Curt H. Davis

Synthesizing multiple remote-sensing techniques for subglacial hydrologic mapping: application to a lake system beneath MacAyeal Ice Stream, West Antarctica

We present an analysis of the active hydrologic system of MacAyeal Ice Stream (MacIS), West Antarctica, from a synthesis of multiple remote-sensing techniques: satellite laser altimetry; satellite image differencing; and hydrologic potential mapping (using a satellite-derived DEM and a bedrock DEM from airborne radio-echo sounding). Combining these techniques augments the information provided by each one individually, and allows us to develop a protocol for studying subglacial hydrologic systems in a holistic manner. Our study reveals five large active subglacial lakes under MacIS, the largest of which undergoes volume changes of at least 1.0 km 3 . We discuss the hydrologic properties of this system and present evidence for links between the lakes. At least three of the lakes are co-located with sticky spots, i.e. regions of high local basal shear stress. We also find evidence for surface elevation changes due to ice-dynamic effects (not just water movement) caused by changes in basal resistance. Lastly, we show that satellite radar altimetry is of limited use for monitoring lake activity on fast-flowing ice streams with surfaces that undulate on � 10 km length scales.

An autoregressive model for analysis of ice sheet elevation change time series

We present an autoregressive (AR) model that can effectively characterize both seasonal and interannual variations in ice sheet elevation change time series constructed from satellite radar or laser altimeter data. The AR model can be used in conjunction with weighted least squares regression to accurately estimate any longer term linear trend present in the cyclically varying elevation change time series. This approach is robust in that it can account for seasonal and interannual elevation change variations, missing points in the time series, signal aperiodicity, time series heteroscedasticity, and time series with a noninteger number of yearly cycles. In addition, we derive a theoretically valid estimate of the uncertainty (standard error) in the long-term linear trend. Monte Carlo simulations were conducted that closely emulated actual characteristics of five-year elevation change time series from Antarctica. The Monte Carlo results indicate that the autoregressive approach yields long-term linear trends that are less biased than two other approaches that have been recently used for analysis of ice sheet elevation change time series. In addition, the simulation results demonstrate that the variability (uncertainty) of the long-term linear trend estimates from the AR approach is in very good agreement with the derived theoretical standard error estimates.

Snow-stratification investigation on an Antarctic ice stream with an X-band radar system

An X-band FMCW radar was used to determine the feasibility of observing annual snow-accumulation layers in Antarctica with a high-resolution inexpensive radar system. The formation of layering boundaries, their resultant electromagnetic discontinuity and their detection by reflected energy are presented. Large returns from depths corresponding to reasonable positions for annual layers were found. The average accumulation rates calculated from the radar returns agree with those measured in a previous pit study done in the same area. The detection of the annual accumulation layers with this system implies a simple, inexpensive mobile radar could be used to profile large areas allowing the distorting effects of local topography to be removed. This type of system with a concurrent pit study could provide insight into the effect of sub-surface strata on spaceborne or airborne microwave remote sensing.

Improved Methods for Analysis of Decadal Elevation-Change Time Series Over Antarctica

In this paper, several techniques to improve the processing and analysis of decadal elevation-change time series (ECTS) constructed using satellite radar altimeter data over the Antarctic ice sheet are described. First, a method that improves both the quality and quantity of ice-sheet ECTS is introduced. By dynamically selecting the reference month used in constructing ECTS for a given local region, the maximum number of elevation-change estimates are used in a matrix processing technique. This can improve ECTS quality while also generating ECTS in new areas, leading to a significant increase in spatial coverage. Next, an improved autoregressive (IAR) approach is presented for modeling nonlinear medium-period variations in decadal Antarctic ECTS. This builds upon previous work where an AR model was used to characterize seasonal and interannual variations in ice-sheet ECTS. The improved approach avoids overdecomposition by adopting an iterative local average filter to estimate nonlinear medium-period trends. Monte Carlo simulations show that the IAR method significantly outperforms the AR method when realistic nonlinear trends are present. Because of these characteristics, the IAR model is able to adequately characterize seasonal, interannual, and medium-period nonlinear signals present in decadal ECTS and extract their long-term trends with very small error. This is important for accurate measurement of decadal elevation change over the Antarctic ice sheet and for assessing the impact of these changes on the global sea level

Automated object extraction through simplification of the differential morphological profile for high-resolution satellite imagery

This paper presents an approach for automated, multi-scale object extraction from high-resolution satellite imagery. Our algorithm combines techniques from mathematical morphology and principal components analysis (PCA) to identify building footprints in scenes. There are three major components in the algorithm: First, the differential morphological profile (DMP) of the image is constructed using structuring elements (SE) of varying sizes. Several preprocessing techniques are applied to the DMP. Next, the values for an entire profile at a given pixel are combined into a k-dimensional vector representing that pixel, where k is the number of resolution levels. PCA is applied to the set of pixel vectors to reduce the number of dimensions needed to represent the data of the DMP, yet capture a significant portion of their variance. We call the intermediate results of PCA the Eigen-opening image and Eigen-closing image. In the final stage of processing, candidate objects are extracted from the Eigen images. Applying a minimal amount of heuristics we can automatically merge the Eigen images into a set of objects. Additionally, spatial information is used to correlate man-made objects with their shadows from the closing profile, if they exist. The efficiency of this algorithm, coupled with it’s robustness, allow it to be useful as an online object extraction tool for geospatial applications.

