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Modelling tree height-diameter relationships in complex tropical rain forest ecosystems remains a challenge because of characteristics of multi-species, multi-layers, and indeterminate age composition. Effective modelling of such complex systems required innovative techniques to improve prediction of tree heights for use for aboveground biomass estimations. Therefore, in this study, deep learning algorithm (DLA) models based on artificial intelligence were trained for predicting tree heights in a tropical rain forest of Nigeria. The data consisted of 1736 individual trees representing 116 species, and measured from 52 0.25 ha sample plots. A K-means clustering was used to classify the species into three groups based on height-diameter ratios. The DLA models were trained for each species-group in which diameter at beast height, quadratic mean diameter and number of trees per ha were used as input variables. Predictions by the DLA models were compared with those developed by nonlinear least squares (NLS) and nonlinear mixed-effects (NLME) using different evaluation statistics and equivalence test. In addition, the predicted heights by the models were used to estimate aboveground biomass. The results showed that the DLA models with 100 neurons in 6 hidden layers, 100 neurons in 9 hidden layers and 100 neurons in 7 hidden layers for groups 1, 2, and 3, respectively, outperformed the NLS and NLME models. The root mean square error for the DLA models ranged from 1.939 to 3.887 m. The results also showed that using height predicted by the DLA models for aboveground biomass estimation brought about more than 30% reduction in error relative to NLS and NLME. Consequently, minimal errors were created in aboveground biomass estimation compared to those of the classical methods.

Selective harvesting is a common silvicultural practice to ensure sustainable use of uneven-aged forests, but our understanding of how multiple forest attributes interact to regulate aboveground biomass (AGB) dynamics following selective harvesting remains limited. Using repeated measurements of 13,996 stem-mapped trees distributed in a broad-leaved Korean pine forest (BKPF), we evaluated the recovery trajectory of AGB over a 7-year period following selective harvesting of different intensity: low (15.8% of basal area removed), medium (29.4%) and high (51.4%). In particular, we examined the effects of (1) tree species diversity, (2) stand structure and (3) the post-harvest density on the rate of AGB accumulation (the net annual increment). Despite the enhanced growth of residual trees by all harvesting treatments, rates of AGB accumulation declined with increasing harvesting intensity due to high mortality losses: mean mortality rates of high stands were more than double those of the unharvested stands. Small trees (DBH

Articolul prezintă rezultatele unui exercițiu de inventariere pe scară mică, realizat într-un arboret (u.a. 34B, U.P. I Săliște, Ocolul silvic Obștea Săliște) care include duglas verde (DF) și molid de Sitka (MOS, specie exotică utilizată foarte rar în România), instalat prin plantare (1,5 x 1,5 m) în anul 1962 în completarea regenerării naturale a fagului. La 60 de ani, duglasul se comportă mai bine decât molidul de Sitka în sens biometric, iar cele mai relevante caracteristici ale celor două specii sunt după cum urmează: - diametrul de bază maxim: 67,1 cm (DU) și 37,7 cm (MOS); - diametrul mediu al suprafeței de bază dg: 41,81 cm (DU) și 27,43 cm (MOS); - înălțimea maximă: 39,5 m (DU) și 30,5 m (MOS); - înălțimea corespunzătoare lui dg: 32,00 m (DU) și 24,95 m (MOS); - indicele de zveltețe mediu: 84 (DU) și 90 (MOS); - diametrul mediu al coroanei: 462 cm (DU) și 361 cm (MOS); - ambele specii prezintă un elagaj natural imperfect, ca și în alte culturi instalate în întreaga Europă. Duglasul verde s-a adaptat la condițiile locale ca și în alte părți din România, unde se întâlnește pe cca 12.700 ha, și prezintă un potențial ridicat de producție de lemn. Molidul de Sitka a fost capabil să supraviețuiască în condiții locale dificile (spre exemplu, altitudine mare, climat temperat-continental, foarte diferit de cel oceanic din arealul natural și de pe coastele nordice și vestice ale Europei), fără a produce la fel de mult lemn ca în alte țări europene gen Marea Britanie și Irlanda.

In order to use the airborne LiDAR intensity in conjunction with the height-derived information for forest modeling and classification purposes, radiometric correction is deemed to be a critical pre-processing requirement. In this study, we implemented a LiDAR scan line correction (LSLC) and an overlap-driven intensity correction (OIC) to remove the stripe artifacts that appeared within the individual flight lines and overlapping regions of adjacent flight lines of a multispectral LiDAR dataset. We tested the effectiveness of these corrections in various land/forest cover types in a temperate mixed mature forest in Ontario, Canada. Subsequently, we predicted three plot level forest attributes, i.e., basal area (BA), quadratic mean diameter (QMD), and trees per hectare (TPH), using different combinations of height and intensity metrics derived from the multispectral LiDAR data to determine if LiDAR intensity data (corrected and uncorrected) improved predictions over models that utilize LiDAR height-derived information only. The results show that LSLC can reduce the intensity banding effect by 0.19–23.06% in channel 1 (1550 nm) and 4.79–66.87% in channel 2 (1064 nm) at the close-to-nadir region. The combined effect of LSLC and OIC is notable particularly at the swath edges. After implementing both methods, the intensity homogeneity is improved by 5.51–12% in channel 1, 6.37–42.93% in channel 2, and 6.48–33.77% in channel 3 (532 nm). Our results further demonstrate that BA and QMD predictions in our study area gained little from additional LiDAR intensity metrics. Intensity metrics from multiple LiDAR channels and intensity normalized difference vegetation index (NDVI) metrics did improve TPH predictions up to 7.2% in RMSE and 1.8% in Bias. However, our lowest TPH prediction errors (%RMSE) were still approximately 10% larger than for BA and QMD. We observed only minimal differences in plot level BA, QMD, and TPH predictions between models using original and corrected intensity. We attribute this to: (i) the lower effectiveness of radiometric correction in forest versus grassland, bare soil and road land cover types, and (ii) the effect of spatial resolution on intensity noise.

