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Abstract. As part of the Boreal-Ecosystem Atmosphere Study (BOREAS), an investigation is being made of the use of satellite data including shuttle imaging radar-C (SIR-C), X-band synthetic aperture radar (XSAR), and Landsat-Thematic Mapper data for estimating total and component aboveground woody biomass in boreal forest study sites in Canada. The goal of this paper is to present progress in mapping above ground woody biomass over portions of the BOREAS southern study area using spaceborne sensor data. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass are used to estimate biomass across the landscape. The procedure involves image classification with SAR and Landsat data and development of simple mapping techniques using combinations of SAR channels. The analysis uses measurements from forest stands representing a range of biomass and structures. Field measurements included plot level mensuration (species, stem diameter, height, density, and basal area) and tree geometry measurements (leaf, branch, bole size, and angle distributions). The results indicate that aboveground biomass can be estimated to within about 1.6 kg/m 2 and up to about 15 kg/m 2 across the SIR-C image evaluated. A general method produced equivalent results with those obtained by treating forest type (pine, spruce, and aspen) separately. The biomass mapping was extended to bole, branch, and foliage components from relationships with total aboveground biomass developed from detailed tree measurements. Average biomass within the imaged area was estimated to be about 7.3 kg/m 2 with biomass components of bole, branch, and foliage comprising 83, 12, and 5% of the total. Examination of the scaling of biomass estimates from remote sensing images of varying resolution shows that information at scales useful for ecosystem models can be obtained. In addition, the biomass estimation technique provides similar information at different image resolutions. IntroductionEstimates of regional and global forest biomass are important for understanding and monitoring ecosystem responses to climate change and human activities. Knowledge of the distribution and structure of aboveground woody biomass is important for assessing production and decomposition rates in forests. In addition, ecosystem model predictions of primary production rates could be improved by including the effect of woody plant tissue respiration which can be related to tree trunk biomass. Scientists interested in the fate of global carbon are using field measurements and remote sensing observations over diverse ecosystems to address these issues.A [1995a] reported on initial efforts to map cover type and estimate biomass over a portion of BOREAS southern study area. In the present paper, we discuss efforts to improve upon this earlier work and develop forest cover and biomass maps in a gridded format from SAR data suitable for use with ecosystem models. The method for developing the algorithm is discussed, including field measurements and im-29,599

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Abstract-Automatic detection of human motion is important for security and surveillance applications. Compared to other sensors, radar sensors present advantages for human motion detection and identification because of their all-weather and day-and-night capabilities, as well as the fact that they detect targets at a long range. This is particularly advantageous in the case of remote and highly cluttered radar scenes. The objective of this paper is to investigate human motion in highly cluttered forest medium to observe the characteristics of the received Doppler signature from the scene. For this purpose we attempt to develop an accurate model accounting for the key contributions to the Doppler signature for the human motion in a forest environment. Analytical techniques are combined with full wave numerical methods such as Method of Moments (MoM) enhanced with Fast Multipole Method (FMM) to achieve a realistic representation of the signature from the scene. Mutual interactions between the forest and the human as well as the attenuation due to the vegetation are accounted for. Due to the large problem size, parallel programming techniques that utilize a Graphics Processing Unit (GPU) based cluster are used.