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Protecting and enhancing forest carbon sinks is considered a natural solution for mitigating climate change. However, the increasing frequency, intensity, and duration of droughts due to climate change can threaten the stability and growth of existing forest carbon sinks. Extreme droughts weaken plant hydraulic systems, can lead to tree mortality events, and may reduce forest diversity, making forests more vulnerable to subsequent forest disturbances, such as forest fires or pest infestations. Although early warning metrics (EWMs) derived using satellite remote sensing data are now being tested for predicting post-drought plant physiological stress and mortality, applications of unmanned aerial vehicles (UAVs) are yet to be explored extensively. Herein, we provide twenty-four prospective approaches classified into five categories: (i) physiological complexities, (ii) site-specific and confounding (abiotic) factors, (iii) interactions with biotic agents, (iv) forest carbon monitoring and optimization, and (v) technological and infrastructural developments, for adoption, future operationalization, and upscaling of UAV-based frameworks for EWM applications. These UAV considerations are paramount as they hold the potential to bridge the gap between field inventory and satellite remote sensing for assessing forest characteristics and their responses to drought conditions, identifying and prioritizing conservation needs of vulnerable and/or high-carbon-efficient tree species for efficient allocation of resources, and optimizing forest carbon management with climate change adaptation and mitigation practices in a timely and cost-effective manner. 

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. 

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. 

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. 

Canopy disturbance explains liana abundance and distribution within tropical forests and thus may also explain the widespread pattern of increasing liana abundance; however, this hypothesis remains untested. We used a 10‐year study (2007–2017) of 117,100 rooted lianas in an old‐growth Panamanian forest to test whether local canopy disturbance explains increasing liana abundance. We found that liana density increased 29.2% and basal area 12.5%. The vast majority of these increases were associated with clonal stem proliferation following canopy disturbance, particularly in liana‐dense, low‐canopy gaps, which had far greater liana increases than did undisturbed forest. Lianas may be ecological niche constructors, arresting tree regeneration in gaps and thus creating a high‐light environment that favours sustained liana proliferation. Our findings demonstrate that liana abundance is increasing rapidly and their ability to proliferate via copious clonal stem production in canopy gaps explains much of their increase in this and possibly other tropical forests.