El objetivo de este trabajo fue seleccionar modelos hipsométricos locales y generales para estimar alturas totales (h; m) de Eucalyptus globulus Labill. en macizos del sudeste de Buenos Aires, Argentina. Del conjunto inicial de 53 modelos candidatos, fueron seleccionados 14 para su validación. Se emplearon 236 árboles apeados para el ajuste y la validación cruzada, efectuando posteriormente una validación independiente con 743 árboles, cuyos datos de altura y diámetro a la altura del pecho (d) se obtuvieron en un inventario no destructivo. Las ecuaciones locales tuvieron en general la menor calidad de ajuste y precisión. Los mejores resultados con ecuaciones generales se lograron incorporando variables de rodal como el diámetro medio cuadrático (dg; cm), el número de individuos vivos por hectárea (N) y la altura dominante (H0; m). La inclusión de otras variables como la edad, el índice de sitio, el diámetro máximo y el área basal no mejoraron los indicadores de ajuste y validación. Se seleccionaron los tres modelos que obtuvieron los mejores estadísticos de ajuste y validación (Coeficiente de determinación ajustado R2 aj.>0,69; Eficiencia del modelo>0,76; Raíz del error medio cuadrático, The objective of this work was to select local and general hypsometric models to estimate total heights (h; m) of Eucalyptus globulus Labill. trees growing in stands of the southeast of Buenos Aires Province, Argentina. Out of 53 candidate models evaluated, 14 were selected for validation. Model fitting and cross-validation were performed from a sample of 236 felled trees, subsequently carrying out an independent validation with height and diameter at breast height (d) data from another sample of 743 individuals, obtained in a non-destructive inventory. Fitting statistics and precision were in general the poorest for local equations. For generalized models, the best results were achieved by incorporating stand variables such as quadratic mean diameter (dg; cm), number of living individuals per hectare (N) and dominant height (H0; m). The inclusion of other variables such as age, site index, maximum diameter, and basal area did not improve the fit and validation indicators. Three models with the best fit and validation statistics were finally selected (R2 aj.>0,69; Model efficiency>0,76; Root mean square error, Facultad de Ciencias Agrarias y Forestales

The relative growth rate (RGRnv) is the standardized measurement of forest growth, whereby excluding the size differences between individuals allows their performance to be compared equally. The RGRnv model was developed using the National Forest Inventory (NFI) data on the Daxing’an Mountains, in Northeast China, which contain Dahurian larch (Larix gmelinii Rupr.), white birch (Betula platyphylla Suk.), and mixed coniferous–broadleaf forests. Four predictor variables—i.e., quadratic mean diameter (Dq), stand basal area (G), average tree height (Ha), and altitude (A)—and four different methods—i.e., the nonlinear mixed-effects models (NLME), three nonlinear quantile regression (NQR3), five nonlinear quantile regression (NQR5), and nine nonlinear quantile regression (NQR9) models—were used in this study. All the models were validated using the leave-one-out method. The results showed that (1) the mixed coniferous–broadleaf forest presented the highest RGRnv, (2) the RGRnv was negatively correlated with the four predictors, and the heteroscedasticity reduced significantly after the weighting function was integrated into the models, and (3) the quantile regression models performed better than NLME, and NQR9 outperformed both NQR3 and NQR5. To make more accurate predictions, parameters of the adjusted mixed-effects and quantile regression models should be recalculated and localized using sampled RGRnv in each region and then applied to predict all the other RGRnv of plots. MAPE% indicates the mean absolute percentage error. The values were stable when the sample numbers were greater than or equal to six across the three forest types, which showed relatively accurate and lowest-cost prediction results.

The leaf area index (LAI) is a key characteristic of forest stand aboveground net productivity (ANP), and many methods have been developed to estimate the LAI. However, every method has flaws, e.g., methods may be destructive, require means or time and/or show intrinsic bias and estimation errors.A relationship using basal area (G) and stand age to estimate LAI was proposed by Sonohat et al. (2004). We used literature data in addition to data form measurements campaign made in the northern half of France to build a data set with large ranges of pedoclimatic conditions, stand age and measured LAI. We validated the Sonohat et al. (2004) relationship and attempted to improve or modify it using other stand/dendrometric characteristics that could be predictors of the LAI.The result is a series of three models using the G, age and/or quadratic mean diameter (Dg), and the models were able to estimate the LAI of an oak only even-aged forest stand with good confidence (root mean square error, RMSE < 0.75) While G is the main predictor here, age and Dg could be used conjointly or exclusively given the available data, with variable precision in the estimations.Although these models could not, by construction, relate to the interannual variability of the LAI, they may provide the theoretical LAI of an untouched forest (no meteorological, biotic or anthropogenic perturbation) in recent years. additionally, the use of this model may be more interesting than an LAI measurement campaign, depending on the means to be invested in such a campaign.

The techniques for conducting forest inventories have been established over centuries of land management and conservation. In recent decades, however, compelling new tools and methodologies in remote sensing, computer vision, and data science have offered innovative pathways for enhancing the effectiveness and comprehension of these sampling designs. Now with the aid of Unmanned Aerial Systems (UAS) and advanced image processing techniques, we have never been closer to mapping forests at field-based inventory scales. Our research, conducted in New Hampshire on complex mixed-species forests, used natural color UAS imagery for estimating individual tree diameters (diameter at breast height (dbh)) as well as stand level estimates of Basal Area per Hectare (BA/ha), Quadratic Mean Diameter (QMD), Trees per Hectare (TPH), and a Stand Density Index (SDI) using digital photogrammetry. To strengthen our understanding of these forests, we also assessed the proficiency of the UAS to map the presence of large trees (i.e., >40 cm in diameter). We assessed the proficiency of UAS digital photogrammetry for identifying large trees in two ways: (1) using the UAS estimated dbh and the 40 cm size threshold and (2) using a random forest supervised classification and a combination of spectral, textural, and geometric features. Our UAS-based estimates of tree diameter reported an average error of 19.7% to 33.7%. At the stand level, BA/ha and QMD were overestimated by 42.18% and 62.09%, respectively, while TPH and SDI were underestimated by 45.58% and 3.34%. When considering only stands larger than 9 ha however, the overestimation of BA/ha at the stand level dropped to 14.629%. The overall classification of large trees, using the random forest supervised classification achieved an overall accuracy of 85%. The efficiency and effectiveness of these methods offer local land managers the opportunity to better understand their forested ecosystems. Future research into individual tree crown detection and delineation, especially for co-dominant or suppressed trees, will further support these efforts.

Understanding the relationship between size and density is an important issue for stand density management in terms of self-thinning in even-aged stands. This study was conducted to determine the maximum stand density and evaluate the size–density relationship based on the Competition Density Rule (C-D rule) for Korean red pine, Korean white pine, and Japanese larch in Korea. The study area covered permanently monitored plots of even-aged plantations established for investigating and modeling long-term growth changes through silviculture. Data were applied using the first inventory within the permanent plots before implementing artificial thinning according to research design. To predict the maximum stem number by species, the C-D rule was applied, and the three-parameter method was shown to be better than the four-parameter method. According to the results based on the C-D rule with three parameters, stand density index by species was 1041 trees (Korean red pine, Pinus densiflora), 1089 trees (Korean white pine, P. koraiensis), and 1005 trees (Japanese larch, Larix kaempferi) in 25 cm of quadratic mean diameter. In the relationship between the maximum stem number and quadratic mean diameter with log–log scale, the intercept for Korean red pine, Korean white pine, and Japanese larch was 10.317, 10.901, and 11.331, and the slope by species was −1.047, −1.214, and −1.373, respectively. To analyze the size–density relationship, the sample plots were classified based on relative spacing and relative density. In regression analysis with log–log scale using the censored sample plot data, maximum size–density relationship was explained the most with quadratic mean diameter rather than mean stem volume and dominant height.

