Warner, T. A., Miller, T. A., La Puma, I. P., Nolan, L. A., Skowronski, N. S., & Maxwell, A. E., 2022. Exploring golden eagle habitat preference using lidar-based canopy bulk density. Remote Sensing Letters, 13(6), 556-567. DOI: 10.1080/2150704X.2022.2055985
Zhang, J., J. Li, A. Bao, T. A. Warner, L. Li, C. Chang, J. Bai & T. Liu, 2022. Characterizing seasonal and long-term dynamics of a lacustrine wetland in Xinjiang, China, using dense time-series remote sensing imagery. International Journal of Remote Sensing, 43 (14): 5502-552513. DOI: 10.1080/01431161.2022.2135415
Zhou, C., H. Lan, R. Bürgmann, T. A. Warner, J. J. Clague, L. Li, Y. Wu, X. Zhao Y. Zhang, and J. Yao, 2022. Application of an improved multi-temporal InSAR method and forward geophysical model to document subsidence and rebound of the Chinese Loess Plateau following land reclamation in the Yan’an New District. Remote Sensing of Environment 279: 113102. DOI: 10.1016/J.RSE.2022.113102/
Zhou, Y., J. Shunping & T. A. Warner, 2022. Regional Spatiotemporal Patterns of Fire in the Eurasian Subarctic Based on Satellite Imagery. Remote Sensing 14(24), 6200. DOI: 10.3390/rs14246200
Maxwell, A. E., Warner, T. A., & Guillén, L. A., 2021. Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 1: Literature Review. Remote Sensing, 13(13), 2450. DOI: 10.3390/rs13132450.
Maxwell, A. E., Warner, T. A., & Guillén, L. A., 2021. Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 2: Recommendations and Best Practices. Remote Sensing, 13(13), 2591. DOI: 10.3390/rs13132591.
Ramezan, C.A., T.A. Warner, A.E. Maxwell and B.S. Price, 2021. Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data. Remote Sensing 13 (3), 368. DOI: 10.3390/rs13030368
Remote Sensing in the Geology and Geography Department at WVU
Remote sensing is an exciting field of study, especially with the current interest in lidar, high spatial resolution imagery, and object-oriented analysis. In the Department of Geology and Geography at WVU, remote sensing is part of a core emphasis on Geographic Information Science (GISc).
My research interests include the spatial properties of remotely sensed images, lidar, high spatial resolution imagery, thermal imagery, machine learning classification, wildfire mapping, and information literacy. I have a particular interest in the use of remote sensing for promoting transparency and non-proliferation. A list of past students is available here.
The Cooperative Ecosystems Studies Units (CESU) highlighted training in remote sensing that I periodically help provide to the US Natural Resources Conservation Service (NRCS).
I occasionally run workshops on how to write and publish remote sensing papers.