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Spaceborne SAR interferometry (InSAR) has the potential of detecting forest change on a global scale with fine (meter-level) spatial resolution as well as on a monthly/weekly basis regardless of day or night. This is significant to characterize the land-use change and its impact on climate change. In this paper, both single-pass and repeat-pass SAR interferometry from spaceborne sensors are combined in order to detect and quantify (with Normalized RMSE ≤ 30%) forest disturbance at a large scale (dozens of kilometers) however with a fine spatial resolution (< 1 hectare) based on two newly developed approaches. The single-pass InSAR approach is not only able to detect forest disturbance but also capable of characterizing meter (or even sub-meter) level change of forest phase-center (mean) height due to forest growth and/or degradation. These methods are extensively validated with the past and current spaceborne single-pass and repeat-pass InSAR missions (i.e. JAXA's ALOS-1, ALOS-2 and DLR's TanDEM-X) over subtropical forests in Australia as well as tropical forests in Brazil. Such techniques also serve as observing prototypes for the fusion of the future spaceborne InSAR missions (such as NASA-ISRO's NISAR and DLR's TanDEM-L).

This paper describes of a ground-based synthetic radar (SAR) which can take short- and long-term radar cross-section measurements that can be used for the monitoring of dynamic forest characteristics through the instrument's sensitivity to the dielectric constants for the soil and woody structures. The SAR image is generated through the successive transmission/reception of radar echoes from a tram-mounted sensor that sits 20 meters above the forest floor. Processing of the data into range and azimuthally resolved imagery is achieved through the backprojection algorithm.

Yang Lei and Paul Siqueira *Department of Electrical and Computer Engineering, University of Massachusetts Amherst,151 Holdsworth Way, Amherst, MA 01003, USA; E-Mail: ylei@ecs.umass.edu* Author to whom correspondence should be addressed; E-Mail: siqueira@ecs.umass.edu;Tel.: +1-413-577-0623; Fax: +1-413-545-4652.External Editors: Nicolas Baghdadi and Prasad S. ThenkabailReceived: 27 July 2014; in revised form: 10 October 2014 / Accepted: 11 October 2014 /Published: 24 October 2014Abstract: This paper describes a novel, simple and efficient approach to estimate forestheight over a wide region utilizing spaceborne repeat-pass InSAR correlation magnitudedata at L-band. We start from a semi-empirical modification of the RVoG model thatcharacterizes repeat-pass InSAR correlation with large temporal baselines (e.g., 46 daysfor ALOS) by taking account of the temporal change effect of dielectric fluctuation andrandom motion of scatterers. By assuming (1) the temporal change parameters andforest backscatter profile/extinction coefficient follow some mean behavior across eachinteferogram; (2) there is minimal ground scattering contribution for HV-polarization; and(3) the vertical wavenumber is small, a simplified inversion approach is developed to linkthe observed HV-polarized InSAR correlation magnitude to forest height and validatedusing ALOS/PALSAR repeat-pass observations against LVIS lidar heights over the HowlandResearch Forest in central Maine, US (with RMSE

A better understanding of ecosystem processes requires accurate estimates of forest biomass and structure on global scales. Recently, there have been demonstrations of the ability of remote sensing instruments, such as radar and lidar, for the estimation of forest parameters from spaceborne platforms in a consistent manner. These advances can be exploited for global forest biomass accounting and structure characterization, leading to a better understanding of the global carbon cycle. The popular techniques for the estimation of forest parameters from radar instruments, in particular, use backscatter intensity, interferometry, and polarimetric interferometry. This paper analyzes the uncertainty in biomass estimates derived from single-season L-band cross-polarized (HV) radar backscatter over temperate forests of the Northeastern United States. An empirical approach is adopted, relying on ground-truth data collected during field campaigns over the Harvard and Howland Forests in 2009. The accuracy of field biomass estimates, including the impact of the diameter-biomass allometry, is characterized for the field sites. A single-season radar data set from the National Aeronautics and Space Administration Jet Propulsion Laboratory's L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar instrument is analyzed to assess the accuracy of the backscatter-biomass relationships with a theoretical radar error model.

