Unmanned aerial vehicles (UAVs) provide a new research tool to obtain high spatial and temporal resolution imagery at a reduced cost. Rapid advances in miniature sensor technology are leading to greater potentials for ecological research. We demonstrate one of the first applications of UAV lidar and hyperspectral imagery and a fusion method for individual plant species identification and 3D characterization at submeter scales in south‐eastern Arizona, USA. The UAV lidar scanner characterized the individual vegetation canopy structure and bare ground elevation, whereas the hyperspectral sensor provided species‐specific spectral signatures for the dominant and target species at our study area in leaf‐on condition. We hypothesized that the fusion of the two different data sources would perform better than either data type alone in the arid and semi‐arid ecosystems with sparse vegetation. The fusion approach provides 84–89% overall accuracy (kappa values of 0.80–0.86) in target species classification at the canopy scale, leveraging a wide range of target spectral responses in the hyperspectral data and a high point density (50 points/m²) in the lidar data. In comparison, the hyperspectral image classification alone produced 72–76% overall accuracies (kappa values of 0.70 and 0.71). The UAV lidar‐derived digital elevation model (DEM) is also strongly correlated with manned airborne lidar‐derived DEM (R² = 0.98 and 0.96), but was obtained at a lower cost. The lidar and hyperspectral data as well as the fusion method demonstrated here can be widely applied across a gradient of vegetation and topography to monitor and detect ecological changes at a local scale.

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

Airborne‐based Light Detection and Ranging (LiDAR) offers the potential to measure snow depth and vegetation structure at high spatial resolution over large extents and thereby increase our ability to quantify snow water resources. Here we present airborne LiDAR data products at four Critical Zone Observatories (CZO) in the Western United States: Jemez River Basin, NM, Boulder Creek Watershed, CO, Kings River Experimental Watershed, CA, and Wolverton Basin, CA. We make publicly available snow depth data products (1 m² resolution) derived from LiDAR with an estimated accuracy of <30 cm compared to limited in situ snow depth observations.

Feedbacks among vegetation dynamics, pedogenesis, and topographic development affect the “critical zone”—the living filter for Earth's hydrologic, biogeochemical, and rock/sediment cycles. Assessing the importance of such feedbacks, which may be particularly pronounced in water‐limited systems, remains a fundamental interdisciplinary challenge. The sky islands of southern Arizona offer an unusually well‐defined natural experiment involving such feedbacks because mean annual precipitation varies by a factor of five over distances of approximately 10 km in areas of similar rock type (granite) and tectonic history. Here we compile high‐resolution, spatially distributed data for Effective Energy and Mass Transfer (EEMT: the energy available to drive bedrock weathering), above‐ground biomass, soil thickness, hillslope‐scale topographic relief, and drainage density in two such mountain ranges (Santa Catalina: SCM; Pinaleño: PM). Strong correlations exist among vegetation‐soil‐topography variables, which vary nonlinearly with elevation, such that warm, dry, low‐elevation portions of these ranges are characterized by relatively low above‐ground biomass, thin soils, minimal soil organic matter, steep slopes, and high drainage densities; conversely, cooler, wetter, higher elevations have systematically higher biomass, thicker organic‐rich soils, gentler slopes, and lower drainage densities. To test if eco‐pedo‐geomorphic feedbacks drive this pattern, we developed a landscape evolution model that couples pedogenesis and topographic development over geologic time scales, with rates explicitly dependent on vegetation density. The model self‐organizes into states similar to those observed in SCM and PM. Our results highlight the potential importance of eco‐pedo‐geomorphic feedbacks, mediated by soil thickness, in water‐limited systems.

Mountains are vital to ecosystems and human society given their influence on global carbon and water cycles. Yet the extent to which topography regulates montane forest carbon uptake and storage remains poorly understood. To address this knowledge gap, we compared forest aboveground carbon loading to topographic metrics describing energy balance and water availability across three headwater catchments of the Boulder Creek Watershed, Colorado, USA. The catchments range from 1800 to 3500 m above mean sea level with 46–102 cm/yr mean annual precipitation and −1.2° to 12.3°C mean annual temperature. In all three catchments, we found mean forest carbon loading consistently increased from ridges (27 ± 19 Mg C ha) to valley bottoms (60 ± 28 Mg C ha). Low topographic positions held up to 185 ± 76 Mg C ha, more than twice the peak value of upper positions. Toe slopes fostered disproportionately high net carbon uptake relative to other topographic positions. Carbon storage was on average 20–40 Mg C ha greater on north to northeast aspects than on south to southwest aspects, a pattern most pronounced in the highest elevation, coldest and wettest catchment. Both the peak and mean aboveground carbon storage of the three catchments, crossing an 11°C range in temperature and doubling of local precipitation, defied the expectation of an optimal elevation‐gradient climatic zone for net primary production. These results have important implications for models of forest sensitivity to climate change, as well as to predicted estimates of continental carbon reservoirs.