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Despite reported health benefits of urban greenspace (gs), the epidemiological evidence is less clear for allergic disease. To address a limitation of previous research, we examined the associations of medium- and high-resolution residential gs measures and tree and/or grass canopies with allergic outcomes for children enrolled in the longitudinal cincinnati childhood allergy and air pollution study (ccaaps). We estimated residential gs based on 400 m radial buffers around participant addresses (n = 478) using the normalized differential vegetation index (ndvi) and land cover-derived urban greenspace (ugs) (tree and grass coverage, combined and separate) at 30 m and 1.5–2.5 m resolution, respectively. Associations between outdoor aeroallergen sensitization and allergic rhinitis at age 7 and residential gs measures at different exposure windows were examined using multivariable logistic regression models. A 10% increase in ugs-derived grass coverage was associated with an increased risk of sensitization to grass pollens (adjusted odds ratio [aor]: 1.27; 95% confidence interval = 1.02–1.58). For each 10% increase in ugs-derived tree canopy coverage, nonstatistically significant decreased odds were found for grass pollen sensitization, tree pollen sensitization, and sensitization to either (aor range = 0.87–0.94). Results similar in magnitude to ugs-tree canopy coverage were detected for ndvi and allergic sensitizations. High-resolution (down to 1.5 m) gs measures of grass- and tree-covered areas showed associations in opposite directions for different allergy outcomes. These data suggest that measures strongly correlated with tree canopy (e.g., ndvi) may be insufficient to detect health effects associated with proximity to different types of vegetation or help elucidate mechanisms related to specific gs exposure pathways.

The health benefits of exposure to trees and plants is a rapidly expanding field of study. Research has shown that exposure is associated with improvements in a wide range of health outcomes including cardiovascular disease, birth outcomes, respiratory disease, cancer, mental health and all-cause mortality

Studying the impacts of prenatal atmospheric heavy-metal exposure is challenging, because biological exposure monitoring does not distinguish between specific sources, and high-resolution air monitoring data is lacking for heavy metals. Bioindicators - animal or plant species that can capture environmental quality - are a low-cost tool for evaluating exposure to atmospheric heavy-metal pollution that have received little attention in the public-health literature. We obtained birth records for Portland, Oregon live births (2008-2014) and modeled metal concentrations derived from 346 samples of moss bioindicators collected in 2013. Exposure estimates were assigned using mother's residential address at birth for six metals with known toxic and estrogenic effects (arsenic, cadmium, chromium, cobalt, nickel, lead). Associations were evaluated for continuous (cts) and quartile-based (Q) metal estimates and three birth outcomes (preterm birth (PTB;37 weeks)), very PTB (vPTB;32 weeks), small for gestational age (SGA; 10th percentile of weight by age and sex)) using logistic regression models with adjustment for demographic characteristics, and stratified by maternal race. Chromium and cobalt were associated with increased odds of vPTB (chromium - odds ratio (OR)

A wealth of evidence links microbial exposure to better human immune function. However, few studies have examined whether exposure to plant diversity is protective of immune diseases, despite the fact that plant leaves support ~1026 bacterial cells. Using the Centers for Disease Control and Prevention's 500 cities project data, we found that a 1-SD increase in exposure to taxonomic plant diversity is associated with a 5.3 (95% CI: 4.2–6.4; p

We tested the hypothesis that exposure to the natural environment is associated with improved academic performance. Specifically, we examined the association between individual-level standardized math and reading test scores and exposure to the natural environment using data from Portland Public Schools (17,918 students attending 83 schools for the math model and 19,459 students attending 90 schools for the reading model). We found that a 1- SD increase in tree cover within 200 m of a child’s home was associated with moving from the 50th percentile to the 51st percentile on math tests. A 1- SD increase in tree cover within 100 m of a child’s school was associated with moving from the 50th percentile to the 56th percentile on reading tests. Finally, a 1- SD increase in road density within 100 m of a child’s home was associated with moving from the 50th percentile to the 47th percentile on reading tests.

