Site Index Model Research Articles

Dynamic site index models sensitive to ecoregional variability for Scots pine stands in Western Black Sea Region of Türkiye.

Site productivity, defined as the production amount of the stand at a specific age, has a significant impact on the growth of the stand and site index is used as an indicator of site productivity. The objective of this study is to develop ecoregion-based dynamic site index models for Scots pine (Pinus sylvestris L.) stands in the Kastamonu and Sinop regions of Türkiye. The mixed-effects modeling approach allowing for the inclusion of ecoregions in the models was used to develop dynamic site index models, and the models derived from seven base models were tested. The best model was selected based on statistical criteria. As a result of statistical analyses and graphical examinations, the King-Prodan model was found to yield the best predictive results in terms of growth patterns. The site index model based on the King-Prodan method produced a coefficient of determination (R2) of 0.977. The statistical criteria for this model are as follows: Akaike information criterion (AIC) of 4931.052, Bayesian information criterion (BIC) of 4968.933, root mean square error (RMSE) of 1.218, and mean error (ME) of - 0.036. The F-test was used to test whether there was a statistically significant difference in dominant heights between ecoregions. The results demonstrated that the dominant heights exhibited statistically significant differences among the ecoregions. Consequently, it is of paramount importance to utilize ecoregion-based dynamic site index models in order to achieve reliable and accurate predictions.

A low-cost alternative to LiDAR for site index models: applying repeated digital aerial photogrammetry data in the modelling of forest top height growth

Abstract Environmental and forest structural information derived from remote sensing data has been found suitable for modelling forest height growth and site index and therefore forest productivity assessment, with the advances in airborne laser scanning (ALS) playing a major role in this development. While there is growing interest in the use of ALS-derived point clouds, point clouds from high-resolution digital aerial photography (DAP) are also often used for mapping and estimating forest ecosystem properties due to their lower acquisition costs. In this study, we document the applicability of bi-temporal DAP data for developing top height (TH) growth models for Scots pine stands. Our results indicate that DAP data can function as an alternative to traditional TH measurements used in growth modelling when corrected based on a limited sample of field-measured reference TH values. As the correction cannot be constant for each DAP dataset due to the different parameters during data acquisition, we propose a straightforward method for the bias correction of DAP-derived TH estimates. By undertaking iterative random sampling, we were able to find the minimum number of reference measurements needed to calculate the TH correction in order to achieve the desired accuracy of the TH estimations based on DAP. Here, we used ALS data as the reference data; however, the ALS measurements can be replaced by any other reliable source of TH values. The presented method for determining TH can be used not only for site index and forest growth modelling but also in forest inventories.

A growth-effective age-based periodic site-index for the estimation of dynamic forest site productivity under environmental changes

Key messageA novel periodic site index is introduced for the quantification of dynamic forest site productivity. The measure is age-independent, sensitive to environmental changes and efficient for the estimation and prediction of stand height and stand volume increment.ContextAccurate and up-to-date prediction of site productivity is crucial for the sustainable management of forest ecosystems, especially under environmental changes.AimsThe aim of this study was to introduce a novel concept: a periodic site index based on growth-effective age for the quantification of dynamic forest site productivity.MethodsThe growth-effective age based periodic site index is estimated from repeated or multi-temporal measurements of stand dominant height. Furthermore, a recursive procedure to update the underlying site index model is presented by using repeated measurements of stand dominant height. The database used in this study comprised repeated measurements of 945 Norway spruce (Picea abies L.) experimental plots at 508 different locations in Southwest Germany.ResultsThe evaluation shows that periodic site index is statistically superior to the conventional site index, based on chronological stand age, for estimating stand height and stand volume increment. The analysis of temporal differences between growth-effective stand age and chronological stand age and between periodic site index and conventional site index in the period 1900 to 2020 reveals trends referring to stand age and site productivity, which corroborate earlier regional studies on forest growth trends due to environmental changes.ConclusionsThe periodic site index is a better indicator for site productivity than conventional site index. Under conditions of environmental changes, conventional site index is biased, whereas the growth-effective age based site index provides an unbiased estimate of stand height development. With the more widespread application of remote sensing techniques, such as airborne laser scanning, the availability of multi-temporal stand height data will increase in the near future. The novel concept provides an adaptive modeling approach perfectly suited to these data for an improved estimation and prediction of forest site productivity under environmental changes and can straightforwardly be applied also to uneven-aged and multi-species stands.

