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Abstract Background Moderately severe or major trauma (injury severity score (ISS) > 8) is common, often resulting in physical and psychological problems and leading to difficulties in returning to work. Vocational rehabilitation (VR) can improve return to work/education in some injuries (e.g. traumatic brain and spinal cord injury), but evidence is lacking for other moderately severe or major trauma. Methods ROWTATE is an individually randomised controlled multicentre pragmatic trial of early VR and psychological support in trauma patients. It includes an internal pilot, economic evaluation, a process evaluation and an implementation study. Participants will be screened for eligibility and recruited within 12 weeks of admission to eight major trauma centres in England. A total of 722 participants with ISS > 8 will be randomised 1:1 to VR and psychological support (where needed, following psychological screening) plus usual care or to usual care alone. The ROWTATE VR intervention will be provided within 2 weeks of study recruitment by occupational therapists and where needed, by clinical psychologists. It will be individually tailored and provided for ≤ 12 months, dependent on participant need. Baseline assessment will collect data on demographics, injury details, work/education status, cognitive impairment, anxiety, depression, post-traumatic distress, disability, recovery expectations, financial stress and health-related quality of life. Participants will be followed up by postal/telephone/online questionnaires at 3, 6 and 12 months post-randomisation. The primary objective is to establish whether the ROWTATE VR intervention plus usual care is more effective than usual care alone for improving participants’ self-reported return to work/education for at least 80% of pre-injury hours at 12 months post-randomisation. Secondary outcomes include other work outcomes (e.g. hours of work/education, time to return to work/education, sickness absence), depression, anxiety, post-traumatic distress, work self-efficacy, financial stress, purpose in life, health-related quality of life and healthcare/personal resource use. The process evaluation and implementation study will be described elsewhere. Discussion This trial will provide robust evidence regarding a VR intervention for a major trauma population. Evidence of a clinically and cost-effective VR intervention will be important for commissioners and providers to enable adoption of VR services for this large and important group of patients within the NHS. Trial registration ISRCTN: 43115471. Registered 27/07/2021.

Objectives Major trauma centres (MTCs) save lives but rehabilitation to support return-to-work (RTW) is lacking. This paper describes development of a vocational rehabilitation intervention (the ROWTATE intervention) to support RTW following traumatic injury.Design Sequential and iterative person-based approach in four stages—Stage 1: review of evidence about the efficacy and mechanisms of RTW interventions; Stage 2: interviews (n=38) and focus groups (n=25) with trauma survivors and service providers in five UK MTCs to identify the issues, and challenges faced postinjury; Stage 3: codesign workshops (n=43) with trauma stakeholders in MTCs to conceptually test and identify intervention delivery barriers/enablers; Stage 4: meetings (n=7) with intervention development working group (IDWG) to: (1) generate guiding principles, (2) identify key intervention features (process, components, mechanisms) to address unmet rehabilitation needs; (3) generate a logic model and programme theory to illustrate how the intervention works; and (4) develop a training package to support delivery.Results Trauma survivors described unmet needs relating to early advice about RTW; psychological support; pain management; hidden disabilities (eg, fatigue); estimating recovery; and community, amputee and musculoskeletal rehabilitation. Mechanisms of effective interventions identified in the review included early intervention, colocation, employer engagement, case coordination and work accommodations. Intervention features identified by IDWG members (n=13) from stages 1 and 2 were use of stepped-care approaches by occupational therapists (OTs) and clinical psychologists (CPs), OT/CP formulation for complex cases, assessment of mental health problems, individually tailored rehabilitation including vocational goal setting, cross-sector coordination/communication, employer engagement, phased RTW, education/advice for family/employers, exploration of work alternatives, ongoing review of physical and mental health needs, work stability monitoring. Conceptual testing ratified the logic model. Geography and long waiting lists were identified as potential delivery barriers.Conclusions Real-world testing of the intervention is underway in a randomised controlled trial.

Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for the classification and discrimination of microbial species and strains, primarily sourced from individuals with invasive infections. These samples were obtained from various children with suspected sepsis infections with bacteria and fungi originating at different sites. We conducted a comprehensive analysis utilising 212 isolates from 14 distinct genera, comprising 202 bacterial and 10 fungal isolates. With the spectral analysis taking several weeks, we present the incorporation of quality control samples to mitigate potential variations that may arise between different sample plates, especially when dealing with a large sample size. The results demonstrated a remarkable consistency in clustering patterns among 14 genera when subjected to principal component analysis (PCA). Particularly, Candida, a fungal genus, was distinctly recovered away from bacterial samples. Principal component discriminant function analysis (PC-DFA) allowed for distinct discrimination between different bacterial groups, particularly Gram-negative and Gram-positive bacteria. Clear differentiation was also observed between coagulase-negative staphylococci (CNS) and Staphylococcus aureus isolates, while methicillin-resistant S. aureus (MRSA) was also separated from methicillin-susceptible S. aureus (MSSA) isolates. Furthermore, highly accurate discrimination was achieved between Enterococcus and vancomycin-resistant enterococci isolates with 98.4% accuracy using partial least squares-discriminant analysis. The study also demonstrates the specificity of FT-IR, as it effectively discriminates between individual isolates of Streptococcus and Candida at their respective species levels. The findings of this study establish a strong groundwork for the broader implementation of FT-IR and chemometrics in clinical and microbiological applications. The potential of these techniques for enhanced microbial classification holds significant promise in the diagnosis and management of invasive bacterial infections, thereby contributing to improved patient outcomes.

Abstract In Canada, the combustion of forest biomass for bioenergy production has been increasing with an associated increase in residual wood ash. Wood ash is typically landfilled as waste but there is growing interest in applying wood ash to the soils of commercial forests. Ideally, wood ash supplies nutrients that may have been removed through biomass harvesting, increases soil pH, which improves nutrient availability, and potentially improves site productivity, but there is also potential for detrimental effects, such as toxicity, that impair soil functions. The objective of this study was to investigate the effects of wood ash application on soil organic matter attributes at eight experimental sites across Canada that are examining the effects of wood ash application on site fertility, productivity, and soil biodiversity. Wood ash application had an effect on total carbon (TC) and total nitrogen, microbial biomass carbon (MBC), hot water extractable carbon (HWEC), mineralizable C, sand size C, and HWEC and MBC normalized to TC, but changes were typically restricted to single sites or differed in their direction, that is, positive or negative. Based on the limited and inconsistent effects of ash on established indictors of soil quality measured in this study, there does not appear to be any advantageous or detrimental effects of adding wood ash to forest soil quality.

Abstract There is interest in utilizing wood ash as an amendment in forestry operations as a mechanism to return nutrients to soils that are removed during harvesting, with the added benefit of diverting this bioenergy waste material from landfill sites. Existing studies have not arrived at a consensus on what the effects of wood ash amendments are on soil biota. We collected forest soil samples from studies in managed forests across Canada that were amended with wood ash to evaluate the effects on arthropod, bacterial and fungal communities using metabarcoding of F230, 16S, 18S and ITS2 sequences as well as enzyme analyses to assess its effects on soil biotic function. Ash amendment did not result in consistent effects across sites, and those effects that were detected were small. Overall, this study suggests that ash amendment applied to managed forest systems in amounts (up to 20 Mg ha−1) applied across the 8 study sties had little to no detectable effects on soil biotic community structure or function. When effects were detected, they were small, and site-specific. These non-results support the application of wood ash to harvested forest sites to replace macronutrients (e.g., calcium) removed by logging operations, thereby diverting it from landfill sites, and potentially increasing stand productivity.

Abstract Background Larval connectivity between distinct benthic populations is essential for their persistence. Although connectivity is difficult to measure in situ, it can be predicted via models that simulate biophysical interactions between larval behaviour and ocean currents. The blue mussel (Mytilus Edulis L.) is widespread throughout the northern hemisphere and extensively commercialised worldwide. In the Irish Sea, this industry represents ~ 50% of Welsh shellfisheries, where cultivation is mainly based on wild spat. However, the main sources and amount of spat varied interannually (1100 tonnes harvest in 2014 against zero in 2018). The aim of this study is to characterise the structure and dynamics of the blue mussel metapopulation within the northern part of the Irish Sea. Methods We develop a Lagrangian particle tracking model, driven by a high-resolution (from 30 to 5000 m) validated unstructured coastal hydrodynamic model of the Irish Sea, to simulate spatial and temporal variability of larval dispersal and connectivity between distinct mussel populations and potential settlement areas. Results Our results showed that: (1) larvae positioned near the surface were strongly influenced by wind-driven currents suggesting that connectivity networks had the potential to span hundreds of kilometres; (2) in contrast, larvae positioned deeper in the water column were driven by tidal currents, producing intricate spatial patterns of connectivity between mussel beds over tens of kilometres that were consistent over time. Conclusions Dispersal of mussel larvae in the tidally energetic Irish Sea during the April–May spawning season is potentially driven by wind-driven surface currents, as confirmed by fisherman observations of inter-annual variability in wild spat collection. These results have important implications for metapopulation dynamics within the context of climate change and sustainable shellfisheries management (i.e. gain and loss of populations and harvest areas according to wind conditions).

