# Archaeological LiDAR in Mediterranean Karst Landscapes. A Multiproxy Dating Method for Archaeological Landscape and a Case Study From Prehistoric Kras Plateau (Slovenia)

Edisa Lozić, Benjamin Štular

► To cite this version:

Edisa Lozić, Benjamin Štular. Archaeological LiDAR in Mediterranean Karst Landscapes. A Multi-proxy Dating Method for Archaeological Landscape and a Case Study From Prehistoric Kras Plateau (Slovenia). Archaeological Prospection, 2024, 10.1002/arp.1954. hal-04688471

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RESEARCH ARTICLE

OPEN ACCESS

# Archaeological LiDAR in Mediterranean Karst Landscapes. A Multiproxy Dating Method for Archaeological Landscape and a Case Study From Prehistoric Kras Plateau (Slovenia)

Edisa Lozić ID | Benjamin Štular ID

Institute of Archaeology, Research Centre of the Slovenian Academy of Sciences and Arts, Ljubljana, Slovenia

**Correspondence:** Benjamin Štular (benjamin.stular@zrc-sazu.si)

**Received:** 17 February 2023 | **Revised:** 14 May 2024 | **Accepted:** 4 August 2024

**Funding:** This research was funded by Aris Slovenian Research and Innovation Agency Grants N6-0132, J6-4609 and P6-0064.

**Keywords:** airborne laser scanning (ALS) | archaeological LiDAR | kernel density estimation | landscape chronology | Mediterranean karst landscapes | taskscape

## ABSTRACT

The case study area is a small but typical prehistoric landscape in the Kras Plateau on the north coast of the central Mediterranean. The Late Bronze and Iron Age Kras Plateau was an emblematic Mediterranean archaeological landscape dotted with numerous hillforts. Since the mid-20th century, the landscape had been overgrown with some of the most archaeology-hostile vegetation, severely impeding landscape archaeology until archaeological LiDAR revealed thousands of archaeological features that attest to a carefully constructed and managed agro-pastoral landscape. However, these discoveries were hampered by insecure chronology typical of any LiDAR guided analysis. This case study meticulously documented two prehistoric hillforts and a previously unknown agro-pastoral landscape with hundreds of archaeological features. The focus of the article, however, was on establishing a more precise and objective dating method. We proposed a multiproxy method to date the archaeological landscape. It combines relative dating using remote sensing data and historical maps; dating based on historical context; relative stratigraphic dating; indirect dating based on associated archaeological finds; and dating by association. Particularly, we focused on the association-based dating of archaeological features. We proposed a method based on the concept of taskscapes that relies on kernel density estimation. Using these methods, we objectively demonstrated that agro-pastoral landscape features documented with archaeological LiDAR were contemporaneous with Late Bronze Age and Iron Age hillforts and have no connection to the post-medieval landscape. The latter has important methodological implications for the prehistoric archaeology of Mediterranean karst landscapes, where backdating post-Medieval landscapes is a common practise.

## 1 | Introduction

Airborne LiDAR, where available, has become an indispensable component in landscape archaeology (e.g., Cohen, Klassen, and Evans 2020; Chase, Chase, and Chase 2020; Swieder 2021). Archaeologists use perception and comprehension to interpret enhanced visualizations of high-resolution raster grids interpolated from airborne LiDAR point clouds (e.g., Crutchley 2006;

Challis et al. 2011; Lozić and Štular 2021; Štular, Eichert, and Lozić 2021). The results have proven to be extremely effective in detecting archaeological features, significantly improving our understanding of archaeological sites, monuments and landscapes, particularly in forested areas (recently, e.g., Blatrix et al. 2022; Cuenca and López 2020; Dorison 2022; Fernández-Lozano et al. 2022; Fontana 2022; Lieskovský et al. 2022; Lozić 2021; Snitker et al. 2022).

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>
However, successful applications of airborne LiDAR in ar-
chaeology (hereafter referred to as archaeological LiDAR)
in Mediterranean environments remain relatively under-
represented (although see Bernardini et al. 2020; Doneus,
Doneus, and Cowley 2022; Laharnar, Lozić, and Štular 2019;
Mlekuž 2015, 2018b). This is largely due to the exceptional ar-
chaeological articulation of the Mediterranean area, the diver-
sity of geomorphological settings and the unique vegetation. In
the Mediterranean karst, in particular, the inhospitable vegeta-
tion and largely eroded soils severely limit traditional prospec-
tion methods and thus the dating of the features.

This paper tackles these issues in a case study using archaeo-
logical LiDAR in a challenging Mediterranean karst environ-
ment on the Kras Plateau. The Kras Plateau (Italian: *il Carso*,
German: *der Karst*) is situated on the north coast of the central
Mediterranean in the so called Caput Adriae region, which con-
nects the Balkan and Italian Peninsulas (Figure 1). The majority
of its total area of approximately 500 km² is in Slovenia, with a

small portion in Italy, northeast of Venice. It is the namesake
and a reference site for karst-like landscapes worldwide (Ferk
and Zorn 2015).

In the past decade there were several publications of LiDAR-
derived information from the Kras Plateau (Bernardini
et al. 2020; Laharnar, Lozić, and Štular 2019; Mlekuž 2015,
2018b), however, archaeologists are yet to critically engage with
this potential (sensu Garstki 2022). The primary challenge for
the newly acquired information to be used in high-level ar-
chaeological interpretation is to establish a reliable landscape
chronology.

Thus, the purpose of this article was to address this challenge by
advancing the methodology for determining the chronology of
the archaeological features within the case study area. We have
developed a multiproxy approach that integrates relative dating
via remote sensing and historical maps, dating with historical
context, relative stratigraphic dating, dating with archaeological

<figure><img src="image_3.png" /><figcaption>FIGURE 1 | Kras Plateau: Study area and its sites (red); hillforts (circles); the Bronze Age barrows (triangles); the Iron Age towers (squares); Thiessen polygons of the Bronze Age central settlements. Data sources: (Bratina 2021a; Novaković 2001a; Teržan and Turk 2021).</figcaption></figure>
finds and dating by association. Methodologically, we have con-
centrated primarily on the latter. To this end, we have proposed
an approach based on the concept of taskscapes, which we are
modelling with kernel density estimation. This methodology
is directly applicable in many Mediterranean environments
with similar characteristics. Furthermore, we believe that it
can be used with minor modifications in any archaeological
LiDAR study.

**2 | Archaeological and Environmental Background**

**2.1 | Archaeological Context**

In the modern sense, archaeology in Kras dates back to the late
1800s, when prehistoric sites could be easily identified in the
then barren landscape (Burton 1877; de Marchesetti 1903). It
was dominated by the study of walled hilltop settlements or hill-
forts and the landscape. At that time, the Kras Plateau landscape
was described as a ‘rocky desert’, which left an indelible impres-
sion on archaeologists and still influences our interpretation of
prehistory today.

The earliest hillforts were erected at the latest in the mid-2nd
millennium BCE (Bratina 2018, 2021a) and they shaped the cul-
tural landscape for almost two millennia. Apart from the monu-
mental walls, which are occasionally reminiscent of Cyclopean
masonry (cf., Zupančič and Vinazza 2015), the density of
the hillforts is perhaps the most striking feature. Novaković
(Novaković 2001b) lists around 120 hillforts in the Kras Plateau
or more than one per four km². A number that dwarfs the 65
village communities (Slovenian katastrska občina) recorded in
the same area in 1825 (Zorn, Kumer, and Ferk 2015).

