Between Land and Sea: An Airborne LiDAR Field Survey to Detect Ancient Sites in the Chekka Region/Lebanon Using Spatial Analyses

Abstract

The interdisciplinary project "Between Land and Sea" combines geological, geomorphological and paleo-environmental approaches to identify archaeological remains of the Chekka region (Lebanon). In order to record the topography of this area, the first ever scientific airborne LiDAR data acquisition in Lebanon was conducted in autumn 2018. This work describes not only the acquisition and processing of the LiDAR data, but also the attempt to derive possible archaeological sites from the generated elevation model based on methods for spatial analysis. Using an "inverted mound" (iMound) algorithm, areas of possible settlement structures could be identified, which were classified regarding their probability of a possible ancient site using a deductive predictive model. A preliminary validation of some of the detected favoured areas using high-resolution aerial images has shown that the methods applied can provide hints to previously undiscovered sites. It was possible to identify probable ancient wall remains at several detected locations. In addition, least-cost path analyses were performed to reconstruct possible trade and transport routes from the Lebanon Mountains to the Mediterranean coast. The combination of the results of the iMound detection and classification as well as the calculated path system could point to the strategic location of the modern village of Kfar Hazir as a kind of traffic junction. Moreover, reconstructed main transport routes provide indications of heavily frequented roads and may form the basis for further investigations. To validate the results, upcoming field surveys will be realized on site.

Full text

Open Archaeology 2020; 6: 248268
Research Article
Jakob Rom*, Florian Haas, Manuel Stark, Fabian Dremel, Michael Becht, Karin Kopetzky,
Christoph Schwall, Michael Wimmer, Norbert Pfeifer, Mahmoud Mardini, Hermann Genz
Between Land and Sea: An Airborne LiDAR Field
Survey to Detect Ancient Sites in the Chekka
Region/Lebanon Using Spatial Analyses
Open Access. © 2020 Jakob Rom et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution-
NonCommercial-NoDerivatives 4.0 License.
https://doi.org/10.1515/opar-2020-0113
received July 27, 2020; accepted August 18, 2020.
Abstract: The interdisciplinary project “Between Land and Sea” combines geological, geomorphological and paleo-
environmental approaches to identify archaeological remains of the Chekka region (Lebanon). In order to record
the topography of this area, the first ever scientific airborne LiDAR data acquisition in Lebanon was conducted in
autumn 2018. This work describes not only the acquisition and processing of the LiDAR data, but also the attempt
to derive possible archaeological sites from the generated elevation model based on methods for spatial analysis.
Using an “inverted mound” (iMound) algorithm, areas of possible settlement structures could be identified, which
were classified regarding their probability of a possible ancient site using a deductive predictive model. A preliminary
validation of some of the detected favoured areas using high-resolution aerial images has shown that the methods
applied can provide hints to previously undiscovered sites. It was possible to identify probable ancient wall remains
at several detected locations. In addition, least-cost path analyses were performed to reconstruct possible trade and
transport routes from the Lebanon Mountains to the Mediterranean coast. The combination of the results of the iMound
detection and classification as well as the calculated path system could point to the strategic location of the modern
village of Kfar Hazir as a kind of traffic junction. Moreover, reconstructed main transport routes provide indications
of heavily frequented roads and may form the basis for further investigations. To validate the results, upcoming field
surveys will be realized on site.
Keywords: LiDAR, Levante, Spatial Analysis, least-cost path, Chekka
1 Introduction
Due to the civil war in Lebanon (1975–1990) archaeological activities came to a halt and only in the last decade was
intensive scientific research in this country resumed. While some regions are better known, others are still waiting
to be investigated. Belonging to the latter is the region between Ras Chekka and Enfeh and its hinterland up to the
Mount Lebanon massif (ca. 300 km²). Although it is known from ancient texts that people living in this area during the
Bronze and Iron Ages were in contact with the important civilizations of their time (Kopetzky, 2010; Cohen-Weinberger
& Goren, 2004), hardly any physical evidence of these periods was detected in this part of Lebanon. Recent publications
concentrate on the Tells of Fadous-Kfarabida and Batroun south of the study area (Badreshany & Genz, 2009; Genz,
2016; Höflmayer et al., 2014; Genz et al., 2018; Genz, 2010), on Enfeh north of it (Panayot-Haroun, 2015; Semaan &
Salama, 2019) and on Roman temples in the hinterland (Fares, 2010).
*Corresponding author: Jakob Rom, Catholic University of Eichstaett-Ingolstadt, Ostenstrasse 14, Eichstaett, 85072, Germany, E-mail: JRom@ku.de
Florian Haas, Manuel Stark, Fabian Dremel, Michael Becht, Catholic University of Eichstaett-Ingolstadt, Eichstätt, Germany
Karin Kopetzky, Christoph Schwall, Austrian Academy of Sciences, Vienna, Austria
Michael Wimmer, Norbert Pfeifer, Vienna University of Technology, Vienna, Austria
Mahmoud Mardini, The Cyprus Institute, Nicosia, Cyprus
Hermann Genz, American University of Beirut, Beirut, Lebanon

