The Horizontal Distribution of Branch Biomass in European Beech: A Model Based on Measurements and TLS Based Proxies

Forest biomass is currently among the most important and most researched target variables in forest monitoring. The common approach of observing individual tree biomass in forest inventory is to assign the total tree biomass to the dimensionless point of the tree position. However, the tree biomass, in particular in the crown, is horizontally distributed above the crown projection area. This horizontal distribution of individual tree biomass (HBD) has not attracted much attention—but if quantified, it can improve biomass estimation and help to better represent the spatial distribution of forest fuel. In this study, we derive a first empirical model of the branch HBD for individual trees of European beech (Fagus sylvatica L.). We destructively measured 23 beech trees to derive an empirical model for the branch HBD. We then applied Terrestrial Laser Scanning (TLS) to a subset of 17 trees to test a simple point cloud metric predicting the branch HBD. We observed similarities between a branch HBD and commonly applied taper functions, which inspired our HBD model formulations. The models performed well in representing the HBD both for the measured biomass, and the TLS-based metric. Our models may be used as first approximations to the HBD of individual trees—while our methodological approach may extend to trees of different sizes and speciesThis research was funded by the Forest Research Institute of the German Federal State of Rheinland-Pfalz (FAWF) in Trippstadt. We also thank the Marie Sklodowska-Curie Action fellow QUAFORD and the Ramón y Cajal Tenure Track awarded to C.P.-CS

Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes designbased and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, modelbased, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters. Keywords: Design-based inference, Model-assisted estimation, Model-based inference, Hybrid inference, National forest inventory, Remote sensing, Samplin

Alcohol consumption, cardiac biomarkers, and risk of atrial fibrillation and adverse outcomes

Aims There is inconsistent evidence on the relation of alcohol intake with incident atrial fibrillation (AF), in particular at lower doses. We assessed the association between alcohol consumption, biomarkers, and incident AF across the spectrum of alcohol intake in European cohorts.Methods and results In a community-based pooled cohort, we followed 107 845 individuals for the association between alcohol consumption, including types of alcohol and drinking patterns, and incident AF. We collected information on classical cardiovascular risk factors and incident heart failure (HF) and measured the biomarkers N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin I. The median age of individuals was 47.8 years, 48.3% were men. The median alcohol consumption was 3 g/day. N = 5854 individuals developed AF (median follow-up time: 13.9 years). In a sex- and cohort-stratified Cox regression analysis alcohol consumption was non-linearly and positively associated with incident AF. The hazard ratio for one drink (12 g) per day was 1.16, 95% CI 1.11-1.22, P Conclusions In contrast to other cardiovascular diseases such as HF, even modest habitual alcohol intake of 1.2 drinks/day was associated with an increased risk of AF, which needs to be considered in AF prevention.</p

Unequal probability sampling in fixed area plots of stem volume with and without prior inclusion probabilities

The impact of guessing auxiliary population attributes, as opposed to relying on actual values from a prior survey, was quantified for three unequal probability sampling methods of tree stem volume (biomass). Reasonable prior guesses (no-list sampling) yielded, in five populations and 35 combinations of population size and sample size, results at par with sampling with known auxiliary predictors (list sampling). Realized sample sizes were slightly inflated in no-list sampling with probability proportional to predictions ( PPP ). Mean absolute differences from true totals and root mean square errors in no-list-sampling schemes were only slightly above those achieved with list sampling. Stratified sampling generally outperformed PPP and systematic sampling, yet the latter is recommended due to consistency between observed and expected mean square errors and overall robustness against a systematic bias in no-list settings.

Stepwise estimators for three-phase sampling of categorical variables

Three-phase sampling can be a very effective design for the estimation of regional and national forest cover type frequencies. Simultaneous estimation of frequencies and sampling variances require estimation of a large number of parameters; often so many that consistency and robustness of results becomes an issue. A new stepwise estimation model, in which bias in phase one and two is corrected sequentially instead of simultaneously, requires fewer parameters. Simulated three-phase sampling tested the new model with 144 settings of sample sizes, the number of classes and classification accuracy. Relative mean absolute deviations and root mean square errors were, in most cases, about 8% lower with the stepwise method than with a simultaneous approach. Differences were a function of design parameters. Average expected relative root mean square errors, derived from the assumption of a Dirichlet distribution of cover-type frequencies, tracked the empirical root mean square errors obtained from repeated sampling with - 10%. Resampling results indicate that the relative bias of the most frequent cover types was slightly inflated by the stepwise method. For the least common cover type, the simultaneous method produced the largest relative bias.

Model-calibrated k-nearest neighbor estimators

A generalized difference, a model-calibrated (MC), and a pseudo-empirical likelihood (PEMLE) kNN estimator of a population mean and its sampling variance was assessed with simulated simple random (SRS) and one-stage cluster sampling (CLU) from three artificial and one actual multivariate populations. The number of nearest neighbors (k) for imputing values of a target variable varied from one to eight. The design-based MC estimator had the lowest bias, but bias varied among populations and target variables. In terms of root mean squared errors (RMSEs), the estimators had similar performance, yet RMSEs of MC and PEMLE were less variable. Results were uneven across populations and target variables. The value of k had little effect on RMSE suggesting an advantage of choosing a low value that retains most of the attribute variance in a map. Nominal confidence intervals computed from MC estimators of variance achieved overall the best coverage rate. Rankings of the estimators in SRS and CLU designs were similar. We recommend MC for practical kNN applications in forest inventories for pixel-level predictions and derived estimates.201