Michael J. Falkowski
Ph.D., University of Idaho, 2008
Areas of Expertise
Remote sensing and geospatial analysis
Areas of Interest
Increasing concerns over climate change, biodiversity loss, and ecosystem disturbance have increased the demand for spatially explicit, large area ecosystem characterizations. To meet this need, my research is focused upon solving applied problems in ecosystem science and sustainable forest management. I am particularly interested in developing methods to quantify and monitor vegetation structure and composition across large spatial extents, primarily via remote sensing and spatial modeling.
Quantifying ecosystem composition and structure—in a spatially explicit manner&emdash;is paramount to the development and evaluation of effective sustainable forest management practices and policies.”
I conduct research in a diversity of areas including LiDAR remote sensing of forest structure, spatial modeling of forest species composition and wildlife habitat suitability, as well as remote sensing of active fire and post-fire effects, among others. Although each of the aforementioned research areas are focused upon unique problems, there is one common thread throughout; integrating cutting edge spatial technologies with fundamental field measurements and established ecological theory to gain a better understanding of the natural environment.
Pond, N.C., Froese, R. E., Deo, R.K., and Falkowski, M.J. 2014. Multiscale validation of an operational model of forest inventory attributes developed with constrained remote sensing data. Canadian Journal of Remote Sensing 40:43–59.
Brosofske, K., Froese, R.E., Falkowski, M.J., and Banskota, A. 2014. A review of methods for mapping and prediction of inventory attributes for operational forest management. Forest Science (In press).
Hess, A.N., Falkowski, M.J., Pocewicz A., Webster, C.R., Martinuzzi, S., and A.J. Storer. 2013. Employing lidar data to identify butterfly habitat characteristics of four contrasting butterfly species across a diverse landscape. Remote Sensing Letters 4:354-363.
Henareh Khalyani, A., Falkowski, M.J., and Mayer, A.L. 2012. Classification of Landsat images based on spectral and topographic variables for land cover change detection in Zagros forests. International Journal of Remote Sensing 33:6956-6974.
Hudak, A.T., Strand, E.K., Vierling, A.L, Byrne, J.C., Eitel, J., Martinuzzi, S.,and Falkowski, M.J. 2012. Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys. Remote Sensing of Environment 123:25-40.
Hummel, S., Hudak, A, Uebler, E, Falkowski, M.J., and K. Megown. 2011. Comparable accuracy and cost of stand exam vs. LiDAR data informs landscape management on the Malheur National Forest. Journal of Forestry, July/August.
Falkowski, M.J., Hudak, A.T., Crookston, N., Gessler, P.E., and Smith A.M.S., 2010. Landscape- scale parameterization of a tree-level forest growth model: a k-NN imputation approach incorporating LiDAR data. Canadian Journal of Forest Research 40:184-199.
Falkowski, M.J., Wulder, M.A., White, J.C., and Gillis, M.D., 2009. Supporting large area, sample-based forest inventories with very high spatial resolution satellite imagery. Progress in Physical Geography 33(3):403-423.
Falkowski, M.J., Evans, J.S., Martinuzzi, S., Gessler, P.G., and Hudak, A.T., 2009. Characterizing forest succession with lidar data: An evaluation for the inland northwest,USA. Remote Sensing of Environment 113:946-956.
Falkowski, M.J., Smith, A.M.S., Gessler, P.E., Hudak, A.T., Vierling, L.A., and Evans, J.S., 2008. The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. Canadian Journal of Remote Sensing 34:S2, S338-S350.
Hudak, A.T., Crookston, N.L., Evans, J.S., Hall, D.E., and Falkowski M.J., 2008. Nearest-neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing of Environment 112:2232-2245.
Hudak, A.T., Evans, J.S., Crookston, N.L., Falkowski, M.J., Steigers, B., Taylor R., and Hemingway, H., 2007. Aggregating pixel-level basal area predictions derived from LiDAR data to industrial forest stands in Idaho. Proceedings of the 3rd Forest Vegetation Simulator Conference.
Falkowski M.J., Smith A.M.S., Hudak, A.T., Gessler, P.E., Vierling, L.A., and Crookston, N.L., 2006. Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of LiDAR data. Canadian Journal of Remote Sensing 32:153-161.