Between Land and Sea: An Airborne LiDAR Field Survey to Detect Ancient Sites in the Chekka Region ... 249
The project “Between Land and Sea: The Chekka region in Lebanon”, funded by the Austrian Science Fund, intends
to investigate the ecological and economical basis of an ancient cultural landscape, which for millennia connected
the timber growing areas in the mountains to the shores of the Mediterranean. The Bay of Chekka is a natural harbor
and thus an ideal mooring place for ships. Here, two ancient settlements, Tell el-Heri and especially Tell Mirhan, are
evidence for this Mediterranean trade (Kopetzky et al., 2019). However, aside from some Roman and medieval remains,
no earlier archaeological features are known so far.
This area is a typical Mediterranean landscape with a complex topography covered with macchia and olive groves,
which makes a traditional survey on foot nearly impossible. In addition to the invisibility of the ground, modern
constructions of buildings and infrastructure have changed this ancient landscape severely at such a rapid speed that
conventional methods cannot keep up with and it is feared that archaeological sites will be lost. Thus, it was decided to
support the regional field survey with LiDAR technology to detect endangered ancient structures more quickly.
The use of airborne LiDAR (Light Detection and Ranging) in archaeology has become more and more important in
the last few decades. Especially in highly vegetated areas, this method can provide detailed surface information with
an option to see “through” dense vegetation. Consequently, it has been used for archaeological prospection in forested
regions around the world like in Belize (Chase et al., 2011), in Cambodia (Evans et al., 2013), in Slovenia (Štular et al.,
2012), in the United States (Davis et al., 2019) and in the Kingdom of Tonga (Freeland et al., 2016).
Spatial analyses that go beyond a visual inspection and interpretation of a LiDAR derived model are rarely used in
archaeological prospection, some of those focus on (automated) feature detection (e.g. Freeland et al., 2016; Masini et
al., 2018; Davis et al., 2019; Verhagen & Drăguţ, 2012; Cerrillo-Cuenca, 2017). Others concentrate on visibility analyses
and calculation of viewsheds in order to recognize spatial patterns (e.g. Vinci & Bernardini, 2017; Doneus & Kühteiber,
2013). One of the most common spatial analyses of digital elevation models in archaeology is the reconstruction of road
or path networks with least-cost paths (LCP) (e.g. Diwan & Doumit, 2017; Verhagen & Jeneson, 2012; Seifried & Gardner,
2019; Palmisano, 2017; Howey, 2007) in order to reconstruct the network between known settlements and between
settlements and their hinterland. The calculation of LCPs is based on the assumption that people optimize frequently
used paths over a long period of time. Because of the variety of new GIS tools and fast developing computing capacities,
LCP analyses have been frequently used since the beginning of this millennium (Herzog, 2013). A broad overview of this
method including multiple variations and possible problems and errors is described in Herzog (2014).
The presented work deals with the first scientific airborne LiDAR data acquisition in the Lebanon as well as the
processing of the data and their evaluation regarding possible archaeological sites in the Chekka region. The main
objective of this study was the detection of favoured areas for possible ancient settlements using an inverted Mound
(iMound) algorithm and their classification with a deductive predictive model (Verhagen & Whitley, 2012; McCoy &
Ladefoged, 2009; Danese et al., 2014) based on several spatial derivations of the LiDAR model. As a result, locations
with a high probability of archaeologically interesting findings were identified, which can be taken into consideration
in the planning of future regional surveys. As a second objective these very local areas were put in a larger context
performing LCP analyses in order to get an idea of possible trade and transport routes of timber (especially cedar) from
their source areas (mountains) to the coast, as they were the main commodity in ancient times.
2 Study Area
The study area is part of the Lebanese Levantine coast at the eastern part of the Mediterranean Sea, and is located
within the triangle of Byblos in the south, Tripoli in the north and Baalbek in the east (Fig. 1). It forms the hinterland
of the modern city of Chekka, a predominantly industrial town with a large cement factory and many quarrying areas
close by. The region extends from the coast around Chekka to the highest peaks of the Lebanon Mountains (more than
3000 m) over an altitudinal range of nearly 1800 m, including different landscape units. Beginning from the West, the
narrow coastal plain merges into badlands, which have been deeply incised into a large alluvial plain. This plain was
formed by the directly linked river Abu Ali, which drains the upper part of the catchment. Two other major rivers (Nahr
el Jaouz and Nahr el Ouadi) with their tributaries drain the southern parts of this region and have formed a complex
hilly landscape with steep slopes and deeply incised valleys.
The region around Chekka is dominated by Mesozoic limestone and tertiary sediment deposits. While the
sedimentary deposits with their fine to coarse grain sizes are dominated by fluvial and gravitational (landslides)

250 Jakob Rom et al.
geomorphic processes (badlands), most of the limestone parts show strongly pronounced karst forms. These limestone
layers include embedded phosphate nodules and chert bands, which are known as the Chekka Formation (Walley,
1997). This material is now used on a large scale for cement production (Kopetzky et al., 2019). Beside the quarrying
activity, the whole area has been massively transformed and changed by anthropogenic impacts, as e.g. terracing for
olive groves, road constructions and buildings.
Today’s climate is characterized by cool, wet winters and hot, dry summers. Mean annual rainfall usually ranges
between 700 and 1000 mm with a high variance in time and space and with the highest values in the mountain region.
Since the beginning of the Holocene, drier and cooler as well as wetter and warmer phases have alternated in the
eastern Mediterranean (Psomiadis et al., 2018). These changing conditions have had a strong impact on the vegetation
(e.g. the tree line) as well as on the geomorphic dynamics and thus on human activity (Broodbank, 2013) within the
Levant.
Without anthropogenic disturbances, the natural vegetation would be composed of macchia with deciduous
species (e.g. oaks) at lower altitudes evergreen species with increasing altitudes. The Lebanon cedar (cedrus libani)
would occur under the current climatic conditions from elevations between 800 and 2100 m (Messinger et al., 2015).
Due to overexploitation in the past, it can only be found in twelve reserves in Lebanon today (Hajar et al., 2010). It can
be assumed, however, that the lower “cedar line” was subject to fluctuations due to the changing climatic conditions
during the Holocene.
3 Materials and Methods
3.1 Data Acquisition
The ALS data acquisition was extremely challenging, as there was no previous experience in this region for such a kind
of investigation. The main problem were the restrictions that apply to the Lebanese airspace. Thus, airborne data could
only be collected by the Lebanese military using UH-1D helicopters (Fig. 2).
A Riegl VP-1 Helicopter Pod system was used, consisting of a VUXSYS LR laser scanner, including an inertial
mounting unit (IMU), a global navigation satellite system antenna (GNSS; the system used is able to acquire GPS and
Figure 1: Study area in northern Lebanon. Three geomorphological landscape units are marked in the Chekka region (coastal plain, badland
area, alluvial plain of the Abu Ali River). The locations of the dGNSS reference stations are marked as well. The elevation model is based on
ALOS Global Digital Surface Model ©JAXA.

Between Land and Sea: An Airborne LiDAR Field Survey to Detect Ancient Sites in the Chekka Region ... 251
GLONAS satellite information) and two Sony Alpha 6000 cameras (riegl.com, see Tab. 1). The system was connected via
cable to a toughbook inside the helicopter, so that the scanner could be powered (by on-board electronics), operated
and the data could be streamed to the PC, where they were stored in real time. Since there was no applicable mount
for the VP-1 POD available for this type of helicopter, a prototype for such a mount had to be developed in Eichstätt,
which could then be adapted to an exhibition helicopter of the German Museum Munich. After transporting the mount
to Lebanon, it had to be approved by a security commission of the Lebanese Air Force in Beirut. After successful
approval, a flight plan was drawn up in cooperation with the military leadership, which divided the area into single
flight sections. In the end, the area could be surveyed in a total of 14 flying hours on three consecutive days (divided
into 7 flights of 2 hours each) along parallel flight strips (strip orientation north-south, 400 m distance between strips)
and along profile flights through the main valleys. An eighth flight was scheduled but could not be carried out due to
a defect of the helicopter.
Besides the airborne part of the data acquisition, the processing of the trajectory (see 3.2.1) also required the recording
of differential GNSS raw data (dGNSS) with a temporal resolution of 1 Hz by a ground team within the study area. These
data were used in a postprocessing procedure to correct the raw information of the GNSS antenna on the helicopter in
order to eliminate errors, which are caused by e.g. tropospheric/ionospheric refraction. Due to the large differences in
elevation within the study area and as specified by the flight plan, a total of 3 locations (see Fig. 1) were chosen as dGNSS
reference stations. Two systems were deployed redundantly on each location (Stonex S9III and Stonex S9i).
The large helicopter used turned out to be very suitable for data acquisition and the pilots benefited from a radar
system able to determine the flight altitude above ground, unlike commercial helicopters. This enabled the constant
checking of the pre-calculated ideal flight parameters. Nevertheless, due to the extreme differences in altitude (valleys
and hills) in certain areas, it was not always possible to adjust the flight height quickly to the topography, which resulted
in varying point resolutions of the point cloud. An attempt to compensate for this was made by sufficient overlaps
between the flight strips and by additional flight routes through the valleys. Despite the overall successful flight mission,
problems with the GPS satellite availability occurred during single flights. Dips in the GPS signal occurred and were
Figure 2: Self-made construction to mount the Riegl VP-1 POD on a Bell UH-1D helicopter of the Lebanese Army.
Table 1: Scan parameters and their settings used during the ALS data acquisition.
Scan parameter Settings
Platform Bell UH-D
Sensor Riegl VUX LR
Flight height - m above ground
Flight speed  kn ( km/h)
Pulse repetition rate  kHz
Swath width  m
Points per m² 

