Title :
Model-Based Estimation of Forest Canopy Height in Red and Austrian Pine Stands Using Shuttle Radar Topography Mission and Ancillary Data: A Proof-of-Concept Study
Author :
Brown, Charles G., Jr. ; Sarabandi, Kamal ; Pierce, Leland E.
Author_Institution :
Lawrence Livermore Nat. Lab., Livermore, CA, USA
fDate :
3/1/2010 12:00:00 AM
Abstract :
In this paper, accurate tree stand height retrieval is demonstrated using C-band Shuttle Radar Topography Mission (SRTM) height and ancillary data. The tree height retrieval algorithm is based on modeling uniform tree stands with a single layer of randomly oriented vegetation particles. For such scattering media, the scattering phase center height, as measured by SRTM, is a function of tree height, incidence angle, and the extinction coefficient of the medium. The extinction coefficient for uniform tree stands is calculated as a function of tree height and density using allometric equations and a fractal tree model. The accuracy of the proposed algorithm is demonstrated using SRTM and TOPSAR data for 15 red pine and Austrian pine stands (TOPSAR is an airborne interferometric synthetic aperture radar). The algorithm yields root-mean-square (rms) errors of 2.5-3.6 m, which is a substantial improvement over the 6.8-8.3-m rms errors from the raw SRTM minus National Elevation Dataset Heights.
Keywords :
fractals; remote sensing by radar; synthetic aperture radar; topography (Earth); vegetation mapping; Austrian pine stands; C-band Shuttle Radar Topography Mission; National Elevation Dataset Heights; SRTM data; TOPSAR data; allometric equations; ancillary data; extinction coefficient; forest canopy height; fractal tree model; incidence angle; red pine stands; remote sensing; root-mean-square errors; scattering phase center height; synthetic aperture radar; tree density; tree height retrieval algorithm; tree stand height retrieval; uniform tree stands; vegetation particles; Interferometry; remote sensing; synthetic aperture radar (SAR);
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2009.2031635