Natural regeneration has been proposed as a cost-effective forest restoration approach for both small and large-scale initiatives. However, attributes for assessing the success of forest restoration through natural regeneration may vary among them in spatial patterns depending on the scale of analysis and on environmental gradients. Here we analysed the spatial patterns of recovery completeness (i.e. how similar attributes in restored forests are to the same attributes in reference forests) in response to environmental factors in a Mediterranean forest landscape of Central Chile. We evaluated (1) forest recovery completeness using basal area (BA), quadratic mean diameter (QMD), adult species density (ASD), adult species richness (ASR), and seedling species richness (SSR); (2) the spatial congruence of recovery completeness estimated by each of these indicators; and (3) the environmental factors potentially shaping these spatial patterns. We used field measurements and geospatial information sources to quantify and predict indicator responses by fitting boosted regression tree models. To assess the spatial congruence of predictions we overlaid high-level recovery completeness values for all indicators. Overall recovery completeness in the study area was 72.7%, suggesting positive prospects for attaining fully restored forests. Recovery completeness had a resulted higher for diversity (92.3%-99.6%) than structural forest attributes (33.5%-76.9%); however, spatial congruence among recovery indicators was low due to the uneven spatial responses of each indicator. The maximum potential spatial congruence was, Universidad de La Frontera, Fundación de Apoyo a la Investigación del Estado de Sao Paulo

Whole-tree harvesting (WTH) is increasingly used to extract forest biomass for energy and commercial wood products. Slash burning, which is used for fuels reduction and site preparation, also reduces aboveground biomass. Yet effects of incremental biomass reduction, from either WTH or slash burning, on long-term forest productivity and composition are poorly understood. This research uses data from a 50-year-old study in northern mixedwood (Picea – Abies – hardwood) stands on the Penobscot Experimental Forest in Maine, U.S.A., to address these concerns. Clearcutting was conducted in 1964–65 with WTH, stem-only harvesting (SOH), and SOH with post-harvest prescribed burning of logging residues (SOHB). Growing stock, composition, and soil properties (O horizon thickness and soil drainage) were measured 50 years after treatment. Hardwood composition (percent of total basal area) increased from pre-treatment levels in all treatments but was greater in SOHB than SOH and WTH. Eastern white pine (Pinus strobus), though a minor species, was significantly more abundant in WTH than SOH or SOHB. Results indicated no other significant differences in species composition, or in stand structure or productivity (total basal area, stem density, dominant height, quadratic mean diameter, and total above-ground carbon stock) among treatments. Independent of treatment, we observed relationships between soil properties and stem density and quadratic mean diameter (qmd), such that lower stem density and greater qmd were observed on sites with greater O horizon thickness. These findings suggest that relative to SOH, WTH and SOHB do not degrade northern mixedwood stand productivity as expressed by stand structure and stocking 50 years after a single treatment, even on a site with low to moderate production potential. Nevertheless, species shifts associated with clearcutting (i.e., shade-tolerant conifer to intolerant hardwood composition) and prescribed burning in this forest type should be considered in light of the potential application of either for intensive silviculture treatments.

Errors in individual tree detection and delineation affect diameter distribution predictions based on crown attributes extracted from the detected trees. We develop a methodology for circumventing these problems. The method is based on matching cumulative distribution functions of field measured tree diameter distributions and crown radii distributions extracted from airborne laser scanning data through individual tree detection presented by Vauhkonen and Mehtatalo (2015). In this study, empirical distribution functions and a monotonic, nonlinear model curve are introduced. Tree crown radius distribution produced by individual tree detection is corrected by a method taking into account that all trees cannot be detected. The evaluation is based on the ability of the developed model sequence to predict quadratic mean diameter and total basal area. The studied data consists of 36 field plots in a typical boreal managed forest area in eastern Finland. The suggested enhancements to the model sequence produce improved results in most of the test cases. Most notably, in leaveone-out cross-validation experiments the modified models improve RMSE of basal area 13% in the full data and RMSE of quadratic mean diameter and basal area 69% and 11%, respectively, in pure pine plots. Better modeling of the crown radius distribution and improved matching between crown radii and stem diameters add the operational premises of the full distribution matching.

Airborne laser scanning (ALS) is a reliable source of accurate information for forest stand inventory attributes including height, cover, basal area, and volume. The commonly applied area-based approach (ABA) allows the derivation of wall-to-wall geospatial coverages representing each of the modeled attributes at a grid-cell level, with spatial resolutions typically between 20 and 30 m. The ABA predictive models are developed using stratified inventory data from field plots, the requirement for which can increase the overall cost of the ALS-based inventory. Parsimonious use of ground plots is a key means to control variable costs in the operational implementation of the ABA. In this paper, we demonstrate how the prediction accuracy of Lorey's height (HL, m), quadratic mean diameter (QMD, cm), and gross volume (V, m3) vary when existing ABA models are transferred to different areas or are applied to point cloud data with different characteristics than those on which the original model was developed. Specifically, we consider three scenarios of model transferability: (i) same point cloud characteristics, different areas; (ii) different point cloud characteristics, same areas; and (iii) different point cloud characteristics, different areas. We generated area-based models using three modeling approaches: linear regression (OLS), random forests (RF), and k-nearest neighbour (kNN) imputation. Results indicated that the prediction accuracy of area-based models varied by attribute and by modeling approach. We found that when the models were transferred their prediction accuracy decreased, with an average increase in relative bias up to 22.04%, and increase in relative RMSE up to 29.31%. Prediction accuracies for HL were higher than those of QMD or V when models were transferred, and had the lowest average increase in relative bias and relative RMSE of

Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We developed a methodology for the identification of FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean and Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied and clusters (FSTs) were determined in coniferous and deciduous forests using four forest structural variables obtained from forest inventory data – quadratic mean diameter (QMD), Gini coefficient (GC), basal area larger than mean (BALM) and density of stems (N) –. Then, classification and regression tree analysis (CART) were used to extract the empirical threshold values for discriminating those clusters. Based on the classification trees, GC and BALM were the most important variables in the identification of FSTs. Lower, medium and high values of GC and BALM characterize single storey FSTs, multi-layered FSTs and exponentially decreasing size distributions (reversed J), respectively. Within each of these main FST groups, we also identified young/mature and sparse/dense subtypes using QMD and N. Then we used similar structural predictors derived from ALS – maximum height (Max), L-coefficient of variation (Lcv), L-skewness (Lskew), and percentage of penetration (cover), – and a nearest neighbour method to predict the FSTs. We obtained a greater overall accuracy in deciduous forest (0.87) as compared to the coniferous forest (0.72). Our methodology proves the usefulness of ALS data for structural heterogeneity assessment of forests across biogeographical regions. Our simple two-tier approach to FST classification paves the way toward transnational assessments of forest structure across bioregions.

