On the processing of aerial LiDAR data for supporting enhancement, interpretation and mapping of archaeological features

On the processing of aerial LiDAR data for supporting enhancement, interpretation and mapping of archaeological features Rosa Lasaponara 1, Nicola Masini2 2 GisSearch Lab. - CNR-IBAM (Institute of Archaeological and Architectural Heritage), Potenza, Italy 1 ARGON Lab. - CNR-IMAA (Institute of Methodologies for Environmental Analysis ), Potenza, Italy

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Magnetic Method GPR Electrical Method LiDAR Aerial prospection Electromagnetic Method Satellite Remote Sensing Ground Truth Ground Remote Sensing How detect Archaeological Features?

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Neolithic settlement in Apulia (Souther Italy) ,[object Object],[object Object],[object Object],[object Object]

AIRBORNE/SATELLITE OPTICAL DATA : LIMITS i) the impossibility of surveying archaeological features of areas covered by dense vegetation; ii) the difficulty in detecting archaeological features related to microrelief ( shadow-marks ) also in case of bare ground surfaces; Limits of optical remotely sensed data How overcome these limits LiDAR Shadow marks : micro-topographic relief variations that can be made visible by shadowing in low sunlight angle conditions.

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Airborne Laser Scanner (ASL) or LiDAR (Light Detection And Ranging)

[object Object],[object Object],* FW offers improved capabilities especially in areas with complex morphology and/or dense vegetation cover Scanners ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Airborne laser scanning (ALS) sensors can penetrate vegetation canopies allowing the underlying terrain elevation to be accurately modeled The most exciting characteristics : Vegetation Filtering!!

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Study case: medieval village of Yrsum

Study case: medieval village of Yrsum Archaeological features detected by optical (aerial/satellite) remote sensing 1) Ditch and cropmarks related to buried walls of the castle (A) 2) Microrelief related to some buried buildings of the medieval village (B) Issues to be addressed : 1) More detailed map of archaeological features 2) Reconstruction of forma urbis 3) Identification of building phases of the medieval village 4) Geomorphological pattern

WORKFLOW DATA PROCESSING ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],The identification of archaeological features for both bare and densely vegetated areas, needs a DTM with a high accuracy. For this aim, it is crucial to carry out the classification of terrain and off terrain objects by applying adequate filtering methods

DATA FILTERING The identification of archaeological features for both bare and densely vegetated areas, needs a DTM with a high accuracy. For this aim, it is crucial to carry out the classification of terrain and off terrain objects by applying adequate filtering methods (in detail, see Sithole and Vosselman 2004). Filtering methods available slope-based, block-minimum, surface-based clustering / segmentation Assessment of Filtering methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Best results in separating points on a ground surface from other points are obtained by the surface-based methods which assume as discriminant function a parametric surface with a corresponding buffer which defines a region in 3D space where ground points are expected to reside. (Axelsson 2000; Briese & Pfeifer 2001; Elmqvist 2001; Sohn & Dohman 2002; Wack & Wimmer 2002; Sithole & Vosselman, 2004) ( Sithole & Vosselman, 2004 )

CLASSIFICATION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],representation of main parameters to construct TIN * Routine of TerraScan (Soininen, 2005) Triangulation Irregular Network (TIN) densification method by Axelsson (2000) ,[object Object],[object Object],[object Object],[object Object],[object Object]

CLASSIFICATION Digital Terrain Model (DTMs) are obtained by the discrimination of on-terrain from off-terrain points (Classification) by using the diverse laser measurements : (i) height; (ii) intensity; (iii) echo width ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

POST PROCESSING Shading procedures to emphasize archaeological features ,[object Object],[object Object],z=60°     ,[object Object],[object Object],[object Object]

Zenith=45°/Azimuth=0° Zenith=45°/Azimuth=90° Zenith=45°/Azimuth=180° Zenith=45°/Azimuth=270° Principal Component Analysis PCA1 PCA3 PCA4 Hill shaded DTM AIRBORNE LASER SCANNING POST PROCESSING: Principal Component Analysis

POST PROCESSING: Convexity and Slope maps Convexity Slope DTM DTM with archaeological interpretation a a' Profile a-a’

Slope map Both of maps put clearly in evidence the microrelief referable to the layout of buried buildings of Irsi medieval village Profile convexity map

Mapping of archaeological features and reconstruction of “forma urbis” “

Archaeological interpretation Paleo landlside Urban sector extra moenia Square ( platea )? Castle/Motte Niche of detachment B A C Landslide foot Landslide slope

Virtual reconstruction: scenario I (Foundation) Castle Bailey (basse court)

Virtual reconstruction: scenario II (urban expansion) B A Castle Castle Bailey (basse court)

Under the vegetation …..the Historical Landscape! The small dimensions of fielddivisions suggest an intensive farming in this area, likely related to vineward, vegetable gardens and fruit trees which supplied people living in Yrsum Ancient field divisions

On the processing of aerial LiDAR data for supporting enhancement, interpretation and mapping of archaeological features

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On the processing of aerial LiDAR data for supporting enhancement, interpretation and mapping of archaeological features