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Article

Contribution of the Sediment Flow Connectivity Index (SfCI) in Landscape Archaeology Investigations: Test Case of a New Interdisciplinary Approach

by
Marina Zingaro
1,
Giovanni Scicchitano
1,*,
Paola Palmentola
2,
Arcangelo Piscitelli
3,
Alberto Refice
4,
Rodolfo Roseto
1,
Giovanni Scardino
1 and
Domenico Capolongo
1,4
1
Department of Earth and GeoEnvironmental Sciences, University of Bari, 70124 Bari, Italy
2
Department of Humanistic Research and Innovation, University of Bari, 70121 Bari, Italy
3
Environmental Surveys Srl, Spin-Off University of Bari, 70124 Bari, Italy
4
Institute for the Electromagnetic Sensing of the Environment-Italian National Research Council (IREA-CNR), 70126 Bari, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(20), 15042; https://doi.org/10.3390/su152015042
Submission received: 12 September 2023 / Revised: 16 October 2023 / Accepted: 17 October 2023 / Published: 19 October 2023
(This article belongs to the Special Issue Geomorphological Mapping for the Geoheritage Resources Sustainability)
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Abstract

:
The integration of geomorphological analysis in archaeological investigations is essential to describe physical geography and land morphology in order to understand the relationship between the environment and human activities. Recently, the sediment flow connectivity index (SfCI) has been demonstrated to be a powerful geomorphic indicator for defining the most sensitive areas to geomorphological modifications in a catchment. This work presents the experimental application of the SfCI for a landscape archaeological analysis in order to assess the contribution of the index to potentially recognize, monitor, and interpret the historical evidence in the evaluation of landscape evolution. The investigation was performed in the basin of Lama Camaggi in the Apulia region (southern Italy), characterized by precious archaeological evidence found on the surface during field surveys in the years 2001–2002 and 2012–2013. The results show (1) the correlation between high-sediment-connectivity areas and areas with high densities of archaeological sites, and (2) the capacity of the SfCI to identify surface processes that may potentially affect the readability of the archaeological records to support data interpretation. These results confirm the advantage of applying an interdisciplinary approach in archaeology and opens innovative research scenarios.

1. Introduction

An interdisciplinary approach based on the comparative assessment of different information sources (geology, geomorphology, geophysics, archaeology, history, topography, cartography, toponymy, literature, epigraphy, etc.) is essential to reconstruct paleolandscapes. Indeed, the availability of various data is useful in all phases of archaeological studies, from the preliminary analysis of sites to planning field surveys, from the detection of archaeological evidence to the interpretation of discovery contexts, and from the monitoring to the preservation of cultural heritage [1,2,3,4,5,6]. In this wide framework of archaeological investigations, the analysis of the evolution of the landforms (i.e., geomorphology) plays a very important role to better understand: (a) the relationship between environment and human activities over time, (b) the unfolding of historical events, (c) the interrelation of surface-occurring phenomena and the surficial distribution of archaeological data, and (d) the natural and anthropogenic processes that affect heritage assets [7,8,9,10,11]. As demonstrated in the literature, all these fields of research, in which the contribution of geomorphology is indispensable, are closely interconnected: the preservation of cultural heritage requires the examination of past events and the reconstruction of the interaction of nature and human actions involved in the archaeological evidence [7,10,11,12,13,14,15,16]. Over the years, researchers have highlighted the importance of jointly investigating natural and cultural heritage in order to sustain it through its promotion and protection [17,18,19,20,21]. It is therefore evident that an important tool for achieving the sustainability goals defined by the United Nations (Sustainability Development Goals—SDG 8 and 11, [22]) is the analysis of the forms and processes occurring on the Earth’s surface applied to archaeological investigations. In particular, the morphological dynamics of the surface and its modification over time have always influenced human choices, and, moreover, conditioned the possibility of site discovery and the readability of surface archaeological traces [23,24,25,26]. Furthermore, erosion, transport, and deposition processes that shape landforms continue to modify the physical environment to which the archaeological heritage is closely connected, conditioning its cultural value and preservation. In fact, archaeological heritage, either standing above soil or still buried, is affected by surface mobility. This should therefore be quantitatively analyzed to better define the dynamics of discovered remains, together with their conservation and interpretation [27,28,29]. Therefore, it becomes necessary to describe and consider these processes in the analysis of archaeological surface evidence in order to exploit the advantages of geomorphometry so to monitor their evolution and understand their significance and relevance.
During the past years, geomorphologists focused on sediment connectivity that described sediment paths within a catchment by investigating the contiguity of landscape components and their interaction in geomorphic, hydrological, and ecological systems [30,31,32,33,34,35]. Various aspects have been explored, and new approaches and methods have been developed, providing helpful tools (indices and models) to evaluate and estimate sediment mobilization and transport from sources to sinks, and the consequent connection with the hydrographic network [36,37,38,39]. From the earliest studies, the increasingly widespread use of geomorphic indicators to assess the processes of the supply, transfer, and storage of sediment on the surface demonstrates the applicability of sediment connectivity in different disciplinary fields: river monitoring and management [40,41,42], geodiversity and biodiversity [43,44], and climatology and pedology [45,46,47]. However, the contribution of sediment connectivity assessment in the analysis of the processes involving surface archaeological evidence has not yet been investigated.
This work presents the experimental application of the sediment flow connectivity index (SfCI) in landscape archaeology analysis. The SfCI is a powerful geomorphological indicator for defining the most sensitive areas to geomorphological modifications in a catchment; it has been previously tested to correlate the effects of various events that occur in a catchment, conditioned by water and sediment displacement [48,49,50]. The investigations were performed in the Lama Camaggi basin, in the Apulia region (southern Italy), which is characterized by a large presence of archaeological evidence, found on the surface during field surveys in the years 2001–2022 and 2012–2013. The present work analyses the relationship between sediment connectivity and the occurrence of surface archaeological data by assessing whether the SfCI can provide useful information to reconstruct and preserve paleolandscapes. Then, the experimentation aims to assess the role of phenomena, such as surface water outflow, downslope wash, or sediment burial, in the evaluation of the reliability of archaeological records. The main objective is to explore the potential of the SfCI to monitor and interpret surface archaeological evidence. The relevance to define the potential contribution of the SfCI in landscape archaeology investigations lies in a promising opening of new research scenarios in geoarchaeology.

2. Study Area

The rather small basin of Lama Camaggi (210 km2), located in the north–central area of the Apulia region (southern Italy), extends from the border between the high and low part of the carbonate Murge plateau, whose evolution is controlled by climate and sea-level changes [51,52,53,54], to the Adriatic coast (Figure 1). Lama Camaggi is a characteristic fluvio-karst valley in the Apulian landscape that is intersected by caves and depressions (such as collapse dolines) and a surface runoff and drainage network of valleys, locally named Lame and Gravine, incised in Plio–Pleistocene calcarenites and Mesozoic limestones. The lithological aspect, the change of the base (sea) level, and the low gradient of the topography conditioned the generation and the morphological features of these valleys that are characterized by subvertical walls and flat bottoms filled with colluvial and alluvial deposits [10,54,55]. These valleys represent the main hydrographic network of the region. The main characteristics of the Lama Camaggi catchment are: (1) the ephemeral hydrological regime of the streams; (2) the overall low-slope angles alternating with the presence of the Murge hills, such as Monte Santa Barbara (262 m msl) and Monte Faraone (232 m msl); (3) the low spatial variability in precipitation, soil units, and land use. Furthermore, during extreme-rainfall events, flood phenomena occurred along Lama Camaggi near the towns of Andria and Barletta, where some sections of the channel have been buried and diverted.
The morphology of the area of the Lama Camaggi catchment has influenced the anthropic dynamics since the earliest times. In fact, the analysis of the archaeological traces here found (see Section 3.2 and Figure 1) demonstrate that topography affected the historical evolution of the ancient settlements, as well as the anthropic displacement in the territory from the Prehistoric to the Medieval age [56]. Furthermore, the ancient track of the Via Traiana Roman road was identified in an area that crosses the catchment in the east–west direction [57,58,59]. These geomorphological aspects, coupled with the archaeological evidence, make the basin of Lama Camaggi a suitable test area to investigate the applicability of the SfCI in archaeology.