Data describing aircraft position and attitude are essential to computing return positions from ranging data collected during airborne laser scanning (ALS) campaigns. However, these data are often excluded from the products delivered to the client and their recovery after the contract is complete can require negotiations with the data provider, may involve additional costs, or even be infeasible. This paper presents a rigorous, fully automated, novel method for recovering aircraft positions using only the point cloud. The study used ALS data from five acquisitions in the US Pacific Northwest region states of Oregon and Washington and validated derived aircraft positions using the smoothed best estimate of trajectory (SBET) provided for the acquisitions. The computational requirements of the method are reduced and precision is improved by relying on subsets of multiple-return pulses, common in forested areas, with widely separated first and last returns positioned at opposite sides of the aircraft to calculate their intersection, or closest point of approach. To provide a continuous trajectory, a cubic spline is fit to the intersection points. While it varies by acquisition and parameter settings, the error in the computed aircraft position seldom exceeded a few meters. This level of error is acceptable for most applications. To facilitate use and encourage modifications to the algorithm, the authors provide a code that can be applied to data from most ALS acquisitions.

ObjectivesDetermine whether patients who live in greener and more walkable neighbourhoods live longer, and take fewer opioids, following hip or knee arthroplasty.DesignRetrospective cohort study.SettingResidential environment following surgery at one of 54 New Zealand hospitals.ParticipantsAll people who received a total hip or knee arthroplasty at a publicly-funded hospital in New Zealand in 2006 and 2007 (7449 hip arthroplasties and 6558 knee arthroplasties).Primary and secondary outcome measureTime to all-cause mortality and number of postsurgical opioid prescriptions.ResultsPatients who lived in greener neighbourhoods, as measured by the Normalised Difference Vegetation Index, lived longer following hip or knee arthroplasty (standardised OR: 0.95, 95% CI 0.92 to 0.99). However, when we estimated separate hip-arthroplasty-only and knee-arthroplasty-only models, greenness was only significantly associated with greater longevity following hip arthroplasty. Similarly, patients who lived in greener neighbourhoods took fewer opioids in the 12 months following hip or knee arthroplasty (standardised OR: 0.97, 95% CI 0.95 to 0.99), but in separate hip-arthroplasty-only and knee-arthroplasty-only models, greenness was only significantly associated with lower opioid use following hip arthroplasty. Walkability was not significantly associated with postsurgical opioid use or postsurgical longevity. All ORs were adjusted for sex, ethnicity, age, presurgical chronic health conditions, presurgical opioid use, social deprivation and length of hospital stay.ConclusionsConsistent with the literature on enhanced-recovery programme, people who lived in greener neighbourhoods took fewer opioids, and lived longer, following hip arthroplasty. Improving access to the natural environment may therefore be an effective component of postsurgical recovery programme.

Low-cost methods to measure forest structure are needed to consistently and repeatedly inventory forest conditions over large areas. In this study we investigate low-cost pushbroom Digital Aerial Photography (DAP) to aid in the estimation of forest volume over large areas in Washington State (USA). We also examine the effects of plot location precision (low versus high) and Digital Terrain Model (DTM) resolution (1 m versus 10 m) on estimation performance. Estimation with DAP and post-stratification with high-precision plot locations and a 1 m DTM was 4 times as efficient (precision per number of plots) as estimation without remote sensing and 3 times as efficient when using low-precision plot locations and a 10 m DTM. These findings can contribute significantly to efforts to consistently estimate and map forest yield across entire states (or equivalent) or even nations. The broad-scale, high-resolution, and high-precision information provided by pushbroom DAP facilitates used by a wide variety of user types such a towns and cities, small private timber owners, fire prevention groups, Non-Governmental Organizations (NGOs), counties, and state and federal organizations.

