While satellite-derived global vegetation structure products are powerful and easy to use, their utility for studying spatial patterns within heterogeneous landscapes such as forest-savanna mosaics has not been extensively evaluated. We explored the application of global vegetation structure products in heterogeneous landscapes by comparing them with Airborne Laser Scanning. Specifically, we assessed the accuracy and bias of two fractional cover products, MODIS Vegetation Continuous Fields (VCF) and Hansen Global Forest Change (GFC), and one global canopy height model, Global Forest Canopy Height Model (CHM), in comparison with the same variables derived from local ALS point clouds. We found that there were limitations to all three products. MODIS VCF was less accurate than its reported accuracy by at least 3%, and GFC was 10% less accurate than MODIS VCF. While Global CHM had a similar magnitude of error to its reported product accuracy, product agreement was much lower (R2 0.19 vs. R2 0.61). We also found that the context of the analysis is important when choosing whether to use one fractional product over the other. Global products should be applied with caution in heterogeneous landscapes. Increased training and validation from these landscapes could improve the performance of these products and their utility for landscape-scale ecological research.

Airborne Laser Scanning (ALS) technology has transformed modern archaeology by unveiling hidden landscapes beneath dense vegetation. However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques. We address this limitation with Archaeoscape (available at https://archaeoscape.ai), a novel large-scale archaeological ALS dataset spanning 888 km^2 in Cambodia with 31,141 annotated archaeological features from the Angkorian period. Archaeoscape is over four times larger than comparable datasets, and the first ALS archaeology resource with open-access data, annotations, and models. We benchmark several recent segmentation models to demonstrate the benefits of modern vision techniques for this problem and highlight the unique challenges of discovering subtle human-made structures under dense jungle canopies. By making Archaeoscape available in open access, we hope to bridge the gap between traditional archaeology and modern computer vision methods.