3. Materials and Methods

3.1. Geomorphological Analysis

3.1.1. SfCI Computation

As presented in [48], the SfCI is based on a mapping approach that includes functional aspects in the structural component of sediment connectivity. Structural connectivity indicates the physical contiguity of morphological units in a hydrographic catchment, while functional connectivity indicates the interaction between morphological units through geomorphic processes [31,47,60]. In particular, in the SfCI, the connectivity is defined as the connection by sediment transport, considering the mobilization (erosion) of sediment and its transfer along the channel to the outlet in the lateral and longitudinal directions; then, assessing sediment linkages through material transport. The index is founded on two main assumptions: (i) if there is no sediment mobilization, there is no connection; (ii) the greater the sediment mobilization in a cell, the greater the possibility that mobilized sediment reaches other cells [36,48]. This combination of structural and functional components is supported by the definition of a soil stability index (recognized as a functional property of sediment connectivity, [61]) and by the use of a flow-routing algorithm (considered as a proxy for runoff processes) to estimate water- and sediment-contributing areas, thus defining sediment paths according to the steepest descent direction. Clearly, the latter is a simplified method to describe the complex dynamics of sediment transport that partly limits the analysis; nevertheless, it is justified by the initial assumptions (i-ii) and by the common application of similar contributing-area approaches in sediment connectivity assessment ([61,62] and reference therein).
The computation of the SfCI consists of three main steps: (1) the computation of a sediment mobility map, (2) the derivation of the SfCI map through flow routing, and (3) the (optional) production of a simplified SfCI map for applicative purposes (see Figure 1 in [48]). The sediment mobility map estimates the potential detachment and mobilization of sediment, while the sediment-flow-accumulation algorithm estimates the potential sediment fluxes through slope-driven flow accumulation.
The sediment mobility map (SM) derives from the product of two factors, SM1 (potential sediment detachment, controlled by rainfall, soil stability, and land use) and SM2 (potential movement towards surrounding cells, controlled by topographic and morphological aspect of the surface). SM is defined as:
S M = S M 1 · S M 2
with
S M 1 = R S I L
S M 2 = S R u
where R is a rainfall index, SI is a soil stability index, L is a land-use index, S is a slope index, and Ru is a surface ruggedness index (which describes the topographic variability as the mean difference of height between a central pixel and its surrounding cells; see [48]). Each of these indices is dimensionless and determined by ranking the values of all the corresponding variables through a qualitative approach based on the a priori interpretation of erosion surface processes [35,48]. In this simplified approach, 1 is assigned to the maximum value, 0.05 (in order to avoid null values) is assigned to the minimum value, and the range from 0.05 to 1 is assigned to intermediate values in relatively uniform intervals [48,49].
The sediment flow connectivity map is obtained by propagating the SM values (initial seed values) through a classical flow-accumulation algorithm F [63,64], which allows us to simulate a “sediment contributing area” for each cell according to a steepest slope principle. In this way, after iterations, the cells with the highest flow-accumulated values are sediment-active cells (i.e., cells that contribute to sediment flux), and the cells with the lower flow-accumulated values are sediment-inactive cells (i.e., the cells that are not on a sediment pathway). The SfCI is given by:
S f C I = log 10 ⁡ F ( S M )
The applicative SfCI (a_SfCI) map is derived by applying a mean filter through a rectangular window to the SfCI map in order to aggregate similar connectivity data over map areas (and not single paths). This smoother map is classified in three connectivity classes (high, medium, and low) defined through a natural break index (Jenks). This sediment connectivity representation helps highlighting hotspots (corresponding to high-sediment-connectivity areas) in the catchment, i.e., areas most sensitive to geomorphological modification. Therefore, the a_SfCI constitutes the main tool to directly apply the sediment connectivity index to fluvial, alluvial, and (here) archaeological contexts [48,49].

3.1.2. SfCI Data Processing

All maps derived for the SfCI computation (i.e., rainfall, soil stability, land-use, slope, and ruggedness indices maps) have here a spatial resolution of 8 m that corresponds to the resolution of the regional DEM used in the extraction of surface characteristics. The regional DEM of the Apulian territory was realized by combining the photogrammetry and technical regional cartography with a vertical accuracy of 1 m; the dataset, which is composed by tiles in ASCII format, can be downloaded from the UTM33N-WGS84 projection system from the regional WebGIS site (http://www.sit.puglia.it; accessed on 12 September 2023).
Rainfall data correspond to the mean annual precipitation (MAP) recorded by rain-gauge stations (located in Apulia, Campania and Basilicata regions) in the period 1921–2020, freely available from Puglia, Dipartimento di Protezione Civile Regione Puglia (https://protezionecivile.puglia.it; accessed on 12 September 2023). Data were converted to isohyet maps by applying inverse distance-weighted interpolation (Shepard, 1968) [65]. In particular, the range of the MAP values over the Lama Camaggi basin, corresponding to 517–598 (mm/y), was extracted from the isohyet map and rescaled into index values from 0.05 to 1 (see Table 1 and Figure 2a).
Soil stability data were obtained from the classification of soil properties, such as thickness and permeability, considered for the evaluation of the layer saturation and drainage-runoff capacity of the soil surface, respectively (Cevasco et al., 2014 [66]; Zingaro et al., 2019 [48]; Zingaro et al., 2020 [49]). Soil units and relative properties come from a regional soil map (1:100.000 scale) and from the ACLA (Agro-ecological characterization of Apulia) dataset (https://pugliacon.regione.puglia.it; accessed on 12 September 2023). First, the classes of permeability (from 1 to 6) and the values of the thickness of the soil units were examined and classified (high, medium, and low permeability and thickness; see Table 2); then, a matrix of combined properties was developed (Table 3) and the definition of three classes of the soil stability index, with corresponding values from 0.05 to 1, was derived (Table 4 and Figure 2b). In addition, the field observation of the soil units described in the database was applied.
Land-use data were derived from the regional soil-use map (updated to 2011), available on the WebGis site (http://www.sit.puglia.it; accessed on 12 September 2023). The classification of the land-use index was defined by assigning high values to classes that can favor sediment detachment (e.g., poorly vegetated, croplands) and low values to classes that can obstruct sediment detachment (grasslands, pastures, shrubs). The classification is reported in Table 5 and Figure 2c.
Slope and ruggedness indices maps were obtained by computing slope and ruggedness from the regional DEM (see Section 3.1.1) and classifying the corresponding ranges of values. In particular, the TRI (terrain ruggedness index, Wilson et al., 2007) [67] was computed by using a tool implemented in the “gdaldem” module of the GDAL/OGR Geospatial Data Abstraction software library, version 3.7.2 (Open Source Geospatial Foundation, https://gdal.org; accessed on 12 September 2023). The ranges of the slope and ruggedness were rescaled into values from 0.05 to 1 (see Table 6 and Table 7 and Figure 3).
All the steps of the mapping procedure were carried out using ArcMap®, version 10.8 (ESRI, www.esri.com; accessed on 12 September 2023) and QGIS®, version 3.16.1 (Open Source Geospatial Foundation Project, http://qgis.org; accessed on 12 September 2023) software.