Study on the relationship between net primary productivity and site quality in Japanese larch plantations in mountainous areas of eastern Liaoning.

Plantation forests enhance carbon storage in terrestrial ecosystems in China. Larix kaempferi (Lamb.) Carrière (Lamb.) (Larix olgensis Henry) is the main species for afforestation in the eastern Liaoning Province. Therefore, it is important to understand the correlation between the site class and carbon sink potential of Larix kaempferi plantations in Liaoning Province for afforestation and carbon sink in this area. The model was fitted using three classical theoretical growth equations: the Richards model, the Korf model, and the Hossfeld model. This study used the forest resource inventory data for management in Liaoning Province in 2011 to build six dynamic height-age models for a Larix kaempferi plantation in Dandong City regardless of base-age. The optimal model derived by the generalized algebraic difference approach (GADA) method was compared with the model derived by the algebraic difference approach (ADA) method. The superiority of GADA was demonstrated by comparison. The Levenberg-Marquardt algorithm was used to fit the model. The statistical and biological characteristics were considered synthetically when comparing the models. The best model was screened out by statistical analysis and graphic analysis. The results show that the differential height-age model derived from Richards equation can well explain the growth process of Larix kaempferi in Dandong City, Liaoning Province under different conditions. The site index model based on Richards equation and derived by GADA was used to calculate the site class of a Larix kaempferi plantation in Dandong City. The net primary productivity (NPP) value from the past ten years was extracted from the MOD17A3HGF data set. Spearman correlation analysis and Kendall correlation analysis were used to show that there is a significant positive correlation between NPP value and site class of Larix kaempferi plantation in Dandong City. Among them, the highest growth occurred in 2016; NPP increased by about 3.914 gC/m2/year for every two increases in height-age grade; the lowest increase in NPP was in 2014; NPP increased by about 2.113 gC/m2/year for every two increases in height-age grade; and for every two increases in height-age grade in the recent ten years, the average NPP value increased by about 2.731 gC/m2/year.

Relationships between site index modeling of crimean juniper and habitat factors

The aim of this study is to examine the relationships between height growth and some site factors of Crimean juniper in southwestern Anatolia. The data was collected from 130 sample plots selected from 10 districts in western Anatolia. The age and height of the Crimean juniper were measured in each sample plot. Surface soil samples collected from each sample plot were analyzed for pH, organic matter, carbonate content, texture, stoniness, available water capacity, wilting point, and field capacity. All site index values were calculated at a standard age of 100 years old. Spearman and Pearson correlation analyses were used to determine the relationships between plus tree and site factors. According to Spearman's correlation analysis, the site index showed a negative relationship with limestone. According to the results of the Pearson correlation analysis, the numerical variables LATTUD, SLOPE, SURSTON, and HINDEX had a 1% relationship with the site index, and RINDEX had a 5% relationship. Once the binary relationships had been determined, the multivariate relationships were modelled using generalized additive models. The best site index model was obtained using a generalized additive model 1 (R = 0.63), including latitude, longitude, altitude, slope, and stoniness.

