Stand-level Parameters Research Articles

Inferring the tree regeneration niche from inventory data using a dynamic forest model

Abstract. The regeneration niche of trees is governed by many processes and factors that are challenging to determine. Besides a species's geographic distribution, which determines if seeds are available, a myriad of local processes in forest ecosystems (e.g., competition and pathogens) exert influences on tree regeneration. Consequently, the representation of tree regeneration in dynamic forest models is a notoriously complicated process which often involves many subprocesses that are often data deficient. The ForClim forest gap model solved this problem by linking species traits to regeneration properties. However, this regeneration module was never validated with large-scale data. Here, we compare this trait-based approach with an inverse calibration approach where we estimate regeneration parameters directly from a large dataset of unmanaged European forests. The inverse calibration was done using Bayesian inference, estimating shade and drought tolerance as well as the temperature requirements for 11 common tree species along with the intensity of regeneration (i.e., the maximum regeneration rate). We find that the parameters determining the species' light niche (i.e., light requirements) are similar for the trait-based and calibrated values for both model variants, but only a more complex model variant that included competition between recruits leads to plausible estimates of the drought niche. The trait-derived temperature niche did not match to the estimates from either model variant using inverse calibration. The parameter estimates differed between the complex and the simple model, with the estimates for the complex model being closer to the trait-based parameters. In both model variants, the calibration strongly changed the parameters that determine regeneration intensity compared to the default. We conclude that the regeneration niche of trees can be recovered from a large forestry dataset in terms of the stand-level parameters light availability and regeneration intensity, while abiotic drivers (temperature and drought) are more elusive. The higher performance (better fit to hold out) of the inversely calibrated models underpins the importance of informing dynamic models by real-world observations. Future research should focus on even greater environmental coverage of observations of demographic processes in unmanaged forests to verify our findings at species range limits under extreme climatic conditions.

Using LiDAR Data to Estimate Biomass in Afforested Bottomland Oak Sites in the Southern United States

Abstract The extent of bottomland hardwood forests in the Lower Mississippi Alluvial Valley (LMAV) has diminished, and federal programs like the Conservation Reserve Program provide incentives to afforest marginal agricultural areas with oaks to provide ecosystem services. Remote sensing technologies, like light detection and ranging (LiDAR), can be used to estimate biomass of these stands to potentially allow landowners to take advantage of carbon markets, but data are expensive to collect. Therefore, we determined whether freely available low-density LiDAR data could capture variability in tree- and stand-level characteristics in the LMAV, including aboveground biomass. We found that multiple regression LiDAR models captured more variability in tree-level than stand-level parameters and including soil type generally improved models. Model r2 values predicting tree and stand parameters including tree height, height to the live crown, quadratic mean diameter, crown area, trees per hectare, stand basal area, and stand biomass ranged from 0.34 to 0.82 and root mean square percent error (RMSPE) ranged from 7% to 36%. Specifically, models for stand biomass had an RMSE of about 19 Mg/ha or about 19% of mean values across sites. Therefore, freely available LiDAR data was useful in evaluating afforested bottomland oak sites for tree- and stand-level structural components in the LMAV.

Integrating UAV-SfM and Airborne Lidar Point Cloud Data to Plantation Forest Feature Extraction

A low-cost but accurate remote-sensing-based forest-monitoring tool is necessary for regularly inventorying tree-level parameters and stand-level attributes to achieve sustainable management of timber production forests. Lidar technology is precise for multi-temporal data collection but expensive. A low-cost UAV-based optical sensing method is an economical and flexible alternative for collecting high-resolution images for generating point cloud data and orthophotos for mapping but lacks height accuracy. This study proposes a protocol of integrating a UAV equipped without an RTK instrument and airborne lidar sensors (ALS) for characterizing tree parameters and stand attributes for use in plantation forest management. The proposed method primarily relies on the ALS-based digital elevation model data (ALS-DEM), UAV-based structure-from-motion technique generated digital surface model data (UAV-SfM-DSM), and their derivative canopy height model data (UAV-SfM-CHM). Following traditional forest inventory approaches, a few middle-aged and mature stands of Hinoki cypress (Chamaecyparis obtusa) plantation forests were used to investigate the performance of characterizing forest parameters via the canopy height model. Results show that the proposed method can improve UAV-SfM point cloud referencing transformation accuracy. With the derived CHM data, this method can estimate tree height with an RMSE ranging from 0.43 m to 1.65 m, equivalent to a PRMSE of 2.40–7.84%. The tree height estimates between UAV-based and ALS-based approaches are highly correlated (R2 = 0.98, p < 0.0001), similarly, the height annual growth rate (HAGR) is also significantly correlated (R2 = 0.78, p < 0.0001). The percentage HAGR of Hinoki trees behaves as an exponential decay function of the tree height over an 8-year management period. The stand-level parameters stand density, stand volume stocks, stand basal area, and relative spacing are with an error rate of less than 20% for both UAV-based and ALS-based approaches. Intensive management with regular thinning helps the plantation forests retain a clear crown shape feature, therefore, benefitting tree segmentation for deriving tree parameters and stand attributes.

