Mathematics – Probability
Scientific paper
Sep 2007
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007georl..3418405s&link_type=abstract
Geophysical Research Letters, Volume 34, Issue 18, CiteID L18405
Mathematics
Probability
2
Biogeosciences: Remote Sensing, Global Change: Land/Atmosphere Interactions (1218, 1843, 3322), Biogeosciences: Carbon Cycling (4806), Global Change: Global Climate Models (3337, 4928), Biogeosciences: Biodiversity
Scientific paper
Recent empirical studies have shown that multi-angle spectral data can be useful for predicting canopy height, but the physical reason for this correlation was not understood. We follow the concept of canopy spectral invariants, specifically escape probability, to gain insight into the observed correlation. Airborne Multi-Angle Imaging Spectrometer (AirMISR) and airborne Laser Vegetation Imaging Sensor (LVIS) data acquired during a NASA Terrestrial Ecology Program aircraft campaign underlie our analysis. Two multivariate linear regression models were developed to estimate LVIS height measures from 28 AirMISR multi-angle spectral reflectances and from the spectrally invariant escape probability at 7 AirMISR view angles. Both models achieved nearly the same accuracy, suggesting that canopy spectral invariant theory can explain the observed correlation. We hypothesize that the escape probability is sensitive to the aspect ratio (crown diameter to crown height). The multi-angle spectral data alone therefore may not provide enough information to retrieve canopy height globally.
Blair Bryan J.
Chiu J. C.
Ganguly Sangram
Huang Dong
Jenkins J. P.
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