The Wishart classification, utilizing polarimetric parameters alpha (α) and entropy (H), was performed on airborne L-band Synthetic Aperture Radar to identify dominant scattering mechanisms within each pixel. The scattering classes were then attributed to structural forms of either excurrent (large, central stem with minimal branching) or decurrent (diffuse branching) trees. To test the classification for a variety of forest structures, three contrasting study areas were chosen; a wooded savanna at the Injune Landscape Collaborative Project in Queensland, Australia, a managed transitional boreal-hardwood forest at the Howland Research Forest in Howland, Maine, and a transitional hardwood-pine forest at the Harvard Forest in Petersham, Massachusetts. Two questions are answered in this study: can the polarimetric parameters α and H characterize structurally similar areas within forests? And can they consistently differentiate these areas across multiple study areas? It is shown that the classification explained nearly 80% of the forest composition at the Injune Collaborative Research Site, 47% at the Howland Research Forest and 40% at the Harvard Forest. Classification accuracy decreases with high levels of H, which is a limiting factor at Howland and Harvard forests where canopy heterogeneity, density and moisture content are higher. When high-H Wishart classes (H > 0.9) are omitted from analysis, accuracy improves to 83% and 86% for the Injune Collaborative Research Site and Harvard Forest respectively. The success of the Wishart classification at low-H indicates that there is a potential for using polarimetric information to characterize forest composition in particular landscapes; namely those that do not exhibit a high, homogeneous H response.

High performance and reliability are important aspects of space-based systems. In many cases correct system functionality must be continuously monitored to ensure the validity of collected data. In this research we develop a new digital circuit which can detect minute phase differences in time-varying analog signals. These phase shifts can be determined for both a single channel input versus a known model of the signal and across two channels with simultaneously-sampled data. Phase shift data can be used in spaceborne systems to either confirm correct system operation or identify potential problems. Current systems primarily transfer data to the Earth for error checking rather than performing error analysis in real-time. The benefits of our field-programmable gate array (FPGA)-based approach are evaluated using a 3 gigasamples per second (GSamp/s) data acquisition system developed as part of the NASA Surface Water Ocean Topography (SWOT) initiative. Phase calculations with an error of less than 0.021 deg (0.006% of 360 deg) are determined using our adaptive approach. The high accuracy of differential phase detection allows for the real-time monitoring of environmental metrics, such as temperature fluctuations, which affect signal phase.

Quantification of global carbon storage, carbon flux and disturbance in forested regions is of critical importance to refining our understanding of ecosystem processes, climate modeling and climate change. Remote sensing instruments, such as lidar and radar provide a means of obtaining highly accurate and well resolved biomass estimates over global scales. This has sparked interest in mission concepts such as DESDynI. One of the core objectives of the proposed DESDynI mission was global carbon accounting and monitoring through a combination of lidar and radar measurements. In this article, the relationship between field biomass and lidar metrics is analyzed using data from coordinated field measurements and lidar overflights at the Harvard and Howland Forests in North-Eastern United States to assess the performance of a potential biomass mapping instrument. Results show that the performance of lidar estimates of biomass vary significantly between the two sites even though they belong to the same northern temperate forest ecoregion. An attempt is made to isolate the reasons behind the dissimilarities. While RMS errors as low as 30 tons/ha can be seen, these are limited to biomass ranges of up to 300 tons/ha.

This paper applies the forest height inversion approach developed in [1] along with the automatic mosaicking algorithm described in [2], and generates a state mosaic map for the US state of New Hampshire (NH) by utilizing the spaceborne repeat-pass ALOS/PALSAR InSAR correlation magnitude data and the airborne lidar data from NH GRANIT database over the White Mountain National Forest (WMNF). Since the forest height map for the US state of Maine (ME) has already been generated using ALOS/PALSAR InSAR data and a small strip of LVIS lidar data over the Howland forest in central Maine, a two-state mosaic, i.e., ME+NH mosaic, can be generated using the LVIS lidar strip with the GRANIT lidar data serving as a separate validation site so that the quality (e.g. error propagation) of the ME+NH mosaic map can be determined. In addition to providing the mosaic map of forest height, this forest height inversion approach is also modified to generate a forest disturbance map over the ground validation site at WMNF where ground truth (such as GRANIT lidar) data is available. The approaches along with the analysis described in this paper will serve as observing prototypes for the data fusion of future spaceborne repeat-pass InSAR missions (e.g. NASA's NISAR [3]) and lidar missions (e.g. NASA's GEDI [4]) in characterizing the large-scale forest height as well as disturbance events (e.g. selective logging and/or forest degradation).

