Basal Area Models Research Articles

Evaluation of the Performance of Climatic Scenarios in the Basal Area Model of Trees (Case Study: the Farim Forest)

Evaluation of the Performance of Climatic Scenarios in the Basal Area Model of Trees (Case Study: the Farim Forest)

Developing the Additive Systems of Stand Basal Area Model for Broad-Leaved Mixed Forests.

Stand basal area (SBA) is an important variable in the prediction of forest growth and harvest yield. However, achieving the additivity of SBA models for multiple tree species in the complex structure of broad-leaved mixed forests is an urgent scientific issue in the study of accurately predicting the SBA of mixed forests. This study used data from 58 sample plots (30 m × 30 m) for Populus davidiana × Betula platyphylla broad-leaved mixed forests to construct the SBA basic model based on nonlinear least squares regression (NLS). Adjustment in proportion (AP) and nonlinear seemingly unrelated regression (NSUR) were used to construct a multi-species additive basal area prediction model. The results identified the Richards model (M6) and Korf model (M1) as optimal for predicting the SBA of P. davidiana and B. platyphylla, respectively. The SBA models incorporate site quality, stand density index, and age at 1.3 m above ground level, which improves the prediction accuracy of basal area. Compared to AP, NSUR is an effective method for addressing the additivity of basal area in multi-species mixed forests. The results of this study can provide a scientific basis for optimizing stand structure and accurately predicting SBA in multi-species mixed forests.

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

Modeling basal area yield using simultaneous equation systems incorporating uncertainty estimators

Abstract Over the last three decades, many growth and yield systems developed for the southeast USA have incorporated methods to create a compatible basal area (BA) prediction and projection equation. This technique allows practitioners to calibrate BA models using both measurements at a given arbitrary age, as well as the increment in BA when time series panel data are available. As a result, model parameters for either prediction or projection alternatives are compatible. One caveat of this methodology is that pairs of observations used to project forward have the same weight as observations from a single measurement age, regardless of the projection time interval. To address this problem, we introduce a variance–covariance structure giving different weights to predictions with variable intervals. To test this approach, prediction and projection equations were fitted simultaneously using an ad hoc matrix structure. We tested three different error structures in fitting models with (i) homoscedastic errors described by a single parameter (Method 1); (ii) heteroscedastic errors described with a weighting factor ${w}_t$ (Method 2); and (iii) errors including both prediction ($\overset{\smile }{\varepsilon }$) and projection errors ($\tilde{\varepsilon}$) in the weighting factor ${w}_t$ (Method 3). A rotation-age dataset covering nine sites, each including four blocks with four silvicultural treatments per block, was used for model calibration and validation, including explicit terms for each treatment. Fitting using an error structure which incorporated the combined error term ($\overset{\smile }{\varepsilon }$ and $\tilde{\varepsilon}$) into the weighting factor ${w}_t$ (Method 3), generated better results according to the root mean square error with respect to the other two methods evaluated. Also, the system of equations that incorporated silvicultural treatments as dummy variables generated lower root mean square error (RMSE) and Akaike’s index values (AIC) in all methods. Our results show a substantial improvement over the current prediction-projection approach, resulting in consistent estimators for BA.

Modeling spatial patterns of longleaf pine needle dispersal using long-term data

BackgroundPredicting patterns of fire behavior and effects in frequent fire forests relies on an understanding of fine-scale spatial patterns of available fuels. Leaf litter is a significant canopy-derived fine fuel in fire-maintained forests. Litter dispersal is dependent on foliage production, stand structure, and wind direction, but the relative importance of these factors is unknown.ResultsUsing a 10-year litterfall dataset collected within eighteen 4-ha longleaf pine (Pinus palustris Mill.) plots varying in canopy spatial pattern, we compared four spatially explicit models of annual needle litter dispersal: a model based only on basal area, an overstory abundance index (OAI) model, both isotropic and anisotropic litter kernel models, and a null model that assumed no spatial relationship. The best model was the anisotropic model (R2 = 0.656) that incorporated tree size, location, and prevailing wind direction, followed by the isotropic model (R2 = 0.612), basal area model (R2 = 0.488), OAI model (R2 = 0.416), and the null model (R2 = 0.08).ConclusionsAs with previous studies, the predictive capability of the litter models was robust when internally validated with a subset of the original dataset (R2 = 0.196–0.549); however, the models were less robust when challenged with an independent dataset (R2 = 0.122–0.319) from novel forest stands. Our model validation underscores the need for rigorous tests with independent, external datasets to confirm the validity of litter dispersal models. These models can be used in the application of prescribed fire to estimate fuel distribution and loading, as well as aid in the fine tuning of fire behavior models to better understand fire outcomes across a range of forest canopy structures.