Extracting Forest Parameters based on Stand Automatic Segmentation Algorithm

Forest stand segmentation is a critical process for forest management and inventory. The forest stand segmentation accuracy will determine the forest stand level parameters quality. In this study, we developed an automatic forest stand segmentation algorithm based on ArboLiDAR, a software used to process Light Detection and Ranging (LiDAR) point cloud data. We then optimized the parameters for the algorithm to the Dayekou forest area on Qilian Mountain in China to find the most suitable parameters for automatic stand segmentation. Further, we extracting the forest parameters at the stand level based on Bysh method. Our results showed that the limited region growing method based on the gradient is the most suitable one for analyzing automatic stand segmentation in the studied area. Among our tested parameters groups, the fifth group contains the optimal parameters for the studied area. In addition, for forest parameters, the R2 of mean height (H), average diameter at breast height (D), basal area (G), and Stand volume (V) is 0.744, 0.720, 0.562, 0.696, respectively. The RMSE value is 5.24%, 28.57%, 19.93%, and 17.66%, respectively. Our study serves as a technical basis and reference for future studies that perform more efficient analyses on forest resource inventory in China.

ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA

Abstract. Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR) is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs). The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM) from a digital surface model (DSM), and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC) and a variable window filter (VWF), are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits") that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.

ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA

Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR) is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs). The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM) from a digital surface model (DSM), and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC) and a variable window filter (VWF), are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits") that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.

Characterization of the structure, dynamics, and productivity of mixed-species stands: review and perspectives

The growth and yield of mixed-species stands has become an important topic of research since there are certain advantages of this type of forest as regards functions and services. However, the concepts and methods used to characterize mixed stands need to be understood, as well as harmonized and standardized. In this review we have compiled a set of measures, indices, and methods at stand level to characterize the structure, dynamics, and productivity of mixed stands, and we discuss the pros and cons of their application in growth and yield studies. Parameters for the characterization of mixed stand structure such as stand density, species composition, horizontal (intermingling) and vertical tree distribution pattern, tree size distribution, and age composition are described, detailing the potential as well as the constraints of these parameters for understanding resource capture, use, and efficiency in mixed stands. Furthermore, a set of stand-level parameters was evaluated to characterize the dynamics of mixed stands, e.g. height growth and space partitioning, self- and alien-thinning, and growth partitioning among trees. The deviations and changes in the behaviour of the analysed parameters in comparison with pure stand conditions due to inter-specific interactions are of particular interest. As regards stand productivity, we reviewed site productivity indices, the growth–density relationship in mixed stands as well as methods to compare productivity in mixed versus monospecific stands. Finally, we discuss the main problems associated with the methodology such as up-scaling from tree to stand level as well as the relevance of standardized measures and methods for improving forest growth and yield research in mixed stands. The main challenges are also outlined, especially the need for qualitatively sound data.

Tree Species Composition and Diversity in Oban Forest Reserve, Nigeria

Tree species composition and diversity were assessed in Oban Forest Reserve. Three stage sampling procedure was used to collect data on tree parameters – diameter at breast height (dbh); diameters over bark at the base, middle and top; merchantable height and total height using a minimum dbh limit of ≥10 cm in the tertiary sample plots. A total of 808 trees were measured and 72 species identified in the study area. Stand level parameters were estimated and tree diversity assessed. Results indicate that an average number of stems encountered per hectare were 306. Population densities of individual tree species ranged from 1 to 22 ha-1. Population densities of species were poor. The basal area/ha estimated is 34.67m2 and the species richness index obtained was 10.605, which indicate high species richness. The value of Shannon’s Index (HI) is 3.795 which is quite high.The results show that the forest reserve is a well-stocked tropical rainforest in Nigeria. The relative richness of the forest reserve in terms of individual tree species does not correlate well with the abundance because the abundance of each of the species was quite low and density poor. While there is paucity of most species, Staudtia stipitata was the most abundant (22 tree/ha). The forest has reverted back to the turbulent agrading stage of the forest growth cycle.

