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Funded Research

Evaluation of synergies between VNIR-SWIR and TIR imagery in a Mediterranean-climate Ecosystem

Roberts, Dar: UC Santa Barbara (Project Lead)

Project Funding: 2011 - 2013

NRA: 2010 NASA: HyspIRI Preparatory Activities Using Existing Imagery   

Funded by NASA

Abstract:
Much of the potential of a combined VNIR-SWIR imaging spectrometer and TIR imaging system for the analysis of vegetation remains largely unexplored. Obvious synergies include hyperspectral measures of plant stress and physiological function, such as the Photochemical Reflectance Index (PRI) and canopy temperature, in which plant stress would be expected to be expressed both as a change in the type of xanthophyll activated as well as a change in canopy temperatures. Another synergy is the potential of VNIR-SWIR imagery as a means improving temperature emissivity separation by providing column water vapor. VNIR-SWIR estimates of fractional cover should help partition temperatures between green leaves, senesced canopy materials and bare soil. Finally, combined VNIR-SWIR & TIR data provide the opportunity to evaluate how canopy dominants (expressed by plant species or functional types) modify retrieved canopy temperatures and how temperatures in a landscape vary among vegetation types and phenophase. In this research, we propose to evaluate synergies between a VNIR-SWIR and TIR imaging system utilizing AVIRIS and MASTER data acquired over Coal Oil Point, California on June 19, 2008. We will augment the 2008 AVIRIS analysis, which is limited to the coastal region and one date, with additional with field spectra, ecophysiological analyses, thermal radiometer measurements and destructive harvests to explore seasonal changes in plant biophysical, physiological, and optical properties. To account for regions burned during the 2008 and 2009 fires in the Santa Barbara Front Range and expand spatial coverage we will rely on a 2004 AVIRIS image that has already been mapped to plant species at the 20 m and 64 m resolutions. Specific questions we will address include: 1) What is the potential for improved temperature emissivity separation using VNIR-SWIR column water vapor? 2) What is the relationship between moisture content and emissivity? 3) What is the relationship between common hyperspectral measures of plant stress and physiological function (i.e., PRI, leaf water content) and canopy temperature in imagery (16 and 64 m) and observed in ground-based measurements? 4) How does canopy temperature vary as a function of plant functional type and species at these scales? This question can be addressed using MASTER data and AVIRIS from 2008 augmented by data from 2004. 5) How is canopy temperature and emissivity impacted by changes in canopy cover, notably changes in green leaf fraction, non-photosynthetic vegetation and bare soil? How is temperature partitioned among the various components of cover? This research complements existing NASA supported research evaluating the temporal, spatial and spectral requirements for mapping plant functional types and species by adding a thermal element. It leverages extensively off of existing research in the area and routine measures of environmental conditions at Coal Oil Point. If an additional MASTER-AVIRIS pair authorized for acquisition are obtained in 2010, these will be included in the analysis.

Publications:

Roberts, D. A., Dennison, P. E., Roth, K. L., Dudley, K., Hulley, G. 2015. Relationships between dominant plant species, fractional cover and Land Surface Temperature in a Mediterranean ecosystem. Remote Sensing of Environment. 167, 152-167. DOI: 10.1016/j.rse.2015.01.026

Roberts, D. A., Quattrochi, D. A., Hulley, G. C., Hook, S. J., Green, R. O. 2012. Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey mission. Remote Sensing of Environment. 117, 83-101. DOI: 10.1016/j.rse.2011.07.021

Roberts, D., Alonzo, M., Wetherley, E. B., Dudley, K. L., Dennison, P. E. 2017. 9. Multiscale Analysis of Urban Areas Using Mixing Models in: Integrating Scale in Remote Sensing and GIS. CRC Press, 247-282. DOI: 10.1201/9781315373720-10


More details may be found in the following project profile(s):