Interactive effects of functional traits and rainfall event size on stemflow in a tropical montane cloud forest

AbstractThis study analyses how rainfall (P) event size influences the effect of functional traits on stemflow volume of woody plants in a tropical montane cloud forest. In addition, it was investigated whether multiple regression models based on functional traits provide better predictions of stemflow volume than allometric equations using only basal area (BA). Eighteen functional traits of size, structure, bark and leaf of woody plants in the overstory, midstory and understory were analysed. Stemflow volume was determined to a greater extent by size traits, mainly basal area and crown area, with these relationships becoming stronger as P event size increased. Stemflow volume was also influenced by other traits that affect the efficiency with which individuals produced stemflow. The crown area/basal area ratio showed the strongest correlation with the stemflow production efficiency metrics (funnelling ratio and stemflow proportion). Bark traits had a negative effect on funnelling ratio and stemflow proportion, with a tendency to be stronger in smaller P events, while vascular epiphyte cover showed the opposite trend. Multiple regression models with two to three variables explained 43 to 81% of stemflow volume variation, depending on P event size. For P events <15 mm, the fit was generally better for multiple regression models than for basal area models, while for P events >15 mm, differences among models were small. Functional traits largely define stemflow at the level of individual plants, but this relationship is mediated by P event size.

A model of cross-sectional growth of Cunninghamia lanceolata plantation in Hunan province with climate effects

Objective: The influence of climate on forest stands cannot be ignored, but most of the previous forest stand growth models were constructed under the presumption of invariant climate and could not estimate the stand growth under climate change. The model was constructed to provide a theoretical basis for forest operators to take reasonable management measures for fir under the influence of climate. Methods: Based on the survey data of 638 cedar plantation plots in Hunan Province, the optimal base model was selected from four biologically significant alternative stand basal area models, and the significant climate factors without serious covariance were selected by multiple stepwise regression analysis. The optimal form of random effects was determined, and then a model with climatic effects was constructed for the cross-sectional growth of fir plantations. Results: Richards formula is the optimal form of the basic model of stand basal area growth. The coefficient of adjustment was 0.8355; the average summer maximum temperature and the water vapor loss in Hargreaves climate affected the maximum and rate of fir stand stand growth respectively, and were negatively correlated with the stand growth. The adjusted coefficient of determination of the fir stand area break model with climate effects was 0.8921, the root mean square error (RMSE) was 3.0792, and the mean relative error absolute value (MARE) was 9.9011; compared with the optimal base model, improved by 6.77%, RMSE decreased by 19.04%, and MARE decreased by 15.95%. Conclusion: The construction of the stand cross-sectional area model with climate effects indicates that climate has a significant influence on stand growth, which supports the rationality of considering climate factors in the growth model, and it is important for the regional stand growth harvest and management of cedar while improving the accuracy and applicability of the model.

Comparing basal area growth models for Norway spruce and Scots pine dominated stands

Models that predict forest development are essential for sustainable forest management. Constructing growth models via regression analysis or fitting a family of sigmoid equations to construct compatible growth and yield models are two ways these models can be developed. In this study, four species-specific models were developed and compared. A compatible growth and yield stand basal area model and a five-year stand basal area growth model were developed for Scots pine ( L.) and Norway spruce ( (L.) Karst.). The models were developed using data from permanent inventory plots from the Swedish national forest inventory and long-term experiments. The species-specific models were compared, using independent data from long-term experiments, with a stand basal area growth model currently used in the Swedish forest planning system Heureka (Elfving model). All new models had a good, relatively unbiased fit. There were no apparent differences between the models in their ability to predict basal area development, except for the slightly worse predictions for the Norway spruce growth model. The lack of difference in the model comparison showed that despite the simplicity of the compatible growth and yield models, these models could be recommended, especially when data availability is limited. Also, despite using more and newer data for model development in this study, the currently used Elfving model was equally good at predicting basal area. The lack of model difference indicate that future studies should instead focus on model development for heterogeneous forests which are common but lack in growth and yield modelling research.Pinus sylvestrisPicea abies

