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Protecting the future of forests in the United States and other countries depends in part on our ability to monitor and map forest health conditions in a timely fashion to facilitate management of emerging threats and disturbances over a multitude of spatial scales. Remote sensing data and technologies have contributed to our ability to meet these needs, but existing methods relying on supervised classification are often limited to specific areas by the availability of imagery or training data, as well as model transferability. Scaling up and operationalizing these methods for general broadscale monitoring and mapping may be promoted by using simple models that are easily trained and projected across space and time with widely available imagery. Here, we describe a new model that classifies high resolution (~1 m2) 3-band red, green, blue (RGB) imagery from a single point in time into one of four color classes corresponding to tree crown condition or health: green healthy crowns, red damaged or dying crowns, gray damaged or dead crowns, and shadowed crowns where the condition status is unknown. These Tree Crown Health (TCH) models trained on data from the United States (US) Department of Agriculture, National Agriculture Imagery Program (NAIP), for all 48 States in the contiguous US and spanning years 2012 to 2019, exhibited high measures of model performance and transferability when evaluated using randomly withheld testing data (n = 122 NAIP state x year combinations; median overall accuracy 0.89–0.90; median Kappa 0.85–0.86). We present examples of how TCH models can detect and map individual tree mortality resulting from a variety of nationally significant native and invasive forest insects and diseases in the US. We conclude with discussion of opportunities and challenges for extending and implementing TCH models in support of broadscale monitoring and mapping of forest health. [ABSTRACT FROM AUTHOR]

Develops models of tree crown radius for several conifer species in California. Forest inventory variables; Use of ordinary and weighted least squares method; Estimation of canopy cover.

Unmanned Aerial Systems (UAS) have emerged as a platform capable of providing valuable data on vegetation structure, health, and productivity. The platform provides maximum temporal flexibility in data collection and customizability of spatial footprints which is important for mapping and monitoring vegetation conditions especially for high spectrally and structurally heterogeneous landscapes. In this letter, we investigate the potential improvement in structural information when using sensor payloads that include infrared bands (730–810 nm) in addition to the typical visible bands of many UAS sensor payloads. Specifically, we use derived point clouds and imagery collected from UAS-mounted sensors at a test site in Louisville, Kentucky, USA to assess whether multispectral sensors improve estimates of vegetation structure for plant greenness (leaf-on) and senescence (leaf-off) periods. Results indicate that including detailed multispectral reflectance information beyond the visible portion of the electromagnetic spectrum aids in estimating structural characteristics of woody vegetation, regardless of season. While both leaf-on and leaf-off periods result in reasonable crown height estimates, leaf-on conditions also improve radial estimates, likely due to continuous canopy surfaces. These findings advance research for remote sensing analyses assessing structural composition in heterogeneous landscapes where varying levels of vegetation structure have implications on land use and land function. [ABSTRACT FROM AUTHOR]

Abstract: The forest canopy is the medium for energy, mass, and momentum exchanges between the forest ecosystem and the atmosphere. Tree crown size is a critical aspect of canopy structure that significantly influences these biophysical processes in the canopy. Tree crown size is also strongly related to other canopy structural parameters, such as tree height, diameter at breast height and biomass. But information about tree crown sizes is difficult to obtain and rarely available from traditional forest inventory. The study objective was to test the hypothesis that a model previously developed for estimation of tree crown size can be generalized across sensors and sites. Our study sites include the Racoon Ecological Management Area in southeast Ohio, USA and the Duke Forest in North Carolina Piedmont, USA. We sampled a series of circular plots in the summers of 2005 and 2007. We derived average tree crown diameter (CD) for trees with diameter at breast height (DBH) greater than 6.4cm (2.5 in) for each sampling plot. We developed statistical models using image spatial information from Ikonos and QuickBird images as the independent variable and CD for stands in Ohio as the dependent variable. The models provide an explanation of tree crown size for the hardwood stands comparable to other approaches (R 2 =∼0.5 and RMSE=0.83m). Moreover, the models that estimate tree crown size using the ratio of image variances at two spatial resolutions can be applied across sensors and sites, i.e. the statistical models developed with Ikonos images can be applied directly to estimate tree crown size with QuickBird image, and the statistical models developed in Ohio can be applied directly to estimate tree crown size with images in North Carolina. These results indicate that the model developed based on image variance ratio at two spatial resolutions can be used to take advantage of existing sampling plot data and images to estimate CD with more recent images, enhancing the efficiency of forest resources inventory and monitoring. [Copyright &y& Elsevier]

Water supply is a critical component of tree physiological health, influencing a tree's photosynthetic activity and resilience to disturbances. The climatic regions of the western United States are particularly at risk from increasing drought, fire, and pest interactions. Existing methods for quantifying drought stress and a tree's relative resilience against disturbances mostly use moderate-scale (20–30 m) multispectral satellite sensor data. However, tree water status (i.e., water stress) quantification using sensors like Landsat and Sentinel are error-prone given that the spectral reflectance of pixels are a mixture of the dominant tree canopy, surface vegetation, and soil. Uncrewed aerial systems (UAS) equipped with multispectral sensors could potentially provide individual tree water status. In this study, we assess whether the simulated band equivalent reflectance (BER) of a common UAS optical multispectral sensor can accurately quantify the foliar moisture content and water stress status of individual trees. To achieve this, water was withheld from groups of Douglas-fir and western white pine saplings. Then, measurements of each sapling's foliar moisture content (FMC) and spectral reflectance were converted to BER of a consumer-grade multispectral camera commonly used on UAS. These bands were used in two classification models and three regression models to develop a best-performing FMC model for predicting either the water status (i.e., drought-stressed or healthy) or the foliar moisture content of each sapling, respectively. Our top-performing models were a logistic regression classification and a multiple linear regression which achieved a classification accuracy of 96.55% and an r2 of 82.62, respectively. These FMC models could provide an important tool for investigating tree crown level water stress, as well as drought interactions with other disturbances, and provide land managers with a vital indicator of tree resilience. [ABSTRACT FROM AUTHOR]