Modeling dominant height with USGS 3DEP LiDAR to determine site index in even-aged loblolly pine (Pinus taeda L.) plantations in the southeastern US

Abstract Accurate quantification and mapping of forest productivity are critical to understanding and managing forest ecosystems. Local LiDAR or photogrammetric surveys have been used to obtain reliable estimates of canopy heights, yet these acquisitions can entail substantial expenses. Therefore, we developed models using freely available US Geological survey (USGS) LiDAR data for prediction of dominant height to map site index across loblolly pine (Pinus taeda L.) plantations in the southeastern US. We used 2017–2020 national USGS 3D Elevation Program LiDAR acquisitions and explored how different height percentiles, grid output resolutions, time difference between LiDAR and ground acquisitions, tree height, and dominant height definition affected the proposed model. We built the dominant height models using 1301 ground plots. The final regression model was constructed with the 95th percentile of the height distribution of the first returns above-ground and had values of R2 = 0.89, RMSE = 1.55 m, and RRMSE = 7.66 per cent at a 20-m pixel grid, yet all the examined percentile-resolution combinations were acceptable. No effect evidence was found for time difference when the flight was less than 4 months in advance or after the ground measurement, and it was also found independent of pulse density when this variable was lower than 9.5 pulses m−2. Using the recorded age of the plantations, we assessed the error propagation when translating dominant height to site index in two site index models, obtaining an RRMSE lower than 10 per cent in both. We found that USGS LiDAR acquisitions can be reliably used to map dominant height at a large scale, and consequently used to map forest productivity when age is known. This ability adds more value to a tool proven widely applicable in time and space and offers a great opportunity for stakeholders in different fields of use.

Variations in the Forest Productivity of Pinus patula Plantations in Tanzania: The Need for an Improved Site Classification System

The productivity of forests in sub-Saharan Africa is often summarized into large compartments or site classes. However, the classification of forest productivity levels based on the original site index model in Tanzania and the techniques applied to generate the model did not include the micro-toposequence variations within compartments. This may create false expectations of wood supply and hinder the estimation of sustainable harvesting processes. This study analyzed variations in forest productivity and the site index in P. patula stands in two forest plantations of Tanzania to assess the applicability and generality of the present site classification system. We used dominant height as a proxy for forest productivity in 48 plots at the Sao Hill forest plantation (SHFP) and 24 plots at the Shume forest plantation (SFP). We stratified the sampling plots in each site class along the soil catena and recorded the elevation, slope, and slope positions (summit, mid, and lower). Our results showed that the site classes did not generally match the previously assigned site classes and the productivity of a given site class varied between the two plantations. We found a consistently higher productivity than that implied by the original site index in SFP, while in SHFP, the productivity was both higher and lower than estimated in different compartments. Both elevations and slope significantly contributed to predicting the productivity variations within site classes. Overall, the results indicate that physiographic factors affect variations in forest productivity within the assigned site classes. We recommend a more comprehensive site productivity assessment that takes into account physiographic variations and hence provides more accurate information for sustainable forest plantation management in Tanzania and in the region at large.

Improving the Site Index and Stand Basal Area Model of Picea asperata Mast. by Considering Climate Effects

Improving the Site Index and Stand Basal Area Model of Picea asperata Mast. by Considering Climate Effects

Soil-based assessment of site productivity for southern pine plantations in the coastal plain of the southeastern US: (II) slash pine

Non-industrial private forest landowners and foresters often rely on outdated site index models for forest productivity predictions, which hinders accurate assessment. To address this issue, 157 slash pine plots were established in the coastal plain of Georgia, USA, to collect tree and soil data. Using regression tree and linear regression models, we identified modified Cooperative Research in Forest Fertilization soil groups and surface soil extractable phosphorus as significant predictors of site index. The optimal regression tree model (R2 = 0.490, RMSE = 1.72 m) outperformed the linear regression model. Updated site index and simulated mean annual increments for slash pine were compared with long-term experimental study data, as well as with previous findings for loblolly pine in the same region (Zhao et al., 2024a). The new predictive models will help non-industrial private forest landowners evaluate slash pine productivity in the Coastal Plain region, applicable across diverse sites.