The Convention Concerning the Protection of the World Cultural and Natural Heritage (WHC), adopted by United Nations Educational, Scientific and Cultural Organization (UNESCO) on November 16, 1972, aims to ensure the identification, protection, conservation, presentation, and transmission to future generations of the world's cultural and natural heritage. The WHC works toward these goals by emphasizing the Outstanding Universal Value (OUV) of heritage sites and the unique contribution such places can make to conservation and human development agendas.1UNESCO, United Nations Educational, Scientific and Cultural Organization. Operational Guidelines for the Implementation of the World Heritage Convention. Paris, France: UNESCO, 2021.Google Scholar As of the end of January 2023, the WHC has been signed by 194 state parties, covering 1,157 sites (including 900 cultural, 218 natural, and 39 mixed properties), 55 of which are considered to be in danger. These sites, totaling an area of more than 370 million hectares are designated as World Heritage (WH) sites (https://whc.unesco.org/en/list/). WH sites have played a significant role in the sustainable development of society globally and helped effectively maintain and preserve the cultural diversity and global biodiversity of the Earth.2Luo L. Wang X. Guo H. Contribution of UNESCO designated sites to the achievement of sustainable development goals.Innovation. 2022; 3100227Google Scholar However, WH sites around the world are experiencing significant impacts caused, in large part, by increasing anthropogenic threats (such as the rapid spread of urban sprawl, deforestation, resource overexploitation, air pollution, armed conflict, excessive tourism, and poor management).3Allan J.R. Venter O. Maxwell S. et al.Recent increases in human pressure and forest loss threaten many Natural World Heritage Sites.Biol. Conserv. 2017; 206: 47-55Crossref Scopus (104) Google Scholar There is also a range of overarching, long-term global challenges posed by climate change, extreme and severe weather events and geological hazards.4Vousdoukas M.I. Clarke J. Ranasinghe R. et al.African heritage sites threatened as sea-level rise accelerates.Nat. Clim. Chang. 2022; 12: 256-262Crossref Scopus (39) Google Scholar These threats, anthropogenic or otherwise, have put an unprecedented amount of pressure on the protection of both cultural and natural properties at the heritage sites. There is no conclusive solution to effectively protect heritage sties in the foreseeable future, and the public is becoming increasingly conscious about the negative impacts some anthropogenic activities have brought to heritage sites, exposing them to various risks for irreversible damage and loss. In light of this predicament, UNESCO, alongside its conservation partners, has been actively calling for the use of non-intrusive space technology (ST) to better evaluate and understand the detrimental factors surrounding WH sites and devise effective plans to mitigate negative impacts on OUV through the accurate identification, documentation, monitoring, and understanding of WH sites and their changes across large spatiotemporal scales. For this commentary, ST refers mainly to remote sensing (RS) based on Earth observation (EO) from space, as well as other geospatial information technologies such as geographic information systems (GIS) and global navigation satellite systems (GNSS). Even though ST was not developed intentionally for WH protection, it provides an advanced set of innovative and flexible tools that integrate scientific research into heritage science, which forms the evidential base to support measures for the protection of WH sites. ST also opens up paths of methodological innovation to facilitate future conservation of cultural and natural properties with new paradigms. On the basis of a half-century review of the applications of ST in the field of WH, we find that ST has effectively reshaped our means for WH conservation in four major domains of application: mapping, monitoring, modeling, and management of WH sites (Figure 1A). ST, as an objective and reliable "eye" from above for heritage experts, has enabled detailed mapping of heritages at various scales. This application commonly involves the acquisition and updatable analysis of multimodal datasets, combined with near real-time survey and investigation of WH sites and their contextual environments. ST is also an invaluable tool in identifying and designating potential new WH sites. The widespread integration of ST has played a crucial role in UNESCO's routine monitoring and emergency monitoring of cultural and natural heritages as a countermeasure to the challenges presented by global change. In particular, ST has made major contributions to fine-grained monitoring at very high resolutions in time and space and in assessing change caused by natural and/or anthropogenic processes. The combined application of RS, GIS, and augmented reality has facilitated the production of three-dimensional (3D) modeling of WH sites. In addition, photogrammetry and laser scanning can be used in WH surveys for generating 3D documentation of cultural and natural properties. Heritage information models of some popular WH sites have already been actively used in the fields of architecture study and digital ecology. The ability of ST to contribute to the conservation, planning, and management of WH sites has already been commonly used in the following domains: the definition of authenticity and integrity (AI), the mapping of OUV attributes of cultural and natural assets, the development and maintenance of heritage databases, 3D modeling and digital reconstruction, and informing WH management for sustainable development. The UNESCO WHC refers to its "strategic objectives" as the "5 C's": credibility, conservation, capacity building, communication, and communities.1UNESCO, United Nations Educational, Scientific and Cultural Organization. Operational Guidelines for the Implementation of the World Heritage Convention. Paris, France: UNESCO, 2021.Google Scholar Here, we propose five key contributions ("5 D's") of ST for conservation work involving WH sites (see Figure 1B): data, driver, discovery, digital transformation, and decision support. ST and the 5 D's, together with the WHC, constitute the "turbine model" for safeguarding WH that is proposed in this commentary and will assist in achieving the 5 C's over the next 50 years. By providing massive multi-source data, ST plays a crucial role in the WH governance chain. Archived data together with ongoing data acquisition record changes at WH sites and provide evidence for their conservation. The data collected through ST will, in turn, serve as an invaluable store of not only raw observations but also derivative products that can be extracted from imagery.5Levin N. Ali S. Crandall D. et al.World Heritage in danger: big data and remote sensing can help protect sites in conflict zones.Global Environ. Change. 2019; 55: 97-104Crossref Scopus (50) Google Scholar As a result of this data mechanism, ST will be able to mitigate the problem of lacunae in WH data by offering global coverage and consistency. ST is driving a revolution to become a new paradigm of WH protection. It provides advanced tools, such as RS data processing and GIS spatial analysis powered by machine learning, which can be used for dynamic monitoring and spatiotemporal analysis of changes that are occurring at WH sites. From this enhanced monitoring method, WH specialists and managers are empowered with the tools to monitor change over time and to assess whether conservation efforts to mitigate the impacts of natural and anthropogenic processes are contributing to positive outcomes toward sustainability. ST plays a key role in aiding frontier research across multiple disciplines related to heritage science. Scientific discoveries based on the use of new geospatial technologies and big EO data can be applied to the quantitative assessment of the state of WH protection. They can also be used to support deepened multidimensional interpretations of the OUV of WH sites, to guide the sustainable use of WH, and to improve accessibility so that all stakeholders may obtain unbiased and open information, thus increasing the credibility of WH as a global brand. ST provides a means of digital transformation that can be used for identifying, understanding, visualizing, presenting, and interpreting WH sites and their OUV in the digital space. In the future, ST will promote the transformation of WH protection through studies such as tangible heritage, digitalized heritage, or both. GIS-aided digital transformation will greatly improve the quality and efficiency of global communication and the dissemination of information related to WH sites, promoting public awareness of protection concepts and smoothing the implementation of actions. ST is becoming an invaluable tool to provide decision-making support along the entire chain of WH governance, from mapping to monitoring, modeling, and management. ST-based solutions could offer spatial intelligence to inform strategic decision making for multiple stakeholders, including local communities, WH sectors, institutions, and organizations. It offers an innovative path for capacity building and capacity enhancement in relation to the global governance of WH. These solutions are delivered in numerous ways, including databases, technologies, protocols, visualization platforms, and analytical systems. WH is under the influence of a wide array of anthropogenic and natural processes, giving the heritage bodies and national and international organizations promoting the preservation of WH sites an unprecedented opportunity to use science, technology, and innovation, including ST, to experiment innovative solutions and promote the role of WH in achieving sustainable development. Given that natural and anthropogenic challenges can present similar issues in various regions across the globe, it is evident that sharing knowledge and expertise, as well as developing collective strategies to leverage ST, while tailoring our methods to local circumstances, will be fundamental in developing a global approach for WH that is successful in the long run. The past 50 years of applications have resulted in continuous innovation in the development of ST to become powerful, cutting-edge tools for use in heritage documentation, monitoring, assessment and protection, and sustainable development. Although the progress is promising, we must underscore that relying solely on ST is insufficient; the greatest benefits are achieved by integrating ST with a variety of disciplines, such as management, economics, sustainability, sociology, education, ethics, politics, law, art, and aesthetics. Through closer and sustained engagement with a broader cross-section of society and local communities, ST can contribute to opening new horizons for WH protection over the course of the twenty-first century. This work was supported by the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals (grant CBAS2022IRP09) and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (grant 2023135). The viewpoints expressed here are those of the authors and do not necessarily reflect the official policy or position of their institutions. The authors declare no competing interests.

