Multi-scale relief model (MSRM): a new algorithm for the visualization of subtle topographic change of variable size in digital elevation models.

Morphological analysis of landforms has traditionally relied on the interpretation of imagery. Although imagery provides a natural view of an area of interest (AOI) images are largely hindered by the environmental conditions at the time of image acquisition, the quality of the image and, mainly, the lack of topographical information, which is an essential factor for a correct understanding of the AOI's geomorphology. More recently digital surface models (DSMs) have been incorporated into the analytical toolbox of geomorphologists. These are usually high-resolution models derived from digital photogrammetric processes or LiDAR data. However, these are restricted to relatively small areas and are expensive or complex to acquire, which limits widespread implementation. In this paper, we present the multi-scale relief model (MSRM), which is a new algorithm for the visual interpretation of landforms using DSMs. The significance of this new method lies in its capacity to extract landform morphology from both high- and low-resolution DSMs independently of the shape or scale of the landform under study. This method thus provides important advantages compared to previous approaches as it: (1) allows the use of worldwide medium resolution models, such as SRTM, ASTER GDEM, ALOS, and TanDEM-X; (2) offers an alternative to traditional photograph interpretation that does not rely on the quality of the imagery employed nor on the environmental conditions and time of its acquisition; and (3) can be easily implemented for large areas using traditional GIS/RS software. The algorithm is tested in the Sutlej-Yamuna interfluve, which is a very large low-relief alluvial plain in northwest India where 10 000 km of palaeoriver channels have been mapped using MSRM. The code, written in Google Earth Engine's implementation of JavaScript, is provided as Supporting Information for its use in any other AOI without particular technical knowledge or access to topographical data. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data.

This paper presents an innovative multisensor, multitemporal machine-learning approach using remote sensing big data for the detection of archaeological mounds in Cholistan (Pakistan). The Cholistan Desert presents one of the largest concentrations of Indus Civilization sites (from ca 3300 to 1500 BC). Cholistan has figured prominently in theories about changes in water availability, the rise and decline of the Indus Civilization, and the transformation of fertile monsoonal alluvial plains into an extremely arid margin. This paper implements a multisensor, multitemporal machine-learning approach for the remote detection of archaeological mounds. A classifier algorithm that employs a large-scale collection of synthetic-aperture radar and multispectral images has been implemented in Google Earth Engine, resulting in an accurate probability map for mound-like signatures across an area that covers ca 36,000 km2 The results show that the area presents many more archaeological mounds than previously recorded, extending south and east into the desert, which has major implications for understanding the archaeological significance of the region. The detection of small (30 ha) suggests that there were continuous shifts in settlement location. These shifts are likely to reflect responses to a dynamic and changing hydrological network and the influence of the progressive northward advance of the desert in a long-term process that culminated in the abandonment of much of the settled area during the Late Harappan period.ER

The seeds of commerce: a network analysis-based approach to the Romano-British transport system

Communication routes are an important subject in the study of the human past. They allowed interactions between communities and the dispersal of goods and ideas. Their study, therefore, can shed light on the way in which communities inhabited the landscape, related to each other and were affected by macro-regional trends. Many methods, such as archaeomorphological analysis and Least Cost Route modelling (LCR), have been devised and are routinely employed for the reconstruction of ancient routes. Their analysis in terms of communication, trade or historical significance, however, has usually been left unexplored. This is probably due to the connected nature of routes, which form communication networks: these are shaped by interconnected nodes and extend over territories surpassing the regional scale in such a way that even a change in a single node or link can affect the whole network. Consequently, the partial reconstruction of communication networks provided by the aforementioned methods does not usually allow a holistic analysis. In this paper the relatively well understood British Roman road network is employed to explore the analytical possibilities offered by a combination of Social Network Analysis, Spatial Network Analysis and spatial interpolation-based distribution analysis. The British road network has been reconstructed using published data but also a variation of LCR in which cost surfaces are derived from cultural data obtained from large-scale cultural inventories. The distribution of introduced food plants during the Roman period serve as an excellent proxy for the study of trade along the network and its historical consequences. This multi-period archaeobotanical dataset has some evident advantages to other types of material remains: archaeobotanical remains are not reused as, for example, amphorae and, accordingly, they reflect a distribution pattern based on consumption or commerce. Some of them are imported (as they cannot be produced locally) and, consequently, their distribution would be applied through usage of the main routes. The results suggest a continuous inflow of exotics but highlight their changing transport routes, their differential access and the particular weight of certain nodal sites in the development of this commerce with direct impact on urbanisation and the overall economy of Britannia. The Roman road network acted as a major factor in the distribution of sites, their political and economic importance and their permanence or disappearance as global economic trends changed over time

