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

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

OUTLINE

a) Archaeological feature detection: Potential and limits of optical remote sensingb)LiDAR technologyc) Data processingd) Post processinge) Study casef) Results and discussion

Magnetic MethodMagnetic Method GPRGPR Electrical MethodElectrical Method

LiDARLiDAR

Aerial prospectionAerial prospection

Electromagnetic MethodElectromagnetic Method

Satellite Remote SensingSatellite Remote Sensing

Ground TruthGround Truth

Ground Remote SensingGround Remote Sensing

How detect Archaeological Features?

Changes of soil constituents

Variations of moisture content

Nutrient deficiencies

Differences in the plant growth





Optical remote sensing

•Traditional aerial archaeology•Multispectral satellite imagery•Hyperspectral airborne data

buried masonry

crop-marks related to buried structures (positive presences)

stressed vegetationhealthy veget. healthy veget. healthy vegetation very healthy veget. healthy veget.

buried ditch

crop-marks related to ditches (negative presences)

sandy (or permeable) soil

clayey soil

buried masonry

soil-marks related to buried structures (positive presences)

buried ditchsandy (or permeable) soil

clayey soil

damper soilless damp soil less damp soil

soil-marks related to ditches (negative presences)

soil-marks and shadows marks related to buried structures

sunbeams

buried masonry

less damp s.damper soil damper s.shaded anddamper s.

buried masonry



buried ditch



clayey soil

buried masonry



clayey soil




sunbeams

buried masonry


Crop marksdifferences of height or color in crops which are under stress due to lack of water or deficiencies in other nutrients


Soil marks: traces of archaeological features visible on bare ground sites

Neolithic settlement in Apulia (Souther Italy)









Optical remote sensing

•Traditional aerial archaeology•Multispectral satellite imagery•Hyperspectral airborne data



Soil marks: traces of archaeological features visible on bare ground sites

Neolithic settlement in Apulia (Souther Italy)

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

buried masonry



buried ditch



clayey soil

buried masonry



clayey soil




sunbeams

buried masonry


buried masonry



buried ditch



clayey soil

buried masonry



clayey soil




sunbeams

buried masonry


Shadow marks : micro-topographic relief variations that can be made visible by shadowing in low sunlight angle conditions.

It provides direct range measurements mapped into 3D point clouds between a laser scanner and earth’s topography.

The laser scanner, mounted to an aeroplane or helicopter, emits near infrared pulses, at a frequency rate of 30.000 to 100.000 pulses per second, into different directions along the flight path towards the terrain surface.

Each pulse could be reflected one or more times from objects (ground surface, vegetation, buildings, etc.), whose position is determined by computing

the time delay between emission and each received echo, the angle of the emitted laser beam, the position of the scanner (determined using differential global positioning system and an inertial measurement unit).

Airborne Laser Scanner (ASL) or LiDAR (Light Detection And Ranging)

conventional scanners or discrete echo scanners delivers only the first and last echo, thus losing many other reflections

full-waveform (FW) scanners detects the entire echo waveform for each emitted laser beam*.

* FW offers improved capabilities especially in areas with complex morphology and/or dense vegetation cover

Scanners

Scanner employed by CNR

Scanner Riegel LMS –Q560, full waveform (unlimited echoes)

Density from 19 pts/m2 to 150 pt/m2

Scanning frequency = from 70.000 Hz (at 1500 m flight altitude) to 200.000 Hz (at 500 m flight altitude)

Number of recorded echoes : unlimited;

Wave length of laser = 1550nm

Pulse =3.5 ns

Max flight altitude = 3000 m

Airborne laser scanning (ALS) sensors can penetrate vegetation canopies allowing the underlying terrain elevation to be accurately modeled

The most exciting characteristics : Vegetation Filtering!!