Polycyclic aromatic hydrocarbons (PAHs) are widespread pollutants that are costly to measure with air quality instruments. We used a spatially balanced sample of an epiphytic moss, Orthotrichum lyellii, to develop fine-scale maps of atmospheric PAHs across residential areas of Portland, Oregon. The unusual abundance of this stress-tolerant species enabled us to systematically collect a large sample (n = 350) on a 1 × 1km grid across the city. This provided the unique opportunity to investigate potential effects of more than 20 factors on PAH concentrations in moss, including emissions sources, tree canopy cover, relative elevation, short-term weather, and nuance of sample collection. We measured 20 PAHs in moss samples and assigned them to 4 groups for analysis based on molecular weight (MW): the Naphthalene, Low, Medium, and High MW groups. Spatial regression models explained a modest amount of variation in PAH concentrations in moss, with spatial structure increasing as MW increased. Motor vehicle emissions were the only PAH source we detected and was highly significant in all models. Deciduous tree cover correlated with lower PAH concentrations in moss for all except the high MW group. Short-term weather and nuance of sample collection, like height on the sampled tree, tree taxonomic family, and whether sample was primarily collected from a branch or bole, had surprisingly large effects on all groups. Overall, results aligned with expectations based on each group's dispersal potential due to MW. All PAH groups were highest over downtown Portland, along major highways, and lowest in parks and outer neighborhoods, although the details varied. Our results supported two common themes in studies directly measuring PAHs: the importance of roads as an emissions source in residential areas, and the potential for trees to reduce atmospheric concentrations. Moreover, the large effects of weather and sampling nuance have critical implications for the design of future studies.

Background:The biodiversity hypothesis posits that declining biodiversity may be responsible, at least in part, for the global increase in immune diseases. However, few studies have been able to demonstrate a link between exposure to biodiversity and specific health outcomes. Methods: We test whether exposure to plant diversity protects against childhood acute lymphoblastic leukemia (ALL) by promoting immune maturation. Our sample consisted of all children born in New Zealand from 1998-2011 (n=899,126; 264 ALL cases), which we followed from birth to age five. We calculated plant-diversity metrics using the Global Biodiversity Information Facility, which contains over two million geocoded plant records in New Zealand. Findings: Consistent with previous research, children who had always lived in an urban area, or who had an older mother, were at greater risk for ALL, whereas children with older siblings were at lower risk. In addition, we found that plant-diversity metrics based on the maximum number of plant genera a child was exposed to during the first two years of life were protective of ALL. Specifically, exposure to the highest tertile of plant diversity was associated with a reduction in ALL risk of 35% (95% CI: 11%-53%). Interpretation: Exposure to plant diversity, and associated microbial communities, may be a viable public-health intervention to reduce the risk of ALL and possibly other immune diseases. Funding Statement: Geoffrey Donovan acknowledges the receipt of a fellowship from the OECD Co-operative Research Programme: Biological Resource Management for Sustainable Agricultural Systems in 2019. No other funding was received. Declaration of Interests: The authors declare no competing financial interests. Ethics Approval Statement: Our study was classified as out of scope by the New Zealand Health and Disabilities Ethics Committee, approved by Statistics New Zealand (approval # MAA2019-07), and conducted in accordance with national and international ethical guidelines for observational studies.

Aquatic vegetation can shelter coastlines from energetic waves and tidal currents, sometimes enabling accretion of fine sediments. Simulation of flow and sediment transport within submerged canopies requires quantification of vegetation geometry. However, field surveys used to determine vegetation geometry can be limited by the time required to obtain conventional caliper and ruler measurements. Building on recent progress in photogrammetry and computer vision, we present a method for reconstructing three-dimensional canopy geometry. The method was used to survey a dense canopy of aerial mangrove roots, called pneumatophores, in Vietnam’s Mekong River Delta. Photogrammetric estimation of geometry required 1) taking numerous photographs at low tide from multiple viewpoints around 1 m2 quadrats, 2) computing relative camera locations and orientations by triangulation of key features present in multiple images and reconstructing a dense 3D point cloud, and 3) extracting pneumatophore locations and diameters from the point cloud data. Step 3) was accomplished by a new ‘sector-slice’ algorithm, yielding geometric parameters every 5 mm along a vertical profile. Photogrammetric analysis was compared with manual caliper measurements. In all 5 quadrats considered, agreement was found between manual and photogrammetric estimates of stem number, and of number × mean diameter, which is a key parameter appearing in hydrodynamic models. In two quadrats, pneumatophores were encrusted with numerous barnacles, generating a complex geometry not resolved by hand measurements. In remaining cases, moderate agreement between manual and photogrammetric estimates of stem diameter and solid volume fraction was found. By substantially reducing measurement time in the field while capturing in greater detail the 3D structure, photogrammetry has potential to improve input to hydrodynamic models, particularly for simulations of flow through large-scale, heterogenous canopies.