Detection of Body Dump Sites and Clandestine Burials: a GIS-Based Landscape Approach

Forensic archaeology has been of inestimable help in the location and excavation of clandestine burials (Hunter 1996a:16-17, 1996b; Killam 2004; Levine et al. 1984). Landscape archaeology techniques have been adapted and widely applied for such purposes. In this article, an adaptation of one of the most common applications of archaeological GIS, that is predictive site location, is applied to the detection of body dump sites and clandestine burial sites, bringing together in the process the fields of landscape archaeology, forensic sciences, and GIS

LEGACIES OF CHANGE: THE SHAPING OF CULTURAL LANDSCAPES IN A MARGINAL MEDITERRANEAN MOUNTAIN RANGE, THE GARRAF MASSIF, NORTH-EASTERN SPAIN

International audienceHuman conceptions of landscape have influenced the shaping of landscapes as much as landscape configurations have modelled human perceptions. In this article a new theoretical approach to long-term cumulative landscape change is tested on the Garraf Massif (Baix Llobregat, north-eastern Spain). Thanks to its ancient occupation, physical character and location near to the city of Barcelona, the area provides a good illustration of how human-induced landscape change has shaped new, and sometimes conflicting, landscape perceptions. These perceptions then play an equally active role in altering the previously ‘inherited landscape’ in a long-term cyclical process that can be studied through the combined use of historical, palaeoenvironmental and archaeological records

The seeds of commerce: A network analysis-based approach to the Romano-British transport system

Communication routes are an important subject in the study of the human past. They allowed interactions between communities and the dispersal of goods and ideas. Their study, therefore, can shed light on the way in which communities inhabited the landscape, related to each other and were affected by macro-regional trends. Many methods, such as archaeomorphological analysis and Least Cost Route modelling (LCR), have been devised and are routinely employed for the reconstruction of ancient routes. Their analysis in terms of communication, trade or historical significance, however, has usually been left unexplored. This is probably due to the connected nature of routes, which form communication networks: these are shaped by interconnected nodes and extend over territories surpassing the regional scale in such a way that even a change in a single node or link can affect the whole network. Consequently, the partial reconstruction of communication networks provided by the aforementioned methods does not usually allow a holistic analysis. In this paper the relatively well understood British Roman road network is employed to explore the analytical possibilities offered by a combination of Social Network Analysis, Spatial Network Analysis and spatial interpolation-based distribution analysis. The British road network has been reconstructed using published data but also a variation of LCR in which cost surfaces are derived from cultural data obtained from large-scale cultural inventories. The distribution of introduced food plants during the Roman period serve as an excellent proxy for the study of trade along the network and its historical consequences. This multi-period archaeobotanical dataset has some evident advantages to other types of material remains: archaeobotanical remains are not reused as, for example, amphorae and, accordingly, they reflect a distribution pattern based on consumption or commerce. Some of them are imported (as they cannot be produced locally) and, consequently, their distribution would be applied through usage of the main routes. The results suggest a continuous inflow of exotics but highlight their changing transport routes, their differential access and the particular weight of certain nodal sites in the development of this commerce with direct impact on urbanisation and the overall economy of Britannia. The Roman road network acted as a major factor in the distribution of sites, their political and economic importance and their permanence or disappearance as global economic trends changed over time

Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation

Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied landscape contexts. This paper presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. Twenty-eight years of Landsat 5 data, a total of 1711 multi-spectral images, have been bulk processed using Google Earth Engine© Code Editor and cloud computing infrastructure. The use of multi-temporal data has allowed us to overcome seasonal cultivation patterns and long-term visibility issues related to recent crop selection, extensive irrigation and land-use patterns. The application of this approach on the Sutlej-Yamuna interfluve (northwest India), a core area for the Bronze Age Indus Civilisation, has enabled the reconstruction of an unsuspectedly complex palaeo-river network comprising more than 8000 km of palaeo-channels. It has also enabled the definition of the morphology of these relict courses, which provides insights into the environmental conditions in which they operated. These new data will contribute to a better understanding of the settlement distribution and environmental settings in which this, often considered riverine, civilisation operated