The size of the hillforts varied greatly, from 900 to more than
250 000 m². The hillfort layouts appear to have been determined
by morphology. A distinction was made between hillforts with
simple and complex ground plans, the latter comprised of an el-
evated ‘citadel’ and a ‘lower town’. A positive correlation was
discovered between the size of the ‘citadel’ and the total size of
the enclosed area, possibly implying that the extent of the set-
tlement mirrored the size of the population (Novaković 2001a).
Following the rank-size model, hillforts were divided into large,
high-ranking central settlements and small, specialized low-
ranking settlements. Each high-ranking hillfort would have
controlled a settlement niche that comprised good agricul-
tural land, pasture land and specialized low-ranking hillforts
(Slapšak 1988, 1995). However, at the time when this model
was developed, agricultural potential was estimated by back-
dating the landscape documented on the 1825 map (Novaković,
Simoni, and Mušić 1999a, 1999b; Slapšak 1999), although a dis-
crepancy between the thus reconstructed agricultural potential
and hilltop positioning was noted (Novaković 2001a, 2001b).

Two chronological phases of the Kras prehistory were identified
based on changes in burial rites (Novaković 2001a, 2001b) and
settlement pottery (Bratina 2021a; Vinazza 2021): the earlier
phase was dated to the Middle and Late Bronze Age and the later
to Iron Age. The latest research places the 8th/9th century BCE
as a watershed between the two periods (Teržan 2021). While

the landscape was always relatively densely populated with hill-
forts, a densification process was observed. In the earlier phase,
11 territorial organization units with 39 securely dated hillforts
were identified, and in the later phase, 18 units with 57 hillforts
were identified (Figure 1). This process was interpreted as a
transition from individual lineage territories to communal ter-
ritories (Novaković 2001a, 2001b) or polities.

It was only when LiDAR data became available in 2015 that direct evidence of prehistoric land use in Kras was discovered (Mlekuž 2015). Identified features include cairnfields, boundary walls, enclosures of varying sizes and rectangular fields. The latter resemble brickwork fields (sensu Riley 1980) and are sometimes associated with funnel trackways. There were several attempts by early archaeologists to interpret the cairns as burial mounds, but this was refuted by several archaeological excavations between 1890s and 2021. These features, discovered in their thousands, attest to a carefully constructed and managed agricultural landscape. They were most likely raised in connection with herding activities and probably reflect the development of new livestock rearing practises in the Middle to Late Bronze Age in the Kras Plateau (Bernardini et al. 2020; Laharnar, Lozić, and Štular 2019; Mlekuž 2015, 2018b).

Recently, invaluable new information on the chronology of
individual hillforts was obtained (Bratina 2014, 2018, 2019,
2021a; Fabec, Tolar, and Vinazza 2014; Fabec and Vinazza 2018;
Vinazza 2012, 2021). Additionally, a new type of site was dis-
covered, namely Iron Age towers (Teržan and Turk 2014, 2021).
The hillforts with their associated towers and boundary walls
were understood as a ‘line of surveillance and defence’ that ‘con-
trolled and defended their ... frontier’ (Teržan and Turk 2021)
as a response to ‘military conflicts’ and perhaps a ‘conquest’
(Teržan 2021). This interpretation is at odds with the prevail-
ing modern understanding of hillforts, which emphasizes the
interplay of symbolic, social and cosmological functions along-
side their potentially violent implications. Many recent studies
emphasize internal social dynamics, such as the socio-political
environment in which agricultural practises and economic
strategies were embedded, as a critical motivation for hill-
fort construction (e.g., Armit 2007; Čučković 2017; Lock 2011;
Sharibles 1991) and even take into account nuances such as
acoustic affordances (Hincks and Johnston 2022).

**2.2 | Environmental Context**

As with almost every other archaeological landscape in the
Mediterranean, the prehistory of the Kras Plateau can only be
understood in its environmental context. Geomorphologically,
Kras is a plateau with an average altitude of about 550 m asl,
situated between the two ridges to the south and north. The
plateau is further divided by a longitudinal ridge into the
Divaško-Komen Kras (Slovenia) and the Trieste Carso (Italy).
Geologically, it is dominated by Cretaceous and Tertiary lime-
stones, with some Dolomite and rare Flysch patches. As the
water gradually dissolved the carbonate rock, a distinctive karst
landscape with typical surface and subterranean features was
formed. In Kras, the most important surface features are dolines
(enclosed karst depressions, locally called vrtače). The terrain is
also characterized by the absence of surface water and relatively
flat plains. The reddish brown (*terra rossa*), post-carbonate brown and rendzina soils are moderately fertile but extremely shallow (e.g., Gams et al. 1993; Gams and Gabrovec 1999; Parise 2012; Zorn et al. 2020).

Kras has a sub-Mediterranean climate that varies greatly depending on exact location. An important climatic feature is the bora, a violent north-westerly wind that causes severe aeolian erosion in open landscapes. Until a century ago, the Kras was a rocky, bare land, sometimes called a ‘rocky desert’. However, thanks to the forestation that started in the late 19th century, the predominant vegetation today is dwarf forest and shrubland. The most common plant communities are European hop-hornbeam with downy oak (*Ostryo carpinifoliae*–*Quercetum pubescentis*), European hop-hornbeam with autumn moor grass (*Seslerio autumnalis*–*Ostryetum carpinifoliae*) and sessile oak with autumn moor grass (*Quercetum petraeae*–*Seslerio autumnalis*) (Breg Valjavec, Zorn, and Čarni 2018; Zorn, Kumer, and Ferk 2015).

The extremely shallow soils and small amount of surface water mentioned above have historically been limiting factors for agriculture in the Kras. These conditions permitted only small-scale subsistence farming based on sheep and goat grazing. This resulted in a distinct subsistence model and the associated engineered landscape known in the Western Mediterranean as *paysages agro-karstiques* (Figure 2) (Gams et al. 1993; Nicod 1987).

However, the *paysages agro-karstiques* are not as ancient as was often assumed. It has long been known that Kras was subject to a devastating erosion event or process at some point in the pre-Modern past. The majority of attempts to reconstruct this event assumed that the erosion was caused by human-made deforestation (e.g., Čehovin 1993; Kranjc 2004; Zorn, Kumer, and Ferk 2015). Analysis of the Holocene pollen record revealed predominantly forest vegetation until approximately the 1st century AD (Roman period). After that, non-arboreal vegetation gradually increased, peaking after the 11th century (Culiberg 1995).

<figure><img src="image_4.png" /><figcaption>**FIGURE 2** | Kostanjevica na Krasu (Slovenia), a view from a balloon taken during the World War I. reveals a barren landscape often perceived as a 'rocky desert' (Konstanjevica vom Ballon; K.u.K. Festungs – Ballon-Abteilung 1/3 B, Photo-Kontrolle, Photo 2H; courtesy of Goriški muzej Kromberk – Nova Gorica, Inv. No. 05.04.Kostanjevica-zracni-2H).</figcaption></figure>

Due to the specific conditions, environmental reconstruction in Mediterranean landscapes such as the Kras Plateau requires a special approach (Walsh 2016). To this end, recently, much better proxy data for soil erosion has been found by Fabec: the rate of aeolian soil deposition in dolines. The ratio between the Bronze Age, the Iron Age, the Roman period and the medieval/post-medieval period was approximately 2:1:0:4 (Fabec 2018). This means that the earliest notable erosion occurred in the Bronze Age. The associated charcoal and pottery fragments are a strong indication that it was caused by humans, probably through the introduction of new landscape management systems. Erosion was much slower in the Iron Age and came to a complete halt in the Roman period, indicating the efficient management of a stable landscape. By far the most significant erosion process took place in the medieval and post-Medieval periods.

