Bogdan Strimbu
Research Scientist III - Bioinformatica
Publications
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article
Diameter And Height Modeling For Accurate Prediction Of Tree Size In A Douglas-Fir Rainforest |
West Todd; Strimbu Bogdan | Forestry, 2025 | |
AbstractModern forest inventories increasingly integrate ground and aerial datasets. Often, prediction of both tree heights from ground measurements and tree diameters from aerial point clouds is thus required. This study jointly evaluates 77 fixed-effect regression forms predicting either (1) total height or (2) diameter at breast height of individual trees. Three conifer, three broadleaved, and a group of less common tree species in North America's central Pacific Temperate Rainforest are considered. Prediction accuracy was dominated by selection of base model form and differences between naturally regenerated and plantation stands, resulting in model efficiencies near 90% for height and 85% for diameter. Inclusion of generalizing stand structure and physiographic variables increased height model efficiency by 0.0%-1.2%, comparable to the 0.0%-0.9% increase from generalizing diameter models. This broad evaluation and selection process enables increased forest inventory accuracy and improved tree growth prediction by evaluating new and existing allometric model forms, creating or substantially revising allometric models for study area species, and establishing a basis for further model development in any forest. Generalized additive models, in particular, were preferred to nonlinear or linear regressions in 65% of species and response variable combinations, indicating opportunity to revise nonlinear regressions to better utilize their greater interpretability, quicker fitting, and rapid evaluation times. |
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article
Impact Of Climate On The Growth And Yield Of The Main Tree Species From Romania Using Dendrochronological Data |
Gheorghe Marin; Strimbu Bogdan M. | Plants-Basel, 2025 | |
AbstractNational Forest Inventories (NFIs) offer a comprehensive and consistent dataset for forest analysis, enabling the refinement of growth and yield models by integrating regional environmental factors. This study investigates the influence of climate on the growth of three dominant tree species in Romania: Norway spruce (Picea abies L. Karst), European beech (Fagus sylvatica L.), and Sessile oak (Quercus petraea (Matt.) Liebl). Increment core analysis revealed a general increase in diameter growth since 1960, partially correlated with temperature trends. Repeated measures analysis confirmed significant variations in radial growth across ecoregions. The analysis further explored the impact of climatic variables on diameter at breast height (DBH) and basal area (BA) growth and yield. Among nine climatic attributes and their combinations, total precipitation and average growing season temperature significantly affected DBH and BA growth. However, yield was largely insensitive to precipitation, with only Sessile oak yield showing a temperature dependence. Beyond ecoregion and climate, the growth and yield of DBH and BA exhibited positive correlations with the calendar year, age, and previous growth/yield values. Notably, DBH and BA growth demonstrated a dependence on the preceding four to five years, whereas yield was significantly influenced only by the previous year. The observed influence of both the calendar year and previous years suggests a prolonged environmental memory in tree growth and yield responses. |
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article
Assessment Of Herbaceous Vegetation Classification Using Orthophotos Produced From The Image Acquired With Unmanned Aerial Systems |
Wickramarathna S.; Goetz J.; III; Souder J.; Protzman B.; Shepard B.; Herban S.; Mauro F.; Hailemariam T.; Strimbu B.M. | Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 2023 | |
AbstractArguably the most popular remote-sensing products are classified images. However, there are no definitive procedures to assess classification accuracy that simultaneously consider resources available and field efforts. The explosive usage of unmanned aerial systems (UAS) in land surveys adds new challenges to classification assessment, as orthorectified images usually contain significant artifacts. This study aims to identify the optimal ratio between training and validation sample size within a supervised classification approach applied to UAS orthophotos. As a case study, we used a wetland area west of Portland, OR, USA, treated with various glyphosate formulations to control Phalaris arundinacea, commonly known as reed canary grass. A completely randomized design with five replications and six glyphosate formulations was used to assess P. arundinacea vigor following repeated herbicide applications. The change in P. arundinacea vitality was monitored with high-resolution four-band imagery acquired with a SlantRange 3PX camera installed on a DJI Matrice 210. The orthophotos created from images were produced with Pix4D, which was subsequently preprocessed