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Highlights • Multi-seasonal optical imagery and ALS was compared for modelling forest attributes. • Optical and ALS intensity data convey distinct information about forest structure. • ALS data alone exhibited strong predictive power for stem density. • The synergy of both data sources led to more accurate model of BA and species mixture. • Sentinel-2 A imagery holds great potential for enhancing ALS-based forest inventories. Abstract Advanced forest resource inventory (FRI) information is of critical importance for sustainable forest management. FRIs are dependent on remote sensing data and processing methods, along with field calibration/validation to generate cost-effective options for modelling forest inventory and biophysical variables over large areas. The objective of this study was to examine the impact of combining multi-seasonal multispectral satellite imagery with airborne laser scanning (ALS) data for estimating basal area, species mixture and stem density for an uneven-aged tolerant hardwood forest in Ontario, Canada. Using random forest (RF) regression as a non-parametric diagnostic technique, three multispectral optical sensors (i.e., Landsat-5 TM, Sentinel-2 A and WorldView-2) were compared to examine the most cost-effective sensor configuration for modelling FRI variables. The contribution of spectral predictors derived from these optical sensors as well as ALS height and intensity metrics were evaluated using RF variable importance. As part of our variable selection framework, all predictor variables were grouped into relatively independent clusters using a hierarchical variable clustering technique, which revealed the distinctiveness between information contained in spectral predictors, height- and intensity-based metrics. This indicates that ALS intensity data carry unique information complementary to passive near-infrared data for forest characterization. ALS data alone did not result in accurate models for basal area and species mixture, but predictive accuracies were improved significantly with the addition of spectral predictors. Compared to single-date images, multi-seasonal imagery proved to be more accurate for modelling FRI variables, especially when combined with ALS data. Despite its limited spatial resolution, Sentinel-2 A was found to be the most cost-effective image source for enhancing ALS-based FRI models. Using variables identified by the variable selection procedure, best subsets regression outperformed the RF models developed for diagnostic analysis, resulting in a suite of accurate and parsimonious predictive models, with coefficients of determination of 0.73, 0.90 and 0.67, for basal area, species mixture, and stem density, respectively. [ABSTRACT FROM AUTHOR]

Land cover and land use classifications from remote sensing are increasingly becoming institutionalized framework data sets for monitoring environmental change. As such, the need for robust statements of classification accuracy is critical. This paper describes a method to estimate confidence in classification model accuracy using a bootstrap approach. Using this method, it was found that classification accuracy and confidence, while closely related, can be used in complementary ways to provide additional information on map accuracy and define groups of classes and to inform the future reference sampling strategies. Overall classification accuracy increases with an increase in the number of fields surveyed, where the width of classification confidence bounds decreases. Individual class accuracies and confidence were non-linearly related to the number of fields surveyed. Results indicate that some classes can be estimated accurately and confidently with fewer numbers of samples, whereas others require larger reference data sets to achieve satisfactory results. This approach is an improvement over other approaches for estimating class accuracy and confidence as it uses repetitive sampling to produce a more realistic estimate of the range in classification accuracy and confidence that can be obtained with different reference data inputs. [ABSTRACT FROM AUTHOR]

A wide variety of sampling techniques and strategies are needed to analyze polycyclic aromatic compounds (PACs) and interpret their distributions in various environmental media (i.e., air, water, snow, soils, sediments, peat and biological material). In this review, we provide a summary of commonly employed sampling methods and strategies, as well as a discussion of routine and innovative approaches used to quantify and characterize PACs in frequently targeted environmental samples, with specific examples and applications in Canadian investigations. The pros and cons of different analytical techniques, including gas chromatography – flame ionization detection (GC-FID), GC low-resolution mass spectrometry (GC-LRMS), high performance liquid chromatography (HPLC) with ultraviolet, fluorescence or MS detection, GC high-resolution MS (GC-HRMS) and compound-specific stable (δ13C, δ2H) and radiocarbon (Δ14C) isotope analysis are considered. Using as an example research carried out in Canada's Athabasca oil sands region (AOSR), where alkylated polycyclic aromatic hydrocarbons and sulfur-containing dibenzothiophenes are frequently targeted, the need to move beyond the standard list of sixteen EPA priority PAHs and for adoption of an AOSR bitumen PAC reference standard are highlighted. Image 1 • Review of methods to collect samples for PAC analysis in the Canadian environment. • Review of techniques used to quantify or characterize PACs in environmental samples. • High-resolution MS and compound-specific isotope analysis offer unparalleled insight. • The list of 16 EPA PAHs is insufficient for studies in Canada's oil sands region. • The adoption of an Athabasca oil sands bitumen PAC reference standard is proposed. A comprehensive review of sampling methods, strategies and analytical techniques used to quantify and characterize PACs in the Canadian environment. [ABSTRACT FROM AUTHOR]

