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Multispectral satellite imagery and airborne laser scanning techniques for the
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LANDSCAPE ARCHAEOLOGY ON THE NORTHERN
FRONTIER OF THE ROMAN EMPIRE
AT POROLISSUM
An interdisciplinary research project
CORPVS LIMITIS IMPERII ROMANI

DAC I A P OROL I SSE N SI S ( I ) : P O RO L I S SU M

P OROL I SSUM M O N O G R A P H S 2

Editor
CORIOLAN HORAȚIU OPREANU
ROMANIAN ACADEMY – INSTITUTE OF ARCHAEOLOGY AND HISTORY OF ART CLUJ-NAPOCA

LANDSCAPE ARCHAEOLOGY
ON THE NORTHERN FRONTIER
OF THE ROMAN EMPIRE
AT POROLISSUM
An interdisciplinary research project

Edited by
Coriolan Horațiu Opreanu and Vlad-Andrei Lăzărescu

Mega Publishing House

Cluj‑Napoca
2016
This volume was supported by a grant of the Romanian National Authority for Scientific Research,
CNCS-UEFISCDI, project number PN-II-PT-PCCA-2011-3.1-0924.

ISBN 978-606-543-787-6

DTP and cover:

Francisc BAJA

© Editors, 2016

Editura Mega | www.edituramega.ro

e‑mail: mega@edituramega.ro
 Contents

List of Illustrations 7
List of Tables 13
Authors and Contributors 15
Acknowledgements 17
Introduction (Coriolan Horațiu Opreanu, Vlad-Andrei Lăzărescu) 19

I
DEFINING THE ROMAN LIMES OF DACIA

Dănuț Petrea, Iuliu Vescan, Ștefan Bilașco & Ioan Tanțău

Natural landscape of the researched area 31

Coriolan Horațiu Opreanu & Vlad-Andrei Lăzărescu

The limes as a contact zone between Orbis Romanus and Barbaricum 43

Coriolan Horațiu Opreanu & Vlad-Andrei Lăzărescu

The province of Dacia 49

II
INTERDISCIPLINARY RESEARCH METHODS AND THE STUDY OF THE
ROMAN LIMES IN DACIA POROLISSENSIS

Dan Ștefan
Geophysical surveys and the reconstruction of the ancient landscape 115

Sorina Fărcaș, Ioan Tanțău, Roxana Grindean, Andrei Marian Panait & Andrei Cosmin Diaconu
Palaeoenvironmental research methods 121

Ștefan Bilașco, Iuliu Vescan, Ioan Fodorean & Raularian Rusu

GIS and Spatial Analysis 133

Anamaria Roman & Tudor-Mihai Ursu

Multispectral satellite imagery and airborne laser scanning techniques for the detection of
archaeological vegetation marks 141
 III
LANDSCAPES OF THE NORTH-WESTERN LIMES OF ROMAN DACIA

Ioan Tanțău, Roxana Grindean, Andrei Marian Panait, Andrei Cosmin Diaconu & Sorina Fărcaș
Palaeoenvironmental reconstructions since 5000 BC 153

Roxana Grindean, Ioan Tanțău & Sorina Fărcaș

Human impact and land-use since 5000 BC 165

Tudor-Mihai Ursu & Anamaria Roman

Ancient and present floristic, vegetation, and ethnopharmacological aspects regarding
Porolissum area 173

Ioan Rus, Iuliu Vescan & Ștefan Bilașco

3D Modeling using UAV and photogrammetric techniques 225

Anamaria Roman, Tudor-Mihai Ursu, Vlad-Andrei Lăzărescu & Coriolan Horațiu Opreanu
Multi-sensor surveys for the interdisciplinary landscape analysis and archaeological feature
detection at Porolissum 237

Coriolan Horațiu Opreanu & Vlad-Andrei Lăzărescu

Remote Sensing at Porolissum: an archaeological perspective 263

Vlad-Andrei Lăzărescu, Ștefan Bilașco & Iuliu Vescan

Big Brother is watching you! Approaching Roman surveillance and signalling at Porolissum 275

Conclusions (Coriolan Horațiu Opreanu) 305

Bibliography 309
 Authors and Contributors

Ștefan Bilașco PhD is Lecturer and researcher at Babeș-Bolyai University of Cluj-Napoca,

Faculty of Geography with the scientific areas of competence in GIS-Spatial Analysis,
Geoinformatics, Cartography and Topography.

