Uncovering archaeological landscapes at Angkor using lidar

Bayesian regression versus machine learning for rapid age estimation of archaeological features identified with lidar at Angkor

Lidar (light-detection and ranging) has revolutionized archaeology. We are now able to produce high-resolution maps of archaeological surface features over vast areas, allowing us to see ancient land-use and anthropogenic landscape modification at previously un-imagined scales. In the tropics, this has enabled documentation of previously archaeologically unrecorded cities in various tropical regions, igniting scientific and popular interest in ancient tropical urbanism. An emerging challenge, however, is to add temporal depth to this torrent of new spatial data because traditional archaeological investigations are time consuming and inherently destructive. So far, we are aware of only one attempt to apply statistics and machine learning to remotely-sensed data in order to add time-depth to spatial data. Using temples at the well-known massive urban complex of Angkor in Cambodia as a case study, a predictive model was developed combining standard regression with novel machine learning methods to estimate temple foundation dates for undated Angkorian temples identified with remote sensing, including lidar. The model's predictions were used to produce an historical population curve for Angkor and study urban expansion at this important ancient tropical urban centre. The approach, however, has certain limitations. Importantly, its handling of uncertainties leaves room for improvement, and like many machine learning approaches it is opaque regarding which predictor variables are most relevant. Here we describe a new study in which we investigated an alternative Bayesian regression approach applied to the same case study. We compare the two models in terms of their inner workings, results, and interpretive utility. We also use an updated database of Angkorian temples as the training dataset, allowing us to produce the most current estimate for temple foundations and historic spatiotemporal urban growth patterns at Angkor. Our results demonstrate that, in principle, predictive statistical and machine learning methods could be used to rapidly add chronological information to large lidar datasets and a Bayesian paradigm makes it possible to incorporate important uncertainties-especially chronological-into modelled temporal estimates.

Lidar reveals pre-Hispanic low-density urbanism in the Bolivian Amazon

Archaeological remains of agrarian-based, low-density urbananism^1–3 have been reported to exist beneath the tropical forests of Southeast Asia, Sri Lanka and Central America^4–6. However, beyond some large interconnected settlements in southern Amazonia^7–9, there has been no such evidence for pre-Hispanic Amazonia. Here we present lidar data of sites belonging to the Casarabe culture (around ad 500 to ad 1400)^10–13 in the Llanos de Mojos savannah–forest mosaic, southwest Amazonia, revealing the presence of two remarkably large sites (147 ha and 315 ha) in a dense four-tiered settlement system. The Casarabe culture area, as far as known today, spans approximately 4,500 km², with one of the large settlement sites controlling an area of approximately 500 km². The civic-ceremonial architecture of these large settlement sites includes stepped platforms, on top of which lie U-shaped structures, rectangular platform mounds and conical pyramids (which are up to 22 m tall). The large settlement sites are surrounded by ranked concentric polygonal banks and represent central nodes that are connected to lower-ranked sites by straight, raised causeways that stretch over several kilometres. Massive water-management infrastructure, composed of canals and reservoirs, complete the settlement system in an anthropogenically modified landscape. Our results indicate that the Casarabe-culture settlement pattern represents a type of tropical low-density urbanism that has not previously been described in Amazonia.

Two remarkably large sites in southwest Amazonia, belonging to the Casarabe culture, include complex civic-ceremonial architecture and large water-management infrastructure, representing a type of tropical low-density urbanism that has not previously been described in Amazonia.

Digital Deforestation: Comparing Automated Approaches to the Production of Digital Terrain Models (DTMs) in Agisoft Metashape

This paper tests the suitability of automated point cloud classification tools provided by the popular image-based modeling (IBM) software package Agisoft Metashape for the generation of digital terrain models (DTMs) at moderately-vegetated archaeological sites. DTMs are often required for various forms of archaeological mapping and analysis. The suite of tools provided by Agisoft are relatively user-friendly as compared to many point cloud classification algorithms and do not require the use of additional software. Based on a case study from the Mycenaean site of Kastrouli, Greece, the mostly-automated, geometric classification tool “Classify Ground Points” provides the best results and produces a quality DTM that is sufficient for mapping and analysis. Each of the methods tested in this paper can likely be improved through manual editing of point cloud classification.

A Bibliometric and Visual Analysis of Global Urban Resilience Research in 2011–2020: Development and Hotspots

Urban resilience (UR), which promotes the implementation of resilient cities, has received widespread attention. The purpose of this study is to visualize the knowledge background, research status, and knowledge structure of relevant literatures by using a Citespace based scientometrics survey. The results show that UR is an increasingly popular topic, with 2629 articles published during the study period. (1) The most prolific publications and journals involved in the flourishment of UR research were identified by co-citation. The United States was the most productive contributor, with numerous publications and active institutions. Journal of Cleaner Production, Sustainability, International Journal of Disaster Risk Reduction were the three most cited journals. (2) Co-occurrence analysis was employed to determine the highly productive keywords, and subject categories in the UR domain, including “environmental science & ecology”, “environmental sciences, “science & technology”, “environmental studies”, “green & sustainable science & technology”, and “water resources”. (3) The diversity of highly cited authors in different countries and regions confirmed the evolution of UR studies. (4) Furthermore, the classification of UR knowledge was performed in the form of clusters and knowledge structure to achieve ten distinct sub-domains (e.g., Urban floods and stormwater management, Urban ecosystem services, Urban landscapes, and Trauma). This study provides an overview of UR research and research topics so that future researchers can identify their research topics and partners.

Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data

Advanced deep learning methods combined with regional, open access, airborne Light Detection and Ranging (LiDAR) data have great potential to study the spatial extent of historic land use features preserved under the forest canopy throughout New England, a region in the northeastern United States. Mapping anthropogenic features plays a key role in understanding historic land use dynamics during the 17th to early 20th centuries, however previous studies have primarily used manual or semi-automated digitization methods, which are time consuming for broad-scale mapping. This study applies fully-automated deep convolutional neural networks (i.e., U-Net) with LiDAR derivatives to identify relict charcoal hearths (RCHs), a type of historical land use feature. Results show that slope, hillshade, and Visualization for Archaeological Topography (VAT) rasters work well in six localized test regions (spatial scale: <1.5 km2, best F1 score: 95.5%), but also at broader extents at the town level (spatial scale: 493 km2, best F1 score: 86%). The model performed best in areas with deciduous forest and high slope terrain (e.g., >15 degrees) (F1 score: 86.8%) compared to coniferous forest and low slope terrain (e.g., <15 degrees) (F1 score: 70.1%). Overall, our results contribute to current methodological discussions regarding automated extraction of historical cultural features using deep learning and LiDAR.

Rethinking the Landscape: Emerging Approaches to Archaeological Remote Sensing

An emerging arena of archaeological research is beginning to deploy remote sensing technologies—including aerial and satellite imagery, digital topographic data, and drone-acquired and terrestrial geophysical data—not only in support of conventional fieldwork but also as an independent means of exploring the archaeological landscape. This article provides a critical review of recent research that relies on an ever-growing arsenal of imagery and instruments to undertake innovative investigations: mapping regional-scale settlement histories, documenting ancient land use practices, revealing the complexity of settled spaces, building nuanced pictures of environmental contexts, and monitoring at-risk cultural heritage. At the same time, the disruptive nature of these technologies is generating complex new challenges and controversies surrounding data access and preservation, approaches to a deluge of information, and issues of ethical remote sensing. As we navigate these challenges, remote sensing technologies nonetheless offer revolutionary ways of interrogating the archaeological record and transformative insights into the human past.

Historical socioecological transformations in the global tropics as an Anthropocene analogue

Large, low-density settlements of the tropical world disintegrated during the first and second millennia of the CE. This phenomenon, which occurred in South Asia, Southeast Asia, and Mesoamerica, is strongly associated with climate variability and extensive landscape transformation. These profound social transformations in the tropical world have been popularized as "collapse," yet archaeological evidence suggests a more complex and nuanced story characterized by persistence, adaptation, and resilience at the local and regional scales. The resulting tension between ideas of climate-driven collapse and evidence for diverse social responses challenges our understanding of long-term resilience and vulnerability to environmental change in the global tropics. Here, we compare the archetypal urban collapse of the Maya, in modern Belize, Guatemala, Honduras, and Mexico, during the 8th to 11th centuries CE, and the Khmer in modern Cambodia, Laos, Thailand, and Vietnam during the 14th to 15th centuries CE. We argue that the social response to environmental stress is spatially and temporally heterogenous, reflecting the generation of large-scale landesque capital surrounding the urban cores. Divergences between vulnerable urban elite and apparently resilient dispersed agricultural settlements sit uncomfortably with simplistic notions of social collapse and raise important questions for humanity as we move deeper into the Anthropocene.

Provisioning an Early City: Spatial Equilibrium in the Agricultural Economy at Angkor, Cambodia

A dominant view in economic anthropology is that farmers must overcome decreasing marginal returns in the process of intensification. However, it is difficult to reconcile this view with the emergence of urban systems, which require substantial increases in labor productivity to support a growing non-farming population. This quandary is starkly posed by the rise of Angkor (Cambodia, 9th-fourteenth centuries CE), one of the most extensive preindustrial cities yet documented through archaeology. Here, we leverage extensive documentation of the Greater Angkor Region to illustrate how the social and spatial organization of agricultural production contributed to its food system. First, we find evidence for supra-household-level organization that generated increasing returns to farming labor. Second, we find spatial patterns which indicate that land-use choices took transportation costs to the urban core into account. These patterns suggest agricultural production at Angkor was organized in ways that are more similar to other forms of urban production than to a smallholder system.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10816-021-09535-5.

Humans as geomorphic agents: Lidar detection of the past, present and future of the Teotihuacan Valley, Mexico

As humans are the primary geomorphic agents on the landscape, it is essential to assess the magnitude, chronological span, and future effects of artificial ground that is expanding under modern urbanization at an alarming rate. We argue humans have been primary geomorphic agents of landscapes since the rise of early urbanism that continue to structure our everyday lives. Past and present anthropogenic actions mold a dynamic "taskscape" (not just a landscape) onto the physical environment. For example, one of the largest Pre-Columbian metropolitan centers of the New World, the UNESCO world heritage site of Teotihuacan, demonstrates how past anthropogenic actions continue to inform the modern taskscape, including modern street and land alignments. This paper applies a multi-scalar, long durée approach to urban landscapes utilizing the first lidar map of the Teotihuacan Valley to create a geospatial database that links modern and topographic features visible on the lidar map with ground survey, historic survey, and excavation data. Already, we have recorded not only new features previously unrecognized by historic surveys, but also the complete erasure of archaeological features due to modern (post-2015) mining operations. The lidar map database will continue to evolve with the dynamic landscape, able to assess continuity and changes on the Teotihuacan Valley, which can benefit decision makers contemplating the stewardship, transformation, or destruction of this heritage landscape.

