Paul Siqueira

Vertical structure of vegetated land surfaces from interferometric and polarimetric radar

This paper describes the estimation of parameters characterizing the vertical structure of vegetated land surfaces, from combined interferometric and polarimetric radar data. Physical models expressing radar observations in terms of parameters describing vegetated land surfaces are the foundation for parameter estimation techniques. Defining a general complex cross correlation enables the unified development of models for interferometry and polarimetry, including polarimetric interferometry. Three simple physical models in this paper express this complex cross correlation in terms of vegetation parameters: (1) a randomly oriented volume, (2) a randomly oriented volume with a ground return, and (3) an oriented volume. For the first two models the parameters include vegetation height, extinction coefficient, underlying topography, and another parameter depending on ground electrical properties and roughness. For the oriented volume, additional parameters depend on the refractivity, extinction coefficients, and backscattering characteristics of waves propagating along eigenpolarizations of the vegetation volume. The above models show that the interferometric cross-correlation amplitude and the polarimetric {HHHH/VVVV} ratio both change by about 1% per meter of vegetation height change, for experimental conditions typical of airborne and spaceborne interferometric radars. These vertical-structure sensitivities prompt a parameter estimation demonstration with two-baseline TOPSAR interferometric and zero-baseline polarimetric data from the Boreal Ecosystem-Atmosphere Study (BOREAS) Southern Study Area in Prince Albert National Park, Saskatchewan, Canada. The demonstrations show the feasibility of measuring vegetation height to better than 4.2 m, underlying topography to better than 6.5 m, and the ratio of ground-to-volume power to better than 10%, using interferometry and polarimetry, coupled with parameter-constraining assumptions, concerning the degree of surface roughness. This paper suggests that single-baseline and multibaseline fully polarimetric interferometry have the potential to obviate the need for such assumptions, thereby making parameter estimation more robust, accurate, and realistic.

Estimation of forest biophysical characteristics in Northern Michigan with SIR-C/X-SAR

A three-step process is presented for estimation of forest biophysical properties from orbital polarimetric SAR data. Simple direct retrieval of total aboveground biomass is shown to be ill-posed unless the effects of forest structure are explicitly taken into account. The process first involves classification by (1) using SAR data to classify terrain on the basis of structural categories or (2) a priori classification of vegetation type on some other basis. Next, polarimetric SAR data at L- and C-bands are used to estimate basal area, height and dry crown biomass for forested areas. The estimation algorithms are empirically determined and are specific to each structural class. The last step uses a simple biophysical model to combine the estimates of basal area and height with ancillary information on trunk taper factor and wood density to estimate trunk biomass. Total biomass is estimated as the sum of crown and trunk biomass. The methodology is tested using SIR-C data obtained from the Raco Supersite in Northern Michigan on Apr. 15, 1994. This site is located at the ecotone between the boreal forest and northern temperate forests, and includes forest communities common to both. The results show that for the forest communities examined, biophysical attributes can be estimated with relatively small rms errors: (1) height (0-23 m) with rms error of 2.4 m, (2) basal area (0-72 m/sup 2//ha) with rms error of 3.5 m/sup 2//ha, (3) dry trunk biomass (0-19 kg/m/sup 2/) with rms error of 1.1 kg/m/sup 2/, (4) dry crown biomass (0-6 kg/m/sup 2/) with rms error of 0.5 kg/m/sup 2/, and (5) total aboveground biomass (0-25 kg/m/sup 2/) with rms error of 1.4 kg/m/sup 2/. The addition of X-SAR data to SIR-C was found to yield substantial further improvement in estimates of crown biomass in particular. However, due to a small sample size resulting from antenna misalignment between SIR-C and X-SAR, the statistical significance of this improvement cannot be reliably established until further data are analyzed. Finally, the results reported are for a small subset of the data acquired by SIR-C/X-SAR. >

The "Myth" of the minimum SAR antenna area constraint

A design constraint traceable to the early days of spaceborne synthetic aperture radar (SAR) is known as the minimum antenna area constraint for SAR. In this paper, it is confirmed that this constraint strictly applies only to the case in which both the best possible resolution and the widest possible swath are the design goals. SAR antennas with area smaller than the constraint allows are shown to be possible, have been used on spaceborne SAR missions in the past, and should permit further, lower-cost SAR missions in the future.

The calculated performance of forest structure and biomass estimates from interferometric radar

Abstract Vertical structure and biomass are key characteristics of the forest random medium. This paper calculates the power and interferometric synthetic aperture radar (InSAR) sensitivity to tree height and vegetation density as it manifests in extinction, using a homogeneous, random-volume model of the forest medium, and accounting for speckle and thermal noise. Tree height and extinction are both related to biomass within the context of this simple model. Signal and noise calculations show that InSAR coherence and phase are more sensitive than radar power to structure and biomass to 10% variations in structure parameters over a wide range of medium to high density forests. For example, for extinctions of 0.2 db m−1 and other parameters as noted in the text, the sensitivity of InSAR coherence to 10% changes in tree height exceeds observation errors for trees shorter than about 37 m, as opposed to 14 m for radar power. InSAR phase sensitivity to 10% structural and associated biomass changes exceeds observation errors for all tree heights. Only for the very lowest densities, for extinctions less than 0.15 db m−1, does radar power sensitivity to density exceed that of InSAR; even at low densities, InSAR sensitivity to tree height outperforms that of radar power in these calculations.

