|Chopping, Mark - MONTCLAIR UNIV|
|Su, Lihong - MONTCLAIR UNIV|
|Martonchik, John - NASA JET PROP. LAB|
|Laliberte, Andres - NEW MEXICO STATE UNIV|
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 26, 2006
Publication Date: November 24, 2007
Citation: Chopping, M., Su, L., Rango, A., Martonchik, J.V., Peters, D.C., Laliberte, A. 2008. Remote sensing of woody shrub cover in desert grasslands using MISR with a geometric-optical canopy reflectance model. Remote Sensing of Environment. 112:19-34 Interpretive Summary: Very high resolution imagery from space platforms can be used to map shrub cover and monitor changes, but the high resolution data is very expensive to use. An alternative is to use moderate resolution mulitangle remote sensing data from the Multiangle Imaging Spectro Radiometer (MISR) instrument currently in orbit. The MISR data can't be used by itself, but must be combined with a geometric-optical model for shrub cover mapping which isolates the soil-understory background signal from the combined upper canopy/background signal. This approach was tested at 5.25 km2 area on the Jornada Experimental Range encompassing a wide range of canopy conditions. This approach yielded a close similarity to the high resolution shrub mapping over 90% of the study area. Further refinements are needed before operational agencies can use MISR for shrub mapping, but it is the first time that moderate resolution (an affordable) satellite remote sensing data have been used to obtain an extensive map of woody shrub cover in desert grasslands.
Technical Abstract: A new method is described for the retrieval of fractional cover of large woody plants (shrubs) at the landscape scale using moderate resolution multiangle remote sensing data from the Multiangle Imaging SpectroRadiometer (MISR) and an hybrid geometric-optical (GO) canopy reflectance model. Remote sensing from space is the only feasible method for regularly mapping woody shrub cover over large areas, an important application because extensive woody shrub encroachment into former grasslands has been seen in arid and semiarid grasslands around the world during the last 150 years. The major difficulty in applying GO models in desert grasslands is the spatially dynamic nature of the combined soil and understory background reflectance: the background is important and cannot be modeled as either a Lambertian scatterer or by using a fixed bidirectional reflectance distribution function (BRDF). Candidate predictors of the background BRDF at the Sun-target-MISR angular sampling configurations included the volume scattering kernel weight from a Li-Ross BRDF model; diffuse brightness (rho 0) from the Modified Rahman-Pinty-Verstraete (MRPV) BRDF model; other Li- Ross kernel weights (isotropic, geometric); and MISR near-nadir bidirectional reflectance factors (BRFs) in the blue, green, and near infra-red bands. The best method was multiple regression on the weights of a kernel-driven model and MISR nadir camera blue, green, and near infra-red bidirectional reflectance factors. The results of forward modeling BRFs for a 5.25 km2 area in the USDA, ARS Jornada Experimental Range using the Simple Geometric Model (SGM) with this background showed good agreement with the MISR data in both shape and magnitude, with only minor spatial discrepancies. The simulations were shown to be accurate in terms of both absolute value and reflectance anisotropy over all 9 MISR views and for a wide range of canopy configurations (r2=0.78, RMSE = 0.013, N = 3969). Inversion of the SGM allowed estimation of fractional shrub cover with a root mean square error (RMSE) of 0.03 but a relatively weak correlation (r2=0.19) with the reference data (shrub cover estimated from high resolution IKONOS panchromatic imagery). The map of retrieved fractional shrub cover was an approximate spatial match to the reference map. Deviations reflect the first-order approximation of the understory BRDF in the MISR viewing plane; errors in the shrub statistics; and the 12 month lag between the two data sets.