ALBERT

Description: We have used surface elevation measurements acquired by the Ice, Cloud,and land Elevation Satellite Geoscience Laser Altimeter System (GLAS) and EnviSAT Radar Altimeter 2 (RA-2) satellite altimeters to assess the elevation change of the 13 700-km 2 Devon Ice Cap (DIC) in Arctic Canada between 2002 and 2008. We present algorithms for the retrieval of elevation change rates over ice caps using data acquired from these satellites. A comparison of GLAS elevation data to those acquired by the RA-2 shows reasonable agreement between the two instruments; the root mean square elevation change difference was 56 cm, and the correlation coefficient between the two data sets was 0.68. Using only RA-2 elevation measurements, which are spatially and temporally more continuous, we determined the elevation change rate of the areas of the DIC where the surface geometry allows the RA-2 retracker to maintain lock. This includes most of the DIC, excluding large parts of the eastern half of the ice cap. The elevation change rate was found to be insignificant given a statistical estimate of the measurement error (-0.09 ± 0.29 m/a). We also present an assessment of the regional variations of the DIC elevation change, including a significant -0.71 ± 0.49 m/a elevation change rate of the 1980-km 2 western arm. Furthermore, we present evidence of a localized 2-m drop in the surface elevation of the South Croker Bay Glacier during summer 2007. This drop is apparent within both satellite data sets, and we interpret this signal to reflect a sudden speedup of the glacier.

Description: Investigations in data quality and uncertainty modeling are becoming key topics in geoinformation science. This paper models a collection of outcomes from a standard segmentation algorithm as a random set. It quantifies extensional uncertainties of extracted objects using statistical characteristics of random sets. The approach is applied to a synthetic data set and vegetation patches in the Poyang Lake area in China. These patches are of interest as they have both sharp and vague boundaries. Results show that random sets provide useful spatial information on uncertainties using their basic parameters like the mean, level sets, and variance. The number of iterations to achieve a stable covering function and the sum of the variances are good indicators of boundary sharpness. The coefficient of variation has a positive relation with the degree of uncertainty. An asymmetry ratio reflects the uneven gradual changes along different directions where broad boundaries exist. This paper shows that several characteristics of extensional uncertainty of segmented objects can be quantified numerically and spatially by random sets.

Description: Improving watershed nutrient budgets, ecosystem models, and our understanding of the impact of land-use management on ecosystem functioning depends on the development of remote sensing methods that can predict aspects of the nitrogen (N) cycle. This is particularly true for temperate managed grasslands, which constitute a large portion of agricultural land and, at times, export a significant amount of N to aquatic systems and the atmosphere. Although foliar N is often remotely sensed, we explore the use of spectroscopy to predict the foliar isotopic ratio of $^{15}hbox{N}$ to $^{14} hbox{N}$ , i.e., $delta{^{15}hbox{N}}$ . Foliar $delta{^{15}hbox{N}}$ has been shown in global surveys and site-specific studies to reflect N availability and the amount of N lost to the atmosphere. We built a data set of the canopy reflectance of plots in managed pastures and hay lands, which we then harvested for laboratory analysis. For the spectra of dried and ground samples, we calculated the normalized band depth (NBD) of three absorption features most likely to correlate with $delta{^{15}hbox{N}}$ . In these data, foliar N and $delta{^{15}hbox{N}}$ were not correlated, and we found weak, but significant, linear models with $delta{^{15} hbox{N}}$ for the NBD of the 2100-nm feature known to relate to foliar N. The canopy spectra, which inherently reflect the vegetation structure, correlated better with $delta{^{15} hbox{N}}$ than the spectra of dried and- - ground samples. These results suggest that near-term advances in estimating $delta{^{15}hbox{N}}$ and aspects of pasture management style are likely to be related to, or to include, the quantification of the vegetation structure.

Description: High-resolution (submeter) orbital imagers have opened up new possibilities for Mars topographic mapping with unprecedented precision. While the typical sensor model for Martian orbiters has been the linear-array charge-coupled device (CCD), the High-Resolution Imaging Science Experiment (HiRISE) instrument is based on a more complicated structure involving a combination of 14 separate linear-array CCDs. To take full advantage of this high-resolution capability without compromising imaging geometry, we have developed a rigorous photogrammetric model for HiRISE stereo image processing in which third-order polynomials are used to model the change in exterior-orientation parameters over time. A coarse-to-fine hierarchical matching approach was developed, and its performance was evaluated based on manually matched image points and manually measured features for a test area at the Mars Exploration Rover Spirit landing site. Using automatically selected tie points, we performed bundle adjustment (BA) to improve the accuracy of image pointing data and remove or reduce inconsistencies between the stereo pair and inconsistencies between overlapping CCDs in the same image mosaic. A method for the incorporation of jitter terms into the BA was developed and proved to be effective. We created a 1-m-resolution digital elevation model and an orthophoto using this methodology and compared them with topographic products from the U.S. Geological Survey.

