Airborne laser bathymetry – detecting and recording submerged archaeological sites from the air

A new generation of airborne bathymetric laser scanners utilises short green laser pulses for high resolution hydrographic surveying in very shallow waters. The paper investigates its use for the documentation of submerged archaeological structures, introducing the concept of airborne laser bathymetry and focussing on a number of challenges this novel technology still has to face. Using this method, an archaeological pilot study on the northern Adriatic coast of Croatia has revealed sunken structures of a Roman villa. The results demonstrate the potential of this novel technique to map submerged archaeological structures over large areas in high detail in 3D, for the first time providing the possibility for systematic, large-scale archaeological investigation of this environment. The resulting maps will provide unique means for underwater heritage management.     

Mapping by matching: a computer vision-based approach to fast and accurate georeferencing of archaeological aerial photographs

To date, aerial archaeologists generally apply simple rectification procedures or more expensive and time-consuming orthorectification algorithms to correct their aerial photographs in varying degrees for geometrical deformations induced by the topographical relief, the tilt of the camera axis and the distortion of the optics. Irrespective of the method applied, the georeferencing of the images is commonly determined with ground control points, whose measurement and identification is a time-consuming operation and often limits certain images from being accurately georeferenced. Moreover, specialised software, certain photogrammetric skills, and experience are required. Thanks to the recent advances in the fields of computer vision and photogrammetry as well as the improvements in processing power, it is currently possible to generate orthophotos of large, almost randomly collected aerial photographs in a straightforward and nearly automatic way. This paper presents a computer vision-based approach that is complemented by proven photogrammetric principles to generate orthophotos from a range of uncalibrated oblique and vertical aerial frame images. In a first phase, the method uses algorithms that automatically compute the viewpoint of each photograph as well as a sparse 3D geometric representation of the scene that is imaged. Afterwards, dense reconstruction algorithms are applied to yield a three-dimensional surface model. After georeferencing this model, it can be used to create any kind of orthophoto out of the initial aerial views. To prove the benefits of this approach in comparison to the most common ways of georeferencing aerial imagery, several archaeological case studies are presented. Not only will they showcase the easy workflow and accuracy of the results, but they will also prove that this approach moves beyond current restrictions due to its applicability to datasets that were previously thought to be unsuited for convenient georeferencing.     

Surfaces from the Visual Past: Recovering High<Emphasis Type="Bold">-</Emphasis>Resolution Terrain Data from Historic Aerial Imagery for Multitemporal Landscape Analysis

Historic aerial images are invaluable sources of aid to archaeological research. Often collected with large-format photogrammetric quality cameras, these images are potential archives of multidimensional data that can be used to recover information about historic landscapes that have been lost to modern development. However, a lack of camera information for many historic images coupled with physical degradation of their media has often made it difficult to compute geometrically rigorous 3D content from such imagery. While advances in photogrammetry and computer vision over the last two decades have made possible the extraction of accurate and detailed 3D topographical data from high-quality digital images emanating from uncalibrated or unknown cameras, the target source material for these algorithms is normally digital content and thus not negatively affected by the passage of time. In this paper, we present refinements to a computer vision-based workflow for the extraction of 3D data from historic aerial imagery, using readily available software, specific image preprocessing techniques and in-field measurement observations to mitigate some shortcomings of archival imagery and improve extraction of historical digital elevation models (hDEMs) for use in landscape archaeological research. We apply the developed method to a series of historic image sets and modern topographic data covering a period of over 70 years in western Sicily (Italy) and evaluate the outcome. The resulting series of hDEMs form a temporal data stack which is compared with modern high-resolution terrain data using a geomorphic change detection approach, providing a quantification of landscape change through time in extent and depth, and the impact of this change on archaeological resources.     

Archaeological prospection of forested areas using full-waveform airborne laser scanning

Airborne laser scanning (ALS) is a potential tool for recognising and measuring topographic earthwork features in wooded areas. To explore its potential for archaeological reconnaissance in a densely forested area, a test scan covering an Iron Age hillfort in the eastern part of Austria was carried out during the first phase of a research project.     

Mitigating Global Climate Change: Why Are Some Countries More Committed Than Others?

This article analyzes factors affecting countries' commitment to mitigating global climate change within the scope of existing international institutions. The commitment level is operationalized as an ordinal variable ranging from an agreement with the international institutions (signature and ratification of the Framework Convention on Climate Change) to the actual implementation of the internationally negotiated modes of behavior (enactment of domestic environmental policies). A theoretical model of governments' decisionmaking is presented and tested for 91 countries at different levels of economic development with different domestic institutions. A given national government selects its commitment level depending on its incentives and ability to affect global emissions of greenhouse gases (GHG). An ordered logistic regression model is employed to analyze the factors affecting the levels of national commitment. Empirical analysis suggests that national commitment is significantly affected by the national government's incentives and the ability to affect the global level of GHG emissions, impacted more by the incentives than by the ability, and not affected by the aggregate levels of economic benefits.