Abstract
Digital elevation models derived from airborne laser scanning have found worldwide application in archaeology and other disciplines. A key feature that makes these models so valuable lies in their capacity to represent micro-relief features indicating traces of past human activity. While detection of these often faint traces in vegetated areas benefits from maximum leaf-off conditions during data acquisition, countrywide collection of data must make compromises and often cannot take place in the most appropriate seasons. In this paper, we identify the impact of leaf-on conditions on the distribution of ground returns and present what types of archaeological objects might remain unnoticeable if the flight date is outside the desirable time window. Comparing five ALS data acquisition campaigns from both leaf-off (April and November) and leaf-on conditions (May and June), we demonstrate how foliage affects the morphology of relief features as recorded in ALS derivatives, and we identify other effects on archaeological interpretation caused by changing vegetation conditions. The results encourage evaluation of countrywide general-purpose data for their applicability in archaeology.
Highlights
Airborne Laser Scanning (ALS, known as airborne lidar) has found wide application in archaeology, proving to be an efficient method for documenting relief features over large, even vegetated areas [1,2,3,4,5,6,7]
Several factors affect the quality of ALS products, including the flying platform and scanning device, survey parameters, date and time of data acquisition, point cloud extraction algorithms, and geo-referencing techniques, as well as software and parameters used for data filtering and classification [13,14,15,16]
The distribution of ALS survey points is an interplay between many factors, including flight parameters, the density and structure of vegetation/ground cover, and phenological state of vegetation
Summary
Airborne Laser Scanning (ALS, known as airborne lidar) has found wide application in archaeology, proving to be an efficient method for documenting relief features over large, even vegetated areas [1,2,3,4,5,6,7]. ALS data are often collected countrywide by order of governmental organisations and usually made available as georeferenced and filtered point clouds or various ALS derivatives. Very often, these datasets are affordable or even provided for free [8,9,10]. Several factors affect the quality of ALS products, including the flying platform and scanning device, survey parameters (i.e., opening angle, pulse rate, field of view, flying height above ground level, overlap between neighbouring scan stripes, flying velocity), date and time of data acquisition, point cloud extraction algorithms (whenever a full-waveform scanner is in operation), and geo-referencing techniques, as well as software and parameters used for data filtering and classification [13,14,15,16].
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