Dynamic Height Growth Equations and Site Index-Based Biomass Models for Young Native Species Afforestations in Spain

The expansion of forested areas through afforestation and reforestation is widely recognized as a highly effective natural solution for mitigating climate change. Accurately assessing the potential carbon uptake capacity of newly afforested areas requires modelling tools to estimate biomass stocks, including site index curves and biomass models. Given the unique conditions in terms of tree size, uniform spacing, and tree allometries observed in young afforestations compared to natural stands, specific tools are necessary. In Spain, over 800,000 ha has been afforested with native forest species since 1992, but specific modelling tools for these plantations are lacking. Using data from 370 stem analyses collected across an extensive network of plots in young afforestations, we developed dynamic height growth and site index models for the main native species (five pines and five oaks) commonly used in afforestation in Spain. We compared various nonlinear models, such as ADA (algebraic difference approach) and GADA (generalized algebraic difference approach) expansions. The developed site index models were then used to predict the total biomass stored in the afforestation. Our results underscore the necessity for specific site index models tailored to afforestations, as well as the potential of the established site index in predicting biomass and carbon fixation capacity in these young forests.

Dynamic height growth models for highly productive pedunculate oak (Quercus robur L.) stands: explicit mapping of site index classification in Serbia

Key messageWe applied the generalized algebraic difference approach (GADA) to develop dynamic models of height growth for pedunculate oak (Quercus robur L.) in Serbia. According to the dominant heights, the studied region comprises some of Europe’s most productive sites for pedunculate oak. Therein, we have generated a map showing the current site index class of stands. Such a map could be used to enhance forest management and evaluate climate change impacts.ContextAlthough sustainable forest management requires reliable prediction of forest site productivity, such indicators are currently unavailable for pedunculate oak sites in Serbia. The site index (SI) curves represent the most commonly used indirect scale for site productivity classification. The dynamic equations derived by the Generalized Algebraic Difference Approach (GADA) are the state-of-the-art approach in growth modeling, but they have not been widely applied for studying the height dynamics of pedunculate oak.AimsThe main objectives of this study were to develop the first dynamic site index curves for pedunculate oak in Serbia and subsequently to provide stand-level maps with predicted site indices.MethodsWe have tested five flexible polymorphic equations with variable asymptotes derived by the GADA approach. Models were calibrated using artificially established growth trajectories obtained from 3636 detailed temporary sample plots. The selection of the most suitable model was accomplished according to (1) quantitative measures of goodness of fit, (2) the analysis of residual scattering, and (3) the biological plausibility of obtained height growth curves.ResultsAfter correcting the error terms with a continuous first-order autoregressive structure and conducting a three-stage performance analysis, the GADA dynamic site index model derived from the Hossfeld base equation shows the best overall properties. Insight into the oscillations of relative error suggested that 100 years is the most suitable age for site index referencing. Comparison with existing height growth models revealed greater flexibility and a considerably better representation of the height growth dynamic of pedunculate oak in the studied region. Additionally, we have produced a spatially explicit map showing the expected SI100 for 1907 stands with pedunculate oak within 22 management units.ConclusionDynamic SI-curves based on GADA will serve forest practitioners to update management plans and serve as a reference point for benchmarking the impact of climate change and for developing adaptation strategies. The utilized approach allowed unbiased estimation of SI100 across all age classes so that the results could be mapped at a broader scale. This study provides the second known application of the dynamic model for pedunculate oak in Europe but the first that includes some of the most productive sites in the species distribution range.

Site Index Model for Southern Subtropical Masson Pine Forests Using Stand Dominant Height

