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Large‐scale disturbances, such as megafires, motivate restoration at equally large extents. Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS) flights can provide an efficient solution to the tradeoff of precision and spatial extent in detecting demographic rates from the air. We flew two, sequential UAS flights at two sagebrush (Artemisia tridentata) common gardens to measure the survival and growth of individual plants. The accuracy of Bayesian‐optimized segmentation of individual shrub canopies was high (73–95%, depending on the year and site), and remotely sensed survival estimates were within 10% of ground‐truthed survival censuses. Stand age structure affected remotely sensed estimates of growth; growth was overestimated relative to field‐based estimates by 57% in the first garden with older stands, but agreement was high in the second garden with younger stands. Further, younger stands (similar to those just after disturbance) with shorter, smaller plants were sometimes confused with other shrub species and bunchgrasses, demonstrating a need for integrating spectral classification approaches that are increasingly available on affordable UAS platforms. The older stand had several merged canopies, which led to an underestimation of abundance but did not bias remotely sensed survival estimates. Advances in segmentation and UAS structure from motion photogrammetry will enable demographic rate measurements at management‐relevant extents. [ABSTRACT FROM AUTHOR]

An increasing number of threats, both natural (e.g. fires, drought) and anthropogenic (e.g. agriculture, infrastructure development), are likely to affect both availability and quality of plants that grouse rely on for cover and food. As such, there is an increasing need to monitor plants and their use by grouse over space and time to better predict how changes in habitat quality influence the behavior of grouse. We use the greater sage-grouse Centrocercus urophasianus to showcase how new technology can be used to advance our understanding of the ecology, behavior and conservation of grouse. We demonstrate how laser, spectral and chemical detectors and unmanned aerial systems can be used to measure structural and phytochemical predictors of habitat quality at several spatial scales. We also demonstrate how advanced biotelemetry systems and robotic animals can be used to measure how habitat quality influences fine-scale habitat use, movement and reproductive effort of grouse. Integrating these technologies will allow researchers to better assess and manage the links among habitat quality (safety and food), resource acquisition (foraging behavior) and reproductive behaviors of grouse.

Understanding interactions between environmental stress and genetic variation is crucial to predict the adaptive capacity of species to climate change. Leaf temperature is both a driver and a responsive indicator of plant physiological response to thermal stress, and methods to monitor it are needed. Foliar temperatures vary across leaf to canopy scales and are influenced by genetic factors, challenging efforts to map and model this critical variable. Thermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf‐level resolutions. We used a UAS equipped with a thermal camera to assess temperature variation among genetically distinct populations of big sagebrush (Artemisia tridentata), a keystone plant species that is the focus of intensive restoration efforts throughout much of western North America. We completed flights across a growing season in a sagebrush common garden to map leaf temperature relative to subspecies and cytotype, physiological phenotypes of plants, and summer heat stress. Our objectives were to (1) determine whether leaf‐level stomatal conductance corresponds with changes in crown temperature; (2) quantify genetic (i.e., subspecies and cytotype) contributions to variation in leaf and crown temperatures; and (3) identify how crown structure, solar radiation, and subspecies‐cytotype relate to leaf‐level temperature. When considered across the whole season, stomatal conductance was negatively, non‐linearly correlated with crown‐level temperature derived from UAS. Subspecies identity best explained crown‐level temperature with no difference observed between cytotypes. However, structural phenotypes and microclimate best explained leaf‐level temperature. These results show how fine‐scale thermal mapping can decouple the contribution of genetic, phenotypic, and microclimate factors on leaf temperature dynamics. As climate‐change‐induced heat stress becomes prevalent, thermal UAS represents a promising way to track plant phenotypes that emerge from gene‐by‐environment interactions. [ABSTRACT FROM AUTHOR]

Plant communities are composed of complex phenotypes that not only differ among taxonomic groups and habitats but also change over time within a species. Restoration projects (e.g. translocations and reseeding) can introduce new functional variation in plants, which further diversifies phenotypes and complicates our ability to identify locally adaptive phenotypes for future restoration. Near‐infrared spectroscopy (NIRS) offers one approach to detect the chemical phenotypes that differentiate plant species, populations, and phenological states of individual plants over time. We use sagebrush (Artemisia spp.) as a case study to test the accuracy by which NIRS can classify variation within taxonomy and phenology of a plant that is extensively managed and restored. Our results demonstrated that NIRS can accurately classify species of sagebrush within a study site (75–96%), populations of sagebrush within a subspecies (99%), annual phenology within a population (>99%), and seasonal phenology within individual plants (>97%). Low classification accuracy by NIRS in some sites may reflect heterogeneity associated with natural hybridization, translocation of nonlocal seed sources from past restoration, or complex gene‐by‐environment interactions. Advances in our ability to detect and interpret spectral signals from plants may improve both the selection of seed sources for targeted conservation and the capacity to monitor long‐term changes in vegetation. [ABSTRACT FROM AUTHOR]