3.2. Archaeological Analysis

Historical analysis and field surveys have been performed in the Lama Camaggi basin in recent years as part of various scientific research and urban-planning investigations. In particular, archaeological evidence found during the activities performed in the years 2001–2002 and 2012–2013 in the territory of Andria (involving part of the Lama Camaggi basin) are considered here [56,68]. Following a preliminary landscape study, the archaeologists of the University of Bari (https://www.uniba.it; accessed on 12 September 2023) and the Superintendence of Archaeology, Fine Arts and Landscape (https://sabapba.cultura.gov.it/; accessed on 12 September 2023) carried out systematic surface surveys in the areas of greatest geomorphological and historical interest, according to the current guidelines of landscape archaeology investigations [14,69,70,71]. As described in the related literature [56,68], tools, such as cartographic support (map of the Italian Military Geographic Institute, digital terrain model—DTM, orthophotos, hydrogeomorphological, and cadastral maps), global positioning system (GPS) devices, and site and topographical unit cards, were used in the field; digital database and GIS platforms were used in the processing phase. The evidence (areas of archaeological material, ancient structures) identified on the surface, henceforth called archaeo-records (Figure 1), were recognized as traces of the anthropogenic presence in the territory from Prehistory to the Modern Age, made easier by the morphological characteristics of the natural landscape. In fact, through the historical interpretation of this evidence, the archaeologists described the anthropic choice to occupy this area, above all in the Prehistoric and Protohistoric ages, as strongly determined by the availability of shelter and defense in hills and caves (represented by the karst and tectonic morphologies of the Murge hills), and the proximity of primary sources (such as water, wild fruits and plants, game, wood). Thus, deep valleys (Lama Camaggi and other smaller valleys in the catchment) could represent advantageous territories and could be exploited as reference lines for moving around the territory from the high part of the Murge plateau to the Adriatic coast [10,72]. A greater human organization of spaces could then be ascribed to the Archaic–Classical and Roman ages, documented by the traces of settlements (such as the sites of Monte Santa Barbara, Tavernola, and Quadrone) and roads. Moreover, historical analysis shows that the Via Traiana and other minor roads continue to follow the orientation of the natural forms (see Figure 2 and Figure 3 in [56]). Archaeological remains (pottery artifacts, ruins, cisterns, masserias) demonstrate that the territory was highly populated in the Medieval and Modern ages.
This archaeological analysis, which is based on past works, was applied here with reference to two main aspects: (1) acquiring the location of archaeo-records in the Lama Camaggi basin and (2) understanding the cultural significance of this evidence closely related to the landscape context.

3.3. SfCI and Archaeo-Records Comparison

In order to compare sediment connectivity values with the archaeological occurrence, a visual analysis was applied. In particular, the a_SfCI map and archaeo-records were overlapped to identify a potential spatial correspondence of high-sediment-connectivity areas with archaeo-records areas. Moreover, a further focus on surface-occurring phenomena was attempted by using the SfCI map in order to observe the sediment paths in areas affected by the presence of the archaeological evidence.

4. Results

4.1. SM, SfCI, and a_SfCI Maps

Figure 4 shows the SM, SfCI, and a_SfCI maps derived from Equations (1)–(4). In the SM map (Figure 4a), most of the Lama Camaggi basin is characterized by low sediment mobility (blue cells), with higher values of mobility (orange–red cells) predominantly distributed in two parts of the catchment. This sediment mobility pattern is mostly conditioned by the lower stability of soils in these areas of the basin rather than by the rainfall rate, land use, and topography. In fact, the presence of thinner and less permeable soils (see Section 3.1.2 and Figure 2b) appears to be the most important contributor to increased sediment mobility, given the low spatial variability of other mobility factors (see Figure 2 and Figure 3).
The SfCI map (Figure 4b) shows values of the index ranging between −4.5 (very low) and 5.6 (very high). Sediment paths are identified by high SfCI values (red cells) in greater sediment mobility areas, showing that sediment flow tends to concentrate only in those parts of the basin where sediment supply in the drainage network occurs from local sediment sources (higher areas). Disconnected areas (green cells) are present in almost the entire basin, with a greater extent in the northern part.
In the a_SfCI map (Figure 4c), areas with low, medium, and high sediment connectivity are more visible (green, yellow, and red, respectively). The spatial distribution of sediment connectivity shown by the map makes the presence of hotspot areas (red) in the Lama Camaggi basin even more identifiable in two main regions of the catchment.

4.2. SfCI Comparison with Archaeological Occurrence

Figure 5 shows the visual comparison between the sediment connectivity areas and archaeological occurrence areas through the overlap of the a_SfCI map and archaeo-records. As visible in the figure, most areas with archaeological evidence are located in one of the two main hotspot regions, thus showing a higher tendency of areas most active in geomorphological dynamics (i.e., affected by high sediment connectivity) to host areas characterized by the presence of archaeological data on the surface (detailed box).
Figure 6 and Figure 7 focus the analysis in the most significant area for the presence of archaeological evidence (the site of Monte Santa Barbara, see Section 3.2 and Figure 7a) by observing the location of the archaeo-records within the sediment paths (SfCI map, Figure 6b). The comparison shows that there are archaeological areas (highlighted in light blue in Figure 6 and visible in photos in Figure 7b–d) receiving a high contribution of water and sediment due to their location on sediment flows (marked by red arrows in Figure 6a,b).