Unmanned Aerial Vehicles (UAVs) are already broadly employed for 3D modeling of large objects such as trees and monuments via photogrammetry. The usual workflow includes two distinct steps: image acquisition with UAV and computationally demanding post-flight image processing. Insufficient feature overlaps across images is a common shortcoming in post-flight image processing resulting in the failure of 3D reconstruction. Here we propose a real-time control system that overcomes this limitation by targeting specific spatial locations for image acquisition thereby providing sufficient feature overlap. We initially benchmark several implementations of the Scale-Invariant Feature Transform (SIFT) feature identification algorithm to determine whether they allow real-time execution on the low-cost processing hardware embedded on the UAV. We then experimentally test our UAV platform in virtual and real-life environments. The presented architecture consistently decreases failures and improves the overall quality of 3D reconstructions.

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.

Summary Background Several small experimental studies and cross-sectional observational studies have shown that exposure to the natural environment might protect against attention-deficit hyperactivity disorder (ADHD) or moderate the symptoms of ADHD in children. We aimed to assess whether exposure to the natural environment protects against ADHD and whether this hypothesised protective effect varies across a child's life course. Methods We did a longitudinal study with data collected from all children born in New Zealand in 1998, excluding those without an address history, those who were not singleton births, and those who died or emigrated before 18 years of age. We used Statistics New Zealand's Integrated Data Infrastructure to identify children with ADHD and to define covariates. ADHD was defined according to hospital diagnosis or pharmacy records (two or more prescriptions for ADHD drugs). Exposure to green space for each year of a child's life (from gestation to 18 years of age) was estimated at the meshblock level (the smallest geographical unit for which the New Zealand Census reports data) using normalised difference vegetation index (NDVI), and land-use data from Landcare Research New Zealand. We used logit models to assess the associations between ADHD prevalence and minimum, maximum, and mean lifetime NDVI, as well as rural living, controlling for sex, ethnicity, mother's educational level, mother's smoking status, mother's age at parturition, birth order, antibiotic use, and low birthweight. Findings Of the 57 450 children born in New Zealand in 1998, 49 923 were eligible and had available data, and were included in the analysis. Children who had always lived in a rural area after 2 years of age were less likely to develop ADHD (odds ratio [OR] 0·670 [95% CI 0·461–0·974), as were those with increased minimum NDVI exposure after age 2 years (standardised OR for exposure vs first quartile: second quartile 0·841 [0·707–0·999]; third quartile 0·809 [0·680–0·963]; fourth quartile 0·664 [0·548–0·805]). In early life (prenatal to age 2 years), neither rural living nor NDVI were protective against ADHD. Neither mean nor maximum greenness was significantly protective against ADHD. Interpretation Rurality and increased minimum greenness were strongly and independently associated with a reduced risk of ADHD. Increasing a child's minimum lifetime greenness exposure, as opposed to maximum or mean exposure, might provide the greatest increment of protection against the disorder. Funding None.

