Improving the precision of dynamic forest parameter estimates using Landsat

The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is well established.When reducing the variance of post-stratification estimates for forest change parameters such as forest growth, it is logical to use a change-related strata map. At the stand level, a time series of Landsat images is ideally suited for producing such a map. In this study, we generate strata maps based on trajectories of Landsat Thematic Mapper-based normalized difference vegetation index values, with a focus on post-disturbance recovery and recent measurements. These trajectories, from1985 to 2010, are converted to harmonic regression coefficient estimates and classified according to a hierarchical clustering algorithm from a training sample. The resulting strata maps are then used in conjunction with measured plots to estimate forest status and change parameters in an Alabama, USA study area. These estimates and the variance of the estimates are then used to calculate the estimated relative efficiencies of the post-stratified estimates. Estimated relative efficiencies around or above 1.2 were observed for total growth, total mortality, and total removals, with different strata maps being more effective for each. Possible avenues for improvement of the approach include the following: (1) enlarging the study area and (2) using the Landsat images closest to the time of measurement for each plot. Multitemporal satellite-derived strata maps show promise for improving the precision of change parameter estimates

Refined forest land use classification with implications for United States national carbon accounting

The United States provides annual estimates of carbon sources and sinks as part of its National Green-house Gas Inventory (NGHGI). Within this effort, carbon stocks and fluxes are reported for six land use categories that are relevant to economic sectors and land use policy. The goal of this study is to develop methodologies that will allow the US to align with an internationally agreed upon forest land use definition which requires forest to be able to reach 5 m in height at maturity. Models to assess height potential are available for a majority of US forests except for woodland ecosystems. We develop a set of models to assess height potential in these systems. Our results suggest that ∼13.5 million ha of forests are unlikely to meet the international definition of forests due to environmental limitations to maximum attainable height. The incorporation of this height criteria in the NGHGI results in a carbon stock transfer of ∼848 Tg from the forest land use to woodland land use (a sub-category of grasslands) with minimal effect on sequestration rates. The development of a forest land use definition sensitive to climatic factors in this study enables a land use classification system that can be responsive to climate change effects on land uses themselves while being more consistent across a host of international and domestic carbon reporting efforts

Improving the precision of dynamic forest parameter estimates using Landsat

The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is well established.When reducing the variance of post-stratification estimates for forest change parameters such as forest growth, it is logical to use a change-related strata map. At the stand level, a time series of Landsat images is ideally suited for producing such a map. In this study, we generate strata maps based on trajectories of Landsat Thematic Mapper-based normalized difference vegetation index values, with a focus on post-disturbance recovery and recent measurements. These trajectories, from1985 to 2010, are converted to harmonic regression coefficient estimates and classified according to a hierarchical clustering algorithm from a training sample. The resulting strata maps are then used in conjunction with measured plots to estimate forest status and change parameters in an Alabama, USA study area. These estimates and the variance of the estimates are then used to calculate the estimated relative efficiencies of the post-stratified estimates. Estimated relative efficiencies around or above 1.2 were observed for total growth, total mortality, and total removals, with different strata maps being more effective for each. Possible avenues for improvement of the approach include the following: (1) enlarging the study area and (2) using the Landsat images closest to the time of measurement for each plot. Multitemporal satellite-derived strata maps show promise for improving the precision of change parameter estimates

Refined forest land use classification with implications for United States national carbon accounting

The United States provides annual estimates of carbon sources and sinks as part of its National Green-house Gas Inventory (NGHGI). Within this effort, carbon stocks and fluxes are reported for six land use categories that are relevant to economic sectors and land use policy. The goal of this study is to develop methodologies that will allow the US to align with an internationally agreed upon forest land use definition which requires forest to be able to reach 5 m in height at maturity. Models to assess height potential are available for a majority of US forests except for woodland ecosystems. We develop a set of models to assess height potential in these systems. Our results suggest that ∼13.5 million ha of forests are unlikely to meet the international definition of forests due to environmental limitations to maximum attainable height. The incorporation of this height criteria in the NGHGI results in a carbon stock transfer of ∼848 Tg from the forest land use to woodland land use (a sub-category of grasslands) with minimal effect on sequestration rates. The development of a forest land use definition sensitive to climatic factors in this study enables a land use classification system that can be responsive to climate change effects on land uses themselves while being more consistent across a host of international and domestic carbon reporting efforts

Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data

The majority of the aboveground biomass on the Earth’s land surface is stored in forests. Thus, forest biomass plays a critical role in the global carbon cycle. Yet accurate estimate of forest aboveground biomass (FAGB) remains elusive. This study proposed a new conceptual model to map FAGB using remotely sensed data from multiple sensors. The conceptual model, which provides guidance for selecting remotely sensed data, is based on the principle of estimating FAGB on the ground using allometry, which needs species, diameter at breast height (DBH), and tree height as inputs. Based on the conceptual model, we used multiseasonal Landsat images to provide information about species composition for the forests in the study area, LiDAR data for canopy height, and the image texture and image texture ratio at two spatial resolutions for tree crown size, which is related to DBH. Moreover, we added RaDAR data to provide canopy volume information to the model. All the data layers were fed to a Random Forest (RF) regression model. The study was carried out in eastern North Carolina. We used biomass from the USFS Forest Inventory and Analysis plots to train and test the model performance. The best model achieved an R2 of 0.625 with a root mean squared error (RMSE) of 18.8 Mg/ha (47.6%) with the “out-of-bag” samples at 30 × 30 m spatial resolution. The top five most important variables include the 95th, 85th, 75th, and 50th percentile heights of the LiDAR points and their standard deviations of 85th heights. Numerous features from multiseasonal Sentinel-1 C-Band SAR, multiseasonal Landsat 8 imagery along with image texture features from very high-resolution imagery were selected. But the importance of the height metrics dwarfed all other variables. More tests of the conceptual model in places with a broader range of biomass and more diverse species composition are needed to evaluate the importance of other input variables

Online dietary intake estimation : Reproducibility and validity of the Food4Me food frequency questionnaire against a 4-day weighed food record

©Rosalind Fallaize, Hannah Forster, Anna L Macready, Marianne C Walsh, John C Mathers, Lorraine Brennan, Eileen R Gibney, Michael J Gibney, Julie A Lovegrove. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 11.08.2014. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.Background: Advances in nutritional assessment are continuing to embrace developments in computer technology. The online Food4Me food frequency questionnaire (FFQ) was created as an electronic system for the collection of nutrient intake data. To ensure its accuracy in assessing both nutrient and food group intake, further validation against data obtained using a reliable, but independent, instrument and assessment of its reproducibility are required. Objective: The aim was to assess the reproducibility and validity of the Food4Me FFQ against a 4-day weighed food record (WFR). Methods: Reproducibility of the Food4Me FFQ was assessed using test-retest methodology by asking participants to complete the FFQ on 2 occasions 4 weeks apart. To assess the validity of the Food4Me FFQ against the 4-day WFR, half the participants were also asked to complete a 4-day WFR 1 week after the first administration of the Food4Me FFQ. Level of agreement between nutrient and food group intakes estimated by the repeated Food4Me FFQ and the Food4Me FFQ and 4-day WFR were evaluated using Bland-Altman methodology and classification into quartiles of daily intake. Crude unadjusted correlation coefficients were also calculated for nutrient and food group intakes. Results: In total, 100 people participated in the assessment of reproducibility (mean age 32, SD 12 years), and 49 of these (mean age 27, SD 8 years) also took part in the assessment of validity. Crude unadjusted correlations for repeated Food4Me FFQ ranged from .65 (vitamin D) to .90 (alcohol). The mean cross-classification into "exact agreement plus adjacent" was 92% for both nutrient and food group intakes, and Bland-Altman plots showed good agreement for energy-adjusted macronutrient intakes. Agreement between the Food4Me FFQ and 4-day WFR varied, with crude unadjusted correlations ranging from .23 (vitamin D) to .65 (protein, % total energy) for nutrient intakes and .11 (soups, sauces and miscellaneous foods) to .73 (yogurts) for food group intake. The mean cross-classification into "exact agreement plus adjacent" was 80% and 78% for nutrient and food group intake, respectively. There were no significant differences between energy intakes estimated using the Food4Me FFQ and 4-day WFR, and Bland-Altman plots showed good agreement for both energy and energy-controlled nutrient intakes. Conclusions: The results demonstrate that the online Food4Me FFQ is reproducible for assessing nutrient and food group intake and has moderate agreement with the 4-day WFR for assessing energy and energy-adjusted nutrient intakes. The Food4Me FFQ is a suitable online tool for assessing dietary intake in healthy adults.Peer reviewedFinal Published versio