For the past three years, the University of Massachusetts and Harvard Forest have been working on an automated system for collecting remote sensing data about a regrowing region in Petersham, Massachusetts. The system consists of a two 15 m towers separated by a 50 m span and connected to one another by a pair of taut cables which support a tram that can travel between the two towers. This paper will discuss the operation of the system, the suite of instruments on-board the tram, and a radar system that is being constructed to operate on the tram. It is anticipated that this system will be able to perform measurements on a periodic basis for the purpose of monitoring the biophysical characteristics of the surrounding area.

The ability to discern malignant from benign tissue in excised human breast specimens in Breast Conservation Surgery (BCS) was evaluated using a prototype 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. This result is similar to what has previously been obtained using Terahertz pulsed imaging (TPI) techniques. We will discuss the specific advantages of Single frequency THz imaging (SFTI) compared with TPI for potentially allowing the development of much faster, more compact and less expensive cancer imaging systems that could be adapted for employment in the operating room. The system design and characterization of the prototype SFTI system are 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.

In this paper, a real-time algorithm that operates on data provided by a network of weather radars in the form of moments is presented. The algorithm implementation, performance, and abilities are presented and discussed. As part of this paper, a methodology for mapping from native spherical coordinates to a common network grid is developed and demonstrated. Metrics which gauge the quality of mapping and quality of data are derived, and their use from a network point of view is demonstrated. The main product of the algorithm, which is an implementation of a vector velocity estimation technique based on a maximum likelihood approach, is shown, and its results are displayed, compared, and discussed. The application of a methodology for velocity unfolding and reflectivity display enhancement is presented as well.

In this paper we quantify the effects of temporal decorrelation in repeat pass synthetic aperture radar interferometry (InSAR). Temporal decorrelation causes significant uncertainties in vegetation parameter estimates obtained using various InSAR techniques, which are desired on a global scale. Because of its stochastic nature temporal decorrelation is hard to model and isolate. In this paper we analyze temporal decorrelation statistically as observed in a large swath of SIR-C L-Band InSAR data collected over the eastern United States, with a repeat pass duration of one day in October 1994 and a near zero perpendicular baseline. The very small baseline for this particular pair makes the effect of volumetric scattering on correlation magnitude statistics nearly imperceptible, allowing for a quantitative analysis of temporal effects alone. The swath analyzed in this paper spans more than a million hectares of terrain comprised primarily of deciduous and evergreen forests, agricultural land, water and urban areas. The relationships of these different land-cover types, phenology and weather conditions (i.e. precipitation and wind) on the measures of interferometric correlation is analyzed in what amounts to be the most geographically extensive analysis of this phenomenon to date.

Forest canopy height is essential information for many forest management activities and is a critical parameter in models of ecosystem processes. Several methods are available to measure canopy height from single-tree to regional and global scales, but the methods vary widely in their sensitivities, leading to different height estimates even for identical stands. We compare four technologies for estimating canopy height in pine and hardwood forests of the Piedmont region of North Carolina, USA: (1) digital elevation data from the global Shuttle Radar Topography Mission (SRTM) C-band radar interferometry, (2) X- and P-band radar interferometry from the recently developed airborne Geographic Synthetic Aperture Radar (GeoSAR) sensor, (3) small footprint lidar measurements (in pine only), and (4) field measurements acquired by in situ forest mensuration. Differences between measurements were smaller in pine than in hardwood forests, with biases ranging from 5.13 to 12.17 m in pine (1.60–13.77 m for lidar) compared to 6.60–15.28 m in hardwoods and RMSE from 8.40 to 14.21 m in pine (4.73–14.92 m for lidar) compared to 9.54–16.84 in hardwood. GeoSAR measurements of canopy height were among the most comparable measurements overall and showed potential for successful calibration, with R

