EXTRACTION OF FOREST BIOPHYSICAL PARAMETERS USING POLARIMETRIC SAR Michael Wollersheim and Michael J. Collins M. Wollersheim is with the Department of Electrical and Computer Engineering:
[email protected] M. Collins is with the Department of Geomatics Engineering:
[email protected] Schulich School of Engineering, University of Calgary Calgary, AB Canada T3A 1X7
1. INTRODUCTION Sustainable management of the world’s forest resources is one of the most pressing environmental issues of our time. Demand for forest products is increasing worldwide while forested surfaces continue to decrease [1]. This presents a significant need for improved forest management procedures offering accurate, quick and cost-effective forest inventories. While field measurements provide the most accurate estimates of forest biophysical parameters, they produce small local datasets. Radar remote sensing using SAR offers the ability to image at a scale large enough to produce regional datasets and at resolutions that are comparable to optical satellites. Most SAR research to date utilizes L or P-band radar systems capable of penetrating through moderately dense vegetation. Radar systems operating at C-band such as those used by Environment Canada’s Convair-580 and Canada Space Agency’s Radarsat-2 can provide information on the structure of a canopy surface [2]. It has been shown that there is a significant relationship between the structure of the forest canopy and sub-canopy [2]. This suggests that there is potential for C-band SAR measurements to extract sub-canopy parameters in spite of the fact that the short wavelength signal often does not penetrate below the canopy layer. In this study, forest biophysical parameters are related to polarimetric measurements made from Convair-580 C-band SAR data. This has the potential to increase the value of recently launched Radarsat-2 through the development of data models and identification of polarimetric tools which are useful for forest resource management. 2. STUDY SITE AND DATA ACQUISITION 2.1. Site Description The Petawawa Research Forest is located 200 km west of Ottawa and 180 km east of North Bay in Canada. The experimental forest encompasses 100 km2 of mixed mature forest and is characterized by white, red and jack pine, white and black spruce, poplar and red oak. About 85% of the Petawawa Research Forest is productive forest land. The growing stock is estimated to be 1.5 million m3. Extensive ground measurements have been made in this forest resulting in an inventory of more than 1600 stands with known species composition, age, area, canopy closure, height, stocking, basal area, and volume. 2.1. Convair-580 Acquisition Two acquisitions of Convair-580 fully polarimetric data were collected over the Petawawa Research Forest. The first acquisition took place on June 8, 2002 and the second took place on October 14, 2004. The data were polarimetrically calibrated by CCRS using the POLGASP SAR processor and delivered in a single-look complex (SLC) format with four constituent polarizations. The spatial resolution is approximately 6 meters in the azimuth and slant range directions. 3. METHODOLOGY Numerous studies have been performed which relate the SAR horizontal and vertical co- and cross-polarization radar crosssection (RCS) measurements to biophysical parameters. These techniques have had limited success at C-Band attributed largely to the signal’s low usable dynamic range [3][4][5]. More recently, efforts have been made to exploit the statistical
information and the polarimetric information in the SAR signal to find stronger relationships that have a higher dynamic range [6][7]. In this study, in addition to examining the relationships of the linear polarization RCS measurements, several statistical and polarimetric parameters have been investigated. The statistical parameters are related to the texture of the SAR data and include the standard deviation of the RCS measurements and the order parameter of the K-distributed SAR clutter. Many of the polarimetric parameters are derived from the 3-dimensional polarimetric signature plots and include the received power extrema, Van Zyl coefficient of variation, inter-channel coherence amplitude and phase, Kostinski and Boerner scattered wave parameters and Touzi’s discriminators [7]. Other polarimetric parameters investigated include the Cloude parameters [7] and ratios of the linear RCS measurements [2]. The strength of the relationships between SAR variables and forest parameters is known to be dependent on the forest type being studied [2]. The polarimetric SAR response will vary depending on the structural and dielectric properties of the target forest stands. For this reason, only stands which were found to be dominated by a single species were used for this study. A species has been considered dominant if it composes 70% or greater of the stand species composition. The species included in this study are white pine, red pine, jack pine, white spruce, black spruce, poplar and red oak. Relationships are formed by performing single linear regression first, followed by a multiple linear regression technique which utilizes the most promising SAR variables as determined by the single linear regression. Stands selected for the models are determined according to the species being studied and confined to within a desired range of radar incidence angle. The results are analyzed by computing the coefficient of determination r2 and using an F-test to verify statistical significance of the relationships. Scatter plots of the data points and residual errors are also inspected to identify poor model fit or bias. 4. RESULTS AND CONCLUSIONS It was found that the forest biophysical parameters that were most related to the SAR measurements were the volume and basal area for all species. The r2 coefficient varied considerably depending on the incident angle range, number of sample stands, and the species being modeled. Nevertheless, promising relationship emerged suggesting that C-band polarimetric SAR measurements could be a useful tool in measuring sub-canopy forest parameters. Species classes were merged to examine the benefit of stratifying the forest stands by species prior to regression. The results were considerably worse and few statistically significant regressions were found. Stratification thus proved to be an essential step in the modeling process. The best SAR parameters to use for regression depended on the species and forest parameter being modeled. In nearly all cases, the polarimetric and statistical SAR variables outperformed the linear polarized RCS measurements thus demonstrating the value of having full polarimetric information. 11. REFERENCES [1] T. Le Toan, A. Beaudoin, J. Riom, and D. Guyon, “Relating Forest Biomass to SAR Data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 30, no. 2, pp. 402-411, March 1992. [2] R.M. Green, “Relationships between polarimetric SAR backscatter and forest canopy and sub-canopy biophysical properties,” International Journal of Remote Sensing, vol. 19, no. 12, pp. 2395-2412, 1998. [3] M.C. Dobson, F.T. Ulaby, T. Le Toan, A. Beaudoin, E.S. Kasischke, N. Christensen, “Dependence of Radar Backscatter on Coniferous Forest Biomass,” IEEE Transactions on Geoscience and Remote Sensing, vol. 30, no. 2, pp. 412-415, 1992. [4] K.J. Ranson, G. Sun, “Mapping Biomass of a Northern Forest Using Multifrequency SAR Data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 32, no. 2, pp. 388-396, 1994. [5] M.L. Imhoff, “Radar Backscatter and Biomass Saturation: Ramifications for Global Biomass Inventory,” IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 2, pp. 511-518, 1995. [6] H. Wang, K. Ouchi, M. Watanabe, M. Shimada, T. Tadono, A. Rosenqvist, S.A. Romshoo, M. Matsuoka, T. Moriyama, S. Uratsuka, “In Search of the Statistical Properties of High Resolution Polarimetric SAR Data for the Measurement of Forest Biomass Beyond the RCS Saturation Limits,” IEEE Geoscience and Remote Sensing Letters, vol. 3, no. 4, pp. 495-499, October 2006. [7] R. Touzi, W. Boerner, J.S. Lee, E. Luneberg, “A review of polarimetry in the context of synthetic aperture radar: concepts and information extraction,” Canadian Journal of Remote Sensing, vol. 30, no. 3, pp. 380-407, January 2004.