In this study, we examined the associations between field-assessed floristic and structural habitat values for mature forest and GIS-derived variables to assess whether high conservation value forests could be predicted for strategic reservation at a landscape scale. We investigated the Eucalyptus regnans forests of the Victorian Central Highlands in south-eastern Australia, where several extensive wildfires in the last century have left little mature forest. We assessed the extent to which the floristic composition and a suite of habitat-related structural variables could be explained by two forest inventory GIS variables (percentage senescence and site productivity) and whether explanatory capacity improved from inclusion of additional environmental variables (climate, soils, topography, structure and spatial location). Results showed that the floristic composition was weakly related to productivity, but not to percentage senescence. Four habitat-related structural variables were positively related to percentage senescence (density of old-growth eucalypts, the quadratic mean diameter (QMD) of both live and dead eucalypts, and the maximum eucalypt form class (a proxy for tree hollows)) while the volume of CWD had a marginally significant positive relationship. Three structural variables were related to productivity (the maximum eucalypt form class, the QMD of dead understorey trees and of dead eucalypts). However, in all cases the explanatory power of percentage senescence and productivity was weak (proportion of deviance explained by the models

Rapid development and cultivation of willow Short Rotation Coppice (SRC), driven by international companies, demand reliable, easily applicable and cheap, non-destructive biomass yield estimation methods that are not yet available in Lithuania. During the last years, knowledge transfer between research institutions and industry across Europe has therefore been an important and fostered topic in this area. This study focuses on non-destructive biomass estimation methods, developed by German research institutions, and evaluates them under Lithuanian growth conditions. The common allometric power equation (APE), quadratic mean diameter (QMD), and yield appraisal (YA) approaches were tested for estimation bias, precision and accuracy. In total, data from 39 commercial willow SRC were used for the analyses. According to the results, mean annual oven dried biomass increment was higher in German willow SRC compared to Lithuanian willow SRC. However, there were no statistically significant differences between the scaling coefficients of plot specific allometric power equations. The developed country-specific common APE were characterize by very high coefficients of determination (R2 >0.96). Predictions of both common models were in line with predictions of plot-specific models and none of these models produced a higher bias or a significantly lower precision compared to plot-specific APE models. Compared with the other applied methods (QMD and YA), common APEs produced the most accurate estimations for Lithuanian and German willow SRC. Based on our findings we conclude that the transfer of non-destructive biomass estimation methods between Lithuania and Germany is scientifically sound and offers an important synergetic potential for local industries.

In a high, middle-aged stand of European hornbeam, on a site of mesophilous pedunculate oak, European hornbeam and narrow-leaved ash in the unflooded part of Ravni Srem (Carpino betuli-Quercetum roboris (Anić, 1959) Rauš 1971. subass. Fraxinetosum angustifoliae Tomić, 2007), the following tree species from the natural forest community were present: narrow-leaved ash, field maple and field elm. Pedunculate oak, a primary edificator of the community and economically the most important species was absent. The stand is 55 years old and originates from a failed regeneration of pedunculate oak. Thus, the silvicultural prescription is restoration. The number of trees in the unthinned part of the stand (EP 1) was 1217 trees per hectare and in the thinned part (EP 2) 530 trees per hectare. In the thinned part of the stand, 43.5% of the total number of trees and 40% of the total number of trees of European hornbeam were counted, compared to the unthinned part of the stand. The quadratic mean diameter (dg) and dominant trees diameter (Dg20) in the unthinned part of the stand were 17.4 and 23.6 cm and in the thinned part of the stand 19.0 and 27.6 cm. The Lorey's mean height and the dominant heights were between 22.2 and 23.9 m on EP 1 and 20.5 to 22.2 on EP 2. The amount of European hornbeam seed on the soil surface in different stands is similar. In the unthinned part of the stand, the mean number of European hornbeam seeds was 1195 pcs m-2 (100%) and in the thinned part 1177 pcs m-2 (98.5%). The mean number of healthy seeds on the soil surface in the thinned and unthinned stands was 1117-1142 pcs m-2. The seeds from previous years participate with 12.2-15.0% (136-171 pcs m-2) and the seeds from the 2018 mast year 85.0-87.8% (971-981 pcs m-2), with 95% of the seeds being healthy (971-981 pcs m-2). Due to the effect of thinnings, the stands that were dominated by European hornbeam become more mixed, which is a new element that defines the process of the restoration in the future. Seedbank in the middle-aged stand designated for restoration under different managing strategies is made up of 85-88% of healthy seeds from 2018, with 12-15% from previous years.

Made available in DSpace on 2022-04-29T08:30:19Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-07-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate small openings and cracks in the forest canopy, enabling the collection of structural information in complex forests. Our objective was to identify the most significant LiDAR metrics and machine learning techniques to estimate the stand and diversity variables in a disturbed heterogeneous tropical forest. Data were collected in a remnant of the Brazilian Atlantic Forest with different successional stages. LiDAR metrics were used in three types of transformation: (i) raw data (untransformed), (ii) correlation analysis, and (iii) principal component analysis (PCA). These transformations were tested with four machine learning techniques: (i) artificial neural network (ANN), ordinary least squares (OLS), random forests (RF), and support vector machine (SVM) with different configurations resulting in 27 combinations. The best technique was determined based on the lowest RMSE (%) and corrected Akaike information criterion (AICc), and bias (%) values close to zero. The output forest variables were mean diameter at breast height (MDBH), quadratic mean diameter (QMD), basal area (BA), density (DEN), number of tree species (NTS), as well as Shannon–Waver (H’) and Simpson’s diversity indices (D). The best input data were the new variables obtained from the PCA, and the best modeling method was ANN with two hidden layers for the variables MDBH, QMD, BA, and DEN while for NTS, H’and D, the ANN with three hidden layers were the best methods. For MDBH, QMD, H’and D, the RMSE was 5.2–10% with a bias between −1.7% and 3.6%. The BA, DEN, and NTS were the most difficult variables to estimate, due to their complexity in tropical forests; the RMSE was 16.2–27.6% and the bias between −12.4% and −0.24%. The results showed that it is possible to estimate the stand and diversity variables in heterogeneous forests with LiDAR data. São Paulo State University (UNESP), Roberto Simonsen 305 Department of Cartography São Paulo State University (UNESP), Roberto Simonsen 305 Department of Forestry Federal Rural University of Amazonia (UFRA), Tv. Pau Amarelo s/n ERATOSTHENES Centre of Excellence Laboratory of Remote Sensing and Geo-Environment Department of Civil Engineering and Geomatics School of Engineering and Technology Cyprus University of Technology Finnish Geospatial Research Institute (FGI) National Land Survey of Finland, Geodeetinrinne 2 São Paulo State University (UNESP), Roberto Simonsen 305 Department of Cartography São Paulo State University (UNESP), Roberto Simonsen 305 FAPESP: 2013/50426-4