Thus, the *paysages agro-karstiques* were a post-medieval adaptation to the ‘rocky desert’ landscape characterized by extremely shallow soils, severe lack of surface water and intensive land improvement. For the sake of clarity in this text, this landscape will be referred to as post-medieval.

# 3 | Methods and Materials

## 3.1 | LiDAR and Other Data Sources

The analysis of LiDAR-derived data and information or LiDAR survey was conducted in a 12 km² area between the villages of Kostanjevica na Krasu and Lipe (Figure 3). The

<figure><img src="image_5.png" /><figcaption>**FIGURE 3** | Case study area. (a) Aerial imagery from 2014 (approximately concurrent with data acquisition; source Web 3 n.d.); (b) enhanced VAT visualization of LiDAR-derived 0.5 m DFM (see Appendices 1 and 2 for para- and metadata); (c) DFM Confidence Map (1 lowest, 6 highest data confidence).</figcaption></figure>
parameters of the data acquisition and the processing work-
flow are explained in the Appendices 1 and 2. The area was
selected for three reasons. First, it was known beforehand that
two prehistoric hillforts and a potential settlement site in the
valley are situated there. This was an assurance that sufficient
relevant new information on prehistoric archaeology would
be documented to make a meaningful contribution to knowl-
edge about prehistoric Kras. Second, in the last half-century,
any worthwhile archaeological survey has been thwarted by
difficult vegetation and geomorphology. Third, the area was
known to be rich in post-prehistoric archaeology, foremost the
numerous traces of the Isonzo front line from World War I,
which further complicates the documentation of prehistoric
archaeology. The latter two circumstances were deliberately
chosen to introduce information noise into the case study in
order to demonstrate the potential of LiDAR-derived data for
landscape archaeology in the most challenging Mediterranean
karst environment.

At the time of data acquisition, around two-thirds of the study
area was covered by dwarf deciduous forest and very dense
shrubbery, which is a type of maquis vegetation. Maquis, typ-
ical of the Kras Plateau and Mediterranean karst in general, is
very unfavourable for archaeological surveys, whether LiDAR
guided or not (e.g., Grammer et al. 2017), as it has a negative im-
pact on data quality (Doneus, Banaszek, and Verhoeven 2022;
Štular, Lozić, and Eichert 2023; Štular and Lozić 2020). The ef-
fect of maquis on LiDAR data is clearly visible on the DFM con-
fidence map, a tool developed to assign a confidence or quality
level between 1 (lowest) and 6 to each grid cell (Štular, Lozić,
and Eichert 2021). The maquis areas have values between 1 and
3 (Figure 3c).

The rest of the area was made up of meadows and fields that had
been heavily reworked by ploughing and aggressive field im-
provement typical of post-medieval Mediterranean karst. As a

result, while the quality of the LiDAR data was high with values
between 4 and 6 in DFM confidence map (Figure 3c), the visibil-
ity of potential archaeological features predating post-medieval
period was very low.

Additional data sources used in our study were: archaeological topographic data from the Archaeological Cadastre of Slovenia (ARKAS; Web 1 n.d.) and Registry of Portable Heritage (RKD; Web 2 n.d.), digital orthophotos from 2009, 2012, 2014, 2015 and 2017 (Web 3) and historic cadastral maps from around 1825 (Web 2 n.d.; Web 3 n.d.).

## 3.2 | Interpretive Mapping and LiDAR Fieldwork Archives

The central analytical part of this study was the interpretative
mapping. This is desk-based archaeological interpretation that
identifies, interprets and maps archaeological sites and features.
It is based on the analysis of the enhanced visualizations of the
LiDAR-derived digital feature model (DFM) in conjunction with
additional data sources unified in a GIS environment (Lozić and
Štular 2021). The result of the interpretative mapping was a geo-
database containing detected archaeological features mapped as
points (e.g., cairns) or as lines (e.g., trenches or linear embank-
ments). In addition to mapping the features, rich metadata was
also recorded (Appendix 3; Figure 4).

Technically speaking, there is a choice to be made in any map-
ping project between lines and points on the one hand and poly-
gons on the other. The decision between the two is a question
of scale and/or time budget. We have based our decision on
scale: our data is suitable for displaying at scales of up to 1:2000
(‘100% crop size’ corresponds to a scale of 1:1890). Mapping any-
thing other than the largest features, such as ramparts, with
polygons at this scale would not only be wasted effort, but also

<figure><img src="image_6.png" /><figcaption>**FIGURE 4** | Kostanjevica na Krasu (Slovenia), interpretation of archaeological features derived from LiDAR data (see Appendices 1 and 2 for para- and metadata). The interactive map or EMap is available at https://hdl.handle.net/20.500.12102/ariadne-plus-pilot.</figcaption></figure>
misleading. Thus, we have mapped all features with lines and points. The only exceptions are the ramparts of the Grmača and Ambrož hillforts.

In terms of content, the focus of this paper and thus also of the interpretative mapping was on prehistory. Therefore, features that were clearly recognized as (much) later were not mapped, in particular modern features, bomb craters from the World War I and post-medieval features. All features that could not be clearly interpreted as non-prehistoric were mapped. In addition, World War I trenches were mapped as they represent an important interpretative feature of the prehistoric landscape, that is, their presence explains the missing sections of the prehistoric record.

The decisions described above may appear arbitrary or even inconsistent. However, they ought to be made in 'feature-rich' landscapes such as the Kras region due to the sheer quantity of features. For instance, choosing not to map thousands of post-medieval field boundaries and World War I bomb craters, while mapping thousands of presumably prehistoric cairns, is vital for managing the resources of mapping projects.

We refer to the resulting LiDAR-derived DFMs, their enhanced visualizations and the geodatabase generated by interpretative mapping as the LiDAR fieldwork archive. Fieldwork archives are datasets that contain what is commonly referred to as processed data, that is, datasets that contain measured values or values generated by a calculation or analysis of measured values that can be geolocated (Opitz 2022). The LiDAR fieldwork archive of this case study is stored in the persistent open access repository Zenodo (Lozić and Štular 2022) and as a persistent interactive map or EMaP (Web 6 n.d.; Figure 4; Štular and Lozić 2022).

## 3.3 | Multiproxy Landscape Chronology

Archaeological landscapes are often described as palimpsests (e.g., Bailey 2007). This concept refers to the human-made alteration of the land, where the features of the former use are overlaid by the new use, but are still detectable, thus revealing changes in land use. However, the exact chronology is often difficult to discern. This is the main weakness of our results and of most archaeological LiDAR: a loose and often indirectly argued chronology.

