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 (<5 ha) to large mounds (>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.

Dynamique holocène de la végétation et occupation des Pyrénées andorranes depuis le Néolithique ancien, d'après l'analyse pollinique de la tourbière de Bosc dels Estanyons (2180 m, Vall del Madriu, Andorre)

a b s t r a c tPrevious research acknowledges the ancient and complex land-use history of European mountainous areas, which are characterised by a remarkable regional variability in terms of human practices and patterns of occupation during the Holocene. However, the combined palaeoenvironmental and archaeological study of highland human management at a micro-regional scale remains a largely unexplored research field, especially in the Pyrenees. A combined pollen, stomata, non-pollen palynomorphs (NPPs) and macrocharcoal study was carried out at three nearby alpine and subalpine peat basins from a relatively small territory (ca.1700 ha) at the Madriu valley (Andorra, eastern Pyrenees), following a fine spatialresolution strategy. The purpose was to test the suitability of high altitudinal palaeoecological sequences when reconstructing past small-scale land-use variability. The palaeoecological results of those peat records are compared and further integrated with archaeological local data, and together underline the marked complexity of high mountain land-use system over the Holocene period. Main phases of microregional land-use and landscape variability can be distinguished from the middle Neolithic to the early Bronze Age and from the Roman Period to the Modern Era. Conversely, several phases of homogeneous landscape management are distinguishable during the early Neolithic, and from the late Bronze to the late Iron Age. Results drawn from this study show that landscape variability is not necessarily connected to topographic or climatic parameters, and underline the role of social, economical and cultural parameters in the land-use organisation and the landscape shaping of high mountain spaces since Prehistory.

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Although high mountain areas have traditionally been viewed as predominantly grazing areas, with low population and a high degree of land-use stasis, recent research suggest that land-use complexity and change over time has been underestimated. This interdisciplinary palaeoenvironmental analysis has been carried out on the Pradell calcareous fen, located in the eastern Pre-Pyrenees (Spain) at 1975 m a.s.l., and it comprises different environmental indicators: pollen, stomata, non-pollen palynomorphs, macrocharcoal particles, lithostratigraphy, sedimentology and geochemistry. The results of this high temporal resolution study are integrated with archaeological data, and together provide strong evidence for the complexity of the high-mountain land-use system over the last 1500 years. Archaeological fieldwork has shown the rise of highland mining activities during the Roman period. Later, frequent fires resulted from the farming and settlement that followed the Christian conquest. Geochemical analysis of sediment cores records late-Mediaeval metal production, while the expansion of feudal cropping and the advent of several Mediaeval crises are clearly recorded in both the pollen and the historical data. Finally, the rise of a mixed economy system based on transhumance, farming, metallurgy and woodland exploitation was established during Modern and Contemporary times. The high correlation between the palaeoenvironmental, archaeological and historical data at the Pradell fen stresses the value of calcareous fens for palaeoenvironmental reconstructions of historical landscapes. Results obtained also depict high mountain landscapes as the result of the long-term interaction of many human practices, including mining and smelting, grazing, cropping and tree exploitation for the production of wood, charcoal and resin.

Abstract: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.

A range of data sources are now used to support the process of archaeological prospection, including remote sensed imagery, spy satellite photographs and aerial photographs. This paper advocates the value and importance of a hitherto under-utilised historical mapping resource—the Survey of India 1” to 1-mile map series, which was based on surveys started in the mid–late nineteenth century, and published progressively from the early twentieth century AD. These maps present a systematic documentation of the topography of the British dominions in the South Asian Subcontinent. Incidentally, they also documented the locations, the height and area of thousands of elevated mounds that were visible in the landscape at the time that the surveys were carried out, but have typically since been either damaged or destroyed by the expansion of irrigation agriculture and urbanism. Subsequent reanalysis has revealed that many of these mounds were actually the remains of ancient settlements. The digitisation and analysis of these historic maps thus creates a unique opportunity for gaining insight into the landscape archaeology of South Asia. This paper reviews the context within which these historical maps were created, presents a method for georeferencing them, and reviews the symbology that was used to represent elevated mound features that have the potential to be archaeological sites. This paper should be read in conjunction with the paper by Arnau Garcia et al. in the same issue of Geosciences, which implements a research programme combining historical maps and a range of remote sensing approaches to reconstruct historical landscape dynamics in the Indus River Basin.

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.