Disentangling direct and indirect effects of island area on plant functional trait distributions

AbstractAimSpecies diversity on islands generally increases with island area. This might arise either from direct effects of island area via neutral assembly processes or from indirect effects via habitat and structural differences between islands that scale positively with island area. Here, we tested whether community‐weighted functional trait means of woody plants are directly or indirectly affected by island area to elucidate how functional traits mediate the assembly on differently sized islands.LocationTwenty‐eight tropical islands (25 m2 – 12,000 m2) in the Raja Ampat archipelago, Indonesia.TaxonWoody angiosperms.MethodsStudied islands had a shared geological history but differed in terms of area, habitat quality expressed by soil depth, forest structure expressed by tree basal area and degree of isolation. Traits studied were seed and fruit mass, tree height, wood density, leaf mass per area, leaf nitrogen concentration and chlorophyll content (estimated from chlorophyll‐meter units) and summarised as community‐weighted means (CWM) for each island. Using liner regression, we tested whether CWMs were correlated to island area and basal area and structural equation models (SEMs) to test on direct and indirect effects of island area, basal area, soil depth and isolation on trait distributions.ResultsCWM of seed mass, tree height and chlorophyll content increased with both island area and basal area, whereas leaf nitrogen concentration decreased with increasing basal area. Fruit mass was not correlated to island area and basal area. SEMs revealed that the shifts in tree height, wood density, leaf nitrogen concentration and chlorophyll content were caused directly by basal area, which in turn was directly and positively affected by both island area and soil depth. Differences in seed mass among islands were explained by combined effects of basal area, island area and isolation, whereas fruit mass was only explained by isolation.Main conclusionsTrait values shifted systematically across islands of different sizes. Being small and having light seeds are prevailing trait combinations for establishing on small islands with simple forest structure. For establishment on larger islands with more complex forest structures, species are taller, have heavier seeds, higher chlorophyll content and lower leaf N concentrations. We conclude that mechanisms affecting CWM on islands directly link to ecological differences between islands like forest structure – and only indirectly to island area.

Additivity of stand basal area predictions in canopy stratifications for natural oak forests

Stand basal area is an important variable for evaluating forest productivity. In this study, four types of growth models were used for modeling the stand basal area of natural oak forests in Hunan Province, China. The results showed that the Schumacher model (Eq. (3)) performed the best. Because of different canopy layers in natural oak secondary forests, it is important to develop stand basal area models considering canopy stratification. Three widely used canopy stratification methods were used for natural oak forests: the clustering analysis of tree height, classification scheme of the International Union of Forest Research Organizations (IUFRO) standard, and canopy competition cut-off height (CCH) method. According to the standards set forth in the National Standards of the People’s Republic of China (GB/T 26424-2010), the forests were divided into two layers: upper and lower. Among the three canopy stratification approaches, the IUFRO method was selected as the best. In addition, it should be noted that the sum of the basal area predictions at different layers can generate inconsistent results, as compared to predictions from the total stand basal area model. To solve the additivity of stand basal area predictions, the additive stand basal area growth model was established by nonlinear seemingly unrelated regression (NSUR). Comparing with the whole stand basal area growth model without stratification, the R2 values of basal area growth models with stratification increased by 4.78% (from 0.9277 to 0.9721). In addition, compared with equations estimated separately, the NSUR method increased R2 by 0.82% (from 0.9721 to 0.9801). The results reveal that the NSUR method can be applied to solve the additivity of stand basal area predictions at each layer and can improve model precision, which provides helpful insights into developing stand growth models in natural forests.

A Multivariate Hybrid Stochastic Differential Equation Model for Whole-Stand Dynamics

The growth and yield modeling of a forest stand has progressed rapidly, starting from the generalized nonlinear regression models of uneven/even-aged stands, and continuing to stochastic differential equation (SDE) models. We focus on the adaptation of the SDEs for the modeling of forest stand dynamics, and relate the tree and stand size variables to the age dimension (time). Two different types of diffusion processes are incorporated into a hybrid model in which the shortcomings of each variable types can be overcome to some extent. This paper presents the hybrid multivariate SDE regarding stand basal area and volume models in a forest stand. We estimate the fixed- and mixed-effect parameters for the multivariate hybrid stochastic differential equation using a maximum likelihood procedure. The results are illustrated using a dataset of measurements from Mountain pine tree (Pinus mugo Turra).

