Summary: Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine‐scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine‐scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process‐based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

Forest health is an important variable that we need to monitor for forest management decision making. However, forest health is difficult to assess and monitor based merely on forest field surveys. In the present study, we first derived a comprehensive forest health indicator using 15 forest stand attributes extracted from forest inventory plots. Second, Pearson’s correlation analysis was performed to investigate the relationship between the forest health indicator and the spectral and textural measures extracted from SPOT-5 images. Third, all-subsets regression was performed to build the predictive model by including the statistically significant image-derived measures as independent variables. Finally, the developed model was evaluated using the coefficient of determination (R²) and the root mean square error (RMSE). Additionally, the produced model was further validated for its performance using the leave-one-out cross-validation approach. The results indicated that our produced model could provide reliable, fast and economic means to assess and monitor forest health. A thematic map of forest health was finally produced to support forest health management.

Converting secondary natural forests (SFs) to Chinese fir plantations (CFPs) represents one of the most important (8.9 million ha) land use changes in subtropical China. This study estimated both biomass and soil C stocks in a SF and a CFP that was converted from a SF, to quantify the effects of land use change on ecosystem C stock. After the forest conversion, biomass C in the CFP (73 Mg·ha⁻¹) was significantly lower than that of the SF (114 Mg·ha⁻¹). Soil organic C content and stock decreased with increasing soil depth, and the soil C stock in the 0–10 cm layer accounted for more than one third of the total soil C stock over 0–50 cm, emphasizing the importance of management of the top soil to reduce the soil C loss. Total ecosystem C stock of the SF and the CFP was 318 and 200 Mg·ha⁻¹, respectively, 64% of which was soil C for both stands (205 Mg·ha⁻¹ for the SF and 127 Mg·ha⁻¹ for the CFP). This indicates that land use change from the SF to the CFP significantly decreased ecosystem C stock and highlights the importance of managing soil C.

Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson’s correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination ( R a d j 2 ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models ( R a d j 2 > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.

We monitored the water quality and hydrological conditions of a horizontal subsurface constructed wetland (HSSF-CW) in Beijing, China, for two years. We simulated the area-based constant and the temperature coefficient with the first-order kinetic model. We examined the relationships between the nitrogen (N) removal rate, N load, seasonal variations in the N removal rate, and environmental factors—such as the area-based constant, temperature, and dissolved oxygen (DO). The effluent ammonia (NH₄⁺-N) and nitrate (NO₃⁻-N) concentrations were significantly lower than the influent concentrations (p < 0.01, n = 38). The NO₃⁻-N load was significantly correlated with the removal rate (R² = 0.96, p < 0.01), but the NH₄⁺-N load was not correlated with the removal rate (R² = 0.02, p > 0.01). The area-based constants of NO₃⁻-N and NH₄⁺-N at 20 °C were 27 ± 26 (mean ± SD) and 14 ± 10 m∙year⁻¹, respectively. The temperature coefficients for NO₃⁻-N and NH₄⁺-N were estimated at 1.004 and 0.960, respectively. The area-based constants for NO₃⁻-N and NH₄⁺-N were not correlated with temperature (p > 0.01). The NO₃⁻-N area-based constant was correlated with the corresponding load (R² = 0.96, p < 0.01). The NH₄⁺-N area rate was correlated with DO (R² = 0.69, p < 0.01), suggesting that the factors that influenced the N removal rate in this wetland met Liebig’s law of the minimum.

A nonlinear equation system for individual tree diameter growth and mortality of natural Mongolian oak forests was developed based on 13,360 observations from 195 permanent sample plots in Northeast China. Weighted regression was used in a distance-independent diameter growth equation for dealing with heterocedasticity. Since diameter growth and mortality models have common predictors including the diameter at breast height (DBH), stand basal area (BA), basal-area-in-larger trees (BAL), and site index (SI), parameters were estimated using nonlinear three-stage least squares (N3SLS) and seemingly unrelated regression (SUR) which accounts for correlations of errors across models. The system equation provided better projection than individual fitting of the equation based on maximum likelihood estimation. Compared with the separate tree growth model, the simultaneous equations using N3SLS and SUR produced more efficient parameter estimation and smaller bias. Furthermore, N3SLS had more accurate projection. Overall, the simultaneous model will facilitate the growth and yield projection for better management of Mongolian oak forests in the region.