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Recent developments in remote sensing (RS) technology have made several sources of auxiliary data available to support forest inventories. Thus, a pertinent question is how different sources of RS data should be combined with field data to make inventories cost-efficient. Hierarchical model-based estimation has been proposed as a promising way of combining: (i) wall-to-wall optical data that are only weakly correlated with forest structure; (ii) a discontinuous sample of active RS data that are more strongly correlated with structure; and (iii) a sparse sample of field data. Model predictions based on the strongly correlated RS data source are used for estimating a model linking the target quantity with weakly correlated wall-to-wall RS data. Basing the inference on the latter model, uncertainties due to both modeling steps must be accounted for to obtain reliable variance estimates of estimated population parameters, such as totals or means. Here, we generalize previously existing estimators for hierarchical model-based estimation to cases with non-homogeneous error variance and cases with correlated errors, for example due to clustered sample data. This is an important generalization to take into account data from practical surveys. We apply the new estimation framework to case studies that mimic the data that will be available from the Global Ecosystem Dynamics Investigation (GEDI) mission and compare the proposed estimation framework with alternative methods. Aboveground biomass was the variable of interest, Landsat data were available wall-to-wall, and sample RS data were obtained from an airborne LiDAR campaign that produced simulated GEDI waveforms. The results show that generalized hierarchical model-based estimation has potential to yield more precise estimates than approaches utilizing only one source of RS data, such as conventional model-based and hybrid inferential approaches. [ABSTRACT FROM AUTHOR]

The pipe model approach was compared with foliage biomass models by using the cross-sectional area at the tree crown base for predicting foliage biomass of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). We evaluated the impacts of site type, fertilization, and climate on the relationship between foliage biomass and cross-sectional area at the tree crown base, referred as to the pipe model ratio. Our hypotheses were that (i) the pipe model approach is a more precise and accurate method for foliage prediction than the traditional biomass models and (ii) the pipe model ratio for foliage does not explicitly depend on any single environmental driver. Data used here consisted of felled trees from Finnish and Swedish biomass studies. These data were analyzed by linear mixed models with different covariates, and the uncertainties of different modelling approaches were evaluated. The pipe model outperformed other models for Scots pine but not for Norway spruce. Results showed larger pipe model ratios for Scots pine in herb-rich forests compared with those of trees in subxeric heath forest. Results from fertilized trees indicated that the addition of nitrogen temporarily increased foliage biomass. [ABSTRACT FROM AUTHOR]

Background: Under the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in Northern Europe and is being adopted in many parts of the world. In the boreal forests, however, most of the carbon (85%) is stored in the soil organic (SO) matter. While this very important carbon pool is "invisible" to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data. Results: ALS-based models of dominant height, mean diameter, and biomass were fitted using the field observations and were used to predict mean tree biophysical properties across the entire study area (50 km2) which was in turn used to estimate the biomass carbon stocks and the litter production that feeds into the soil. For the soil carbon pool estimation, we used the Yasso15 model. The methodology was based on (1) approximating the initial soil carbon stocks using simulations; (2) predicting the annual litter input based on the predicted growing stocks in each cell; (3) predicting the soil carbon dynamics of the annual litter using the Yasso15 soil carbon model. The estimated total carbon change (standard errors in parenthesis) for the entire area was 0.741 (0.14) Mg ha−1 yr−1. The biomass carbon change was 0.405 (0.13) Mg ha−1 yr−1, the litter carbon change (e.g., deadwood and leaves) was 0.346 (0.027) Mg ha−1 yr−1, and the change in SO carbon was − 0.01 (0.003) Mg ha−1 yr−1. Conclusions: Our results show that ALS data can be used indirectly through a chain of models to estimate soil carbon changes in addition to changes in biomass at the primary level of forest management, namely the forest stands. Having control of the errors contributed by each model, the stand-level uncertainty can be estimated under a model-based inferential approach. [ABSTRACT FROM AUTHOR]

A correction is presented to the article Scots pine and Norway spruce foliage biomass in Finland and Sweden — testing traditional models vs. the pipe model theory, which appeared in the February,19, 2020 issue.

A new sampling strategy for forest inventories is presented. The most important difference from the traditional sampling strategies is that auxiliary variables from remote sensing are incorporated into the sampling design. The sample is selected to match population distributions of the auxiliary variables as well as possible. This is achieved by a double sampling approach, where auxiliary variables are extracted for a large first-phase sample. The second selection is done by the local pivotal method and produces an even thinning of the first-phase sample. Thus, we make sure that the selected second-phase sample becomes much more representative of the population than what is possible by the use of traditional designs. The potential of implementing the new strategy for the temporary clusters within the Swedish national forest inventory is evaluated with five auxiliary variables: the geographical coordinates, elevation, predicted tree height, and predicted basal area. The increased representativity that we achieve with the new strategy induces up to 95% reduction of the variance of the sample means of the remote sensing auxiliary variables compared with traditional designs. For this reason, we conclude that the new strategy that will be implemented in the forthcoming Swedish national forest inventory has a great potential to achieve large improvements in estimation of many important forest attributes. [ABSTRACT FROM AUTHOR]

Background: Use of d,l-sotalol for rhythm control in patients with atrial fibrillation (AF) has raised safety concerns. Previous randomized studies are few and not designed for mortality outcome., Objective: The purpose of this study was to compare the incidences of mortality and ventricular arrhythmias in AF patients treated with d,l-sotalol for rhythm control vs matched control patients treated with cardioselective beta-blockers., Methods: This population-based cohort study included AF patients from the Swedish National Patient Registry (2006-2017) who underwent rhythm control after a second cardioversion. Incidence rates (IRs) and adjusted hazard ratios (aHRs) for mortality and a composite endpoint of cardiac arrest/death and ventricular arrhythmias were calculated for the overall cohort and a 1:1 propensity score matched cohort of d,l-sotalol vs beta-blocker treatment., Results: Among patient treated with d,l-sotalol (n = 4987) and beta-blocker (n = 27,078) (mean follow-up 458 days), all-cause mortality was lower in patients treated with d,l-sotalol: IR 1.21; 95% confidence interval 0.95-1.52 vs 2.42 (2.26-2.60) deaths per 100 patient-years; aHR 0.66 (0.52-0.83). The difference in mortality persisted in the propensity score matched comparison (n = 4953 in each group): aHR 0.63 (0.48-0.86). No differences were observed in the composite outcome: IR in propensity cohorts 2.13 (1.78-2.52) vs 2.07 (1.73-2.53) events per 100 years; aHR 1.01 (0.78-1.29)., Conclusion: There was no excess mortality with d,l-sotalol compared with cardioselective beta-blockers in patients undergoing rhythm control treatment for AF after a second cardioversion. Our results indicate that the risk associated with d,l-sotalol treatment for AF can be mitigated by careful patient selection and strict adherence to follow-up protocols., (Copyright © 2023 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.)