Andrei Cosmin Diaconu is PhD student at Babeş-Bolyai University of Cluj-Napoca, Faculty

of Biology and Geology with research interests including geology, climate reconstruction,
palaeoenvironment, invertebrate palaeoecology, fire history and human impact.

Sorina Fărcaș PhD is Senior Researcher and Director of the Institute of Biological Research
Cluj-Napoca (IBRC), branch of the NIRDBS.  Her scientific areas of competence include
Quaternary palynology, past and present vegetation ecology and conservation.

Ioan Fodorean PhD is Associate Professor and researcher at Babeș-Bolyai University of

Cluj-Napoca, Faculty of Geography with the scientific areas of competence in GIS-Spatial
Analysis, Cartography and Topography.

Roxana Grindean PhD is Assistant Researcher at Babeş-Bolyai University of Cluj-Napoca,

Faculty of Biology and Geology with research interests including geology, palynology, pal-
aeoecology, forest ecology and environmental archaeology.

Vlad-Andrei Lăzărescu PhD is Junior Researcher at the Institute of Archaeology and

History of Art Cluj-Napoca, member in the Editorial Board of the archaeological yearbook
“Ephemeris Napocensis”. His scientific interests focus upon Late Antiquity and new tech-
nologies and techniques used in archaeology.

Coriolan Horațiu Opreanu PhD is Deputy Director of the Institute of Archaeology and
History of Art and Associate Proffesor at ‘Babeș-Bolyai University’ Cluj-Napoca, Director
of the excavations at Porolissum, Director of the project “Seeing the Unseen. Landscape
Archaeology on the Northern Frontier of the Roman Empire at Porolissum” and Editor of
the archaeological yearbook “Ephemeris Napocensis”. His main research interest is focused
on archaeology of the Roman frontiers, Roman urban archaeology, Late Roman and Early
mediaeval archaeology, relations between the Roman and barbarian worlds, Roman mili-
tary history.

Andrei Marian Panait is PhD student at Babeş-Bolyai University of Cluj-Napoca, Faculty

of Biology and Geology with research interests including geology, past aeolian processes,
peat development and carbon accumulation, sedimentary geochemistry and environmental
changes.
16 Authors and Contributors

Dănuț Petrea PhD is Professor and researcher at Babeș-Bolyai University of Cluj-Napoca,

Faculty of Geography with the scientific areas of competence in Geomorphology and Spatial
Planning.

Anamaria Roman PhD is Researcher at IBRC, branch of the NIRDBS, and member in the
Executive Council of the Romanian Plant Phytosociological Society (SFR). Her research
interests focus on vegetation, landscape ecology and technologies such as Remote Sensing,
enabling ecosystem assessment in the context of past, present or future environmental
changes due to human activities.

Ioan Rus PhD is Assistant Professor and researcher at Babeș-Bolyai University of Cluj-
Napoca, Faculty of Geography with the scientific areas of competence in Geology,
Photogrammetry and Remote Sensing.

Raularian Rusu PhD is Assistant Professor and researcher at Babeș-Bolyai University of

Cluj-Napoca, Faculty of Geography with the scientific areas of competence in Urbanism,
Spatial Planning and Regional Geography.

Dan Ștefan PhD is Senior Researcher at the National Museum of Eastern Carpathians
Sfântu Gheorghe with the scientific areas of competence in near surface Topography,
Geophysics and Remote Sensing

Ioan Tanțău PhD is Assistant Professor and researcher at Babeș-Bolyai University of Cluj-
Napoca, Department of Geology, with the scientific areas of competence in Geology and
Quaternary palaeoenvironments.

Tudor-Mihai Ursu PhD is Scientific Researcher at IBRC, branch of the NIRDBS, and mem-
ber of the SFR. His research interests include landscape ecology, plant taxonomy, studies of
past and present vegetation and Remote Sensing.