Angkor is widely known for its durable religious monuments of stone and brick, but architecture beyond these temples was mostly of non-durable material that decayed long ago, leaving only faint traces inscribed in the Earth's surface. As a result, the nature of human settlement in the Angkorian world has long been a focus of debate and disagreement. Recently, however, archaeological work combining aerial and ground investigations has brought the lived-in spaces of the Angkorian world into much sharper focus. There is a very strong correlation between religious monuments and occupation areas. Larger monuments usually have dense, well-structured occupation in their immediate area, which is in turn surrounded by more diffuse settlement extending over a much wider area. Few settlements are neatly enclosed by walls or moats, and boundaries are generally difficult to define, challenging our conventional definition of 'cities' and blurring the classic distinction between 'rural' and 'urban' spaces.

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.

The apparent decline of Angkor and the Khmer Empire between the 13th to 15th centuries has long been the focus of discussion and debate. Most explanations have focused on warfare, in particular a 1431 invasion by neighbouring Ayutthaya, as the ultimate cause of Angkor's demise. In this chapter, we offer a critical review of the literature. Drawing on new perspectives arising from the last 20 years of archaeological research, we argue that depopulation of the Angkor region was likely a slow and gradual process and not a sudden or violent event as implied by conventional notions of 'collapse'. Recent research instead points to a constellation of social, cultural, and environmental factors that contributed to varying degrees to a shift in political power away from the Angkor region in the mid-first millennium CE, accompanied by broad demographic changes that unfolded over centuries.

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...

The vast agro-urban settlements that developed in the humid tropics of Mesoamerica and Asia contained both elite civic-ceremonial spaces and sprawling metropolitan areas. Recent studies have suggested that both local autonomy and elite policies facilitated the development of these settlements; however, studies have been limited by a lack of detail in considering how, when, and why these factors contributed to the evolution of these sites. In this paper, we use a fine-grained diachronic analysis of Angkor’s landscape to identify both the state-level policies and infrastructure and bottom-up organization that spurred the growth of Angkor as the world’s most extensive pre-industrial settlement complex. This degree of diachronic detail is unique for the ancient world. We observe that Angkor’s low-density metropolitan area and higher-density civic-ceremonial center grew at different rates and independently of one another. While local historical factors contributed to these developments, we argue that future comparative studies might identify similar patterns.

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.

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.