Pseudomonas aeruginosa undergoes diversification during infection of the cystic fibrosis (CF) lung. Understanding these changes requires model systems that capture the complexity of the CF lung environment. We previously identified loss-of-function mutations in the 2-component regulatory system sensor kinase gene pmrB in P. aeruginosa from CF lung infections and from experimental infection of mice. Here, we demonstrate that, while such mutations lowered in vitro minimum inhibitory concentrations for multiple antimicrobial classes, this was not reflected in increased antibiotic susceptibility in vivo. Loss of PmrB impaired aminoarabinose modification of LPS, increasing the negative charge of the outer membrane and promoting uptake of cationic antimicrobials. However, in vivo, this could be offset by increased membrane binding of other positively charged molecules present in lungs. The polyamine spermidine readily coated the surface of PmrB-deficient P. aeruginosa, reducing susceptibility to antibiotics that rely on charge differences to bind the outer membrane and increasing biofilm formation. Spermidine was elevated in lungs during P. aeruginosa infection in mice and during episodes of antimicrobial treatment in people with CF. These findings highlight the need to study antimicrobial resistance under clinically relevant environmental conditions. Microbial mutations carrying fitness costs in vitro may be advantageous during infection, where host resources can be utilized.

Societal Impact Statement Large areas of tropical forest are degraded. While global tree cover is being mapped with increasing accuracy from space, much less is known about the quality of that tree cover. Here we present a field protocol for rapid assessments of forest condition. Using extensive field data from Tanzania, we show that a focus on remotely‐sensed deforestation would not detect significant reductions in forest quality. Radar‐based remote sensing of degradation had good agreement with the ground data, but the ground surveys provided more insights into the nature and drivers of degradation. We recommend the combined use of rapid field assessments and remote sensing to provide an early warning, and to allow timely and appropriately targeted conservation and policy responses. Summary Tropical forest degradation is widely recognised as a driver of biodiversity loss and a major source of carbon emissions. However, in contrast to deforestation, more gradual changes from degradation are challenging to detect, quantify and monitor. Here, we present a field protocol for rapid, area‐standardised quantifications of forest condition, which can also be implemented by non‐specialists. Using the example of threatened high‐biodiversity forests in Tanzania, we analyse and predict degradation based on this method. We also compare the field data to optical and radar remote‐sensing datasets, thereby conducting a large‐scale, independent test of the ability of these products to map degradation in East Africa from space. Our field data consist of 551 ‘degradation’ transects collected between 1996 and 2010, covering >600 ha across 86 forests in the Eastern Arc Mountains and coastal forests. Degradation was widespread, with over one‐third of the study forests—mostly protected areas—having more than 10% of their trees cut. Commonly used optical remote‐sensing maps of complete tree cover loss only detected severe impacts (≥25% of trees cut), that is, a focus on remotely‐sensed deforestation would have significantly underestimated carbon emissions and declines in forest quality. Radar‐based maps detected even low impacts (