Passive restoration in Araucaria Forest: useful ecological indicators in monitoring successional advancement in exotic tree plantation landscapes

Monitoring successional advancement is a complex field involving a constant search for applied ecological indicators which facilitate monitoring of secondary forests for both active and passive restoration. In this study, the authors investigate the successional advancement of floristics and tree structure within Araucaria Forest (AF) fragments under passive restoration in a context where exotic tree plantations (mainly Pinus L. genus) dominate the landscape. The ecological indicators used were floristic dissimilarity (β‐diversity inferences), indicator species, ecological groups of species, basal area, and species abundance distribution (SAD) models (α‐diversity inferences). A total of 182 tree species belonging to 91 genera and 43 botanical families were identified. A high β diversity was verified for which each site has indicator species (for the locations CD—Dicksonia sellowiana; CO—Cryptocarya aschersoniana; and PG—Pinus taeda), where pioneer species contributed to much of the abundance. Different SAD models are useful for describing passive restoration sites in exotic tree plantation landscapes, namely Lognormal, Mandelbrot, and Zipf. SAD models together with basal area, taxonomic group (e.g. Myrtaceae assemblage), and tree abundance in ecological groups are strategic ecological indicators for monitoring successional advancement in AF.

Basal area growth models for Eucalyptus tereticornis Smith stands on the Colombian Atlantic coast

Eucalyptus tereticornis is an important species used in reforestation programs in Colombia. Information on the dynamics and development of the E. tereticornis stands is required to improve management planning. This study compares nine basal area growth models, evaluating their goodness of fit and prediction, and describes their linkage to a thinning response model for E. tereticornis plantations. The evaluated models showed a good fit to the data, the R2adj ranged between 0.90 - 0.92 and 0.69 - 0.86 for the basal area projection and prediction models, respectively. The root of the mean square error (RMSE) ranged between 1.080 m2 ha-1 - 1.343 m2 ha-1 for basal area projection models and 1.671 m2 ha-1 - 2.206 m2 ha-1 for basal area prediction models. The selected basal area model for unthinned stands depends on the age, stand density, and dominant height. For the thinned stands, the basal area was predicted using a competition index that depends on the age and the dominant height of the stand. The competition index had an R2adj = 0.87, and a standard error of estimate of 0.031%. The system of equations presented a slight tendency to overestimate with a mean error of -0.14 m2 ha-1 and a RMSE of 0.696 m2 ha-1. This way, the developed models have the potential to be applied to unthinned and thinned stands with different ages, productivity, and planting densities. The developed models provide new tools to support forest management and research of the species growing in plantations.

Spatially Quantifying Forest Loss at Landscape-scale Following a Major Storm Event

Large scale forest disturbances are becoming more frequent across the world, and remote sensing must play a role in informing and prioritizing immediate, short-term and long-term disaster response and recovery. However, such evaluations from remote sensing are currently limited (e.g., burned area severity and change NDVI) and do not always explicitly relate to change in resources of interest. Herein we demonstrate a novel method to predict basal area loss, validated by independent field evaluations. Hurricane Michael made landfall on Mexico Beach in the Florida panhandle as a Category 5 storm on October 10th, 2018. The storm affected roughly 2 million hectares of largely forested land in the area. In this study, we use Sentinel-2 imagery and 248 forest plots collected prior to landfall in 2018 in the forests impacted by Hurricane Michael to build a general linear model of tree basal area across the landscape. The basal area model was constrained to areas where trees were present using a tree presence model as a hurdle. We informed the model with post-hurricane Sentinel-2 imagery and compared the pre- and post- hurricane basal area maps to assess the loss of basal area following the hurricane. The basal area model had an r-squared value of 0.508. Plots were revisited to ground truth the modelled results; this showed that the model performed well at categorizing forest hurricane damage. Our results validate a novel method to create a landscape scale spatial dataset showing the location and intensity of basal area loss at 10-m spatial resolution which can be used for quantifying forest disturbances worldwide.

