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Timothy Warner

PROFESSOR OF GEOGRAPHY AND GEOLOGY

Tim specializes in remote sensing applications and the spatial analysis of images. He utilizes optical and thermal imagery, as well as lidar, to study terrestrial features including biogeographical and geological phenomena.

Remote Sensing Laboratory

PI: Tim Warner, Professor of Geography and Geology

Research

I have broad interests in remote sensing, including the spatial properties of images, lidar data, thermal imagery, machine learning, and information literacy. Much of my recent work has focused on time-series analysis of remotely sensed data. I am currently working on multi-temporal lidar for mapping widlfire fuel consumption. I have a particular interest in the use of remote sensing for promoting nuclear transparency and non-proliferation.

Teaching

Geog 107 Physical Geography, Geog/Geol 455/655 Introduction to Remote Sensing, Geog/Geol 755 Advanced Remote Sensing.

Recent Publications

Gaertner, B., N. Zegre, T.A. Warner, R. Fernandez; Y. He, and E. Merriam, 2019. Contribution of Growing Season Length to Water Cycle Intensification: Implications for Long Term Forest Evapotranspiration in the central Appalachian Mountains, USA. Science of the Total Environment 650: 1371-1381. DOI: 10.1016/j.scitotenv.2018.09.129

Li, J., T.A. Warner, Y. Wang; J. Bai; and A. Bao, 2019. Mapping Glacial Lakes Partially Obscured by Mountain Shadow for Time Series and Regional Mapping Applications. International Journal of Remote Sensing, DOI: 10.1080/01431161.2018.1433343

Maxwell, A., and T.A. Warner, 2019. Is high spatial resolution DEM data necessary for mapping palustrine wetlands? International Journal of Remote Sensing. 40(1): 118-137. DOI: 10.1080/01431161.2018.1506184

Ramezan, C.A., T.A Warner and A.E. Maxwell, 2019. Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification. Remote Sensing 11, 185. DOI: 10.3390/rs11020185

Warner, T. A., 2019. How to write an effective peer-review report: an editor’s perspective. International Journal of Remote Sensing. 40(13): 4871-4875. DOI: 10.1080/01431161.2019.1596342