Forest inventories are constrained by resource-intensive fieldwork, while unmanned aerial systems (UASs) offer rapid, reliable, and replicable data collection and processing. This research leverages advancements in photogrammetry and market sensors and platforms to incorporate a UAS-based approach into existing forestry monitoring schemes. Digital imagery from a UAS was collected, photogrammetrically processed, and compared to in situ and aerial laser scanning (ALS)-derived plot tree counts and heights on a subsample of national forest plots in Oregon. UAS- and ALS-estimated tree counts agreed with each other (r2 = 0.96) and with field data (ALS r2 = 0.93, UAS r2 = 0.84). UAS photogrammetry also reasonably approximated mean plot tree height achieved by the field inventory (r2 = 0.82, RMSE = 2.92 m) and by ALS (r2 = 0.97, RMSE = 1.04 m). The use of both nadir-oriented and oblique UAS imagery as well as the availability of ALS-derived terrain descriptions likely sustain a robust performance of our approach across classes of canopy cover and tree height. It is possible to draw similar conclusions from any of the methods, suggesting that the efficient and responsive UAS method can enhance field measurement and ALS in longitudinal inventories. Additionally, advancing UAS technology and photogrammetry allows diverse users access to forest data and integrates updated methodologies with traditional forest monitoring.

National forests have a wealth of natural amenities that attract over 175 million recreational visitors a year. Although natural amenities draw visitors to national forests, many of the recreational activities that they engage in require built amenities, such as roads, campgrounds, boat ramps, and trails. We estimate regression models of the effect of two common built amenities—campgrounds and picnic areas—on national-forest visitation controlling for natural amenities and accounting for the endogenous relationship between visitation and built amenities. We found that campgrounds and picnic areas are significantly and positively correlated with visitation.

We assessed the association between the natural environment and asthma in 49,956 New Zealand children born in 1998 and followed up until 2016 using routinely collected data. Children who lived in greener areas, as measured by the normalized difference vegetation index, were less likely to be asthmatic: a 1 s.d. increase in normalized difference vegetation index was associated with a 6.0% (95% CI 1.9-9.9%) lower risk of asthma. Vegetation diversity was also protective: a 1 s.d. increase in the number of natural land-cover types in a child's residential meshblock was associated with a 6.7% (95% CI 1.5-11.5%) lower risk. However, not all land-cover types were protective. A 1 s.d. increase in the area covered by gorse (Ulex europaeus) or exotic conifers, both non-native, low-biodiversity land-cover types, was associated with a 3.2% (95% CI 0.0-6.0%) and 4.2% (95% CI 0.9-7.5%) increased risk of asthma, respectively. The results suggest that exposure to greenness and vegetation diversity may be protective of asthma.

Recent increases in forest diseases have produced significant mortality in boreal forests. These disturbances influence merchantable volume predictions as they affect the distribution of live and dead trees. In this study, we assessed the use of lidar, alone or combined with multispectral imagery, to classify trees and predict the merchantable volumes of 61 balsam fir plots in a boreal forest in eastern Canada. We delineated single trees on a canopy height model. The number of detected trees represented 92% of field trees. Using lidar intensity and image pixel metrics, trees were classified as live or dead with an overall accuracy of 89% and a kappa coefficient of 0.78. Plots were classified according to their class of mortality (low/high) using a 10.5% threshold. Lidar returns associated with dead trees were clipped. Before clipping, the root mean square errors were of 22.7 m3 ha−1 in the low mortality plots and of 39 m3 ha−1 in the high mortality plots. After clipping, they decreased to 20.9 m3 ha−1 and 32.3 m3 ha−1 respectively. Our study suggests that lidar and multispectral imagery can be used to accurately filter dead balsam fir trees and decrease the merchantable volume prediction error by 17.2% in high mortality plots and by 7.9% in low mortality plots.