We successfully transformed Pinus sylvestris yield tables into diameter distribution models. The best results were obtained with the parameter recovery method based on both mean and quadratic mean diameter, which explained 70% of the variability of frequencies by diameter classes and provided better results in the analysis of errors. On the other hand, the method based on stand density, dominant diameter and quadratic mean diameter explained less variability of frequencies by diameter classes (64.4%). Old datasets used to develop yield table models can be recovered to transform those yield tables into diameter distribution models that provide a more detailed description of size variability and forest structure. We used archived measurements collected to develop yield table models for Pinus sylvestris L in central Spain, to transform those yield tables into a diameter distribution model by using parameter recovery methods. We compared two different parameter recovery methods, one based on both mean and quadratic mean diameter and another one based on dominant diameter, stand density and quadratic mean diameter and used a set of 104 even aged plots to analyze the performance of the said methods for the transformation of Pinus sylvestris L yield tables in central Spain into a diameter distribution model. The parameter recovery method based on both mean and quadratic mean diameter explained 70% of the variability of frequencies by diameter classes and provided better results than the method based on stand density, dominant diameter and quadratic mean diameter that explained 64.4% of the variability of frequencies by diameter classes. However, more important than the method itself were the errors that propagated from the models predicting the different variables used in the parameter recovery. Based on the results from the analysis of errors by diameter classes, the method using both mean and quadratic mean diameter outperformed the method using dominant diameter, stand density and quadratic mean diameter and is the best option to transform P. sylvestris yield tables into diameter distribution models.

16 Pág. Instituto de Ciencias Forestales (ICIFOR), Over recent decades, climate change has been particularly severe in the Mediterranean basin, where the intensity and frequency of drought events have had a significant effect on tree growth and mortality. In this context, differences in structural and physiological strategies between tree species could help to mitigate the damage inflicted by climate variability and drought events. Here, we used dendroecological approaches to observe common associations (synchrony) between indexed ring width in Pinus pinea and P. pinaster, as a measure of degree of dependence on climate variation or growth sensitivity to climate, as well as to analyze species growth responses to drought events through the Lloret’s indices of resistance, recovery and resilience. Based on data from 75 mixed and pure plots installed in the Northern Plateau of Spain, we used modeling tools to detect the effect of the mixture, along with climate and stand-related variables, on the short-term responses and long-term growth sensitivity to climate. Our results showed a trade-off between resistance and recovery after the drought episodes. In addition, different attributes of tree species, such as age and size as well as stand density seemed to act synergistically and compensate drought stress in different ways. The presence of age and quadratic mean diameter as covariates in the final synchrony model for P. pinaster reflected the influence of other variables as modulators of growth response to climate. Furthermore, differences in growth synchrony in mixed and monospecific composition suggested the existence of interactions between the two species and some degree of temporal niche complementarity. In mixed stands, P. pinaster exhibited a lower sensitivity to climate than in monospecific composition, whereas P. pinea enhanced its resistance to extreme droughts. These results allowed us to identify the species-specific behavior of P. pinea and P. pinaster to mitigate vulnerability to climate-related extremes., We are grateful to the Forest Service of the Junta de Valladolid for their support during the field work. We also thank Adam Collins for revising the English. M. V. acknowledges the FPI scholarship program from the Spanish Ministry of Education. J.G.A. was supported by a Ramon y Cajal fellowship (RYC-2016-20528). This contribution has been supported within the framework of the projects PCIN2017-026, AGL2014- 51964-C2-2-R, AGL2017-83828-C2.1R and RTA2013-00011-C2.1, of the Spanish Ministry of Economy and Competitiveness, and 778322-CARE4C H2020. Finally, the authors are grateful to the anonymous reviewers whose comments and suggestions significantly improved both clarity and precision of the paper.

Butt rot (BR) damages associated with Norway spruce (Picea abies [L.] Karst.) account for considerable economic losses in timber production across the northern hemisphere. While information on BR damages is critical for optimal decision-making in forest management, the maps of BR damages are typically lacking in forest information systems. We predicted timber volume damaged by BR at the stand-level in Norway using harvester information of 186,026 stems (clear-cuts), remotely sensed, and environmental data (e.g. climate and terrain characteristics). We utilized random forest (RF) models with two sets of predictor variables: (1) predictor variables available after harvest (theoretical case) and (2) predictor variables available prior to harvest (mapping case). We found that forest attributes characterizing the maturity of forest, such as remote sensing-based height, harvested timber volume and quadratic mean diameter at breast height, were among the most important predictor variables. Remotely sensed predictor variables obtained from airborne laser scanning data and Sentinel-2 imagery were more important than the environmental variables. The theoretical case with a leave-stand-out cross-validation achieved an RMSE of 11.4 $m^3ha^{-1}$ (pseudo $R^2$: 0.66) whereas the mapping case resulted in a pseudo $R^2$ of 0.60. When the spatially distinct k-means clusters of harvested forest stands were used as units in the cross-validation, the RMSE value and pseudo $R^2$ associated with the mapping case were 15.6 $m^3ha^{-1}$ and 0.37, respectively. This indicates that the knowledge about the BR status of spatially close stands is of high importance for obtaining satisfactory error rates in the mapping of BR damages., Comment: 22 pages, 6 figures, 3 tables

Increasing demand for tropical hardwood and logging bans for natural teak forests have led to expanding teak plantations. Forest growth factors, briefly summarized in this chapter, may guide the establishment of new teak plantations. In contrast, existing timberland investments usually require accurate timber production estimations and projections that rely on mathematical models to inform silvicultural, managerial, and financial decisions. A thorough revision of the scientific literature on teak growth and yield modeling suggests that the state-space approach is perhaps the most suitable mathematical technique. Thus, the state-space approach model was used for projections. The system of equations, composed of the dominant height and basal area, is used to estimate stand production. A comprehensive but straightforward method that accounts for site productivity and thinnings allows for basal area projection over time, and diameter and volume estimation. Since teak timber value depends largely on the size of the logs extracted from the plantation, we also present a simplified but useful approach to estimate the average girth and to split the total commercial volume into timber product classes. An example wraps up this chapter with three basal area projections, thinning schemes, volume estimations, and timber product class distributions for the three financially feasible site index classes. Simulations indicate that at rotation age at year 20, stands end with 100 trees per hectare, quadratic mean diameter of 32 and 51 cm, total volume ranging from 112 to 153 m3/ha, and commercial volume ranging from 56 to 77 m3/ha, respectively, for the lowest and highest site index classes.