5. Discussion

The geomorphological processes modify the environment where human actions take place, thus affecting evolution, the finding conditions, and the preservation of the ancient sites [9,25]. The application of geomorphological indicators that define the most sensitive areas to modifications can contribute to assessing the effects of surface phenomena on landscape archaeology investigations, thus helping to understand the cultural value of surface evidence. The processes of sediment erosion, transport, and deposition can, respectively, expose, move, and bury the archaeological material, thus influencing the context of discovery, the opportunity of detection, and the state of analysis and interpretation, in addition to the possibility of preservation [9,12,28,29]. An archaeological record uncovered on the surface by soil erosion could be transported and then deposited and discovered, just as it could be covered and buried again. Similarly, eroded material could be dispersed and never again found. Again, the transported sediment could be deposited to layers of storage covering potential archaeological deposits. These and other situations define the role of surface natural phenomena, here represented by sediment connectivity and described by the SfCI in the postdepositional dynamics of the archaeological record.
The computation of the SfCI index in the Lama Camaggi basin, which can be considered an archaeological-prone area for the significant presence of evidence (see Section 3.2), allows for the evaluation of the potential contribution of sediment connectivity to the archaeological landscape analysis. The results first define hotspot areas, where greater sediment mobilization and transport increase exposure to morphological changes (Figure 4); then, they suggest that the areas with the greatest archaeological occurrence mostly fall in hotspot areas (Figure 5 and Figure 6). This agreement establishes a connection between geomorphological-modification-prone areas and archaeological-prone areas, potentially making the SfCI a useful tool for evaluating the vulnerability of the evidence to the surface processes. In fact, the greater displacement of sediment (due to intense linkage with the sources and the paths of sediment flow) in hotspot areas results in greater preservation precariousness of the archaeological records on the surface, and thus the potential loss of information. It might be argued that sediment and archaeological material move on the surface according to the same geomorphological dynamics, assuming that the transport process is similar to that controlling water flow (simplified in the SfCI calculus as a basic propagation in the steepest descent direction; [48]). Moreover, [49] demonstrated the applicability of the SfCI in flood susceptibility, proving the connection between morphological and hydraulic conditions, partly described by the index and flood dynamics. It is thus plausible that the effects of surface-occurring phenomena (processes of supply, transfer and the storage of sediment, and extreme events) could affect both areas of archaeological material and ancient structures. If this is true, archaeo-records found in sediment-hotspot areas of the Lama Camaggi basin could be considered heritage emergencies to be monitored (1) to reduce the risk of damaging and biasing and (2) to interpret their historical value. The latter aspect can be deduced from the results of the comparative analysis shown in Figure 6. In fact, the finding of archaeological material in areas where sediment flow occurs cannot but be conditioned by surface processes. This could mean that the evidence documented on the surface may not be indicative of an archaeological deposit in the area of discovery, but may have been transported from another site along sediment paths according to a process of surface water outflow or downslope wash, above all in higher regions. For example, archaeo-records near Monte Santa Barbara (Figure 8) could be related to the same namesake site by interpreting the materials as sliding down the valley along the slope of the hillside (potential sediment source) by sediment flow (schematically reconstructed in Figure 8b). This data analysis is supported by the consistency of materials found in the archaeo-records and on the site of Monte Santa Barbara (Figure 8c).
In relation to the reconstruction of surface dynamics, the SfCI might provide information even when archaeological evidence is not in the hotspot areas. In fact, if it is known that low-sediment-connectivity areas are representative of poor occurrence or the lack of supply and mobilization of sediment (as explained in Section 4.1), archaeo-records, such as those located in the Quadrone site (Figure 9), might be surface traces of potential archaeological deposits. In this perspective, the SfCI could be useful in preliminary excavation investigations and in preventive archaeology evaluation to support the detection of remains [73,74]. However, it should be considered that, in flat areas (as with the Quadrone site), sediment tends to accumulate in layers. Therefore, the presence of a potential archaeological deposit would not be detectable by traces on the surface, being covered by burying colluvial and alluvial sediment. It follows that the historical interpretation of the archaeological material on the surface could be greatly affected by these considerations, which would change the meaning of the surface data. As this study shows, the experimental application of the SfCI in the Lama Camaggi basin demonstrates the contribution of sediment connectivity to the interrelation of surface processes influencing the readability of archaeological data through an interdisciplinary approach. The possibility of using a geomorphological indicator in landscape investigations can improve detection, monitoring, and analysis activities, opening new research scenarios in geoarchaeology. Moreover, the applicability of geomorphometry in mitigating the impact of damage to archaeological evidence fits very well with the new trends in the research community, aimed at providing a structured framework of innovative methodologies for risk assessment and the management of cultural heritage [20,75]. Indeed, the present test case contributes to developing a new interdisciplinary approach, useful for enhancing the research effort towards sustainability. In particular, this experimentation can be considered a step forward to the achievement of SDG 11 [22], which aims to protect and safeguard the world’s natural and cultural heritage.

6. Conclusions

The applicability of the sediment flow connectivity index (SfCI) was tested in the Lama Camaggi basin, a high-density archaeological area, in order to explore the potential contribution of sediment connectivity to landscape archaeology investigations. A visual comparative assessment of the SfCI maps and archaeo-records found during field surveys in the years 2001–2022 and 2012–2013 was applied. The main results show (1) the correlation between areas affected by high sediment connectivity and areas characterized by the occurrence of surface archaeological data, and (2) the potential of the SfCI to support the interpretation of data by identifying the interrelation of surface processes.
The results suggest that the computation of the SfCI might be useful to assess the readability and the conservation of archaeological evidence. Therefore, this work has the advantage to be a novelty in the current research scenario for applying a new approach based on the integration of data and methods.
On the other hand, the experimental study has limitations that can be summarized as follows:
  • The simplified approach of our index may overlook: (i) the complex dynamics of sediment transport (represented by the use of a flow-accumulation algorithm, and justified by the assumptions on which the index itself is based); (ii) the different characteristics of the various factors involved in the calculation (represented by the intermediate dimensionless indices, i.e., rainfall, soil stability, land use, slope, and ruggedness, defined by normalization procedure);
  • Some characteristics of the Lama Camaggi basin (such as the poor spatial variability of precipitation, overall low slopes) could affect the results; which, however, were shown to be such that they did not invalidate the test. It should be specified that our study area, while not constituting an exemplar case study, represents a good test case because of the significant presence of surface archaeological evidence and the presence of fluvio-karst valleys that correspond to the main surface hydrographic network;
  • The spatial resolution of maps (8 m) partially limits the sediment connectivity analysis and the subsequent assessment, especially when considered in relation to the spatial extent of the archaeological evidence.
Considering these limitations, the index can be claimed as a first guide to identify sites to be investigated by excavations. Indeed, subsequent geoarchaeological investigations at the site scale could provide information on the presence of archaeological records (along and/or downstream of sediment paths and/or buried by sediment layers, etc.) so as to possibly validate retrospectively the SfCI contribution to landscape archaeology analyses. Indeed, the most important outcome of the present work is to provide the scientific community with the opportunity to apply the geomorphological indicator of sediment connectivity in evaluating the vulnerability and the historical significance of archaeological evidence. This awareness can be used in the future to further develop this interdisciplinary approach, contributing to one of the sustainability goals.

Author Contributions

Conceptualization, M.Z., G.S. (Giovanni Scicchitano), and D.C.; methodology, M.Z., G.S. (Giovanni Scicchitano), D.C., A.R. and P.P.; formal analysis, M.Z., G.S. (Giovanni Scicchitano) and D.C.; investigation, M.Z., G.S. (Giovanni Scicchitano), D.C., G.S. (Giovanni Scardino), and R.R.; data curation, M.Z., G.S. (Giovanni Scicchitano), D.C., G.S. (Giovanni Scardino), and R.R.; writing—original draft preparation, M.Z., G.S. (Giovanni Scicchitano), D.C., A.R. and P.P.; writing—review and editing, M.Z., G.S. (Giovanni Scicchitano), A.R. and D.C.; supervision, M.Z., G.S. (Giovanni Scicchitano), and D.C.; project administration, G.S. (Giovanni Scicchitano) and D.C.; funding acquisition, D.C. and A.P. All authors have read and agreed to the published version of the manuscript.

Funding

The present work was developed within RIPARTI project, supported by the program POC PUGLIA FESRT-FSE 2014/2020, Azione 10.4 (scientific coordinator prof. Domenico Capolongo). The work was realized with the support of the Environmental Surveys Srl, Spin-Off, University of Bari.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available from the authors upon request.