Background: Several recent studies have identified a relationship between the natural environment and improved health outcomes. However, for practical reasons, most have been observational, cross-sectional studies. Purpose: A natural experiment, which provides stronger evidence of causality, was used to test whether a major change to the natural environment—the loss of 100 million trees to the emerald ash borer, an invasive forest pest— has influenced mortality related to cardiovascular and lower- respiratory diseases. Methods: Two fixed-effects regression models were used to estimate the relationship between emerald ash borer presence and county-level mortality from 1990 to 2007 in 15 U.S. states, while controlling for a wide range of demographic covariates. Data were collected from 1990 to 2007, and the analyses were conducted in 2011 and 2012. Results: There was an increase in mortality related to cardiovascular and lower-respiratory-tract illness in counties infested with the emerald ash borer. The magnitude of this effect was greater as infestation progressed and in counties with above-average median household income. Across the 15 states in the study area, the borer was associated with an additional 6113 deaths related to illness of the lower respiratory system, and 15,080 cardiovascular-related deaths. Conclusions: Results suggest that loss of trees to the emerald ash borer increased mortality related to cardiovascular and lower-respiratory-tract illness. This finding adds to the growing evidence that

Urban networks of air-quality monitors are often too widely spaced to identify sources of air pollutants, especially if they do not disperse far from emission sources. The objectives of this study were to test the use of moss bio-indicators to develop a fine-scale map of atmospherically-derived cadmium and to identify the sources of cadmium in a complex urban setting. We collected 346 samples of the moss Orthotrichum lyellii from deciduous trees in December, 2013 using a modified randomized grid-based sampling strategy across Portland, Oregon. We estimated a spatial linear model of moss cadmium levels and predicted cadmium on a 50 m grid across the city. Cadmium levels in moss were positively correlated with proximity to two stained-glass manufacturers, proximity to the Oregon–Washington border, and percent industrial land in a 500 m buffer, and negatively correlated with percent residential land in a 500 m buffer. The maps showed very high concentrations of cadmium around the two stained-glass manufacturers, neither of which were known to environmental regulators as cadmium emitters. In addition, in response to our findings, the Oregon Department of Environmental Quality placed an instrumental monitor 120 m from the larger stained-glass manufacturer in October, 2015. The monthly average atmospheric cadmium concentration was 29.4 ng/m3, which is 49 times higher than Oregon's benchmark of 0.6 ng/m3, and high enough to pose a health risk from even short-term exposure. Both stained-glass manufacturers voluntarily stopped using cadmium after the monitoring results were made public, and the monthly average cadmium levels precipitously dropped to 1.1 ng/m3 for stained-glass manufacturer #1 and 0.67 ng/m3 for stained-glass manufacturer #2.

Tree invasions have been documented throughout Northern Hemisphere high elevation meadows, as well as globally in many grass and forb-dominated ecosystems. Tree invasions are often associated with large-scale changes in climate or disturbance regimes, but are fundamentally driven by regeneration processes influenced by interactions between climatic, topographic, and biotic factors at multiple spatial scales. The purpose of this research was to quantify spatiotemporal patterns of meadow invasion; and how climate, larger landforms, topography, and overstory trees have interactively influenced tree invasion. We combined airborne Light Detection and Ranging (LiDAR) characterizations of landforms, topography, and overstory vegetation with historical climate, field measurements of snow depth, tree abundance, and tree ages to reconstruct spatial and temporal patterns of tree invasion over five decades in a subalpine meadow complex in the Oregon Cascade Range, USA. Proportion of meadow occupied by trees increased from 8 % in 1950 to 35 % in 2007. Larger landforms, topography, and tree canopies interactively mediated regional climatic controls of tree invasion by modifying depth and persistence of snow pack, while tree canopies also influenced seed source availability. Landscape context played an important role mediating snow depth and tree invasion; on glacial landforms tree invasion was negatively associated with spring snowfall, but on debris flows tree invasion was not associated with snow fall. The importance of snow, uncertain climate change impacts on snow, and mediation of snow by interacting and context dependent factors in complex mountain terrain poses substantial hurdles for understanding how these ecotones may respond to future climate conditions.