The evolution of form and function of trees of diverse species has taken place over hundreds of millions of years, while urban environments are relatively new on an evolutionary time scale, representing a novel set of environmental constraints for trees to respond to. It is important to understand how trees of different species, planted in these anthropogenically‐structured urban ecosystems, are responding to them. Many theories have been advanced to understand tree form and function, including several that suggest the fractal‐like geometry of trees is a direct reflection of inherent and plastic morphological and physiological traits that govern tree growth and survival. In this research, we analyzed the "fractal dimension" of thousands of tree crowns of many different tree species, growing in different urban environments across the United States, to learn more about the nature of trees and their responses to urban environments at different scales. Our results provide new insights regarding how tree crown fractal dimension relates to balances between hydraulic‐ and light‐capture‐related functions (e.g., drought and shade tolerance). Our findings indicate that trees exhibit reduced crown fractal dimension primarily to reduce water loss in hotter cities. More specifically, the intrinsic drought tolerance of the studied species arises from lower surface to volume ratios at both whole‐crown and leaf scales, preadapting them to drought stress in urban ecosystems. Needle‐leaved species showed a clear trade‐off between optimizing the fractal dimension of their crowns for drought vs. shade tolerance. Broad‐leaved species showed a fractal crown architecture that responded principally to inherent drought tolerance. Adjusting for the temperature of cities and intrinsic species effects, the fractal dimension of tree crowns was lower in more heavily urbanized areas (with greater paved area or buildings) and due to crowns conflicting with utility wires. With expectations for more urbanization and generally hotter future climates, worldwide, our results add new insights into the physiological ecology of trees in urban environments, which may help humans to provide more hospitable habitats for trees in urbanized areas and to make better decisions about tree selection in urban forest management. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Comprehensive assessment of individual-tree crown condition by the US Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program has its origins in the concerns about widespread forest decline in Europe and North America that developed in the late 1970s and early 1980s. Programs such as the US National Acid Precipitation Assessment Program, US National Vegetation Survey, Canadian Acid Rain National Early Warning System, and joint US-Canadian North American Sugar Maple Decline Project laid the groundwork for the development of the US Forest Service crown-condition indicator. The crown-condition assessment protocols were selected and refined through literature review, peer review, and field studies in several different forest types during the late 1980s and early 1990s. Between 1980 and 2011, 126 publications relating specifically to the crown-condition indicator were added to the literature. The majority of the articles were published by the US Department of Agriculture, Forest Service or other State or Federal government agency, and more than half were published after 2004. [ABSTRACT FROM AUTHOR]

The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2 shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling. Detailed maps of tree species can be used for forest analysis, biogeographic research and ecosystem monitoring. This study provides individual tree species maps for 24 sites in the United States National Ecological Observatory Network (NEON), covering 80 tree species and 100 million individuals. [ABSTRACT FROM AUTHOR]

Optimizing forest plantation management has become imperative due to increasing forest product demand, higher fertilization and management costs, declining land availability, increased competition for land use, and the growing demands for carbon sequestration. Precision forestry refers to the ability to use data acquired with technology to support the forest management decision-making process. LiDAR can be used to assess forest metrics such as tree height, topographical position, soil surface attributes, and their combined effects on individual tree growth. LiDAR opens the door to precision silviculture applied at the tree level and can inform precise treatments such as fertilization, thinning, and herbicide application for individual trees. This study uses ALS LiDAR and other ancillary data to assess the effect of scale (i.e., stand, soil type, and microtopography) on dominant height and site index measures within loblolly pine plantations across the southeastern United States. This study shows differences in dominant height and site index across soil types, with even greater differences observed when the interactions of microtopography were considered. These results highlight how precision forestry may provide a unique opportunity for assessing soil and microtopographic information to optimize resource allocation and forest management at an individual tree scale in a scarce higher-priced fertilizer scenario. [ABSTRACT FROM AUTHOR]

Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is to associate sensor data into individual crowns. While dozens of crown detection algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics. There is a need for a benchmark dataset to minimize differences in reported results as well as support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the USA National Ecological Observatory Network's Airborne Observation Platform with multiple types of evaluation data, we created a benchmark dataset to assess crown detection and delineation methods for canopy trees covering dominant forest types in the United States. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 400 field-annotated crowns, and 3,000 canopy stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation data sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as overlapping field-annotated crowns. We provide an example submission and score for an open-source algorithm that can serve as a baseline for future methods. Author summary: Combining RGB, LiDAR and hyperspectral sensor data from the USA National Ecological Observatory Network's Airborne Observation Platform with multiple types of evaluation data, we created a benchmark dataset to assess crown detection and delineation methods for canopy trees covering dominant forest types in the United States. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 400 field-annotated crowns, and 3,000 canopy stem points from a wide range of forest types. [ABSTRACT FROM AUTHOR]

Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source data set of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Network's Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses and cross-region comparisons encompassing forest types from most of the United States. [ABSTRACT FROM AUTHOR]

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

Highlights • We proposed a new LiDAR-based profile area change (PAC) method for fire severity assessment. • PAC metrics are highly correlated to field measured changes in basal area and leaf area index. • PAC metrics outperformed traditional LiDAR-based canopy cover and tree height change metrics, and Landsat-derived RdNBR. • PAC metrics can be applied at multiple scales from individual trees to plot-level fire assessments. Abstract This study proposes a new method (profile area change, PAC) to quantify fire-induced forest structural changes at the individual tree and clump of trees scales using pre- and post-fire LiDAR data. The PAC measures the difference in profile area summarized from pre- and post-fire LiDAR points. We applied the PAC method to assess the effects of the 2013 American Fire in the Sierra Nevada, California, USA. Our LiDAR PAC metrics were compared to changes in commonly used LiDAR-derived canopy cover and tree height metrics at tree level, and to Landsat-8 imagery-derived relative differenced normalized burn ratio (RdNBR) at the 30m pixel level. A quantitative validation using field measured changes in basal area and leaf area index (LAI) confirmed that correlations between PAC metrics and field measurements (R2 ≥ 0.67) were significantly higher than those from canopy cover or tree height metrics (R2 ≤ 0.43), and much stronger than that from RdNBR (R2 ≤ 0.26). The PAC metrics can also be used to infer the extent of tree canopy disturbance caused by fire, based on whether the majority of biomass loss occurred above or below the tree crown base height. Mapping of canopy disturbance indicated that over half (57.0%) of the American Fire region had tree canopy loss from fire, 22.5% of trees had sub-canopy loss, while the remaining area had no detectable tree canopy change. Overall, the LiDAR PAC metric, as a simple and integrated method, demonstrated promising potential in characterizing fine-grained changes in forest structure. The method can be beneficial for forest managers in evaluating fire-induced environmental and economic losses, and provide useful information for forest restoration design. [ABSTRACT FROM AUTHOR]

Abstract: Fires spreading in elevated vegetation, such as chaparral or pine forest canopies, are often more intense than fires spreading through surface vegetation such as grasslands. As a result, they are more difficult to suppress, produce higher heat fluxes, more firebrands and smoke, and can interact with, or create, local weather conditions that lead to dangerous fire behavior. Such wildland fires can pose a serious threat to wildland–urban interface communities. A basic building block of such fires is a single tree. In the work presented here, a number of individual trees, of various characteristics, were burned without an imposed wind in the Large Fire Laboratory of the National Institute of Standards of Technology. A numerical model capable of representing the spatial distribution of vegetation in a tree crown is presented and evaluated against tree burning experiments. For simplicity, the vegetation was assumed to be uniformly distributed in a tree crown represented by a well defined geometric shape (cone or cylinder). Predictions of the time histories of the radiant heat flux and mass loss rates for different fuel moisture contents and tree heights compared favorably to measured values and trends. This work is a first step toward the development and application of a physics-based computer model to spatially complex, elevated, vegetation present in forest stands and in the wildland–urban interface. [Copyright &y& Elsevier]