To this end, we applied a method that can be described by the term multiproxy, in which multiple methods are combined to reduce the effects of equifinality of interpretation (e.g., Birks and Birks 2006). Specifically, we used five methods to determine the chronology of individual features and feature types: relative dating using remote sensing data and historical maps; dating by historical context; relative stratigraphic dating; dating by associated archaeological finds; and dating by association. Given the challenges involved in determining landscape chronology, it is vital that the LiDAR fieldwork archive contains metadata that describes the confidence in dating for each feature.

The first method was used for relative dating of features using remote sensing data and historical maps. Aerial and satellite imagery was used to identify modern features, for example, haystacks,

fallen trees and footpaths. Historical maps—in our case, Franciscan cadastral maps from around 1825 (Gašperič 2022; Web 4 n.d.; Web 5 n.d.; scale 1:2880, colour plates)—were used to detect features existing circa 1825. This method is very fruitful, for example, for the study of drystone walls typical of post-medieval Kras, which is an emerging topic for archaeological LiDAR in Mediterranean landscapes (Doneus, Doneus, and Cowley 2022).

The second method was dating by historical context. In our case study, there was a very important historic event that shaped the landscape: World War I. The Isonzo front line, where Italy and the Austro-Hungarian Empire fought between 1914 and 1917, crosses our study area. Based on known analogies (e.g., Košir 2020), the features of the front line (e.g., zigzag trenches, bunkers in the dolines and bomb craters) could be dated with a high degree of confidence.

The third method was relative stratigraphic dating (Doneus, Neubauer, et al. 2022; Neubauer et al. 2022). For example, the linear features, cairns and the long wall are overlaid in several places by drystone walls, which are shown as plots on the 1825 map. This allows relative dating for a handful of features. Moreover, given the uniform morphology and distribution, it can be inferred that all the cairnfields and associated linear features predate 1825. The method therefore provides a relative chronology for feature types.

The fourth and most widely accepted method for dating archaeological features in the landscape is indirect dating of associated artefacts or radiometric dating of suitable materials (e.g., Verhagen 2013). Unfortunately, in our case study, as in any Mediterranean karst landscape, this is very difficult. The preservation of organic material for radiometric dating is very poor, and the most common find, settlement pottery, can only be dated very loosely. In our case, the hillforts were dated with associated pottery.

The final method is dating by association. It is based on establishing the contemporaneity of an undated feature or group of features with another feature or group of features, which was dated by one of the other four methods mentioned above. This is achieved by establishing an interpretative link between the two in a repeatable, transparent and objective way. A straightforward example was a path documented in LiDAR data linking a village with a meadow, both documented on the 1825 map; this path was therefore in use circa 1825 (Štular 2011).

Often a more complex application is required. In our case study and most commonly in Mediterranean archaeology–hillfort sites have been dated (with varying degrees of precision), but not the agro-pastoral landscape. The method we propose is suitable for establishing the association between settlements and agro-pastoral landscape. With minor modifications, the method can be used for any activity in the landscape.

Our specific application draws upon a well-established site catchment analysis principle which was theoretically first proposed by von Thünen (1826). In any agricultural system engaging in fieldwork demands significantly more effort compared to tasks in meadows or pastures, and these, in turn, require more effort
than forest work or summer pasture (both intensive, but seasonal undertakings). Consequently, the dwellings are strategically positioned at the centre to facilitate optimal accessibility, with the three zones of activity extending outwards in concentric circles. The innermost ring is dedicated to the most labour-intensive tasks. Tilling the field, particularly within a hoe-based farming system, stands as the quintessential and most time-intensive activity within this inner zone. The intermediate ring hosts activities of moderate exertion, such as livestock tending or the collection of winter fodder. The outermost ring encompasses seasonal and akin tasks, for instance, the collection of firewood or summer grazing. Owing to geographical constraints like mountains or water resources, practical instances more closely resemble pizza-slice-shaped segments or cross-sections (e.g., Astill and Grant 1988; Aston 1985; Lozić 2021; Vita-Finzi and Higgs 1970). 'Living' such landscape results in an 'agricultural taskscape'. The concept of taskscapes is well-acknowledged and thus needs no introduction (Ingold 2017; Ingold and Bradley 1993).

It is rare that taskscapes are detected archaeologically. Nevertheless, under suitable conditions archaeological LiDAR emerges as the ideal tool for such undertaking. For example, our case study exemplifies a taskscape identified by a dense pattern of cairns and linear features. In order to calculate the density of these features and consequently visualize the taskscapes, a kernel density estimation is applied. Kernel density estimation is a method that provides valuable insight into the intensity and concentration of point data, such as archaeological sites or features. It creates a smooth, continuous surface by placing a kernel function at each point in the dataset and summing these functions to generate a density surface. The kernel function determines the shape and size of the area of influence around each point. Commonly used kernel functions include Gaussian, Epanechnikov and Uniform kernels. The bandwidth parameter controls the size of the kernel and influences the degree of smoothing applied to the data. There are two parameters in this analysis. The first is bandwidth: a smaller bandwidth results in a higher resolution surface with more detail; a larger bandwidth produces a smooth surface with a lower resolution. The kernel type is the second parameter (e.g., Baxter, Beardah, and Wright 1997; Bonnier, Finné, and Weiberg 2019). We have empirically determined that the 100m bandwidth and 'kernel densities' parameter are the most appropriate for our application. We consider the resulting kernel density to be a proxy for the agricultural taskscape because the density of features is directly related to the amount of labour dwellers invested in agricultural activities in the landscape.

Using this method we have mapped both the post-medieval and agro-pastoral agricultural taskscapes. The post-medieval landscape was diligently documented in the 1825 cadastral map (for the archaeological uses of this map see Pleterski 2013; Štular 2006; Štular 2011). To document the 1825 agricultural taskscape we mapped the centre of each plot and weighted each plot with a value of 3 for fields, a value of 2 for meadows, and a value of 1 for forests and pastures (the area of the plots was not taken into account). Agro-pastoral agricultural taskscape was mapped using LiDAR-derived data. After empirically testing various settings, we mapped the vertices of field systems and the centre points of cairns, weighted with values of 3 and 2, respectively. There was no direct evidence for the forests.

In short, the kernel density method was used to calculate the density of agro-pastoral features such as field boundaries and cairn fields. In any landscape governed primarily by agro-pastoral activities, the following pattern emerges: a settlement in the centre and a gradually decreasing kernel density away from the centre.

# 4 | Results

## 4.1 | Hillforts and Potential Settlement

In the entire project, we documented 1108 archaeological features, of which 431 are prehistoric, 58 Roman or Late Roman, 6 medieval and 300 from World War I (Lozić 2016). Here we only focus on prehistoric features from the limited study area. There are three settlements within the study area: hillforts Grmača, Ambrož, and the potential settlement site Cerkvence (Figure 1).

The Grmača and Ambrož hillforts are situated on the northern ridge of the Kras, which rises above the central plateau and descends northward into the Vipava valley; lengthwise, it slopes gradually from east to west. The Grmača hillfort was first recorded by Carlo Marchesetti in the early 1900s. The ramparts were recognized, and a large cairn directly above the entrance in the eastern part of the hillfort was considered to be the remains of a tower (de Marchesetti 1903). During the 1973 topographical survey, an additional 2.20m wide fortification wall and a smaller enclosure, a 'citadel,' were documented within the hillfort. A further rampart that partitions the settlement at the southwest end was also recorded (Web 1 n.d.). Several fragments of prehistoric pottery and burnt plaster clay were collected from the surface of the rampart (Slapšak 1974; Svoljšak 1967). The earliest pottery from this site stems from the first half of the second millennium CE, and there is evidence of pottery from the first millennium as well (Bratina 2021b).

