J.U.H.; Lippitt, C.D.; Vierling, L.A.; Schulthess, U.; Litvak, M.E. Remote Sensing Based Simple Models of GPP in Both Disturbed and Undisturbed Piñon-Juniper Woodlands in the Southwestern U.S.. Remote Sens. 2016, 8, 20.

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