Copyright of Annals of Forest Science (EDP Sciences) is the property of EDP Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The application of lidar data to assist with forest inventory is common around the world. However, the determination of tree species is still somewhat elusive. Lidar data collected using UAS (uncrewed aircraft systems) platforms offer high density point cloud data for areas from a few to several hundred hectares. General point cloud metrics computed using these data captured differences in the crown structure that proved useful for species classification. For our study, we manually adjusted plot and tree locations to align field trees and UAS lidar point data and computed common descriptive metrics using a small cylindrical sample of points designed to capture the top three meters and leader of each tree. These metrics were used to train a random forest classifier to differentiate between two conifer species, Douglas fir and western hemlock, common in the Pacific Northwest region of the United States. Our UAS lidar data had a single swath pulse density of 90 pulses/m2 and an aggregate pulse density of 556 pulses/m2. We trained classification models using both height and intensity metrics, height metrics alone, intensity metrics alone, and a small subset of five metrics, and achieved overall accuracies of 91.8%, 88.7%, 78.6%, and 91.5%, respectively. Overall, we showed that UAS lidar data captured morphological differences between the upper crowns of our two target species and produced a classification model that could be applied over large areas. [ABSTRACT FROM AUTHOR]

Studies of drivers shaping forest communities frequently include abiotic or biotic factors, while their interactive effects remain understudied. Here, we combined data on two prominent abiotic gradients, climatic moisture deficit and wildfire probability, along with tree functional neighbourhoods (i.e. trait differences of close neighbours) to assess variation in survival and growth of 56 tree species in the northwestern US.We asked two questions: (1) How does functional neighbourhood dissimilarity vary with environmental gradients? and (2) How do demographic rates of tree species in the northwestern United States vary with the interactive effects of environmental gradients and functional neighbourhood? We expected functional neighbourhoods to become more similar as environmental stress increased, due to a convergence of species towards an optimum stress tolerance strategy. We also predicted the interactive effects of abiotic and biotic factors on tree demography and high variation in species‐specific responses to these interactive effects due to divergent species life history strategies.Functional neighbourhoods defined by dissimilarities in stem conductivity, litter decomposition, resprouting ability and specific leaf area changed with climate, shifting to more diverse neighbourhoods as climatic moisture deficit and wildfire probability increased. Results supported interactive effects of the functional neighbourhood and climatic moisture deficit or wildfire probability on tree demography, but only when the identity of dominant species was considered. Species‐specific responses were highly variable in their direction and magnitude and often demonstrated opposing effects of climate and the functional neighbourhood and climatic moisture deficit on tree demography.Synthesis. Our findings show that climate and tree neighbourhood functional dissimilarity jointly impact tree demography; however, the effects are species‐specific. Results of this study highlight the need to consider the interactive effects of abiotic and biotic contexts and individual species responses to their environment to adequately understand tree persistence under current and future climate conditions. [ABSTRACT FROM AUTHOR]

We investigated the branching and leaf display of dominant forest shrubs and understory trees in central New Jersey to determine (1) whether branching differentiation occurs in shrubs which reach optimum development in different successional environments, (2) the contrast in branching of small trees between field and forest and (3) the nature of within-crown branching plasticity in a mature canopy tree. We discovered that shrubs do not differ in gross branching structure (ratio of terminal to supporting branches) and propose that branch angle, length and alteration of leaf orientation may be significant display characters. Small trees exhibit markedly variable response to open v. closed habitats, confirming the expected increase in branching ratio in open environments. Within, but not outside, the forest, earlier successional species were more variable in branching. A single canopy tree crown also demonstrated alteration of leaf display components, including increased length and wider angle of branches, but not branch ratio in the shaded, lower crown. We suggest some of the fine-scale morphological traits of shrub branching which may be important in determining their leaf display. Finally, we discuss differences in shrub and tree habit, such as cloning and the presumably reduced costs of support in shrubs, which may explain the failure of shrubs to exploit the same component of branching strategy as trees. [ABSTRACT FROM AUTHOR]

Recent severe ice storms across the United States severely damaged trees resulting in extensive electrical power outages. Furthermore, trees and branches can fall on nearby roads, blocking traffic flow and reducing the safety of drivers. In this study, trees subjected to ice loads were analyzed using the finite element method and Monte Carlo simulation to develop analytical fragility curves. Two-dimensional, fractal trees were constructed with randomly generated geometric and mechanical parameters for four deciduous tree species: Acer saccharum, Tilia americana, Fagus grandifolia, and Quercus alba. Two load case scenarios were considered – with and without the effects of leaves – which were then subjected to varying ice accumulation thicknesses. The resulting fragility curves suggest that leaves have a substantial impact on tree branch damage under ice loads, which is significant because of the increase in unseasonably early ice storms due to climate change. [ABSTRACT FROM AUTHOR]

Arthropod diversity is often linked to variation in resource use, dispersal ability, habitat connectivity, and climate factors that differ across spatial scales. The aim of this research was to examine how species richness, functional diversity, and community composition of two taxa differing in functional roles and dispersal ability are structured across spatial scales and to identify the importance of vegetation, climate, and landscape in explaining these patterns at different scales. Organisms were collected from tree canopies using insecticidal fogging in the summer of 2000 from 96 trees in 24 stands of 6 deciduous forest sites in 2 ecoregions of the eastern United States. Taxonomic and functional beta diversity of ants (Hymenoptera: Formicidae) and spiders (Araneae) were partitioned across four hierarchical spatial scales (individual tree, forest stand, site, and ecoregion). The contributions of climatic, landscape, and vegetation variables were determined using model selection. Ant and spider species richness, functional diversity, and community composition differed between taxa and across spatial scales. Alpha diversity (within trees) was lower than expected for both taxa and types of diversity, with host tree species supporting different species of ants and spiders. While the beta components of species diversity among trees and forest stands were greater than expected for both taxa, spiders also showed significant levels of beta diversity among sites. Functional beta diversity was less scale‐dependent than taxonomic beta diversity. Stand‐level patterns of beta diversity were significantly predicted by variation in climate and landscape connectivity. The effects of climate and landscape fragmentation on the diversity and community structure of both taxa indicate that anthropogenic climate change and land use change will alter canopy arthropod communities. Results also suggest that patterns of diversity among fragmentation metrics are influenced by differences in dispersal ability. [ABSTRACT FROM AUTHOR]

Understanding the severity and extent of near surface critical zone (CZ) disturbances and their ecosystem response is a pressing concern in the face of increasing human and natural disturbances. Predicting disturbance severity and recovery in a changing climate requires comprehensive understanding of ecosystem feedbacks among vegetation and the surrounding environment, including climate, hydrology, geomorphology, and biogeochemistry. Field surveys and satellite remote sensing have limited ability to effectively capture the spatial and temporal variability of disturbance and CZ properties. Technological advances in remote sensing using new sensors and new platforms have improved observations of changes in vegetation canopy structure and productivity; however, integrating measures of forest disturbance from various sensing platforms is complex. By connecting the potential for remote sensing technologies to observe different CZ disturbance vectors, we show that lower severity disturbance and slower vegetation recovery are more difficult to quantify. Case studies in montane forests from the western United States highlight new opportunities, including evaluating post‐disturbance forest recovery at multiple scales, shedding light on understory vegetation regrowth, detecting specific physiological responses, and refining ecohydrological modeling. Learning from regional CZ disturbance case studies, we propose future directions to synthesize fragmented findings with (a) new data analysis using new or existing sensors, (b) data fusion across multiple sensors and platforms, (c) increasing the value of ground‐based observations, (d) disturbance modeling, and (e) synthesis to improve understanding of disturbance. Key Points: Increasing remote sensing capabilities improve observations of disturbance severity and recovery in varying critical zone settingsCase studies present how new approaches from multiple platforms and sensors better quantify post‐disturbance recovery of montane forestsWe suggest integrating the new approaches to refine remote sensing capabilities and resolve critical zone processes at multiple scales [ABSTRACT FROM AUTHOR]