252 Jakob Rom et al.
concentrated in a relatively limited area, probably the result of the deliberate manipulation of the GPS signal on the
ground by military units. This problem ultimately affected individual narrow sections of three flight strips. In certain
cases, the resulting errors could be eliminated by the postprocessing of the trajectory and a final strip adjustment, but
one flight strip had to be partly removed from the data set.
3.2 Data Processing
3.2.1 Raw Data Processing
The processing of the raw data followed a three-step workflow, including the calculation of a precise trajectory (flight
line), the combination of the trajectory with the raw scan data and a final strip adjustment.
The trajectory was derived by the software package PosPac MMS (Applanix), using the information acquired during
the flight by the GNSS antenna and the IMU, both mounted on the VP-1. This unprocessed trajectory information
from the scanner system was combined afterwards with the data of the dGNSS ground stations, which were operated
during the flights within the single flight areas (see Fig. 1), resulting in a processed trajectory. Then this highly accurate
reconstruction of the flight path was transferred to the software package Riegl Riprocess.
Within Riprocess, the raw scan data were linked to the processed trajectory and point clouds were derived for every
single flight strip of the mission. These strips as well as the processed trajectory information were then exported and
a final strip adjustment was performed by using the approach of Glira et al. (2015) as implemented in the point cloud
processing software OPALS (Pfeifer et al., 2014). In order to serve as reference data for the adjustment, the most reliable
strips were selected based on quality criteria and adjusted in advance. Consequently, the rest of the strips could be
adjusted using more flexible spline trajectory correlation models (Glira et al., 2016). These were necessary due to the
significantly varying trajectory quality also within single strips. As a final result about 2.7x109 points within 180 flight
strips were exported as pointclouds (LAS files) and served as data base for all following processing steps.
3.2.2 Filtering and Classification
After the postprocessing of the raw data, the resulting pointclouds had to be filtered regarding positive and negative
outliers and classified, especially concerning ground points. For the following steps the GIS-software SAGA (System for
Automated Geoscientific Analyses) (Conrad et al., 2015) with the extension LIS Pro 3D of Laserdata (www.laserdata.
at) was used (Petrini-Monteferri et al., 2009) in combination with the programming language Python and the statistics
software R.
The workflow for the processing of the ALS data in order to generate a Digital Terrain Model (DTM) was created
based on the morphological classification in Hilger (2017) and is shown in Figure 3. At first, the raw pointcloud was
divided into 50 x 50 m tiles. This small size of 2500 m² per part was chosen in order to reduce the computation time
for the following steps. The tiling of the pointcloud was followed by a removal of the outliers. All points with errors
in z-direction (low and air points) were filtered using the SAGA LIS tool “Remove Isolated Points (PC)”. The next and
most important step was the ground classification itself, which is based on the work of Hilger (2017). Within each cell
of the tiled pointcloud (50 x 50 m), the lowest point was declared as a ground point (seed point). These seed points
were meshed in a triangulated irregular network (TIN), which worked as a preliminary ground surface. Next, the cell
size of the tiles was reduced step by step until it reached a size of 10 x 10 m. With every step, new lowest points were
detected and integrated into the TIN. After that procedure, a previously unclassified point was only declared as ground,
if the angle between the TIN plane and the line connecting the point and the closest valid ground point is less than
40° and the point is located within a distance of 1.5 m towards the TIN mesh. After new points were verified as ground,
the TIN mesh was updated and the threshold parameters were checked again for all unclassified points until finally no
further points could be assigned to the class “ground” (Ma et al., 2018). After the ground classification, the classified
pointcloud was carefully checked and corrected manually. The last step included the creation of a DTM using only
ground points. To exclude even low vegetation from the model, the z-value of only the lowest point per cell was used to
determine the cell value. The created DTM with a cell size of 1 m² is the base for all following evaluation steps.

Between Land and Sea: An Airborne LiDAR Field Survey to Detect Ancient Sites in the Chekka Region ... 253
3.3 Data Evaluation by Spatial Analysis
The derived DTM was analysed using spatial calculations in order to detect possible ancient settlements and other traces
of anthropogenic structures. These analyses were used in a final step to detect favoured areas for possible archaeological
sites. The basic workflow of the spatial analyses is shown in figure 4. First of all, areas had to be extracted from the input
DTM that could be considered as potential sites for settlement structures (as described below, the term iMound is used
for these areas). Then those areas were classified regarding their potential as a possible archaeological site. In the end
LCP analyses intend to reconstruct possible trade and transport routes from the Lebanese Mountains towards the coast
in order to link the coastal sites with their hinterland.
3.3.1 iMound Detection
Regarding the settlements, the main focus of the presented study was on tells and settlements on hills, as a pre-
selection had to be made due to the size of the study area. Many of the known inland sites in the region are either
situated at elevated locations (temples of Hardine and Ain Akrine) or on partly artificial tells (Tell el-Heri and Tell
Arqa). Consequently, the first task was to extract hill-shaped landforms from the DTM, which were analysed afterwards
whether they would have been a suitable place to settle for ancient people or not.
To accomplish this an inverted Mound (iMound) algorithm was used, which was originally developed by Freeland
et al. (2016) in order to detect burial mounds in the Kingdom of Tonga. Figure 5 illustrates the detection algorithm of the
Figure 3: Workflow of the filtering and classification of the raw pointcloud data.
Figure 4: Workflow of the data evaluation by spatial analysis.

