Search

Several recent longitudinal studies have found that exposure to the natural environment is associated with lower non-accidental mortality. However, most of these studies used the normalized difference vegetation index (NDVI) as an exposure metric; and because NDVI might not be sensitive enough to adequately capture changes in urban vegetation, these studies might lack true longitudinal variation in exposure. Therefore, we used a natural experiment to assess the impact of 30 years of tree planting by the nonprofit Friends of Trees on non-accidental, cardiovascular, lower-respiratory, and accidental mortality in Portland, Oregon (mortality data were provided by the Oregon Health Authority). We estimated autoregressive mixed models of Census-tract level mortality rate (deaths per 100,000 population) associated with trees planted, including a tract-level random effect. All models used data from the American Community Survey to control for year, race, education, income, and age. Each tree planted in the preceding 15 years was associated with significant reductions in non-accidental (-0.21, 95 % CI: −0.30, −0.12) and cardiovascular mortality (-0.066, 95 % CI: −0.11, −0.027). Furthermore, the dose–response association between tree planting and non-accidental mortality increased in magnitude as trees aged and grew. Each tree planted in the preceding 1–5 years was associated with a reduction in mortality rate of −0.154 (95 % CI: −0.323, 0.0146), whereas each tree planted in the last 6–10 and 11–15 years was associated with a reduction in mortality rate of −0.262 (95 % CI: −0.413, −0.110) and −0.306 (95 % CI: −0.527, −0.0841) respectively. Using US EPA estimates of a value of a statistical life, we estimated that planting a tree in each of Portland’s 140 Census tracts would generate $14.2 million in annual benefits (95 % CI: $8.0 million to $20.4 million). In contrast, the annual cost of maintaining 140 trees would be $2,716–$13,720.

Objectives Determine whether patients who live in greener and more walkable neighbourhoods live longer, and take fewer opioids, following hip or knee arthroplasty.Design Retrospective cohort study.Setting Residential environment following surgery at one of 54 New Zealand hospitals.Participants All 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 measure Time to all-cause mortality and number of postsurgical opioid prescriptions.Results Patients 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.Conclusions Consistent 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.

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.

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.

The United States Forest Service Forest Inventory and Analysis (FIA) Program provides a diverse selection of data used to assess the status of the nation’s forests using sample locations dispersed throughout the country. Airborne laser scanning (ALS) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize forest structure in three dimensions. This study investigates the potential of discrete return ALS data for modeling forest aboveground biomass (AGBM) and gross volume (gV) at FIA plot locations in the Malheur National Forest, eastern Oregon utilizing three analysis levels: (1) individual subplot (r = 7.32 m); (2) plot, comprising four clustered subplots; and (3) hectare plot (r = 56.42 m). A methodology for the creation of three point cloud-based airborne LiDAR metric sets is presented. Models for estimating AGBM and gV based on LiDAR-derived height metrics were built and validated utilizing FIA estimates of AGBM and gV derived using regional allometric equations. Simple linear regression models based on the plot-level analysis out performed subplot-level and hectare-level models, producing R2 values of 0.83 and 0.81 for AGBM and gV, utilizing mean height and the 90th height percentile as predictors, respectively. Similar results were found for multiple regression models, where plot-level analysis produced models with R2 values of 0.87 and 0.88 for AGBM and gV, utilizing multiple height percentile metrics as predictor variables. Results suggest that the current FIA plot design can be used with dense airborne LiDAR data to produce area-based estimates of AGBM and gV, and that the increased spatial scale of hectare plots may be inappropriate for modeling AGBM of gV unless exhaustive tree tallies are available. Overall, this study demonstrates that ALS data can be used to create models that describe the AGBM and gV of Pacific Northwest FIA plots and highlights the potential of estimates derived from ALS data to augment current FIA data collection procedures by providing a temporary intermediate estimation of AGBM and gV for plots with outdated field measurements.

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.

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.

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.