The emerald ash borer (EAB), Agrilus planipennis Fairmaire, is the most destructive invasive pest on ash (Fraxinus spp.) in the United States. We determined whether ash trees injected with emamectin benzoate (EB) could protect untreated neighboring ash trees. We also determined whether the selective treatment of ash trees with EB injections had adverse effects on the establishment of introduced larval parasitoids Tetrastichus planipennis Yang and Spathius galinae Belokobylskij & Strazenac. In experiment one, trees were treated with EB and then retreated 3 years later. Five years post initial treatment, we found that 90% of treated ash trees retained healthy crowns, significantly higher than those of untreated control ash trees (16%). For experiment two, trees only received one treatment of EB and after 2 years 100% of treated ash trees retained healthy crowns, significantly higher than those of untreated ash trees (50%). In both experiments, we found that distance from the central EB-treated tree was not a significant predictor for tree health or presence of EAB exit holes. Although distance from the EB-treated trees appeared to have a significant positive relationship with woodpecker feeding signs on neighboring trees, such relationships did not result in significant differences in the proportion of neighboring ash trees retaining healthy crowns between EB treatment and control plots. The introduced EAB parasitoids appeared to have established equally well between treatment and control plots. Findings are discussed in the context of integration of EB trunk injection with biological control for protection of North American ash against EAB. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Early cone formation is a consistent issue in Fraser fir Christmas tree plantations in the eastern United States. Growers must remove cones by hand, resulting in significant labor costs, as cones degrade the aesthetic value of Christmas trees. In this study, we conducted two series of experiments in collaboration with several Christmas tree farms to determine the effectiveness of applying plant growth regulators (PGRs), specifically GA-inhibitors, in reducing cone formation and shoot growth in Fraser fir. In the first experiments we investigated the effectiveness of paclobutrazol, applied as a soil injection or as a foliar spray in reducing cone formation. The highest rate of soil application (300 mL of paclobutrazol per tree) reduced cumulative coning by approximately 38%. Leader growth control of soil applied paclobutrazol increased with application rate. In the second set of experiments, we compared four GA-inhibitors (paclobutrazol, chlormequat chloride, uniconazole-p, daminozide) applied as foliar sprays on coning and leader growth. Overall, paclobutrazol was the most effective compound for reducing coning and shoot growth. The results confirm earlier findings that application of GA-inhibitors can reduce, but not eliminate, coning. Likewise, PGR application can reduce shoot growth and possibly increase tree density but will not eliminate the need for shearing. [ABSTRACT FROM AUTHOR]

Thousand cankers disease ( TCD), a lethal fungal dieback of eastern black walnut ( Juglans nigra), caused by Geosmithia morbida, and spread by the walnut twig beetle, Pityophthorus juglandis, was documented in 2009 to be very destructive in the western United States and was identified in the native range of J. nigra at Knoxville, Tennessee, in 2010, and in 2011 at Richmond, Virginia. Beginning late 2010, we studied branch dieback levels (per cent live crown) and new TCD symptom development at these two quarantined locations monthly for 3 years. Of the 106 trees studied (53 at each location), 31 trees had low live crown ratings of 70 to 0% with little change over the 3 years of the study. One per cent of the trees developed new symptoms on a per-year basis. Thus, a moderate level of TCD (mean = 76% live crown) was present in these two locations, and most trees were in a quiescent or dormant TCD condition for 3 years, an important finding not previously reported. We found new TCD symptoms developed in Richmond in 2011 and 2012 when precipitation from January 1 to the end of August was low (60-64 cm), and not when the precipitation in Richmond was higher (99 cm). In late 2012, in Richmond, soil water potential assays indicated that some black walnut trees were under severe physiological stress (−15 bars). In contrast, in 2013, high precipitation levels (99 and 130 cm) and high soil water potentials (−0.1 to −3 bars) at both locations were associated with extensive new foliage and stem growth and recovery from TCD. Further research is needed on water relationships in regard to TCD and black walnut health. [ABSTRACT FROM AUTHOR]

Laurel wilt is a destructive vascular disease of trees in the laurel family (Lauraceae) caused by a nonnative insect/pathogen complex. This study monitored the recent spread and impact of laurel wilt in sassafras (Sassafras albidum [Nutt.] Nees) from the Gulf-Atlantic Coastal Plain region of the southeastern United States (US) into the adjacent Piedmont/Sandhills and Mountain regions. Laurel wilt was detected at thirteen of forty-six sassafras sites including seven outside the Coastal Plain. Compared to nondiseased sites, sassafras mortality due to laurel wilt increased rapidly from 2018 to 2020 and occurred in all diameter classes monitored (≥ 5 cm diameter at breast height, dbh). Flight trapping for the laurel wilt vector, the redbay ambrosia beetle (RAB, Xyleborus glabratus Eichhoff), with α -copaene lures did not enhance early detection of latent laurel wilt infections. Seasonal flight activity of the RAB in the Piedmont and Mountains suggested two generations per year with little to no flight from December through March. Study Implications Sassafras is the most widely-distributed lauraceous tree species in the US and is valued for its ecological functions, durable wood, and numerous cultural uses. Our results strongly suggest the potential for substantial loss of the existing sassafras resource as laurel wilt continues spreading through the eastern US. Survey traps baited with current lures for the primary insect vector are unlikely to improve early detection of new laurel wilt infections. The georeferenced network of plots established for this project provides baseline data for future monitoring efforts and could be revisited to look for evidence of sassafras resistance to the laurel wilt pathogen. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