In this paper, we present a time-domain method for estimating the phase, amplitude, and timing of a test signal, referenced either to a theoretical version of the signal, or to another signal undergoing a similar transformation as the measured signal. The use of time-domain basis for this method allows for its direct application to data samples collected by a digital oscilloscope or a dedicated A/D converter, and precludes the need for conversion into in-phase and quadrature components. The estimation process described in this paper is based on a maximum likelihood formulation, which allows for the estimate performance to be related to errors associated with sampling and is shown to achieve the Cramer-Rao lower bound for variance. Measurements such as the ones described in this paper are important for characterizing interferometric and polarimetric radar systems, as well as for determining the number of observations necessary for achieving a given degree of accuracy in the measurement. By including a statistical description of the estimation process, we enable the ability for using the technique for evaluating hypotheses describing the measurement error model. This last point is critical because it creates a mechanism for accepting or rejecting system model scenarios based on the signal-to-noise ratio and the number of digitized samples.

Snow characteristics, such as Snow Water Equivalent (SWE) and snow grain size, are essential to monitor the global hydrological cycle and thus an indicator of climate change. This paper demonstrates an InSAR scattering model for dense medium such as snow considering the multiple scattering effect through the use of Quasi-Crystalline Approximation (QCA) and Percus-Yevick pair distribution function. Based on the simplified versions of the model, simulated results are shown for the problem of snow characteristics retrieval. First, it is noticed that the Ka-band InSAR phase has better sensitivity to the snow grain size, while the L-band InSAR phase has better sensitivity to the snow depth. Then, the Ka- and L-band InSAR correlation measurements are utilized to retrieve the snow volume parameters along with the ground parameters simultaneously. The InSAR scattering model and the retrieval approach proposed in this paper can be a complimentary tool for other techniques of retrieving snow characteristics.

In this paper, an airborne Ka-band (35 GHz) FMCW scatterometer and its preliminary results are presented. The paper describes the system, the calibration and the data processing needed to study the response of different targets to the scatterometer. The results obtained in the first flights include normalized radar cross-section measurements of inland water bodies and a tree height estimation algorithm.

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 obse...

The Large-Scale Biosphere-Atmosphere (LBA) experiment in Amazonia represents a unique opportunity to make a comprehensive study of the Amazon region and its component ecosystems. To make sense of the field studies and to generalize the results from them, remote sensing techniques and comprehensive mapping are critical elements for producing region-wide science results. In this paper, we present the processing work done for one such mapping campaign, that of the JERS-1 L-band two season (low and high-flood) SAR data collected in 1995 and 1996. Specifically, to make a useable resource for other LBA researchers to explore, the datasets from the two seasons had to be coregistered (to a sub-pixel level), radiometrically calibrated, and interpreted to verify the quality of the data. This paper describes the techniques used to achieve these goals and an estimate of the land cover based on a simple classification of the data.

The JERS-1 Amazon Multi-season 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 completely map the Amazon River floodpain (and surrounding areas) twice at high resolution.

In late September 1995, the National Space Development Agency of Japan (NASDA) began a new phase of operations for the Japanese Earth Remote Sensing satellite (JERS-1) Synthetic Aperture Radar (SAR)--the Global Rain Forest Mapping (GRFM) project. The first rainforest area to be mapped was the Amazon basin, between September and November of that year (the low flood season for much of the region), in support of the JERS-1 Amazon Multi-season Study (JAMMS), sponsored by NASA. This data acquisition was repeated 6 months later to acquire a second map of the Amazon, during the high flood season in May/June of 1996. The main objective of the JAMMS project was to generate a map of inundation over the Amazon basin by comparing data from the high- and low-flood seasons. Most of the data collected during these two phases of the JAMMS project, a total of ∼5000 frames of data, was received and processed by the Alaska SAR Facility (ASF), then sent to the Jet Propulsion Laboratory (JPL) and NASDA for post-processing and...