Centro de Investigación Forestal (CIFOR), Mixed-species stands have been found to be more productive than would be expected from the performance of their component species in monocultures due to facilitation and complementarity between species, although these interactions depend on the combination of species present. Our study focuses on monospecific and mixed-species stands of Scots pine and Norway spruce using 20 triplets established in nine countries along a climatic gradient across Europe. Differences in mean tree and stand characteristics, productivity and stand structure were assessed. Basal area increment in mixed stands was 8% higher than expected while volume increment was only 2% greater. Scots pine trees growing in mixed-species stands showed 11% larger quadratic mean diameter, 7% larger dominant diameter, 17% higher basal area and 25% higher stand volume than trees growing in monospecific stands. Norway spruce showed only a non-significant tendency to lower mean values of diameters, heights, basal area, as well standing volume in mixtures than monocultures. Stand structure indices differed between mixed stands and monocultures of Scots pine showing a greater stratification in mixed-species stands. Furthermore, the studied morphological traits showed little variability for trees growing in monospecific stands, except for diameter at breast height, crown length and crown length ratio. For trees growing in mixed stands, all the morphological traits of the trees were identified as different. Some of these morphological traits were associated with relative productivity. Nevertheless, relative productivity in mixed-species stands was not related to site conditions., We thank national funders of REFORM project (Spanish Ministry of Science and Innovation: PCIN2017-026; Research Council of Lithuania: S-SUMFOREST-17-1) and those funders for the support to non-participants in the REFORM project (Denmark: Contract between Danish Ministry of Environment and Food and Department of Geosciences and Natural Resource Management of UCPH; Estonia: Estonian University of Life Sciences projects number P180024MIME and P200029MIME; Poland: EU CARE4C project (GA 778322) supported by the Polish Government MNiSW2018-2021 matching fund (W117/H2020/2018); Slovakia: APVV-15-0265) that allowed the establishment and measurement of the triplets. A grant for a research stay was received by RR-P from the CARE4C project (Carbon smart forestry under climate change, H2020-MSCA-RISE-2017)., 13 Pág.

35 Pág., Research Highlights: This is the first study that has considered forest management and wildfires in the balance of living biomass and carbon stored in Mediterranean forests. Background and Objectives: The Kyoto Protocol and Paris Agreement request countries to estimate and report carbon emissions and removals from the forest in a transparent and reliable way. The aim of this study is to forecast the carbon stored in the living biomass of Spanish forests for the period 2000–2050 under two forest management alternatives and three forest wildfires scenarios. Materials and Methods: To produce these estimates, we rely on data from the Spanish National Forest Inventory (SNFI) and we use the European Forestry Dynamics Model (EFDM). SNFI plots were classified according to five static (forest type, known land-use restrictions, ownership, stand structure and bioclimatic region) and two dynamic factors (quadratic mean diameter and total volume). The results were validated using data from the latest SNFI cycle (20-year simulation). Results: The increase in wildfire occurrence will lead to a decrease in biomass/carbon between 2000 and 2050 of up to 22.7% in the medium–low greenhouse gas emissions scenario (B2 scenario) and of up to 32.8% in the medium–high greenhouse gas emissions scenario (A2 scenario). Schoolbook allocation management could buffer up to 3% of wildfire carbon loss. The most stable forest type under both wildfire scenarios are Dehesas. As regards bioregions, the Macaronesian area is the most affected and the Alpine region, the least affected. Our validation test revealed a total volume underestimation of 2.2% in 20 years. Conclusions: Forest wildfire scenarios provide more realistic simulations in Mediterranean forests. The results show the potential benefit of forest management, with slightly better results in schoolbook forest management compared to business-as-usual forest management. The EFDM harmonized approach simulates the capacity of forests to store carbon under different scenarios at national scale in Spain, providing important information for optimal decision-making on forest-related policies., This research was funded by the European Union’s Horizon 2020 research and innovation program (Grant No. 633464 “Distributed, Integrated and Harmonized Forest Information for Bioeconomy Outlooks (DIABOLO)”); by the Spanish National Funding Agency (Grant No. AGL2016-76769-C2-1-R. “Influencia del régimen de perturbaciones y la gestión en el balance de carbono, estructura y dinámica de las masas forestales”) and by the Spanish Ministry of Agriculture, Fishing and Food (Encomienda de gestión EG17-042 “Soporte científico a la generación de información Forestal”).

Accurately quantifying the size&ndash, density relationships is important to predict stand development, estimate stand carrying capacity and prescribe silvicultural treatments. Parametric methods, such as segmented regression, were proposed to estimate the complicated trajectory of size&ndash, density relationships. However, applying nonparametric methods to assess stand development has not been explicitly examined. In this study, we compared parametric and nonparametric methods for estimating size&ndash, density relationships for Japanese cedar plantations in Taiwan. Specifically, we compared the efficacy of two segmented regression models with the penalized spline and random forest for regression methods. We also examined various stages in stand development for old-growth Japanese cedar stands. Data collected from 237 Japanese cedar permanent plots were used in model fitting and validation. Results indicated that the parametric and nonparametric methods used in this study can provide reliable estimates of the size&ndash, density relationship for Japanese cedar. Higher accuracy was achieved before the stands diverged from the self-thinning line. The penalized spline approach behaved consistently well regardless of datasets or stages in stand development, while the predictability of the random forest algorithm slightly decreased when the validation data was fitted. The results of this study provide insights on the use of methods to quantify the size&ndash, density relationships as well as enhance the understanding of long-term stand development.

Tree height is an important trait in forest science and is highly associated with the site quality from which the trees are measured. However, other factors, such as competition and species interaction, may yield better estimates for individual tree height when taken into account, but these variables have so far been challenging in model fitting. We propose a hybrid approach using genetic algorithms for variables selection and a machine learning algorithm (random forest) for fitting models of individual tree heights. We compare our proposed hybrid method with a mixed-effects model and random forest model using a dataset of 5,608 trees and 189 environmental variables (forest inventory-based variables, soil, topographic, climate, spectral, and geographic) from sites in southeastern Brazil. The tree height models were evaluated using the coefficient of determination, absolute bias, and root means square error (RMSE) based on the validation of dataset performance. The optimal set of variables of the proposed method include the ratio of diameter at breast height to quadratic mean diameter, distance independent competition index, dominant height, the soil silt and boron content. Our findings showed that the proposed hybrid method achieved an accuracy comparable with other methodologies in estimating the total height of the individual trees, and such a modelling approach could have broader applications in forestry and ecological science where a studied response trait has a large number of potential explanatory variables.