254 Jakob Rom et al.
mounds step by step. After a low-pass filter was applied to the DTM in order to remove noise from the model, the DTM
was flipped: Hills became pits and vice versa. Now the sinks were filled using the algorithm of Wang and Liu (2006),
known from hydrologic modelling toolboxes. The next step was to subtract this filled model from the original one so
that only the filled sinks (which are in fact inverted mounds) remain. By using thresholds for minimum mound height
(1 m) and maximum plain area (8 ha) the valid iMounds were extracted (Fig. 5) and the DTM was flipped back. All the
following investigations were then conducted on these hill-shaped landforms.
3.3.2 iMound Classification
The detected iMounds are primarily landforms and not archaeological features. In order to establish whether those areas
may have been suitable locations for ancient settlers or not, they were subjected to a classification with a deductive
predictive model. Several parameters will be introduced in the following paragraphs based on which a score value was
calculated (see section 3.3.2.5) estimating the probability of an ancient settlement at this location.
3.3.2.1 Visual Inspection
The beginning of the analyses was a visual examination that resulted in the designation of a first visual parameter (a)
and a second parameter (r). The iMounds were manually checked in a multi-stage procedure and evaluated with respect
to their location and possible recognizable structures. The first inspection included a visual check of all iMounds using
satellite images. For incorrect or completely unsuitable hills, both visual parameters for score calculation (a and r) were
set to 0. These include, for example, modern anthropogenic hills in quarrying areas or areas on the narrow ridges of
Figure 5: Step by step calculation of the iMounds on an example near Enfeh. A) Input DTM. B) Application of a low-pass filter. C) Inverting the
model. D) Filling sinks after Wang and Liu (2006). E) Subtraction of the filled model from the input model and application of a 1 m threshold
for minimum mound height (encircled in black). F) Digitized iMounds mapped on the input DTM visualized by hillshading.

Between Land and Sea: An Airborne LiDAR Field Survey to Detect Ancient Sites in the Chekka Region ... 255
geological structures. For all remaining iMounds the parameter a was set to 1. The second check was carried out only
for these valid areas (a = 1).
Each iMound was scanned for possible ancient (wall) structures based on different visualization methods of
the DTM. These included Analytical Hillshades (simple relief shading) by one light source (cf. Kokalj et al., 2013),
primarily using the SAGA tool “Combined Shading”, which creates the impression of diffuse light scattering in
combination with the slope. The height of the light source above the horizon was set between 15° and 35° and the
result was displayed in a grey scale with a linear histogram stretch (Fig. 6A). The slope gradient in degree was also
displayed using a linear color scheme (yellow to red). Due to the very high values, both anthropogenic walls (including
agricultural terraces, building remains) as well as natural steep structures could be identified (Fig. 6B). In addition,
Local Relief Models (Fig. 6E) were calculated using the improved methodology from Hesse (2010) and by varying the
used filter radii between 6 and 15 m to be able to detect small-scale features on the flat to medium relief iMound areas.
The Sky View Factor (cf. Kokalj et al., 2011; Zakšek et al., 2011) was also applied and was mostly calculated using 16
sectors and radii between 6 and 20 m (Fig. 6F). Finally, positive and negative openness (cf. Doneus, 2013) were also
derived in order to visualize the individual iMounds (Fig. 6C and 6D). Both the number of sectors and the used radii
were in accordance with the Sky View Factor.
Only in the case of recognizable structures using the described visualization methods or a particularly suitable
location of the iMound, the parameter r was set to 1; in the case of no recognizable structures, the corresponding
iMound remains at r = 0. In the end, three types of iMounds can be distinguished based on the visual inspection:
Incorrect or unsuitable iMounds (a = 0, r = 0), suitable areas without any indications of archaeological structures (a =
1, r = 0) and suitable areas with features indicating past settlements or a very favourable spatial location (a = 1, r = 1).
Figure 6: Applied visualization methods on the example of Tell el-Heri. A) Hillshade (Combined Shading). B) Slope map. C) Positive
Openness. D) Negative Openness. E) Local Relief Model. F) Sky View Factor.

256 Jakob Rom et al.
3.3.2.2 Availability of Fresh Water
The availability of fresh water was crucial for ancient settlements, which is why this factor was probably one of the
most important in the choice of a settlement location. This assumption was included in the calculation of the score, as
it is a vital component in other predictive models as well (cf. Brandt et al. 1992). The current river system was derived
indirectly from the DTM using a flow accumulation approach based on the relief parameters, whereby a grid cell must
have a potential hydrological catchment area of at least 10,000 m² in order to be classified as a river of sufficient size
(cf. Wilson & Gallant, 2000; Quinn et al., 1991). The derived river network served as a basis for further calculations.
Two restrictions must be taken into account: (i) the river network has changed more or less strongly since ancient
times, although at least in the deeply incised valleys of the larger rivers, especially at higher altitudes in the study
area, no drastic changes in flow path are expected. And (ii) surely not all derived flow paths indicate perennial rivers.
Hydrological data from the American University of Beirut show that there are only three perennial river sections (Abu
Ali, Nahr el Ouadi and Nahr el Jaouz) throughout the year under the present day climatic and hydrological conditions,
which surely have changed during the Holocene. However, the basis and accuracy of this dataset remains unclear and
it was decided to use the before mentioned minimum hydrological catchment size (10,000 m²) for a probable perennial
river.
To get an idea of which iMounds are in a good position regarding the availability of fresh water, LCPs of each
iMound (starting from the centre of the polygon) to the nearest calculated river or known spring were computed. A
map of presently active springs was provided by Professor Joanna Doummar from the Department of Geology at the
American University of Beirut. If the LCPs are calculated based on the DTM with a cell size of 1 m², the computational
steps will require a high computing power with long processing times. Another problem with small cell sizes is that the
LiDAR data set includes modern roads, which can be easily recognized with a 1 m grid. Since the modern roads usually
have more favourable inclination angles than their immediate surroundings, the LCP algorithms prefer to follow the
modern roads automatically. As a consequence, the DTM was converted to a cell size of 10 x 10 m in order to prevent
this bias, by using a mean value upscaling method. This 10 m grid does no longer contain any relicts of modern roads
or other small-scale features and the LCPs could be calculated purely based on the relief parameters.
The parameter D describes the accumulated costs of each iMound to reach the next river or spring using the 6th
degree polynomial function by Herzog (2010) (see chapter 3.3.3.1) for calculating the cost grid. Olaya (2004) proposed to
exclude high slope values from the computation of LCPs because very steep slopes can be dangerous for hikers. As the
presented calculations offer the possibility to cross hillsides, this threshold was set to a high value (45°).
3.3.2.3 Area and Circularity
Unrealistic large iMounds probably covering more than one hilltop were avoided by setting the maximum iMound size
to 8 ha, the size of the largest Bronze and Iron Age sites known in this region. Nevertheless, the detection of a mound
with a large area is a better indication for a valid ancient settlement than a small area, because settlements had to find
enough space on the hilltops. However, the shape of an iMound does not necessarily represent the extent of a possible
tell. The latter may well extend beyond the boundaries of the digitized mound. In addition, contrary to the previous
parameter, no ranking order can be established. This means for example that an 8 ha iMound is not necessarily more
suitable than a 5 ha one. Only the fact that too small areas are more likely to indicate natural structures than tells can
be considered in the score calculation. To achieve this, the area parameter (A) for iMounds larger than 0.3 ha was set to
1. For smaller iMounds, the value 0.5 was assumed for this parameter.
Tells are often nearly circular, in particular manmade tells (cf. Menze et al., 2006). This applies also to the outlines
of several known tells close to the study area, for example Tell el-Heri, Tell Arqa and Tell Ardeh. For these reasons, high
circularity of an iMound is considered a significant indicator of a likely settlement location. The circularity (parameter
C) was calculated after Skau (1951):
=(4
)