Carbon stock assessment using forest canopy density mapper in agroforestry land in Berau, East Kalimantan, Indonesia

Abstract. Hartoyo APP, Prasetyo LB, Siregar IZ, Supriyanto, Theilade I, Siregar UJ. 2019. Carbon stock assessment using forest canopy density mapper in agroforestry land in Berau, East Kalimantan, Indonesia. Biodiversitas 20: 2661-2676. In the Reducing Emissions from Deforestation and forest Degradation (REDD+) program, remote sensing is the most important tool for measuring forest cover and carbon dynamic, including the utilization of software Forest Canopy Density (FCD) mapper. However, there have been rarely untested the accuracy of FCD applied in agroforestry landscapes to support carbon stock assessment compared to conventional field measurement data. This research was aimed to investigate the correlation between: (i) the value of FCD (%) and tree stand density (N/ha), (ii) the value of FCD (%) and basal area (m2/ha), (iii) the value of FCD (%) and total carbon stock (Mg C/ha), and iv) total carbon stock and percentage of canopy closure (%). Tree stand density, basal area, carbon stock and canopy profile were conventionally measured by trained members of local communities. The results of this study showed that the R2 between FCD and tree density was 37.7% (r = 61.4%), while the R2 between FCD and the basal area was 3.33% (r = 18.3%). The result of normality and heteroscedasticity tests showed that FCD was more accurate and precise in estimating the tree stand density model than the basal area model. Total carbon stock differed significantly (p<0.1) from tree density with R2 = 27.7% (r = 27.3%). Total carbon can be predicted using FCD with total carbon (Mg C/ha) = 13.005 + 0.826 FCD. Our findings suggest that FCD can be used as a new method to estimate tree density and total carbon stock cheaply, efficiently and accurately to support carbon stock assessment in agroforest practices. In carbon assessment, total carbon stock can also be estimated using canopy closure measurement.

Transferability of Lidar-derived Basal Area and Stem Density Models within a Northern Idaho Ecoregion

A patchwork of disjunct lidar collections is rapidly developing across the USA, often acquired with different acquisition goals and parameters and without field data for forest inventory. Airborne lidar and coincident field data have been used to estimate forest attributes across individual lidar extents, where forest measurements are collected using project-specific inventory designs. This research explores predicting forest attributes at locations not represented in the training data by combining lidar and field measurements from ecologically similar forests. Using field measurements from six lidar units, random forests regression models were created by systematically withholding forest inventory data from one lidar unit and using the forest inventory data from the other five units to predict basal area and stem density at the withheld unit. Results indicate that BA models produce more accurate predictions than stem density models when transferred to a lidar unit that did not contain field data. Relative root mean square errors calculated from the withheld field plots ranged between 32.3%–50.1% for BA and 40.7%–67.3% for stem density models. It is concluded that forest managers may use predictive models constructed from ecologically similar forests to obtain a preliminary estimate of resources, until local field measurement can be obtained.RÉSUMÉUn ensemble disparate de collections de données lidar se développe rapidement aux États-Unis, souvent acquises avec des objectifs et des paramètres différents et sans données de terrains relatives aux inventaires forestiers. Des données de terrain concomitantes sont utilisées pour estimer des attributs forestiers au sein d’étendues de lidar aéroporté individuelles, pour lesquelles les mesures d’intérêt correspondent à des modèles d’inventaires déterminés. En combinant des mesures de terrain et de lidar provenant de forêts écologiquement similaires, cette étude examine la prédiction d’attributs forestiers à des endroits où des données d’entrainement sont absentes. En utilisant des mesures de terrain, des modèles de régression produits par des forêts d’arbres décisionnels ont été créés pour six unités de lidar. Un procédé systématique a été suivi par lequel les données de chaque unité sont retenues et la surface terrière et densité des tiges de cette unité sont ensuite estimés avec les données des cinq unités restantes. Les résultats indiquent que les estimations de surface terrière sont plus robustes que pour la densité de tiges lorsque les modèles sont transférés à une unité de lidar pour laquelle les données de terrain sont absents. Les erreurs quadratiques moyennes relatives calculées pour les échantillons retenus sont entre 32.3 % et 50.1 % pour les modèles de surface terrière et entre 40.7 % et 67.3 % pour la densité des tiges. Nous concluons que, jusqu'à ce que des mesures de terrain locales puissent être acquises, il est possible d’obtenir des estimations préliminaires de ressources forestières en utilisant des modèles prédictifs construits à partir d'échantillons de forêts écologiquement similaires.