Acknowledgments

The study was carried out using archaeological data provided by previous research authorized by the Superintendence of Archaeology, Fine Arts and Landscape of Italian Heritage Ministry, made available by the municipality of the town of Andria and realized by archaeologists of the University of Bari. A part of the soil analysis was applied by the collaboration of Massimo Caldara (Dept. Earth and GeoEnvironmental Sciences, University of Bari) and Raffaele Lopez.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Brancato, R. How to Access Ancient Landscapes? Field Survey and Legacy Data Integration for Research on Greek and Roman Settlement Patterns in Eastern Sicily. Groma 2020, 4, 1–32. [Google Scholar] [CrossRef]
  2. Doneus, M.; Banaszek, Ł.; Verhoeven, G.J. The Impact of Vegetation on the Visibility of Archaeological Features in Airborne Laser Scanning Datasets from Different Acquisition Dates. Remote Sens. 2022, 14, 858. [Google Scholar] [CrossRef]
  3. Kempf, M. Modeling Multivariate Landscape Affordances and Functional Ecosystem Connectivity in Landscape Archeology. Archaeol. Anthropol. Sci. 2020, 12, 159. [Google Scholar] [CrossRef]
  4. Satellite Monitoring of Geo-Hazards Affecting Cultural Heritage. In A Research Agenda for Heritage Planning; Stegmeijer, E.; Veldpaus, L. (Eds.) Edward Elgar Publishing: Cheltenham, UK, 2021; ISBN 978-1-78897-463-9. [Google Scholar]
  5. Storchi, P. Tannetum: Mutamenti Ambientali, Considerazioni Storiche e Fotografia Aerea per La Localizzazione Della Città e La Ricostruzione Del Territorio. Agri Centuriati Int. J. Landsc. Archaeol. 2014, 11, 61–82. [Google Scholar] [CrossRef]
  6. Zingaro, M.; Scicchitano, G.; Capolongo, D. The Innovative Growth of Space Archaeology: A Brief Overview of Concepts and Approaches in Detection, Monitoring, and Promotion of the Archaeological Heritage. Remote Sens. 2023, 15, 3049. [Google Scholar] [CrossRef]
  7. Dall’Aglio, P. Topografia Antica, Geoarcheologia e Discipline Paleoambientali. J. Anc. Topogr. 2011, 21, 7–24. [Google Scholar]
  8. Ferrari, K. Studying Evolving Landscapes: Geomorphology as a Research Tool for Landscape Archaeology. In TRAC 2016: Proceedings of the Twenty-Sixth Theoretical Roman Archaeology Conference: Sapienza University of Rome, Rome, Italy, 16–19 March 2016; Open Library of Humanities: London, UK, 2016; ISBN 88-7140-770-9. [Google Scholar]
  9. Wells, L. A Geomorphological Approach to Reconstructing Archaeological Settlement Patterns Based on Surficial Artifact Distribution. In Earth Sciences and Archaeology; Springer: New York, NY, USA, 2001; pp. 107–141. ISBN 978-1-4613-5433-8. [Google Scholar]
  10. Zingaro, M.; Mastronuzzi, G. Il Popolamento Antico Di Lama Diumo-San Giorgio in Relazione Alle Forme Del Paesaggio. Agri Centuriati 2017, 14, 39–56. [Google Scholar]
  11. Luo, L.; Wang, X.; Guo, H.; Lasaponara, R.; Shi, P.; Bachagha, N.; Li, L.; Yao, Y.; Masini, N.; Chen, F.; et al. Google Earth as a Powerful Tool for Archaeological and Cultural Heritage Applications: A Review. Remote Sens. 2018, 10, 1558. [Google Scholar] [CrossRef]
  12. Howland, M.D.; Jones, I.W.N.; Najjar, M.; Levy, T.E. Quantifying the Effects of Erosion on Archaeological Sites with Low-Altitude Aerial Photography, Structure from Motion, and GIS: A Case Study from Southern Jordan. J. Archaeol. Sci. 2018, 90, 62–70. [Google Scholar] [CrossRef]
  13. Forti, L.; Brandolini, F.; Oselini, V.; Peyronel, L.; Pezzotta, A.; Vacca, A.; Zerboni, A. Geomorphological Assessment of the Preservation of Archaeological Tell Sites. Sci. Rep. 2023, 13, 7683. [Google Scholar] [CrossRef]
  14. Parcak, S.H. GIS, Remote Sensing, and Landscape Archaeology. In Online Only—Archaeology; Oxford Handbooks Editorial Board, Ed.; Oxford University Press: Oxford, UK, 2017; ISBN 978-0-19-993541-3. [Google Scholar]
  15. Tapete, D. Remote Sensing and Geosciences for Archaeology. Geosciences 2018, 8, 41. [Google Scholar] [CrossRef]
  16. Tarolli, P.; Sofia, G. Human Topographic Signatures and Derived Geomorphic Processes across Landscapes. Geomorphology 2016, 255, 140–161. [Google Scholar] [CrossRef]
  17. Contillo, L.; Zingaro, M.; Capolongo, D.; Corrado, G.; Schiattarella, M. Geomorphology and Geotourism for a Sustainable Development of the Daunia Mts, Southern Italy. J. Maps 2022, 18, 418–427. [Google Scholar] [CrossRef]
  18. Bonazza, A.; Bonora, N.; Duke, B.; Spizzichino, D.; Recchia, A.P.; Taramelli, A. Copernicus in Support of Monitoring, Protection, and Management of Cultural and Natural Heritage. Sustainability 2022, 14, 2501. [Google Scholar] [CrossRef]
  19. Gray, M. Geodiversity, Geoheritage and Geoconservation for Society. Int. J. Geoheritage Park. 2019, 7, 226–236. [Google Scholar] [CrossRef]
  20. Spizzichino, D.; Margottini, C.; Brustia, E.; Cigna, F.; Comerci, V.; Dessì, B.; Guerrieri, L.; Iadanza, C.; Leoni, G.; Tapete, D.; et al. Satellite Monitoring Applied to Natural Hazards and Cultural Heritage: The PROTHEGO Project 2017. In Proceedings of the 11th Remote Sensing Thematic Workshop, Bologna, Italy, 27–28 June 2017. [Google Scholar]
  21. Wang, Y.; Wu, F.; Li, X.; Chen, L. Geotourism, Geoconservation, and Geodiversity along the Belt and Road: A Case Study of Dunhuang UNESCO Global Geopark in China. Proc. Geol. Assoc. 2019, 130, 232–241. [Google Scholar] [CrossRef]
  22. United Stations. The Sustainable Development Goals Report; UN: New York, NY, USA, 2022. [Google Scholar]
  23. Cavalazzi, M. “Looking through the Keyhole”: Problems and Research Strategies for Landscape Archaeology in an Alluvial Plain with a High Rate of Vertical Growth. The Case of Bassa Romagna and South-Eastern Po Valley. Groma. Doc. Archaeol. 2021, 5. [Google Scholar] [CrossRef]
  24. Cremaschi, M.; Storchi, P.; Perego, A. Geoarchaeology in an Urban Context: The Town of Reggio Emilia and River Dynamics during the Last Two Millennia in Northern Italy. Geoarchaeology 2018, 33, 52–66. [Google Scholar] [CrossRef]
  25. Herz, N.; Garrison, E.G. Geomorphology in Archaeology. In Geological Methods for Archaeology; Oxford University Press: Oxford, UK, 1998; ISBN 978-0-19-509024-6. [Google Scholar]
  26. Franceschelli, C.; Marabini, S. Lettura Di Un Territorio Sepolto: La Pianura Lughese in Età Romana; Studi e Scavi. Nuova Serie; Ante quem: Bologna, Italy, 2007; ISBN 978-88-7849-027-7. [Google Scholar]
  27. Cuca, B.; Agapiou, A. Impact of land use change to the soil erosion estimation for cultural landscapes: Case study of paphos disrict in cyprus. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2017, 42, 25–29. [Google Scholar] [CrossRef]
  28. De Angeli, S.; Battistin, F. Archaeological Site Monitoring and Risk Assessment Using Remote Sensing Technologies and GIS. In A Research Agenda for Heritage Planning: Perspectives from Europe; Edward Elgar Publishing: Cheltenham, UK, 2021; p. 145. [Google Scholar]
  29. Wainwright, J. Erosion of Archaeological Sites: Results and Implications of a Site Simulation Model. Geoarchaeology 1994, 9, 173–201. [Google Scholar] [CrossRef]
  30. Baartman, J.E.M.; Masselink, R.; Keesstra, S.D.; Temme, A.J.A.M. Linking Landscape Morphological Complexity and Sediment Connectivity: Landscape Morphological Complexity and Sediment Connectivity. Earth Surf. Process. Landf. 2013, 38, 1457–1471. [Google Scholar] [CrossRef]
  31. Bracken, L.J.; Turnbull, L.; Wainwright, J.; Bogaart, P. Sediment Connectivity: A Framework for Understanding Sediment Transfer at Multiple Scales: Sediment Connectivity: Sediment Transfer at Multiple Scales. Earth Surf. Process. Landf. 2015, 40, 177–188. [Google Scholar] [CrossRef]
  32. Cavalli, M.; Vericat, D.; Pereira, P. Mapping Water and Sediment Connectivity. Sci. Total Environ. 2019, 673, 763–767. [Google Scholar] [CrossRef] [PubMed]
  33. Fryirs, K.A.; Brierley, G.J.; Preston, N.J.; Kasai, M. Buffers, Barriers and Blankets: The (Dis)Connectivity of Catchment-Scale Sediment Cascades. CATENA 2007, 70, 49–67. [Google Scholar] [CrossRef]
  34. Fryirs, K. (Dis)Connectivity in Catchment Sediment Cascades: A Fresh Look at the Sediment Delivery Problem. Earth Surf. Process. Landf. 2013, 38, 30–46. [Google Scholar] [CrossRef]
  35. Wohl, E. Connectivity in Rivers. Prog. Phys. Geogr. Earth Environ. 2017, 41, 345–362. [Google Scholar] [CrossRef]
  36. Ali, G.; Oswald, C.; Spence, C.; Wellen, C. The T-TEL Method for Assessing Water, Sediment, and Chemical Connectivity. Water Resour. Res. 2018, 54, 634–662. [Google Scholar] [CrossRef]