A History of Universalism: Conceptions of the Internationality of Science from the Enlightenment to the Cold War

That science is fundamentally universal has been proclaimed innumerable times. But the precise geographical meaning of this universality has changed historically. This article examines conceptions of scientific internationalism from the Enlightenment to the Cold War, and their varying relations to cosmopolitanism, nationalism, socialism, and 'the West'. These views are confronted with recent tendencies to cast science as a uniquely European product

Identification of Surgeon Burnout via a Single-Item Measure

BackgroundBurnout is endemic in surgeons in the UK and linked with poor patient safety and quality of care, mental health problems, and workforce sustainability. Mechanisms are required to facilitate the efficient identification of burnout in this population. Multi-item measures of burnout may be unsuitable for this purpose owing to assessment burden, expertise required for analysis, and cost.AimsTo determine whether surgeons in the UK reporting burnout on the 22-item Maslach Burnout Inventory (MBI) can be reliably identified by a single-item measure of burnout.MethodsConsultant (n = 333) and trainee (n = 217) surgeons completed the MBI and a single-item measure of burnout. We applied tests of discriminatory power to assess whether a report of high burnout on the single-item measure correctly classified MBI cases and non-cases.ResultsThe single-item measure demonstrated high discriminatory power on the emotional exhaustion burnout domain: the area under the curve was excellent for consultants and trainees (0.86 and 0.80), indicating high sensitivity and specificity. On the depersonalisation domain, discrimination was acceptable for consultants (0.76) and poor for trainees (0.69). In contrast, discrimination was acceptable for trainees (0.71) and poor for consultants (0.62) on the personal accomplishment domain.ConclusionsA single-item measure of burnout is suitable for the efficient assessment of emotional exhaustion in consultant and trainee surgeons in the UK. Administered regularly, such a measure would facilitate the early identification of at-risk surgeons and swift intervention, as well as the monitoring of group-level temporal trends to inform resource allocation to coincide with peak periods

Burnout Among Surgeons in the UK During the COVID-19 Pandemic: A Cohort Study

BackgroundSurgeon burnout has implications for patient safety and workforce sustainability. The aim of this study was to establish the prevalence of burnout among surgeons in the UK during the COVID-19 pandemic.MethodsThis cross-sectional online survey was set in the UK National Health Service and involved 601 surgeons across the UK of all specialities and grades. Participants completed the Maslach Burnout Inventory and a bespoke questionnaire. Outcome measures included emotional exhaustion, depersonalisation and low personal accomplishment, as measured by the Maslach Burnout Inventory-Human Services Survey (MBI-HSS).ResultsA total of 142 surgeons reported having contracted COVID-19. Burnout prevalence was particularly high in the emotional exhaustion (57%) and depersonalisation (50%) domains, while lower on the low personal accomplishment domain (15%). Burnout prevalence was unrelated to COVID-19 status; however, the greater the perceived impact of COVID-19 on work, the higher the prevalence of emotional exhaustion and depersonalisation. Degree of worry about contracting COVID-19 oneself and degree of worry about family and friends contacting COVID-19 was positively associated with prevalence on all three burnout domains. Across all three domains, burnout prevalence was exceptionally high in the Core Trainee 1–2 and Specialty Trainee 1–2 grades.ConclusionsThese findings highlight potential undesirable implications for patient safety arising from surgeon burnout. Moreover, there is a need for ongoing monitoring in addition to an enhanced focus on mental health self-care in surgeon training and the provision of accessible and confidential support for practising surgeons