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Optical remote sensing data have been widely used for estimating forest aboveground biomass (AGB). However, the use of optical images is often restricted by the saturation of spectral reflectance for forests that have multilayered and complex canopy structures and high AGB values and by the effect of spectral reflectance from underlayer shrub, grass, and bare soil for young stands. This usually leads to overestimations and underestimations for smaller and larger values, respectively, and makes it very challenging to improve the estimation accuracy of forest AGB. In this study, a novel methodology was proposed by incorporating stand age as a dummy variable into four models to improve the estimation accuracy of the Pinus densata forest AGB in Yunnan of Southwestern China. A total of eight models, including two parametric models (LM: linear regression model and LMC: LM with combined variables), two nonparametric models (RF: random forest and ANN: artificial neural network) without the age dummy variable, and four corresponding models with the age dummy variable (DLM, DLMC, DRF, and DANN), were compared to estimate AGB. Landsat 8 Operational Land Imager (OLI) images and 147 sample plots were acquired and utilized. The results showed that (1) compared with the two parametric models, the two nonparametric algorithms resulted in significantly greater estimation accuracies of Pinus densata forest AGB, and the increases of accuracy varied from 8% to 32% for 100 modeling plots and from 12% to 35% for 47 test plots based on root mean square error (RMSE); (2) compared with the models without the age dummy variable, the models with the age dummy variable greatly reduced the overestimations for the plots with AGB values smaller than 70 Mg/ha and the underestimations for the plots with AGB values larger than 180 Mg/ha and, thus, significantly improved the overall estimation accuracy by 14% to 42% for the modeling plots and by 32% to 44% for the test plots based on RMSE; and (3) the texture measures derived from the Landsat 8 OLI images contributed more to improving the estimation accuracy than the original spectral bands and other transformations. This implied that two nonparametric models, coupled with the use of the age dummy variable and texture measures, offered a great potential for improving the estimation accuracy of Pinus densata forest AGB.

This article explores an important property of the intrinsic estimator that has received no attention in literature: the age, period, and cohort estimates of the intrinsic estimator are not unique but vary with the parameterization and reference categories chosen for these variables. We give a formal proof of the non-uniqueness property for effect coding and dummy variable coding. Using data on female mortality in the United States over the years 1960-1999, we show that the variation in the results obtained for different parameterizations and reference categories is substantial and leads to contradictory conclusions. We conclude that the non-uniqueness property is a new argument for not routinely applying the intrinsic estimator.

Chinese fir is a crucial afforestation and timber species in southern China. Accurate estimation of its stand biomass is vital for forest resource assessment, ecological industry development, and ecosystem management. However, traditional biomass prediction methods often face limitations in terms of accuracy and efficiency, highlighting the need for more robust modeling approaches. This study utilized data from 154 forest stands in Guangdong Province to develop biomass regression models that incorporate random effects and dummy variables. The models were based on airborne LiDAR-derived metrics. Among 41 highly correlated LiDAR variables, only two-5% cumulative height percentile and leaf area index-were retained in the final model. The results revealed that the logistic mixed-effects model was the most effective for estimating leaf biomass, while the empirical mixed-effects model was better suited for other biomass components. Nonlinear models outperformed linear models, with the nonlinear mixed-effects model (incorporating stand age as a random effect) achieving the highest predictive accuracy. Furthermore, machine learning techniques further improved model performance (R² = 0.855 to 0.939). Validation with independent test samples confirmed the robustness and reliability of the nonlinear mixed-effects model. This study highlights the effectiveness of airborne LiDAR data in providing efficient and precise estimates of stand biomass. It also emphasizes the significant role of stand developmental stages in biomass modeling. The findings contribute to the development of a rigorous and scalable framework for large-scale artificial forest biomass estimation, which has important implications for forest resource monitoring, ecological industry development, and ecosystem management strategies.