where A is the area and p the perimeter of the iMound polyg
ones:
()=
Parameter s represents the slope of a grid cell in percent. A similar calculation of the cost grid was
suggested for example by Bell and Lock (2000).
1) LCP based on Tobler (1993)
The Tobler hiking f unction is the most common method for calculating LCPs in archaeology (Herzog,
2010). The formula was developed on an empirical basis and was e.g. published in Tobler (1993). The
formula of the speed of a walker as a function of the slope (V(s)) is:
()=6.|.|
where s represents the mathematical slope. To calculate a cost grid for LCP applications, the reciprocal
of this formula must be used. It describes how long a hiker needs for a certain distance depending on
the slope and is expressed in time per distance (e.g. seconds per meter).
2) LCP based on Llobera and Sluckin (2007)
The third method of constructing the cost grid is based on the considerations of Llobera and Sluckin
(2007), specified by Herzog (2013):
()=1+(

š)²
og (2010):
()=1337.8+278.19517.3978.199+93.419+19.825+ 1.64
Again, s represents the mathematical slope. The formula is a mathematical adaptat
where A is the area and p the perimeter of the iMound polygon. This formula calculates values close to 1 for more or less
round features whereas irregularly shaped areas receive values close to 0.

Between Land and Sea: An Airborne LiDAR Field Survey to Detect Ancient Sites in the Chekka Region ... 257
3.3.2.4 Visibility
Visibility probably played a major role in the choice of a location for ancient settlements. Therefore, viewshed analyses
are part of many predictive models (cf. Danese et al., 2014). If one could overlook large parts of the surrounding
landscape when standing on the hill, it was possible to recognize approaching threats at an early stage. It was also
possible to control and monitor trade routes when visibility was wide. The radius of the viewsheds computed for the
iMounds was set to 2100 m because the field experiments of Fábrega-Álvarez and Parcero-Oubiña (2019) indicate that it
is not possible to detect motion of a man-sized object in a vegetated environment at a larger distance.
To be able to perform visibility calculations in an acceptable computing time, the DTM with a cell size of 10 x 10 m
was used and the viewshed for each iMound was calculated only from the highest topographic point plus an estimated
eye height of 1.60 m. The resulting binary grid was buffered using the established radius of 2100 m detection range (Fig.
7) and the visible area within this buffer radius was summed up for each iMound, resulting in parameter V.
3.3.2.5 Score Calculation
All parameters of the spatial analysis were combined in a predictive model to calculate a score for each iMound. This
score indicates the probability of archaeological features to be expected in the areas according to the variables computed
here. The value of the score (S) can range from 1 (very likely) to 0 (very unlikely) and is composed of the parameters
described above, whose values can also vary from 1 to 0. The parameters D and V were normalized and adjusted so
that the value 1 was assigned to the best and the value 0 to the worst fitting iMound. The normalized parameters are
indicated by the subscript “no” in the formula (Dno, Vno). Exceptions are, as described above, the area (A) and also the
circularity (C), the latter directly resulting from the formula in chapter 3.3.2.3.
Figure 7: Calculation of the visibility (V) using the example of iMound ID 66. Visible areas are marked in white, invisible areas in black.

258 Jakob Rom et al.
S=[(Dno+A+C+Vno+r)*a]/5
If indications of structures were registered during the visual inspection (r = 1), this can indicate possible archaeological
sites. The parameter a in this formula ensures that a score value was only assigned to valid iMounds.
3.3.3 LCP Analysis
The iMound classification in order to detect potential settlement areas on hills was followed by simple LCP analyses
to reconstruct former trade routes and paths from mountainous regions to the coast. Three main destinations on the
Mediterranean coast were selected. These include the well-known tells of Mirhan and el-Heri as well as a spot further
south representing the ancient sites of modern Batroun (cf. Markoe, 2000). Batroun itself is located outside the area,
which was surveyed by LiDAR and is therefore not included in the DTM, but the distance from the selected destination
to the modern city center is only 4 km and the path is quite flat as it follows the plain of Nahr el Jaouz. As starting points
for the LCP calculations, the midpoints of only those iMounds were used, whose score value exceeds 0.5. This ensures
that only areas with a high probability for ancient settlements were included in this calculation. The computation of the
LCPs was again based on an upscaled DTM with a cell size of 10 x 10 m (c.f. 3.3.2.2).
Four different slope-dependent cost functions were applied for calculating the LCPs, these cost functions are
described within the next chapter. As all of these depend on the slope of the DTM only, the resulting path networks
likely concentrate on the incised and low-slope flood plains of the main river sections. Even though these plain areas
seem well suited for paths, narrow passages and river meander prevent easy progress and require frequent crossing
of the rivers. As this leads to considerable problems, especially in the case of large rivers, it was decided to adjust the
calculation of LCPs there. For this purpose, the main river sections, according to the data of the American University of
Beirut (Abu Ali, Nahr el Ouadi, Nahr el Jaouz), were buffered (Buffer radius: 25 m) and provided with very high costs, so
that crossing the river was made considerably more difficult, but not impossible.
3.3.3.1 LCP Methods
Four different methods were used to calculate the main cost grids. In order to get near to an anisotropic (direction
dependent) calculation of the cost surface, the aspect grid was used to set the direction of maximum cost in the SAGA
tool “Accumulated Cost” (Olaya, 2004). Once the costs for each of the destinations were accumulated, the final LCPs
could be created.
1. LCP based only on the slope
The first method is the simplest. The slope gradient is directly included in the calculation of the cost grid. In
principle, flat routes are preferred to steeper ones:
Cost(s)=s
Parameter s represents the slope of a grid cell in percent. A similar calculation of the cost grid was suggested for
example by Bell and Lock (2000).
2. LCP based on Tobler (1993)
The Tobler hiking function is the most common method for calculating LCPs in archaeology (Herzog, 2010). The
formula was developed on an empirical basis and was e.g. published in Tobler (1993). The formula of the speed of
a walker as a function of the slope (V(s)) is:
V(s)=6e-3.5|s+0.05|
where s represents the mathematical slope. To calculate a cost grid for LCP applications, the reciprocal of this
formula must be used. It describes how long a hiker needs for a certain distance depending on the slope and is
expressed in time per distance (e.g. seconds per meter).