  37. Heckmann, T.; Schwanghart, W. Geomorphic Coupling and Sediment Connectivity in an Alpine Catchment—Exploring Sediment Cascades Using Graph Theory. Geomorphology 2013, 182, 89–103. [Google Scholar] [CrossRef]
  38. Najafi, S.; Dragovich, D.; Heckmann, T.; Sadeghi, S.H. Sediment Connectivity Concepts and Approaches. CATENA 2021, 196, 104880. [Google Scholar] [CrossRef]
  39. Poeppl, R.E.; Parsons, A.J. The Geomorphic Cell: A Basis for Studying Connectivity: The Geomorphic Cell. Earth Surf. Process. Landforms 2018, 43, 1155–1159. [Google Scholar] [CrossRef]
  40. Kalantari, Z.; Cavalli, M.; Cantone, C.; Crema, S.; Destouni, G. Flood Probability Quantification for Road Infrastructure: Data-Driven Spatial-Statistical Approach and Case Study Applications. Sci. Total Environ. 2017, 581, 386–398. [Google Scholar] [CrossRef]
  41. Kondolf, G.M.; Boulton, A.J.; O’Daniel, S.; Poole, G.C.; Rahel, F.J.; Stanley, E.H.; Wohl, E.; Bång, A.; Carlstrom, J.; Cristoni, C.; et al. Process-Based Ecological River Restoration: Visualizing Three-Dimensional Connectivity and Dynamic Vectors to Recover Lost Linkages. Ecol. Soc. 2006, 11, 13–37. [Google Scholar] [CrossRef]
  42. Poeppl, R.E.; Keesstra, S.D.; Maroulis, J. A Conceptual Connectivity Framework for Understanding Geomorphic Change in Human-Impacted Fluvial Systems. Geomorphology 2017, 277, 237–250. [Google Scholar] [CrossRef]
  43. Doyle, M.W.; Ensign, S.H. Alternative Reference Frames in River System Science. BioScience 2009, 59, 499–510. [Google Scholar] [CrossRef]
  44. Lizaga, I.; Quijano, L.; Palazón, L.; Gaspar, L.; Navas, A. Enhancing Connectivity Index to Assess the Effects of Land Use Changes in a Mediterranean Catchment: Effects of Land Use Changes in a Mediterranean Catchment. Land Degrad. Develop. 2018, 29, 663–675. [Google Scholar] [CrossRef]
  45. Coulthard, T.J.; Van De Wiel, M.J. Modelling Long Term Basin Scale Sediment Connectivity, Driven by Spatial Land Use Changes. Geomorphology 2017, 277, 265–281. [Google Scholar] [CrossRef]
  46. Ludwig, J.A.; Bastin, G.N.; Chewings, V.H.; Eager, R.W.; Liedloff, A.C. Leakiness: A New Index for Monitoring the Health of Arid and Semiarid Landscapes Using Remotely Sensed Vegetation Cover and Elevation Data. Ecol. Indic. 2007, 7, 442–454. [Google Scholar] [CrossRef]
  47. Turnbull, L.; Wainwright, J.; Brazier, R.E. A Conceptual Framework for Understanding Semi-Arid Land Degradation: Ecohydrological Interactions across Multiple-Space and Time Scales. Ecohydrology 2008, 1, 23–34. [Google Scholar] [CrossRef]
  48. Zingaro, M.; Refice, A.; Giachetta, E.; D’Addabbo, A.; Lovergine, F.; De Pasquale, V.; Pepe, G.; Brandolini, P.; Cevasco, A.; Capolongo, D. Sediment Mobility and Connectivity in a Catchment: A New Mapping Approach. Sci. Total Environ. 2019, 672, 763–775. [Google Scholar] [CrossRef]
  49. Zingaro, M.; Refice, A.; D’Addabbo, A.; Hostache, R.; Chini, M.; Capolongo, D. Experimental Application of Sediment Flow Connectivity Index (SCI) in Flood Monitoring. Water 2020, 12, 1857. [Google Scholar] [CrossRef]
  50. Zingaro, M. Advanced Analysis and Integration of Remote Sensing and in Situ Data for Flood Monitoring. Rend. Online Della Soc. Geol. Italy 2021, 54, 41–47. [Google Scholar] [CrossRef]
  51. Iurilli, V.; Cacciapaglia, G.; Selleri, G.; Palmentola, G.; Mastronuzzi, G. Karst Morphogenesis and Tectonics In South-Eastern Murge (Apulia, Italy). Geogr. Fis. Din. Quat. 2009, 32, 145–155. [Google Scholar]
  52. Mastronuzzi, G.; Sansò, P. Quaternary Coastal Morphology and Sea Level Changes. Puglia 2003, 437, 184. [Google Scholar] [CrossRef]
  53. Sabato, L.; Tropeano, M. Geologia Della Puglia. In Geositi della Puglia; Regione Puglia: Puglia, Italy, 2015; pp. 48–53. [Google Scholar]
  54. Fiore, A.; Gallicchio, S.; Giandonato, P.B.; Iurilli, V.; Mastronuzzi, G.; Pieri, P.; Sabato, L.; Sansò, P.; Selleri, G.; Simone, O.; et al. IL PATRIMONIO GEOLOGICO DELLA PuGLIA: INIZIATIVA EDITORIALE DELLA SIGEA PuGLIA. In Geologia dell’Ambiente; Il patrimonio geologico della Puglia: Puglia, Italy, 2010. [Google Scholar]
  55. Mastronuzzi, G.; Sansò, P. Pleistocene Sea-Level Changes, Sapping Processes and Development of Valley Networks in the Apulia Region (Southern Italy). Geomorphology 2002, 46, 19–34. [Google Scholar] [CrossRef]
  56. Zingaro, M. Evoluzione Storica Del Popolamento Antico in Agro Di Andria (Puglia). J. Anc. Topogr. 2018, 28, 95–104. [Google Scholar]
  57. Ashby, T.; Gardner, R. The Via Traiana. Pap. Br. Sch. Rome 1916, 8, 104–171. [Google Scholar] [CrossRef]
  58. Ruta, R. La via Traiana Tra Canosa Ed Egnazia: Problemi Di Topografia e Di Toponomastica. Atene Roma 1983, 28, 174–179. [Google Scholar]
  59. Uggeri, G. La Viabilità Romana Nel Salento; Grafischena: Fasano, Italy, 1983. [Google Scholar]
  60. Wainwright, J.; Turnbull, L.; Ibrahim, T.G.; Lexartza-Artza, I.; Thornton, S.F.; Brazier, R.E. Linking Environmental Régimes, Space and Time: Interpretations of Structural and Functional Connectivity. Geomorphology 2011, 126, 387–404. [Google Scholar] [CrossRef]
  61. Heckmann, T.; Cavalli, M.; Cerdan, O.; Foerster, S.; Javaux, M.; Lode, E.; Smetanová, A.; Vericat, D.; Brardinoni, F. Indices of Sediment Connectivity: Opportunities, Challenges and Limitations. Earth Sci. Rev. 2018, 187, 77–108. [Google Scholar] [CrossRef]
  62. Altmann, M.; Haas, F.; Heckmann, T.; Liébault, F.; Becht, M. Modelling of Sediment Supply from Torrent Catchments in the Western Alps Using the Sediment Contributing Area (SCA) Approach. Earth Surf. Process. Landf. 2021, 46, 889–906. [Google Scholar] [CrossRef]
  63. Jenson, S.K.; Domingue, O. Extracting Topographic Structure from Digital Elevation Data for Geographic Information System Analysis. Photogramm. Eng. Remote Sens. 1988, 54, 1593–1600. [Google Scholar]
  64. Schwanghart, W.; Scherler, D. Short Communication: TopoToolbox 2—MATLAB-Based Software for Topographic Analysis and Modeling in Earth Surface Sciences. Earth Surf. Dyn. 2014, 2, 1–7. [Google Scholar] [CrossRef]
  65. Shepard, D. A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 23rd ACM National Conference, New York, NY, USA, 27–29 August 1968; pp. 517–524. [Google Scholar] [CrossRef]
  66. Cevasco, A.; Pepe, G.; Brandolini, P. The influences of geological and land use settings on shallow landslides triggered by an intense rainfall event in a coastal terraced environment. Bull. Eng. Geol. Environ. 2014, 73, 859–875. [Google Scholar] [CrossRef]
  67. Wilson, M.F.; O’Connell, B.; Brown, C.; Guinan, J.C.; Grehan, A.J. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope. Mar. Geod. 2007, 30, 3–35. [Google Scholar] [CrossRef]
  68. Perfido, P.; Angiulli, S.; Cascione, V.; Milella, S. Realizzazione Di Un Archivio Informatizzato Di Dati Contenti Tutte Le Informazioni Archeologiche Del Territorio Comunale Di Andria (Ba) Implementate Su Un GIS; Relazione Tecnica; Technical Report: Bari, Italy, 2005. [Google Scholar]
  69. Belvedere, O. La ricognizione di superficie. Bilancio e prospettive. J. Anc. Topogr. 2010, 20, 31–40. [Google Scholar]
  70. Dall’Aglio, P. Il Survey e La Ricerca Storico-Topografica. In La Topografia Antica; CLUEB: Bologna, Italy, 2000; pp. 233–240. [Google Scholar]
  71. Renfrew, C.; Bahn, P.G. Archaeology: Theories, Methods, and Practice, 7th ed.; Thames & Hudson: London, UK, 2016; ISBN 978-0-500-29210-5. [Google Scholar]
  72. Fornaro, A. La Viabilità. In La Puglia Centrale dall’Età del Bronzo all’Alto Medioevo. Archeologia e Storia. Atti del Convegno di Studi (Bari, 15–16 Giugno 2009); Bretschneider George: Rome, Italy, 2010; pp. 377–382. [Google Scholar]
  73. Abate, N.; Lasaponara, R. Preventive Archaeology Based on Open Remote Sensing Data and Tools: The Cases of Sant’Arsenio (SA) and Foggia (FG), Italy. Sustainability 2019, 11, 4145. [Google Scholar] [CrossRef]
  74. Danese, M.; Masini, N.; Biscione, M.; Lasaponara, R. Predictive Modeling for Preventive Archaeology: Overview and Case Study. Open Geosciences 2014, 6, 42–55. [Google Scholar] [CrossRef]
  75. Charisma Cultural Heritage Academy for Risk Management. Study on Risk Assessment and Management of Cultural Heritage across Europe. In Risk Management for Cultural Heritage; WARREDOC: Perugia, Italy, 2022. [Google Scholar]
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Figure 1. Lama Camaggi basin in the north–central of the Apulia region (location area at the top left), with topographic, geomorphological, and archaeological features. Base map: ESRI National Geographic (on the left) model and Digital Elevation model (on the right).