Background While traumatic brain injury (TBI) is recognized as an important risk factor for cognitive health, most research has been conducted on military servicemembers and athletes, often postmortem—primarily (or exclusively) male populations with limited causes of TBI, access to healthcare and monitoring not typical of the general population. The generalizability of these studies, and the impact on TBI at the population level, remains unclear; and little research has been conducted on TBIs among the general population, including women, nor using non‐postmortem methods. Method Using TBI and cognition data from the Health and Retirement Study, this paper analyses longitudinal changes in cognition and dementia risk among participants who have experienced TBI after age 50, including differences by sex/gender, TBI cause, and severity. Cognition was measured using a 27‐point scale and included immediate and delayed recall, numeracy, and orientation questions. For TBI, participants were asked a series of questions about whether they had had a traumatic brain injury, the date of the injury, cause of that injury, and various sequelae. A dummy variable was coded into having had a TBI or not, and variables were created to indicate the earliest and latest TBI each participant had. Sex/gender was gather in the initial wave and two options (male and female) were given. Multilevel regression was conducted with cognition as the outcome variable and including time since TBI, source of TBI, and sequelae count, as well as sex/gender. Demographic variables, including age, race/ethnicity, education, wealth, veteran status, and histories of homelessness and incarceration were also included. Result In the fully adjusted model, years‐since‐TBI was statistically significantly associated with a decrease in cognition (b = ‐0.05, p = 0.008). Interpersonal violence as the cause of TBI was also negatively associated with cognition (b = ‐0.74, p = 0.004). Neither sex/gender nor TBI sequelae had a statistically significant association with cognition Conclusion TBI may have long‐lasting effects on cognitive health well after its occurrence, and these may be compounded when that injury is due to interpersonal violence. More research is needed to analyze the relationship between violence, TBI, and later‐life cognition.

Increasing the carbon sink function of forests is critical for achieving carbon (C) neutrality in the context of global climate change. Past studies have focused on the estimation of forest biomass or C storage, while those on forest C sink potential remain limited. In particular, there remain few systematic investigations to define the forest C sink, to characterize the synergistic influencing factors, and to develop related quantitative analysis methods. The development of scientific C enhancement strategies requires the construction of C density-age models integrating multiple stand factors. These models allow accurate quantification of the gap (∆C) between actual and maximum C sequestration capacity. This study used permanent sample plot data to develop and validate a novel multi-model assessment approach for quantifying the C sink potential of Larix olgensis plantations in Heilongjiang Province, China, and to translate the results into precise management tools. An Average-Level Model (ALM) was established to define baseline C sequestration. Three innovative potential assessment models were then proposed: (1) the Empirical Upper Boundary Model (PLM1); (2) the Dummy Variable Model (PLM2); and (3) the Quantile Regression Model (PLM3). These models define the maximum C sequestration capacity from distinct perspectives. PLM1 (R2 = 0.7910) characterized the theoretical upper limit of C sink potential (79.86 Mg·ha−1), making it suitable for macro-strategic goal setting, though it is somewhat dependent on extreme data points. PLM2 (R2 = 0.7943) achieved the best fit, and when combined with measurable stand conditions (site class index [SCI] > 16 m, stand density index [SDI] > 800 trees·ha−1), it provides clear guidance for management practices. Although PLM3 showed a lower goodness-of-fit (R2 = 0.1056), it provided reasonable parameter estimates and robust predictions, offering a reliable upper-bound reference for C sink project planning and risk control. At a stand age of 60 years (yr), the C sink enhancement potentials (“∆” C) corresponding to the three models were 15.73, 14.48, and 13.26 Mg·ha−1, representing increases of 24.53%, 22.58%, and 20.68%, respectively, over the average level (64.13 Mg·ha−1); the peak C sequestration rates of the models were 104.3%, 82.7%, and 60.5% higher than that of the ALM, with peak times occurring earlier at 9, 7, and 11 yr, respectively, underscoring the importance of the early management. The multi-model assessment approach developed here facilitates “precision carbon enhancement” by quantifying C sink potential across its theoretical, achievable, and robust upper-bound dimensions. This quantification provides both mechanistic insights into C sequestration processes and a critical link between theoretical understanding and practical forest management. This work holds significant value for advancing forestry C sinks in service of national strategies.