Between Land and Sea: An Airborne LiDAR Field Survey to Detect Ancient Sites in the Chekka Region ... 259
3. LCP based on Llobera and Sluckin (2007)
The third method of constructing the cost grid is based on the considerations of Llobera and Sluckin (2007),
specified by Herzog (2013):
=(4
)

where A is the area and p the perimeter of the iMound polyg
ones:
()=
Parameter s represents the slope of a grid cell in percent. A similar calculation of the cost grid was
suggested for example by Bell and Lock (2000).
1) LCP based on Tobler (1993)
The Tobler hiking f unction is the most common method for calculating LCPs in archaeology (Herzog,
2010). The formula was developed on an empirical basis and was e.g. published in Tobler (1993). The
formula of the speed of a walker as a function of the slope (V(s)) is:
()=6.|.|
where s represents the mathematical slope. To calculate a cost grid for LCP applications, the reciprocal
of this formula must be used. It describes how long a hiker needs for a certain distance depending on
the slope and is expressed in time per distance (e.g. seconds per meter).
2) LCP based on Llobera and Sluckin (2007)
The third method of constructing the cost grid is based on the considerations of Llobera and Sluckin
(2007), specified by Herzog (2013):
()=1+(

š)²
og (2010):
()=1337.8+278.19517.3978.199+93.419+19.825+ 1.64
Again, s represents the mathematical slope. The formula is a mathematical adaptat
where s represents the slope gradient and š a critical inclination value. This formula was developed primarily with
regard to wheeled vehicles, which were most likely used in ancient times in the region around Chekka. For the use
of carts š must not be set too high. Herzog (2013) recommends values between 8 and 16%, in this work a threshold
value of 10% was used.
4. LCP based on Herzog (2010)
The last method is a 6th degree polynomial function developed by Herzog (2010):
=(4
)

where A is the area and p the perimeter of the iMound polyg
ones:
()=
Parameter s represents the slope of a grid cell in percent. A similar calculation of the cost grid was
suggested for example by Bell and Lock (2000).
1) LCP based on Tobler (1993)
The Tobler hiking f unction is the most common method for calculating LCPs in archaeology (Herzog,
2010). The formula was developed on an empirical basis and was e.g. published in Tobler (1993). The
formula of the speed of a walker as a function of the slope (V(s)) is:
()=6.|.|
where s represents the mathematical slope. To calculate a cost grid for LCP applications, the reciprocal
of this formula must be used. It describes how long a hiker needs for a certain distance depending on
the slope and is expressed in time per distance (e.g. seconds per meter).
2) LCP based on Llobera and Sluckin (2007)
The third method of constructing the cost grid is based on the considerations of Llobera and Sluckin
(2007), specified by Herzog (2013):
()=1+(

š)²
og (2010):
()=1337.8+278.19517.3978.199+93.419+19.825+ 1.64
Again, s represents the mathematical slope. The formula is a mathematical adaptat
Again, s represents the mathematical slope. The formula is a mathematical adaptation to physiological data from
Minetti et al. (2002), which were obtained at slope gradient values between -0.45 and +0.45, and is thus based on
the energy that a pedestrian must expend.
3.3.3.2 Validation of the LCPs
The best LCP method was selected by comparing the LCPs with the recent main traffic routes derived from OpenStreetMap
(OSM) data (www.openstreetmap.org). Trails, residential roads, and similar pathways were removed from the data set.
It is quite likely that the locations of the main traffic roads today are more or less comparable to the main routes in
ancient times. To be able to make a statement about the quality of the LCP methods, a buffer of radius 50 m was created
around the LCPs. If OSM roads were detected within these buffer zones, the corresponding path sections of the LCPs
were marked. The ratio of total length and corresponding path sections can provide information about the quality of the
different LCP methods (Tab. 2). A hit rate of 45% for the Llobera and Sluckin method means that almost half of the road
sections within a radius of 50 m actually contain modern main roads as recorded in the OSM data set.
Based on this validation, the best fitting method to calculate LCPs in the considered area around Chekka turned
out to be the method after Llobera and Sluckin (2007). Thus, it was used for the reconstruction of the ancient paths. In
order to provide a comparable alternative to the Llobera and Sluckin paths, the method based on Herzog (2010) was
additionally used for the reconstruction.
4 Results and Discussion
4.1 DTM
The calculated DTM with a cell size of 1 m² effectively covers an area of about 290 km² and extends from the
Mediterranean coast to altitudes of 1780 m in the southeast of the study area (Fig. 8). The arithmetic mean of the density
of all recorded points is 9.977 pts/m², but in central areas and regions well covered by the flight strips, significantly
higher density values were achieved. Due to the GPS problems during the recording of the data (see chapter 3.1), only
minor inaccuracies in the strip adjustment of the affected flight strip occurred.
The classification of the pointclouds led to a reliable detection of the ground points in large parts of the investigation
area. For an automated approach of filtering and classification, the overall result is satisfying. Even in densely forested
areas, it was possible to detect the ground beneath the leaf canopy using the LiDAR method. Only a few of the 1 m²
cells did not contain any ground points resulting in small gaps in the DTM. These originate from buildings or appear

260 Jakob Rom et al.
Figure 8: LiDAR generated DTM of the overflown area in a 1 m raster with the calculated river network. Rivers with year-round discharge
(according to the data provided by the American University of Beirut) are marked in bold. In areas not covered by the LiDAR DTM, a hillshade
of the ALOS Global Digital Surface Model ©JAXA is shown in grayscale.
Table 2: Validation of the LCP methods using OSM reference data. The total length of all reconstructed routes is shown in the second
column. The third column shows the sum of the reconstructed routes that are comparable to OSM data. The ratio of both is shown in the last
column.
Method Total Length [m] Valid Length [m] Valid [%]
Slope ,, , .
Tobler () ,, , .
Llobera and Sluckin () ,, , .
Herzog () ,, , .

