Abstract
This study investigates the potential of lidar and hyperspectral data for prediction of canopy chlorophyll (Chl) and carotenoid concentrations for a spatially complex boreal mixedwood. First, canopy scale application of hyperspectral reflectance and derivative indices are used to estimate Chl concentration. Second, lidar data analyses is conducted to identify structural metrics related to Chl concentration. Third, lidar metrics and hyperspectral indices are combined to determine if Chl concentration estimates can be improved further. Of the hyperspectral indices considered, only the derivative chlorophyll index (DCI) and the red‐edge inflection point (λp) are shown to be good predictors of Chl concentration when mixed‐species plots are included in the analysis (i.e., for total chlorophyll concentration (a+b), r 2 = 0.79, RMSE = 4.6 µg cm−2 and r 2 = 0.78, RMSE = 4.5 µg cm−2 for DCI and λp, respectively). Integrating mean lidar first return heights for the 25th percentile with the hyperspectral DCI index further strengthens the relationship to canopy Chl concentration (i.e., for Chl(a+b), r 2 = 0.84, RMSE = 3.5 µg cm−2). Maps of total chlorophyll concentration for the study site reveal distinct spatial patterns that are indicative of the spatial distribution of species at the site.
Acknowledgements
The authors gratefully acknowledge the field efforts of Denzil Irving, Bob Oliver, David Atkinson, Björn Prenzel, Chris Hopkinson, Laura Chasmer, Brock McLeod, Adam Thompson and Lauren MacLean. We would also like to thank Al Cameron and Lincoln Rowlinson for establishing a cruise‐line trail and on‐site assistance with tree species identification. Garry Koteles provided valuable information regarding tree and understory species and did all of the field measurements for the NFI validation plots. The Ontario Forest Research Institute (OFRI) provided technical expertise (Maara Packalen) for sampling and processing of leaves, use of laboratory facilities, and performed the pigment concentration and foliar nutrient analyses. Financial assistance for this work was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), Fluxnet‐Canada (NSERC, BIOCAP Canada, and the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS)), the Ontario Graduate Scholarship Program (OGS), the Centre for Research in Earth and Space Technology (CRESTech), and Queen's University.