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The prediction of growing stock volume is one of the commonest applications of remote sensing to support the sustainable management of forest ecosystems. In this study, we used data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 1st nationwide Airborne Laser Scanning (ALS) survey to develop predictive yield models for the three major commercial tree forest species (Eucalyptus globulus, Pinus pinaster and Pinus radiata) grown in north-western Spain. Integration of both types of data required prior harmonization because of differences in timing of data acquisition and difficulties in accurately geolocating the SNFI plots. The harmonised data from 477 E. globulus, 760 P. pinaster and 191 P. radiata plots were used to develop predictive models for total over bark volume, mean volume increment and total aboveground biomass by relating SNFI stand variables to metrics derived from the ALS data. The multiple linear regression methods and several machine learning techniques (k-nearest neighbour, random trees, random forest and the ensemble method) were compared. The study findings confirmed that multiple linear regression is outperformed by machine learning techniques. More specifically, the findings suggest that the random forest and the ensemble method slightly outperform the other techniques. The resulting stand level relative RMSEs for predicting total over bark volume, annual increase in total volume and total aboveground biomass ranged from 30.8–38.3%, 34.2–41.9% and 31.7–38.3% respectively. Although the predictions can be considered accurate, more precise geolocation of the SNFI plots and coincide temporarily with the ALS data would have enabled use of a much larger and robust field database to improve the overall accuracy of estimation.

\"Completely updated and expanded new edition of this widely cited book, Modelling Forest Growth and Yield, 2nd Edition synthesizes current scientific literature, provides insights in how models are constructed, gives suggestions for future developments, and outlines keys for successful implementation of models.The book describes current modeling approaches for predicting forest growth and yield and explores the components that comprise the various modeling approaches. It provides the reader with the tools for evaluating and calibrating growth and yield models and outlines the steps necessary for developing a forest growth and yield model\"--

Context Mediterranean managed dry-edge pine forests maintain biodiversity and supply key ecosystem services but are threatened by climate change and are highly vulnerable to desertification. Forest management through its effect on stand structure can play a key role on forest stability in response to increasing aridity, but the role of forest structure on drought resilience remains little explored. Objectives To investigate the role of tree growth and forest structure on forest resilience under increasing aridity and two contrasting policy-management regimes. We compared three management scenarios; (i) “business as usual”-based on the current harvesting regime and increasing aridity—and two scenarios that differ in the target forest function; (ii) a “conservation scenario”, oriented to preserve forest stock under increasing aridity; and (iii), a “productivity scenario” oriented to maintain forest yield under increasingly arid conditions. Methods The study site is part of a large-homogeneous pine-covered landscape covering sandy flatlands in Central Spain. The site is a dry-edge forest characterized by a lower productivity and tree density relative to most Iberian Pinus pinaster forests. We parameterized and tested an analytical size-structured forest dynamics model with last century tree growth and forest structure historical management records. Results Under current management (Scenario-i), increasing aridity resulted in a reduction of stock, productivity, and maximum mean tree size. Resilience boundaries differed among Scenario-ii and -Scenario-iii, revealing a strong control of the management regime on resilience via forest structure. We identified a trade-off between tree harvest size and harvesting rate, along which there were various possible resilient forest structures and management regimes. Resilience boundaries for a yield-oriented management (Scenario-iii) were much more restrictive than for a stock-oriented management (Scenario-ii), requiring a drastic decrease in both tree harvest size and thinning rates. In contrast, stock preservation was feasible under moderate thinning rates and a moderate reduction in tree harvest size. Conclusions Forest structure is a key component of forest resilience to drought. Adequate forest management can play a key role in reducing forest vulnerability while ensuring a long-term sustainable resource supply. Analytical tractable models of forest dynamics can help to identify key mechanisms underlying drought resilience and to design management options that preclude these social-ecological systems from crossing a tipping point over a degraded alternate state.