Abstract: Contemporary techniques predicting habitat suitability under climate change projections often underestimate availability of thermal refuges. Habitat structure contributes to thermal heterogeneity at a variety of spatial scales, but quantifying microclimates at organism‐relevant resolutions remains a challenge. Landscapes that appear homogeneous at large scales may offer patchily distributed thermal refuges at finer scales. We quantified the relationship between vegetation structure and the thermal environment at a scale relevant to small, terrestrial animals using a new approach for mapping fine‐scale thermal heterogeneity. We expected that vegetation would create attenuated microclimates and that the influence of vegetation structure would vary seasonally. We measured shrub volume, horizontal cover and operative temperature (Te) in a sagebrush‐steppe habitat in Idaho, USA, at 534 microsites across two study sites of c. 1 km2 each. We modelled relationships between habitat structure and both mean daily maximum temperature ( T ¯ max) and mean diurnal temperature range ( DTR ¯) for each study site during summer and winter. Aerial imagery from unmanned aerial systems was used to estimate shrub volume and canopy cover at 1‐m resolution, and we applied the best fit model to map thermal heterogeneity across broader extents. Increasing shrub volume and cover was associated with lower T ¯ max and DTR ¯, but strengths of the relationships differed between study sites. There was considerable heterogeneity in availability of thermal refuges across sagebrush‐steppe rangelands that have traditionally been considered relatively homogeneous. This technique can help ecologists and land managers identify critical thermal refuges that large‐scale climate modelling can overlook and thus contribute to an understanding of animal–habitat relationships under changing climates and land uses. [ABSTRACT FROM AUTHOR]

Abstract: Assessing habitat quality is a primary goal of ecologists. However, evaluating habitat features that relate strongly to habitat quality at fine‐scale resolutions across broad‐scale extents is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging the gap between relatively high spatial resolution, low spatial extent field‐based habitat quality measurements and lower spatial resolution, higher spatial extent satellite‐based remote sensing. Our goal in this study was to evaluate the potential for UAS structure from motion (SfM) to estimate several dimensions of habitat quality that provide potential security from predators, and forage for pygmy rabbits (Brachylagus idahoensis) in a sagebrush steppe environment. At the plant and patch scales, we compared UAS‐derived estimates of vegetation height, volume (estimate of food availability) and canopy cover to estimates from ground‐based terrestrial laser scanning (TLS) and field‐based measurements. Then, we mapped habitat features across two sagebrush landscapes in Idaho, USA, using point clouds derived from UAS SfM. At the individual plant scale, the UAS‐derived estimates matched those from TLS for height (R2 = 0.85), volume (R2 = 0.94) and canopy cover (R2 = 0.68). However, there was less agreement with field‐based measurements of height (R2 = 0.67), volume (R2 = 0.31) and canopy cover (R2 = 0.29). At the patch scale, UAS‐derived estimates provided a better fit to field‐based measurements (R2 = 0.51–0.75) than at the plant scale. Landscape‐scale maps created from UAS were able to distinguish structural heterogeneity between key patch types. Our work demonstrates that UAS was able to accurately estimate habitat heterogeneity for a key terrestrial vertebrate at multiple spatial scales. Given that many of the vegetation metrics we focus on are important for a wide variety of species, our work illustrates a general remote sensing approach for mapping and monitoring fine‐resolution habitat quality across broad landscapes for use in studies of animal ecology, conservation and land management. [ABSTRACT FROM AUTHOR]

Water-limited ecosystems encompass approximately 40% of terrestrial land mass and play a critical role in modulating Earth’s climate and provisioning ecosystem services to humanity. Spaceborne remote sensing is a critical tool for characterizing ecohydrologic patterns and advancing the understanding of the interactions between atmospheric forcings and ecohydrologic responses. Fine to medium scale spatial and temporal resolutions are needed to capture the spatial heterogeneity and the temporally intermittent response of these ecosystems to environmental forcings. Techniques combining complementary remote sensing datasets have been developed, but the heterogeneous nature of these regions present significant challenges. Here we investigate the capacity of one such approach, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, to map Normalized Difference Vegetation Index (NDVI) at 30 m spatial resolution and at a daily temporal resolution in an experimental watershed in southwest Idaho, USA. The Dry Creek Experimental Watershed captures an ecotone from a sagebrush steppe ecosystem to evergreen needle-leaf forests along an approximately 1000 m elevation gradient. We used STARFM to fuse NDVI retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) and Landsat during the course of a growing season (April to September). Specifically we input to STARFM a pair of Landsat NDVI retrievals bracketing a sequence of daily MODIS NDVI retrievals to yield daily estimates of NDVI at resolutions of 30 m. In a suite of data denial experiments we compared these STARFM predictions against corresponding Landsat NDVI retrievals and characterized errors in predicted NDVI. We investigated how errors vary as a function of vegetation functional type and topographic aspect. We find that errors in predicting NDVI were highest during green-up and senescence and lowest during the middle of the growing season. Absolute errors were generally greatest in tree-covered portions of the watershed and lowest in locations characterized by grasses/bare ground. On average, relative errors in predicted average NDVI were greatest in grass/bare ground regions, on south-facing aspects, and at the height of the growing season. We present several ramifications revealed in this study for the use of multi-sensor remote sensing data for the study of spatiotemporal ecohydrologic patterns in dryland ecosystems. [ABSTRACT FROM AUTHOR]