Between Land and Sea: An Airborne LiDAR Field Survey to Detect Ancient Sites in the Chekka Region ... 261
within very dense vegetation. The resulting gaps were thus filled by using a spline-interpolation. Figure 9 shows the
differences between the unfiltered Digital Surface Model, the DTM (only ground points), and the final gap-filled DTM.
In many publications concerning archaeological prospection LiDAR derived DTMs are used because of the numerous
advantages of this method. Archaeological features are often made visible quickly and easily by a simple hillshading.
Examples can be found in Chase et al. (2011) for Central America, where the DTM clearly identifies Mayan structures,
as well as in Evans et al. (2013) for the Khmer temples of Angkor in Cambodia. In the work presented here, however, no
archaeologically relevant features can be made visible without great effort or just using simple visualization methods.
One reason is that the region in the hinterland of Chekka has been strongly and almost completely reworked in modern
times, whereas, for example, Chase et al. (2011) found almost unchanged landscapes in the rainforest of Belize since
Mayan times. The second reason is that a very long time has passed since the Bronze Age, when settlements can be
assumed to have been established due to the intensive cedar wood trade in this area. The remains of settlements have
either been destroyed by modern construction, or have decayed to a point where they only have a minimal influence
on the topography. Because of the elapsed time, the strong modern overprinting and the partly strong relief, a direct
detection of settlement remains from ancient times was not feasible.
4.2 iMound Detection and Classification
590 areas in the study area were classified as iMounds. Each iMound is clearly identified by an individual identification
number (ID). By visual inspection of the respective areas 314 mounds could be assigned to level 1 (a = 0, r = 0), 157 to
level 2 (a = 1, r = 0) and 119 to level 3 (a = 1, r = 1). Many of the mounds are concentrated in the areas east of the coastal
plain. This strongly fragmented landscape shows a diversified geomorphological setting including many natural hill
structures, which were identified by the iMound algorithm.
For iMounds classified to levels 2 and 3, the probability of an ancient settlement at this location was estimated
by a score (see 3.3.2.5). A high score reflects suitable parameter values for an ancient settlement. The locations of
the iMounds as well as their individual score values are presented in figure 10. The calculated score is theoretically
distributed between 0 and 1, the achieved values vary between 0.258 (iMound ID 89) and 0.869 (iMound ID 243). In
addition, known and presently active springs are mapped in figure 10 as well. Table 3 shows the iMounds with the 10
highest scores and their parameters for calculating the score value. It is remarkable that seven of the ten highest scores
are located in the alluvial plain of the Abu Ali River, mainly near the modern towns of Kfar Hazir and Amioun. The
reasons for these high scores in this region are, on the one hand, low slope values and, on the other hand, short and
relatively direct paths to rivers. In addition, the visibility in plains shows high values, because on hills - in an otherwise
flat area - it is possible to overlook large parts of the surroundings. Due to the large number of iMounds with a high
score, probably only a part of them indicate tell locations in this landscape.
As it is very difficult to detect archaeologically interesting structures only by visualizing the DTM, the iMound
detection and classification was supposed to point to areas of possible ancient settlements. However, the calculated
Figure 9: Effects of the ground classification and the interpolation using the example of a small section. A: Digital Surface Model, which
includes buildings and vegetation. B: DTM, only ground points are included. The artificial terraces become clearly visible. C: DTM after gaps
were filled by spline-interpolation.

262 Jakob Rom et al.
areas must not be considered as complete. It is very likely that villages were also formed between the detected hilltops,
which are not covered by this methodology. However, the iMounds as an automated calculation result provide large-
area indications of favourable settlement locations, especially tells or settlements on hilltop structures, without the
need to search the DTM manually with great time expenditure.
4.3 LCP Analysis
For all iMounds with a score > 0.5 (n = 164) LCPs were calculated to one of the three destinations Tell Mirhan, Tell el-Heri
or Batroun. The algorithm automatically selects the best destination for each path. Figure 10 shows the results of these
analyses. Based on the validation (see 3.3.3.2), the paths were calculated using the method according to Llobera &
Sluckin (blue) and Herzog (red). The intensity of the color indicates how many paths overlap at this point. In this way,
possible main routes can be determined. Numerous coinciding LCPs, computed by using both methods, indicate that
these apparently very suitable paths were also heavily used in past times. When looking at the reconstructed paths in
figure 10, two routes are particularly noticeable:
First, the southern route from the high mountains in the southeast to the destination at Batroun near the coast.
Especially in the last third, many paths of both methods merge beside the valley of Nahr el Jaouz, and even cross the
river at the same location. Shortly before arriving at the destination, 32 Llobera & Sluckin paths and 38 Herzog paths
follow the same trail. It should be stated again that high costs were attributed to traverse the river Nahr el Jaouz due to
the river morphology. Nevertheless, the possibility of ancient routes along sections of the floodplain remains.
The second remarkable route with large overlaps of both methods leads from the alluvial plain of the Abu Ali River
through the badlands towards Tell Mirhan. The reason for this large number of overlaps of the LCPs is that many paths
starting in the eastern study area join at Kfar Hazir in order to cross the terrain step from the alluvial plain through the
badland area towards the coastal plain. As a result, 67 Herzog paths and 79 Llobera & Sluckin paths follow a similar
route towards Tell Mirhan. The fact that nearly all LCPs of those iMounds located east of the plain meet at Kfar Hazir
is very remarkable. It implies that today the easiest way from the plain towards the coast without any real alternative
is through Kfar Hazir. In fact, the modern main road is located exactly at this point. It has to be considered that the
landscape, especially the geomorphic very active badlands, has changed a lot during the last millennia, but that even
in ancient times the best way out of the mountains towards Tell Mirhan was probably through this narrow passage. The
village of Kfar Hazir is therefore of great importance when considering the LCP results.
Table 3: The ten iMounds with the highest score including all parameters used for the calculation. A: Area, C: Circularity, D: Availability of
fresh water (accumulated costs), V: Visibility, r=1 if manmade structures are visible in the LiDAR data.
ID A [m²] C D V [m²] r Score
 , . . ,,  .
 , . . ,,  .
 , . . ,,  .
 , . . ,,  .
 , . . ,,  .
 , . . ,,  .
 , . . ,,  .
 , . . ,,  .
 , . . ,,  .
 , . . ,,  .

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With the help of the calculation of digital relief parameters from the DTM, it was also possible to derive least-cost
Voronoi polygons for the three destinations. This means that for each pixel of the elevation model, the destination that
can be reached most easily on the basis of the underlying LCP formula (in this case the formula according to Llobera
and Sluckin, see 3.3.3.1) was calculated. The calculation itself is similar to the derivation of hydrological catchment
areas from a DTM. However, it is not based on the elevation model itself, but on the cost grid from the LCP calculation.
Figure 10 clearly shows that especially the least-cost Voronoi polygon for Tell Mirhan covers large parts of the study
area, though the size of the least-cost Voronoi polygons of Tell Mirhan and Batroun can only be determined when
considering additional destinations and extending the study area. This observation supports the thesis that Tell Mirhan
was of great importance as a harbor town in ancient periods (Kopetzky et al., 2019). In contrast, the Voronoi polygon for
Tell el-Heri is relatively small and only covers areas up to a maximum of 9 km inland. For all areas further east, it seems
to be easier to move to the coast towards Tell Mirhan or Batroun. This could be an indication that el-Heri was not a large
port city but served other purposes.
The LCP methods used are based on the slope-dependent cost functions combined with high costs in the buffers
around the main rivers. A more detailed analysis including other topographic components (vegetation cover, soil
properties, smaller river systems) or even social and cultural components (visibility) could provide further information
for the accurate reconstruction of ancient road systems (Herzog, 2013).
4.4 Examples of Favoured Areas
The iMound classification has led to the identification of many potentially archaeologically interesting areas. Using high-
resolution RGB aerial images (ground resolution ca. 0.05 m), which were taken by the two Sony Alpha 6000 cameras
during the LiDAR flight, these sites have been examined visually. However, due to the intensive modern construction
Figure 10: Results of the iMound detection and classification as well as the LCP analysis. Known springs are mapped as well. Further
explanations in the text.