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.

In this paper, an airborne Ka-band (35GHz) FMCW interferometric synthetic aperture radar (InSAR) and its preliminary processing results are presented. Among the issues that had to be addressed in the processing of the SAR imaginer was the correction of range ambiguities through the use of a phase gradient algorithm. In this paper, the results of the 35 GHz InSAR are presented to demonstrate the height mapping capability of the UMass 35 GHz radar system.

A methodology, example and accuracy assessment are given for a continental-scale mosaic of the Amazon River basin at 100 m resolution using the JERS-1 satellite. This unprecedented resource of L-band SAR data collected by JERS-1 during the low-flood season of the river amounts to a collection of 57 orbits of the satellite and a total of some 1500 1 k/spl times/1 kB images. Interscene overlap both in the along-track and cross-track directions allows common reference points to be used to correct individual scene geolocation inaccuracies that have been derived from the satellite ephemeris. The set of common reference points is assembled into a matrix formulation that is used to solve for individual scene geometric offsets. By correcting for these offsets, each scene is placed within a global coordinate system, which can then be used as the basis for creating a final, visually seamless mosaic. The methodology employed in this approach allows for a mathematical foundation to be applied to the mosaicking process as well as providing a unique, traceable solution for correctly geolocating satellite imagery.

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.

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 Maxwell's 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.

This paper describes the estimation of vertical-structure parameters from combined interferometric and polarimetric radar data.

We propose the use of multiantenna radars for precipitation measurement from moving platforms. The primary motivation is measurement of vertical motion from spaceborne radars. Preliminary analysis of the concept and application to a specific example indicate that such a system would be feasible

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 method's 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.

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.

In remote sensing, the propagation of electromagnetic fields through random media is often of concern. We may wish to characterize the effects of clouds or water droplets along the line of sight between an airplane and a radar installation, or we may be interested in using radar to probe the random medium itself, such as in determining snow depth and particle size. In all of these problems it is necessary to predict the propagation constant in the "random" medium. The purpose of this paper is to characterize the accuracy of the quasi-crystalline approximation and other associated methods of determining the effective permittivity for two-dimensional (2-D) random media. A numerical method based on the method of moments is used as a gauge for comparison with the theoretical methods. After deriving the 2-D quasi-crystalline approximation and presenting the numerical method, the behavior of the effective permittivity is analyzed for a range of particle sizes, volume fractions and dielectric losses. From this analysis, regions of validity for the theoretical methods are determined. An investigation is also given which explores the effect of particle arrangement methods on the pair distribution function which, in turn, is shown to have a significant effect on the imaginary component of the effective permittivity.

Electromagnetic volume scattering theories for random media using a coherent field approach require some understanding of the statistical behavior of the locations of individual scatterers within the random medium. This knowledge manifests itself as the correlation function or pair distribution function, which is used to characterize the medium. This article presents a method for simulating the arrangements of particles of arbitrary shape within a very dense random medium, thus allowing for these functions to be calculated numerically. To demonstrate the significance of the Monte Carlo simulation, the problem of scattering from a two-dimensional dense random medium is considered. Using a first-order solution based on the Born approximation, it is shown that the approximate theoretically derived correlation function may lead to significant errors in the prediction of the backscatter response.

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. >

This paper investigates the ability of radar image segmentation to produce meaningful, structurally homogenous objects with respect to lidar-derived forest metrics. A comparative approach is taken to determine if radar-derived segments perform better in this respect than arbitrary, square segments or landcover-derived segments. It is found that segmentation of UAVSAR co- and cross-polarization backscatter magnitudes results in increased lidar homogeneity on the segment level relative to the arbitrary and landcover segmentations.