1. Each year, the National Ecological Observatory Network's (NEON) Airborne Observation Platform (AOP) collects high-resolution hyperspectral imagery, discrete and waveform lidar, and digital photography at a subset of 81 terrestrial and aquatic research sites throughout the United States. These open remote sensing data, together with NEON in situ sensor measurements and field observations, enable researchers to characterize ecological processes at multiple spatial and temporal scales. 2. Here we describe the sampling design for the AOP that aims to meet the diverse research needs of the ecological science community within the operational constraints affecting airborne data collection. Our spatial sampling protocol captures NEON instrumented systems, field plots and environmental gradients around each site while considering the context of airspace restrictions and remote sensing instrument capabilities. We use time series of moderate resolution imaging spectroradiometer (MODIS) satellite and PhenoCam near-surface observations to define temporal sampling windows based on vegetation peak foliar greenness. We developed a probabilistic model based on MODIS reflectance imagery and Monte Carlo simulation to estimate sampling durations for cloud-free data collection at each site. 3. Agreement in the estimated phenophase transition dates between MODIS Enhanced Vegetation Index and PhenoCam Green Chromatic Coordinate varied by vegetation class. Results from both sensors show that some vegetation classes have relatively consistent interannual peak greenness start-and end-dates, while others experience high year-to- year variability in green-up and senescence. In addition to phenological variability among sites, certain vegetation forms demonstrate distinct, asynchronous responses to climate, resulting in non-overlapping peak greenness periods within a single site. Results from flight campaigns showed that the cloud-likelihood model underestimated actual cloud conditions by 13%-26%, depending on the probability used. 4. Where interannual or intra-site phenology is highly variable or clouds are a persistent problem, it becomes challenging to schedule domain deployments so that all sites are flown in cloud-free conditions while their vegetation communities are in peak greenness. Despite limitations, application of cloud and peak greenness models to airborne sampling results in significant improvements to AOP data quality. Although most applicable to airborne sampling with hyperspectral and lidar instruments in piloted aircraft, these methods may be a valuable resource to deployment of Unmanned Aerial Vehicles for ecological research. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Field Ornithology is the property of Resilience Alliance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Static models of individual tree crown attributes such as height to crown base and maximum branch diameter profile have been developed for several commercially important species. Dynamic models of individual branch growth and mortality have received less attention, but have generally been developed retrospectively by dissecting felled trees; however, this approach is limited by the lack of historic stand data and the difficulty in determining the exact timing of branch death. This study monitored the development of individual branches on 103 stems located on a variety of silvicultural trials in the Pacific Northwest, USA. The results indicated that branch growth and mortality were significantly influenced by precommercial thinning (PCT), commercial thinning, fertilization, vegetation management, and a foliar disease known as Swiss needle cast [caused by Phaeocryptopus gaeumannii (T. Rohde) Petr.]. Models developed across these datasets accounted for treatment effects through variables such as tree basal area growth and the size of the crown. Insertion of the branch growth and mortality equations into an individual-tree modeling framework, significantly improved short-term predictions of crown recession on an independent series of silvicultural trials, which increased mean accuracy of diameter growth prediction (reduction in mean bias). However, the static height to crown base equation resulted in a lower mean square error for the tree diameter and height growth predictions. Overall, individual branches were found to be highly responsive to changes in stand conditions imposed by silvicultural treatments, and therefore represent an important mechanism explaining tree and stand growth responses. [Copyright &y& Elsevier]

Abstract: In the 1970s, public opposition to clearcut harvesting in hardwood forests of the eastern United States led forest managers and scientists to consider alternative practices that retain a low-density overstory forest cover. From 1979 to 1984, a form of clearcut-with-reserves harvesting was applied in 80-year-old Appalachian mixed-hardwoods to create four experimental stands with two-aged structures. The residual stand basal area averaged 5.3m2/ha, comprising an average of 36 reserve trees/ha. The reserve trees were evenly distributed throughout the stand, initially with considerable space between their crowns, thus providing the sunlight and seedbed conditions needed to recruit desirable shade-intolerant reproduction after harvest. This study examined the response of the 100-year-old reserve trees and the development of the 20-year-old natural reproduction located in their immediate vicinity. Diameter at breast height (Dbh), height, and relative position were recorded for all reproduction ≥2.5cm within transects adjacent to northern red oak (Quercus rubra L.) and yellow-poplar (Liriodendron tulipifera L.) reserve trees. Each transect was divided into five zones, which represented positions relative to the reserve tree crown edge, and basal area was computed for each of three shade tolerance classes within each zone. A repeated measures ANOVA was used to compare basal area of reproduction by tolerance classes and zone. In general, basal area of reproduction, particularly that of shade-intolerant species, increased with distance from the reserve tree. Regression analyses also indicated that dbh and height of reproduction was positively related to distance from the reserve trees. Although height growth of reserve trees was similar for both species, northern red oak exhibited significantly greater dbh and crown radial growth than yellow-poplar. The results indicated that reserve trees influence the growth rate and species composition of reproduction in their immediate vicinity. Basal area of reproduction increased from 10.1 to 17.7m2/ha with increasing distance from the reserve trees. Basal area of intolerant species more than doubled along the same gradient. Basal area of reproduction in the two-age stands was 30–40% less than that observed in even-aged stands on similar growing sites, but the reduction was offset by growth of the reserve trees. The surface area covered by the reserve tree crowns increased approximately 88% for northern red oak and 44% for yellow-poplar. Since the sphere of influence of reserve trees increases over time, forest managers must consider their long-term impact on reproduction when prescribing clearcut-with-reserves harvests and other practices that involve retaining trees for many years. [Copyright &y& Elsevier]

Detecting crop phenology with satellite time series is important to characterize agroecosystem energy-water-carbon fluxes, manage farming practices, and predict crop yields. Despite the advances in satellite-based crop phenological retrievals, interpreting those retrieval characteristics in the context of on-the-ground crop phenological events remains a long-standing hurdle. Over the recent years, the emergence of near-surface phenology cameras (e.g., PhenoCams), along with the satellite imagery of both high spatial and temporal resolutions (e.g., PlanetScope imagery), has largely facilitated direct comparisons of retrieved characteristics to visually observed crop stages for phenological interpretation and validation. The goal of this study is to systematically assess near-surface PhenoCams and high-resolution PlanetScope time series in reconciling sensor- and ground-based crop phenological characterizations. With two critical crop stages (i.e., crop emergence and maturity stages) as an example, we retrieved diverse phenological characteristics from both PhenoCam and PlanetScope imagery for a range of agricultural sites across the United States. The results showed that the curvature-based Greenup and Gu-based Upturn estimates showed good congruence with the visually observed crop emergence stage (RMSE about 1 week, bias about 0–9 days, and R square about 0.65–0.75). The threshold- and derivative-based End of greenness falling Season (i.e., EOS) estimates reconciled well with visual crop maturity observations (RMSE about 5–10 days, bias about 0–8 days, and R square about 0.6–0.75). The concordance among PlanetScope, PhenoCam, and visual phenology demonstrated the potential to interpret the fine-scale sensor-derived phenological characteristics in the context of physiologically well-characterized crop phenological events, which paved the way to develop formal protocols for bridging ground-satellite phenological characterization. [ABSTRACT FROM AUTHOR]

Columbia, MO, is one of the fastest growing cities in the United States. The rapid urbanization is jeopardizing the city's urban climax forests, which are defined as any woodland community of more than 7.53 ha dominated by plant species such as oak and hickory. The increasing urban pressure places heavy demands on city planners to seek better management approaches to ensure that these plant species, which are native to Missouri, are protected even when threatened by new developments. Traditionally, planners are identifying these areas by surveys, which are costly, time consuming, and conducted on an as-needed basis. The current study is to test the feasibility of high-resolution satellite and airborne imagery for the identification of these forests and to assist planners in preserving them. In order to identify these climax forests, 4-m multispectral IKONOS images for April and August 2000 and 25-cm and 1-m multispectral airborne photographs for September and November 2001 were acquired and used in this study. These images were classified using a traditional classifier [maximum likelihood (ML)] and a rule-based classifier [classification and regression tree (CART)]. Results show that the images taken in September using the ML classifier are more useful in identifying the tree species in the study area. Among the spatial resolutions used, 1-m images proved to be optimal in recognizing trees and at the same time minimizing shadows. [ABSTRACT FROM AUTHOR]

