Canopy chlorophyll concentration estimation using hyperspectral and lidar data for a boreal mixedwood forest in northern Ontario, Canada

  • Authors:

  • V. Thomas;P. Treitz;J. H. Mccaughey;T. Noland;L. Rich

  • Affiliations:

  • Department of Geography, Faculty of Arts and Science, Queen's University, Kingston, ON, K7L 3N6, Canada;Department of Geography, Faculty of Arts and Science, Queen's University, Kingston, ON, K7L 3N6, Canada;Department of Geography, Faculty of Arts and Science, Queen's University, Kingston, ON, K7L 3N6, Canada;Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste. Marie, ON P6A 2E5, Canada;Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste. Marie, ON P6A 2E5, Canada

  • Venue:
  • International Journal of Remote Sensing
  • Year:
  • 2008

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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.