Iuliu Vescan PhD is Assistant Professor and researcher at Babeș-Bolyai University of

Cluj-Napoca, Faculty of Geography with the scientific areas of competence in GIS-Spatial
Analysis, Urbanism, Spatial Planning and Environmental Impact Assessment.
 Acknowledgements

T his volume summarizes the research supported between 2012–2016 by the proj-
ect ‘Seeing the Unseen. Landscape Archaeology on the Northern Frontier of the
Roman Empire at Porolissum’ of the Romanian National Authority for Scientific Research,
CNCS-UEFISCDI (project number PN-II-PT-PCCA-2011-3.1-0924). As director of the
project I am grateful to the colleagues who were members of the interdisciplinary research
group for their professional approach, but also for their team spirit (Sorina Fărcaș, Ioan
Fodorean, Roxana Grindean, Vlad-A. Lăzărescu, Dănuț Petrea, Anamaria Roman, Ioan Rus,
Ioan Tanțău, Tudor Ursu, Iuliu Vescan). I appreciate at higher level the scientific accomplish-
ments of the young researcher colleagues and I consider their performance as a key-factor
of making a go of the project. Many thanks go also to our collaborators Andrei Diaconu,
Monica Gui, Andrei Panait, Raularian Rusu, Dan Ștefan and Cristian Ștefan.

Coriolan H. Opreanu
 Multispectral satellite imagery and airborne
laser scanning techniques for the detection
of archaeological vegetation marks

Anamaria Roman & Tudor-Mihai Ursu

1. Introduction

I n what follows, we introduce a brief overview of the theoretical background and

technical aspects regarding the Remote Sensing (RS) techniques that employed
vegetation proxies in order to discover and map subsurface archaeological remains at the
Roman Archaeological Site from Porolissum. Also, Earth Observation with multiple sen-
sors enabled the interdisciplinary interpretation of the historical human impact on the
environment.
RS is the acquisition of information about an object or phenomenon without making
any physical contact with it, by using electromagnetic radiation or acoustical waves that
are reflected or emanate from the targets of interest417. Satellite imagery, aerial photogra-
phy, Airborne Laser Scanning (ALS, commonly referred to as LiDAR-Light Detection and
Ranging), geophysics, ground spectroscopy, and terrestrial laser scanners, are all considered
remote sensing techniques418 and have been successfully applied in order to investigate the
environment, including the archaeological landscapes419.
The non-destructive RS techniques have opened up new horizons and possibilities for
landscape archaeology during the last decades, becoming a well-established tool for the
study of archaeological and cultural landscapes all over the world420, including conflict or
war damaged sites421. The improvements of satellite sensors in terms of spatial and spectral
characteristics (e.g. WorldView–2 launched in 2009) attracted even more the interest of sci-
entists, who started applying image processing techniques for interdisciplinary approaches.
These were employed in developing methodologies for systematic monitoring of cultural
heritage sites and monuments422 and for identifying traces of past human activity423.
417
  SCOLLAR 1990; REES 2013.
418
  JOHNSON 2006.
419
  JOHNSON/OUIMET 2013; RISBØL ET ALII 2013; LASAPONARA ET ALII 2014; AGAPIOU/LYSANDROU
2015.
420
 PAVLIDIS/FRASER/OGLEBY 2001; CAMPANA 2003; SCARDOZZI 2009; BOFING/HESSE 2010;
AGAPIOU/HADJIMITSIS/ALEXAKIS 2012; BENNETT ET ALII 2012; LASAPONARA/MASINI 2012;
WEISHAMPEL ET ALII 2012; RISBØL ET ALII 2013; LASAPONARA ET ALII 2014; ATZBERGER ET ALII
2014; CHASE ET ALII 2014; AGAPIOU/LYSANDROU 2015.
421
  SCARDOZZI 2009.
422
  AGAPIOU/LYSANDROU 2015; CHEN/LASAPONARA/MASINI 2015.
423
  PAPPU ET ALII 2010; MOREHART 2012; SARRIS ET ALII 2013; DE LAET ET ALII 2015.
142 Anamaria Roman & Tudor-Mihai Ursu