Leaf-off individual trees in a deciduous forest in the eastern USA are detected and analysed in small footprint, high sampling density lidar data. The data were acquired February 1, 2001, using a SAAB TopEye laser profiling system, with a sampling density of approximately 12 returns per square meter. The sparse and complex configuration of the branches of the leaf-off forest provides sufficient returns to allow the detection of the trees as individual objects and to analyse their vertical structures. Initially, for the detection of the individual trees only, the lidar data are first inserted in a 2D digital image, with the height as the pixel value or brightness level. The empty pixels are interpolated, and height outliers are removed. Gaussian smoothing at different scales is performed to create a three-dimensional scale-space structure. Blob signatures based on second-order image derivatives are calculated, and then normalised so they can be compared at different scale-levels. The grey-level blobs with the strongest normalised signatures are selected within the scale-space structure. The support regions of the blobs are marked one-at-a-time in the segmentation result image with higher priority for stronger blobs. The segmentation results of six individual hectare plots are assessed by a computerised, objective method that makes use of a ground reference data set of the individual tree crowns. For analysis of individual trees, a subset of the original laser returns is selected within each tree crown region of the canopy reference map. Indices based on moments of the first four orders, maximum value and number of canopy and ground returns, are estimated. The indices are derived separately for height and laser reflectance of branches for the two echoes. Significant differences (p<0.05) are detected for numerous indices for three major native species groups: oaks (Quercus spp.), red maple (Acer rubrum) and yellow poplar (Liriodendron tuliperifera). Tree species classification results of different indices suggest a moderate to high degree of accuracy using single or multiple variables. Furthermore, the maximum tree height is compared to ground reference tree height for 48 sample trees and a 1.1-m standard error (R2=68% (adj.)) within the test-site is observed. [Copyright &y& Elsevier]

Discusses the characteristics of the crown structure of old-growth Douglas-fir in the western Cascade Range, Washington. Structural complexity of the crown; Occurrence of numerous vertical gaps between branches; Relationship between structure and function of old-growth forests.

Airborne lidar is often used to calculate forest metrics about trees, but it may also provide a wealth of information about the space between trees. Forest canopy gaps are defined by the absence of vegetative structure and serve important roles for wildlife, such as facilitating animal movement. Forest canopy gaps also occur around snags, keystone structures that provide important substrates to wildlife species for breeding, roosting and foraging.We wanted to test a method for quantifying canopy gaps around individual snags and live trees, with the working hypothesis that snags would have more gaps surrounding them overall than live trees. We evaluated canopy gaps around individual snags (n = 270) and live trees (n = 2,186) and evaluated correlations between canopy structure and snag occurrence in dense conifer stands of the Idaho Panhandle National Forest, United States. We paired airborne lidar with ground reference data collected at fixed‐radius plots (n = 53) to evaluate local gap structure. The r package ForestGapR was used to quantify canopy gaps throughout the canopy to determine where the differences were greatest. A canopy space profile was created for each tree by mapping gaps (a) vertically every 2 m in height (2–50 m above‐ground), and (b) horizontally across small (16 m2), medium (36 m2) and large (64 m2) footprint sizes.Our results suggest that this method is robust for quantifying canopy gaps around individual trees. The canopy space profiles were distinctly different for snags and live trees, with more canopy gaps within the area surrounding snags relative to live trees. The greatest differences occurred at mid‐canopy heights (~20 m above‐ground) and at the smallest footprint size (16 m2).These results show potential to improve the understanding of gap dynamics in closed‐canopy conifer forests, and we suggest that snag modelling could be improved by incorporating lidar‐derived canopy gap analyses alongside existing methodologies. [ABSTRACT FROM AUTHOR]

Canopy openness is an important forest characteristic related to understory light environment and productivity. Although many methods exist to estimate canopy openness, comparisons of their performance tend to focus on relatively narrow ranges of canopy conditions and forest types. To address this gap, we compared two popular approaches for estimating canopy openness, traditional spherical densiometer and modern smartphone hemispherical photography, across a large range of canopy conditions (from closed canopy to large gaps) and forest types (from low-elevation broadleaf to high-elevation conifer forests) across four states in the northeastern United States. We took 988 field canopy openness measurements (494 per instrument) and compared them across canopy conditions using linear regression and t -tests. The extensive replication allowed us to quantify differences between the methods that may otherwise go unnoticed. Relative to the densiometer, smartphone photography overestimated low canopy openness (<10%) but it underestimated higher canopy openness (>10%), regardless of forest type. [ABSTRACT FROM AUTHOR]

Aim: Empirical biodiversity – forest structure relationships (BSRs) underlie the use of forest structure as a remotely sensible proxy of biodiversity. However, little is known about how BSRs generalize to continental scales or how climate interacts with structure to drive local patterns in plant diversity. Resolving these research gaps in macrosystems ecology will strengthen our understanding of the biogeography of plant diversity, with implications for global‐scale biodiversity mapping. Location: USA. Time period: Contemporary. Major taxa studied: Vascular plants and trees. Methods: We combined climate variables with field measurements and airborne lidar from all forest and woodland plots in the National Ecological Observatory Network (NEON) to characterize the role of climate in constraining BSRs across the United States. Spatial generalized linear mixed models were used to quantify the individual and joint effects of structure and climate on vascular plant and tree diversity. Results: These findings provide evidence for broad‐scale BSRs across the USA; namely, between plant/tree diversity and forest structural metrics in the vertical and horizontal planes. Vascular plant diversity was positively related to horizontally heterogeneous and bottom‐skewed canopies, whereas tree diversity was positively associated with canopy cover and structural heterogeneity. In addition, climate variables related to stress (negatively), energy (positively) and seasonality (negatively) affected broad‐scale patterns in diversity, with water availability (but not temperature) exerting significant effects on structural conditions. Importantly, climate and structure interact to explain variance in BSRs along coldness, mean temperature and evapotranspiration gradients. Main conclusions: Our findings reinforce the importance of local context dependence in assessing non‐stationary biogeographical patterns in forest biodiversity, as well as the unique ways that forest structure and climate interact to constrain plant diversity. Beyond these theoretical insights, this study provides an empirical foundation for generalizing remotely sensed estimates of climate and forest structure to model biodiversity over vast extents. [ABSTRACT FROM AUTHOR]

Invasive herbivores can have dramatic impacts in new environments by altering landscape composition, displacing natives, and causing plant decline and mortality. One of the most recent invasive insects in the United States, the spotted lanternfly (Lycorma delicatula), has the potential to cause substantial economic and environmental impacts in agriculture and forestry. Spotted lanternfly exhibits a broad host range, yet reports of late-season movement from the surrounding landscapes onto select tree species in suburban environments have been reported. In this study, we aimed to evaluate the fidelity of spotted lanternfly attack on specific, individual trees within the same species during this movement period. In 2018 and 2019, we observed that individual red (Acer rubrum L. [Sapindales: Sapindaceae]) and silver maple (Acer saccharinum L. [Sapindales: Sapindaceae]) trees were preferentially attacked over other nearby trees of the same species. Foliar elemental composition was a good predictor of spotted lanternfly attack numbers, indicating that individual variation in nutrients may influence spotted lanternfly attraction to and/or retention on maple trees. Our data also confirm reports of late-season movement from surrounding landscapes throughout autumn. Collectively, our results show that spotted lanternfly exhibits some fidelity to particular trees in the landscape during this movement period. While other potential mechanisms also contribute to host plant selection by spotted lanternfly, our data show that host nutritional profiles influence spotted lanternfly infestation of suburban trees at the landscape scale. Our data establish that late-season infestations of suburban trees by spotted lanternfly occurred and that variation in host quality should be further considered in the management of this invasive insect pest. [ABSTRACT FROM AUTHOR]