Key messageThe proposed height–diameter model applicable to many tree species in the multi-layered and mixed stands across Czech Republic shows a high accuracy in the height prediction. This model can be useful for estimating forest yield and biomass, and simulation of the vertical stand structures.We developed a generalized nonlinear mixed-effects height–diameter (H–D) model applicable to Norway spruce (Picea abies (L.) Karst.), European beech (Fagus sylvatica L.) and other conifer and broadleaved tree species using the modelling method that includes dummy variables accounting for species-specific height differences and random component accounting for within- and between-sample plot height differences and randomness in the data. We used two large datasets: the first set (model fitting dataset) originated from permanent research sample plots and second set (model-testing dataset) originated from the Czech national forest inventory (NFI) sample plots. The former dataset comprises 224 sample plots with 29,390 trees and the latter dataset comprises 14,903 sample plots with 382,540 trees, each representing wide variabilities of tree size, ecological zone, growth condition, stand structure and development stage, and management regime across the country. Among the four versatile growth functions evaluated as base functions with diameter at breast height (DBH) included as a single predictor, the Chapman-Richards function showed the most attractive fit statistics. This function was then extended through the integration of other predictor variables, which better describe the stand density (stand basal area), stand development and site quality (dominant height), competition (ratio of DBH to quadratic mean DBH), that would act as modifiers of the original parameters of the function. The mixed-effects H–D model described a large part of the variations in the H–D relationships (\\[R_{{{\\text{adj}}}^{2}\\] = 0.9182; RMSE = 2.7786) without substantial trends in the residuals. Testing this model against model-testing dataset confirmed the model’s high accuracy. The model may be used for estimating forest yield and biomass, and therefore will serve as an important tool for decision making in forestry.

An increasing number of studies provide evidence that mixed-species stands can overyield monocultures. But it is still hardly understood, how the overyielding at the stand level emerges from the tree, canopy, and size structure. Analyses of 42 triplets with 126 mixed and mono-specific plots in middle-aged, two-species stands of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies [L.] Karst.), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), and European beech (Fagus sylvativa L.) in Central Europe revealed that mixed-species compared with mono-specific stands can have (1) higher tree numbers, higher right skewness and kurtosis of the size distribution, higher inequality of tree sizes, and thereby higher stocking densities and sum of crown projection areas, (2) growth–size relationships with stronger size asymmetry of growth and higher inequality of size growth, and (3) higher stand productivity coupled with higher maximum stand density, canopy space filling, and size asymmetry. These differences depend on the species assemblage. They suggest a deeper entrance of light into the canopy as well as a higher light interception and light-use efficiency as main causes of the overyielding and overdensity. We discuss implications for research and silviculture and draw conclusions for designing and managing resource-efficient production systems.

The installation of research or permanent plots is a very common task in growth and forest yield research. At young ages, tree height is the most commonly measured variable, so the location of individuals is necessary when repeated measures are taken and if spatial analysis is required. Identifying the coordinates of individual trees and re-measuring the height of all trees is difficult and particularly costly (in time and money). The data used comes from three Pinus pinaster Ait. and three Pinus radiata D. Don plantations of 0.8 ha, with an age ranging between 2 and 5 years and mean heights between 1 and 5 m. Five individual tree detection (ITD) methods are evaluated, based on the Canopy Height Model (CHM), where the height of each tree is identified, and its crown is segmented. Three CHM resolutions are used for each method. All algorithms used for individual tree detection (ITD) tend to underestimate the number of trees. The best results are obtained with the R package, ForestTools and rLiDAR. The best CHM resolution for identifying trees was always 10 cm. We did not detect any differences in the relative error (RE) between Pinus pinaster and Pinus radiata. We found a pattern in the ITD depending on the height of the trees to be detected: the accuracy is lower when detecting trees less than 1 m high than when detecting larger trees (RE close to 12% versus 1% for taller trees). Regarding the estimation of tree height, we can conclude that the use of the CHM to estimate height tends to underestimate its value, while the use of the point cloud presents practically unbiased results. The stakeout of forestry research plots and the re-measurement of individual tree heights is an operation that can be performed by UAV-based LiDAR scanning sensors. The individual geolocation of each tree and the measurement of heights versus pole and/or hypsometer measurement is highly accurate and cost-effective, especially when tree height reaches 1–1.5 m.