Revealing Archaeological Sites under Mediterranean Forest Canopy Using LiDAR: El Viandar Castle (husum) in El Hoyo (Belmez-Córdoba, Spain)

Light detection and Ranging (LiDAR) technology is a valuable tool for archaeological prospection in areas covered by dense vegetation. Its capacity to penetrate dense forest environments enables it to detect archaeological remains scattered over orographically complex areas. LiDAR-derived digital terrain models (DTMs) have made an exceptional contribution towards identifying topographic landscapes of archaeological interest. In this study, we focus on an area of intense historic settlement from the Chalcolithic to the Middle Ages, which today is completely covered by Mediterranean forest. Due to the dense canopy, and the fact that it is a protected area on private land, it has never been analyzed. To reveal the settlement, we primarily used a series of LiDAR mapping surveys to gather data and analyzed other open access remote sensing resources from the National Geographic Institute of Spain (IGN). The IGN LiDAR data proved to be of particular interest. These resources enabled us to detect an ancient fortress (El Viandar Castle) and its surrounding settlement. LiDAR, in conjunction with other products, was fundamental in identifying the site. Equally, the mapping surveys enabled us to analyze the limits and scope of the IGN airborne LiDAR and other free access remote sensing products. Our background in this research demonstrates that low-cost products applied to LiDAR research in archaeology have major limitations when it is necessary to have a high level of spatial resolution in order to define the layout and the main components of an archaeological site.

Classification of airborne 3D point clouds regarding separation of vegetation in complex environments

Classification of outdoor point clouds is an intensely studied topic, particularly with respect to the separation of vegetation from the terrain and manmade structures. In the presence of many overhanging and vertical structures, the (relative) height is no longer a reliable criterion for such a separation. An alternative would be to apply supervised classification; however, thousands of examples are typically required for appropriate training. In this paper, an unsupervised and rotation-invariant method is presented and evaluated for three datasets with very different characteristics. The method allows us to detect planar patches by filtering and clustering so-called superpoints, whereby the well-known but suitably modified random sampling and consensus (RANSAC) approach plays a key role for plane estimation in outlier-rich data. The performance of our method is compared to that produced by supervised classifiers common for remote sensing settings: random forest as learner and feature sets for point cloud processing, like covariance-based features or point descriptors. It is shown that for point clouds resulting from airborne laser scans, the detection accuracy of the proposed method is over 96% and, as such, higher than that of standard supervised classification approaches. Because of artifacts caused by interpolation during 3D stereo matching, the overall accuracy was lower for photogrammetric point clouds (74-77%). However, using additional salient features, such as the normalized green-red difference index, the results became more accurate and less dependent on the data source.

Diachronic modeling of the population within the medieval Greater Angkor Region settlement complex

Angkor is one of the world's largest premodern settlement complexes (9th to 15th centuries CE), but to date, no comprehensive demographic study has been completed, and key aspects of its population and demographic history remain unknown. Here, we combine lidar, archaeological excavation data, radiocarbon dates, and machine learning algorithms to create maps that model the development of the city and its population growth through time. We conclude that the Greater Angkor Region was home to approximately 700,000 to 900,000 inhabitants at its apogee in the 13th century CE. This granular, diachronic, paleodemographic model of the Angkor complex can be applied to any ancient civilization.

Detecting Classic Maya Settlements with Lidar-Derived Relief Visualizations

In the past decade, Light Detection and Ranging (lidar) has fundamentally changed our ability to remotely detect archaeological features and deepen our understanding of past human-environment interactions, settlement systems, agricultural practices, and monumental constructions. Across archaeological contexts, lidar relief visualization techniques test how local environments impact archaeological prospection. This study used a 132 km2 lidar dataset to assess three relief visualization techniques—sky-view factor (SVF), topographic position index (TPI), and simple local relief model (SLRM)—and object-based image analysis (OBIA) on a slope model for the non-automated visual detection of small hinterland Classic (250–800 CE) Maya settlements near the polities of Uxbenká and Ix Kuku’il in Southern Belize. Pedestrian survey in the study area identified 315 plazuelas across a 35 km2 area; the remaining 90 km2 in the lidar dataset is yet to be surveyed. The previously surveyed plazuelas were compared to the plazuelas visually identified on the TPI and SLRM. In total, an additional 563 new possible plazuelas were visually identified across the lidar dataset, using TPI and SLRM. Larger plazuelas, and especially plazuelas located in disturbed environments, are often more likely to be detected in a visual assessment of the TPI and SLRM. These findings emphasize the extent and density of Classic Maya settlements and highlight the continued need for pedestrian survey to ground-truth remotely identified archaeological features and the impact of modern anthropogenic behaviors for archaeological prospection. Remote sensing and lidar have deepened our understanding of past human settlement systems and low-density urbanism, processes that we experience today as humans residing in modern cities.

Beyond Never-Never Land: Integrating LiDAR and Geophysical Surveys at the Johnston Site, Pinson Mounds State Archaeological Park, Tennessee, USA

Archaeologists often use near-surface geophysics or LiDAR-derived topographic imagery in their research. However, rarely are the two integrated in a way that offers a robust understanding of the complex historical palimpsests embedded within a social landscape. In this paper we present an integrated aerial and terrestrial remote sensing program at the Johnston Site, part of the larger Pinson Mounds landscape in the American MidSouth. Our work at Johnston was focused on better understanding the history of human landscape use and change so that we can begin to compare the Johnston Site with other large Middle Woodland (200 BC–AD 500) ceremonial centers in the region. Our research allowed us to examine the accuracy of an early map of the Johnston Site made in the early 20th century. However, our integrated remote sensing approach allows us to go well beyond testing the usefulness of the map; it helps identify different uses of the site through time and across space. Our research emphasizes the importance of an integrated remote sensing methodology when examining complex social landscapes of the past and present.