Erratum

Erratum

Predicting live and dead tree basal area of bark beetle affected forests from discrete-return lidar

Bark beetle outbreaks have killed large numbers of trees across North America in recent years. Lidar remote sensing can be used to effectively estimate forest biomass, but prediction of both live and dead standing biomass in beetle-affected forests using lidar alone has not been demonstrated. We developed Random Forest (RF) models predicting total, live, dead, and percent dead basal area (BA) from lidar metrics in five different beetle-affected coniferous forests across western North America. Study areas included the Kenai Peninsula of Alaska, southeastern Arizona, north-central Colorado, central Idaho, and central Oregon, U.S.A. We created RF models with and without intensity metrics as predictor variables and investigated how intensity normalization affected RF models in Idaho. RF models predicting total BA explained the most variation, whereas RF models predicting dead BA explained the least variation, with live and percent dead BA models explaining intermediate levels of variation. Important metrics varied between models depending on the type of BA being predicted. Generally, height and density metrics were important in predicting total BA, intensity and density metrics were important in predicting live BA, and intensity metrics were important in predicting dead and percent dead BA. Several lidar metrics were important across all study areas. Whether needles were on or off beetle-killed trees at the time of lidar acquisition could not be ascertained. Future work, where needle conditions at the time of lidar acquisition are known, could improve upon our analysis and results. Although RF models predicting live, dead, and percent dead BA did not perform as well as models predicting total BA, we concluded that discrete-return lidar can be used to provide reasonable estimations of live and dead BA. Our results also showed which lidar metrics have general utility across different coniferous forest types.

Stand basal area model for Cunninghamia lanceolata (Lamb.) Hook. plantations based on a multilevel nonlinear mixed-effect model across south-eastern China

Based on a multilevel nonlinear mixed-effect model approach, a stand basal area model was developed for Cunninghamia lanceolata (Lamb.) Hook. plantations belonging to the National Forest Inventory in China. The database consists of 583 plots embracing 18 different blocks within three seed source sites in this study. Of the plots, 80% were randomly selected for model fitting and 20% were carried out for model validation. The modified Chapman–Richards and Schumacher models were evaluated to find a basic model. The explanatory variables included stand dominant height, stand age and total number of stems per hectare. After selection of the basic model, the fitting and predictive ability of a multilevel nonlinear mixed-effect model was analysed. Site-, block- and plot-level random-effects terms were assessed for their contributions to improve model prediction over the ordinary least squares (OLS) method widely used in forest management. In addition, within-plot variance–covariance structure was taken into account owing to the repeated measurements and hierarchical structure of the data set. When evaluating the predictive accuracy of the final model, the first measurement was used for estimation of random parameters. The Chapman–Richards model was finally selected for the basic model based on model-fitting statistics, and both the fitting model and validation data with site-, block- and plot-level random effects showed a substantial improvement compared with the OLS method. After taking into account a reasonable variance–covariance structure, the model performed better than the model developed using only random effects.

Exploiting tree shadows on snow for estimating forest basal area using Landsat data

Basal area (BA) is a basic structural and ecological attribute of forests that is often used to describe forest composition, estimate volume of wood, and guide management decisions. BA is the sum of cross-sectional area of trees measured at 1.37m above ground surface, per unit area, and is most commonly measured in-situ. The objective of this study was to supply estimates of BA for oak woodlands and savannas on the 12,828.5ha Sherburne National Wildlife Refuge in Central Minnesota to guide management efforts. We used winter and summer Landsat imagery, combined with field measurements, to assess the potential for improving forest BA estimates by taking advantage of the high spectral contrast between sunlit snow, forest canopy elements, and shadows projected onto snow ground cover. We explained up to 90% of measured variation in BA using partial least squares regression models calibrated using single- and multiple-date winter Landsat data (R2=0.898, RMSE=2.79m2ha−1), which performed better than models calibrated using summer imagery (R2=0.762, RMSE=3.85m2ha−1). Success of the winter-based BA models may be driven, in part, by potential geometric/allometric relationships between cast shadow and forest BA, but definitive proof of this is a topic for future research. This method of BA estimation is not refuge-specific and may be extended for regional use to manage oak forest wherever winter snow coverage is consistent. Additional research is needed to determine the degree of robustness to variations in the empirical relationship between BA and tree shading patterns across different forest functional types.