Toni Schenk

Intermittent thinning of Jakobshavn Isbræ, West Greenland, since the Little Ice Age

Rapid thinning and velocity increase on major Greenland outlet glaciers during the last two decades may indicate that these glaciers became unstable as a consequence of the Jakobshavn effect (Hughes, 1986), with terminus retreat leading to increased discharge from the interior and consequent further thinning and retreat. To assess whether recent trends deviate from longer-term behavior, we measured glacier surface elevations and terminus positions for Jakobshavn Isbrae, West Greenland, using historical photographs acquired in 1944, 1953, 1959, 1964 and 1985. These results were combined with data from historical records, aerial photographs, ground surveys, airborne laser altimetry and field mapping of lateral moraines and trimlines, to reconstruct the history of changes since the Little Ice Age (LIA). We identified three periods of rapid thinning since the LIA: 1902-13, 1930-59 and 1999-present. During the first half of the 20th century, the calving front appears to have been grounded and it started to float during the late 1940s. The south and north tributaries exhibit different behavior. For example, the north tributary was thinning between 1959 and 1985 during a period when the calving front was stationary and the south tributary was in balance. The record of intermittent thinning, combined with changes in ice-marginal extent and position of the calving front, together with changes in velocity, imply that the behavior of the lower parts of this glacier represents a complex ice-dynamical response to local climate forcings and interactions with drainage from the interior.

Airborne laser scanning for high‐resolution mapping of Antarctica

In order to evaluate the potential of airborne laser scanning for topographic mapping in Antarctica and to establish calibration/validation sites for NASAs Ice, Cloud and land Elevation Satellite (ICESat) altimeter mission, NASA, the U.S. National Science Foundation (NSF), and the U.S. Geological Survey (USGS) joined forces to collect high-resolution airborne laser scanning data. In a two-week campaign during the 2001–2002 austral summer, NASAs Airborne Topographic Mapper (ATM) system was used to collect data over several sites in the McMurdo Sound area of Antarctica (Figure 1a). From the recorded signals, NASA computed laser points and The Ohio State University (OSU) completed the elaborate computation/verification of high-resolution Digital Elevation Models (DEMs) in 2003. This article reports about the DEM generation and some exemplary results from scientists using the geomorphologic information from the DEMs during the 2003–2004 field season.

Fusing Imagery and 3D Point Clouds for Reconstructing Visible Surfaces of Urban Scenes

The automatic reconstruction of urban scenes from sensory input data is a daunting task. By and large the task remains unresolved, although a considerable amount of research has been devoted to its solution. Many of the proposed methods are either too application dependent, or address only some aspects of the general problem. Moreover it appears that solutions based on a single sensor source, for example intensity images or laser point clouds, lead to partial solutions. In this paper we propose the reconstruction of visible surfaces from multi-sensor data, embedded in a fusion framework. We postulate that the reconstructed surface is an intermediate and application independent representation of the scene, similar to the 2.5 D sketch proposed by Marr in his vision paradigm. In contrast to the viewer based 2.5 D sketch, our reconstructed surface is represented in a suitable 3D Cartesian reference system. It contains explicit surface information, including shape and surface discontinuities. We argue that such an explicit description greatly benefits applications, such as object recognition, populating or updating GIS, change detection, city modeling, and true orthophoto generation. This is because the 3D object space enables more powerful reasoning methods to aid object recognition and image understanding as opposed to the traditional approach of reasoning in the 2D image space. Another strong motivation for the proposed application independent surface reconstruction scheme is the multi-source scenario with imaging and laser point data, and possibly hyperspectral data. These widely disparate data sets contain common (redundant), complementary and occasionally conflicting information about the surface. The paper discusses the notion of different surfaces and their relationships. Major emphasis is placed on the development of a general, true 3D surface representation scheme that copes with the problem of multi layer surfaces (e.g. multiple overpass).

Towards automatic aerial triangulation

Digital photogrammetry has a great influence on aerial triangulation. Several digital aerial triangulation systems are now in various stages of development. Two distinct different approaches can be observed: interactive systems which require human operator guidance and softcopy workstations; and automatic systems. Automatic aerial triangulation systems attempt to reduce the aerial triangulation problem to a batch process, with little or no help of a human operator. Todays systems are close to meet this challenge, but the identification and measurement of control points remains an interactive task. The paper focuses on automatic aerial triangulation. Major effort is spent on identifying essential tasks that are independent of existing systems. The tasks, such as selecting suitable tie points, determining accurate approximations and matching multiple images, are derived from the objectives of digital aerial triangulation and by considering the potential of image processing and computer vision. The solution of these essential tasks brings a myriad of challenging problems. The concluding remarks comment on the differences between traditional and digital approaches and discuss the consequences.

