Center for Transformative Environmental Monitoring Programs

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* indicates references utilizing CTEMPs instruments, technical support or guidance 

2023

• *Haagsma, M., Hagerty, C.H., Kroese, D.R. and Selker, J.S., 2023. Detection of soil-borne wheat mosaic virus using hyperspectral imaging: from lab to field scans and from hyperspectral to multispectral data. Precision Agriculture, pp.1-19. 10.1007/s11119-022-09986-0

2022

• *Hardage,  K., S.J. Wheelock, R. Gaffney, T. O’Halloran, B. Serpa, G. Grant, M. Coppoletta, A. Csank, C. Tague, M. Staudacher and S. Tyler (2022). Soil Moisture and micrometeorological differences across reference and thinned stands during extremes of precipitation, Southern Cascade Range. Frontiers in Forests and Global Change. https://doi.org/10.3389/ffgc.2022.898998
• *Safa, H., 2022. Water and Energy Budgets of Snow-Covered Montane Forests: Observations, Remote Sensing, and Modeling (Doctoral dissertation, University of Nevada, Reno).

2021

• *Cramer, A.S., 2021. Mapping Temporal Changes of Erosion and Potentially Acid Generating Material on Abandoned Mine Lands Using Remotely Piloted Aerial Systems (Master's thesis, University of Nevada, Reno).
• *Cramer, A.S., Calvin, W.M., McCoy, S.W., Breitmeyer, R.J., Haagsma, M. and Kratt, C., 2021. Mapping Potentially Acid Generating Material on Abandoned Mine Lands Using Remotely Piloted Aerial Systems. Minerals, 11(4), p.365.
• *Davis, J., Lautz, L., Kelleher, C., Vidon, P., Russoniello, C. and Pearce, C., 2021. Evaluating the geomorphic channel response to beaver dam analog installation using unoccupied aerial vehicles. Earth Surface Processes and Landforms, 46(12), pp.2349-2364.
• *Haagsma, M. 2021. Hyperspectral Disease Detection - Strategies Addressing Time, Space, Machine Learning, and Plant Susceptibility (Doctoral Dissertation, Oregon State University).
• *Haagsma, M., Page, G.F., Johnson, J.S., Still, C., Waring, K.M., Sniezko, R.A. and Selker, J.S., 2021. Model selection and timing of acquisition date impacts classification accuracy: A case study using hyperspectral imaging to detect white pine blister rust over time. Computers and Electronics in Agriculture, 191, p.106555. https://doi.org/10.1016/j.compag.2021.106555
• *Ready, A.A., 2021. Phenology-based UAV remote sensing for classifying invasive annual grasses to the species level (Doctoral dissertation, University of Nevada, Reno).
• *Weisberg, P.J., Dilts, T.E., Greenberg, J.A., Johnson, K.N., Pai, H., Sladek, C., Kratt, C., Tyler, S.W. and Ready, A., 2021. Phenology-based classification of invasive annual grasses to the species level. Remote Sensing of Environment, 263, p.112568.

2020

• *Haagsma, M., Page, G.F., Johnson, J.S., Still, C., Waring, K.M., Sniezko, R.A. and Selker, J.S., 2020. Using Hyperspectral Imagery to Detect an Invasive Fungal Pathogen and Symptom Severity in Pinus strobiformis Seedlings of Different Genotypes. Remote Sensing, 12(24), p.4041. https://doi.org/10.3390/rs12244041

2019

• *Blonder, B., Graae, B.J., Greer, B., Haagsma, M., Helsen, K., Kapás, R.E., Pai, H., Rieksta, J., Sapena, D., Still, C.J. and Strimbeck, R., 2019. Remote sensing of ploidy level in quaking aspen (Populus tremuloides Michx.). Journal of Ecology. https://doi.org/10.1111/1365-2745.13296
• *Kostadinov, T.S., Schumer, R., Hausner, M., Bormann, K.J., Gaffney, R., McGwire, K., Painter, T.H., Tyler, S. and Harpold, A.A., 2019. Watershed-scale mapping of fractional snow cover under conifer forest canopy using lidar. Remote sensing of environment, 222, pp.34-49. https://doi.org/10.1016/j.rse.2018.11.037
• *Kratt, C. B., Woo, D. K., Johnson, K. N., Haagsma, M., Kumar, P., Selker, J., & Tyler, S. (2019). Field trials to detect drainage pipe networks using thermal and RGB data from unmanned aircraft. Agricultural Water Management, 105895.
• *Pestana, S., Chickadel, C.C., Harpold, A., Kostadinov, T.S., Pai, H., Tyler, S., Webster, C. and Lundquist, J.D. (2019). Bias Correction of Airborne Thermal Infrared Observations Over Forests Using Melting Snow. Water Resources Research. https://doi.org/10.1029/2019WR025699

2018

• *Abolt, C., T. Caldwell, B. Wolaver, and H. Pai (2018), Unmanned aerial vehicle-based monitoring of groundwater inputs to surface waters using an economical thermal infrared camera, Optical Engineering, 57, 053113, doi:10.1117/1.Oe.57.5.053113
• *Burnett, J. and M.G. Wing. 2018. A low-cost near-infrared digital camera for fire detection and monitoring. International Journal of Remote Sensing. 39(3), 741-753.
• *Higgins CW,  Liu Z, Wing MG, Kelley J, Sayde C, Burnett J, Predosa R, Holmes HA.(2018). A High Resolution Measurement of the Morning ABL Transition Using Distributed Temperature Sensing and an Unmanned Aircraft System.  Journal of Environmental Fluid Mechanics, https://doi.org/10.1007/s10652-017-9569-1.
• Kelleher, C., C. A. Scholz, L. Condon, and M. Reardon (2018), Drones in geoscience research: The sky is the only limit, Eos, 99, https://doi.org/10.1029/2018EO092269. Published on 22 February 2018.