T-matrix determination of effective permittivity for three-dimensional dense random media

In this paper, we present a full wave method for determining the effective permittivity for random media in three dimensions. The type of media addressed is composed of spherical dielectric particles in a homogeneous dielectric background. The particle volume fraction ranges from 0 to 40% and dielectric contrast may be significantly different from the background medium. The method described relies on the T-matrix approach for solving Maxwells equations using a spherical wave expansion in conjunction with a Monte-Carlo simulation for calculating the mean scattered field confined within a prescribed fictitious boundary. To find the effective permittivity, the mean scattered field is compared with that of a homogeneous scatterer whose shape is defined by the fictitious boundary and its dielectric constant is varied until the scattered fields are matched. A complete description of the T-matrix approach is given along with an explanation of why the recursive form of this technique (RATMA) cannot be used for addressing this problem. After the method development is completed, the results of our numerical technique are compared against the theoretical methods of the quasi crystalline approximation and the effective field approximation to demonstrate the region of validity of the theoretical methods. The examples contained within the paper use between 30 and 120 included spheres (with radii ranging from from ka=0.6 to 0.8) within a larger, fictitious sphere of diameter kD=8.4.

The JERS Amazon Multi-season Mapping Study (JAMMS): Observation strategies and data characteristics

The Japanese Earth Resources Satellite (JERS-1) Amazon Multiseason Mapping Study (JAMMS), part of the Global Rain Forest Mapping (GRFM) project led by the National Space Development Agency of Japan (NASDA), had an ambitious agenda to map the entire Amazon river floodplain (and surrounding areas) twice at high resolution. The observation strategy carried out by NASDA for the JAMMS project and the other elements of the GRFM project (1995-1997) constituted the first time that a spaceborne Synthetic Aperture Radar (SAR) successfully implemented a continental scale, coordinated seasonal mapping campaign. This observation strategy, chosen around the flooding cycle of the major river systems, was designed to provide the first high-resolution measurement of inundation extent by the Amazon river and its tributaries. In order for the scientific community at large to be able to exploit this dataset, the characteristics of the data (resolution, radiometric and geometric calibration, coverage, and ability to be mosaicked) must be well understood. We find that the quantization of the Alaska SAR Facility (ASF) imagery impacts the range of backscatter values that may be observed, in contrast to the NASDA processed imagery. The noise equivalent σ 0 is -15 dB at worst, but improves to about -20 dB at the centre of the swath. The resolution of the ASF imagery is slightly worse than that processed by NASDA. The initial geolocation accuracy of the ASF processed imagery is quite poor, but may be improved during the mosaicking process. The relative radiometric calibration of the data may be improved to about 0.2 dB by comparing the calibration of overlapping imagery, and through a careful analysis of cross-track trends in the data.

Numerical scattering analysis for two-dimensional dense random media: characterization of effective permittivity

A new numerical method for determining effective permittivity of dense random media in two dimensions is presented. The core of the method is to compare the average scattered field of a random collection of scatterers confined within an imaginary boundary with the scattered field from a homogeneous dielectric of the same shape as the imaginary boundary. The two-dimensional (2-D) problem is aggressively studied to provide insight into the dependence of the methods convergence on particle size, boundary shape, and boundary dimension. A novel inverse scattering method is introduced based on the method of moments (MoM), which greatly reduces the computation time and increases the flexibility of the procedure to analyze a variety of geometries. Results from this 2-D method may be used directly to compare with theoretical methods for determining effective permittivity such as the Polder-Van Santen (1946) mixing formula or field techniques such as the quasi-crystalline approximation.

First P-band results using the GeoSAR mapping system

GeoSAR is a program to develop a dual frequency airborne radar interferometric mapping instrument designed to meet the mapping needs of a variety of users in government and private industry. Program participants are the Jet Propulsion Laboratory (JPL), Calgis, Inc., and the California Department of Conservation with funding provided initially by DARPA and currently by the National Imagery and Mapping Agency. Begun to address the critical mapping needs of the California Department of Conservation to map seismic and landslide hazards throughout the state, GeoSAR is currently undergoing tests of the X-band and P-band radars designed to measure the terrain elevation at the top and bottom of the vegetation canopy. Maps created with the GeoSAR data will be used to assess potential geologic/seismic hazard (such as landslides), classify land cover, map farmlands and urbanization, and manage forest harvests. This system is expected to be fully operational in 2002. In this paper we describe an experiment conducted at Californias Latour State Demonstration Forest located near the city of Redding. This experiment marks the first operation of the-P-band radar in a vegetated area.

Uncertainty of Forest Biomass Estimates in North Temperate Forests Due to Allometry: Implications for Remote Sensing

Estimates of above ground biomass density in forests are crucial for refining global climate models and understanding climate change. Although data from field studies can be aggregated to estimate carbon stocks on global scales, the sparsity of such field data, temporal heterogeneity and methodological variations introduce large errors. Remote sensing measurements from spaceborne sensors are a realistic alternative for global carbon accounting; however, the uncertainty of such measurements is not well known and remains an active area of research. This article describes an effort to collect field data at the Harvard and Howland Forest sites, set in the temperate forests of the Northeastern United States in an attempt to establish ground truth forest biomass for calibration of remote sensing measurements. We present an assessment of the quality of ground truth biomass estimates derived from three different sets of diameter-based allometric equations over the Harvard and Howland Forests to establish the contribution of errors in ground truth data to the error in biomass estimates from remote sensing measurements.

Development and Testing of a Single Frequency Terahertz Imaging System for Breast Cancer Detection

The ability to discern malignant from benign tissue in excised human breast specimens in Breast Conservation Surgery (BCS) was evaluated using single frequency terahertz radiation. Terahertz (THz) images of the specimens in reflection mode were obtained by employing a gas laser source and mechanical scanning. The images were correlated with optical histological micrographs of the same specimens, and a mean discrimination of 73% was found for five out of six samples using Receiver Operating Characteristic (ROC) analysis. The system design and characterization is discussed in detail. The initial results are encouraging but further development of the technology and clinical evaluation is needed to evaluate its feasibility in the clinical environment.