Wildland fires have a multitude of ecological effects in forests, woodlands, and savannas across the globe. A major focus of past research has been on tree mortality from fire, as trees provide a vast range of biological services. We assembled a database of individual-tree records from prescribed fires and wildfires in the United States. The Fire and Tree Mortality (FTM) database includes records from 164,293 individual trees with records of fire injury (crown scorch, bole char, etc.), tree diameter, and either mortality or top-kill up to ten years post-fire. Data span 142 species and 62 genera, from 409 fires occurring from 1981-2016. Additional variables such as insect attack are included when available. The FTM database can be used to evaluate individual fire-caused mortality models for pre-fire planning and post-fire decision support, to develop improved models, and to explore general patterns of individual fire-induced tree death. The database can also be used to identify knowledge gaps that could be addressed in future research. Measurement(s) plant morphology trait • tree mortality • fire • tree fire injury • wildfire Technology Type(s) digital curation Factor Type(s) year of data collection • geographic location of fire • tree fire injury Sample Characteristic - Organism trees Sample Characteristic - Environment forest ecosystem Sample Characteristic - Location Cascades Region • Blue Mountains • Far Northern Rockies • Sierra Nevada • Piedmont Province • Region of Piedmont • Atlantic and Gulf Coastal Plain Floristic Province • Northern Rocky Mountains Provincial Park Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12369293 [ABSTRACT FROM AUTHOR]

Insects and pathogens are widely recognized as contributing to increased tree vulnerability to the projected future increasing frequency of hot and dry conditions, but the role of parasitic plants is poorly understood even though they are common throughout temperate coniferous forests in the western United States. We investigated the influence of western hemlock dwarf mistletoe (Arceuthobium tsugense) on large (≥45.7 cm diameter) western hemlock (Tsuga heterophylla) growth and mortality in a 500 year old coniferous forest at the Wind River Experimental Forest, Washington State, United States. We used five repeated measurements from a long‐term tree record for 1,395 T. heterophylla individuals. Data were collected across a time gradient (1991–2014) capturing temperature increases and precipitation decreases. The dwarf mistletoe rating (DMR), a measure of infection intensity, varied among individuals. Our results indicated that warmer and drier conditions amplified dwarf mistletoe effects on T. heterophylla tree growth and mortality. We found that heavy infection (i.e., high DMR) resulted in reduced growth during all four measurement intervals, but during warm and dry intervals (a) growth declined across the entire population regardless of DMR level, and (b) both moderate and heavy infections resulted in greater growth declines compared to light infection levels. Mortality rates increased from cooler‐wetter to warmer‐drier measurement intervals, in part reflecting increasing mortality with decreasing tree growth. Mortality rates were positively related to DMR, but only during the warm and dry measurement intervals. These results imply that parasitic plants like dwarf mistletoe can amplify the impact of climatic stressors of trees, contributing to the vulnerability of forest landscapes to climate‐induced productivity losses and mortality events. [ABSTRACT FROM AUTHOR]

Research Highlights: Bottomland oaks receive less attention than upland species, however their adaptations to flooding and summer water stress will extend our understanding of the oak genus and links between physiology and leaf anatomy. Background and objectives: Determining links between leaf anatomy and physiology can aid in parameterizing dynamic global vegetation models for oak systems, therefore we sought to (1) compare leaf anatomic, nutrient, and physiological parameters for bottomland oaks differing in flood tolerance, (2) determine correlations across parameters and determine which anatomic and nutrient parameters best predict photosynthetic capacity metrics, and (3) compare these data with reported literature values for oaks across the globe. Materials and Methods: We measured CO2 response curves (A/Ci) on leaves from Nuttall, Shumard, swamp chestnut, water and white oak seedlings planted in the Southeastern United States (US) and estimated stomatal size and density, epidermal cell size, vein density, leaf mass per area (LMA) and nitrogen (N) concentrations. Principal component analysis among these leaf anatomic and nutrient parameters was used to determine the best predictors of photosynthetic parameters including Rubisco-limited carboxylation rate (VCmax) and electron transport limited carboxylation rate (Jmax). Results: We found that although physiological parameters were similar, flood-tolerant oaks had lower leaf N concentrations and larger, more infrequent stomata than less flood-tolerant species. Leaf epidermal properties were correlated withNconcentrations and a principal component capturing this correlation as well as principal components correlated with mesophyll conductance and leaf carbon concentrations were found to best explain variation in VCmax and Jmax. These Southeastern US oaks exhibited similar leaf physiological parameters and LMA as oaks reported in the literature but differed in leaf epidermal and stomatal properties as well as leaf N concentrations increasing the reported range of these parameters within the oak genus. Conclusions: Therefore, leaf anatomy and nutrient parameters as opposed to physiology differed across flood tolerance and between bottomland oaks and broader literature values. [ABSTRACT FROM AUTHOR]

Allometric biomass equations were developed based on harvests of 198 trees from 15 field sites in the north-central USA, with the trees representing 4 hybrid poplar genomic groups and a total of 11 clones within these groups. Specifically, equations were developed to describe woody (branch + stem) total dry weight (TDW) as a function of diameter at breast height (DBH), along with hypothesis tests of differences among genomic groups and clones for equation intercepts and slopes. Inclusion of groups or clones improved model fit (r2 = 0.90 or 0.91, respectively) compared to the generic model consisting of only DBH (r2 = 0.85). Differences in equation parameters translated into significant differences among groups and clones for estimated TDW when compared at mean DBH (20 cm). Equations were also developed to describe branch-to-stem weight ratio (BSR) as a function of TDW and tree height (H), also with hypothesis tests of differences in intercepts and slopes among genomic groups and clones. Inclusion of genomic groups somewhat improved model fit (r2 = 0.57) compared to the generic model consisting of only TDW and H (r2 = 0.53), whereas model fit improved more markedly with the inclusion of clones (r2 = 0.75). Our results indicate that group- and clone-specific equations (rather than generic ones) are warranted for hybrid poplars, and that group-specific equations are adequate for estimating TDW whereas clone-specific equations are more appropriate for estimating BSR. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) air pollution varies spatially and temporally in concentration and composition and has been shown to cause or exacerbate adverse effects on human and ecological health. Biomonitoring using airborne tree leaf deposition as a proxy for particulate matter (PM) pollution has been explored using a variety of study designs, tree species, sampling strategies, and analytical methods. In the USA, relatively few have applied these methods using co-located fine particulate measurements for comparison and relying on one tree species with extensive spatial coverage, to capture spatial variation in ambient air pollution across an urban area. Here, we evaluate the utility of this approach, using a spatial saturation design and pairing tree leaf samples with filter-based PM2.5 across Pittsburgh, Pennsylvania, with the goal of distinguishing mobile and stationary sources using PM2.5 composition. Co-located filter and leaf-based measurements revealed some significant associations with traffic and roadway proximity indicators. We compared filter and leaf samples with differing protection from the elements (e.g., meteorology) and PM collection time, which may account for some variance in PM source and/or particle size capture between samples. To our knowledge, this study is among the first to use deciduous tree leaves from a single tree species as biomonitors for urban PM2.5 pollution in the northeastern USA. [ABSTRACT FROM AUTHOR]