A thinning routine for large-scale biogeochemical mechanistic ecosystem models

Biogeochemical mechanistic models (BGC models) are used to model the carbon balance of forest ecosystems. Since European forests are managed intensively, a crucial part of carbon modelling is integrating management and thinning routines in the modelling process. In this study, forest inventory data are used to derive information concerning forest management practice. Based on this, a harvesting model is calibrated for simulating the ‘business as usual’ management that can be used in large-scale BGC models. Our approach is based on data from the Austrian National Forest Inventory. The model comprises two sub-models: (1) a logistic model to assess the probability of an inventory point to be thinned and (2) a non-parametric model based on empirical probability density maps to assess the thinning intensity. Since BGC models operate on the stand level, only stand level parameters are integrated in the model such as standing timber carbon, site quality, cover type, elevation and age. A comparison of the predicted and observed proportion of thinned points and the thinning intensity suggests that the model is able to correctly mimic the management regime derived from the inventory data. No systematic trends in the results are evident. Using this thinning model in combination with a mechanistic model will enable assessment of the overall carbon stored in managed forest ecosystems, especially in large-scale modelling applications.

Comparing growth patterns of jack pine and black spruce in mixed natural stands and plantations

This study examined changes in individual tree height and diameter at breast height and stand-level gross total volume over time, comparing plantation-grown jack pine (Pinus banksiana) and black spruce (Picea mariana) to wildfire-origin, mixed conifer stands. Mensurational data were collected from fixed-area plots in 32 plantations (15 jack pine; 17 black spruce; age 6–64 y) and 21 fire-origin conifer-dominated stands (jack pine/black spruce mixtures; age 4–91 y). Using a generalized three-parameter modified-Weibull function, we described relationships for individual tree and standlevel measures of growth and age for managed and natural origin stands, separately, and then compared the functions to assess growth pattern differences between managed and natural stands. Both the black spruce and jack pine plantations consistently outperformed the natural stands based on all measured individual and stand-level parameters, and significantly lowered the age to operability of the trees. For example, the modeled gross total volume at age 50 resulted in “improved” volumes over the natural stands of 35% and 50% for plantation-grown jack pine and black spruce, respectively. The results from this study suggest that establishing and maintaining desired species at moderate and relatively uniform densities on productive boreal sites can significantly increase yields and shorten the age to operability by nearly 15 y.

Individual Tree Diameter, Height, and Volume Functions for Longleaf Pine

Currently, little information is available to estimate individual tree attributes for longleaf pine (Pinus palustris Mill.), an important tree species of the southeastern United States. The majority of available models are local, relying on stem diameter outside bark at breast height (dbh, cm) and not including stand-level parameters. We developed a set of individual tree equations to predict tree height (H, m), stem diameter inside bark at 1.37 m height (dbhIB, cm), stem volume outside bark (VOB, m 3 ), and stem volume inside bark (VIB ,m 3 ), as well as functions to determine merchantable stem volume ratio (both outside and inside bark) from the stump to any top diameter. Local and general models are presented for each tree attribute. General models included stand-level parameters such as age, site index, dominant height, basal area, and tree density. The user should decide which model type to use, depending on data availability and level of accuracy desired. To our knowledge, this is the first comprehensive individual tree-level set of equations reported for longleaf pine trees, including local and general models, which can be applied to longleaf pine trees over a large geographical area and across a wide range of ages and stand characteristics. The system presented here provides important new tools for supporting future longleaf pine management decisions.

Epiphytic lichens in jack pine and black spruce plantations in northern Ontario, Canada: diversity and community patterns

This study was an investigation of epiphytic lichen communities in mid-rotation (45 to 55 years old) managed stands of jack pine (Pinus banksiana) and black spruce (Picea mariana) in northern Ontario. Lichen community structure was compared between pine and spruce stands, and relationships between community structure and environment (substrate, microclimate, and stand-level parameters) were explored. We also assessed habitat associations of lichens with differing growth forms and functional traits. Nonmetric multidimensional scaling was used to analyze community structure, and results showed that differences between pine and spruce stands were most strongly related to stand basal area and light levels, but were also associated with other factors such as branch diameter. In addition, distinct differences in occurrence patterns between functional groups were observed, with fruticose lichens more common in pine stands, and asexual foliose lichens more common in spruce stands. This study represents the first detailed examination of epiphytic lichen community composition in boreal plantation forests in North America. Our results suggest that the conifer species chosen to regenerate a harvested stand will influence epiphytic lichen communities, which may have further implications for ecosystem processes such as nutrient cycling, and for wildlife species dependent on lichens, such as woodland caribou (Rangifer tarandus caribou).

