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ABSTRACT This article presents results from an unmanned aircraft system (UAS) aerial remote sensing study to improve understanding of Pueblo agricultural features in the Northern Rio Grande area of New Mexico that were in use by the 13th centuryad. It builds on previous archaeological research that has focused on recording precontact and historic Pueblo agricultural practices, pollen analyses and paleoclimatic reconstruction. Evidence suggests that Pueblo people were successfully growing crops including maize, cotton and wheat, in areas where, based on environmental conditions, they could not necessarily grow. This study seeks to better understand the environmental modifications employed by Pueblo peoples to enable growth of these crops. Cobble‐bordered gravel mulch field systems, thought to retain heat and moisture, are located throughout the study area. This article discusses the utility of airborne photogrammetry to locate and map gravel mulch fields on the landscape. Geographic information system (GIS) analysis of the UAS‐derived digital surface model includes slope, aspect and water flow direction and sink to shed light on gravel mulch field function. The article also discusses the potential of handheld and airborne infrared imaging for assessing the thermoregulation of these fields. Final consideration of how the survey results align with the priorities of the Tewa people for future arid‐land farming demonstrates additional utility of the approach. 

Advances built into recent sUASs (drones) offer a compelling possibility for field-based data collection in logistically challenging and GPS-denied environments. sUASs-based photogrammetry generates 3D models of features and landscapes, used extensively in archaeology as well as other field sciences. Until recently, navigation has been limited by the expertise of the pilot, as objects, like trees, and vertical or complex environments, such as cliffs, create significant risks to successful documentation. This article assesses sUASs’ capability for autonomous obstacle avoidance and 3D flight planning using data collection scenarios carried out in Black Mesa, Oklahoma. Imagery processed using commercial software confirmed that the collected data can build photogrammetric models suitable for general archaeological documentation. The results demonstrate that new capabilities in drones may open up new field environments previously considered inaccessible, too risky, or costly for fieldwork, especially for all but the most expert pilots. Emerging technologies for drone-based photogrammetry, such as the Skydio 2+ considered here, place remote, rugged terrain within reach of many archaeological research units in terms of commercial options and cost. 

Abstract Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil–plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red–green–blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management.