Our analysis of the data and information derived from the LiDAR revealed further details (Figure 5). The ground plan of the hillfort has an elliptical shape. The preserved length of the defensive walls' ruins is 253.3m, the width is up to 13.4m, and the height is up to 4.62m. A previously undetected wall was documented in the southern part of the complex; its length is 78.0m, its width is up to 13.4m, and its height is up to 5.1m, measured from the outside. The western part of the outer hillfort walls was reused for the World War I fortifications in the form of zigzag trenches. In the south-eastern part of the hillfort, in the place where Marchesetti assumed the existence of tower, there is a large bomb crater, which also destroyed the junction between the outer and inner hillfort walls. The original total length of the fortification walls was 428.3m, and it enclosed an area of 12779m². The two entrances to the hillfort, located in the northeast and southeast of the hillfort, belong to the type of 'entrance with gaps in the rampart' (sensu Dular and Tecco Hvala 2007). Within the hillfort, four rectangular and two linear archaeological features were documented. These features do not seem to be part of an internal division of the hillfort. Following the example of the neighbouring Ambrož hillfort (see below), the most likely interpretation is that these are the remains of a Late Roman settlement (perhaps circa 4th century CE). Another possibility is that they are the remains of the World
<figure><img src="image_7.png" /><figcaption>**FIGURE 5** | Grmača hillfort. Above: Enhanced VAT visualization of 0.5m DFM (see Appendices 1 and 2 for meta- and paradata); below: Archaeological interpretation.</figcaption></figure>

War I fortifications. The interpretation cannot be ascertained without further fieldwork, but the morphology favours the first possibility.

The Ambrož hillfort, named after the ruins of the church of Sv. Ambrož/ St Ambrose (Smole 1979), is located on the top of the hill Trstelj. In the early 1900s, the discovery of numerous fragments of prehistoric pottery from inside the site was reported (de Marchesetti 1903). The 1973 topographical survey measured the perimeter of the hillfort at 630 m. Of this, 470 m were fortified with walls, the ruins of which were preserved at a height of 2 m, measured from the inside, and 10 to 12 m, measured from the outside of the hillfort. There were no ramparts discovered on the

<figure><img src="image_8.png" /><figcaption>**FIGURE 6** | Ambrož hillfort. Above: Enhanced VAT visualization of 0.5m DFM (see Appendices 1 and 2 for meta- and paradata); below: Archaeological interpretation.</figcaption></figure>

southwest side, where there is a cliff. Further 340 m long ruins of the walls were documented to the east and north of the hillfort (Web 1 n.d.).

Analysis of the LiDAR data revealed that the Ambrož hillfort is well preserved (Figure 6). The complex has an elliptically shaped ground plan and a tangential type entrance in the northeast. The second entrance might have been on the west side, but it was destroyed by a most likely post-medieval cut through the walls. However, such destruction seems too laborous to be made simply to provide access to a pasture in the interior of the hillfort, and in our experience, such post-medieval entrances were always made on the site of an existing original
entrance. The total length of the ruins of the wall is 574.1 m, and the enclosed area is 22 727 m². The width of the wall's ruins is up to 16.8 m, and the height on the outside is up to 9.6 m. The pottery from this site is also dated to the first half of the second millennium BCE and the first millennium BCE (Bratina 2021b).

The interior of the hillfort is divided into two parts: a smaller upper section, or ‘citadel,’ and a larger lower section. The division was made in part by a wall and in part by taking advantage of the terrain. The path that connected the two parts was also documented. Furthermore, the remains of at least 38 buildings were mapped. Of these, 13 buildings were located in the ‘citadel’ and at least 25 in the ‘lower town’. Similar buildings in the neighbouring Notranjska region (which shares Iron Age material culture with the Kras Plateau) were first built in the Late Iron Age and remained in use in some sites until the Late Roman period (Laharnar 2022). A similar longevity of settlement plans on the level of plots can be observed, for example, in many European cities, where they persist for more than a millennium (e.g., Kelleher and Štular 2009). North and northeast of the central hillfort area, four terraces with possible remains of at least three buildings were detected. A similar use of the outer area of a hillfort was documented at Ajdovščina above Rodik (Mušič 1999; Slapšak 1995).

Cerkvence site was identified as a site of archaeological interest in the 1973 archaeological topography (Web 1 n.d.). During archaeological excavations, a secondary refuse of prehistoric pottery was found (Bratina 2001).

The analysis of the LiDAR data did not reveal any additional information at this site. Any archaeological features that may have been there were levelled by intensive agriculture in the 1900s.

## 4.2 | Agro-Pastoral Landscape

Over an area of 224.7 hectares, 216 cairns and 206 linear features (total length 23 718 m) were documented in the landscape beyond the sites described above. The most ubiquitous archaeological features in the case study area are cairnfields. The largest is positioned on the plateau south and southeast of the Cerkvence site. Furthermore, there are two in the foothills south of the Ambrož hillfort and one each south and north of the Grmača hillfort. Most of the cairns (95%) are located on flats or moderate slopes of 6 degrees or less (Figures 4 and 7).

There are two types of cairnfields, enclosed and scattered. Cairnfield enclosures in Kras are often rectangular with rounded corners, although the only such enclosure in our case study is irregularly shaped (Figure 4a). The enclosing structure is 515.5 m long, up to 0.2 m high, and 2.2 m wide; it encloses an area of 14 592 m², with 16 cairns scattered between 25 and 35 m apart. The field inspection indicated that the enclosure was most likely built as a drystone wall around 1 m wide; therefore, based on the volume of the ruins, the original height can be expected to be less than 0.5 m. These enclosures could have been stalls for cows, sheep, or goats, according to one interpretation (Mlekuž 2015, 2018b). Based on the reconstructed shape, this enclosure on its own could not serve as a passage barrier for

sheep or goats, but might be more effective for cows. However, the stall interpretation does not explain the cairns inside the enclosure. The scattered cairnfields are more densely packed, with a distance of 10 to 20 m between the individual cairns. Cairns are irregular in shape and are occasionally interspersed among linear features that appear randomly. The interpretation of cairnfields is not yet clear. One possibility is that these are clearance cairns, that is, refuse stones cleared from the landscape to improve its use for agriculture. This interpretation is based on the 19th century anthropological analogy, where pastures were transformed into meadows through the process of stone removal (Mlekuž 2018b). However, in the cited anthropological analogies the result of such processes are cairns in lines or straight linear features deposited at the edge of the plots. Furthermore, in the context of the post-medieval landscape, the clearing of meadows with sickles for the purpose of harvesting grass for winter fodder was minimal, resulting in sparse and small clearance cairns. It was the meadows that were cleared for scything that produced the largest clearance cairns (Gams et al. 1993). As the scythes were only invented at the very end of the Iron Age (e.g., Bartoli 2017), there was no need for intensive clearing of the stones that would result in the observed cairnfields.

The second interpretation for the cairnfields is that they were constructed to foster a certain type of hoe farming or agroforestry. If so, the cairns and associated linear features would compensate for two of the main shortcomings for agriculture in Mediterranean karst: shallow soils and proneness to summer drought. Concentrating soil in earthworks would create more room for root systems, and stones would improve soil moisture retention (Laharnar, Lozić, and Štular 2019).