Fusion of Spectral and Spatial Information for Automated Change Detection in High Resolution Satellite Imagery

Here we propose a pixel-based change detector utilizing both spectral and spatial features. Traditional pixel- based change detection methods that only utilize spectral data are inherently sensitive to spectral variation. This presents problems in mitigating the impact of spectral changes due to uninteresting types of change without severely limiting the sensitivity of the detector. Here we introduce a change detector that utilizes both spectral and spatial information, including linear features and texture measures, as a method to decrease sensitivity to spectral variation and increase detection rates. For each pixel, a fuzzy value representing the similarity of each pixel feature (both spectral and spatial) is computed. All features are then fused into a single overall similarity score by weighted averaging. This is followed by thresholding and morphological extraction of the detected regions of change. The algorithm was evaluated using panchromatic and pan-sharpened multi-spectral imagery of Springfield, Missouri acquired during different seasons and covering approximately 20 square kilometers of urban, suburban, and rural terrain. Preliminary results show a 70% change detection probability for types of change unrelated to seasonal variation with rates of only 2.4 uninteresting detections per km 2 and 0.09 false alarms per km 2 .

Mining image content associations for visual semantic modeling in geospatial information indexing and retrieval

Query methods using visual semantics play an important role in horizontal interoperability of geospatial databases. However, a common practice is to manually label visual semantics of images using text annotations. This approach is subjective and, more importantly, impractical when dealing with large-scale geospatial image databases. In this paper, we propose a knowledge discovery (KDD) framework to link low-level image features with high-level visual semantics in an attempt to automate the process of retrieving semantically similar images. Our framework first extracts association rules that correlate semantic terms with discrete intervals of individual features. It then applies possibility functions to mathematically model visual semantics. Our approach provides a unique way to query image databases using semantics, and to potentially make available a knowledge exchange method for the geospatial community.

High resolution digital elevation model and a Web-based client-server application for improved flood plain management

Digital elevation models (DEMs) are used more and more frequently in flood plain management. Examples include flood plain models, visualization, flood hazard assessment, and determination of flood-plain elevation. One of the major problems in developing accurate high resolution DEMs is that traditional data sources do not possess enough horizontal resolution and vertical accuracy for flood-plain studies. In this study, a high-resolution DEM was generated using digitally scanned NAPP aerial photos in conjunction with highly accurate ground control from a rapid-static GPS survey. The high resolution DEM and an ortho-mosaic of the NAPP images developed in this study were made available to St. Charles County government officials through the World Wide Web using Client-Server technology. The design and development of this technology utilized ArcViewIMS, ArcView GIS, Java, JavaScript, HTML and Avenue programming. This Web-based tool allows the user to query a point in the ortho-mosaic to determine horizontal position and vertical elevation. In addition, common mapping functions such as zoom, pan, download and print are also incorporated in the Web-based tool.

The effect of sub-surface volume scattering on the accuracy of ice-sheet altimeter retracking algorithms

The NASA and ESA retracking algorithms are compared with an algorithm based upon a combined surface and volume (S/V) scattering model. First, the S/V, NASA, and ESA algorithms were used to retrack over 400,000 altimeter return waveforms from the Greenland and Antarctic ice sheets. The surface elevations from the S/V algorithm were compared with the elevations produced by the NASA and ESA algorithms to determine the relative accuracy of these algorithms when subsurface volume-scattering occurs. The results show that the NASA algorithm produced surface elevations within 35 to 50 cm of the S/V algorithm, while the performance of the ESA algorithm was slightly worse. Next, by analyzing several thousand satellite crossover points from the Antarctic dataset, the authors determined the retracking algorithm that produced the most repeatable surface elevations. The elevations derived from the S/V algorithm had the smallest RMS error for the region of the East Antarctic plateau examined. The ESA algorithm produced erroneous estimates of elevation change when seasonal variations were present; it measured 0.7 to 1.6-m change in elevation over a 6-month period on the East Antarctic plateau where accumulation rates are only 10 cm/year.<<ETX>>

A Combined Surface/volume Scattering Retracking Algorithm for Ice Sheet Satellite Altimetry

Here we develop an algorithm that is based upon a combined surface/volume scattering model that can be used to retrack individual altimeter waveforms over the ice sheets. Because the combined model is non-linear, an iterative least-squares procedure is used to fit the combined model to the return wave- forms. The retracking algorithm is comprised of two distinct sections. The algorithms first section generates initial model parameter estimates from a filtered altimeter waveform. The second section uses the initial estimates, the theoretical model, and the waveform data to generate corrected parameter estimates. Then, based upon the convergence of the mean-squared error of the model fit to the waveform, or the convergence of the model parameters themselves, the algorithm repeats itself using the corrected estimates in place of the initial estimates, or terminates with the final model parameters. This retracking algorithm can be used to assess the accuracy of elevations produced from current retracking algorithms when sub- surface volume scattering is present. This is extremely important so that repeated altimeter elevation measurements can be used to accurately detect changes in the mass balance of the ice sheets. In addition, by analyzing the distribution of the model parameters over large portions of the ice sheet, regional and seasonal variations in the near-surface properties of the snowpack can be quantified.