Terrestrial laser scanning to estimate plot-level forest canopy fuel properties

This paper evaluates the potential of a terrestrial laser scanner (TLS) to characterize forest canopy fuel characteristics at plot level. Several canopy properties, namely canopy height, canopy cover, canopy base height and fuel strata gap were estimated. Different approaches were tested to avoid the effect of canopy shadowing on canopy height estimation caused by deployment of the TLS below the canopy. Estimation of canopy height using a grid approach provided a coefficient of determination of R2 = 0.81 and an RMSE of 2.47 m. A similar RMSE was obtained using the 99th percentile of the height distribution of the highest points, representing the 1% of the data, although the coefficient of determination was lower (R2 = 0.70). Canopy cover (CC) was estimated as a function of the occupied cells of a grid superimposed upon the TLS point clouds. It was found that CC estimates were dependent on the cell size selected, with 3 cm being the optimum resolution for this study. The effect of the zenith view angle on CC estimates was also analyzed. A simple method was developed to estimate canopy base height from the vegetation vertical profiles derived from an occupied/non-occupied voxels approach. Canopy base height was estimated with an RMSE of 3.09 m and an R2 = 0.86. Terrestrial laser scanning also provides a unique opportunity to estimate the fuel strata gap (FSG), which has not been previously derived from remotely sensed data. The FSG was also derived from the vegetation vertical profile with an RMSE of 1.53 m and an R2 = 0.87.

[1]  Hans-Gerd Maas,et al.  Automatic forest inventory parameter determination from terrestrial laser scanner data , 2008 .

[2]  S. Ustin,et al.  Generation of crown bulk density for Pinus sylvestris L. from lidar , 2004 .

[3]  Kenji Omasa,et al.  Voxel-Based 3-D Modeling of Individual Trees for Estimating Leaf Area Density Using High-Resolution Portable Scanning Lidar , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Joe H. Scott,et al.  Assessing Crown Fire Potential by Linking Models of Surface and Crown Fire Behavior , 2003 .

[5]  F. M. Danson,et al.  Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data , 2010 .

[6]  Miguel G. Cruz,et al.  Modeling the Likelihood of Crown Fire Occurrence in Conifer Forest Stands , 2004, Forest Science.

[7]  N. Coops,et al.  Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests , 2003 .

[8]  R. Dubayah,et al.  Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest , 2002 .

[9]  M IoannisD.,et al.  Canopy fuel characteristics and potential crown fire behavior in Aleppo pine ( Pinus halepensis Mill . ) forests , 2007 .

[10]  Robert E. Keane,et al.  Estimating canopy fuel characteristics in five conifer stands in the western United States using tree and stand measurements , 2006 .

[11]  Richard Ernst,et al.  Statistical Techniques for Sampling And Monitoring Natural Resources , 2004 .

[12]  Kevin C. Ryan,et al.  Spatial fuel data products of the LANDFIRE Project , 2009 .

[13]  Erik Næsset,et al.  Mapping LAI in a Norway spruce forest using airborne laser scanning , 2009 .

[14]  Pete Watt,et al.  Measuring forest structure with terrestrial laser scanning , 2005 .

[15]  Robert E. Keane,et al.  Estimating forest canopy bulk density using six indirect methods , 2005 .

[16]  P. Radtke,et al.  Ground-based Laser Imaging for Assessing Three-dimensional Forest Canopy Structure , 2006 .

[17]  Åsa Persson,et al.  Identifying species of individual trees using airborne laser scanner , 2004 .

[18]  R. Rothermel,et al.  Modeling moisture content of fine dead wildland fuels input into the BEHAVE fire prediction system , 1986 .

[19]  Nicholas Skowronski,et al.  Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey , 2007 .

[20]  I. Jonckheere,et al.  Influence of measurement set-up of ground-based LiDAR for derivation of tree structure , 2006 .

[21]  S. Ustin,et al.  Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling , 2003 .

[22]  S. Popescu,et al.  A voxel-based lidar method for estimating crown base height for deciduous and pine trees , 2008 .

[23]  Nikos Koutsias,et al.  Classification analyses of vegetation for delineating forest fire fuel complexes in a Mediterranean test site using satellite remote sensing and GIS , 2003 .

[24]  S. Reutebuch,et al.  Estimating forest canopy fuel parameters using LIDAR data , 2005 .

[25]  C. Hopkinson,et al.  Assessing forest metrics with a ground-based scanning lidar , 2004 .

[26]  Benjamin Koetz,et al.  Forest Canopy Gap Fraction From Terrestrial Laser Scanning , 2007, IEEE Geoscience and Remote Sensing Letters.

[27]  Charles H. Wick,et al.  A method of evaluating crown fuels in forest stands. , 1972 .

[28]  J. K. Hiers,et al.  Ground-based LIDAR: a novel approach to quantify fine-scale fuelbed characteristics , 2009 .

[29]  R. Keane,et al.  Chapter 12 - Mapping wildland fuel across large regions for the LANDFIRE Prototype Project , 2006 .

[30]  I. Burke,et al.  Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests , 2005 .

[31]  M. Finney FARSITE : Fire Area Simulator : model development and evaluation , 1998 .

[32]  K. Itten,et al.  Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction , 2006 .

[33]  Jing M. Chen,et al.  Determining digital hemispherical photograph exposure for leaf area index estimation , 2005 .

[34]  Ömer Küçük,et al.  Estimating crown fuel loading for calabrian pine and Anatolian black pine , 2008 .