Razi Ahmed

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.

An Error Model for Biomass Estimates Derived From Polarimetric Radar Backscatter

Estimating the amount of above ground biomass in forested areas and the measurement of carbon flux through the quantification of disturbance and regrowth are critical to develop a better understanding of ecosystem processes. Well-resolved and globally consistent inventories of forest carbon must rely on remote sensing measurements, particularly from polarimetric radars. While a wide variety of studies conducted over the past three decades have shown how radar polarimetric measurements can be used to estimate above ground carbon for regions with less than 100 Mg of biomass per hectare, there is no established methodology for assessing biomass estimation accuracy based on a priori instrument and mission parameters. In this paper, a framework for assessing biomass estimation accuracy is presented that is a blend of the basic imaging physics and empirically derived parameters that describe various relationships between biomass and radar polarimetric observable quantities. The implications of this error model on the design and performance of a polarimetric radar are explored using instrument, mission, and science parameters from a notional Earth observing mission.

Variable Precision Two-Channel Phase, Amplitude, and Timing Measurements for Radar Interferometry and Polarimetry

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.

Analyzing the Uncertainty of Biomass Estimates From L-Band Radar Backscatter Over the Harvard and Howland Forests

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 Laboratorys 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.

A biomass estimate over the harvard forest using field measurements with radar and lidar data

The National Research Councils 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.

A Cross-Track Ku-Band Interferometer for Topographic and Volumetric Depth Measurements

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.

Analysis and error assessment on the use of segmentation for estimating forest structural characteristics from lidar and radar

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.

Some first polarimetric-interferometric multi-baseline and tomographic results at Harvard forest using UAVSAR

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 worlds forests that comprise 86% of the planets 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.

Combining Lidar and InSAR Observations over the Harvard and Duke Forests for Making Wide Area Maps of Vegetation Height

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 GSFCs SLICER instrument. For the Harvard Forest, spaceborne data from JAXAs ALOS/PALSAR mission is used in conjunction with GSFCs 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.

Temporal Decorrelation Studies for Vegetation Parameter Estimation with Space-Borne Radars

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.