Stand dominant height has a close relationship with stand productivity and is not much affected by stand density and thinning within a reasonable density range, making it an excellent indicator for estimating stand site quality. Topographic factors (altitude, aspect, slope, etc.) have a significant influence on the growth process of stand level, and the combination of different site factors increases the randomness of the evaluation of forest productivity. In this paper, with one-way ANOVA, it was determined that the effects of density and management mode on the Masson pine stand dominant height were not significant. The data on the Masson pine stand dominant height in the southern subtropics in Guangxi, China, were analyzed, and the GADA model was established using the nonlinear least squares method, the Bayesian approach, and the one-level nonlinear mixed-effects model with the topographic factor as the random effect, respectively. The results indicated that the nonlinear mixed-effects model had the best fitting performance and the highest prediction accuracy for stand site quality (a 0.27% improvement in R² compared to the least squares method and a 1.30% improvement in R² compared to the Bayesian approach), while the model obtained by the Bayesian approach had more elasticity and biological significance. In summary, when the data distribution is uniform and comprehensive, introducing terrain factors into the establishment of site index models can provide a more scientific basis for estimating the productivity of southern subtropical Masson pine stands under different site conditions. When the data distribution is uneven, applying the Bayesian approach can make the site index model more biologically meaningful. The stand site quality model can predict the potential production capacity of forests, which is an important basis and can support forest management and harvest prediction. The results of this study provide a theoretical and practical basis for the establishment of a reasonable site index model for the Masson pine stand.

Site Quality Evaluation Model of Chinese Fir Plantations for Machine Learning and Site Factors

Site quality evaluation is an important foundation for decision-making and planning in forest management and provides scientific decision support and guidance for the sustainable development of forests and commercial plantations. Site index and site form models were constructed and subsequently compared utilizing fir (Cunninghamia lanceolata) plantations in Nanping City, Fujian Province, China. This papers aim was to construct a site quality classification model, conduct further analysis on the effects of different site factors on the quality of the site, and achieve an assessment of site quality for Chinese fir plantations. An algebraic difference approach was used to establish a site index model and a site form model for Chinese fir in Fujian Province. The suitability of the two models was compared using model accuracy analysis and partial correlation, and the optimal model was chosen for classifying the site quality of the stands. On this basis, a site quality classification model was established using the random forest algorithm, and the importance of each site factor was determined through importance ranking in terms of their impact on site quality. Within the study area, the R2 of the site index model results was 0.581, and the R2 values of the five site form models based on different reference breast diameters, ranked from high to low, were 0.894, 0.886, 0.884, 0.880, and 0.865. The bias correlation coefficient between site form and stand volume was 0.71, and the bias correlation coefficient between site index and stand volume was 0.52. The results confirmed that the site form model is better suited for evaluating the site quality of Chinese fir plantations. The random forest-based site form classification model had a high classification accuracy with a generalization accuracy of 0.87. The factors that had the greatest impact on site form were altitude, canopy closure, and slope gradient, whereas landform had the smallest impact on site form. These results can provide a reference for the evaluation of the site quality of plantations and natural forests in southern China to ensure the long-term sustainable use of forest resources.

Site Index Models for Loblolly Pine Forests in the Southern United States Developed with Forest Inventory and Analysis Data

Abstract Accurate productivity estimates are essential to assess the overall sustainability of forest resources. Site index (SI) models for loblolly pine (Pinus taeda L.) in plantation and natural forests of the southeastern United States were developed using the Forest Inventory and Analysis (FIA) database. We extracted short (~20 years), unbalanced panel data from the FIA database. Ten different nonlinear models derived from the base models using the algebraic difference approach (ADA) or the generalized algebraic difference approach (GADA) were fitted to the extracted data. The performance of the models was ranked based on a variety of fit and evaluation statistics. The results showed that all top three models were derived using the GADA approach. The best model for loblolly pine plantation and natural forest stands was derived from the Hossfeld model and the Chapman–Richards model, respectively. The best-fitted models for planted forests were also compared with previously developed models. This study demonstrated that base-age invariant and polymorphic SI models could be developed using short panel data extracted from FIA data. The SI models presented here can be used as a height growth model component in forest growth and yield model systems. Study Implications: Improved site index equations for assessing the site quality of loblolly pine plantation and natural stands are now available to stakeholders at the policy, management, and operational levels. Activities such as forest management, restoration, and wildlife management, which require site quality data, will benefit from the new models. Furthermore, the approach of deriving panel data based on Forest Inventory and Analysis data offers information on developing and updating models for other species. Finally, the approach of this study, to use permanent plot measurement data in developing growth and yield models, is cost-effective and time-efficient.