2. Archaeological vegetation marks, their characteristics and detection

The detection of archaeological remains through airborne remote sensing techniques
employs both the spectral and geometrical properties of the ground objects. During the first
decades of the 20th century, it was documented that under certain conditions the subsur-
face archaeological structures become visible in the airborne sensed images. If differences
between the embedded structure and the surrounding ground topography exist, these rep-
resent direct indicators of the feature’s location. LiDAR is the choice technique to distin-
guish such minute differences in the geometrical properties of the objects, revealing feature
location. Other differences, mainly regarding the ecophysiological and optical proprieties of
the soil or vegetation cover, are indirect or proxy indicators of feature location, commonly
referred to as vegetation (crop), soil or shadow marks. These can be detected by the spectral
sensors that have the ability to identify even slight changes in soil or vegetation properties.
In order to identify and properly interpret the vegetation data and indices resulted from
multispectral or hyperspectral image analysis, vegetation field surveys are needed, in order
to establish the floristic structure of the plant communities. This step enables the distinction
between vegetation patterns resulted from natural/catastrophic processes and those that
can be considered as a result of the past or present human activities. The Braun-Blanquet
approach424 is generally used for investigating plant communities in the field, combined with
the technique of “itinerary” investigations. The succession of the research phases in estab-
lishing the vegetation types is as follows:
(1) the analytical phase, aiming to identify the qualitative, quantitative and spatial struc-
ture of plant community types, their distribution and spatial extent, along with the intensity
of anthropic pressure;
(2) the synthetic phase, involving the analysis of plant community fragments in order to
assign them to vegetation units (coenotaxonomic units/habitat types).
Vegetation marks, valuable proxy indicators of archaeological feature location, can be
either positive or negative425 and occur when the physical properties of an area are different
enough from those of the surrounding soil and vegetation426. Although the general principles
of vegetation mark development in terms of soil parameters are well understood427, their
detectability is still difficult to predict or model, since their visibility is heavily dependent
on a large number of factors including geology, season, and vegetation type. Positive marks
develop when the underlying archaeological feature increases the content of moisture and
nutrients in soil, promoting the growth and health of plants in that area and causing them
to appear greener and taller than the surrounding vegetation. Conversely, negative marks
appear when the underlying archaeological feature inhibits growth, causing stunting and
early failure in times of stress428 (Fig. 87). Stunting of plant growth and wilting are the most
extreme and straightforward signs, while lower levels of stress induce more subtle changes,
that cannot be recorded by common aerial images (monochrome or in the visible spectrum,
R-Red; G-Green; B-Blue) requiring the use of other spectral combinations such as the NIR
(Near-Infrared) region.
The electromagnetic spectrum includes a wide range of wavebands of different wave-
lengths, visible light forming only a small section of it (Fig.  88). All objects/materials,

424
  WESTHOFF/VAN DER MAAREL 1978.
425
  WILSON 2000.
426
  BECK ET ALII 2007.
427
  EVANS/JONES 1997; HEJCMAN/SMRZ 2010.
428
  BENNETT 2011.
 Multispectral satellite imagery and airborne laser scanning techniques for the detection 143

Fig. 87. The influence of subsurface archaeological remains on the vegetation

growth and the subsequent appearance of the vegetation marks.

including vegetation, absorb and reflect light of different wavelengths depending on their
physical, physiological or chemical properties429. Understanding the spectral language of
plant reflectance (spectral signature) in the context of the electromagnetic spectrum is very
important in environmental studies analyzing, for instance, vegetation stress responses or
land cover change, being also employed in many studies for the comprehensive analysis of
archaeological landscapes430.

Fig. 88. The electromagnetic spectrum, reproduced (after TEMPFLI ET ALII 2009 and BERGMAN ET ALII 2011).

The absorption and reflectance of different light wavelengths by vegetation is a conse-

quence of the cellular structure of the leaf, as shown in Fig. 89. The electromagnetic responses
of green, healthy vegetation is within the green’ visible and near-infrared wavelengths.
  GIBSON 2000.
429

  LASAPONARA/MASINI 2012; BENNETT ET ALII 2013; COMER/HARROWER 2013.

