HR: 0830h
AN: C41B-0967 [PDF]
TI: The Effect of Illumination and Viewing Geometry and Forest Canopies on the Estimation of Snow Cover
Using Remote Sensing
AU: Liu, J
EM: jcliu@bu.edu
AF: Department of Geography, Boston University, 675 Commonwealth Ave., Boston, MA 02215 United States
AU: Woodcock, C E
EM: curtis@bu.edu
AF: Department of Geography, Boston University, 675 Commonwealth Ave., Boston, MA 02215 United States
AU: Melloh, R A
EM: Rae.A.Melloh@erdc.usace.army.mil
AF: US Army Cold Regions Research and Engineering Laboratory, 72 Lyme Road, Hanover, NH 03755 United States
AU: * Davis, R E
EM: Robert.E.Davis@erdc.usace.army.mil
AF: US Army Cold Regions Research and Engineering Laboratory, 72 Lyme Road, Hanover, NH 03755 United States
AB:
With the exception of cloud cover, the largest obstacle to producing a global daily snow cover product using remotely sensed
data is the presence of the forests, which cover much of the seasonally snow-covered portion of the world. The presence of
the forest canopy influences the radiance received by the sensor in such a way that the proportion of viewable snow within a
pixel changes as a function of forest properties, topography and viewing position.
To explore the potential effects of sun angle and viewing geometry of satellite systems such as NOAA AVHRR and MODIS on snow
cover estimation, a program has been written to estimate viewable gap fractions (VGF) across landscapes based on the
Li-Strahler geometric-optical (GO) bidirectional reflectance distribution function (BRDF) model. It computes a VGF map for a
specified illumination and viewing geometry using maps of forest cover and species and terrain images of slope and aspect.
This study explores the effect of illumination and viewing geometry and forest properties on the VGF for the Fool's Creek
Intensive Study Area (ISA) in Fraser Experimental Forest, Colorado. Intensive field measurements of the required parameters
for the GO model and maps of forest properties are used to generate maps of viewable gap fractions. Hemispherical photos are
used to validate model results. The results improve our understanding of the way forest canopies influence the estimation of
snow cover using remotely sensed data.
DE: 1640 Remote sensing
DE: 1833 Hydroclimatology
DE: 1863 Snow and ice (1827)
DE: 1878 Water/energy interactions
SC: Cryosphere [C]
MN: 2003 Fall Meeting