No abstract

The U.S. has been providing national-scale estimates of forest carbon (C) stocks and stock change to meet United Nations Framework Convention on Climate Change (UNFCCC) reporting requirements for years. Although these currently are provided as national estimates by pool and year to meet greenhouse gas monitoring requirements, there is growing need to disaggregate these estimates to finer scales to enable strategic forest management and monitoring activities focused on various ecosystem services such as C storage enhancement. Through application of a nearest-neighbor imputation approach, spatially extant estimates of forest C density were developed for the conterminous U.S. using the U.S.’s annual forest inventory. Results suggest that an existing forest inventory plot imputation approach can be readily modified to provide raster maps of C density across a range of pools (e.g., live tree to soil organic carbon) and spatial scales (e.g., sub-county to biome). Comparisons among imputed maps indicate strong regional differences across C pools. The C density of pools closely related to detrital input (e.g., dead wood) is often highest in forests suffering from recent mortality events such as those in the northern Rocky Mountains (e.g., beetle infestations). In contrast, live tree carbon density is often highest on the highest quality forest sites such as those found in the Pacific Northwest. Validation results suggest strong agreement between the estimates produced from the forest inventory plots and those from the imputed maps, particularly when the C pool is closely associated with the imputation model (e.g., aboveground live biomass and live tree basal area), with weaker agreement for detrital pools (e.g., standing dead trees). Forest inventory imputed plot maps provide an efficient and flexible approach to monitoring diverse C pools at national (e.g., UNFCCC) and regional scales (e.g., Reducing Emissions from Deforestation and Forest Degradation projects) while allowing timely incorporation of empirical data (e.g., annual forest inventory).

Tree canopy cover is a fundamental component of the landscape, and the amount of cover influences fire behavior, air pollution mitigation, and carbon storage. As such, efforts to empirically model percent tree canopy cover across the United States are a critical area of research. The 2001 national-scale canopy cover modeling and mapping effort was completed in 2006, and here we present results from a pilot study for a 2011 product. We examined the influence of two different modeling techniques (random forests and beta regression), two different Landsat imagery normalization processes, and eight different sampling intensities across five different pilot areas. We found that random forest out-performed beta regression techniques and that there was little difference between models developed based on the two different normalization techniques. Based on these results we present a prototype study design which will test canopy cover modeling approaches across a broader spatial scale.

BackgroundAdvancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant’s risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App).Methods and FindingsAnthropometry and childhood obesity risk data were obtained for 1868 UK-born White or South Asian infants in the Born in Bradford cohort. Logistic regression was used to develop prediction equations (at 6±1.5, 9±1.5 and 12±1.5 months) for risk of childhood obesity (BMI at 2 years >91st centile and weight gain from 0–2 years >1 centile band) incorporating sex, birth weight, and weight gain as predictors. The discrimination accuracy of the equations was assessed by the area under the curve (AUC); internal validity by comparing area under the curve to those obtained in bootstrapped samples; and external validity by applying the equations to an external sample. An App was built to incorporate six final equations (two at each age, one of which included maternal BMI). The equations had good discrimination (AUCs 86–91%), with the addition of maternal BMI marginally improving prediction. The AUCs in the bootstrapped and external validation samples were similar to those obtained in the development sample. The App is user-friendly, requires a minimum amount of information, and provides a risk assessment of low, medium, or high accompanied by advice and website links to government recommendations.ConclusionsPrediction equations for risk of childhood obesity have been developed and incorporated into a novel App, thereby providing proof of concept that childhood obesity prediction research can be integrated with advancements in technology.

1As part of the development of the 2011 National Land Cover Database (NLCD) tree canopy 2 cover layer, a pilot project was launched to test the use of high resolution photography coupled 3 with extensive ancillary data to map the distribution of tree canopy cover over four study regions 4 in the conterminous US. Two stochastic modeling techniques, Random Forests (RF) and 5Stochastic Gradient Boosting (SGB), are compared. The objectives of this study were first to 6 explore the sensitivity of RF and SGB to choices in tuning parameters. Second, to compare the 7 performance of the two final models by assessing the importance of, and interaction between, 8 predictor variables, the global accuracy metrics derived from an independent test set, as well as 9 the visual quality of the resultant maps of tree canopy cover. The predictive accuracy of RF and 10 SGB was remarkably similar on all four of our pilot regions. In all four study regions, the 11 independent test set MSE was identical to 3 decimal places, with the largest difference in Kansas 12where RF gave an MSE of 0.0113 and SGB gave an MSE of 0.0117. With correlated predictor 13 variables, stochastic gradient boosting had a tendency to concentrate variable importance in 14 fewer variables, while Random Forest tended to spread importance out amongst more variables. 15 RF is simpler to implement than SGB, as RF both has fewer parameters needing tuning, and also 16 was less sensitive to these parameters. As stochastic techniques, both RF and SGB introduce a 17 new component of uncertainty: repeated model runs will potentially result in different final 18 predictions. We demonstrate how RF allows the production of a spatially explicit map of this 19 stochastic uncertainty of the final model. 20