Mishra, Deepak: University of Georgia (Project Lead)
Cotten, David: University of Georgia, Deptartment of Crop and Soil Science, Lab for Environmental Physics (Co-Investigator)
O'Connell, Jessica: University of Texas at Austin (Co-Investigator)
Hawman, Peter: University of Georgia (Student-Graduate)
Narron, Caroline: University of Georgia (Student-Graduate)
Project Funding:
2017 - 2022
NRA: 2016 NASA: Carbon Cycle Science
Funded by NASA
Abstract:
Coastal marshes are atmospheric carbon sinks, depositing as much as 1713 g C m-2 yr-1 in soils,which has been termed “blue carbon”. However, marshes are threatened by multiple causes,especially sea-level rise. Remote monitoring of landscape Gross Primary Productivity (GPP), a proxy for carbon sequestration potential, helps assess C sinks and facilitate prioritization of restoration and conservation. We propose to create a novel MODIS GPP algorithm for coastal marshes that accounts for the species specific influence of tidal inundation on plant production. We will calibrate our models across four salt and brackish marsh sites across three states (Louisiana, Mississippi, Georgia) and covering three species (Spartina alterniflora, Spartina patens, Juncus roemerianus). Models will be based on a combination of eddy covariance carbon flux data, monitoring of plant level photosynthesis, field and spectral estimates of species composition and plant biophysical variables, as well as accurate measures of marsh surface inundation. The ultimate product will be regional maps of GPP that assists with monitoring coastal marsh blue carbon across the southeastern United States. This work relies on MODIS based remote sensing to scale from carbon flux and field data to regional GPP assessments. These remote sensing GPP models will be based on two approaches: production efficiency models (PEM) which compute GPP from absorbed solar radiation and canopy photosynthesis models (CPM), based on biophysical variables including leaf area index. We will adapt PEM and CPM GPP models to tidal marshes from 500 m tide-indexed MODIS daily surface reflectance data.
As part of this study, we will solve problems that complicate tidal marsh GPP estimates. For example, we will improve marsh surface flooding estimates, generate a plant-centered GPP model that reduces the influence of tidal exchange on carbon accounting, adapt new spectral biophysical indices that account for the influence of wetland moist soils on reflectance, and use chlorophyll flouremetry to measure plant level productivity during high tides, a time when eddy covariance towers can not estimate carbon flux. We also will map species composition and generate light use efficiency estimates for common coastal marsh species. Consequently, we will be able to generate species invariant and tide robust CPM and PEM plant centered GPP. The crux of this proposal is to combine multiple sources of information to generate our ultimate product, regional maps of MODIS derived plant GPP spanning 2000-2020 and perform a comprehensive phenological analysis.
End-users engagement is also important. We will make end-users, such as coastal managers,aware of the tools we will develop, provide access to tools and instructional documents, and train staff to use the tools to inform decision-making. To ease this task, we will develop a Python plug-in for QGIS, an open source geospatial software and host our source code on GitHub to facilitate future and community model development. Applications of our work include estimating CO2 exchange after natural and anthropogenic disasters, modeling the influence of sea level rise on marsh health, understanding coastal C sources and sinks, and use by government agencies to assess restoration trajectories for conservation and management of critical coastal ecosystems.
Publications:
Ghosh, S., Mishra, D. 2017. Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA. Remote Sensing. 9(12), 1340. DOI: 10.3390/rs9121340
O'Connell, J. L., Mishra, D. R., Cotten, D. L., Wang, L., Alber, M. 2017. The Tidal Marsh Inundation Index (TMII): An inundation filter to flag flooded pixels and improve MODIS tidal marsh vegetation time-series analysis. Remote Sensing of Environment. 201, 34-46. DOI: 10.1016/j.rse.2017.08.008
Tao, J., Mishra, D., Cotten, D., O'Connell, J., Leclerc, M., Nahrawi, H., Zhang, G., Pahari, R. 2018. A Comparison between the MODIS Product (MOD17A2) and a Tide-Robust Empirical GPP Model Evaluated in a Georgia Wetland. Remote Sensing. 10(11), 1831. DOI: 10.3390/rs10111831
More details may be found in the following project profile(s):