Objectives This study aimed to: (1) understand the context for delivering a trauma vocational rehabilitation (VR) intervention; (2) identify potential barriers and enablers to the implementation of a VR intervention post-trauma.Design Qualitative study. Data were collected in person or via phone using different methods: 38 semistructured interviews, 11 informal ‘walk-through care pathways’ interviews, 5 focus groups (n=25), 5 codesign workshops (n=43). Data were thematically analysed using the framework approach, informed by the Consolidated Framework for Implementation Research.Setting Stakeholders recruited across five UK major trauma networks.Participants A variety of stakeholders were recruited (n=117) including trauma survivors, rehabilitation physicians, therapists, psychologists, trauma coordinators and general practitioners. We recruited 32 service users (trauma survivors or carers) and 85 service providers.Results There were several issues associated with implementing a trauma VR intervention including: culture within healthcare/employing organisations; extent to which healthcare systems were networked with other organisations; poor transition between different organisations; failure to recognise VR as a priority; external policies and funding. Some barriers were typical implementation issues (eg, funding, policies, openness to change). This study further highlighted the challenges associated with implementing a complex intervention like VR (eg, inadequate networking/communication, poor service provision, perceived VR priority). Our intervention was developed to overcome these barriers through adapting a therapist training package, and by providing early contact with patient/employer, a psychological component alongside occupational therapy, case coordination/central point of contact, and support crossing sector boundaries (eg, between health/employment/welfare).Conclusions Findings informed the implementation of our VR intervention within the complex trauma pathway. Although we understand how to embed it within this context, the success of its implementation needs to be measured as part of a process evaluation in a future trial.

Monitoring the impacts of poaching on wildlife is crucial to the management of ecosystems and wildlife populations. Previous studies have shown that poaching can affect the demography, reproduction and behavior of wildlife. For African elephants (Loxodonta africana), poaching has been shown to affect population numbers, structure, breeding system, behavior and activity patterns. This study investigated whether there were significant differences in group size and composition of African elephants in Ruaha National Park, Tanzania between areas of high- and low poaching levels, based on the 2013 whole-ecosystem aerial census, which we used as a proxy for poaching pressure. Elephant group size and composition were recorded along 417.6 km of monthly transects from May to November 2016, and again from May to November 2017. Comparison of cow/calf group sizes revealed that the group sizes were larger in areas with low poaching pressure. The dependent-to-adult female ratio was higher in areas with low poaching pressure, while the proportion of adult females was higher in areas with high poaching pressure. The proportions of cow/calf and mixed group types were higher in areas with low poaching pressure. A higher proportion of bull groups were seen in areas with high poaching pressure, which could be evidence of a risk response strategy. Therefore, poaching has significantly shaped grouping patterns, composition and has caused reproductive suppression in Ruaha elephants. We recommend a genetic study of this elephant population to establish the degree of relatedness among families to understand the extent of social structure breakdown caused by social stress due to high poaching pressure in the past years.

Aerial-photo interpreted inventories of forest resources, including tree species composition, are valuable in forest resource management, but are expensive to create and can be relatively inaccurate. Because of differences among tree species in their spectral properties and seasonal phenologies, it might be possible to improve such forest resource inventory information (FRI) by using it in concert with multispectral satellite information from multiple time periods. We used Sentinel-2 information from nine spectral bands and 12 dates within a two-year period to model multivariate percent tree species composition in >51,000 forest stands in the FRI of south-central Ontario, Canada. Accuracy of random forest (RF) and convolutional neural network (CNN) predictions were tested using species-specific basal area information from 155 0.25-ha field plots. Additionally, we created models using the Sentinel-2 information in concert with the field data and compared the accuracy of these models and the FRI-based models by use of basal areas from a second (13.7-ha) field data set. Based on average R2 values across species in the two field data sets, the Sentinel-FRI models outperformed the FRI, showing 1.5- and 1.7-fold improvements relative to the FRI for RF and 2.1- and 2.2-fold improvements for CNN (mean R2: 0.141–0.169 (FRI); 0.217–0.295 (RF); 0.307–0.352 (CNN)). Models created with the field data performed even better: improvements relative to the FRI were 2.1-fold for RF and 2.8-fold for CNN (mean R2: 0.169 (FRI); 0.356 (RF); 0.469 (CNN)). As predicted, R2 values between FRI- and field-trained predictions were higher than R2 values with the FRI. Of the 21 tree species evaluated, 8 relatively rare species had poor models in all cases. Our multivariate approach allowed us to use more FRI stands in model creation than if we had been restricted to stands dominated by single species and allowed us to map species abundances at higher resolution. It might be possible to improve models further by use of tree stem maps and incorporation of the effects of canopy disturbances.

