Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), one of the most commercially important tree species in western North America and one of the most valuable timber trees worldwide, was introduced to Europe in 1827. It became a major species for afforestation in Western Europe after WWII, currently grows in 35 countries on over 0.83 million ha and is one of the most widespread non-native tree species across the continent. A lower sensitivity to drought makes Douglas-fir a potential alternative to the more drought-sensitive Norway spruce so its importance in Europe is expected to increase in the future. It is one of the fastest growing conifer species cultivated in Europe, with the largest reported dimensions of 2.3 m in diameter and 67.5 m in height. Pure stands have high productivity (up to 20 m³ ha⁻¹a⁻¹) and production (over 1000 m³ ha⁻¹). The species is generally regenerated by planting (initial stocking density from less than 1000 seedlings ha⁻¹ to more than 4000 ha⁻¹), using seedlings of European provenance derived from seed orchards or certified seed stands. As the range of end-uses of its wood is very wide, the rotation period of Douglas-fir is highly variable and ranges between 40 and 120 years. When the production of large-sized, knot-free timber is targeted, thinnings are always coupled with pruning up to 6 m. There is an increasing interest in growing Douglas-fir in mixtures and managing stands through close-to-nature silviculture, but the species’ intermediate shade tolerance means that it is best managed through group selection or shelterwood systems.

Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis. Therefore, the accuracy of gross primary production (GPP) estimates is expected to improve by removing these components. However, their influence in GPP estimations has not been quantitatively evaluated for deciduous forests. Several vegetation indices have been used recently to estimate the fraction of photosynthetically active radiation absorbed by photosynthetic components (

) for partitioning

{\mathit{APAR}}_{\mathit{green}}

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(photosynthetically active radiation absorbed by photosynthetic components). In this study, the enhanced vegetation index (EVI) estimated

{\mathit{FAPAR}}_{\mathit{green}}

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and to separate the photosynthetically active radiation absorbed by photosynthetic components (

{\mathit{APAR}}_{\mathit{green}}

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) from total APAR observations (

{\mathit{APAR}}_{\mathit{total}}

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) at two deciduous forest sites. The eddy covariance-light use efficiency (EC-LUE) algorithm was employed to evaluate the influence of non-photosynthetic components and to test the performance of

{\mathit{APAR}}_{\mathit{green}}

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in GPP estimation. The results show that the influence of non-photosynthetic components have a seasonal pattern at deciduous forest sites, large differences are observed with normalized root mean square error (RMSE^*) values of

{\mathit{APAR}}_{\mathit{green}}

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-based GPP and

{\mathit{APAR}}_{\mathit{total}}

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-based GPP between tower-based GPP during the early and end stages, while slight differences occurred during peak growth seasons. In addition, daily GPP estimation was significantly improved using the

{\mathit{APAR}}_{\mathit{green}}

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-based method, giving a higher coefficient of determination and lower normalized root mean square error against the GPP estimated by the

{\mathit{APAR}}_{\mathit{total}}

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-based method. The results demonstrate the significance of partitioning

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from

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for accurate GPP estimation in deciduous forests.

Forests are exposed to changing climatic conditions reflected by increasing drought and heat waves that increase the risk of wildfire ignition and spread. Climatic variables such as rain and wind as well as vegetation structure, land configuration and forest management practices are all factors that determine the burning potential of wildfires. The assessment of emissions released by vegetation combustion is essential for determining greenhouse gases and air pollutants. The estimation of wildfire-related emissions depends on factors such as the type and fraction of fuel (i.e., live biomass, ground litter, dead wood) consumed by the fire in a given area, termed the burning efficiency. Most approaches estimate live burning efficiency from optical remote sensing data. This study used a data-driven method to estimate live burning efficiency in a Mediterranean area. Burning severity estimations from Landsat imagery (dNBR), which relate to fuel consumption, and quantitative field data from three national forest inventory data were combined to establish the relationship between burning severity and live burning efficiency. Several proxies explored these relationships based on dNBR interval classes, as well as regression models. The correlation results between live burning efficiency and dNBR for conifers (R = 0.63) and broad-leaved vegetation (R = 0.95) indicated ways for improving emissions estimations. Median estimations by severity class (low, moderate-low, moderate-high, and high) are provided for conifers (0 .44 − 0.81) and broad-leaves (0.64 − 0.86), and regression models for the live fraction of the tree canopy susceptible to burning (< 2 cm, 2 − 7 cm, > 7 branches, and leaves). The live burning efficiency values by severity class were higher than previous studies.

Experiencing urban green and blue spaces (GBSs) can be a nature-based solution to improve mental well-being and cope with negative moods for people exposed to PM_2.5 pollution. In this study, a total of 1257 photos were collected to recognize their posted emotions of Sina Weibo users from 38 parks in 22 cities in Northeast China in 2021, when atmospheric PM_2.5 and landscape metrics were evaluated for GBSs of each park. Autumn and winter had heavy atmospheric PM_2.5 pollutions in resource-dependent cities of Liaoning. Net positive emotions (happy minus sad scores) decreased in larger green spaces. The perception of blue space countered the presentation of sadness only for a limited period over four seasons. High elevation decreased the level of happiness presented in winter. Overall, this study confirms that visiting large urban green spaces at low elevations can benefit the perception of positive sentiments for people exposed to PM_2.5 in autumn. For planning urban forests in Northeast China, more green spaces should be constructed in cities in southern Jilin province to alleviate air PM_2.5 pollution and gain better well-being of local people.

Selective logging is well-recognized as an effective practice in sustainable forest management. However, the ecological efficiency or resilience of the residual stand is often in doubt. Recovery time depends on operational variables, diversity, and forest structure. Selective logging is excellent but is open to changes. This may be resolved by mathematical programming and this study integrates the economic-ecological aspects in multi-objective function by applying two evolutionary algorithms. The function maximizes remaining stand diversity, merchantable logs, and the inverse of distance between trees for harvesting and log landings points. The Brazilian rainforest database (566 trees) was used to simulate our 216-ha model. The log landing design has a maximum volume limit of 500 m³. The nondominated sorting genetic algorithm was applied to solve the main optimization problem. In parallel, a sub-problem (p-facility allocation) was solved for landing allocation by a genetic algorithm. Pareto frontier analysis was applied to distinguish the gradients α-economic, β-ecological, and γ-equilibrium. As expected, the solutions have high diameter changes in the residual stand (average removal of approximately 16 m³ ha⁻¹). All solutions showed a grouping of trees selected for harvesting, although there was no formation of large clearings (percentage of canopy removal < 7%, with an average of 2.5 ind ha⁻¹). There were no differences in floristic composition by preferentially selecting species with greater frequency in the initial stand for harvesting. This implies a lower impact on the demographic rates of the remaining stand. The methodology should support projects of reduced impact logging by using spatial-diversity information to guide better practices in tropical forests.