In order to clarify the combined impact of tree species composition, site quality, and stand age on the growth and harvest of mixed forests, the prediction models of average DBH and stand volume for mixed forests were established, respectively. The interval period and tree species composition coefficient (TSCC) were considered as independent variables. These models were then optimized by using the particle swarm optimization algorithm for reparameterization and evaluating their applicability. It was found that after introducing the site quality grade and TSCC, the average stand height prediction model showed a better fitting result. The fit accuracy of the average DBH prediction model and the stand volume prediction model were both improved with the help of the TSCC, mainly because the tree species composition affects the growth rate of the average stand height and average DBH and the maximum growth rate of the stand volume. The degree of the impact can be sorted as Cunninghamia lanceolata > Pinus massoniana > hard broad-leaved tree species (group). Overall, the established growth and harvest prediction models for mixed forests with the interval period and TSCC as independent variables have high fit accuracy and applicability.

Stem profile modeling is crucial in the forestry sector, particularly for commercially valuable species like teak (Tectona grandis Linn F.), whose value depends on its stem dimensions, heartwood proportion, and age. We proposed a nonlinear mixed-effect model to describe the evolution of the stem and heartwood profiles of clonal teak trees with ages between 4 and 12 years in the Brazilian Amazon. Tapering models were used to estimate the bark, bark-free, and heartwood diameters. Dummy variables were included in each tapering model to estimate each type of diameter and enable compatibility. We used mixed models with age as a random effect in order to improve the accuracy. The Demaerschalk model provided the most accurate and compatible estimates for all three types of stem diameter. Also, age as a random effect significantly improved the model’s accuracy by 7.2%. We observed a progressive increase in the heartwood proportion (14% to 34%) with advancing age, while the proportions of bark (23% to 20%) and sapwood (63% to 45%) showed inverse behavior. The growth rate of the heartwood differed from that of the bark volume, emphasizing the importance of considering the age of heartwood maximization when determining the cutting cycle of the species.

Accurate knowledge of individual tree ages is critical for forestry and ecological research. However, previous methods suffer from flaws such as tree damage, low efficiency, or ignoring autocorrelation among residuals. In this paper, an approach for estimating the ages of individual trees is proposed based on the diameter series of Cinnamomum camphora (Cinnamomum camphora (L.) Presl), Schima superba (Schima superba Gardn. et Champ.), and Liquidambar formosana (Liquidambar formosana Hance). Diameter series were obtained by stem analysis. Panel data contains more information, more variability, and more efficiency than pure time series data or cross-sectional data, which is why diameter series at stump and breast heights were chosen to form the panel data. After choosing a base growth equation, a constraint was added to the equation to improve stability. The difference method was used to reduce autocorrelation and the parameter classification method was used to improve model suitability. Finally, the diameter increment equation of parameter a-classification was developed. The mean errors of estimated ages based on the panel data at breast height for C. camphora, S. superba, and L. formosana were 0.47, 2.46, and −0.56 years and the root mean square errors were 2.04, 3.15 and 2.47 years, respectively. For C. camphora and L. formosana, the estimated accuracy based on the panel data was higher at breast height than at stump height. This approach to estimating individual tree ages is highly accurate and reliable, and provides a feasible way to obtain tree ages by field measurement.

For a given subset of regions, origin–destination flows are decomposed into (1) flows from the subset to its complement, (2) flows from the complement to the subset, (3) flows within the subset, and (4) flows within the complement. With the last flow as the base category, we define three directional dummy variables. We analyze Japan’s inter-municipal migrations by age-sex group using a standard gravity model with directional dummy variables whose regional subsets consist of major cities. Migrations between major cities are more frequent in age groups 30–59, especially for men, than migrations between other municipalities. This observation reflects the fact that many business-related transitions are taking place between major cities. Models with only origin and destination dummies cannot detect such an effect.

This study attempts to estimate the impact of demographic changes on house prices, according to several fixed effects regressions, based on the panel data of 31 administrative units in China covering 16 years (2002–2017). Analyzing the data by region and province, we found that the old-age dependency ratio coefficient was positive: An increase by 1% led to a rise in housing prices by 5.51% when other variables were controlled. Meanwhile, the child dependency ratio coefficient was negative: An increase by 1% led to a decrease in housing prices by 3% when other variables were controlled. A robustness test with dummy variables further verified our findings. On the other hand, regional economic development could also affect the influence of age factors on housing prices. In underdeveloped areas, changes in the age structure of a population have less impact on housing prices. We also found that mobility greatly impacts housing prices. An interaction analysis of age structures, spatial migrations, cultural habits, the hukou system, and other variables is rarely mentioned in the literature, which might otherwise help to supplement previously conducted studies.