Monumental architecture at Aguada Fénix and the rise of Maya civilization

Archaeologists have traditionally thought that the development of Maya civilization was gradual, assuming that small villages began to emerge during the Middle Preclassic period (1000-350 BC; dates are calibrated throughout) along with the use of ceramics and the adoption of sedentism¹. Recent finds of early ceremonial complexes are beginning to challenge this model. Here we describe an airborne lidar survey and excavations of the previously unknown site of Aguada Fénix (Tabasco, Mexico) with an artificial plateau, which measures 1,400 m in length and 10 to 15 m in height and has 9 causeways radiating out from it. We dated this construction to between 1000 and 800 BC using a Bayesian analysis of radiocarbon dates. To our knowledge, this is the oldest monumental construction ever found in the Maya area and the largest in the entire pre-Hispanic history of the region. Although the site exhibits some similarities to the earlier Olmec centre of San Lorenzo, the community of Aguada Fénix probably did not have marked social inequality comparable to that of San Lorenzo. Aguada Fénix and other ceremonial complexes of the same period suggest the importance of communal work in the initial development of Maya civilization.

Assessing the Utility of Open-Access Bathymetric Data for Shipwreck Detection in the United States

Investigation of submerged cultural heritage is an important area of archeological focus. However, the expense of acquiring the necessary data to conduct studies of underwater landscapes is often prohibitive to many researchers. Within the United States, highly resolved bathymetric data are openly available from governmental agencies, and yet little to no marine archaeological exploration has occurred using this information. Here, we investigate the archaeological utility of freely available bathymetric datasets from the National Oceanic and Atmospheric Administration (NOAA) in the United States. These datasets have not previously been utilized for archaeological publications, and include swath bathymetric and topographic LiDAR data, which are widely used by marine archaeologists. We present three case studies from Long Island, New York, coastal Massachusetts (on the Eastern coast of North America), and New Orleans (on the Gulf Coast of North America) to demonstrate the potential of this open-access information for locating shipwreck sites. Results indicate that shipwrecks at varying levels of preservation can be identified at depths up to 160 m, and that even in extremely turbid waters, bathymetric LiDAR can detect some wreckage. Following this assessment, we develop an automated shipwreck detection procedure using an inverse depression analysis. Our results are promising for automated detection methods in marine archaeology research. We argue that archaeologists in the United States should take advantage of these freely available data, as it is possible that these bathymetric data can be used for detection and conservation of cultural and environmental resources even without large funding acquisitions.

Modeling Virtual Angkor: An Evolutionary Approach to a Single Urban Space

Virtual Angkor is a comprehensive, interactive, visual representation of life in the premodern Cambodian city of Angkor. It is the cumulative result of extensive computer graphics research underpinned by a broad investigation in the sources and oriented towards a clear set of educational goals. Virtual Angkor represents the potential for symbiotic platforms that draw on and merge artistic, historical, technical, and scientific expertise. In this overview, considerations of time compression, parameter-driven simulation, modeling fidelity, instancing and duplication, interaction, artistic license, integrating feedback, and ongoing collaborative processes are highlighted.

Ancient Maya wetland fields revealed under tropical forest canopy from laser scanning and multiproxy evidence

We report on a large area of ancient Maya wetland field systems in Belize, Central America, based on airborne lidar survey coupled with multiple proxies and radiocarbon dates that reveal ancient field uses and chronology. The lidar survey indicated four main areas of wetland complexes, including the Birds of Paradise wetland field complex that is five times larger than earlier remote and ground survey had indicated, and revealed a previously unknown wetland field complex that is even larger. The field systems date mainly to the Maya Late and Terminal Classic (∼1,400-1,000 y ago), but with evidence from as early as the Late Preclassic (∼1,800 y ago) and as late as the Early Postclassic (∼900 y ago). Previous study showed that these were polycultural systems that grew typical ancient Maya crops including maize, arrowroot, squash, avocado, and other fruits and harvested fauna. The wetland fields were active at a time of population expansion, landscape alteration, and droughts and could have been adaptations to all of these major shifts in Maya civilization. These wetland-farming systems add to the evidence for early and extensive human impacts on the global tropics. Broader evidence suggests a wide distribution of wetland agroecosystems across the Maya Lowlands and Americas, and we hypothesize the increase of atmospheric carbon dioxide and methane from burning, preparing, and maintaining these field systems contributed to the Early Anthropocene.

How Maya Archaeologists Discovered the 99% Through the Study of Settlement Patterns

This article presents an autobiographical perspective on the changing nature of Maya archaeology, focusing on the role of settlement pattern studies in illuminating the lives of commoners as well as on the traditional emphasis on the ruling elite. Advances in understanding the nature of nonelite peoples in ancient Maya society are discussed, as are the many current gaps in scholarly understandings of pre-Columbian Maya civilization, especially with regard to the diversity of ancient “commoners” and the difficulty in analyzing them as a single group.

Artificial plateau construction during the Preclassic period at the Maya site of Ceibal, Guatemala

Investigations at the Maya site of Ceibal, Guatemala, documented an artificial plateau, measuring 600 x 340 m in horizontal dimensions and 6 to 15 m in height. Unlike highly visible pyramids, such horizontally extensive constructions covered by the rainforest are difficult to recognize on the ground, but airborne laser scanning (LiDAR) revealed its planned form. Excavations carried out over many years provided data on its construction sequence, fill volumes, and labor investments. The initial construction of the plateau occurred around 950 B.C. when a formal ceremonial complex was built in its center. This was the period when the inhabitants of the Maya lowlands were adopting a new way of life with greater reliance on maize agriculture, full sedentism, and ceramic use. The inhabitants of areas surrounding Ceibal, who retained certain levels of residential mobility, probably participated in the construction of the plateau. In this regard, the Ceibal plateau is comparable to monumental constructions that emerged before or during the transition to agriculture or sedentism in other parts of the world. The data from Ceibal compel researchers to examine the social implications of monumental constructions in the Maya lowlands before the establishment of centralized polities with hereditary rulers. Unlike pyramids, where access to the summits may have been limited to privileged individuals, the horizontal monumentality of the plateau was probably more conducive to inclusive interaction. The Ceibal plateau continued to be built up during the Preclassic period (1000 B.C.-A.D. 175), and its fill volume substantially surpassed those of pyramids. Large-scale construction projects likely promoted organizational and managerial innovations among participants, which may have set the stage for later administrative centralization.

