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• Water and carbon cycling is tightly coupled at a piñon-juniper woodland in Utah. • Shallow pools of soil moisture were the dominant control over ecosystem fluxes and tree water potential. • Small summer precipitation events stimulated carbon-water cycling for weeks. Dryland ecosystems occupy a vast swath of the terrestrial land surface and exert a sizeable impact on the cycling of carbon and water globally. These biomes are characterized by tightly coupled carbon and water cycles that respond rapidly to transient pulses in water availability. However, there exist many mechanistic uncertainties regarding the environmental drivers of, and linkages between, plant and ecosystem processes. Thus, drylands are often poorly represented in many vegetation and land surface models. An enhanced understanding of dryland ecosystem function is limited by the lack of long-term, co-located, and frequent measurements of plant and ecosystem processes. At a piñon-juniper woodland in southeastern Utah, USA, we collected a continuous dataset of meteorological conditions, soil water potential from surface to bedrock, tree water potential, and ecosystem carbon and water fluxes from eddy covariance. We found that predawn and midday tree water potential and daily ecosystem fluxes were highly sensitive to fluctuations in soil water availability, particularly in shallower layers, and that daytime variability in atmospheric drivers only loosely controlled these processes. The strong connections between shallow soil water potential, tree water potential, and ecosystem fluxes occurred because of the dominant role of precipitation pulses in driving vegetation activity, as even small pulses of moisture stimulated shallow soil water potential, tree water potential, and evapotranspiration for between 1 and 2 weeks. Carbon fluxes (net ecosystem exchange and gross primary productivity) were sensitive to precipitation pulses for longer, up to 3 weeks. Our results highlight that improved monitoring and sensing of shallow soil moisture can greatly enhance our understanding of dryland ecosystem function. A better mechanistic understanding of the impacts of precipitation pulses is also needed to improve vegetation modeling of dryland ecosystems. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) data enable accurate modeling and mapping of aboveground biomass (AGB), but the limited spatial and temporal extents of ALS data collection limit the capacity for broad-scale carbon accounting. Conversely, while space-based remote sensing instruments provide increased spatial and temporal coverage, it can be difficult to directly link field-level vegetation biometrics to satellite data due to coarser spatial resolution and positional uncertainty. The combined use of ALS and satellite remote sensing data may offer a solution to efficient, accurate, and consistent AGB mapping across time and space. Such airborne-spaceborne data fusion has been demonstrated successfully in high-biomass settings; however, the unique structural conditions of dryland woodland ecosystems, with open canopies and low leaf area indices, pose mapping challenges that require further study. These challenges are particularly acute with large footprint spaceborne lidar, where short, widely-spaced trees may limit the capacity for accurate AGB estimation. In this study, we present a scaled methodological framework for linking field-measured woodland AGB to ALS data and, in turn, linking ALS-modeled AGB to satellite data, using piñon-juniper woodlands in southeastern Utah as a case study. We compare the effectiveness of this scaling approach using two satellite sensors, Landsat 8 OLI (multispectral) and GEDI (lidar). Since the predicted outputs of our local-scale model are being used as inputs to our regional-scale model, we also demonstrate an approach for propagating uncertainty throughout this nested, multiscale analytical framework, leveraging the inherent variability within a random forest's decision trees. Given the positional uncertainty of GEDI footprints, we test a range of different footprint sizes for their relative effects on ALS-GEDI AGB model accuracy. Our local-scale (field-ALS) predictive model was able to account for 74% of variance in AGB, and estimate AGB with a root mean squared error (RMSE) of 14 Mg/ha, a mean absolute error (MAE) of 11.09 Mg/ha. Our regional-scale (ALS-Landsat/GEDI) analysis with propagated uncertainty revealed that the combined use of Landsat and GEDI metrics produced the best predictive model (R2 = 0.68; RMSE = 12.71 Mg/ha; MAE = 9.40 Mg/ha), followed by Landsat-only metrics (R2 = 0.66, RMSE = 13.08 Mg/ha; MAE = 9.71 Mg/ha), and GEDI-only metrics (R2 = 0.49, RMSE = 16.01 Mg/ha; MAE = 12.14 Mg/ha). These results suggest that Landsat may be better-suited than GEDI for estimating AGB in woodland environments where low canopy covers and short trees limit the capacity for precisely characterizing vegetation structure within large-footprint, waveform lidar data. The footprint size analysis revealed that larger simulated footprints (e.g., 30 m radius and greater) produced higher GEDI model accuracies; however, increasing footprint radii beyond 30 m does not significantly increase model accuracy. This research represents an important step forward in improving our capacity for reliably mapping woodland AGB, and provides an early test case for the application of GEDI data to woodland AGB mapping. • We present a scaled analytical approach for accurately mapping woodland biomass. • We use piñon-juniper woodlands of southeastern Utah as a case study. • Scaling from ground to air to space provides accurate, repeatable estimates. • Comparison reveals that Landsat outperforms GEDI in modeling woodland biomass. • Combining Landsat spectral and GEDI waveform metrics produced highest accuracy. [ABSTRACT FROM AUTHOR]

Woodland ecosystems, dominant on nearly 4% of all terrestrial land globally, are faced with a variety of threats, including increasingly prolonged and severe droughts, invasive insect outbreaks, and the rapid spread of pathogens. While many remote sensing methods have been developed for the detection and quantification of mortality in forested environments, woodland ecosystems present unique challenges to accurately mapping tree die-off due to relatively lower canopy covers, smaller and irregularly-shaped tree crowns, and greater influence of understory vegetation and soil cover on reflectance. To address these challenges, we developed a multi-sensor, multi-scale approach combining the analytical strengths of centimeter-resolution unmanned aerial system imagery for interpreting individual tree-level mortality, airborne lidar for crown mapping and quantifying percent canopy mortality, and Landsat imagery for upscaling mortality estimates to a regional scale. This approach utilizes a new algorithm for delineating the shapes of small, irregular woodland tree crowns using lidar. To demonstrate the application of this method, we map the extent and severity of a recent tree mortality event in piñon-juniper (PJ) woodlands of southeastern Utah. Our results suggest that 39% of PJ in this region has experienced some level of mortality, with patches exceeding 50% mortality. An analysis of potential mortality drivers revealed that canopy cover, terrain, and recent winter precipitation conditions are most directly linked with mortality, although the explanatory power of the mortality driver model was low. Our approach demonstrates a methodology that could be used for tree mortality mapping and scaling in a variety woodland ecosystems, and can provide a strong basis for further ecophysiological, ecological, and carbon cycle studies involving woodland tree mortality. Unlabelled Image • Woodland ecosystems globally widespread and ecologically important, yet threatened. • Structural/spectral characteristics of woodlands pose challenges to mapping mortality. • Fusion of UAS imagery, airborne lidar, Landsat time series overcomes limitations. • New lidar-based tree crown delineation algorithm introduced for piñon-juniper trees. • Major PJ mortality event linked to canopy cover, terrain, climate, soil conditions. [ABSTRACT FROM AUTHOR]