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measurements_banner_2.jpg (65612 bytes) Idaho Fire Initiative for Research and Education (IFIRE)

College of Natural Resources









Dr Alistair Smith

alistair [at]


LiDAR Research: Fuels, Structure, and Inventory


The members of the lab in conjunction with a number of regional collaborators are involved in a strong collection of integrated LiDAR research and extension activities. In addition to cutting-edge research we have organized several workshops and conference sessions aimed at disseminating our research to as many stakeholders as possible. As part of this collaboration we are putting together a web-based mini-tutorial on LiDAR and how it can be useful for forestry: Follow This Link for our new Lidar Web Tutorial ...


Production and Assessment of LiDAR Bare Earth Models.


Jeffery S. Evans and Andrew T Hudak at the USDA Forest Service Rocky Mountain Research Station, Moscow are leading the development of a bare earth model (BEM) algorithm that is suitable in large biomass areas. As highlighted in J.Evans and A.Hudak (IEEE Transactions on Geoscience and Remote Sensing, in press) they have developed a new algorithm entitled the 'Progressive Curvature Filter'. A comparison of the Progressive Curvature Filter with the commonly applied 'Block Minimum' is shown below.


                 All LiDAR Returns                                     Block Minimum                                  Progressive Curvature Filter


This algorithm is already widely used by the USDA Agricultural Research Service (ARS), the USDA FS Remote Sensing Applications Center (RSAC), in addition to our research group. As part of ongoing research the lab is collaborating with Dr Tim Link (University of Idaho) and Dr Danny Marks (ARS) in the USDA-ARS Owyhee Wildlands LiDAR Experiment (OWLX). As part of this experiment the lab will be hiring a new MSc student [click here for advert] to conduct accuracy assessment of the Bare Earth Models collected here and in other regional forest datasets.


LiDAR Characterization of Vegetation Structure and Fuels.


In addition to BEM algorithms, our lab have been developing novel methods to extract tree-level characteristics from canopy height models (CHMs). Work by A.M.S. Smith, M.J. Falkowski et al (Falkowski et al 2006; Canadian Journal of Remote Sensing) highlights how 'Spatial Wavelet Analysis' can be applied to extract tree crown widths and maximum tree heights from an open-canopy mixed conifer forest stand in northern Idaho.


Andrew Hudak et al also has considerable experience in integrating LiDAR with multispectral satellite sensor imagery. Following on from Hudak et al (2002) in which LiDAR and Landsat ETM+ data were integrated, Hudak et al (2006) used both ALI (EO-1 Satellite) with LiDAR to model parameters such as Basal Area and Tree Density on Moscow Mountain (see Right).

Complimentary Regional LiDAR Collaborators.


The University of Idaho's Department of Geography, Lidar research by J Rooker Jensen, K Humes, and others have focused on evaluating the technology's capabilities to measure forest inventory within the Nez Perce Reservation, northern Idaho. At the University of Montana's National Center for Fire Landscape Analysis, C Sielestad, L Queen, et al are involved in the assessment of surface fuel models. On a related topic, R.Lawrence at Montana State University is investigating the fusion of polarimteric synthetic aperture radar and hyperspectral imagery of landcover classification in Yellowstone National Park. The research lab is also coordinating with the CLICK group at the USGS - EROS Data Center, who have recently set-up a national depositary for XYZ LiDAR Data. 


Relevant Publications


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, Vol. 32, No. 2, 153-161. (Link to PDF)

Hudak, A.T., Crookston, N.L., Evans, J.S., Falkowski M.J., Smith A.M.S.,Gessler, P.E. and Morgan, P., 2006. Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral data, Canadian Journal of Remote Sensing, Vol. 32, No. 2, 126-138. (Link to PDF)

University of Idaho, Moscow, ID, 83844