264 Jakob Rom et al.
(buildings, infrastructure, terracing) of some areas, it must be assumed that some sites are not shown in the aerial
images. Therefore, on-site visits to verify the results are crucial and will be conducted in the course of the project.
Nevertheless, in some places the aerial images complement the results of the methods presented. An example is the
iMound ID 508 about 2 km northwest of the city of Douma. With a score of 0.645, it achieves an above-average value and
is moreover not situated in any plain but in complex topographical relief, which further strengthens the score. Already
during the visual inspection of the DTM at this location, linear structures were detected and a spring has been mapped
in the immediate vicinity (Fig. 10). The visual observation of aerial images of iMound 508 confirms the evidence. In the
elevation model as well as in the aerial image the linear structures of ancient wall remains are clearly visible (Fig. 11).
The obvious building foundations indicate a probable ancient settlement on this site, which had been unknown to the
authors until this point. A survey on site is necessary for further information on this settlement as well as on the nearby
iMound at Douma (ID 500), which reaches a top-ten score. Unfortunately, this area is completely covered with modern
constructions, thus aerial images cannot provide additional information.
Figure 11: Aerial images showing four iMounds with high scores. Top left: Clear settlement structures of iMound 508. Top right: Round wall
structures of iMound 344. Bottom left: Vegetated structures of iMound 365, encircled in red. Bottom right: Wall structures and old terraces
of iMound 256.

Between Land and Sea: An Airborne LiDAR Field Survey to Detect Ancient Sites in the Chekka Region ... 265
Analogous to this procedure, other structures could be identified on aerial images. Among others, wall remains are
clearly visible in iMounds 256, 344 and 554. All of these iMounds achieve very high score values. With iMound ID 365
near the villages of Bahbouch and Bziza, structures can be identified on aerial images (Fig. 11). Whether the traces of an
ancient settlement still slightly influence the terrain surface or whether these formations are of different origin cannot
be determined by remote sensing methods.
Due to the reasons mentioned above not all iMounds could be verified by aerial images. Among these numerous
unconfirmed sites, the area around the town of Kfar Hazir will be discussed in more detail. Here, at the transition from
the Abu Ali flood plain to the heavily fragmented areas of the badlands, the iMounds with the highest score values can
be found. In addition, all LCPs running in this direction roughly coincide in this area. Based on today’s relief, Kfar Hazir
seems to be the optimal place to overcome the terrain step from the high-altitude flood plain towards the coastal plain.
A settlement at this point would have had an outstanding strategic importance in ancient times, as the roads could
be monitored and large parts of the alluvial plain could be overseen. As a consequence, there is much to suggest that
modern Kfar Hazir can look back on a long settlement history. However, the strong modern imprinting of the landscape
has the consequence that hardly any old structures can be seen in aerial images. More reliable information about Kfar
Hazir as a potential archaeological site can only be obtained by validation on site.
5 Conclusion
The DTM created from the acquired LiDAR data of the Chekka region provides a good database for archaeological
investigations. The spatial resolution as well as the solid filtering and classification of the pointcloud allow further
evaluations. In addition to the spatial analyses presented here, areas of potential archaeological sites could be delimited
without time-consuming examination of all the DTM visualizations.
By identifying iMounds and estimating the probability of an ancient settlement at this location, new possible
site locations have been detected automatically. As the examples of iMounds 256, 344, 365 and 508 show, some of
the detected areas can be verified as archaeologically interesting spots by aerial images. For other calculated areas a
verification on site is necessary.
In order to put the locally restricted iMounds into a larger context, connections between numerous favoured areas
and destinations on the Mediterranean coast were established by calculating LCPs. Thus, possible main trade and
transport routes from the Lebanon Mountains to the coast could be reconstructed. It is noticeable that many of these
routes roughly coincide in some places due to the topography. This is most obvious in the village of Kfar Hazir close to
a terrain step. Such probable past traffic junctions are of archaeological importance because strategic settlements were
often located nearby.
The reconstructed paths, as well as the derived river systems lead to the question of the transport of timber as an
important commodity from the higher altitudes to the coast. The calculated DTM shows parts of the large river courses
of the Abu Ali, Nahr el Ouadi and Nahr el Jaouz. All three river channels are characterized by narrow river valleys
with hardly any straight sections. The problem of log jams is very serious in large parts of the river sections, which
would have made transport via the river systems very labour-intensive. The reconstructed route system could have
been used for the timber transport. A presumed main road from Kfar Hazir to the coast leads directly to Tell Mirhan.
These reasons speak for a preferred transport of timber via a land route, which is also in line with the results of Semaan
(2015). However, a combination of fluvial and terrestrial transport is also conceivable, with a higher probability of water
transport in the upper mountain regions where the Lebanese cedar grows.
It has been shown that LiDAR data acquisition and subsequent spatial analysis can help to answer archaeological
questions. In politically unstable countries like Lebanon, however, such surveys are difficult to conduct. A long and
intensive preparation period and negotiations with political, military and scientific authorities must be expected. Short-
term changes in the data collection process and other unexpected problems may occur. Nevertheless, the recording
and evaluation of a high-resolution DTM leads to valuable results and makes a first evaluation of relatively large areas
possible, which could be shown within this study.

266 Jakob Rom et al.
Acknowledgements: We would like to thank Director General Sarkis el-Khoury, Samar Karam and Rita Lichaa from
the Directorate General of Antiquities, whose support made our research possible. We are indebted to the Lebanese
Army and its LAF commander-in-chief General Joseph Aoun and LAF Brigadier-General Ziad Haikal and to Brigadier
General Abi Rashid from the CIMIC. Grateful thanks go to Major Abdul, Major Al-Arab and Captain Hamd. Our sincere
gratitude goes to the technicians at the Beirut and Hamad air bases and to the excellent pilots of the 12th Squadron of
the Lebanese Air Force for the support and execution of the helicopter flights: Captain Hamd, Captain Alam, Captain
Zayd, Captain Hajj, Lieutenant Khalil, Lieutenant Bou Chayya, Lieutenant Hanna, Lieutenant Ghaya, Lieutenant
Hachem and Lieutenant Haykal. It was a pleasure to fly with you! Additionally, we want to thank Volker Wichmann from
Laserdata for the technical support of the SAGA-LIS software and the automation tools. We also would like to thank the
Flugwerft Oberschleißheim/German Museum Munich for the possibility to test the self-constructed helicopter mount
on the UH-1D in their exhibition. We also want to thank the two reviewers for their critical reviews and comments, which
helped a lot to improve the manuscript.
This research is funded by the Austrian Science Fund (project no. P30581-G25) and by University Research Board
grants from the American University of Beirut (Award Number: 103367, Project Number: 23935).
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