Measurement and remote sensing of LAI in Mountain montane ecosystems
Canadian Journal of Forest Research
We estimated leaf area index (LAI) for Glacier National Park, Montana, U.S.A., by using various methods to measure LAI in the field and correlating these values with Landsat Thematic Mapper data. Ground-based LAI were estimated using sapwood-LAI allometric equations and optical instruments, including the LAI-2000 and a Decagon ceptometer. Optical-based LAI estimates contain nonrandom self-shading; therefore, allometric and optical LAIs were compared to calculate coefficients to correct optical LAI data within similar vegetation types and canopy structural conditions. Least-squares regression models were constructed from pooled the ground-based allometric and corrected optical LAI values and from Landsat Thematic Mapper vegetation indices. Average LAI and satellite indices for defined slope, aspect, and elevation classes were used in the model calculation, as point estimates were generally poor. The normalized difference vegetation index and a mid-infrared corrected simple ratio had the “best fit” with field LAI values. We applied these two models to the Thematic Mapper indices and tested LAI estimation with independent field LAI data. In addition, we tested the effect of spatial resolution on satellite-estimated LAI values by averaging the Thematic Mapper data into 250 x 250 m grid cells (pixels). Our results showed that the normalized difference vegetation index provided the best estimate of LAI and decreased in accuracy with coarser pixels. The corrected simple ratio index overestimated LAI largely because of difficulty deriving the appropriate reflectance scale of mid-infrared correction to apply to this index at the larger landscape scale investigated here. However, mid-infrared correction of the Thematic Mapper indices was a good indicator of understory canopy cover.
© 1997 NRC Research Press
White, J. D., Running S. W., Nemani R. R., Keane R. E., and Ryan K. C. (1997). Measurement and remote sensing of LAI in Mountain montane ecosystems. Canadian Journal of Forest Research: 27(11), 1714-1727, doi: 10.1139/x97-142