TY - JOUR
T1 - Tree species identification in mixed coniferous forest using airborne laser scanning
AU - Suratno, Agus
AU - Seielstad, Carl
AU - Queen, Lloyd
N1 - Funding Information:
This study was funded by the National Center for Landscape Fire Analysis (NCLFA) in the College of Forestry and Conservation of the University of Montana and The USDA Forest Service McIntire-Stennis Program. We thank Eric Rowell, Crystal Stonesifer, Casey Teske, Erik Hakanson, Tim Wallace, Ann Hadlow, Josh Rodriquez, Martin Twer, and R.J. Hannah for field assistance. We also would like to thank Ron Roth (Leica Geosystems, Inc.) for his communications on the technical aspects of ALS50 System. In addition, we thank Jim Riddering for his constructive comments and suggestions.
PY - 2009/11
Y1 - 2009/11
N2 - This study tests the capacity of relatively low density (<1 return/m2) airborne laser scanner data for discriminating between Douglas-fir, western larch, ponderosa pine, and lodgepole pine in a western North American montane forest and it evaluates the relative importance of intensity, height, and return type metrics for classifying tree species. Collectively, Exploratory Data Analysis, Pearson Correlation, ANOVA, and Linear Discriminant Analysis show that structural and intensity characteristics generated from LIDAR data are useful for classifying species at dominant and individual tree levels in multi-aged, mixed conifer forests. Proportions of return types and mean intensities are significantly different between species (p-value < 0.001) for plot-level dominant species and individual trees. Classification accuracies based on single variables range from 49%-61% at the dominant species level and 37%-52% for individual trees. The accuracy can be improved to 95% and 68% respectively by using multiple variables. The inclusion of proportion of return type greatly improves the classification accuracy at the dominant species level, but not for individual trees, while canopy height improves the accuracy at both levels. Overall differences in intensity and return type between species largely reflect variations in the physical structure of trees and stands. These results are consistent with the findings of others and point to airborne laser scanning as a useful source of data for species classification. However, there are still many knowledge gaps that prevent accurate mapping of species using ALS data alone, particularly with relatively sparse datasets like the one used in this study. Further investigations using other datasets in different forest types will likely result in improvements to species identification and mapping for some time to come.
AB - This study tests the capacity of relatively low density (<1 return/m2) airborne laser scanner data for discriminating between Douglas-fir, western larch, ponderosa pine, and lodgepole pine in a western North American montane forest and it evaluates the relative importance of intensity, height, and return type metrics for classifying tree species. Collectively, Exploratory Data Analysis, Pearson Correlation, ANOVA, and Linear Discriminant Analysis show that structural and intensity characteristics generated from LIDAR data are useful for classifying species at dominant and individual tree levels in multi-aged, mixed conifer forests. Proportions of return types and mean intensities are significantly different between species (p-value < 0.001) for plot-level dominant species and individual trees. Classification accuracies based on single variables range from 49%-61% at the dominant species level and 37%-52% for individual trees. The accuracy can be improved to 95% and 68% respectively by using multiple variables. The inclusion of proportion of return type greatly improves the classification accuracy at the dominant species level, but not for individual trees, while canopy height improves the accuracy at both levels. Overall differences in intensity and return type between species largely reflect variations in the physical structure of trees and stands. These results are consistent with the findings of others and point to airborne laser scanning as a useful source of data for species classification. However, there are still many knowledge gaps that prevent accurate mapping of species using ALS data alone, particularly with relatively sparse datasets like the one used in this study. Further investigations using other datasets in different forest types will likely result in improvements to species identification and mapping for some time to come.
KW - Conifer
KW - Intensity
KW - Laser scanning
KW - North America
KW - Tree species
UR - http://www.scopus.com/inward/record.url?scp=70350617798&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2009.07.001
DO - 10.1016/j.isprsjprs.2009.07.001
M3 - Article
AN - SCOPUS:70350617798
SN - 0924-2716
VL - 64
SP - 683
EP - 693
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
IS - 6
ER -