It is typically difficult to burn duff because of high fuel moisture; however, under persistent drought conditions, duff will burn readily. This study investigates the burning of a deep duff layer by the 2016 Rough Ridge Fire, in the southern United States, under drought conditions and evaluates the contribution of duff consumption to fire emissions and air pollution. Fuel loading was measured and used to evaluate the BlueSky framework. Smoke was simulated for three fuel loading and moisture scenarios of field measurement, BlueSky estimated fuel loading, and a hypothetical moist condition. The measured fuels had a very deep duff layer that had accumulated over decades due to the lack of historical fires, most of which was burned by the fire. The burning of this deep duff layer contributed substantially to the increased fire emissions at the fire site and the air pollution in metro Atlanta. In contrast, BlueSky under-predicted duff loading and fire emissions. As a result, no major air pollution episodes were predicted for metro Atlanta. The high-moisture scenario also failed to produce a major air-pollution episode within Atlanta, which highlights the contribution of the drought to the air-pollution episode within Atlanta. Forest duff is composed of decomposing organic materials that are typically unavailable for wildfires to consume because of its high moisture content in the south-eastern United States. However, duff can be burned substantially under persistent drought conditions. This study examines a case of wildfire amid long-term drought and identifies the critical role of duff burning in the production of air pollution in a downwind metropolitan region. The BlueSky framework under-predicted the fuel loading, fuel consumption, and fire emissions obtained based on the field measurements. [ABSTRACT FROM AUTHOR]

Thousand cankers disease (TCD) results from the combined activity of the fungal pathogen, Geosmithia morbida Kolařík, Freeland, Utley, and Tisserat and its principle vector, Pityophthorus juglandis (Blackman) (Coleoptera: Curculionidae: Scolytinae) in Juglans L. spp. and Pterocarya Kunth spp. host plants. TCD has been reported from the eastern and western United States. To evaluate potential for other beetle species to vector the fungus in east Tennessee, specimens were collected using ethanol-baited traps that were suspended beneath crowns of TCD-symptomatic trees. Associations of G. morbida with insect species collected in traps were assessed in an unsuccessful, preliminary culture-based fungal assay, and then with a molecular-based detection method. For culture-based assays, rinsate from washed, individual insects was plated on nutrient media and growing colonies were subcultured to obtain axenic G. morbida cultures for identification. For the molecular-based method, G. morbida presence was detected by amplifying the previously developed, species-specific microsatellite locus GS004. Capillary electrophoresis was used to detect the amplified amplicons and representative reactions were validated using Sanger sequencing. Eleven beetle species were found to carry G. morbida , including Cnestus mutilatus (Blandford), Dryoxylon onoharaensum (Murayama), Hylocurus rudis (LeConte), Monarthrum fasciatum (Say), Monarthrum mali (Fitch), Xyleborinus saxesenii (Ratzeburg) , Xylosandrus crassiusculus (Motschulsky), Xylosandrus germanus (Blandford) (all Coleoptera: Curculionidae: Scolytinae), Stenomimus pallidus (Boheman) (Coleoptera: Curculionidae: Cossoninae), Oxoplatypus quadridentatus (Olivier) (Coleoptera: Curculionidae: Platypodinae), and Xylops basilaris (Say) (Coleoptera: Bostrichidae). These findings raise concerns that alternative subcortical insect species that already occur within quarantined habitats can sustain incidence of introduced G. morbida and contribute to spread within the native range of black walnut, Juglans nigra L. in the eastern United States. [ABSTRACT FROM AUTHOR]

Washington State produces about 70% of total fresh market apples in the United States. One of the primary goals of apple breeding programs is the development of new cultivars resistant to devastating diseases such as fire blight. The overall objective of this study was to investigate high-throughput phenotyping techniques to evaluate fire blight disease symptoms in apple trees. In this regard, normalized stomatal conductance data acquired using a portable photosynthetic system, image data collected using RGB and multispectral cameras, and visible-near infrared spectral reflectance acquired using a hyperspectral sensing system, were independently evaluated to estimate the progression of fire blight infection in young apple trees. Sensors with ranging complexity – from simple RGB to multispectral imaging to hyperspectral system – were evaluated to select the most accurate technique for the assessment of fire blight disease symptoms. The proximal multispectral images and visible-near infrared spectral reflectance data were collected in two field seasons (2016, 2017); while, proximal side-view RGB images and multispectral images using unmanned aerial systems were collected in 2017. The normalized stomatal conductance data was correlated with disease severity rating (r = 0.51, P < 0.05). The features extracted from RGB images (e.g., maximum length of senesced leaves, area of senesced leaves, ratio between senesced and healthy leaf area) and multispectral images (e.g., vegetation indices) also demonstrated potential in evaluation of disease rating (| r | > 0.35, P < 0.05). The average classification accuracy achieved using visible-near infrared spectral reflectance data during the classification of susceptible from symptomless groups ranged between 71 and 93% using partial least square regression and quadratic support vector machine. In addition, fire blight disease ratings were compared with normalized difference spectral indices (NDSIs) that were generated from visible-near infrared reflectance spectra. The selected spectral bands in the range 710–2,340 nm used for computing NDSIs showed consistently higher correlation with disease severity rating than data acquired from RGB and multispectral imaging sensors across multiple seasons. In summary, these specific spectral bands can be used for evaluating fire blight disease severity in apple breeding programs and potentially as early fire blight disease detection tool to assist in production systems. [ABSTRACT FROM AUTHOR]

Identifying and mapping tropical trees at the species level from space can support an improved assessment of forest composition, forest carbon uptake, tree species distribution and preferred habitat as well as a better understanding of the response of forests to climate change. In this study, the development of a validated data and image-processing schema demonstrated the capability of current metre-scale satellite technology (WorldView-3) to identify specific tree species within an unmanaged tropical forest. The experimental site, La Selva Biological Station in Costa Rica, provided access for field validation and spectral data acquisition of individual tree canopies from established canopy towers. It is also a representative biome of diverse lowland Atlantic tropical forests in Central America. The process defined in this paper calibrated and corrected field-acquired ASD field spectra for ten tree species and corrected WorldView-3 image data for viewing and illumination geometry. In addition, assessments of three current atmospheric compensation methods for correcting recent WorldView-3 satellite imagery established the most accurate compensation process for a tropical forest setting. Corrected reflectance in the satellite data matched the spectrometer data to ±0.25% for visible bands and ±0.5% for near-infrared bands. This study shows that spectral data from the satellite and field spectrometer data are nearly equivalent when applying the appropriate atmospheric compensation, band response emulation, and viewing correction processes established in this study. [ABSTRACT FROM AUTHOR]