Error and quality assessment for remotely sensed estimates of leaf area index

Abstract

Four techniques for estimating leaf area index (LAI) from remote sensing data (linear spectral mixture analysis, moisture stress index, modification of spectral vegetation indices, and normalized distance method) were investigated to determine the degree to which the parameters associated with each method influence the quality of computed landscape-level estimates of LAI. A series of Monte Carlo simulations were used to test the quality of each estimation method. These simulations showed that the quality of landscape-level LAI estimates depends on the initial modeling quality and the inherent landscape variability in terms of LAI. A multiscale analysis was also performed across a portion of the Upper Elbow River watershed in Alberta, Canada, using Moderate Resolution Imaging Spectroradiometer (MODIS) data and resampled Satellite pour l’Observation de la Terre (SPOT) imagery. This analysis was performed to determine the extent to which the derived relationships are scale dependent. Spatial statistical analysis revealed the tendency of the correlation of input parameters to vary inversely with distance. These results confirm the validity of the derived estimation relationships. The multiscale analysis also demonstrated that remote estimation techniques have a significant sensitivity to variations in scale, almost irrespective of the remote estimation method used.

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