with ERDAS Imagine 2020 to reduce the noise, shadows, and artifacts. All images were classified with the maximum likelihood classification algorithm. Simple random and stratified random sampling methods were applied to collect training and validation samples, evaluating eight ratios of training to validation samples to assess their classification accuracy. We found that increasing the training-to-validation sample size ratio enhances accuracy, with the 3:1 ratio being the most reliable in classifying P. arundinacea vigor. Our study provides evidence that image preprocessing and enhancement are essential for UAS-based imagery. © Articles by the authors; Licensee UASVM and SHST, Cluj-Napoca, Romania. The journal allows the author(s) to hold the copyright/to retain publishing rights without restriction. |
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article
Coppice Management For Young Sycamore Maple (Acer Pseudoplatanus L.) |
Strimbu Bogdan M.; Nicolescu Valeriu-Norocel | Forests, 2023 | |
AbstractSycamore is a valuable tree not only economically but also ecological and culturally. Even though it has a vigorous regeneration system from its stump, its coppice management has triggered limited formal investigations. Therefore, the present study focused on finding the most suitable coppice strategy for achieving ground coverage and biomass, as well as developing growth and yield models for sycamore maples. Using a series of eight measurements spanning twenty-one years, starting from age six, we found that single-shoot coppices provided superior yields for height than seed-managed trees up to age twelve and up to age twenty for DBH. The coppice trees outperformed the seed trees up to age 10. The yield of DBH and the height for single-shoots and seed-managed trees were described by parsimonious formulations, namely the Schumacher model for DBH and the square root for height. The relationship of DBH-height exhibited a clear linear form, pointing toward the main limitation of the study, namely the confinement to ages less than 20 years. Nevertheless, all the models exhibited a bias R-2 around 80%, except for the height and DBH change throughout time, which was around 67%. |
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article
Efficient Synthetic Generation Of Ecological Data With Preset Spatial Association Of Individuals |
Strimbu Bogdan M.; Paun Andrei; Amarioarei Alexandru; Paun Mihaela; Strimbu Victor F. | Canadian Journal Of Forest Research, 2021 | |
AbstractMany experiments cannot feasibly be conducted as factorials. Simulations using synthetically generated data are viable alternatives to such factorial experiments. The main objective of the present research is to develop a methodology and platform to synthetically generate spatially explicit forest ecosystems represented by points with a predefined spatial pattern. Using algorithms with polynomial complexity and parameters that control the number of clusters, the degree of clusterization, and the proportion of nonrandom trees, we show that spatially explicit forest ecosystems can be generated time efficiently, which enables large factorial simulations. The proposed method was tested on 1200 synthetically generated forest stands, each of 25 ha, using 10 spatial indices: Clark-Evans aggregation index; Ripley's K; Besag's L; Morisita's dispersion index; Greig-Smith index; the size dominance index of Hui; index of nonrandomness of Pielou; directional index and mean directional index of Corral-Rivas; and size differentiation index of Von Gadow. The size of individual trees was randomly generated aiming at variograms such as real forests. We obtained forest stands with the expected spatial arrangement and distribution of sizes in less than 1 h. To ensure replicability of the study, we have provided free, fully functional software that executes the stated tasks. |
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article
Nonlinear Parsimonious Forest Modeling Assuming Normal Distribution Of Residuals |
Strimbu Bogdan M.; Amarioarei Alexandru; Paun Mihaela | European Journal Of Forest Research, 2021 | |
AbstractTo avoid the transformation of the dependent variable, which introduces bias when back-transformed, complex nonlinear forest models have the parameters estimated with heuristic techniques, which can supply erroneous values. The solution for accurate nonlinear models provided by Strimbu et al. (Ecosphere 8:e01945, 2017) for 11 functions (i.e., power, trigonometric, and hyperbolic) is not based on heuristics but could contain a Taylor series expansion. Therefore, the objectives of the present study are to present the unbiased estimates for variance following the transformation of the predicted variable and to identify an expansion of the Taylor series that does not induce numerical bias for mean and variance. We proved that the Taylor series expansion present in the unbiased expectation of mean and variance depends on the variance. We illustrated the new modeling approach on two problems, one at the ecosystem level, namely site productivity, and one at individual tree level, namely stem taper. The two models are unbiased, more parsimonious, and more precise than the existing less parsimonious models. This study focuses on research methods, which could be applied in similar studies of other species, ecosystem, as well as in behavioral sciences and econometrics. |