Modelling understorey light for seedling regeneration in continuous cover forestry canopies

Summary Continuous cover forestry (CCF) aims at enhancing stand structural diversity and favouring natural regeneration. To give guidance on how to manage a CCF stand to achieve seedling growth below canopy, an estimate of light transmittance is required. So far, in the UK, only stand-level parameters have been used by managers to predict the understorey light in CCF stands. We assessed a UK Sitka spruce stand undergoing transformation to CCF and measured canopy transmittance using hemispherical pictures. Stand-level characteristics were found to be highly stand specific and not appropriate to predict seedling growth in CCF stands. We parameterized a detailed light model (4C-A-RTM) and a simple one-layer turbid medium model (BL). A sensitivity analysis was performed to test the effect of key stand structural parameters on the modelled transmittance. Measured transmittance from hemispherical photographs was used to validate the models. Both models tended to underestimate canopy transmittance but were positively related to current-year growth of the below canopy seedlings (R 2 = 0.92, P < 0.001). Comparison of the two models showed that the 4C-A-RTM provided a better estimation of light transmittance across observed canopy structural differences. Furthermore, the inclusion of stand characteristics in the 4C-A-RTM is likely to confer greater applicability across stands.

A simple technique for estimating cubic foot volume yields

A simple stand volume yield equation based on easily obtained stand level parameters was found to provide reasonably accurate estimates of cubic foot volume yields across a wide variety of growth and yield systems, historic yield tables and permanent plot data. Yield estimates based on stand basal area per hectare and average dominant height were generally within 5% of yields obtained from two popular growth and yield simulators for plantation grown southern pines. Hardwood yield estimates were found to be within 10% of existing historic yield tables, a hardwood growth simulator, and existing permanent plot data.

AN INDIVIDUAL-PLANT GROWTH SIMULATION MODEL FOR QUANTIFYING PLANT COMPETITION

Plant competition models traditionally have used population or stand level parameters as a basis for modeling. While such models may be valid with regard to average responses, they fail to account for important factors such as within stand variability and spatial relationships. This translates to an assumption of uniformity in growth characteristics among individual plant,S as well as an equidistant spacing arrangement which are unlikely in real populations. One alternative is to model the growth characteristics of individual plants separately which, when combined as a system, will inherently have popUlation attributes related to competition. Competition models of this type allow for various combinations of growth patterns and spatial arrangements. An individual-plant based simulation model is introduced and the relationships of model parameters with existing concepts in plant competition are discussed. Models are calibrated to wild oat (Avenafatua) and spring barley (Hordeum vulgare) using data from replicated field experiments in Northern Idaho.

Measuring Forest Site Quality Using the Parameters of a Dimensionally Compatible Height Growth Function

Abstract This article presents a new approach to modeling forest site quality which centers on interpreting as measures of site quality the parameters of a stand height growth model. It is argued that site index, which relies on a single height at a particular age, is not capable of reflecting important differences in the shapes of height growth trajectories. In the proposed approach, the parameters of the height growth function and the function itself completely describe the projected dominant height growth curve. In order to make the approach applicable in a wide variety of modeling situations, a method of allowing the number of site quality parameters to vary is proposed. The most flexible model configuration allows the estimation of all parameters separately for each stand and requires remeasurement or stem analysis data that span a relatively long period of time. Less flexible model configurations involve the simultaneous estimation of parameters that are different for each stand (stand-level parameters) with parameters that are the same for all stands (global parameters). Stand-level parameters measure different aspects of site quality that are unique for each stand and global parameters measure different aspects of site quality that are unique for each stand. These less flexible model configurations may be useful when modeling objectives warrant combining stand-level and global parameters or in situations where the mixture of longitudinal and cross-sectional data precludes fitting the most flexible model form. The approach works best with a growth function that offers maximum flexibility with a minimum of parameters and when the parameters of the growth function have intuitive biological interpretations. A new growth function with these properties is presented in this paper. The new growth function is dimensionally compatible and has two biologically meaningful parameters: a rate parameter and a maximum size parameter. The integrated form of the growth function also includes initial conditions which can be specified with data or as an additional parameter to be estimated. Two applications of the approach to modeling dominant height growth of loblolly pine (Pinus taeda L.) are described: one where sufficient longitudinal data allowed fitting several model forms and another where data limitations required the use of less flexible models. For. Sci. 38(2):409-429.