Photogrammetry-based three-dimensional reconstruction of objects is becoming increasingly appealing in research areas unrelated to computer vision. It has the potential to facilitate the assessment of forest inventory-related parameters by enabling or expediting resource measurements in the field. We hereby compare several implementations of photogrammetric algorithms (CMVS/PMVS, CMPMVS, MVE, OpenMVS, SURE and Agisoft PhotoScan) with respect to their performance in vegetation assessment. The evaluation is based on (i) a virtual scene where the precise location and dimensionality of objects is known a priori and is thus conducive to a quantitative comparison and (ii) using series of in situ acquired photographs of vegetation with overlapping field of view where the photogrammetric outcomes are compared qualitatively. Performance is quantified by computing receiver operating characteristic curves that summarize the type-I and type-II errors between the reference and reconstructed tree models. Similar artefacts are observed in synthetic- and in situ -based reconstructions.

Mosses accumulate pollutants from the atmosphere and can serve as an inexpensive screening tool for mapping air quality and guiding the placement of monitoring instruments. We measured 22 elements using 346 moss samples collected across Portland, Oregon, in December 2013. Our objectives were to develop citywide maps showing concentrations of each element in moss and identify potential air pollution “hotspots.” We used simple dot maps, histograms, and summary statistics to describe the distribution of each element. Fifteen metals had highly right-skewed distributions, indicating high metal concentrations (relative to concentrations mea-sured in our dataset) in moss at one or more locations. These metals included high-priority toxics such as cadmium, nickel, lead, and arsenic. Past research shows that element concentrations in moss reflect atmospheric concentrations, although the strength of these relationships varies by element and is unknown for the elements we sampled. Therefore, atmospheric concentrations would need to be measured by an air quality monitor in order to determine whether hotspots suggested by the moss indicator are problematic or pose a health risk. We provide the raw data for all elements we measured to enable scientists, regulators, and citizens to further investigate the importance and possible sources of moss-identified hotspots.

Detailed, precise, three-dimensional (3D) representations of individual trees are a prerequisite for an accurate assessment of tree competition, growth, and morphological plasticity. Until recently, our ability to measure the dimensionality, spatial arrangement, shape of trees, and shape of tree components with precision has been constrained by technological and logistical limitations and cost. Traditional methods of forest biometrics provide only partial measurements and are labor intensive. Active remote technologies such as LiDAR operated from airborne platforms provide only partial crown reconstructions. The use of terrestrial LiDAR is laborious, has portability limitations and high cost. In this work we capitalized on recent improvements in the capabilities and availability of small unmanned aerial vehicles (UAVs), light and inexpensive cameras, and developed an affordable method for obtaining precise and comprehensive 3D models of trees and small groups of trees. The method employs slow-moving UAVs that acquire images along predefined trajectories near and around targeted trees, and computer vision-based approaches that process the images to obtain detailed tree reconstructions. After we confirmed the potential of the methodology via simulation we evaluated several UAV platforms, strategies for image acquisition, and image processing algorithms. We present an original, step-by-step workflow which utilizes open source programs and original software. We anticipate that future development and applications of our method will improve our understanding of forest self-organization emerging from the competition among trees, and will lead to a refined generation of individual-tree-based forest models.

Data from the Women's Health Initiative were used to quantify the relationship between the loss of trees to an invasive forest pest-the emerald ash borer-and cardiovascular disease. We estimated a semi-parametric Cox proportional hazards model of time to cardiovascular disease, adjusting for confounders. We defined the incidence of cardiovascular disease as acute myocardial infarction requiring overnight hospitalization, silent MI determined from serial electrocardiograms, ischemic or hemorrhagic stroke, or death from coronary heart disease. Women living in a county infested with emerald ash borer had an increased risk of cardiovascular disease (HR=1.25, 95% CI: 1.20-1.31).