At this time, the conclusion cannot be reached, and relevant geoarchaeological analysis is ongoing. Regardless, researchers agree that the cairnfields were a direct result of the agro-pastoral activities of the inhabitants of the adjacent hillforts (Bernardini et al. 2020; Laharnar, Lozić, and Štular 2019; Mlekuž 2007, 2018b). Thus, whereas comments on the type of agro-pastoral system cannot yet be made, inferences on the distribution of activities in the landscape can (see Section 5).

Most of the linear features, a type of elongated cairn that can extend for a hundred metres or more, have been interpreted as components of field systems. Although the preservation of the features varies, two distinctive field systems can be distinguished. The first on the slopes between the Grmača and Ambrož hillforts and in the second on the plains south of the Cerkvenca.

First, the fields located between the Grmača and Ambrož hillforts were positioned predominantly (95%) on moderate slopes under 10 degrees. The largest and most structured field system is positioned west of the Ambrož hillfort. It is resembling brickwork fields. At least four rectangular plots could be detected, each about 90 by 120 m in size. Internally, these plots are further divided by low walls running perpendicular to the slope that are reminiscent of undeveloped terracing. The features outside of these plots are less structured (Figure 7). North and east of the Ambrož hillfort, they are enclosing individual plots, which comprise all flat or gently sloped areas. South of Grmača are smaller
<figure><img src="image_9.png" /><figcaption>**FIGURE 7** | Archaeological features representing field systems: (a) case study area with colour coded extracts; (b) archaeological features superimposed on the slope map, green extract west of Ambrož hillfort; (c) archaeological features superimposed on the slope map, blue extract south of the Cerkvence site; (d) VAT visualization of DFM, detail of the Roman centuriation features cutting the prehistoric features.</figcaption></figure>

patches of undeveloped terracing, which are not enclosed. Thus, this field system is an adaptation to agricultural use, probably hoe farming, on moderate slopes near hillforts. As such, it is a predecessor of the Mediterranean terraced landscapes, which, however, were more extensive and built with much more effort on significantly steeper slopes (e.g., Ažman Momirski and Kladnik 2009).

The second type is the field system in the plains south of the Cerkvence site. There is a small area of undeveloped terracing in the rugged terrain, but most of it takes the shape of oval plots and two walls. The two walls seem to be delimiting the area of the cairnfield from the area of oval plots (Figure 7c: purple). The oval plots are placed in flat areas (slope less than 6 degrees) (Figure 7c).

## 4.3 | Territorial Boundaries

Three types of boundaries have been detected in this case study: a long wall, walls integrated into a field system, and Roman centuriation walls. First, we will address the long walls. Several case studies have already revealed the existence of long walls interpreted as territorial boundaries in the prehistoric landscapes of the Kras Plateau (Bernardini et al. 2020) and the neighbouring Notranjska (Laharnar, Lozić, and Štular 2019; Vidojević 2021). Excavation of the Notranjska examples revealed drystone walls more than 1 m wide but not higher than several rows of stone, probably up to 0.5 m. Therefore, these were not constructed to stop invading marauders (cf., Teržan and Turk 2021), but as territorial markers with a possible function of directing the herds (Laharnar, Lozić, and Štular 2019; cf., Chadwick 2008).
Such a boundary wall was documented in our case study area. It was running parallel to the ridge north of Grmača and south of Ambrož hillforts (Figure 4; Figure 7a). The total documented length was 4356 m, and the feature was on average 3.2 m wide and up to 0.5 m high. This is about 30% more volume than the examples excavated in Notranjska, so this particular wall might have been higher. It was not positioned to mark any recognizably territorial boundary, and a definitive interpretation cannot be made without further investigation of a larger area. However, it can be said that it is of a different type from the above-mentioned analogies.

Second, walls integrated into a field system were already mentioned in the description of the fields at the Cerkvence site (Figure 7b, c: purple). The two walls were 668 and 374 m long, respectively, and were meaningfully integrated into the field system. It would appear that these two walls were set up to direct grazing herds, specifically to divert herds away from fields under crops.

Third, elements of what was likely a part of the Roman period land division system were documented. In four locations, which are 1 km or more apart from each other, low linear walls that were perfectly parallel or perpendicular to each other have been documented (Figure 7: red lines). They are, from north to south, 533, 85, 212 and 328 m long. The existence of such features has already been recognized and interpreted as part of the Roman land division or centuriation of the Roman colony *Tergeste* (Mlekuž 2018a). The dating to the Roman period is suggested by a rectangular feature (length: 99 m, width: 55 m), which, according to some analogies (Laharnar, Lozić, and Štular 2019), could be the remains of a Roman villa.

The importance of features dated to the Roman period for the pre-historic landscape is their stratigraphic position. Namely, the documented features cut the above-described field system features at two locations (and are cut numerous times by the post-medieval field boundaries). Therefore, they provide a *terminus ante quem*, a date before which the prehistoric features were erected.

## 4.4 | Chronology

As described above, we were using five dating methods. To document the contemporary (circa 1950s and later) landscape features, relative dating with remote sensing data and contemporary maps was used. World War I features were dated by historical context. Post-medieval landscape was detected by comparison with 1825 maps. Relative stratigraphic dating was used to determine the date before which the prehistoric field system was established, which was provided by Roman centuriation. Indirect dating with archaeological finds was used to date the hillforts to Late Bronze and Iron age. Dating by association provided evidence for the prehistoric origin of the agro-pastoral landscape features in the discussion below.

## 5 | Discussion: Prehistoric Agricultural Taskscape

As elaborated above, four distinct groups of features belonging to four landscape phases were detected in our case study:

contemporary, characterized by dwarf forests; World War I conflict landscape, brutally imposed and abruptly abandoned, characterized by specific military features; post-medieval, characterized by intensive land improvement; and agro-pastoral, characterised by cairnfields and so on. The contemporary and World War I landscapes were easily distinguishable and were not of further interest.

We focused on the post-medieval and agro-pastoral landscapes for two reasons. First, they are of most interest for prehistoric archaeology. Second, until now dissociating the post-medieval landscape from the archaeological interpretation of prehistory was an unresolved issue in the archaeology of the Kras Plateau (see Section 2.1) and often in Mediterranean archaeology in general.

To this end we have mapped both the post-medieval and agro-pastoral agricultural taskscapes using method described above. The post-medieval landscape's kernel density was calculated based on the 1825 cadastral map (Figure 8a). Agro-pastoral agricultural taskscape was mapped with kernel density using the vertices of field systems and the centre points of cairns (Figure 8b). To directly compare the two taskscapes we plotted the profile of kernel density for the selected transect that passes over a prehistoric hillfort and a post-medieval village (Figure 8c).

Having modelled the agricultural taskscapes, the first task of dating by association was to dissociate the post-medieval landscape from prehistory, that is, to demonstrate that the post-medieval landscape cannot be backdated to prehistory. The post-medieval taskscape clearly demonstrates that the post-medieval landscape is directly associated with the post-medieval villages, and it is distinctly dissociated from the prehistoric hillforts (Figure 8a). In a similar manner the agro-pastoral taskscape is demonstrably associated with the Late Bronze Age and Iron Age hillforts (Figure 8b).