• Non-stand replacing (NSR) disturbances, markedly impact forest condition. • Long time series satellite imagery allows for characterisation of change. • Exemplar behaviour of non-stand replacing disturbance is defined from spectral trajectories. • Trends relate to insect damage and drought. Objective information is required to monitor and characterize disturbances and disturbance regimes as related to changing climate and anthropogenic pressures. Forest disturbances occur over a range of spatial and temporal scales, with varying extent, severity, and persistence. To date, most of our understanding of detecting forest disturbances using remotely sensed imagery has been based on detecting abrupt and rapid, stand replacing disturbances, such as those related to wildfire and forest harvesting. Conversely, more continuous, subtle, and gradual non-stand replacing (NSR) disturbances, such as those related to drought stress or insect infestation have been subject to less focus. This can be attributed to the variability of NSR in space and time, as well as detection difficulties due to their often-subtle alterations to forest canopies and structure. Time series remote sensing techniques offer new opportunities for the capture and characterization of NSR disturbances. In this research, we investigate NSR disturbances as detected using a Landsat time series-based disturbance detection algorithm, Composite2Change (C2C) applied to the 650 Mha forested ecosystems of Canada. We first examined three key characteristics of the spectral trajectory of NSR disturbances: pre-disturbance conditions, disturbance severity and disturbance persistence. From these characteristics, we defined seven typical NSR patterns (exemplars) for Canada's forested ecosystems. Using annual information on predicted stand-level cover and biomass, we found that all NSR exemplars resulted in a loss of canopy cover, ranging from an average loss of between 5 and 31%. For instance, the most common exemplar was linked to a 15% reduction in canopy cover and a 13 Mg/ha reduction in biomass. While stands lost biomass, over the duration of the NSR, most stands increased slightly in stand height likely due to growth of the remaining trees or release from competition from the remaining stems. Comparing exemplars across Canada with independent data on NSR disturbance events confirmed that NSR exemplars that had larger severities, and shorter disturbance persistence times, were more commonly associated with insect infestations (e.g. mountain pine beetle (Dendroctonus ponderosae) and spruce budworm (Choristoneura fumiferana)), whereas exemplars with a lower severity, and longer persistence were more commonly associated with drought events. Developing a nomenclature around remote sensing based characteristics of NSR disturbances provides an approach to both map NSR disturbances and better understand their impact on forest attributes, as well as provide tools to assess if these disturbanes are changing over space and time. [ABSTRACT FROM AUTHOR]

Porcine circovirus-associated disease (PCVAD) is clinically manifested by postweaning multisystemic wasting syndrome (PMWS), respiratory and enteric disease, reproductive failure, and porcine dermatitis and nephropathy syndrome (PDNS). Porcine circovirus 2 (PCV2) is an essential component of PCVAD, although an etiologic role in PDNS is not well established. Here, a novel circovirus, designated porcine circovirus 3 (PCV3), was identified in sows that died acutely with PDNS-like clinical signs. The capsid and replicase proteins of PCV3 are only 37% and 55% identical to PCV2 and bat circoviruses, respectively. Aborted fetuses from sows with PDNS contained high levels of PCV3 (7.57 × 10 7 genome copies/ml), and no other viruses were detected by PCR and metagenomic sequencing. Immunohistochemistry (IHC) analysis of sow tissue samples identified PCV3 antigen in skin, kidney, lung, and lymph node samples localized in typical PDNS lesions, including necrotizing vasculitis, glomerulonephritis, granulomatous lymphadenitis, and bronchointerstitial pneumonia. Further study of archived PDNS tissue samples that were negative for PCV2 by IHC analysis identified 45 of 48 that were PCV3 positive by quantitative PCR (qPCR), with 60% of a subset also testing positive for PCV3 by IHC analysis. Analysis by qPCR of 271 porcine respiratory disease diagnostic submission samples identified 34 PCV3-positive cases (12.5%), and enzyme-linked immunosorbent assay detection of anti-PCV3 capsid antibodies in serum samples found that 46 (55%) of 83 samples tested were positive. These results suggest that PCV3 commonly circulates within U.S. swine and may play an etiologic role in reproductive failure and PDNS. Because of the high economic impact of PCV2, this novel circovirus warrants further studies to elucidate its significance and role in PCVAD., Importance: While porcine circovirus 2 (PCV2) was first identified in sporadic cases of postweaning multisystemic wasting syndrome in Canada in the early 1990s, an epidemic of severe systemic disease due to PCV2 spread worldwide in the ensuing decade. Despite being effectively controlled by commercial vaccines, PCV2 remains one of the most economically significant viruses of swine. Here, a novel porcine circovirus (PCV3) that is distantly related to known circoviruses was identified in sows with porcine dermatitis and nephropathy syndrome (PDNS) and reproductive failure. PCV2, which has previously been associated with these clinical presentations, was not identified. High levels of PCV3 nucleic acid were observed in aborted fetuses by quantitative PCR, and PCV3 antigen was localized in histologic lesions typical of PDNS in sows by immunohistochemistry (IHC) analysis. PCV3 was also identified in archival PDNS diagnostic samples that previously tested negative for PCV2 by IHC analysis. The emergence of PCV3 warrants further investigation., (Copyright © 2016 American Society for Microbiology.)