430
144 Anamaria Roman & Tudor-Mihai Ursu

Healthy leaves absorb 70–90% of incident visible radiation, particularly in the blue and red
wavelengths (centered on 450 nm and 670 nm respectively), and reflect most of the green
light (centered on 533 nm) which is why leaves appear green to the human eye431. Green light
is reflected back by the chlorophyll pigment within the chloroplasts in the palisade cells.
Blue and red light are absorbed and used in photosynthesis by the chlorophyll pigment in the
palisade cells. However, wavelengths in the NIR region are mostly reflected and transmitted
through the leaves432, being scattered by the cell interfaces in the mesophyll tissue (Fig. 89).

Fig. 89. Cell structure of a green leaf and interactions with the electromagnetic radiation,
adapted from SUMMY ET ALII 2003 and VERHOEVEN 2011. The cross section trough the leaf
of Ligustrum vulgare is provided by the courtesy of Dorina Podar and Zoltan Balazs.

In healthy leaves 40–60% of the NIR light is reflected, although at canopy level the situ-
ation is more complex, because a range of effects such as additive reflectance, incidence
angle, leaf orientation, shadow and soil background reflectance. A typical spectral reflec-
tance curve for healthy vs. stressed vegetation is given in Fig. 90. Plant stress considerably
changes the spectral properties of vegetation, as the chlorophyll pigment rapidly decays
and loses its absorption properties433, increasing reflectance in the green to reddish part
of the visible region (from around 530 nm to 670 nm) of the electromagnetic spectrum.
Moreover, stress often results in leaf chlorosis: a yellowing discoloration due to chlorophyll
losing dominance over the carotenoids434. Stressed plants reveal, therefore, a different spec-
tral signature which can be observed both in visible light (VIS) (they become yellowish), and
as a generally lower reflectance in the NIR region of the electromagnetic spectrum (Fig. 91).
While images captured in the visible spectrum can be visually interpreted, the ones involv-
ing bands from the invisible wavelengths or their combinations with visible light require a
mathematical expression and intervals to make them interpretable. Vegetation indices are
mainly derived from reflectance data within the Red and NIR bands. They are quantitative mea-
sures, based on vegetation spectral properties that attempt to measure biomass or vegetative
431
  KNIPLING 1970.
432
  SLATON/HUNT/SMITH 2001.
433
  KNIPLING 1970; CARTER 1993.
434
  HENDRY/HOUGHTON/BROWN 1987; STRZALKA/KOSTECKA-GUGALA/LATOWSKI 2003.
 Multispectral satellite imagery and airborne laser scanning techniques for the detection 145

Fig. 90. The spectral reflectance curve of vegetation. The major

absorption and reflectance features are indicated.

Fig. 91. Reflectance curves for healthy vs. stressed vegetation (left) and the stressful
effect of positive subsurface archaeological structures on vegetation (right).

vigor435. They operate by contrasting intense chlorophyll pigment absorption in the red against
the high reflectance of leaf mesophyll in the near infrared. The simplest form of vegetation
index is the ratio between two digital values from the red and near infrared spectral bands.
Theoretical analyses and field studies have shown that vegetation indices are directly related
to light-dependent physiological processes (e.g. photosynthesis) of photosynthetically active
plants, predominant in the upper canopy436. Their use is based on the principle that combin-
ing spectral bands can highlight specific characteristics of vegetation regarding growth and
health that are otherwise not visible from individual bands or true/false colour RGB images.
Vegetation indices aid the identification of contrasts in plant quality, vigor and stress, all of
which could be related to the presence of upstanding or buried archaeological features. More
than 150 such indices are presented in the environmental remote sensing literature. The

  AGAPIOU/LYSANDROU 2015.
435

  GLENN ET ALII 2008.