• *Tyler, S., Jensen, O.P., Hogan, Z., Chandra, S., Galland, L.M. and Simmons, J., 2018. Perspectives on the Application of Unmanned Aircraft for Freshwater Fisheries Census. Fisheries, 43(11), pp.510-516.

2017

• *Bedell, E., Leslie, M., Fankhauser, K., Burnett, J., Wing, M.G. and Thomas, E.A., 2017. Unmanned aerial vehicle-based structure from motion biomass inventory estimates. Journal of Applied Remote Sensing, 11(2), 026026-026026.
• *Pai, H., Malenda, H.F., Briggs, M.A., Singha, K., González‐Pinzón, R., Gooseff, M.N., Tyler, S.W., the AirCTEMPs Team. 2017. Potential for Small Unmanned Aircraft Systems Applications for Identifying Groundwater‐Surface Water Exchange in a Meandering River Reach. Geophysical Research Letters, 44(23) 11868-11877.
• *Powers CW, Predosa R, Higgins CW, Schmale DG III. (2017).Mobile Distributed Sensing of the Air/Water Interface of an Aquatic Environment With Unmanned Surface Vehicle. Journal of Unmanned Vehicle Systems 6(1), 43-56
• *Semke, W., Allen, N., Tabassum, A., McCrink, M., Moallemi, M., Snyder, K., Arnold, E., Stott, D. and Wing, M.G., 2017. Analysis of Radar and ADS-B Influences on Aircraft Detect and Avoid (DAA) Systems. Aerospace, 4(3), 49.

2016

• Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery.
  Michez, A., Piégay, H., Jonathan, L., Claessens, H. & Lejeune, P. ,Int. J. Appl. Earth Obs. Geoinf. 44, 88–94 (2016).
• *Wing, M.G., Burnett, J., Brungardt, J., Dobler, D., Cordell, V. and Sessions, J., 2016. Search and Rescue Operations with an Unmanned Helicopter. International Journal of Remote Sensing Applications, 6, pp.65-75.

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2015

• *Automated Extraction of Forest Road Network Geometry from Aerial LiDAR.
  Beck, S. J. C., Olsen, M. J., Sessions, J. & Wing, M. G. European Journal of Forest Engineering, 1, 21–33 (2015).
• Structure from Motion (SfM ) Photogrammetry.
  Micheletti, N., Chandler, J. H. & Lane, S. N. Geomorphological Techniques Chapter 2 (2.2), 1–12 (2015).
• Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry.
  Woodget, A. S., Carbonneau, P. E., Visser, F. & Maddock, I. P. Earth Surf. Process. Landforms 40, 47–64 (2015).
• *Drone Squadron to Take Earth Monitoring to New Heights.
  Selker, J. S., Tyler, S. W., Higgins, C. & Wing, M. Eos (Washington. DC). (2015).

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2014

• Investigating the geomorphological potential of freely available and accessible structure-from-motion photogrammetry using a smartphone.
  Micheletti, N., Chandler, J. H. & Lane, S. N. Earth Surf. Process. Landforms 486, n/a–n/a (2014).
• Ryan, J.C., Hubbard, A.L., Todd, J., Carr, J.R., Box, J.E., Christoffersen, P., Holt, T.O. and Snooke, N., 2015. Repeat UAV photogrammetry to assess calving front dynamics at a large outlet glacier draining the Greenland Ice Sheet. Cryosphere Discussions, 8(2), pp.2243-2275.
• A Low-cost Unmanned Aerial System for Remote Sensing of Forested Landscapes.
  Wing, M., Burnett, J., Johnson, S., Akay, A. E. & Sessions, J. Int. J. Remote Sens. Appl. 4, 113–120 (2014).
• Remote Sensing and Unmanned Aerial System Technology for Monitoring and Quantifying Forest Fire Impacts.
  Wing, M., Burnett, J. & Sessions, J. Int. J. Remote Sens. Appl. 4, 18–35 (2014).

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2013

• Compact Multipurpose Mobile Laser Scanning System — Initial Tests and Results.
  Glennie, C. et al. Remote Sens. 5, 521–538 (2013).
• Eyes in the Sky: Remote Sensing Technology Development Using Small Unmanned Aircraft Systems.
  Wing, M. et al. J. For. 111, 341–347 (2013).

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2011

• Airborne light detection and ranging (LiDAR) for individual tree stem location, height, and biomass measurements.
  Edson, C. & Wing, M. G. Remote Sensing 3, (2011).

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2009

• Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle.
  Berni, J., Zarco-Tejada, P. J., Suarez, L. & Fereres, E. Geosci. Remote Sensing, IEEE Trans. 47, 722–738 (2009).

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