Figure 1. Lama Camaggi basin in the north–central of the Apulia region (location area at the top left), with topographic, geomorphological, and archaeological features. Base map: ESRI National Geographic (on the left) model and Digital Elevation model (on the right).
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Figure 2. SfCI input data in the Lama Camaggi basin. (a) Rainfall data: isohyets in the value classes (mean annual precipitation). (b) Soil stability data: soil properties conditions in assigned value classes. (c) Land-use data in associated value classes.
Figure 2. SfCI input data in the Lama Camaggi basin. (a) Rainfall data: isohyets in the value classes (mean annual precipitation). (b) Soil stability data: soil properties conditions in assigned value classes. (c) Land-use data in associated value classes.
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Figure 3. SfCI data in the Lama Camaggi basin. (a) Slope index map; (b) Terrain ruggedness index map.
Figure 3. SfCI data in the Lama Camaggi basin. (a) Slope index map; (b) Terrain ruggedness index map.
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Figure 4. SfCI maps of the Lama Camaggi basin. (a) Sediment mobility (SM) map (derived from Equations (1)–(3)), (b) sediment flow connectivity index (SfCI) map, and (c) a_SfCI map.
Figure 4. SfCI maps of the Lama Camaggi basin. (a) Sediment mobility (SM) map (derived from Equations (1)–(3)), (b) sediment flow connectivity index (SfCI) map, and (c) a_SfCI map.
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Figure 5. Visual comparison of the a_SfCI map with archaeo-records.
Figure 5. Visual comparison of the a_SfCI map with archaeo-records.
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Figure 6. Focused visual comparison in the Monte Santa Barbara area. Right: detailed a_SfCI (a) and SfCI (b) maps corresponding to the blue box on the left. Archaeo-records highlighted in light blue are located in hotspot areas (a), and on sediment paths marked by red arrows (b).
Figure 6. Focused visual comparison in the Monte Santa Barbara area. Right: detailed a_SfCI (a) and SfCI (b) maps corresponding to the blue box on the left. Archaeo-records highlighted in light blue are located in hotspot areas (a), and on sediment paths marked by red arrows (b).
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Figure 7. Most significant archeological area. (a) Image of the site of Monte Santa Barbara (from Google Earth satellite images). (b–d) Photos of the fields corresponding to archeo-records highlighted in light blue, in north to south order.
Figure 7. Most significant archeological area. (a) Image of the site of Monte Santa Barbara (from Google Earth satellite images). (b–d) Photos of the fields corresponding to archeo-records highlighted in light blue, in north to south order.
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Figure 8. Interpretation of the relation between sediment connectivity and archaeo-records (highlighted in light blue) near the Monte Santa Barbara site. (a) Detailed SfCI map; (b) Hillshade DEM of the area with schematic reconstruction of potential downslope wash path (red arrow); (c) Archaeological material (sherds of geometric pottery–subgeometric II style–with bichromatic decoration: checkerboard patterns, wavy lines, zigzags, lozenges, squares, circles) from the Monte Santa Barbara site (on the left) and archaeo-records (on the right) documented on the surface during surveys in 2012–2013 (details of archaeological survey results can be found in [56]).
Figure 8. Interpretation of the relation between sediment connectivity and archaeo-records (highlighted in light blue) near the Monte Santa Barbara site. (a) Detailed SfCI map; (b) Hillshade DEM of the area with schematic reconstruction of potential downslope wash path (red arrow); (c) Archaeological material (sherds of geometric pottery–subgeometric II style–with bichromatic decoration: checkerboard patterns, wavy lines, zigzags, lozenges, squares, circles) from the Monte Santa Barbara site (on the left) and archaeo-records (on the right) documented on the surface during surveys in 2012–2013 (details of archaeological survey results can be found in [56]).
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Figure 9. Focused visual comparison in the Quadrone area. Right: detail of the a_SfCI (a) and hillshade DEM (b) maps, corresponding to blue box on the left.
Figure 9. Focused visual comparison in the Quadrone area. Right: detail of the a_SfCI (a) and hillshade DEM (b) maps, corresponding to blue box on the left.
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Table 1. Mean annual precipitation (MAP) in the Lama Camaggi basin classified in rainfall index values. The maximum values correspond to 1, the minimum values correspond to 0.05, and intermediate values correspond to uniform ranges (see text for details).
Table 1. Mean annual precipitation (MAP) in the Lama Camaggi basin classified in rainfall index values. The maximum values correspond to 1, the minimum values correspond to 0.05, and intermediate values correspond to uniform ranges (see text for details).
MAP
(mm per Year)		Rainfall Index Value
517–5220.05
523–5410.25
542–5600.50
561–5790.75
580–5981
Table 2. Classification of soil properties (permeability and thickness) useful to determine the soil stability conditions. Categories of the properties are derived from the relative classes and values (see text for details). LP = low permeability; MP = medium permeability; HP = high permeability; LT = low thickness; MT = medium thickness; HT = high thickness.
Table 2. Classification of soil properties (permeability and thickness) useful to determine the soil stability conditions. Categories of the properties are derived from the relative classes and values (see text for details). LP = low permeability; MP = medium permeability; HP = high permeability; LT = low thickness; MT = medium thickness; HT = high thickness.
Soil PropertiesSoil Properties Classes
Permeability Classes
1HP
2HP
3MP
4MP
5LP
6LP
Thickness Classes
<100 cmLT
100–199 cmMT
>199 cmHT
Table 3. Soil stability classes defined by the matrix of soil properties conditions (see text for details and previous caption for the soil properties abbreviations). HS = high soil stability; MS = medium soil stability; LS = low soil stability.
Table 3. Soil stability classes defined by the matrix of soil properties conditions (see text for details and previous caption for the soil properties abbreviations). HS = high soil stability; MS = medium soil stability; LS = low soil stability.
HTMTLT
HPHSMSMS
MPHSMSLS
LPMSMSLS
Table 4. Soil stability classes with corresponding soil stability index values (see text for details and previous caption for abbreviations).
Table 4. Soil stability classes with corresponding soil stability index values (see text for details and previous caption for abbreviations).
Soil Stability ClassesSoil Index Value
LS0.05
MS0.5
HS1
Table 5. Land-use classes and index values assigned considering the related sediment mobility (see text for details).
Table 5. Land-use classes and index values assigned considering the related sediment mobility (see text for details).
Land-Use ClassesLand-Use Index Value
Urban areas0.05
Grassland/pastures/shrubs0.25
Woods0.50
Croplands0.75
Beaches/Poorly vegetated1
Table 6. Slope ranges rescaled in slope index values from 0.05 and 1 (see text for details).
Table 6. Slope ranges rescaled in slope index values from 0.05 and 1 (see text for details).
Slope (Degree)Slope Index Value
0.00–0.990.05
1.00–15.990.25
16.00–30.990.50
31.00–45.990.75
46.00–60.741
Table 7. Terrain ruggedness index ranges rescaled in TRI values from 0.05 to 1 (see text for details).
Table 7. Terrain ruggedness index ranges rescaled in TRI values from 0.05 to 1 (see text for details).
TRI (m)TRI Value
0.00–0.250.05
0.26–3.000.25
3.01–6.000.50
6.01–9.000.75
9.01–12.651
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Zingaro, M.; Scicchitano, G.; Palmentola, P.; Piscitelli, A.; Refice, A.; Roseto, R.; Scardino, G.; Capolongo, D. Contribution of the Sediment Flow Connectivity Index (SfCI) in Landscape Archaeology Investigations: Test Case of a New Interdisciplinary Approach. Sustainability 2023, 15, 15042. https://doi.org/10.3390/su152015042