Two types of nonparametric modeling techniques and various metrics derived from airborne laser scanning (ALS) data were examined in terms of their utility for modeling stem diameter distributions in an uneven-aged tolerant hardwood forest in Ontario, Canada. Using an area-based approach (ABA), the frequency distribution of trees across 6 size classes was predicted using k-nearest neighbor (k-NN) imputation and Random Forest (RF) regression. Predictor variables derived from ALS height and intensity data were divided into 3 groups: height only, intensity only, and all metrics. Prediction results demonstrated that the first 2 groups of predictor variables exhibited similar predictive accuracy, whereas the synergy of both resulted in enhanced performance. The utility of intensity-based metrics was corroborated by an importance measure obtained from RF. The size class-specific stem density estimation approach based on RF was more accurate and flexible than the simultaneous estimation approach based on k-NN models. After the predicted diameter distributions were grouped into 9 structural groups, heterogeneous accuracy scores revealed the challenges for predicting select diameter distributions. Although successes were observed for certain size classes, there remains additional research (e.g., development of additional metrics or data types) to be done to accurately predict a complete range of size classes.

The giant willow aphid (Tuberolachnus salignus) is a large stem-feeding insect which forms dense colonies on infested plants. Since T. salignus is a new invasive species in New Zealand, we have a poor understanding of the plant chemical responses to aphid infestation. This study aimed to characterize the volatile organic compounds (VOCs) emissions of fifteen different willow species and hybrids growing in New Zealand, and to evaluate changes in response to T. salignus attack in a field trial. Volatiles were collected using a headspace sampling technique and analysed using gas chromatography-mass spectrometry (GC-MS). We found high variability in the volatile profiles of different species and hybrids, with (Z)-3-hexenyl acetate and (E)-β-ocimene being the only common components to all blends. Taxonomically related plants showed an overlapping pattern of VOC emission, and there seemed to be a clear separation between shrub and tree willows. Responses to aphid infestation were variable, with only four species/hybrids showing changes in their total VOC emission, or that of at least one class of VOCs. A weak positive correlation between aphid population estimates and VOC emissions suggests that responses are species-specific and not infestation-dependent. These results reveal useful information about the interaction between T. salignus and its potential host plants for biological control and pest management purposes.

Infestation of willow plants by the giant willow aphid Tuberolachnus salignus (Hemiptera: Aphididae) is associated with copious deposition of sugar-rich honeydew under the plant canopy. We explored the effect of aphid honeydew on the soil biota and biochemical indicators in a two-year field trial. Soil samples from under aphid-infested and control willow trees, as well as samples from black sooty mould spots under the aphid-infested willows were compared; soil samples before aphid inoculation were used as a baseline. The honeydew deposition had a positive effect on the total soil carbon (C), but not on the total soil nitrogen content or soil pH. Microbial biomass C, basal respiration, number of yeast colony forming units, and the geometric mean of activities for six enzymes were significantly higher in honeydew-affected soils than in the control treatment on both years. The honeydew deposition also increased soil meso-fauna abundance, especially in the black sooty mould spots. The soil biochemical properties, which differed before and after aphid infestation, showed considerable overlap between the first and second year post-infestation. The results highlight the cascading effects of T. salignus on soil biological activity and the importance of using a multitrophic approach to explore similar scenarios.