Airborne laser scanning or lidar has now been used by archaeologists for twenty years, with many of the first applications relying on data acquired by public agencies seeking to establish baseline elevation maps, mainly in Europe and North America. More recently, several wide-area acquisitions have been designed and commissioned by archaeologists, the most extensive of which cover tropical forest environments in the Americas and Southeast Asia. In these regions, the ability of lidar to map microtopographic relief and reveal anthropogenic traces on the Earth’s surface, even beneath dense vegetation, has been welcomed by many as a transformational breakthrough in our field of research. Nevertheless, applications of the method have attracted a measure of criticism and controversy, and the impact and significance of lidar are still debated. Now that wide-area, high-density laser scanning is becoming a standard part of many archaeologists’ toolkits, it is an opportune moment to reflect on its position in contemporary archaeological practice and to move towards a code of ethics that is vital for scientific research. The papers in this Special Collection draw on experiences with using lidar in archaeological research programs, not only to highlight the new insights that derive from it but also to cast a critical eye on past practices and to assess what challenges and opportunities remain for developing codes of ethics. Using examples from a range of countries and environments, contributions revolve around three key themes: data management and access; the role of stakeholders; and public education. We draw on our collective experiences to propose a range of improvements in how we collect, use, and share lidar data, and we argue that as lidar acquisitions mature we are well positioned to produce ethical, impactful, and reproducible research using the technique.

Ground penetrating radar, probing, and excavation were used to create a contour map of the topography of a buried laterite pavement forming the spillway of a large abandoned reservoir at the Angkorian‐period city of Koh Ker in Cambodia. Calculations of the flow velocity of water through the spillway, based on the topography of the laterite surface, demonstrate that this outlet was even less adequate for passing the flow of water from the Stung Rongea catchment than had been estimated previously by Lustig, Klassen, Evans, French, & Moffat (2018). We argue that this design flaw contributed substantially to the failure of the reservoir’s dike, possibly during the first rainy season after construction, which may have contributed to Koh Ker’s remarkably short‐lived tenure as the political center of the Khmer Empire.

The Greater Angkor region, in northwestern Cambodia, was home to several successive capitals of the Khmer Empire (9th to 15th centuries CE). During this time, the Khmer developed an extensive agricultural and water management system characterized by top-down state-sponsored hydraulic infrastructure. Archaeological evidence now shows that the well-documented state temples and water management features formed the core of an extended settlement complex consisting of many thousands of ponds, habitation mounds, and community temples. These community temples are difficult to date, and so far, the lack of chronological resolution in surface archaeological data has been the most significant challenge to understanding the trajectory of Angkor's growth and decline. In this paper, we combine heterogeneous archaeological datasets and create diachronic models of the landscape as it was developed for agricultural production. We trace the foundation of new temple communities as they emerge on the landscape in relation to the construction of extensive state-sponsored hydraulic infrastructure. Together, these two forms of water management transformed over 1000 km2 of the Greater Angkor Region into an elaborate engineered landscape. Our results indicate that, over time, autonomous temple communities are replaced by large, state-sponsored agricultural units in an attempt by the state to centralize production.

Alternative models exist for the movement of large urban populations following the 15th-century CE abandonment of Angkor, Cambodia. One model emphasizes an urban diaspora following the implosion of state control in the capital related, in part, to hydroclimatic variability. An alternative model suggests a more complex picture and a gradual rather than catastrophic demographic movement. No decisive empirical data exist to distinguish between these two competing models. Here we show that the intensity of land use within the economic and administrative core of the city began to decline more than one century before the Ayutthayan invasion that conventionally marks the end of the Angkor Period. Using paleobotanical and stratigraphic data derived from radiometrically dated sediment cores extracted from the 12th-century walled city of Angkor Thom, we show that indicia for burning, forest disturbance, and soil erosion all decline as early as the first decades of the 14th century CE, and that the moat of Angkor Thom was no longer being maintained by the end of the 14th century. These data indicate a protracted decline in occupation within the economic and administrative core of the city, rather than an abrupt demographic collapse, suggesting the focus of power began to shift to urban centers outside of the capital during the 14th century.

This study develops a modelling framework by utilizing multi-sensor imagery for classifying different forest and land use types in the Phnom Kulen National Park (PKNP) in Cambodia. Three remote sensing datasets (Landsat optical data, ALOS L-band data and LiDAR derived Canopy Height Model (CHM)) were used in conjunction with three different machine learning (ML) regression techniques (Support Vector Machines (SVM), Random Forests (RF) and Artificial Neural Networks (ANN)). These ML methods were implemented on (a) Landsat spectral data, (b) Landsat spectral band & ALOS backscatter data, and (c) Landsat spectral band, ALOS backscatter data, & LiDAR CHM data. The Landsat-ALOS combination produced more accurate classification results (95% overall accuracy with SVM) compared to Landsat-only bands for all ML models. Inclusion of LiDAR CHM (which is a proxy for vertical canopy heights) improved the overall accuracy to 98%. The research establishes that majority of PKNP is dominated by cashew plantations and the nearly intact forests are concentrated in the more inaccessible parts of the park. The findings demonstrate how different RS datasets can be used in conjunction with different ML models to map forests that had undergone varying levels of degradation and plantations.