AMA Style

Zingaro M, Scicchitano G, Palmentola P, Piscitelli A, Refice A, Roseto R, Scardino G, Capolongo D. Contribution of the Sediment Flow Connectivity Index (SfCI) in Landscape Archaeology Investigations: Test Case of a New Interdisciplinary Approach. Sustainability. 2023; 15(20):15042. https://doi.org/10.3390/su152015042

Chicago/Turabian Style

Zingaro, Marina, Giovanni Scicchitano, Paola Palmentola, Arcangelo Piscitelli, Alberto Refice, Rodolfo Roseto, Giovanni Scardino, and Domenico Capolongo. 2023. "Contribution of the Sediment Flow Connectivity Index (SfCI) in Landscape Archaeology Investigations: Test Case of a New Interdisciplinary Approach" Sustainability 15, no. 20: 15042. https://doi.org/10.3390/su152015042

APA Style

Zingaro, M., Scicchitano, G., Palmentola, P., Piscitelli, A., Refice, A., Roseto, R., Scardino, G., & Capolongo, D. (2023). Contribution of the Sediment Flow Connectivity Index (SfCI) in Landscape Archaeology Investigations: Test Case of a New Interdisciplinary Approach. Sustainability, 15(20), 15042. https://doi.org/10.3390/su152015042

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