Background The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural diversity of forests in an objective and quantitative way across many forest types and sites are still needed, for example to support biodiversity monitoring. The existing approaches to quantify forest structural diversity are based on small geographical regions or single forest types, typically using only small data sets. Results Here we developed an index of structural diversity based on National Forest Inventory (NFI) data of Baden-Württemberg, Germany, a state with 1.3 million ha of diverse forest types in different ownerships. Based on a literature review, 11 aspects of structural diversity were identified a priori as crucially important to describe structural diversity. An initial comprehensive list of 52 variables derived from National Forest Inventory (NFI) data related to structural diversity was reduced by applying five selection criteria to arrive at one variable for each aspect of structural diversity. These variables comprise 1) quadratic mean diameter at breast height (DBH), 2) standard deviation of DBH, 3) standard deviation of stand height, 4) number of decay classes, 5) bark-diversity index, 6) trees with DBH ≥ 40 cm, 7) diversity of flowering and fructification, 8) average mean diameter of downed deadwood, 9) mean DBH of standing deadwood, 10) tree species richness and 11) tree species richness in the regeneration layer. These variables were combined into a simple, additive index to quantify the level of structural diversity, which assumes values between 0 and 1. We applied this index in an exemplary way to broad forest categories and ownerships to assess its feasibility to analyse structural diversity in large-scale forest inventories. Conclusions The forest structure index presented here can be derived in a similar way from standard inventory variables for most other large-scale forest inventories to provide important information about biodiversity relevant forest conditions and thus provide an evidence-base for forest management and planning as well as reporting.

Background Leaf Area Index (LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network (ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands. Methods One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands. Results The correlation coefficients between LAI and stand parameters (stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters (R 2 adj. = 0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4–19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI (SSE (12.1040), MSE (0.1223), RMSE (0.3497), AIC (0.1040), BIC (− 77.7310) and R 2 (0.6392)) compared to the other studied techniques. Conclusion The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

This study developed a stand table projection system for interior Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. glauca (Beissn.) Franco) in British Columbia, Canada. Simulation data, obtained by running PrognosisBC (with 150 year projections) using input derived from 140 permanent sample plots, were utilized for model construction. First, a whole-stand attributes (number of trees per ha, quadratic mean diameter, and arithmetic mean diameter) prediction model was constructed. Weibull parameters for the diameter distribution of the future stand were estimated from the predicted stand attributes using two parameter prediction methods (via seemingly unrelated regression and the cumulative distribution function) and one parameter recovery method. The future stand table was projected with the estimated Weibull parameters and compared with two stand diameter distribution adjustment algorithms, yielding a total of nine parameter estimation-diameter adjustment combinations. The outputs from the stand table projection model were evaluated with repeated measures data covering 20 years from 18 independent plots, as well as compared with the outcomes predicted by PrognosisBC for those plots. The diameter class adjustment procedure applying tree mortality prior to growth, combined with the cumulative distribution parameter prediction method, performed best among the nine combinations we compared. The stand table projection system yielded similar results to PrognosisBC on the evaluation data and is expected to be a useful tool for managing forests sustainably and effectively.

Combining auxiliary variables and field inventory data of forest parameters using the model-based approach is frequently used to produce synthetic estimates for small areas. These small areas arise when it may not be financially feasible to take ground measurements or when such areas are inaccessible. Until recently, these estimates have been calculated without providing a measure of the variance when aggregating multiple pixel areas. This paper uses a Random Forest algorithm to produce estimates of quadratic mean diameter at breast height (QMDBH) (cm), basal area (m2 ha−1), stem density (n/ha−1), and volume (m3 ha−1), and subsequently estimates the variance of multiple pixel areas using a k-NN technique. The area of interest (AOI) is the state owned commercial forests in the Slieve Bloom mountains in the Republic of Ireland, where the main species are Sitka spruce (Picea sitchensis (Bong.) Carr.) and Lodgepole pine (Pinus contorta Dougl.). Field plots were measured in summer 2018 during which a lidar campaign was flown and Sentinel 2 satellite imagery captured, both of which were used as auxiliary variables. Root mean squared error (RMSE%) and R2 values for the modelled estimates of QMDBH, basal area, stem density, and volume were 19% (0.70), 22% (0.67), 28% (0.62), and 26% (0.77), respectively. An independent dataset of pre-harvest forest stands was used to validate the modelled estimates. A comparison of measured values versus modelled estimates was carried out for a range of area sizes with results showing that estimated values in areas less than 10–15 ha in size exhibit greater uncertainty. However, as the size of the area increased, the estimated values became increasingly analogous to the measured values for all parameters. The results of the variance estimation highlighted: (i) a greater value of k was needed for small areas compared to larger areas in order to obtain a similar relative standard deviation (RSD) and (ii) as the area increased in size, the RSD decreased, albeit not indefinitely. These results will allow forest managers to better understand how aspects of this variance estimation technique affect the accuracy of the uncertainty associated with parameter estimates. Utilising this information can provide forest managers with inventories of greater accuracy, therefore ensuring a more informed management decision. These results also add further weight to the applicability of the k-NN variance estimation technique in a range of forests landscapes.

Empirical growth models are widely used to predict the growth and yield of plantation tree species, and the precise estimation of site quality is an important component of these models. The most commonly used proxy for site quality in growth models is Site Index (SI), which describes the mean height of dominant trees at a specified base age. Although SI is widely used, considerable research shows significant site-dependent variation in height for a given volume, with this latter variable more closely reflecting actual site productivity. Using a national dataset, this study develops and describes a stand-level growth and yield model for even-aged New Zealand-grown coast redwood (Sequoia sempervirens). We used a novel modelling approach that quantifies site quality using SI and a volume-based index termed the 300 Index, defined as the volume mean annual increment at age 30 years for a reference regime of 300 stems ha−1. The growth model includes a number of interrelated components. Mean top height is modelled from age and SI using a polymorphic Korf function. A modified anamorphic Korf function is used to describe tree quadratic mean diameter (Dq) as a function of age, stand density, SI and a diameter site index. As the Dq model includes stand density in its formulation, it can predict tree growth for different stand densities and thinning regimes. The mortality model is based on a simple attritional equation improved through incorporation of the Reineke stand density index to account for competition-induced mortality. Using these components, the model precisely estimates stand-level volume. The developed model will be of considerable value to growers for yield projection and regime evaluation. By more robustly describing the site effect, the growth model provides researchers with an improved framework for quantifying and understanding the causes of spatial and temporal variation in plantation productivity.

Stand density management diagrams are average stand-level models that graphically display the relationship between stand yield, density, height and diameter throughout the various stages of forest development in even-aged stands. These are useful tools for designing, displaying and evaluating alternative density regimes in even-aged forest ecosystems to achieve a desired future condition. In this paper, we present a stand density management diagram constructed for sandalwood stands in Karnataka state of India. The relationship between stand density, dominant height, quadratic mean diameter, relative spacing and stand volume is exhibited in one graph. The relative spacing index was used to characterise the growing stock level. Two equations were fitted to the data collected from 19 sample plots measured annually for three years: one relates quadratic mean diameter with stand density and dominant height, whereas the other relates total stand volume with quadratic mean diameter, stand density and dominant he...

