Jessica Mitchell

Photo of Mitchell, Jessica

Jessica Mitchell

Research Faculty

Website: http://www.umt.edu/spatial-analysis-lab/
Curriculum Vitae: View/Download CV

Personal Summary

I joined Univeristy of Montana in August 2018 as director of the Montana Natural Heritage Program's Spatial Analysis Lab and a Research Faculty member in the Department of Ecosystem and Conservation Sciences. Many of my projects use remote sensing to analyze environmental impacts and support land management decisions. My research is currently evaluating methods for scaling vegetation structure, canopy chemistry and biodiversity variables from leaves and individuals to plots, landscapes and regions. Some of the data I work with are traditional plot-based measurements; ground and airborne laser scanning (lidar) and reflectance spectroscopy estimates; and time-series satellite observations. Vegetation applications include mapping plant chemistry and forage quality; invasive plant species; biodiveristy indicators; shrub structure; and land cover. In additon to remote sensing research, my experience includes environmental planning, National Environmental Policy Act (NEPA) assessments, and teaching geospatial courses at the undergraduate and graduate levels.

Education

2011   PhD   Engineering and Applied Sciences, Idaho State Uiniversity

2007    MS   Geographic Information Science, Idaho State Uiniversity

2001    BS    Geography and Environmental Systems, University Maryland 

Field of Study

  Remote Sensing of Vegetation

Selected Publications

Dashti, H., Glenn, N.F., Ustin, S., Mitchell, J.J., Qi, Y., Ilangakoon, N.T., Flores, A.N., Silván-Cárdenas, J.L., Zhao, K., Spaete, L.P. and de Graaff, M.A., 2019. Empirical Methods for Remote Sensing of Nitrogen in Drylands May Lead to Unreliable Interpretation of Ecosystem Function. IEEE Transactions on Geoscience and Remote Sensing.

J. J. Mitchell, N. F. Glenn, K. M. Dahlin, N. T. Ilangakoon, H. Dashti, and M. C. Maloney*, “Integrating Hyperspectral and LiDAR Data in the Study of Vegetation.,” in Hyperspectral Remote Sensing of Vegetation (Volume I), II Ed., P. S. Thenkabail, J. G. Lyon, and A. Huete, Eds. London, Ney York: CRC Press- Taylor and Francis group, 2018, p. 449.

Ilangakoon, N.T., Glenn, N.F., Dashti, H., Painter, T.H., Mikesell, T.D., Spaete, L.P., Mitchell, J.J. and Shannon, K., 2018. Constraining plant functional types in a semi-arid ecosystem with waveform lidar. Remote Sensing of Environment209, pp.497-509.

Olsoy, P.J., Mitchell, J., Glenn, N.F. and Flores, A.N., 2017. Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain. Remote Sensing9(10), p.981.

Li, A., Zhao, W., Mitchell, J., Glenn, N., Germino, M., Sankey, J. and Allen, R. 2017. Aerodynamic roughness length estimation with lidar and imaging spectroscopy in a shrub-dominated dryland. Photogrammetric Engineering and Remote Sensing, 83 (6), 415 – 427.

Mitchell, J., Glenn, N., Anderson, M. and Hruska, R., 2016. Flight Considerations and Hyperspectral Image Classifications for Dryland Vegetation Management from a Fixed-wing UAS. Environmental Management and Sustainable Development, 5(2), 41-57.

Mitchell, J.J., Shrestha, R., Spaete, L.P. and Glenn, N.F., 2015. Combining airborne hyperspectral and LiDAR data across local sites for upscaling shrubland structural information: Lessons for HyspIRI. Remote Sensing of Environment, 167, pp.98-110.

Li, A., Glenn, N. F., Olsoy, P. J., Mitchell, J. J., & Shrestha, R. 2015. Aboveground biomass estimates of sagebrush using terrestrial and airborne LiDAR data in a dryland ecosystem. Agricultural and Forest Meteorology213, 138-147.

Mitchell, J., Moore, C., and Glenn, N. 2013. Single and multi-date Landsat classifications of basalt to support Soil Survey efforts, Remote Sensing, 5(10), 4857-4899.

Hruska, R., Mitchell, J., Anderson, M. and N. Glenn, N, Halford, A. and Baun, C. 2012. Radiometric and geometric analysis of hyperspectral imagery acquired from an Unmanned Aerial Vehicle  (UAV), Remote Sensing, 4, 2736-2752.

Mitchell, J., Glenn, N., Sankey, T., Derryberry, D. and Germino, M. 2012. Hyperspectral remote sensing of sagebrush canopy nitrogen, Remote Sensing of Environment, 124, 217-223.

Glenn. N., Sankey, T., Spaete, L. and Mitchell, J. 2011. Errors in LiDAR-derived shrub height and crown area on sloped terrain, Journal of Arid Environments, 75 (4), 377-382.

Mitchell, J., Glenn, N., Sankey, T., and Derryberry, D., Anderson, M. and Hruska, R. 2011. Sagebrush  canopy height and shape estimations using small footprint LiDAR, Photogrammetric Engineering and Remote Sensing, 77 (5), 521 – 530.

Mitchell, J. J., and Glenn, N. F. 2009. Matched filter subpixel abundance estimates in mixture-tuned  matched filter classifications of leafy spurge (Euphorbia esula L.). International Journal of Remote Sensing, 30(23), 6099-6119.

Mitchell, J., and Glenn, N. F. 2009. Leafy Spurge (Euphorbia esula) Classification Performance Using Hyperspectral and Multispectral Sensors, Rangeland Ecology and Management, 62: 16-27.