436
146 Anamaria Roman & Tudor-Mihai Ursu

most widely used index for archaeological analysis is the well-known Normalized Difference
Vegetation Index (NDVI437) obtained by using the following formula:

Vegetation indices are usually employed in order to monitor seasonal or even long-term
variations of structural, phenological and biophysical parameters of land surface vegeta-
tion cover438. Additionally, on the basis of remotely sensed data, crop marks may be suitably
identified by exploiting vegetation indices that are spectral combinations of different bands.
Although the number of published indices is high, apart from Traviglia439, that compared a
simple Red/NIR ratio, the NDVI and Modified Soil-Adjusted Vegetation Index (MSAVIS2),
there are very few studies that tested vegetation indices systematically as tools for vegeta-
tion mark detection. The vegetation indices used for any archaeological study need to be
selected carefully so that they have a substantial biophysical (as opposed to purely numeri-
cal) basis. This is crucial to the aim of understanding the physical and biological parameters
that influence the representation of archaeological features in the data. Further comparison
tests of various indices are needed to establish their individual effectiveness and their use-
fulness for landscape and land cover analysis.

3. Airborne Laser Scanning Technique

More recently, airborne laser scanning (ALS or LiDAR-Light Detection And Ranging) has
become one of the most important and accurate methods for generating digital terrain mod-
els. These data are used to obtain high-resolution topography and have opened avenues for
the analysis of landslides, hill slope and channelization processes, river morphology, active
tectonics, volcanic landforms and anthropogenic signatures on geomorphology and topog-
raphy440. The derived terrain models support project planning in a variety of areas such as
urban development, mining industry, water and forest management, biodiversity conserva-
tion, and cultural heritage preservation. This non-invasive technique has been employed in
various domains: measuring agricultural productivity441, distinguishing faint archaeological
evidences442, forestry practices443, advancing the science of geomorphology444, measuring vol-
cano uplift445, glacier decline and snowpack446, and providing data for topographic mapping, to
name just a few. Using the LiDAR point cloud data, one can extract specific features, such as
underground ancient structures dimensions or aboveground individual trees parameters447, and
obtain ecosystem level information such as forest biomass or carbon sequestration capacity448.
In archaeological prospection, this technology has been increasingly used in the last
decade and a wide range of techniques for data processing and analysis has been developed.
The comparative evaluation and optimisation of these techniques for archaeological feature
437
  BENNETT ET ALII 2013.
438
  BASSANI ET ALII 2009.
439
  TRAVIGLIA 2006.
440
  TAROLLI/SOFIA/DALLA FONTANA 2012; SOFIA/DALLA FONTANA/TAROLLI 2014.
441
  SAEYS ET ALII 2009.
442
  BENNETT ET ALII 2012.
443
  HYYPPÄ ET ALII 2012.
444
  SOFIA/DALLA FONTANA/TAROLLI 2014.
445
  WHELLEY ET ALII 2014.
446
  ABERMANN ET ALII 2010.
447
  POPESCU/WYNNE/NELSON 2003; POPESCU 2007, EDSON/WING 2011; DALPONTE ET ALII 2014.
448
  LEFSKY ET ALII 2005; POPESCU 2007; GARCÍA ET ALII 2010; LEE/JUNG/YUN 2013.
 Multispectral satellite imagery and airborne laser scanning techniques for the detection 147

Fig. 92. Typical airborne laser scanning system.

detection, however, is more recent449. In the last decade, the interpretation of ALS-derived
DTMs (Airborne Laser Scanning-derived Digital Terrain Models) proved to be a very useful
tool for the extraction of anthropogenic features belonging to different historic environ-
ments450. Although there are several papers that deal with this subject, most of them are
focused primarily on Mediterranean landscapes451, on alluviated landscapes452, and on grass-
land environments453, with just a few examples of steep terrain forests. Nevertheless, even in
these cases at least some archaeological features are above the soil surface, being obscured
only by the forest canopy454.
The fundamental concept of a LiDAR measurement is to send a laser pulse towards
a target and to measure the timing and amount of energy that is scattered back from the
target (Fig. 92). The return signal timing (t) provides measurement of the distance between
449
  DONEUS ET ALII 2008; BENNETT ET ALII 2012; LASAPONARA/COLUZZI/MASINI 2011; STEWARTA/
LASAPONARA/SCHIAVONA 2014.
450
  CHALLIS ET ALII 2008; DONEUS 2013; BENNETT/COWLEY/DE LAET 2014; CARLSON/BAICHTAL
2015; HERRMANN 2016.
451
  POIRIER ET ALII 2013.
452
  CHALLIS/FORLIN/KINCEY 2011.
453
  BENNETT ET ALII 2012; BENNETT ET ALII 2013.
454
  DONEUS/BRIESE 2006; DONEUS ET ALII 2008; ŠTULAR 2011.
148 Anamaria Roman & Tudor-Mihai Ursu