Modelling height growth of temperate mixedwood forests using an age-independent approach and multi-temporal airborne laser scanning data

Forest inventories provide information regarding the status of a range of attributes as well as enabling predictive applications. Growth and yield models are essential tools for sustainable forest management, importantly enabling projections of future forest conditions (such as height growth). To select the most appropriate growth trajectory, site index models are commonly used to quantify the productivity of a given site. However, applying these methods to more complex, multi-species, and multi-age forests can be challenging due to deviations from the assumptions made for even-aged stands. In this study, we provide a comprehensive indicator of site quality for more complex and irregular stand structures by developing age-independent height growth models for various forest types. We used multi-temporal airborne laser scanning (ALS) data from 2005, 2012, and 2018 in the Great Lakes–St. Lawrence forest region in southern Ontario, Canada. The stochastic differential equations approach was used to develop age-independent height models and a height growth rate index as a proxy of site quality from ALS-derived height metrics. We evaluated the sensitivity of the models using two different modelling approaches and found that the model that incorporated data from both periods (i.e., 2005–2012 and 2012–2018) generally provided the lower root mean square error (RMSE) value for most forest types. Overall, our results showed good agreement between the model predictions of top height and observed top height in 2018 from field plots for all forest types. We demonstrated the use of these models by creating a system of height growth curves for each forest type and producing a map of site quality for a mixedwood forest (∼10,000 ha) at a spatial resolution of 25 m. The approach developed herein leverages the accurate, spatially detailed characterization of canopy heights afforded by ALS data and is independent of stand age, which is challenging to measure accurately and is typically not available at a spatial resolution that is commensurate with the ALS data. Additionally, the demonstrated approach can be adapted to other data sources that accurately capture canopy heights (i.e., digital aerial photogrammetric or DAP), thereby increasing the possible geographic extent of height growth estimates.

Individual-tree growth models for lodgepole pine (Pinus contorta) in Iceland

Lodgepole pine (Pinus contorta subsp. contorta) is one of the most important tree species planted in Iceland. The current plantation area is 7100 ha, the first plantations being 80 years old. This study presents models for simulating the development of Icelandic lodgepole pine plantations on an individual-tree basis. The model set consists of a site index model, tree height model, and diameter increment model. Data were collected in 35 permanent sample plots with measurement intervals ranging from 3 to 14 years. The total number of diameter increment observations was 3664. Regression analysis was used in site index and height modelling, and both regression analysis and optimization were tested in diameter increment modelling. An optimization-based model was evaluated to be the most suitable for growth simulations. The use of the developed model set was demonstrated in management optimizations, where the rotation length and cutting schedule were optimized for two young sample plots, one representing poor sites and the other good sites. The optimizations showed that planting is not profitable on poor sites if the discount rate is 4% or higher. The mean annual stem wood harvest was 2.7–4.1 m3ha-1a-1 on the poor site and 8.3–11.7 m3ha-1a-1 on the good site.

Forest stand-by-environment interaction invalidates the use of space-for-time substitution for site index modeling under climate change