Point Cloud-Oriented Inspection of Old Street’s Sustainable Transformation from the Ceramic Industry to Cultural Tourism: A Case Study of Yingge, a Ceramic Town in Taiwan

Yingge, a ceramic-producing town in Northern Taiwan, has experienced three development stages in the 50 years since 1970. The town’s fabric and the second contour evolved through the transformation of its former manufacturing industry into cultural tourism on Old Street. This process of evolution is evidenced through chronological changes of overlaid sections, skylines, and horizontal sections along Old Street since 1970. The street fabric has been shaped by its historical background, government planning strategies, commercial activities, cultural identity, and living patterns. Three-dimensional (3D) scans supported our analysis by capturing and segmenting the vocabularies and overlaid sections with special characteristics and changes. Commercial spaces and open street spaces were found to be mutually influential. A flexible and sometimes hidden spatial structure of fabric was elucidated. Yingge has become a large-scale shopping mall and important window into cultural tourism, with its fabric and contours redeveloped to be consistent with the identity of nearby cities. 3D scanning data were combined with documentation and maps to create a referable connection between reality and chronological data. An augmented reality (AR) application was used to simplify the inspection process through a productive connection between as-built scans and user interactions.

An Analysis of Ground-Point Classifiers for Terrestrial LiDAR

Previous literature has compared the performance of existing ground point classification (GPC) techniques on airborne LiDAR (ALS) data (LiDAR—light detection and ranging); however, their performance when applied to terrestrial LiDAR (TLS) data has not yet been addressed. This research tested the classification accuracy of five openly-available GPC algorithms on seven TLS datasets: Zhang et al.’s inverted cloth simulation (CSF), Kraus and Pfeiffer’s hierarchical weighted robust interpolation classifier (HWRI), Axelsson’s progressive TIN densification filter (TIN), Evans and Hudak’s multiscale curvature classification (MCC), and Vosselman’s modified slope-based filter (MSBF). Classification performance was analyzed using the kappa index of agreement (KIA) and rasterized spatial distribution of classification accuracy datasets generated through comparisons with manually classified reference datasets. The results identified a decrease in classification accuracy for the CSF and HWRI classification of low vegetation, for the HWRI and MCC classifications of variably sloped terrain, for the HWRI and TIN classifications of low outlier points, and for the TIN and MSBF classifications of off-terrain (OT) points without any ground points beneath. Additionally, the results show that while no single algorithm was suitable for use on all datasets containing varying terrain characteristics and OT object types, in general, a mathematical-morphology/slope-based method outperformed other methods, reporting a kappa score of 0.902.

Temple occupation and the tempo of collapse at Angkor Wat, Cambodia

The Angkorian Empire was Southeast Asia’s largest premodern polity. Its ancient temple of Angkor Wat has been a religious center since its construction in the 12th century CE and remains a contemporary icon for the wider Angkor polity, even appearing on Cambodia’s flag. Our paper draws from intensive excavations in the Angkor Wat enclosure to present a fine-grained chronological analysis of the temple’s use history. We focus on where people lived in the temple complex rather than on the temple itself, and identify a chronometric gap that does not fully correlate with documented historical events. This granular study of Angkor Wat’s historical sequence contributes to understanding the tempo of organizational change taking place during Angkor’s collapse.

The 9th–15th century Angkorian state was Southeast Asia’s greatest premodern empire and Angkor Wat in the World Heritage site of Angkor is one of its largest religious monuments. Here we use excavation and chronometric data from three field seasons at Angkor Wat to understand the decline and reorganization of the Angkorian Empire, which was a more protracted and complex process than historians imagined. Excavation data and Bayesian modeling on a corpus of 16 radiocarbon dates in particular demand a revised chronology for the Angkor Wat landscape. It was initially in use from the 11th century CE with subsequent habitation until the 13th century CE. Following this period, there is a gap in our dates, which we hypothesize signifies a change in the use of the occupation mounds during this period. However, Angkor Wat was never completely abandoned, as the dates suggest that the mounds were in use again in the late 14th–early 15th centuries until the 17th or 18th centuries CE. This break in dates points toward a reorganization of Angkor Wat’s enclosure space, but not during the historically recorded 15th century collapse. Our excavation data are consistent with multiple lines of evidence demonstrating the region’s continued ideological importance and residential use, even after the collapse and shift southward of the polity’s capital. We argue that fine-grained chronological analysis is critical to building local historical sequences and illustrate how such granularity adds nuance to how we interpret the tempo of organizational change before, during, and after the decline of Angkor.

Remote Sensing and Historical Morphodynamics of Alluvial Plains. The 1909 Indus Flood and the City of Dera Ghazi Khan (Province of Punjab, Pakistan)

This paper explores the historical inundation of the city of Dera Ghazi Kkan (Punjab, Pakistan) in 1909. The rich documentation about this episode available—including historic news reports, books and maps—is used to reconstruct the historical dynamics between an urban settlement and the river morphodynamics in the Indus alluvial plain. Map and document-based historical regressive analysis is complemented with the examination of images obtained through different Remote Sensing techniques, including the use of new algorithms specifically developed for the study of topography and seasonal water availability which make possible to assess long-term changes in the Indus River basin. This case of study provides an opportunity to examine: (1) how historical hydrological dynamics are reflected in RS produced images; (2) the implications of river morphodynamics in the interpretation of settlement patterning; and (3) the documented socio-political responses to such geomorphological change. The results of this analysis are used to consider the long-term dynamics that have influenced the archaeo/cultural landscapes of the Indus River basin. This assessment provides critical insights for: (1) understanding aspects of the formation, preservation of representation of the archaeological record; (2) identifying traces of morphodynamics and their possible impact over the cultural heritage; and (3) offering insights into the role that recent historical documents can have in the interpretation of RS materials. This paper should be read in conjunction with the paper by Cameron Petrie et al. in the same issue of Geosciences, which explores the Survey of India 1” to 1-mile map series and outlines methods for using these historical maps for research on historical landscapes and settlement distribution.

Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia

Archaeologists often need to date and group artifact types to discern typologies, chronologies, and classifications. For over a century, statisticians have been using classification and clustering techniques to infer patterns in data that can be defined by algorithms. In the case of archaeology, linear regression algorithms are often used to chronologically date features and sites, and pattern recognition is used to develop typologies and classifications. However, archaeological data is often expensive to collect, and analyses are often limited by poor sample sizes and datasets. Here we show that recent advances in computation allow archaeologists to use machine learning based on much of the same statistical theory to address more complex problems using increased computing power and larger and incomplete datasets. This paper approaches the problem of predicting the chronology of archaeological sites through a case study of medieval temples in Angkor, Cambodia. For this study, we have a large dataset of temples with known architectural elements and artifacts; however, less than ten percent of the sample of temples have known dates, and much of the attribute data is incomplete. Our results suggest that the algorithms can predict dates for temples from 821–1150 CE with a 49-66-year average absolute error. We find that this method surpasses traditional supervised and unsupervised statistical approaches for under-specified portions of the dataset and is a promising new method for anthropological inquiry.

Medieval Archaeology Under the Canopy with LiDAR. The (Re)Discovery of a Medieval Fortified Settlement in Southern Italy

Despite the recognized effectiveness of LiDAR in penetrating forest canopies, its capability for archaeological prospection can be strongly limited in areas covered by dense vegetation for the detection of subtle remains scattered over morphologically complex areas. In these cases, an important contribution to improve the identification of topographic variations of archaeological interest is provided by LiDAR-derived models (LDMs) based on relief visualization techniques. In this paper, diverse LDMs were applied to the medieval site of Torre Cisterna to the north of Melfi (Southern Italy), selected for this study because it is located on a hilly area with complex topography and thick vegetation cover. These conditions are common in several places of the Apennines in Southern Italy and prevented investigations during the 20th century. Diverse LDMs were used to obtain maximum information and to compare the performance of both subjective (through visual inspections) and objective (through their automatic classification) methods. To improve the discrimination/extraction capability of archaeological micro-relief, noise filtering was applied to Digital Terrain Model (DTM) before obtaining the LDMs. The automatic procedure allowed us to extract the most significant and typical features of a fortified settlement, such as the city walls and a tower castle. Other small, subtle features attributable to possible buried buildings of a habitation area have been identified by visual inspection of LDMs. Field surveys and in-situ inspections were carried out to verify the archaeological points of interest, microtopographical features, and landforms observed from the DTM-derived models, most of them automatically extracted. As a whole, the investigations allowed (i) the rediscovery of a fortified settlement from the 11th century and (ii) the detection of an unknown urban area abandoned in the Middle Ages.

Evaluating the ability of community-protected forests in Cambodia to prevent deforestation and degradation using temporal remote sensing data

Community forests are known to play an important role in preserving forests in Cambodia, a country that has seen rapid deforestation in recent decades. The detailed evaluation of the ability of community-protected forests to retain forest cover and prevent degradation in Cambodia will help to guide future conservation management. In this study, a combination of remotely sensing data was used to compare the temporal variation in forest structure for six different community forests located in the Phnom Kulen National Park (PKNP) in Cambodia and to assess how these dynamics vary between community-protected forests and a wider study area. Medium-resolution Landsat, ALOS PALSAR data, and high-resolution LiDAR data were used to study the spatial distribution of forest degradation patterns and their impacts on above-ground biomass (AGB) changes. Analysis of the remotely sensing data acquired at different spatial resolutions revealed that between 2012 and 2015, the community forests had higher forest cover persistence and lower rates of forest cover loss compared to the entire study area. Furthermore, they faced lower encroachment from cashew plantations compared to the wider landscape. Four of the six community forests showed a recovery in canopy gap fractions and subsequently, an increase in the AGB stock. The levels of degradation decreased in forests that had an increase in AGB values. However, all community forests experienced an increase in understory damage as a result of selective tree removal, and the community forests with the sharpest increase in understory damage experienced AGB losses. This is the first time multitemporal high-resolution LiDAR data have been used to analyze the impact of human-induced forest degradation on forest structure and AGB. The findings of this work indicate that while community-protected forests can improve conservation outcomes to some extent, more interventions are needed to curb the illegal selective logging of valuable timber trees.

The demise of Angkor: Systemic vulnerability of urban infrastructure to climatic variations

Complex infrastructural networks provide critical services to cities but can be vulnerable to external stresses, including climatic variability. This vulnerability has also challenged past urban settlements, but its role in cases of historic urban demise has not been precisely documented. We transform archeological data from the medieval Cambodian city of Angkor into a numerical model that allows us to quantify topological damage to critical urban infrastructure resulting from climatic variability. Our model reveals unstable behavior in which extensive and cascading damage to infrastructure occurs in response to flooding within Angkor's urban water management system. The likelihood and extent of the cascading failure abruptly grow with the magnitude of flooding relative to normal flows in the system. Our results support the hypothesis that systemic infrastructural vulnerability, coupled with abrupt climatic variation, contributed to the demise of the city. The factors behind Angkor's demise are analogous to challenges faced by modern urban communities struggling with complex critical infrastructure.

