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Using an extensive Douglas-fir data set from southwest Oregon, we examined the (1) performance and suitability of selected prediction strategies, (2) contribution of relative position and stand-density measures in improving tree height (h) prediction values, and (3) effect of different subsampling designs to fill in missing h values in a new stand using a regional nonlinear model. Nonlinear mixed-effects models (NMEM) substantially improved the accuracy and precision of height prediction over the conventional nonlinear fixed-effects model (NFEM) that assumes the observations are independent, particularly when a few trees are subsampled for height. The predictive performance of a correction factor on a NFEM with relative position and stand-density measures was comparable to that of a NMEM when four or more trees were subsampled for height. When two or more heights were randomly subsampled, the NMEM efficiently explained the differences in the height–diameter relationship because of the variations in relative position of trees and stand density without having to incorporate them into the model. When only one height was subsampled, selecting the largest diameter tree in the stand would result in a lower predicted root mean square error (RMSE) than randomly selecting the height, regardless of the model form or fitting strategy used.

High spatial resolution imagery provided by unmanned aerial vehicles (UAVs) can yield accurate and efficient estimation of tree dimensions and canopy structural variables at the local scale. We flew a low-cost, lightweight UAV over an experimental Pinus pinea L. plantation (290 trees distributed over 16 ha with different fertirrigation treatments) to determine the tree positions and to estimate individual tree height (h), diameter (d), biomass (wa), as well as changes in these variables between 2015 and 2017. We used Structure from Motion (SfM) and 3D point cloud filtering techniques to generate the canopy height model and object-based image analysis to delineate individual tree crowns (ITC). ITC results were validated using accurate field measurements over a subsample of 50 trees. Comparison between SfM-derived and field-measured h yielded an R 2 value of 0.96. Regressions using SfM-derived variables as explanatory variables described 79% and 86-87% of the variability in d and wa, respectively. The height and biomass growth estimates across the entire study area for the period 2015-2017 were 0.45 m ± 0.12 m and 198.7 ± 93.9 kg, respectively. Significant differences (t-test) in height and biomass were observed at the end of the study period. The findings indicate that the proposed method could be used to derive individual-tree variables and to detect spatio-temporal changes, highlighting the potential role of UAV-derived imagery as a forest management tool.

Climate change is expected to change the distribution of species. For long-lived, sessile species such as trees, tracking the warming climate depends on seedling colonization of newly favorable areas. We compare the distribution of seedlings and mature trees for all but the rarest tree species in California, Oregon and Washington, United States of America, a large, environmentally diverse region. Across 46 species, the mean annual temperature of the range of seedlings was 0.120°C colder than that of the range of trees (95% confidence interval from 0.096 to 0.144°C). The extremes of the seedling distributions also shifted towards colder temperature than those of mature trees, but the change was less pronounced. Although the mean elevation and mean latitude of the range of seedlings was higher than and north of those of the range of mature trees, elevational and latitudinal shifts run in opposite directions for the majority of the species, reflecting the lack of a direct biological relationship between species’ distributions and those variables. The broad scale, environmental diversity and variety of disturbance regimes and land uses of the study area, the large number and exhaustive sampling of tree species, and the direct causal relationship between the temperature response and a warming climate, provide strong evidence to attribute the observed shifts to climate change.

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One of the challenges often faced in forestry is the estimation of forest attributes for smaller areas of interest within a larger population. Small-area estimation (SAE) is a set of techniques well suited to estimation of forest attributes for small areas in which the existing sample size is small and auxiliary information is available. Selected SAE methods were compared for estimating a variety of forest attributes for small areas using ground data and light detection and ranging (LiDAR) derived auxiliary information. The small areas of interest consisted of delineated stands within a larger forested population. Four different estimation methods were compared for predicting forest density (number of trees/ha), quadratic mean diameter (cm), basal area (m 2 /ha), top height (m), and cubic stem volume (m 3 /ha). The precision and bias of the estimation methods (synthetic prediction (SP), multiple linear regression based composite prediction (CP), empirical best linear unbiased prediction (EBLUP) via Fay-Herriot models, and most similar neighbor (MSN) imputation) are documented. For the indirect estimators, MSN was superior to SP in terms of both precision and bias for all attributes. For the composite estimators, EBLUP was generally superior to direct estimation (DE) and CP, with the exception of forest density.Résumé : Un des défis souvent rencontrés en foresterie est l'estimation des attributs forestiers pour de plus petites zones d'intérêt au sein d'une population plus large qu'est la forêt. Les méthodes d'estimation pour petites zones sont des techniques bien adaptées à l'estimation d'attributs forestiers sur de petites superficies pour lesquelles la taille de l'échantillon existant est faible et l'information auxiliaire est disponible. Quatre méthodes d'estimation pour petites zones ont été sélectionnées et comparées pour estimer une variété d'attributs forestiers sur de petites superficies à l'aide de données terrain et de données auxiliaires dérivées du lidar. Les deux premières méthodes étaient indirectes (la prédiction synthétique (PS) et l'imputation par les plus proches voisins (PPV)); les deux autres étaient composites (la meilleure prédiction empirique linéaire sans biais (PSB) basée sur les modèles de Fay-Herriot et la prédiction composite basée sur la régression linéaire multiple (PC)). Les petites superficies d'intérêt étaient représentées par les peuplements délimités dans une forêt. Les attributs forestiers qui ont été comparés étaient la densité de la forêt (nombre de tiges/ha), le diamètre moyen quadratique (cm), la surface terrière (m 2 /ha), la hauteur maximale (m) et le volume des tiges (m 3 /ha). La précision et le biais des quatre méthodes d'estimation sont documentés. Dans le cas des estimateurs indirects, l'imputation par les PPV était supérieure à la PS en termes de précision et de biais pour tous les attributs. Dans le cas des estimateurs composites, la PSB était généralement supé-rieure à l'estimation direct et la PC, excepté pour la densité de la forêt.[Traduit par la Rédaction]

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L.; Amacher, Michael C.; and Monleon, Vicente J., "Using an epiphytic moss to identify previously unknown sources of atmospheric cadmium pollution" (2016 H I G H L I G H T S• Bio-indicators are a valid method for measuring atmospheric pollutants • We used moss to map atmospheric cadmium in Portland, Oregon • Using a spatial linear model, we identified two stained-glass manufacturers as the major sources of atmospheric cadmium in Portland • After both companies suspended cadmium use, atmospheric levels declined precipitously 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 bioindicators 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 stainedglass 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 G R A P H I C A L A B S T R A C T a b s t r a c t a r t i c l e i n f o Contents lists available at ScienceDirectScience of the Total Environment j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / s c i t o t e n v This document is a U.S. government work and is not subject to copyright in the United States.concentration was 29.4 ng/m 3 , which is 49 times higher than Oregon's benchmark of 0.6 ng/m 3 , 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/m 3 for stained-glass manufacturer #1 and 0.67 ng/m 3 for stained-glass manufacturer #2.Published by Elsevier B.V.