•Irsi o Yrsum o castrum Ursum close to the Northeastern border of Basilicata with Apulia• strategic location : the confluence of the Bradano and Basentello riversa • long human frequentation: iron age, roman period, byzantine age• 12th cent : first documentary source •1123 : Yrsum depends on Episcopate of the near town of Montepeloso•1133: Yrsim depends on the monastery of the french order of Chase Die•13°-14 th cent : inhabitants (550 in 1277 and 5090 in 1320)•1288: description of a part of the village from a documentary source (a church, a square, some houses, grain storage)•1370: The village is lootedd,the monastery is destroyed.•Abandonmnent of the village

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 features2) Reconstruction of forma urbis 3) Identification of building phases of the medieval village4) Geomorphological pattern

WORKFLOW DATA PROCESSING

1) Initial setup and data calibration;

2) Filtering cloud points;3) Classify cloud points;4) Creating delivery products

(DEM, DTM)5) Post processing (shaded

DTMs)6) Archaeological

interpretation

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

on test sites characterized

i) outliers, low or high (such as birds, low-flying aircraft, or errors in the laser range-finder);

ii) spatial and morphological object complexity (such as very large or small objects, complex shape) which typically characterizes a urban setting;

iii) attached objects spanning the gaps between bare-Earth surfaces (bridges, natural/artificial ramps, building

on slopes etc..);

iv) low vegetation on slopes;

v) geomorphologic discontinuities due to steep slopes and sharp ridges.

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

Rationale basis: Starting from a coarse TIN surface obtained from reference points which are neighborhood minima. Densification : new points are added in an iterative way if they meet certain geometric threshold values based

which prescribe possible deviations from the average topographic surface and builds a triangulated model.

In every iteration points (from the point cloud) are added to the TIN if they are below data derived thresholds. The iterative process ends when no more points are below the threshold.

representation of main parameters to construct TIN

* Routine of TerraScan (Soininen, 2005)

Triangulation Irregular Network (TIN) densification method by Axelsson (2000)

threshold parametersIteration angle Iteration distance Terrain angle Max building size

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

The elimination of outliers points is performed through classification of : "low points“

single points or groups of points with an height lower than 0,5 m compared the other points within a ray of 5 m

“air points.points present in the air (i.e. birds, etc..).

isolated points points present in the air not classified as airpoints.

POST PROCESSING

Shading procedures to emphasize archaeological features

Vizualization of elevation data as shaded relief, by lighting the DTM by an hypothetical light sourceSelection of the direction parameters (zenith angle z and

azimuth angle ) : on the base of the difference in height and orientation of the microrelief of possible archaeological interest

z=60°

Single shading is not the most effective method to visualize and detect microrelief (If features and/or objects are parallel to the azimuth angle, will not rise a

shade) The right approach : observing and comparing DTM scenes shaded by using different angles of lighting. In addition the different shaded DTM could processed by using the Principal Components Analysis

Zenith=45°/Azimuth=0° Zenith=45°/Azimuth=90°

Zenith=45°/Azimuth=180° Zenith=45°/Azimuth=270°

Prin

cipa

l Com

pone

nt A

naly

sis PCA1

PCA3

PCA4AIR

BO

RN

E

LA

SER

S

CA

NN

ING

POST PROCESSING: Principal Component Analysis

Hill shaded DTM

POST PROCESSING: Convexity and Slope maps

Convexity

Slope

DTMDTM with archaeological interpretation

a

a'

Profile a-a’

MEDIEVAL SITE OF YRSUM

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”“

Paleo landlside

Urban sectorextra moenia

Square (platea)?

Castle/Motte

Niche of detachment

B

A

C

Landslide foot

Landslide slope

Archaeological interpretation

Virtual reconstruction: scenario I (Foundation)

Castle

Bailey (basse court)

Virtual reconstruction: scenario II (urban expansion)

Castle

Bailey (basse court)

B

A

Castle

Ancient field divisions

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

CONCLUSIONS

a)Archaeological prospection needs an integrated approach of different remote sensing methodsb)LiDAR overcomes some limits of optical imagery in

1) surveying archaeological structures and features covered by vegetation

2) Detecting archaeological features related to microrelief

c) Post processing (PCA of hill shading procedure, convexity maps etc..) is crucial to fully exploit the potential of LiDAR in archaeology and enhance archaeological featuresd) LiDAR provides archaeological information and digital topographic model for historical virtual reconstruction

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

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