Because of its ability to measure the temperature-dependent power of electromagnetic radiation emitted from tissue down to several centimeters beneath the skin, microwave radiometry has long been of interest as a means for identifying the internal tissue temperature anomalies that arise from abnormalities in physiological parameters such as metabolic and blood perfusion rates. However, the inherent lack of specificity and resolution in microwave radiometer measurements has limited the clinical usefulness of the technique. The idea underlying this work is to make use of information (assumed to be available from some other modality) about the tissue configuration in the volume of interest to study and improve the accuracy of anomaly detection and estimation from radiometric data. In particular, knowledge of the specific anatomy and the properties of the overall measurement system enable determination of the signatures of localized physiological abnormalities in the radiometry data. These signatures are used to investigate the accuracy with which the location of an anomaly can be determined from radiometric measurements. Algorithms based on matches to entries in a signature dictionary are developed for anomaly detection and estimation. The accuracy of anomaly identification is improved when the coupling of power from the body to the sensor is optimized. We describe the design of a radiometer waveguide having dielectric properties appropriate for biomedical applications.

In breast conservation surgery, surgeons attempt to remove malignant tissue along with a surrounding margin of healthy tissue. Subsequent pathological analysis determines if those margins are clear of malignant tissue, a process that typically requires at least one day. Only then can it be determined whether a follow-up surgery is necessary. This possibility of re-excision is undesirable in terms of reducing patient morbidity, emotional stress and healthcare. It has been shown that terahertz (THz) images of breast specimens can accurately differentiate between breast carcinoma, normal fibroglandular tissue, and adipose tissue. That study employed the Time-Domain Spectroscopy (TDS) technique. We are instead developing a new technique, Frequency-Domain Terahertz Imaging (FDTI). In this joint project between UMass/Amherst and UMass Medical School/Worcester (UMMS), we are investigating the feasibility of the FDTI technique for THz reflection imaging of breast cancer margins. Our system, which produces mechanically scanned images of size 2cm x 2cm, uses a THz gas laser. The system is calibrated with mixtures of water and ethanol and reflection coefficients as low as 1% have been measured. Images from phantoms and specimens cut from breast cancer lumpectomies at UMMS will be presented. Finally, there will be a discussion of a possible transition of this FDTI setup to a compact and inexpensive CMOS THz camera for use in the operating room.

Quantification of the various components of the carbon cycle budget is key to improved climate modeling and projecting anthropogenic affects on climate in the future. Estimating the levels of above ground biomass contained in the world's forests that comprise 86% of the planet's above ground carbon and monitoring the rate of change to these standing stocks resulting from both natural and anthropogenic disturbances is necessary to solving the carbon cycle sink. Remote sensing is the only viable means of obtaining a global inventory of forest biomass at the hectare scale. The most promising means of obtaining remotely sensed biomass measurements involve using either lidar or radar measurements of vegetation structure coupled with allometric relationships. We have collected repeat-pass L-band fully polarimetric radar data at multiple spatial and temporal baselines to investigate the tree height and structure measurements using polarimetric interferometry techniques. This paper will discuss this experiment and comparison with lidar data.

The high cost and inaccessibility of spaceborne systems necessitates the adaptation of computing resources based on changes in environmental conditions. In this paper, a new, adaptive phase difference detection circuit that can be implemented in reconfigurable hardware is described. This circuit measures both phase changes of a single signal over time and phase drifts between two associated signals. This information can be used for anomaly detection for a variety of spaceborne applications, eliminating the need to transmit data for further terrestrial processing. We demonstrate the benefit of this adaptive circuit using a new state-of-the-art data acquisition system developed for the NASA Surface Water Ocean Topography (SWOT) project. Phase calculations with an error of less than 2% are determined using our adaptive approach.

In this paper we describe a project for designing, developing and translating a THz imaging device for monitoring margins from extracted tissue during surgical breast cancer conservation procedures. In this application, the reflective and transmission properties of extracted tissue are monitored, in near real-time using a fine-beam THz signal which is sensitive to the presence of liquid and bound water content. In this way, it is intended that the extracted tissue will be studied in the operating theatre to determine during surgery, whether or not the region of malignant tissue has been fully excised from the patient. In the early stages of this project, we are determining to what degree an existing THz system at the University of Massachusetts (UMass) in Amherst is able to differentiate between breast carcinoma, normal fibroglandular and adipose tissues. This is achieved through close collaboration with a surgical and radiological team at the UMass-Worcester medical school and involves post-surgical recovered tissues. As part of this work, we are describing the system, measurement methodology, and first results that were obtained to calibrate the imaging system.

