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* indicates references utilizing CTEMPs instruments, technical support or guidance
2024
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Ebony L. Williams1*, Christopher B. Kratt2, Raymond S. Rodolfo3, Mark R. Lapus, Ryan R. Lardizabal4, Aya Shika Bangun1, Amber T. Nguyen1, Scott W. Tyler2, and M. Bayani Cardenas1. Multi‐Scale Thermal Mapping of Submarine Groundwater Discharge in Coastal Ecosystems of a Volcanic Area. Geophysical Research Letters, in press. DOI: 10.1029/2024GL111857
2023
2022
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*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
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*Safa, H., 2022. Water and Energy Budgets of Snow-Covered Montane Forests: Observations, Remote Sensing, and Modeling (Doctoral dissertation, University of Nevada, Reno).
2021
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*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).
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*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.
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*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.
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*Haagsma, M. 2021. Hyperspectral Disease Detection - Strategies Addressing Time, Space, Machine Learning, and Plant Susceptibility (Doctoral Dissertation, Oregon State University).
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*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
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*Ready, A.A., 2021. Phenology-based UAV remote sensing for classifying invasive annual grasses to the species level (Doctoral dissertation, University of Nevada, Reno).
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*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
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*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
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*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
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*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
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*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.
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*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
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*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
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*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.
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*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.
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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.
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*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
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*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.
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*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.
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*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
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*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
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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).
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*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
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*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).
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Structure from Motion (SfM ) Photogrammetry.
Micheletti, N., Chandler, J. H. & Lane, S. N. Geomorphological Techniques Chapter 2 (2.2), 1–12 (2015).
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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).
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*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
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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).
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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.
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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).
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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
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2011
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2009
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