Registering imagery to ICESat data for measuring elevation changes on Byrd Glacier, Antarctica

[1] We present a new approach to derive control information from ICESat data that enables rigorous registration of aerial and satellite imagery. The technique, based on matching terrain features identified from ICESat measurements and aerial imagery, opens the door to transform results of previous studies to a global reference frame. We demonstrate the proposed methodology with historical aerial photographs to determine surface changes between 1979 and 2004 over Byrd Glacier. This is important because there is no satellite radar altimetry coverage south of 81.5� S, which limits mass balance knowledge of outlet glaciers draining the East Antarctic ice sheet through the southern Transantarctic Mountains. Our study indicates that the grounded part of Byrd Glacier is close to being in balance. However, we observe large thinning on the floating part of the glacier, probably induced by increased basal melting. Citation: Schenk, T., B. Csatho, C. J. van der Veen, H. Brecher, Y. Ahn, and T. Yoon (2005), Registering imagery to ICESat data for measuring elevation changes on Byrd Glacier, Antarctica, Geophys. Res. Lett., 32, L23S05, doi:10.1029/2005GL024328.

An agent-based method for automatic building recognition from lidar data

Light detection and ranging (lidar) as a modern and powerful remote sensing technology has proven to be a promising data source for modelling of various three-dimensional (3D) objects such as buildings and trees in urban areas. Nevertheless, because of the adjacency of buildings and other objects, especially trees, the results obtained from most traditional building-recognition algorithms are still dependent on several assumptions and simplifications. This paper presents a multi-agent methodology for automatic building recognition based on the decision-level fusion of textural and spatial information extracted from lidar range and intensity products. In the proposed methodology, two different groups of object-recognition agents are defined for building and tree detection in parallel. The algorithm has two different operational levels based on the types of contextual information. In the first level, both object-recognition agents decide on the types of objects in the study area based on textural information, and the candidates of the building and tree regions are generated. In the second operational level, building- and tree-recognition agents perform some operations at the macro level to modify the candidates of building and tree regions based on spatial information. Evaluation of the results confirms the significant capabilities of the proposed multi-agent algorithm to decrease the conflicts in the field of automatic building recognition in complex urban areas.

Experiments with matching in the object space for aerotriangulation

A method for accurate and reliable image matching for aerotriangulation is described and experimental results are reported. Most matching methods use small image patches because they assume that the object surface around the point is planar. However, in order to increase reliability, large image patches that are more likely to contain significant information are necessary. In the method presented here, the matching is performed in the object space in order to minimize the geometric distortion of the image patches due to the relief. As a result, much larger image patches can be used, which increases both the accuracy and the reliability. Experimental results obtained from six different datasets confirm these expectations. The accuracy varies between 1/5 and 1/12 of a pixel.

Spectral interpretation based on multisensor fusion for urban mapping

This paper is concerned with fusing aerial imagery, LIDAR point clouds, and hyperspectral imagery for the purpose of automated urban mapping. Instead of performing traditional supervised and unsupervised classification of hyperspectral data we propose a region growing approach from seed pixels that originate from fusing LIDAR and aerial imagery. This requires a thorough alignment of all sensors involved - a problem that is solved with sensor invariant features. The common system is the geodetic reference frame in which the LIDAR points are computed. The alignment results in transformations from sensor space to object space and back, avoiding resampling the sensor data. After describing the major aspects, an example demonstrates the feasibility of the proposed fusion approach.

Information fusion of Lidar range and intensity data for automatic building recognition

Lidar has proved to be a promising data source for various mapping and 3D modelling of buildings in urban areas. Therefore, many researchers have been trying to study and develop automatic building recognition algorithms based on Lidar data. But, according to the adjacency of buildings and other objects in urban areas, especially trees, the performance of obtained results from most of these algorithms is still dependent to several assumptions and simplifications. In this article, a multi-agent methodology has been proposed for automatic building recognition based on the decision level fusion of textural and spatial information extracted from Lidar range and intensity products. In this multi-agent methodology, two different groups of object recognition agents are defined for building and tree detection in parallel and the algorithm has two different operational levels based on the types of contextual information. In the first level, both object recognition agents decide about the types of objects in the study area based on textural information, and the candidates of building and tree regions will be generated. In the second operational level, building recognition and tree recognition agents perform some operations in macro level in order to modify the candidates of building and tree regions based on spatial information. The evaluation of obtained results confirms the high capabilities of proposed multi-agent algorithm to decrease the conflicts in the field of automatic building recognition in complex urban areas.

A multi-agent method for automatic building recognition based on the fusion of Lidar range and intensity data

Lidar has proved to be a promising data source for various mapping and 3D modeling of buildings in urban areas. Therefore, many researchers have been trying to study and develop automatic building recognition algorithms based on Lidar data. But, according to the complicated relationships between buildings and other objects in urban areas, especially trees and vegetations, the performance of obtained results from most of these algorithms is still dependent to several assumptions and simplifications. In this paper a multi-agent methodology has been proposed for automatic building recognition based on the fusion of textural and spatial information extracted from Lidar range and intensity data. The evaluation of obtained results confirms the high capabilities of this proposed multi-agent algorithm to decrease the conflicts in the field of automatic building recognition in complex urban areas.