Accurate forest information on tree species, stand age, tree density, and stand volume is required to ensure effective forest management practices. In Japan, forest information is consolidated in forest yield tables and stand density management diagrams (SDMDs) that are specifically designed for major forest plantation species. In this study, we analyzed whether the current full density curve in the SDMD of Hinoki (Chamaecyparis obtusa) plantation stands in Kyushu Island aligns with the characteristics of the existing stands. Data from 18 Hinoki forests were used to measure tree heights, diameters, and densities. Equations were developed to establish relationships between stand factors, and various curves were derived for average height, competition ratio, full density, yield ratio, average diameter, and natural mortality. The results showed that the current full density curve in the SDMD for Hinoki plantation stands did not completely align with the characteristics of the existing Hinoki plantation stands in Kyushu Island. Thus, the full density curve in the SDMD for the Hinoki stands in this region should be significantly adjusted. These results can enhance forest management in Japan and advance SDMD modeling and its application in forest planning and management.

This modelling approach helps to identify the areas where management efforts should be concentrated in order to mitigate near-future drought impact on national forest productivity. Around a quarter of the French temperate oak and beech forests are currently in zones of high vulnerability, where management could thus mitigate the influence of climate change on forest yield.

Plants and their associated soil microorganisms interact with each other and form complex relationships. The effects of slash-and-burn agriculture and logging on aboveground plants and belowground microorganisms have been extensively studied, but research on plant–microbial interkingdom ecological networks is lacking. In this study, using old growth forest as a control, we used metagenomic data (ITS and 16S rRNA gene amplified sequences) and plant data to obtain interdomain species association patterns for three different soil disturbance types (slash-and-burn, clear cutting and selective cutting) in a tropical rainforest based on interdomain ecological network (IDEN) analysis. Results showed that the soil bacterial–fungal and plant–microbe ecological networks had different topological properties among the three forest disturbance types compared to old growth forest. More nodes, links, higher modularity and negative proportion were found in the selective cutting stand, indicating higher stability with increasing antagonistic relationships and niche differentiation. However, the area of slash-and-burn forest yield opposite results. Network module analysis indicated that different keystone species were found in the four forest types, suggesting alternative stable states among them. Different plant species had more preferential associations with specific fungal taxa than bacterial taxa at the genus level and plant–microbe associations lagged behind bacterial–fungal associations. Overall, compared with old growth forests, the bacterial–fungal and plant–microbe ecological networks in the slash-and-burn and clear cutting stands were simpler, while the network in the selective cutting stand was more complex. Understanding the relationships between aboveground plants and belowground microorganisms under differing disturbance patterns in natural ecosystems will help in better understanding the surrounding ecosystem functions of ecological networks.

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabe’s (LM), Willmott’s index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan. Resumo Este estudo teve como objetivo desenvolver e avaliar modelos baseados em dados para previsão da produção florestal em diferentes cenários de mudanças climáticas na divisão florestal Gallies do distrito de Abbottabad, Paquistão. Os modelos Random Forest (RF) e Kernel Ridge Regression (KRR) foram desenvolvidos e avaliados usando dados de produção de duas espécies (pinheiro-azul e abeto-prateado) como uma variável objetiva e dados climáticos (temperatura, umidade, precipitação e velocidade do vento) como preditivos variáveis. A precisão da previsão de ambos os modelos foi avaliada por meio de erro quadrático médio (RMSE), erro absoluto médio (MAE), coeficiente de correlação (r), erro quadrático médio relativo (RRMSE), Legates-McCabe’s (LM), índice de Willmott (WI) e métricas Nash-Sutcliffe (NSE). No geral, o modelo RF superou o modelo KRR devido à sua maior precisão na previsão do rendimento florestal. O estudo recomenda fortemente que o modelo RF seja aplicado em outras regiões do país para previsão do crescimento e produtividade florestal, o que pode ajudar no manejo e planejamento futuro da produtividade florestal no Paquistão.