Furthermore, the prehistoric and post-medieval agricultural taskscapes are mutually exclusive: where there is a peak in post-medieval taskscape there is a valley in prehistoric and vice versa (Figure 8c). This is direct evidence that the prehistoric and post-medieval agro-pastoral economies were based on completely different subsistence models. Therefore, no aspect of post-medieval landscape, taskscape or related material culture can be backdated to prehistory.

Once the agro-pastoral landscape features were dated to prehistory we were also able to comment on how much prehistoric activity occurred in the plains where, as mentioned above, the post-medieval and modern agricultural activities obliterated much of the prehistoric archaeology. Despite the extant area with no data (Figure 8b: white), the density peak in the area of the Cerkvence site located in the plain reveals that there was indeed a minor but none the less distinct centre of prehistoric activity there. This supports the hypothesis for the existence of a specialized settlement or 'farm' (cf., Mlekuž 2015) at the Cerkvence site (Bratina 2001).

Lastly, the connection between the cairnfields with the agro-pastoral activities should be briefly discussed. Unfortunately, despite active investigations using geoarchaeology, microstratigraphy and other methods, we are still unable to understand their
<figure><img src="image_10.png" /><figcaption>**FIGURE 8** | Kernel density. Kernel density of (a) post-medieval and (b) prehistoric agro-pastoral landscape ('X' marks archaeological sites—from left Grmača hillfort, Ambrož hillfort, Cerkvence—'O' marks post-medieval villages—from left Kostanjevica, Temnica, Lipa—and dashed line marks the path of the profile); (c) profile of kernel density (orange for post-medieval and green for prehistoric agro-pastoral landscape).</figcaption></figure>

precise function and therefore cannot meaningfully add to the
discourse summarized above (Section 4.2). However, by map-
ping the taskscape we have established a firm and objective link
between the cairnfields and the hillforts. In the context of the
site catchment analysis, the Ambrož, Grmača and Cerkvence
cairnfields belong to the intermediate ring, where moderate ef-
fort activities take place. This is the best objective, albeit some-
what cryptic, evidence to date for the activities associated with
the prehistoric cairnfields in the Karst and Notranjska region.

6 | Conclusions

The prehistoric Kras Plateau was a relatively typical example of
a Mediterranean agro-pastoral landscape of the Late Bronze and
Iron Ages (cf., Iacono et al. 2021), dotted with numerous hill-
forts fortified with monumental walls reminiscent of Cyclopean
masonry. These imposing structures dominated the prehistoric
landscape (symbolically and/or otherwise) and continue to over-
shadow the archaeological discourse.

Through attentive and mindful interpretation of LiDAR data
in the Kras Plateau case study we have ‘populated’ the archae-
ological landscape with meaningful information. Not only the
hillforts have been documented in unprecedented detail, but

also a previously unknown agro-pastoral landscape was re-
vealed. However, until now, the latter was only loosely dated
and only implicitly associated with the securely dated prehis-
toric hillforts. Thus, recent studies remain embedded in the
traditional focus on hillforts (Teržan 2021). Furthermore, the
prehistoric archaeology of the Kras Plateau continues to be in-
terpreted in the context of post-medieval landscapes typical of
Mediterranean karst.

To advance the state of the art we have set out to establish a
firm chronology for the agro-pastoral landscapes detected with
LiDAR-derived data. We have developed an objective and re-
peatable multiproxy method for dating archaeological features
documented with archaeological LiDAR. It combines: direct
dating using remote sensing data and historical maps; dating
based on historical context; relative stratigraphic dating; dating
of associated archaeological finds; and dating by association.
Particularly, we focused on the latter and proposed a taskscape-
based approach that relies on kernel density modelling.

Our dating of the agro-pastoral landscape in the Late Bronze
and Iron Ages is fully consistent with the above mentioned
geoarchaeological analysis of the dolines, which was the first
to confidently date intensive human intervention in the land-
scape through radiometric dating of the sediments (Fabec 2018).
Furthermore, our results are also in line with the pollen analysis (Culiberg 1995). This convergence of evidence from three different data sources obtained with three different methods is significant because the probability of its accuracy increases exponentially when the same results are independently obtained with different methods and data sources (Štular et al. 2022; Wilson 1998).

Thus, we provide the best prehistoric landscape chronology for the Kras Plateau to date. The agro-pastoral landscape in the case study area was contemporaneous with Ambrož and Grmača hillforts which are firmly dated to the Late Bronze Age and Iron Age. The dwellers of the hillforts exclusively cultivated fields and cairnfields on the gentle slopes adjacent to the hillforts. There were ample similar locations that could be used for cultivation, but they were not. This suggests that the existing fields and cairnfields were sufficient and the landscape was nowhere near carrying capacity. The preference for the location of fields and cairnfields was therefore cultural: because of their proximity to settlements (site-catchment) and/or as a precaution against (perceived) endemic violence. In addition, there was likely a specialized non-fortified settlement or 'farm' at the Cerkvence site.

Finally, we believe that our results have important methodological implications for the archaeology of Mediterranean karst landscapes in general. Indeed, the backdating of post-Medieval landscapes remains a common practise in such archaeological contexts. While we by no means claim that the chronological sequence discovered in our case study is directly and uncritically transferable to other archaeological contexts, we believe that our methodology is. We hope to have provided the archaeologists working in the harsh environments of the Mediterranean with an additional tool to establish reliable chronologies.

## Acknowledgements

The authors would like to thank Tomaž Fabec for bringing the photo used as Figure 2 to our attention, as well as the Goriški muzej in general and Ines Beguš in particular, for granting permission to republish the image. We would also like to thank the anonymous reviewers and editors who helped shape the final version of the article.

## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

All relevant data, metadata and paradata are available in the appendices, the Zenodo repository (10.5281/zenodo.7357286; 10.5281/ZENODO.7509438), and as geoservice with persistent link (https://hdl.handle.net/20.500.12102/ariadne-plus-pilot).

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Appendix 1: Data acquisition and raw data processing metadata (M) and paradata (P) (according to the metadata schema in Lozić and Štular 2021).

