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Abstract ContextUnoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) have become an increasingly popular tool for ecological research. But much of the recent research is concerned with developing mapping and detection approaches, with few studies attempting to link UAS data to ecosystem processes and function. Landscape ecologists have long used high resolution imagery and spatial analyses to address ecological questions and are therefore uniquely positioned to advance UAS research for ecological applications. ObjectivesThe review objectives are to: (1) provide background on how UAS are used in landscape ecological studies, (2) identify major advancements and research gaps, and (3) discuss ways to better facilitate the use of UAS in landscape ecology research. MethodsWe conducted a systematic review based on PRISMA guidelines using key search terms that are unique to landscape ecology research. We reviewed only papers that applied UAS data to investigate questions about ecological patterns, processes, or function. ResultsWe summarize metadata from 161 papers that fit our review criteria. We highlight and discuss major research themes and applications, sensors and data collection techniques, image processing, feature extraction and spatial analysis, image fusion and satellite scaling, and open data and software. ConclusionWe observed a diversity of UAS methods, applications, and creative spatial modeling and analysis approaches. Key aspects of UAS research in landscape ecology include modeling wildlife micro-habitats, scaling of ecosystem functions, landscape and geomorphic change detection, integrating UAS with historical aerial and satellite imagery, and novel applications of spatial statistics. 

Abstract We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing “curve” skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology. 

Abstract Dryland ecosystems cover 40% of our planet's land surface, support billions of people, and are responding rapidly to climate and land use change. These expansive systems also dominate core aspects of Earth's climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite their indispensable ecosystem services and high vulnerability to change, drylands are one of the least understood ecosystem types, partly due to challenges studying their heterogeneous landscapes and misconceptions that drylands are unproductive “wastelands.” Consequently, inadequate understanding of dryland processes has resulted in poor model representation and forecasting capacity, hindering decision making for these at‐risk ecosystems. NASA satellite resources are increasingly available at the higher resolutions needed to enhance understanding of drylands' heterogeneous spatiotemporal dynamics. NASA's Terrestrial Ecology Program solicited proposals for scoping a multi‐year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. A primary goal of the scoping study is to gather input from the scientific and data end‐user communities on dryland research gaps and data user needs. Here, we provide an overview of the ARID team's community engagement and how it has guided development of our framework. This includes an ARID kickoff meeting with over 300 participants held in October 2023 at the University of Arizona to gather input from data end‐users and scientists. We also summarize insights gained from hundreds of follow‐up activities, including from a tribal‐engagement focused workshop in New Mexico, conference town halls, intensive roundtables, and international engagements. 

Li 2 S is the key precursor for synthesizing thio-LISICON electrolytes employed in solid state batteries. However, conventional synthesis techniques such as carbothermal reduction of Li 2 SO 4 aren't suitable for the generation of low-cost, high-purity Li 2 S. Metathesis, in which LiCl is reacted with Na 2 S in ethanol, is a scalable synthesis method conducted at ambient conditions. The NaCl byproduct is separated from the resulting Li 2 S solution, and the solvent is removed by evaporation and thermal annealing. However, the annealing process reveals the presence of oxygenated impurities in metathesis Li 2 S that are not usually observed when recovering Li 2 S from ethanol. In this work we investigate the underlying mechanism of impurity formation, finding that they likely derive from the decomposition of alkoxide species that originate from the alcoholysis of the Na 2 S reagent. With this mechanism in mind, several strategies to improve Li 2 S purity are explored. In particular, drying the metathesis Li 2 S under H 2 S at low temperature was most effective, resulting in high-purity Li 2 S while retaining a beneficial nanocrystal morphology (∼10 nm). Argyrodite electrolytes synthesized from this material exhibited essentially identical phase purity, ionic conductivity (3.1 mS cm −1 ), activation energy (0.19 eV), and electronic conductivity (6.4 × 10 −6 mS cm −1 ) as that synthesized from commercially available battery-grade Li 2 S. 

Monitoring and estimating drought impact on plant physiological processes over large regions remains a major challenge for remote sensing and land surface modeling, with important implications for understanding plant mortality mechanisms and predicting the climate change impact on terrestrial carbon and water cycles. The Orbiting Carbon Observatory 3 (OCO‐3), with its unique diurnal observing capability, offers a new opportunity to track drought stress on plant physiology. Using radiative transfer and machine learning modeling, we derive a metric of afternoon photosynthetic depression from OCO‐3 solar‐induced chlorophyll fluorescence (SIF) as an indicator of plant physiological drought stress. This unique diurnal signal enables a spatially explicit mapping of plants' physiological response to drought. Using OCO‐3 observations, we detect a widespread increasing drought stress during the 2020 southwest US drought. Although the physiological drought stress is largely related to the vapor pressure deficit (VPD), our results suggest that plants' sensitivity to VPD increases as the drought intensifies and VPD sensitivity develops differently for shrublands and grasslands. Our findings highlight the potential of using diurnal satellite SIF observations to advance the mechanistic understanding of drought impact on terrestrial ecosystems and to improve land surface modeling.