Mapping an ancient city with a century of remotely sensed data

The rapidly growing global population places cultural heritage at great risk, and the encroachment of modern settlement on archaeological sites means that valuable information about how past societies worked and interacted with the environment is lost. To manage and mitigate these risks, we require knowledge about what has been lost and what remains, so we can actively decide what should be investigated and what should be preserved for the future. Remote sensing provides archaeologists with some of the tools we need to do this. In this paper we explore the application of multitemporal, multisensor data to map features and chart the impacts of urban encroachment on the ancient city of Jerash (in modern Jordan) by combining archives of aerial photography dating back to 1917 with state-of-the-art airborne laser scanning. The combined results revealed details of the water distribution system and the ancient city plan. This demonstrates that by combining historical images with modern aerial and ground-based data we can successfully detect and contextualize these features and thus achieve a better understanding of life in a city in the past. These methods are essential, given that much of the ancient city has been lost to modern development and the historical imagery is often our only source of information.

Archaeological application of airborne LiDAR to examine social changes in the Ceibal region of the Maya lowlands

Although the application of LiDAR has made significant contributions to archaeology, LiDAR only provides a synchronic view of the current topography. An important challenge for researchers is to extract diachronic information over typically extensive LiDAR-surveyed areas in an efficient manner. By applying an architectural chronology obtained from intensive excavations at the site center and by complementing it with surface collection and test excavations in peripheral zones, we analyze LiDAR data over an area of 470 km² to trace social changes through time in the Ceibal region, Guatemala, of the Maya lowlands. We refine estimates of structure counts and populations by applying commission and omission error rates calculated from the results of ground-truthing. Although the results of our study need to be tested and refined with additional research in the future, they provide an initial understanding of social processes over a wide area. Ceibal appears to have served as the only ceremonial complex in the region during the transition to sedentism at the beginning of the Middle Preclassic period (c. 1000 BC). As a more sedentary way of life was accepted during the late part of the Middle Preclassic period and the initial Late Preclassic period (600–300 BC), more ceremonial assemblages were constructed outside the Ceibal center, possibly symbolizing the local groups’ claim to surrounding agricultural lands. From the middle Late Preclassic to the initial Early Classic period (300 BC-AD 300), a significant number of pyramidal complexes were probably built. Their high concentration in the Ceibal center probably reflects increasing political centralization. After a demographic decline during the rest of the Early Classic period, the population in the Ceibal region reached the highest level during the Late and Terminal Classic periods, when dynastic rule was well established (AD 600–950).

Radar interferometry offers new insights into threats to the Angkor site

The conservation of World Heritage is critical to the cultural and social sustainability of regions and nations. Risk monitoring and preventive diagnosis of threats to heritage sites in any given ecosystem are a complex and challenging task. Taking advantage of the performance of Earth Observation technologies, we measured the impacts of hitherto imperceptible and poorly understood factors of groundwater and temperature variations on the monuments in the Angkor World Heritage site (400 km²). We developed a two-scale synthetic aperture radar interferometry (InSAR) approach. We describe spatial-temporal displacements (at millimeter-level accuracy), as measured by high-resolution TerraSAR/TanDEM-X satellite images, to provide a new solution to resolve the current controversy surrounding the potential structural collapse of monuments in Angkor. Multidisciplinary analysis in conjunction with a deterioration kinetics model offers new insights into the causes that trigger the potential decline of Angkor monuments. Our results show that pumping groundwater for residential and touristic establishments did not threaten the sustainability of monuments during 2011 to 2013; however, seasonal variations of the groundwater table and the thermodynamics of stone materials are factors that could trigger and/or aggravate the deterioration of monuments. These factors amplify known impacts of chemical weathering and biological alteration of temple materials. The InSAR solution reported in this study could have implications for monitoring and sustainable conservation of monuments in World Heritage sites elsewhere.

Urbanism and Anthropogenic Landscapes

Humans consistently modify their environments—both directly and indirectly. However, the linkage between human activity and anthropogenic landscapes intensifies in urban situations. The artificial landscapes and dense concentrations of human populations encountered in urban environments create a centripetal pull for resources that results in continual and distant landscape changes, thus inextricably linking urbanism and anthropogenic landscapes. Examining past and present patterns of urban settlement and environmental impact provides context for this symbiotic relationship. Archaeological data, methodology, and technology offer insight into the similarities and variations in urban anthropogenic landscapes across time and space, suggesting that ancient practices can be compared with contemporary ones and that ancient models may have applicability for future-focused urban planning.

Detecting Precontact Anthropogenic Microtopographic Features in a Forested Landscape with Lidar: A Case Study from the Upper Great Lakes Region, AD 1000-1600

Forested settings present challenges for understanding the full extent of past human landscape modifications. Field-based archaeological reconnaissance in forests is low-efficiency and most remote sensing techniques are of limited utility, and together, this means many past sites and features in forests are unknown. Archaeologists have increasingly used light detection and ranging (lidar), a remote sensing tool that uses pulses of light to measure reflecting surfaces at high spatial resolution, to address these limitations. Archaeology studies using lidar have made significant progress identifying permanent structures built by large-scale complex agriculturalist societies. Largely unaccounted for, however, are numerous small and more practical modifications of landscapes by smaller-scale societies. Here we show these may also be detectable with lidar by identifying remnants of food storage pits (cache pits) created by mobile hunter-gatherers in the upper Great Lakes during Late Precontact (ca. AD 1000–1600) that now only exist as subtle microtopographic features. Years of intensive field survey identified 69 cache pit groups between two inland lakes in northern Michigan, almost all of which were located within ~500 m of a lakeshore. Applying a novel series of image processing techniques and statistical analyses to a high spatial resolution DTM we created from commercial-grade lidar, our detection routine identified 139 high potential cache pit clusters. These included most of the previously known clusters as well as several unknown clusters located >1500 m from either lakeshore, much further from lakeshores than all previously identified cultural sites. Food storage is understood to have emerged regionally as a risk-buffering strategy after AD 1000 but our results indicate the current record of hunter-gatherer cache pit food storage is markedly incomplete and this practice and its associated impact on the landscape may be greater than anticipated. Our study also demonstrates the potential of harnessing commercial-grade lidar for other fine-grained archaeology applications.