Density management diagrams are valuable tools for managing specific forestspecies. The aim of this study was to obtain a density management diagram for teak (Tectona grandis L.) plantations in the State of Tabasco in Mexico. To achieve this objective, a set of 10 plantations were studied, in which 42 plots were established. Two equations were fitted simultaneously, including one related to the quadratic mean diameter, stand density and dominant height and the other which related the total stand volume to the quadratic mean diameter, stand density and dominant height. The results showed that the diagram had an acceptable predictability, thus indicating its usefulness and accuracy in planning silvicultural interventions. This diagram is a very powerful tool that can enable stakeholders to manage teak plantations in the State of Tabasco. Fil: Minoche, Djhon. Universidad de Valladolid; España Fil: Risio Allione, Lucia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis; Argentina Fil: Herrero De Aza, Ceclia. Universidad de Valladolid; España. ECM Environment Engineering; España Fil: Martínez Zurimendi, Pablo. Universidad de Valladolid; España. El Colegio de la Frontera del Sur; México

Self-thinning is fundamental to modern density-based forest management. The process of self-thinning arises from the dynamic interaction of stand growth and mortality at equilibrium conditions. However, despite the dynamic basis for the self-thinning process, it is typically modeled using static size-density data.We tested the ability of a simple stand mortality model to estimate the self-thinning line. We used data from long-term silvicultural experiments for six common Eucalyptus species in southeastern Australia. Our approach built on existing mortality model structure that predicts survival trajectories that follow a self-thinning line. We used Poisson and negative binomial generalized linear models for count data as well as a non-linear least squares procedure on the integrated scale to calibrate the mortality model. Derived self-thinning parameters were compared to parameters calibrated on the static allometry between quadratic mean diameter (Dq) and stand density (N) using two reference methods (linear model and stochastic frontier analysis).Our dynamic mortality models provided estimates of the self-thinning line that were as good as or better than those obtained using reference methods; however, this required accounting for overdispersion of mortality count data. We validated survival trajectories using independent data for the three most abundant eucalypt species and found that they showed excellent behavior.Survival trajectories predicted by the mortality models were consistent with, and accurately estimated, the self-thinning line for the eucalypt species in our study. The simplicity of calibrating mortality models using GLM methods raises the possibility of quantifying how environmental drivers influence the dynamic self-thinning equilibrium.

A densidade básica é uma propriedade-chave de qualidade da madeira para produção de celulose, mas sua determinação demanda muito tempo e altos custos. O desenvolvimento de ferramentas de amostragem não destrutiva, eficiente e de baixo custo torna-se importante, e o Resistógrafo pode ser uma alternativa. Este estudo teve o objetivo de utilizar dados de amplitude de resistência à perfuração coletados pelo Resistógrafo, associados a variáveis dendrométricas, para desenvolver modelos para estimativa de densidade básica em povoamentos clonais de Eucalyptus. Utilizaram-se quatro clones híbridos Eucalyptus grandis x Eucalyptus urophylla distribuídos em plantios comerciais no Vale do Paraíba com idade variando de 2 a 7 anos. Os materiais foram amostrados pelo Resistógrafo a 1,30 m do solo (DAP) e pela retirada de um disco na mesma altura para determinação da densidade básica em laboratório. Foram selecionadas equações lineares simples e múltiplas, iniciando pelo uso de dados do Resistógrafo (amplitude média) como variável preditora, e, posteriormente incluindo variáveis dendrométricas como DAP, idade, altura total, altura média das árvores dominantes e diâmetro médio quadrático. A inclusão destas variáveis aumentou a precisão das estimativas. A melhor equação selecionada para o conjunto dos 4 clones, ajustada, utilizando a amplitude média (Resistógrafo), associada à altura média das árvores dominantes, à idade das árvores e ao diâmetro médio quadrático, apresentou R2aj igual a 68,80%, com erro padrão residual de 0,0201 g/cm3 ou 4,31%. Para cada clone, isoladamente, as equações mais precisas também foram aquelas em que se associou o Resistógrafo às variáveis do indivíduo e da população. Equações ajustadas em função apenas de variáveis dendrométricas apresentaram medidas de precisão superiores às ajustadas em função apenas do Resistógrafo, no caso do conjunto dos 4 clones, e superiores ao Resistógrafo associado a variáveis do indivíduo, no caso de cada clone isoladamente. O Resistógrafo mostrou-se como uma ferramenta eficiente para predição da densidade básica. Entretanto, estimativas tão ou mais precisas podem ser obtidas sem uso do equipamento quando se têm variáveis individuais e da população.

Forest stand biomass or carbon storage in the biomass may be estimated by conversion of the trees volume since roughly 50% of tree biomass is carbon. There is a need to find biomass conversion and expansion factors (BCEFs) for young stands, because those established for older ones may not be relevant due to changes in biomass allocation patterns over stand age and development phases. We developed BCEFs for above- and belowground biomass components of young silver birch stands as separate functions of stand age or variables that reflect growth conditions and the biological status of trees, i.e. top height (H100), mean height (Hg), base diameter (D0), quadratic mean diameter at breast height (Dg) or stand volume (V). Data were collected on 120 pure birch stands that regenerated naturally on post-agricultural lands in Poland. We determined BCEFs based on the stem volume and dry biomass of above- and belowground components. We applied segmented linear regression (SLR) to develop equations for BCEF determination for each of the pre-determined age classes using (a) fixed break points (FBP) found with the Ward method and (b) estimated break points (EBP) derived based on the best fit of regression models during piecewise regression estimation. We found that the use of SLR and non-linear regression (NR) approaches produced similar results in terms of quality of fit. However, non-linear parameter estimates are more efficient than linear ones, as NR seems to be more frequently used and easier to process using existing statistical tools, but at the same time the SLR provides biological interpretation of results (i.e. the point where BCEF value stabilizes and beyond which stand features do not influence BCEF). The largest difference between EBP and FBP occurs for stems, and the smallest for roots. For total biomass and all biomass components except stem, BCEFs are very high for the youngest, shortest and thinnest trees and hence stands with low volume. Their values decrease along with age, height, diameter and volume towards the EBPs. For individuals older, taller, thicker and with higher volume than EBP, there is no influence of age, H100, Hg, D0, Dg and V on BCEF values. We confirmed that H100 might be a good biomass predictor for young birch stands, which is important because measuring tree height is not as time consuming as tree diameter measurements, particularly for young and very dense stands, and this parameter can be obtained from remote sensing technology.