Open AccessImproved estimates of forest vegetation structure and biomass with a LiDAR‐optimized sampling design
Author(s) -
Hawbaker Todd J.,
Keuler Nicholas S.,
Lesak Adrian A.,
Gobakken Terje,
Contrucci Kirk,
Radeloff Volker C.
Publication year - 2009
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2008jg000870
Subject(s) - lidar , sampling (signal processing) , environmental science , remote sensing , sampling design , vegetation (pathology) , elevation (ballistics) , stratified sampling , basal area , random forest , sample (material) , range (aeronautics) , biomass (ecology) , sample size determination , population , statistics , geography , computer science , mathematics , geology , forestry , filter (signal processing) , chemistry , oceanography , pathology , sociology , geometry , medicine , materials science , composite material , chromatography , machine learning , computer vision , demography
LiDAR data are increasingly available from both airborne and spaceborne missions to map elevation and vegetation structure. Additionally, global coverage may soon become available with NASA's planned DESDynI sensor. However, substantial challenges remain to using the growing body of LiDAR data. First, the large volumes of data generated by LiDAR sensors require efficient processing methods. Second, efficient sampling methods are needed to collect the field data used to relate LiDAR data with vegetation structure. In this paper, we used low‐density LiDAR data, summarized within pixels of a regular grid, to estimate forest structure and biomass across a 53,600 ha study area in northeastern Wisconsin. Additionally, we compared the predictive ability of models constructed from a random sample to a sample stratified using mean and standard deviation of LiDAR heights. Our models explained between 65 to 88% of the variability in DBH, basal area, tree height, and biomass. Prediction errors from models constructed using a random sample were up to 68% larger than those from the models built with a stratified sample. The stratified sample included a greater range of variability than the random sample. Thus, applying the random sample model to the entire population violated a tenet of regression analysis; namely, that models should not be used to extrapolate beyond the range of data from which they were constructed. Our results highlight that LiDAR data integrated with field data sampling designs can provide broad‐scale assessments of vegetation structure and biomass, i.e., information crucial for carbon and biodiversity science.