Roth, Keely


Keely Roth


I have a lifelong love of geography. One class in geospatial technologies as an undergraduate convinced me of the potential and importance of remote sensing and spatial analysis for measuring, mapping and monitoring our world.


In my research, I work to develop and refine remote sensing techniques for mapping the composition and functioning of natural ecosystems. I am particularly interested in the validation and scaling of remotely-sensed measures of plant function, and in exploring the spatio-temporal variation of these measurements in different ecosystems. To investigate these relationships, I use a variety of quantitative, statistical methods to analyze data collected in the field and lab together with imagery acquired by lidar, multispectral and hyperspectral sensors. I love to get out in the field, and have had the opportunity to do so across California and in the tidal marshes of southern Louisiana.


Currently, I am a postdoctoral researcher in the CSTARS Lab. I work as part of the NASA Hyperspectral Infrared Imager (HyspIRI) Preparatory Science initiative to assess the capabilities of the mission for characterizing plant functional differences across diverse ecosystems. I am investigating how spectral data express plant function and taxonomy at leaf and canopy scales, and how clustering within these data correspond with leaf traits, phenology, and community composition and structure.


I received both my master’s degree and PhD in Geography from UC Santa Barbara. In my doctoral dissertation, I assessed strategies for using imaging spectroscopy data to map dominant plant species and functional types across contrasting ecosystems. I also evaluated the impact of image spatial resolution on the accuracy and stability of these classifications, as well as on estimates of sub-pixel fractional cover and biophysical spectral indices. More locally, I explored seasonal changes in the spectral and functional behavior of Mediterranean shrub and grassland communities, at both individual plant and image scales.







In Preparation

Roth, K.L., Casas, A., Huesca, M., and Ustin, S.L. (in preparation). Leaf spectral clusters as potential optical leaf functional types within California ecosystems, to be submitted to Remote Sensing of Environment.

Roth, K.L., Roberts, D.A., Emery, N., D’Antonio, C., and Mazer, S. (in preparation). Seasonal patterns of spectral separability, phenology and biophysical status in Mediterranean climate plant communities.


Beland, M., Roberts, D.A., Peterson, S.H., Biggs, T.W., Kokaly, R.F., Piazza, S., Roth, K.L., Khanna, S., and Ustin, S.L. (submitted). Mapping changing distributions of dominant species in oil-contaminated salt marshes of Louisiana using imaging spectroscopy, submitted to Remote Sensing of Environment.

In Revision

Huesca, M., García, M., Roth, K., Casas, A., and Ustin, S.L. (in revision). Canopy structural attributes derived from AVIRIS data in a mixed broadleaf/conifer forest, submitted to Remote Sensing of Environment.

Meerdink, S.K., Roberts, D.A., King, J.Y., Roth, K.L., Dennison, P., Amaral, C.H., and Hook, S.J. (in revision). Linking seasonal foliar traits to VSWIR-TIR spectroscopy across California ecosystems, submitted to Remote Sensing of Environment.

Gosnell, J., Roth, K., Diaz, S., MacDonald, A., Kendall, B., Wilmers, C., Duffield, J. and Seddon, P. (in revision). Beyond success and failure: A population-growth based framework for evaluating reintroduction progress, submitted to Global Ecology and Conservation.


Coates, A., Dennison, P., Roberts, D., and Roth, K. (2015). Monitoring the impacts of severe drought on southern California chaparral species using hyperspectral and thermal infrared imagery, Remote Sensing, 7(11):14276-14291.

Roth, K.L., Roberts, D.A., Dennison, P.E., Peterson, S.H., Alonzo, M. (2015). The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data, Remote Sensing of Environment, 171:45-57.

Roth, K.L., Roberts, D.A., Dennison, P.E., Alonzo, M., Peterson, S.H., and Beland, M. (2015). Differentiating plant species within and across diverse ecosystems with imaging spectroscopy, Remote Sensing of Environment, 167:135-151.

Roberts, D.A., Dennison, P.E., Roth, K.L., Dudley, K. and 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.

Dudley, K.L., Dennison, P.E., Roth, K.L., Roberts, D.A., and Coates, A.R. (2015). A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients, Remote Sensing of Environment, 167:121-134.

Alonzo, M., Roth, K. and Roberts, D. (2013). Identifying Santa Barbara’s urban tree species from AVIRIS imagery using canonical discriminant analysis, Remote Sensing Letters, 4 (5), 513-521.

Roth, K.L., Dennison, P.E. and Roberts, D.A.  (2012). Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data, Remote Sensing of Environment, 127, 139-152.

Roberts, D.A., Roth, K.L. and Perroy, R.P. (2011). Hyperspectral Vegetation Indices. In P.S. Thenkabail, J.G. Lyon, and A. Huete (eds.), Hyperspectral Remote Sensing of Vegetation (pp 309-327) Taylor and Francis.

Schaaf, A.N., Dennison, P.E., Fryer, G.K., Roth, K.L. and Roberts, D.A. (2011). Mapping plant functional types at three spatial resolutions using multiple endmember spectral mixture analysis. GIScience & Remote Sensing, 48, 324-344.

Roberts, D., Bradley, E., Roth, K., Eckmann, T., and Still, C. (2010). Linking physical geography education and research through the development of an environmental sensing network and project-based learning. Journal of Geoscience Education, 58 (5), 262-274.