Description: Within the hyperspectral community, change detection is a continued area of interest. Interesting changes in imagery typically correspond to changes in material reflectance associated with pixels in the scene. Using a physical model describing the sensor-reaching radiance, change detection can be formulated as a statistical hypothesis test. Complicating the problem of change detection is the presence of shadow, illumination, and atmospheric differences, as well as misregistration and parallax error, which often produce the appearance of change. The proposed physical model incorporates terms to account for both direct and diffuse shadow fractions to help mitigate false alarms associated with shadow differences between scenes. The resulting generalized likelihood ratio test (GLRT) provides an indicator of change at each pixel. The maximum likelihood estimates of the physical model parameters used for the GLRT are obtained from the entire joint data set to take advantage of coupled information existing between pixel measurements. Simulation results using synthetic and real imagery demonstrate the efficacy of the proposed approach.

Description: Forest inventories are important tools for the management of forests. In this context, the estimation of the tree stem volume is a key issue. In this paper, we present a system for the estimation of forest stem diameter and volume at individual tree level from multireturn light detection and ranging (LIDAR) data. The proposed system is made up of a preprocessing module, a LIDAR segmentation algorithm (aimed at retrieving tree crowns), a variable extraction and selection procedure, and an estimation module based on support vector regression (SVR) (which is compared with a multiple linear regression technique). The variables derived from LIDAR data are computed from both the intensity and elevation channels of all available returns. Three different methods of variable selection are analyzed, and the sets of variables selected are used in the estimation phase. The stem volume is estimated with two methods: 1) direct estimation from the LIDAR variables and 2) combination of diameters and heights estimated from LIDAR variables with the species information derived from a classification map according to standard height/diameter relationships. Experimental results show that the system proposed is effective and provides high accuracies in both the stem volume and diameter estimations. Moreover, this paper provides useful indications on the effectiveness of SVR with LIDAR in forestry problems.

Description: This paper provides a novel pointwise-adaptive speckle filter based on local homogeneous-region segmentation with pixel-relativity measurement. A ratio distance is proposed to measure the distance between two speckled-image patches. The theoretical proofs indicate that the ratio distance is valid for multiplicative speckle, while the traditional Euclidean distance failed in this case. The probability density function of the ratio distance is deduced to map the distance into a relativity value. This new relativity-measurement method is free of parameter setting and more functional compared with the Gaussian kernel-projection-based ones. The new measurement method is successfully applied to segment a local shape-adaptive homogeneous region for each pixel, and a simplified strategy for the segmentation implementation is given in this paper. After segmentation, the maximum likelihood rule is introduced to estimate the true signal within every homogeneous region. A novel evaluation metric of edge-preservation degree based on ratio of average is also provided for more precise quantitative assessment. The visual and numerical experimental results show that the proposed filter outperforms the existing state-of-the-art despeckling filters.

Description: The advent of routine collection of high-quality digital photography provides for traditional uses, as well as “remote sensing” uses such as the monitoring of environmental indicators. A well-devised monitoring system, based on consistent data and methods, provides the opportunity to track and communicate changes in features of interest in a way that has not previously been possible. Data that are geometrically and radiometrically consistent are fundamental to establishing systems for monitoring. In this paper, we focus on models for the radiometric calibration of mosaics consisting of thousands of images. We apply the models to the data acquired by the Australian Commonwealth Scientific and Industrial Research Organisation and its partners as part of regular systematic acquisitions over the city of Perth for a project known as Urban Monitor. One goal of the project, and hence the model development, is to produce annually updated mosaics calibrated to reflectance at 0.2-m ground sample distance for an area of approximately 9600 $hbox{km}^{2}$ . This equates to terabytes of data and, for frame-based instruments, tens of thousands of images. For the experiments considered in this paper, this requires mosaicking estimates derived from 3000 digital photographic frames, and the methods will shortly be expanded to 30 000 $+$ frames. A key part of the processing is the removal of spectral variation due to the viewing geometry, typically attributed to the bidirectional reflectance distribution function (BRDF) of the land surface. A variety of techniques based on semiempirical BRDF kernels have been proposed in the literature for correcting the BRDF effect in single frames, but mosaics with many frames provide unique challenges. This paper presents and illuminates a complete empirical radiometric calibration method for dig- - ital aerial frame mosaics, based on a combined model that uses kernel-based techniques for BRDF correction and incorporates additive and multiplicative terms for correcting other effects, such as variations due to the sensor and atmosphere. Using ground truth, which consists of laboratory-measured white, gray, and black targets that were placed in the field at the time of acquisition, we calculate the fundamental limitations of each model, leading to an optimal result for each model type. We demonstrate estimates of ground reflectance that are accurate to approximately 10%, 5%, and 3% absolute reflectances for ground targets having reflectances of 90%, 40%, and 4%, respectively.

Description: Spaceborne synthetic aperture radar (SAR) systems operating at lower frequencies, such as P-band, are significantly affected by Faraday rotation (FR) effects. A novel algorithm for calibrating the circular-transmit-and-linear-receive (CTLR) mode spaceborne compact polarimetric SAR using mixed calibrators is proposed, which is able to correct precisely both FR and radar system errors (i.e., channel imbalance and crosstalk). Six sets of mixed calibrators, consisting of both passive calibrators and polarimetric active radar calibrators (PARCs), are investigated. Theoretical analysis and simulations demonstrate that the optimal calibration scheme combines four polarimetric selective mixed calibrators, including two gridded trihedrals and two PARCs, together with total-electron-content measurements by the Global Navigation Satellite System system.