We investigated the potential of using unclassified spectral data for predicting the distribution of three bird species over a ¨400,000 ha region of Michigan’s Upper Peninsula using Landsat ETM+ imagery and 433 locations sampled for birds through point count surveys. These species, Black-throated Green Warbler, Nashville Warbler, and Ovenbird, were known to be associated with forest understory features during breeding. We examined the influences of varying two spatially explicit classification parameters on prediction accuracy: 1) the window size used to average spectral values in signature creation and 2) the threshold distance required for bird detections to be counted as present. Two accuracy measurements, proportion correctly classified (PCC) and Kappa, of maps predicting species’ occurrences were calculated with ground data not used during classification. Maps were validated for all three species with Kappa values >0.3 and PCC >0.6. However, PCC provided little information other than a summary of sample plot frequencies used to classify species’ presence and absence. Comparisons with rule-based maps created using the approach of Gap Analysis showed that spectral information predicted the occurrence of these species that use forest subcanopy components better than could be done using known land cover associations (Kappa values 0.1 to 0.3 higher than Gap Analysis maps). Accuracy statistics for each species were affected in different ways by the detection distance of point count surveys used to stratify plots into presence and absence classes. Moderate-to-large detection distances (100 m and 180 m) best classified maps of Blackthroated Green Warbler and Nashville Warbler occurrences, while moderate detection distances (50 m and 100 m), which ignored remote observations, provided the best source of information for classification of Ovenbird occurrence. Window sizes used in signature creation also influenced accuracy statistics but to a lesser extent. Highest Kappa values of majority maps were typically obtained using moderate window sizes of 9 to 13 pixels (0.8 to 1.2 ha), which are representative of the study species territory sizes. The accuracy of wildlife occurrence maps classified from spectral data will therefore differ given the species of interest, the spatial precision of occurrence records used as ground references and the number of pixels included in spectral signatures. For these reasons, a quantitative examination is warranted to determine how subjective decisions made during image classifications affect prediction accuracies.

Several recent studies have identified a relationship between the natural environment and improved health outcomes. However, for practical reasons, most have been observational, cross-sectional studies.A natural experiment, which provides stronger evidence of causality, was used to test whether a major change to the natural environment-the loss of 100 million trees to the emerald ash borer, an invasive forest pest-has influenced mortality related to cardiovascular and lower-respiratory diseases.Two fixed-effects regression models were used to estimate the relationship between emerald ash borer presence and county-level mortality from 1990 to 2007 in 15 U.S. states, while controlling for a wide range of demographic covariates. Data were collected from 1990 to 2007, and the analyses were conducted in 2011 and 2012.There was an increase in mortality related to cardiovascular and lower-respiratory-tract illness in counties infested with the emerald ash borer. The magnitude of this effect was greater as infestation progressed and in counties with above-average median household income. Across the 15 states in the study area, the borer was associated with an additional 6113 deaths related to illness of the lower respiratory system, and 15,080 cardiovascular-related deaths.Results suggest that loss of trees to the emerald ash borer increased mortality related to cardiovascular and lower-respiratory-tract illness. This finding adds to the growing evidence that the natural environment provides major public health benefits.

[1] An economical approach to improving predictions of hydrological models produced highly accurate representations of ephemeral and perennial stream networks. Traditional drainage network extraction from digital elevation models (DEMs) often yields inaccurate and inconsistent results because of elevation errors. Topographic wetness index maps calculated from alternative terrain representations, produced by adding random errors to a DEM of a subwatershed with low relief, were combined to delineate a stream network that matches one produced by more time-intensive (and costly) differential Global Positioning System (GPS) field methods, particularly with respect to the ephemeral component of the drainage network.

Precise delineation of ephemeral and perennial drainage networks is increasingly important to forest managers seeking to achieve fine-grained linking of ground-based measurements of vegetation and remote sensing data to address issues concerning riparian forest systems. Such issues include economic and ecological impacts of alternative riparian buffer strip configurations, relationships between riparian forest structure and impacts like sediment transport and stream temperature, and linkages between upland and riparian forests and in-channel woody debris. Yet existing maps and digital coverages (e.g., USGS topo quads and DLG files) often contain errors that make them unsuitable for spatial overlay and buffer impact assessment (e.g., assessing the value of buffer strip timber forgone), and yield inconsistent representations of drainage networks.