Estimating plot-level tree structure in a deciduous forest by combining allometric equations, spatial wavelet analysis and airborne LiDAR

Abstract

Object-oriented classification methods are increasingly used to derive plant-level structural information from high-resolution remotely sensed data from plant canopies. However, many automated, object-based classification approaches perform poorly in deciduous forests compared with coniferous forests. Here, we test the performance of the automated spatial wavelet analysis (SWA) algorithm for estimating plot-level canopy structure characteristics from a light detection and ranging (LiDAR) data set obtained from a northern mixed deciduous forest. Plot-level SWA-derived and co-located ground-based measurements of tree diameter at breast height (DBH) were linearly correlated when canopy cover was low (correlation coefficient (r) = 0.80) or moderate (r = 0.68), but were statistically unrelated when canopy cover was high. SWA-estimated crown diameters were not significantly correlated with allometrically based estimates of crown diameter. Our results show that, when combined with allometric equations, SWA can be useful for estimating deciduous forest structure information from LiDAR in forests with low to moderate (<175% projected canopy area/ground area) levels of canopy cover.

Acknowledgements

The authors acknowledge Christoph Vogel and Peter Curtis for their assistance with collecting and providing access to the field survey data. This work was in part supported by a Biosphere-Atmosphere Research and Training (BART) summer REU fellowship from the UMBS to Meyer. Maurer was funded by a National Science Foundation (NSF) Integrative Graduate Education and Research Traineeship (IGERT) fellowship (NSF grant DGE-0504552) awarded by the BART program. LiDAR data were provided through an NSF–NCALM graduate seed award to Hardiman. The field survey was funded by the US Department of Energy's Office of Science (BER) through the Midwestern Regional Center of the National Institute for Global Environmental Change (NIGEC) under Cooperative Agreement DE-FC03-90ER610100, and the Midwestern Regional Center of the National Institute for Climatic Change Research (NICCR) at Michigan Technological University, under Award DE-FC02-06ER64158. Bohrer and Garrity were funded in part by NSF grant DEB-0911461, the US Department of Agriculture–National Institute for Food & Agriculture (NIFA) – Air Quality grant CSREES-OHOR–2009–04566 and by the USDA–Forest Service Northern Research Station, East Lansing, MI, Joint Research Venture 10–JV–11242302–013. Any opinions, findings and conclusions or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the NSF.