Using digital camera and Landsat imagery with eddy covariance data to model gross primary production in restored wetlands

  • Sites: US-Myb, US-Tw1
  • Knox, Sara Helen Dronova, Iryna Sturtevant, Cove Oikawa, Patricia Y. Matthes, Jaclyn Hatala Verfaillie, Joseph Baldocchi, Dennis (2017) Using digital camera and Landsat imagery with eddy covariance data to model gross primary production in restored wetlands, Agricultural and Forest Meteorology, 237–238(), 233-245.
  • Funding Agency: Department of Water Resources, US DOE

  • Wetlands have the ability to accumulate large amounts of carbon (C), and therefore wetland restoration has been proposed as a means of sequestering atmospheric carbon dioxide (CO2) to help mitigate climate change. There is a growing interest in using the C services of wetlands to help reduce habitat loss and finance restoration projects. However, including wetlands in C markets worldwide requires a better mechanistic understanding of CO2 and methane exchange and instruments and models that can accurately and inexpensively monitor and predict these fluxes across global wetlands. Remote sensing technology, including near-surface and satellite instruments/approaches, is an effective tool for modeling C fluxes including gross primary productivity (GPP) from the site to global scale. In this study, we evaluate the potential of using digital cameras as a simple, cost-effective means of estimating GPP in restored wetlands, and assess the suitability of using Landsat data to model GPP in these environments for regional upscaling. Our research focused on restored temperate freshwater marshes due to their high C sequestration potential.

    As observed in other ecosystems, daily GPP was strongly correlated with site greenness derived from camera imagery (GCCcam). Based on this, we show the potential of using GCCcam and eddy covariance data to adapt and parameterize a light use efficiency (LUE) model to predict daily GPP. The LUE model combining GCCcam and meteorological data was able to explain up to 91% of the variation in daily GPP at the restored marshes, and predict annual GPP budgets within 0% to 20% of observed budgets. However, model performance decreased with increasing site complexity, highlighting the need to explicitly consider spatial heterogeneity in LUE models. We also tested a similar model using Landsat-derived indices, and found that although model performance was high at a homogeneous wetland dominated by emergent vegetation, data-model agreement decreased at a site comprised of a mixture of open water and vegetation, reflecting limitations of Landsat data. Nonetheless, we show that digital camera and Landsat imagery can be used to model photosynthesis in restored wetlands, providing low-cost methods for monitoring C capture that can be used in C market-funded wetland conservation and restoration.