Search for: All records

This paper reports the initial results of a pilot study investigating the relationships among long-term land use, settlements, historic population, and their potential influence for understanding and evaluating current and future land use. Most of our work to date has been focused on evaluating chang- ing patterns of historic settlement and its relationship to what we know about the historic environment and landscape. Here, we instead rely on remotely-sensed big data as a first step to see how patterns of past land use are correlated with what we know about current land use and land cover. The pilot study initiates a broader research agenda that better incorporates what we know about past landscapes into contemporary land use decisions and to offer critical insights into how the future could be shaped by integrating information about the past. As a first step, the analysis is intentionally broad so that our next steps can provide the fidelity and resolution to offer place based information for design and planning. Nevertheless, it offers a unique window of perception into current land use and a platform for operationalizing evolutionary uses of the past for better managing, designing, and planning complex land systems and moving beyond analogic uses. 

Constructed landscapes are composed of diverse communities, representing different social strata and perspectives of a place. In turn, the risks associated with inhabiting unpredictable environments are disproportionately felt across urban and rural landscapes. The mitigation and management of risks often fall on farming and smallholder communities, influencing decentralized strategies. These themes are explored in an archaeological context surrounding the confluence of the Upper Usumacinta and Lacantún Rivers in the neotropical Maya lowlands of Chiapas, Mexico. LiDAR data collected recently with the GatorEye unoccupied aerial vehicle (UAV) and NASA’s GLiHT system have aided in the mapping of the archaeological urban centre of Benemérito de las Américas, Primera Sección and the surrounding landscape. These data have revealed coupled settlement with land management, in the form of wetland fields, reservoirs, and riverways, emphasizing the interconnectivity of household practice and land use in the region. 

Airborne laser scanning has proven useful for rapid and extensive documentation of historic cultural landscapes after years of applications mapping natural landscapes and the built environment. The recent integration of unoccupied aerial vehicles (UAVs) with LiDAR systems is potentially transformative and offers complementary data for mapping targeted areas with high precision and systematic study of coupled natural and human systems. We report the results of data capture, analysis, and processing of UAV LiDAR data collected in the Maya Lowlands of Chiapas, Mexico in 2019 for a comparative landscape study. Six areas of archaeological settlement and long-term land-use reflecting a diversity of environments, land cover, and archaeological features were studied. These missions were characterized by areas that were variably forested, rugged, or flat, and included pre-Hispanic settlements and agrarian landscapes. Our study confirms that UAV LiDAR systems have great potential for broader application in high-precision archaeological mapping applications. We also conclude that these studies offer an important opportunity for multi-disciplinary collaboration. UAV LiDAR offers high-precision information that is not only useful for mapping archaeological features, but also provides critical information about long-term land use and landscape change in the context of archaeological resources. 

Documentation and mapping of Benemerito de las Americas. 

We present results from the archaeological analysis of 331 km2 of high-resolution airborne lidar data collected in the Upper Usumacinta River basin of Mexico and Guatemala. Multiple visualizations of the DEM and multi-spectral data from four lidar transects crossing the Classic period (AD 350–900) Maya kingdoms centered on the sites of Piedras Negras, La Mar, and Lacanja Tzeltal permitted the identification of ancient settlement and associated features of agricultural infrastructure. HDBSCAN (hierarchical density-based clustering of applications with noise) cluster analysis was applied to the distribution of ancient structures to define urban, peri-urban, sub-urban, and rural settlement zones. Interpretations of these remotely sensed data are informed by decades of ground-based archaeological survey and excavations, as well as a rich historical record drawn from inscribed stone monuments. Our results demonstrate that these neighboring kingdoms in three adjacent valleys exhibit divergent patterns of structure clustering and low-density urbanism, distributions of agricultural infrastructure, and economic practices during the Classic period. Beyond meeting basic subsistence needs, agricultural production in multiple areas permitted surpluses likely for the purposes of tribute, taxation, and marketing. More broadly, this research highlights the strengths of HDBSCAN to the archaeological study of settlement distributions when compared to more commonly applied methods of density-based cluster analysis. 

En este trabajo describimos los resultados del uso de tecnología lidar en drones en el área Maya entre junio del 2017 y 2018. Nuestro objetivo es desarrollar métodos, procedimientos y estándares apropiados para el uso de lidar en drones en el mapeo de asentamientos antiguos. Se sobrevolaron tres sitios dentro de la región superior del río Usumacinta: Piedras Negras en Guatemala, Budsilha y El Infiernito en México. Estos sitios representan una gama de contextos naturales y culturales ideales para evaluar las aplicaciones de la tecnología lidar en el campo. Los modelos de elevación digital y de superficie digital muestran la utilidad del uso de drones en el área Maya. Esta tecnología es apropiada y rentable para el trabajo de campo, pero aún requiere de una detallada planificación y evaluación de las muestras. Futuros estudios evaluarán métodos y técnicas para filtrar y procesar estos datos.