the instrument and the scattering object (d), where c is the speed of light (1.07925285
x109 km/h).
t = 2 d/c
If the top of the vegetation canopy is measured (t1) along with a measurement of the
local ground height (t2), then the height of the canopy (h) can be calculated:
h = d1- d2= (c/2) t1- t2
The airborne sensor itself comprises the laser and projection mechanism, a Global
Positioning System (GPS) and an Internal Measurement Unit (IMU) along with a control
system that also records the data. The GPS provides location data throughout the survey,
while the IMU records the pitch, yaw and roll of the aircraft during survey. Together, these
measurements are used to process the ALS data and to enable correction for the movement
of the aircraft during survey and alignment to real-world co-ordinates. Although airborne
systems were known to be able to record height to less than 1 m accuracy in the 1970s, the
more recent advancements in GPS and IMU technology enabled the sensor to be used for
topographic mapping455.
ALS data are generally measured by two factors: point density (average number of points
per square meter) and point distance (average separation of points). As part of the process-
ing, error images can be generated to highlight areas where the data are inconsistent, such
as at the overlap of flight lines. Processing and analyzing LiDAR data involves the applica-
tion of different filtering, interpolation and visualization algorithms in order to generate
very high resolution terrain models such as: Digital Terrain Model (DTM), Digital Surface
Model (DSM), Slope Model, Canopy Height Model (CHM), Hill-Shade Models etc.

4. Multi-sensor surveys at the Roman archaeological site from Porolissum

Satellite and airborne sensors were shown to have a great potential for efficient and
uninvasive investigations aiming to discover and to accurately map archaeological sites.
Most of these studies employed high spatial resolution satellite imagery like IKONOS and
QuickBird456. Also, hyperspectral satellite images such as HYPERION and MIVIS have
been found to be helpful in obtaining valuable information, revealing buried architectural
remains457. For the aerial detection and study of vegetation marks (and other archaeological
marks), different kinds of analog film and digital sensors were used. Nevertheless, standard
aerial photographs based on three to four broad VIS and NIR wavebands are not ideal:
certain characteristic spectral absorption features are either averaged out or not recorded
due to the lack of sensitivity to wavelengths outside the VIS-NIR range. Compared to
aerial photography, satellite imagery has only recently become publicly available, and not
as many archaeological investigations have been conducted using such data, mainly due
to constraints of ground resolution, cost, and availability. However the spectral range of
satellite imagery is greater than that of aerial photography due to the capabilities of sat-
ellite multi-spectral sensors, allowing for a more comprehensive analysis. Satellite images
exhibit greater spectral sensitivity and contrast in ground reflectance, in comparison to
aerial photographs. In the last decade, aerial unmanned vehicles or platforms (UAV) have
been developed in order to be able to fly with a payload contain hyperspectral sensors that
455
  BERALDIN/BLAIS/LOHR 2010.
456
 ALTAWEEL 2005; LASAPONARA/MASINI 2005; LASAPONARA/MASINI 2006; LASAPONARA/
MASINI 2007.
457
  TRAVIGLIA 2005; AQDUS/DRUMMOND/HANSON 2008; BASSANI ET ALII 2009.
 Multispectral satellite imagery and airborne laser scanning techniques for the detection 149

