Title :
Stand-Level Stem Volume of Boreal Forests From Spaceborne SAR Imagery at L-Band
Author :
Antropov, Oleg ; Rauste, Yrjo ; Ahola, H. ; Hame, Tuomas
Author_Institution :
Remote Sensing Group, VTT Tech. Res. Centre of Finland, Espoo, Finland
Abstract :
This paper presents a modified robust stem volume retrieval approach suitable for use with L-band SAR imagery. Multitemporal dual-polarization SAR imagery acquired by ALOS PALSAR during the summer-autumn 2007 is used in the study, along with stand-wise forest inventory data from two boreal forest sites situated in central Finland. The average sizes of forest stands at the study sites were 3 ha and 4.8 ha. The method used employs model fitting with an inverted semi-empirical boreal forest model, and takes advantage of the multitemporal aspect in order to improve the stability and accuracy of stem volume estimation. Multitemporal combination of model output in a multivariate regression framework allows volume estimates to be obtained with an RMSE about 43% of the mean of 110 m3 /ha, and a coefficient of determination R 2 of 0.71 in the best case. The methodology used can be employed to produce large-area stem volume maps from dual-polarization ALOS PALSAR imagery mosaics.
Keywords :
data acquisition; geophysical image processing; image segmentation; radar imaging; radar polarimetry; regression analysis; remote sensing by radar; synthetic aperture radar; vegetation; vegetation mapping; AD 2007; L-band SAR imagery; RMSE; boreal forest site; central Finland; determination coefficient; dual-polarization ALOS PALSAR imagery mosaic; inverted semiempirical boreal forest model; large-area stem volume map; model fitting; model output; multitemporal aspect; multitemporal combination; multitemporal dual-polarization SAR imagery; multivariate regression framework; robust stem volume retrieval approach; spaceborne SAR imagery; stability; stand-level stem volume; stand-wise forest inventory data; stem volume estimation; Backscatter; Data models; L-band; Predictive models; Solid modeling; Synthetic aperture radar; Training; ALOS PALSAR; boreal forest; forest inventory; synthetic aperture radar (SAR);
Journal_Title :
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
DOI :
10.1109/JSTARS.2013.2241018