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Abstract In this article, a compressive sensing-based reconstruction algorithm is applied to data acquired from a nodding multibeam Lidar system following a Lissajous-like trajectory. Multibeam Lidar systems provide 3D depth information of the environment, but the vertical resolution of these devices may be insufficient in many applications. To mitigate this issue, the Lidar can be nodded to obtain higher vertical resolution at the cost of increased scan time. Using Lissajous-like nodding trajectories allows for the trade-off between scan time and horizontal and vertical resolutions through the choice of scan parameters. These patterns also naturally subsample the imaged area. In this article, a compressive sensing-based reconstruction algorithm is applied to the data collected during a relatively fast and therefore low-resolution Lissajous-like scan. Experiments and simulations show the feasibility of this method and compare the reconstructions to those made using simple nearest-neighbor interpolation. 

The oak (Quercus) species of eastern North America are declining in abundance, threatening the many socioecological benefits they provide. We discuss the mechanisms responsible for their loss, many of which are rooted in the prevailing view that oaks are drought tolerant. We then synthesize previously published data to comprehensively review the drought response strategies of eastern US oaks, concluding that whether or not eastern oaks are drought tolerant depends firmly on the metric of success. Although the anisohydric strategy of oaks sometimes confers a gas exchange and growth advantage, it exposes oaks to damaging hydraulic failure, such that oaks are just as or more likely to perish during drought than neighboring species. Consequently, drought frequency is not a strong predictor of historic patterns of oak abundance, although long-term climate and fire frequency are strongly correlated with declines in oak dominance. The oaks’ ability to survive drought may become increasingly difficult in a drier future.

 

In this work, point pattern estimators are used to analyze the distribution of measurements from a multi-beam Lidar on a pitching platform. Multi-beam Lidars have high resolution in the horizontal plane, but poor vertical resolution. Placing the Lidar on a pitching base improves this resolution, but causes the distribution of measurements to be highly irregular. In this work, these measurement distributions are treated as point patterns and three estimators are used to quantity how measurements are spaced, which has implications in robotic detection of objects using Lidar sensors. These estimators are used to demonstrate how a pitching trajectory for the platform can be chosen to improve multiple performance criteria, such as increasing the likelihood of detection of an object, or adjusting how closely measurements should be spaced. 

Abstract Hydraulic stress in plants occurs under conditions of low water availability (soil moisture; θ) and/or high atmospheric demand for water (vapor pressure deficit; D). Different species are adapted to respond to hydraulic stress by functioning along a continuum where, on one hand, they close stomata to maintain a constant leaf water potential (ΨL) (isohydric species), and on the other hand, they allow ΨL to decline (anisohydric species). Differences in water-use along this continuum are most notable during hydrologic stress, often characterized by low θ and high D; however, θ and D are often, but not necessarily, coupled at time scales of weeks or longer, and uncertainty remains about the sensitivity of different water-use strategies to these variables. We quantified the effects of both θ and D on canopy conductance (Gc) among widely distributed canopy-dominant species along the isohydric–anisohydric spectrum growing along a hydroclimatological gradient. Tree-level Gc was estimated using hourly sap flow observations from three sites in the eastern United States: a mesic forest in western North Carolina and two xeric forests in southern Indiana and Missouri. Each site experienced at least 1 year of substantial drought conditions. Our results suggest that sensitivity of Gc to θ varies across sites and species, with Gc sensitivity being greater in dry than in wet sites, and greater for isohydric compared with anisohydric species. However, once θ limitations are accounted for, sensitivity of Gc to D remains relatively constant across sites and species. While D limitations to Gc were similar across sites and species, ranging from 16 to 34% reductions, θ limitations to Gc ranged from 0 to 40%. The similarity in species sensitivity to D is encouraging from a modeling perspective, though it implies that substantial reduction to Gc will be experienced by all species in a future characterized by higher D. 

In this article, a compressive sensing (CS) reconstruction algorithm is applied to data acquired from a nodding multi-beam Lidar system following a Lissajous-like trajectory. Multi-beam Lidar systems provide 3D depth information of the environment for applications in robotics, but the vertical resolution of these devices may be insufficient to identify objects, especially when the object is small and/or far from the robot. In order to overcome this issue, the Lidar can be nodded in order to obtain higher vertical resolution with the side-effect of increased scan time, especially when raster scan patterns are used. Such systems, especially when combined with nodding, also yield large volumes of data which may be difficult to store and mange on resource constrained systems. Using Lissajous-like nodding trajectories allows for the trade-off between scan time and horizontal and vertical resolutions through the choice of scan parameters. These patterns also naturally sub-sample the imaged area and the data can be further reduced by simply not collecting each data point along the trajectory. The final depth image must then be reconstructed from the sub-sampled data. In this article, a CS reconstruction algorithm is applied to data collected during a fast and therefore low-resolution Lissajous-like scan. Experiments and simulations show the feasibility of this method and compare its results to images produced from simple nearest-neighbor interpolation. 

The coordination of plant leaf water potential (Ψ_L) regulation and xylem vulnerability to embolism is fundamental for understanding the tradeoffs between carbon uptake and risk of hydraulic damage. There is a general consensus that trees with vulnerable xylem more conservatively regulate Ψ_Lthan plants with resistant xylem. We evaluated if this paradigm applied to three important eastern US temperate tree species,Quercus albaL.,Acer saccharumMarsh. andLiriodendron tulipiferaL., by synthesizing 1600 Ψ_Lobservations, 122 xylem embolism curves and xylem anatomical measurements across 10 forests spanning pronounced hydroclimatological gradients and ages. We found that, unexpectedly, the species with the most vulnerable xylem (Q. alba) regulated Ψ_Lless strictly than the other species. This relationship was found across all sites, such that coordination among traits was largely unaffected by climate and stand age.Quercusspecies are perceived to be among the most drought tolerant temperate US forest species; however, our results suggest their relatively loose Ψ_Lregulation in response to hydrologic stress occurs with a substantial hydraulic cost that may expose them to novel risks in a more drought‐prone future.

 

Land‐use/cover change (LUCC) is an important driver of environmental change, occurring at the same time as, and often interacting with, global climate change. Reforestation and deforestation have been critical aspects of LUCC over the past two centuries and are widely studied for their potential to perturb the global carbon cycle. More recently, there has been keen interest in understanding the extent to which reforestation affects terrestrial energy cycling and thus surface temperature directly by altering surface physical properties (e.g., albedo and emissivity) and land–atmosphere energy exchange. The impacts of reforestation on land surface temperature and their mechanisms are relatively well understood in tropical and boreal climates, but the effects of reforestation on warming and/or cooling in temperate zones are less certain. This study is designed to elucidate the biophysical mechanisms that link land cover and surface temperature in temperate ecosystems. To achieve this goal, we used data from six paired eddy‐covariance towers over co‐located forests and grasslands in the temperate eastern United States, where radiation components, latent and sensible heat fluxes, and meteorological conditions were measured. The results show that, at the annual time scale, the surface of the forests is 1–2°C cooler than grasslands, indicating a substantial cooling effect of reforestation. The enhanced latent and sensible heat fluxes of forests have an average cooling effect of −2.5°C, which offsets the net warming effect (+1.5°C) of albedo warming (+2.3°C) and emissivity cooling effect (−0.8°C) associated with surface properties. Additional daytime cooling over forests is driven by local feedbacks to incoming radiation. We further show that the forest cooling effect is most pronounced when land surface temperature is higher, often exceeding −5°C. Our results contribute important observational evidence that reforestation in the temperate zone offers opportunities for local climate mitigation and adaptation.