Current and Past Projects

Datasets and Tools

Publications

  1. Maxwell, A.E., M.S. Bester, L.A. Guillen, C.A. Ramezan, D.J. Carpinello, Y. Fan, F.M. Hartley, S.M. Maynard, and J.L. Pyron, 2020. Semantic segmentation deep learning for extracting surface mine extents from historic topographic maps, Remote Sensing, 12(24): 1-25. https://doi.org/10.3390/rs12244145.
  2. Maxwell, A.E., and T.A. Warner, 2020. Thematic classification accuracy assessment with inherently uncertain boundaries: an argument for center-weighted accuracy assessment metrics, Remote Sensing, 12(12): 1-21. https://doi.org/10.3390/rs12121905.
  3. Fang, F., B.E. McNeil, T.A. Warner, A.E. Maxwell, G.A. Dahle, E. Eutsler, and J. Li. 2020. Discriminating tree species at different taxonomic levels using multi-temporal WorldView-3 imagery in Washington D.C., USA, Remote Sensing of Environment, 246: 111811. https://doi.org/10.1016/j.rse.2020.111811.
  4. Maxwell, A.E., P. Pourmohammadi, and J. Poyner, 2020. Mapping the topographic features of mining-related valley fills using mask R-CNN deep learning and digital elevation data, Remote Sensing, 12(3): 1-23. https://doi.org/10.3390/rs12030547.
  5. Maxwell, A.E., M. Sharma, J.S. Kite, K.A. Donaldson, J.A. Thompson, M.L. Bell, and S.M. Maynard, 2020. Slope failure prediction using random forest machine learning and LiDAR in an eroded folded mountain belt, Remote Sensing, 12(3): 1-27. https://doi.org/10.3390/rs12030486.
  6. Maxwell, A.E., M.P. Strager, T.A. Warner, C.A. Ramezan, A.N. Morgan, and C.E. Pauley, 2019. Large-area, high spatial resolution land cover mapping using random forests, GEOBIA, and NAIP orthophotography: findings and recommendations, Remote Sensing, 11(12) 1409: 1-27. https://doi.org/10.3390/rs11121409.
  7. 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(2), 185 1-21. https://doi.org/10.3390/rs11020185.
  8. Maxwell, A.E., 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. https://doi.org/10.1080/01431161.2018.1506184.
  9. Fang, F., McNeil, B.E., Warner, T.A., and A.E. Maxwell, 2018. Combining high spatial resolution multi-temporal satellite data with leaf-on LiDAR to enhance tree species discrimination at the crown-level, International Journal of Remote Sensing, 39(23): 9054-9072. https://doi.org/10.1080/01431161.2018.1504343.
  10. Maxwell, A.E., T.A. Warner, and F. Fang, 2018. Implementation of machine learning classification in remote sensing: an applied review, International Journal of Remote Sensing, 39(9): 2784-2817. https://doi.org/10.1080/01431161.2018.1433343.
  11. Liebermann, H., J. Schuler, M.P. Strager, and A. Maxwell, 2018. A work flow and evaluation of using unmanned aerial systems for deriving forest stand characteristics in mixed hardwoods of West Virginia, Geospatial Applications in Natural Resources, 2(1): 23-53.
  12. Maxwell, A.E., T.A. Warner, B.C. Vanderbilt, and C.A. Ramezan, 2017. Land cover classification and feature extraction from National Agriculture Imagery Program (NAIP) orthoimagery: A Review, Photogrammetric Engineering & Remote Sensing, 83(11): 737-747. https://doi.org/10.14358/PERS.83.10.737.
  13. Strager, M.S., M. Thomas-Van Gundy, A.E. Maxwell, 2016. Predicting post-fire change in the Central Appalachians from remotely-sensed data, Geospatial Applications in Natural Resources, 1(2): 1-17.
  14. Maxwell, A.E., T.A. Warner, and M.P. Strager, 2016. Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables, Photogrammetric Engineering & Remote Sensing, 82(6): 437-447. https://doi.org/10.14358/PERS.82.6.437.
  15. Maxwell, A.E., and T.A. Warner, 2015. Differentiating mine-reclaimed grasslands from spectrally similar land cover using terrain variables and object-based machine learning classification, International Journal of Remote Sensing, 36(17): 4384-4410. https://doi.org/10.1080/01431161.2015.1083632.
  16. Maxwell, A.E., T.A. Warner, M.P. Strager, J.F. Conley, and A.L. Sharp, 2015. Assessing machine learning algorithms and image- and LiDAR-derived variables for GEOBIA classification of mining and mine reclamation, International Journal of Remote Sensing, 36(4): 954-978. https://doi.org/10.1080/01431161.2014.1001086.
  17. Merriam, E.R., J.T. Petty, M.P. Strager, A.E. Maxwell, and P.F. Ziemkiewicz, 2015. Complex contaminant mixtures in multi-stressor Appalachian riverscapes, Environmental Toxicology and Chemistry, 34(11): 2603-2610.
  18. Merriam, E.R., J.T. Petty, M.P. Strager, A.E. Maxwell, and P.F. Ziemkiewicz, 2015. Landscape-based cumulative effects models for predicting stream response to mountaintop mining in multi-stressor Appalachian watersheds, Freshwater Science, 34(3): 1006-1019.
  19. Strager, M.P., J.M. Strager, J.S. Evans, J.K. Dunscomb, B.J. Kreps, and A.E. Maxwell, 2015. Combining a spatial model and demand forecasts to map future surface coal mining in Appalachia, PLoS ONE, 10(6): e0128813.10.1371/journal.pone.0128813.
  20. Maxwell, A.E., M.P. Strager, T.A. Warner, N.P. Zégre, and C.B. Yuill, 2014. Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation, GIScience & Remote Sensing, 51(3): 301-320. https://doi.org/10.1080/15481603.2014.912874.
  21. Zegre, N., A. Miller, A. Maxwell, and S. Lamont, 2014. Multi-scale analysis of hydrology in a mountaintop mine-impacted watershed, Journal of the American Water Resources Association, doi: 10.1111/jawr.12184.
  22. Maxwell, A.E., T.A. Warner, M.P. Strager, and M. Pal, 2014. Combining RapidEye satellite imagery and LiDAR for mapping of mining and mine reclamation, Photogrammetric Engineering & Remote Sensing, 80(2): 179-189. https://doi.org/10.14358/PERS.80.2.179-189.
  23. Pal, M., A.E. Maxwell, and T.A. Warner, 2013. Kernel-based extreme learning machine for remote-sensing image classification, Remote Sensing Letters, 4(9): 853-862. https://doi.org/10.1080/2150704X.2013.805279.
  24. Merriam, E.R., J.T. Petty, M.P. Strager, A.E. Maxwell, and P.F. Ziemkiewicz, 2013. Scenario analysis predicts context-dependent stream response to landuse change in a heavily mined central Appalachian watershed, Freshwater Science, 32(4): 1246-1259.
  25. Zegre, N., A. Maxwell, and S. Lamont, 2013. Characterizing streamflow response of a mountaintop-mined watershed to changing land use, Applied Geography, 39: 5-15.
  26. Maxwell, A.E., and M.P. Strager, 2013. Assessing landform alterations induced by mountaintop mining, Natural Science, 5(2A): 52A034. http://dx.doi.org/10.4236/ns.2013.52A034.
  27. Maxwell, A.E., M.P. Strager, C.B. Yuill, and J.T. Petty, 2012. Modeling critical forest habitat in the Southern Coal Fields of West Virginia, International Journal of Ecology, Volume 2012, Article ID 182683, 10 pages.