The National Research Council's decadal survey recommended DESDynI as one of the high priority missions for NASA. The mission envisions an InSAR/Lidar instrument for observing ecosystem structures on global scales with high spatial resolutions. Consistent and highly resolved global maps of biomass and carbon stocks require highly accurate observations of vegetation, in fact it is expected that such accuracies would require a combination of the high vertical precision of Lidar observations and the large spatial extent of SAR/InSAR measurements. Here we analyze radar backscatter data along with biomass estimates from a field campaign conducted in the Harvard forest in Massachusetts, USA.

The recent findings of high heterogeneity of human breast tissue and much lower than predicted dielectric contrast between tumors and their host tissue have raised questions about the potential utility of stand-alone microwave breast imaging techniques. Multimodal approaches that employ microwaves together with other imaging techniques seem more promising. This study investigates a CT-microwave combination in which microwave detection makes use of prior information obtained from volumetric CT scans and knowledge of tissue dielectric properties. In particular, a detailed patient-specific tissue distribution is first obtained from a 3D-CT scan of the breast under exam. It is assumed that from this scan a limited suspect region is identified. Then from recent research results on the dielectric properties of breast tissue, complex permittivity (dielectric constant and conductivity) maps of the breast can be constructed under the hypotheses of normal and cancerous tissue in the suspect region. These in turn can be used with electromagnetic (EM) simulation software to generate empirical distributions for the microwave system observations under each hypothesis. Microwave detection is then performed. Instead of trying to recover a complete dielectric image of the breast from the microwave scan, the question of interest in this approach is simply which hypothesis is more consistent with the observed electromagnetic response of the microwave system. A hypothesis testing method based on the likelihood ratio for the empirical distributions and Receiver Operating Characteristic (ROC) optimization is proposed. The results from a simple idealized test case show good potential and invite further study.

The SAR/InSAR component of the NASA DesdynI mission for measuring vertical vegetation structure from space consists of four possible approaches. These include the use of radar backscatter to estimate biomass, to employ PolInSAR relative phase for measuring the vertical extent, the use of interferometric phase and a ground reference, or the use of interferometric correlation magnitude alone. Temporal decorrelation is a significant contributor to decorrelation of interferometric echoes and is not always separable from volumetric decorrelation hence contributing to uncertainties in vegetation parameter estimates obtained using just correlation magnitude. In this text we analyze data that is close to the best case scenario for isolating temporal decorrelation. With almost zero baseline and a repeat pass of one day, SIR-C data over the eastern US serves as our case study of temporal decorrelation.

To help optimize the real time scanning of an adaptive radar network, it is necessary to define a performance metric for weighing the competing scanning tasks. A point volume vector velocity estimation methodology can be used in this context to derive the confidence bounds of the vector velocity estimator that can, in turn, be utilized as a quality indicator in devising scanning strategies. This document presents the basic derivation of this estimation process and provides examples of processed weather moment data.

In this paper the calibration of the University of Massachusetts Advanced Multi-Frequency Radar (AMFR) is discussed in detail. The calibration is performed primarily through the use of an internal calibration path, and is confirmed through cross-calibration with another calibrated radar. Additionally, the performance of AMFR during the 2007 Canadian CloudSat/CALIPSO Validation Project (C3VP) is demonstrated with respect to calibration and instrument stability. It is shown that the calibration during the C3VP experiment was accurate to within 1.5 dB when compared with a C-band weather radar operated by Environment Canada.