Incorporating Canopy Cover for Airborne-Derived Assessments of Forest Biomass in the Tropical Forests of Cambodia

This research examines the role of canopy cover in influencing above ground biomass (AGB) dynamics of an open canopied forest and evaluates the efficacy of individual-based and plot-scale height metrics in predicting AGB variation in the tropical forests of Angkor Thom, Cambodia. The AGB was modeled by including canopy cover from aerial imagery alongside with the two different canopy vertical height metrics derived from LiDAR; the plot average of maximum tree height (Max_CH) of individual trees, and the top of the canopy height (TCH). Two different statistical approaches, log-log ordinary least squares (OLS) and support vector regression (SVR), were used to model AGB variation in the study area. Ten different AGB models were developed using different combinations of airborne predictor variables. It was discovered that the inclusion of canopy cover estimates considerably improved the performance of AGB models for our study area. The most robust model was log-log OLS model comprising of canopy cover only (r = 0.87; RMSE = 42.8 Mg/ha). Other models that approximated field AGB closely included both Max_CH and canopy cover (r = 0.86, RMSE = 44.2 Mg/ha for SVR; and, r = 0.84, RMSE = 47.7 Mg/ha for log-log OLS). Hence, canopy cover should be included when modeling the AGB of open-canopied tropical forests.

Recent Developments in High-Density Survey and Measurement (HDSM) for Archaeology: Implications for Practice and Theory

HDSM, high-density survey and measurement, is the collective term for a range of new technologies that give us the ability to measure, record, and analyze the spatial, locational, and morphological properties of objects, sites, structures, and landscapes with higher density and more precision than ever before. This article considers HDSM technologies, including airborne lidar, real-time kinematic global navigation satellite system (GNSS) survey, robotic total stations, terrestrial laser scanning, structured light scanning and close-range photogrammetry [CRP, also known as structure from motion (SfM)], and unmanned aerial vehicle (UAV)-based SfM/CRP and scanning, and we discuss the impact of these technologies on contemporary archaeological practice. This article reflects on how the democratization and proliferation of HDSM opens various applications and greatly broadens the set of problems being addressed explicitly and directly through shape and place.

Mapping and characterizing selected canopy tree species at the Angkor World Heritage site in Cambodia using aerial data

At present, there is very limited information on the ecology, distribution, and structure of Cambodia's tree species to warrant suitable conservation measures. The aim of this study was to assess various methods of analysis of aerial imagery for characterization of the forest mensuration variables (i.e., tree height and crown width) of selected tree species found in the forested region around the temples of Angkor Thom, Cambodia. Object-based image analysis (OBIA) was used (using multiresolution segmentation) to delineate individual tree crowns from very-high-resolution (VHR) aerial imagery and light detection and ranging (LiDAR) data. Crown width and tree height values that were extracted using multiresolution segmentation showed a high level of congruence with field-measured values of the trees (Spearman's rho 0.782 and 0.589, respectively). Individual tree crowns that were delineated from aerial imagery using multiresolution segmentation had a high level of segmentation accuracy (69.22%), whereas tree crowns delineated using watershed segmentation underestimated the field-measured tree crown widths. Both spectral angle mapper (SAM) and maximum likelihood (ML) classifications were applied to the aerial imagery for mapping of selected tree species. The latter was found to be more suitable for tree species classification. Individual tree species were identified with high accuracy. Inclusion of textural information further improved species identification, albeit marginally. Our findings suggest that VHR aerial imagery, in conjunction with OBIA-based segmentation methods (such as multiresolution segmentation) and supervised classification techniques are useful for tree species mapping and for studies of the forest mensuration variables.

The Archaeology of Urban Landscapes

Urban centers have inner and outer landscapes whose physical remains can be read as the materialization of social, political, economic, and ritual interactions. Inner landscapes are manifested in architecture and spatial organizations that configure relationships on the basis of economic status, ethnicity, occupation, age grade, and gender within the city. Outer landscapes are composed of the hinterlands on which urban centers depend for resources, including agricultural products and in-migrating laborers who seek economic and social opportunities. Urban-based elites reach deep into the countryside not only as a matter of political control, but also for investment of centralized resources into infrastructure such as canals, roads, and territorial borders. The monumental and household configurations of cities, expressed both at the heart of urban centers and in their countrysides, enable a distinct phenomenology of interaction mapped into daily experiences.

The environmental impact of Cambodia's ancient city of Mahendraparvata (Phnom Kulen)

The Khmer kingdom, whose capital was at Angkor from the 9^th to the 14^th-15^th century, was founded in 802 by king Jayavarman II in a city called Mahandraparvata, on Phnom Kulen. Virtually nothing more is known of Mahandraparvata from the epigraphic sources, but systematic archaeological survey and excavation have identified an array of cultural features that point to a more extensive and enduring settlement than the historical record indicates. Recent remote sensing data have revolutionized our view, revealing the remains of a city with a complex and spatially extensive network of urban infrastructure. Here, we present a record of vegetation change and soil erosion from within that urban network, dating from the 8^th century CE. Our findings indicate approximately 400 years of intensive land use, punctuated by discrete periods of intense erosion beginning in the mid 9^th century and ending in the late 11^th century. A marked change in water management practices is apparent from the 12^th century CE, with implications for water supply to Angkor itself. This is the first indication that settlement on Mahendraparvata was not only extensive, but also intensive and enduring, with a marked environmental impact.