enable the investigations of more wavebands and wavelengths from the electromagnetic
spectrum. Nevertheless, these aerial unmanned platforms cannot cover large investigation
areas like the satellite multi-sensors. In principle, it is clear that remote sensing techniques,
such as airborne laser scanning (ALS) and airborne digital spectral imaging, can signifi-
cantly enhance our understanding of archaeological features within a landscape. ALS allows
greater and more precise measurement of the topography of the ground surface than any
other technology at landscape scale, while digital spectral imagery captures the nature of
vegetation and soil changes not just in the visible wavelengths but also in the near and short-
wave infrared (NIR and SWIR) and thermal regions of the spectrum458.
The multi-sensor approach of archaeological surveys has the advantage of being com-
plex and variable, thus overcoming the inherent weaknesses of every remote sensing tech-
nique used by itself. Archaeological features are very variable in what regards their topology,
topography and structure. Sometimes they can be directly visible at the soil surface or they
can appear only as proxy changes to soil and vegetation caused by sub or near surface fea-
tures. In some cases, they are completely masked by soil, vegetation or other environmental
conditions. In most cases they have been altered to a certain degree by the environmental
factors. It is clear that no single sensor could detect such a wide range of characteristics.
Thus, the advantage of multi-sensor surveys lies in the complementarity of the data that
can be collected by multiple sensors, allowing the detection of multiple characteristics of
the archaeological features. This complementarity not only improves the detection rate but
also promotes the understanding and interpretation of the detected features and their sur-
roundings. The availability of more complex information allows, in the end, for more cor-
rect interpretations and better decisions.
The increased spectral resolution of satellite images and spatial resolution of laser scan-
ning systems, as well as the improved analytical methods available led us to believe that such
terrain images, if generated and analyzed properly, may be a valuable data source to assist
archaeological surveys in the open-grassland or the forested area from Porolissum. The joint
usage of both high-resolution multispectral and airborne laser scanning for archaeological
purposes has been, to our knowledge, only rarely approached before in this environmental
context at European level, and never at national level.
In Romania, the remote sensing techniques are still underused in archaeology: aerial
photography was first used in the 1930s, and although some studies on this subject were
published during the 1970s, the technique is still primarily used only as a tool for study
site illustration. The lack of a broader understanding of the potential of this remote sensing
technique prevented its large scale use for discovering and monitoring archaeological sites
in Romania. A notable exception is the work of I. Oltean and W. Hanson from 2013459, who
have been studying, since 1998, the history and development of the western lowlands of
Romania by the application of archaeological aerial reconnaissance. In what regards ALS
technology, it had only been minimally used for archaeology, mostly in the area of public
awareness at the UNESCO site Sarmizegetusa Regia, as commissioned by the BBC. 
In the case of the Roman archaeological site from Porolissum, the first stage in approach-
ing the spectral properties of the vegetation cover was to identify the plant communities
growing above the presumed subsurface archaeological remains. This step was necessary
since in our research area spectral properties are variable among different archaeological fea-
tures/sites but also within the same site. Therefore, we have relied mainly on the geometrical

  BENNETT/COWLEY/DE LAET 2014.

458

  OLTEAN/HANSON 2013.
459
150 Anamaria Roman & Tudor-Mihai Ursu

properties of the vegetation cover (particularly tree height) which were extracted from high
spatial resolution airborne laser scanning data (LiDAR data). Also, we tested the suitability of
WorldView-2 (WV2) multispectral satellite imagery for archaeological reconnaissance and
interpretation. The WV2 instrument is a “push broom” imager launched in October 2009
by DigitalGlobe and was the most technologically-advanced high-resolution satellite ever
launched, before its twin, WorldView-3, reached orbit in 2014. The WV2 satellite constructs
an image one row at a time as the image of the Earth, focused through the telescope, moves
across the linear detector arrays, which are located on the focal plane. The WV2 satellite
carries two types of sensors that collect, respectively: panchromatic images (0.450-0.800
μm) with cell size 0.46 m at nadir (0.50 m for commercial uses) and multispectral images
(Coastal Blue: 0.400–0.500 μm; Blue: 0.450–0.510 μm; Green 0.510-0.580 μm; Yellow
0.585–0.625 μm: Red 0.630–0.690 μm; Red Edge 0.705–0.7 45 μm; NIR1 0.770–0.895 μm;
NIR2 0.860–1.040 μm) with cell size 1.84 m at nadir (2 m for commercial uses). Operating
at an altitude of 770 km with an inclination of 97.2°, this sun synchronous satellite has an
average revisit time of 1.1 days and is capable of collecting up to 1 million km2 of 8-band
imagery per day. Commercially available products are resampled to 0.5 m (Panchromatic)
and 2.0 m (Multispectral). The nominal swath width is 16.4 km460.

  DIGITAL GLOBE 2013; DIGITAL GLOBE 2014.

460

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