Quantification and prediction of forest site productivity potential under climate changes are core issues of growth and yield modelling and crucial for the effective management of forest stands. Climatic factors typically enter these models as long-term baseline data of mean air temperature and sum of precipitation. Such models frequently are parameterized using spatially distributed data, an approach that can be considered as indirect space-for-time substitution (SFTS), which is based on the assumption that the growth response across spatial environmental gradients is equivalent to the dynamic growth response in time when site factors shift in situ due to environmental changes over time. In site index modelling this assumption has never been thoroughly tested. We applied procedures to test the validity of the SFTS approach using altitudinal transect analysis of data from long-term experimental plots with Norway spruce (Picea abies) in southwest Germany. The following procedures were applied 1. Partitioning of spatial and temporal variance components of site index and air temperature; 2. Test of the equivalence of spatial and temporal variation in models predicting site index from air temperature; 3. Bias analysis of the SFTS- and the altitudinal range-specific individual models. The results show that the response of site index to changes in growing season air temperature as estimated by the SFTS-based approach is significantly different from the observed temporal dynamics within the plots. The estimated sensitivity of site index to changes in growing season air temperature is significantly larger in the SFTS-model. Bias trends increase with increasing air temperature in all altitudinal ranges. We found significant effects of stand-by-environment interaction on site index responses to changing growing season air temperature. Possible reasons for the found differences are discussed. Furthermore, based on an independent data set from the German National Forestry Inventory (NFI), a SFTS-based climate-sensitive site index model was compared to a state-space modelling approach (SSM) which considers the dynamics of environmental changes over time. The results show that the SSM is statistically superior to the SFTS approach, corroborating the findings of the altitudinal transect analysis. Finally, future site index dynamics of Norway spruce were simulated on the basis of climate change scenarios using the SSM approach. We recommend for all climate sensitive forest growth and yield models based on spatially and temporally distributed data to properly account for within- and between-group effects to avoid erroneous model predictions under climate change.

Development of Site Index Model for <i>Cryptomeria japonica</i> Stands by the Current Growth Characteristics in South Korea

Development of Site Index Model for <i>Cryptomeria japonica</i> Stands by the Current Growth Characteristics in South Korea

Modeling the Dominant Height of Larix principis-rupprechtii in Northern China—A Study for Guandi Mountain, Shanxi Province

An accurate estimate of the site index is essential for informing decision-making in forestry. In this study, we developed site index (SI) models using stem analysis data to estimate the site index and the dominant height growth for Larix gmelinii var. principis-rupprechtii in northern China. The data included 5122 height–age pairs from 75 dominant trees in 29 temporary sample plots (TSPs). Nine commonly used growth functions were parameterized using the modeling method, which accounts for heterogeneous variance and autocorrelation in the time-series data and introduces sample plot-level random effects in the model. The results show that the Duplat and Tran-Ha I model with random effects described the largest proportion of the dominant height variation. This model accurately evaluated the site quality and predicted the dominant tree height growth in natural Larix forests in the Guandi Mountain region. As an important supplement in improving methods for site quality evaluation, the model may serve as a fundamental tool in the scientific management of larch forests. The research results can inform an accurate evaluation of the site quality and predict the growth of the dominant height in a larch forest in the Guandi Mountain forest area as well as provide a theoretical basis for forest site quality evaluation at similar sites.

Nueva ecuación dinámica de altura dominante e índice de sitio para Pinus chiapensis (Martínez) Andresen en Puebla y Veracruz, México

Introduction: Pinus chiapensis (Martínez) Andresen lacks in many areas of site index (SI) models to classify timber productivity. Objective: To develop a dynamic SI equation, using the generalized algebraic difference approach (GADA) to describe the dominant height growth pattern and classify the productivity of natural stands of P. chiapensis in Puebla and Veracruz, Mexico. Materials and methods: Four theoretical growth models were used to derive six equations in GADA, fitted to dominant height-age observations from stem analyses of 31 trees. Fitting was performed using the Dummy variable method, which is invariant to the base age; autocorrelation and heteroscedasticity were corrected. Results and discussion: Quantitative evaluation, graphical analysis of residuals and growth trends of equations allowed the selection of an equation derived from the Levakovic II model with higher predictive capacity. With this equation and a base age of 50 years, polymorphic SI curves with variable asymptotes were constructed to classify productivity into low, medium and high, corresponding to SI of 25, 32 and 39 m, respectively. The maximum mean annual increase for the SI of 32 m was 1.07 m∙yr-1 and occurred at 11.08 years. The equation exhibited better performance relative to a previously reported polymorphic equation. Conclusions: It is recommended to use the developed equation to predict dominant height growth and SI of P. chiapensis stands in Puebla and Veracruz, Mexico.