In this paper, two data sets consisting of co-located full-waveform lidar and InSAR observations are discussed, one over the Duke Forest, near Durham, North Carolina, and the other, the Harvard Forest, located in Western Massachusetts. Data for the Duke forest consists of AIRSAR and GeoSAR (both airborne sensors) interferometric SAR observations spanning in frequency from X-band down to P-band, and data from the GSFC's SLICER instrument. For the Harvard Forest, spaceborne data from JAXA's ALOS/PALSAR mission is used in conjunction with GSFC's LVIS instrument. Early work with SLICER and GeoSAR data has used a lookup table approach for generating a table that correlates the InSAR observables of differential height between X-and P-band observations, and X-band correlation magnitude to lidar derived height. This table was then used for estimating heights over the remaining swath, where lidar data was not available. A similar technique can be used for spaceborne data, in this case, over the Harvard Forest. In this paper, the comparison between lidar observations and the InSAR Duke observations are shown, and then followed by a preliminary treatment highlighting relationships in the ALOS/PALSAR Harvard data that can be exploited for similar purposes.

Characterization of the Earth's oceanic and cryospheric topography is among NASA's strategic goals for the next decade. An effective technique for achieving such measurements is radar interferometry. For this technology, use of mm-wave frequencies (Ku- and Ka-band) is appealing due to the proportional size of the space borne structure to the observing wavelength and minimal snow penetration. At such high frequencies, achieving stability in instrument performance and the mechanical deployment structure for accurate measurements is an engineering challenge. To address this, the Microwave Remote Sensing Laboratory at the University of Massachusetts is developing a high performance Ku- and Ka-band interferometer as a prototype for a space borne instrument. The interferometer will be used in a scaled-down ground-based configuration. This paper presents an instrument description of the Ka-band interferometer (the down-converter in particular) and first results from its performance evaluation.

This paper presents an algorithm that derives the vector velocity estimate in a radar network environment considering the velocities that exceed the maximum unambiguous velocity of its observing sensor nodes. At first, an analytical derivation with underlying assumptions is presented and then simulation results are shown and discussed. This unfolding vector velocity algorithm is based on maximum likelihood theory operating on the first three weather radar spectral moments and can potentially provide a real time velocity detection of broader range of velocities than given by the nodal maximum unambiguous velocity.

This paper presents initial results from the lab testing of a Ku-band cross-track interferometric downconverter designed for performing topographic and volumetric depth measurements. This interferometer will be used shortly in the field for measuring topography and to determine its capacity for estimating the volumetric depth of snow.

This paper demonstrates the feasibility of using multialtitude, fixed-baseline INSAR to estimate vertical-structure parameters of forests. Two model scenarios applied to C-band INSAR data collected at 8-km and 4-km altitudes over central Oregon with TOPSAR suggest the need for either a ground surface contribution with a single vegetation layer or a two-layer vegetation model. The two layer model parameters seem more reasonable and qualitatively reflect the field-measured characteristics of the forest studied. The parameters produced from the single set of INSAR cross-correlations in this paper do not constitute a proof that multialtitude INSAR will reliably produce vertical structure parameters, but they do prompt future, more complete demonstrations. A complete set of amplitudes and phases will be used in parameter estimation scenarios, augmenting those shown in (2) and (3), from 2-, 4-, and 8-km altitude INSAR. Including polarimetry in the observation vector will constrain ground/volume power fractions and terrain slope parameters.

The measured backscattering coefficients of three sandy soil surfaces are presented. The measurements were conducted using 35 and 94 GHz polarimetric scatterometers. The measured backscattering coefficients are compared to those predicted by the classical surface scattering models (physical optics and geometric optics). Furthermore, the results are compared to a recently developed empirical model based on observations made at microwave frequencies. Preliminary analysis shows that none of the above models is capable of predicting the measured backscattering coefficients accurately and that volume scattering from the voids inside the soil layer, computed via the radiative transfer theory, must be considered. It is shown that a combination of surface and volume scattering terms will adequately characterize the backscattering from soil surface at millimeter wave frequencies. >

The authors have presented a novel technique for characterizing the electromagnetic behavior of coherent fields in random media. This behavior is characterized by the fundamental physical constant of effective permittivity, where it is shown that reliable, consistent results can be found using existing, full-wave electromagnetic solution techniques. As a result, this work represents a solid step forward in our basic understanding of how electromagnetic fields propagate through random media.