<table><thead><tr><th>Process step</th><th>M/P</th><th>Title</th><th>Content</th></tr></thead><tbody><tr><td rowspan="8">Project planning</td><td>M</td><td>Project name</td><td>Lasersko skeniranje Slovenije</td></tr><tr><td>M</td><td>Brief description</td><td>Countrywide scanning</td></tr><tr><td>P</td><td>Purpose</td><td>General purpose, flood protection</td></tr><tr><td>M</td><td>Platform</td><td>Fixed wing aircraft</td></tr><tr><td>P</td><td>Date of flight(s)</td><td>2014 February</td></tr><tr><td>P</td><td>Operator</td><td>Flycom d.o.o.</td></tr><tr><td>M</td><td>Custodian</td><td>Geodetska uprava Republike Slovenije</td></tr><tr><td>P</td><td>Scanner type</td><td>Full-waveform</td></tr><tr><td rowspan="10">System calibration</td><td>M</td><td>Instruments (laser scanner/INS/GNSS)</td><td>RIEGL LMS-Q780/IGI Aerocontrol Mark II. E 256 Hz/GNSS: Novatel OEMV-3</td></tr><tr><td>M</td><td>Pulse repetition rate [kHz]</td><td>N/A</td></tr><tr><td>M</td><td>Wavelength</td><td>N/A</td></tr><tr><td>M</td><td>Max. scanning angle</td><td>Reported ±30°, actual ±33°</td></tr><tr><td>M</td><td>Scan lines per second</td><td>N/A</td></tr><tr><td>M</td><td>Additional sensors</td><td>None</td></tr><tr><td>M</td><td>Max. scanning angle error</td><td>0.25 mrad</td></tr><tr><td>M</td><td>INS angle accuracy</td><td>Roll/pitch 0.004°; heading 0.01°; position 0.05 m</td></tr><tr><td>M</td><td>INS-GNSS-laser synchronization error</td><td>0.005 ms</td></tr><tr><td rowspan="10">Data acquisition</td><td>M</td><td>Altitude above ground level</td><td>1200–1400 m</td></tr><tr><td>M</td><td>Average speed</td><td>165 kts/105 ms</td></tr><tr><td>M</td><td>Flight strip overlap</td><td>N/A (&lt;5%)</td></tr><tr><td>M</td><td>Swath width</td><td>865 m</td></tr><tr><td>M</td><td>Footprint diameter [m]</td><td>N/A</td></tr><tr><td>M</td><td>Average laser pulse density per m²</td><td>5</td></tr><tr><td>M</td><td>N/E/H accuracy (m)</td><td>±0.02/0.02/0.025</td></tr><tr><td>M</td><td>Vegetation state</td><td>Dormant</td></tr><tr><td>M</td><td>No. of flight strips</td><td>28</td></tr><tr><td rowspan="7">Registering</td><td>P</td><td>Flight trajectory calculation (software/method)</td><td>GraNav (Waypoint-Novatel) v8.50/DGPS</td></tr><tr><td>P</td><td>GNSS and IMU merging (software)</td><td>AeroOffice (IGI) v5.1f</td></tr><tr><td>P</td><td>Raw data analysis (software)</td><td>RiPROCESS v1.5.9: RiANALYZE v6.0.2 (RIEGL)</td></tr><tr><td>P</td><td>Merging of raw data with flight trajectory</td><td>RiPROCESS v1.5.9: RiWORLD v4.5.8 (RIEGL)</td></tr><tr><td>P</td><td>Full-waveform processing and filtering</td><td>N/A</td></tr><tr><td>P</td><td>LAS export (software)</td><td>RiPROCESS (RIEGL) v1.5.9</td></tr><tr><td>M</td><td>LAS format</td><td>1.2</td></tr><tr><td rowspan="2">Strip adjustment</td><td>M</td><td>Coordinate system</td><td>D96/TM</td></tr><tr><td>P</td><td>Strip adjustment (yes/no)</td><td>Yes</td></tr><tr><td></td><td>P</td><td>Strip adjustment (software/method)</td><td>TerraMatch (TerraScan)/line matching</td></tr></tbody></table>
Appendix 2: Point cloud processing and derivation of products paradata (according to the metadata schema in Lozić and Štular 2021).

<table><thead><tr><th>Process step</th><th>Type</th><th>Content</th></tr></thead><tbody><tr><td rowspan="3">Automatic ground point classification</td><td>Software</td><td>LAStools Rapidlasso GmbH</td></tr><tr><td>Filter</td><td>lasground_new</td></tr><tr><td>Settings</td><td>st: 5; g: /; off: 0.05; s+: 1.0; s: 1.0; b: no; terrain type: wilderness; pre-processing: ultra fine</td></tr><tr><td rowspan="5">Object-type classification</td><td>Software</td><td>LAStools, Rapidlasso GmbH</td></tr><tr><td>Filter</td><td>lasheight</td></tr><tr><td>Settings</td><td>Classify between: 0.5 and 2 as 3; classify between: 2 and 5 as 4; classify above: 5 as 5</td></tr><tr><td>Software</td><td>LAStools, Rapidlasso GmbH</td></tr><tr><td>Filter</td><td>lasclassify</td></tr><tr><td rowspan="5">Manual reclassification</td><td>Software</td><td>Building planarity: 0.1; forest ruggedness: 0.4; ground offset: 1.8</td></tr><tr><td>Targeted area</td><td>Within the known site of castle ruins</td></tr><tr><td>Targeted features</td><td>Standing archaeological features (type 3)</td></tr><tr><td>Software</td><td>Global Mapper® 21.1.x, Bluemarblegeo</td></tr><tr><td>Change-detection map</td><td>See Figure 2d</td></tr><tr><td rowspan="3">DFM interpolation</td><td>Software</td><td>Surfer 17.1.x, Goldensoftware®</td></tr><tr><td>Filter</td><td>Kriging</td></tr><tr><td>Settings</td><td>Kriging type: point; Drift:none; No. sectors: 4; Max. all sectors: 64; Max. each sector: 16; Min. all sectors: 8; Radius 1:20; Radius 2:20; cell size: 0.25 m.</td></tr><tr><td rowspan="8">Enhanced visualization</td><td>Software</td><td>RVT 2.2.1, ZRC SAZU</td></tr><tr><td>Filter</td><td>Sky view factor</td></tr><tr><td>Settings</td><td>No. search directions: 32; Search radius: 10; Remove noise: no.</td></tr><tr><td>Software</td><td>RVT 2.2.1. ZRC SAZU</td></tr><tr><td>Filter</td><td>Visualization for archaeological topography (VAT)</td></tr><tr><td>Settings</td><td>setting: steep.</td></tr><tr><td>Software</td><td>WhiteboxTools 1.4.0., University of Guelph's GHRG</td></tr><tr><td>Filter</td><td>DiffFromMeanElev</td></tr><tr><td>Settings</td><td>x dimension: 10; y dimension: 10</td></tr></tbody></table>
Appendix 3: Metadata schema for EMaP and Zenodo data archives.

<table><thead><tr><td>Code</td><td>Description</td></tr></thead><tbody><tr><td>ID</td><td>Consecutive identification number</td></tr><tr><td>Type</td><td>Short description of the type of archaeological features (controlled vocabulary)</td></tr><tr><td>Type confidence</td><td>Reliability of the definition of the type (1 lowest, 3 highest)</td></tr><tr><td>Dating</td><td>Short description of chronology (controlled vocabulary)</td></tr><tr><td>Dating confidence</td><td>Reliability of dating (0 unknown, 1 lowest, 3 highest)</td></tr><tr><td>Description</td><td>Generic description of archaeological feature</td></tr><tr><td>Interpretation</td><td>Interpretation of archaeological feature; reliability is conditioned by Type confidence</td></tr><tr><td>Length</td><td>... of linear features in metres</td></tr><tr><td>Mapping source</td><td>..., e.g., type of visualization of DFM or other remote sensing data</td></tr><tr><td>Visibility</td><td>... of the mapped feature in the mapping source (1 = poor, feature barely recognizable; 2 = fair, feature can be easily recognized, but not the exact morphology; 3 = good, feature and its morphology are fully recognizable)</td></tr><tr><td>Author</td><td>... of the archaeological interpretation</td></tr><tr><td>Date</td><td>... of the archaeological interpretation</td></tr><tr><td>x GK D48</td><td>x coordinate of archaeological feature in Gauss-Krüger system D48; for each linear feature is given coordinate is in the centre point</td></tr><tr><td>y GK D48</td><td>y coordinate as above</td></tr></tbody></table>