Background: Up to 160,000 people incur traumatic brain injury (TBI) each year in the UK. TBI can have profound effects on many areas of human functioning, including participation in work. There is limited evidence of the clinical effectiveness and cost-effectiveness of vocational rehabilitation (VR) after injury to promote early return to work (RTW) following TBI. Objective: To assess the feasibility of a definitive, multicentre, randomised controlled trial (RCT) of the clinical effectiveness and cost-effectiveness of early, specialist VR plus usual care (UC) compared with UC alone on work retention 12 months post TBI. Design: A multicentre, feasibility, parallel-group RCT with a feasibility economic evaluation and an embedded mixed-methods process evaluation. Randomisation was by remote computer-generated allocation. Setting: Three NHS major trauma centres (MTCs) in England. Participants: Adults with TBI admitted for > 48 hours and working or studying prior to injury. Interventions: Early specialist TBI VR delivered by occupational therapists (OTs) in the community using a case co-ordination model. Main outcome measures: Self-reported RTW 12 months post randomisation, mood, functional ability, participation, work self-efficacy, quality of life and work ability. Feasibility outcomes included recruitment and retention rates. Follow-up was by postal questionnaires in two centres and face to face in one centre. Those collecting data were blind to treatment allocation. Results: Out of 102 target participants, 78 were recruited (39 randomised to each arm), representing 39% of those eligible and 5% of those screened. Approximately 2.2 patients were recruited per site per month. Of those, 56% had mild injuries, 18% had moderate injuries and 26% had severe injuries. A total of 32 out of 45 nominated carers were recruited. A total of 52 out of 78 (67%) TBI participants responded at 12 months (UC, n = 23; intervention, n = 29), completing 90% of the work questions; 21 out of 23 (91%) UC respondents and 20 out of 29 (69%) intervention participants returned to work at 12 months. Two participants disengaged from the intervention. Face-to-face follow-up was no more effective than postal follow-up. RTW was most strongly related to social participation and work self-efficacy. It is feasible to assess the cost-effectiveness of VR. Intervention was delivered as intended and valued by participants. Factors likely to affect a definitive trial include deploying experienced OTs, no clear TBI definition or TBI registers, and repatriation of more severe TBI from MTCs, affecting recruitment of those most likely to benefit/least likely to drop out. Limitations: Target recruitment was not reached, but mechanisms to achieve this in future studies were identified. Retention was lower than expected, particularly in UC, potentially biasing estimates of the 12-month RTW rate. Conclusions: This study met most feasibility objectives. The intervention was delivered with high fidelity. When objectives were not met, strategies to ensure feasibility of a full trial were identified. Future work should test two-stage recruitment and include resources to recruit from ‘spokes’. A broader measure covering work ability, self-efficacy and participation may be a more sensitive outcome. Trial registration: Current Controlled Trials ISRCTN38581822. Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 22, No. 33. See the NIHR Journals Library website for further project information.

Delineating canopy gaps and quantifying gap characteristics (e.g., size, shape, and dynamics) are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. Both high spatial resolution optical and light detection and ranging (LiDAR) remote sensing data have been used to identify canopy gaps through object-based image analysis, but few studies have quantified the pros and cons of integrating optical and LiDAR for image segmentation and classification. In this study, we investigate whether the synergistic use of optical and LiDAR data improves segmentation quality and classification accuracy. The segmentation results indicate that the LiDAR-based segmentation best delineates canopy gaps, compared to segmentation with optical data alone, and even the integration of optical and LiDAR data. In contrast, the synergistic use of two datasets provides higher classification accuracy than the independent use of optical or LiDAR (overall accuracy of 80.28% ± 6.16% vs. 68.54% ± 9.03% and 64.51% ± 11.32%, separately). High correlations between segmentation quality and object-based classification accuracy indicate that classification accuracy is largely dependent on segmentation quality in the selected experimental area. The outcome of this study provides valuable insights of the usefulness of data integration into segmentation and classification not only for canopy gap identification but also for many other object-based applications.

Recent advances in remote sensing technology provide sufficient spatial detail to achieve species-level classification over large vegetative ecosystems. In deciduous-dominated forests, however, as tree species diversity and forest structural diversity increase, the frequency of spectral overlap between species also increases and our ability to classify tree species significantly decreases. This study proposes an operational workflow of individual tree-based species classification for a temperate, mixed deciduous forest using three-seasonal WorldView images, involving three steps of individual tree crown (ITC) delineation, non-forest gap elimination, and object-based classification. The process of species classification started with ITC delineation using the spectral angle segmentation algorithm, followed by object-based random forest classifications. A total of 672 trees was located along three triangular transects for training and validation. For single-season images, the late-spring, mid-summer, and early-fall images achieve the overall accuracies of 0.46, 0.42, and 0.35, respectively. Combining the spectral information of the early-spring, mid-summer, and early-fall images increases the overall accuracy of classification to 0.79. However, further adding the late-fall image to separate deciduous and coniferous trees as an extra step was not successful. Compared to traditional four-band (Blue, Green, Red, Near-Infrared) images, the four additional bands of WorldView images (i.e., Coastal, Yellow, Red Edge, and Near-Infrared2) contribute to the species classification greatly (OA: 0.79 vs. 0.53). This study gains insights into the contribution of the additional spectral bands and multi-seasonal images to distinguishing species with seemingly high degrees of spectral overlap.