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article
Development Of Nonlinear Parsimonious Forest Models Using Efficient Expansion Of The Taylor Series: Applications To Site Productivity And Taper |
Amarioarei Alexandru; Paun Mihaela; Strimbu Bogdan | Forests, 2020 | |
AbstractThe parameters of nonlinear forest models are commonly estimated with heuristic techniques, which can supply erroneous values. The use of heuristic algorithms is partially rooted in the avoidance of transformation of the dependent variable, which introduces bias when back-transformed to original units. Efforts were placed in computing the unbiased estimates for some of the power, trigonometric, and hyperbolic functions since only few transformations of the predicted variable have the corrections for bias estimated. The approach that supplies unbiased results when the dependent variable is transformed without heuristic algorithms, but based on a Taylor series expansion requires implementation details. Therefore, the objective of our study is to investigate the efficient expansion of the Taylor series that should be included in applications, such that numerical bias is not present. We found that five functions require more than five terms, whereas the arcsine, arccosine, and arctangent did not. Furthermore, the Taylor series expansion depends on the variance. We illustrated the results on two forest modeling problems, one at the stand level, namely site productivity, and one at individual tree level, namely taper. The models that are presented in the paper are unbiased, more parsimonious, and they have a RMSE comparable with existing less parsimonious models. |
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article
A Parsimonious Approach For Modeling Uncertainty Within Complex Nonlinear Relationships |
Bogdan M. Strimbu; Alexandru Amarioarei; Mihaela Paun | Ecosphere, An Esa Open Access Journal, 2018 | |
AbstractAdvancements in information technology led environmental scientists to the illusion that efforts should be mainly focused on developing models that reduce uncertainty rather than on models adjusted to the existing uncertainty. As a result, environmental relationships are represented by non-parsimonious and suboptimal models, which in many instances could be even wrong. The objective of this research was to provide scientists focused on modeling ecosystem processes with a procedure that supplies parsimonious correct results. The procedure transforms the response variable to achieve a linear model and the normality of the residuals. After the parameters of the transformed model are estimated, the bias induced by back-transforming is corrected. We have computed the bias corrections for 11 of the most popular functions from the power, trigonometric, and hyperbolic families by considering the truncated normal distribution, when necessary. Using generated data, we have shown that the proposed procedure supplies unbiased results. We have identified a sample size artifact of data generation such that when the variance increases the truncation of distribution starts altering the corrections of predicted values, sometimes by more than 50% from the actual values. Our results indicate that uncertainty, measured by variance, impacts the analysis in a non-intuitive way when the defining domain of the response variable is restricted. The subtle way of influencing the development of complex nonlinear models by uncertainty advocates the usage of parsimonious linear models, which are less sensitive to the method of processing data. Finally, ecosystem processes should be modeled with strategies that consider not only processes and computation aspects, but also uncertainty, in particularly reducing variance to levels with no significant impact on the results. |
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article
A Posteriori Bias Correction Of Three Models Used For Environmental Reporting |
Bogdan M Strimbu; Alexandru Amarioarei; John Paul McTague; Mihaela M Paun | Forestry: An International Journal Of Forest Research, 2018 | |
AbstractA plethora of forest models were developed by transforming the dependent variable, which introduces bias if appropriate corrections are not applied when back-transformed. Many recognized models are still biased and the original data sets are no longer available, which suggests ad hoc bias corrections. The present research presents a procedure for bias correction in the absence of needed information from summary statistics. Additionally, we developed a realistic correction of the square root transformation based on a truncated normal distribution. The transformations considered in this study are the logarithm, the square root and arcsine square root. Using simulated data we found that uncorrected back-transformation created biases by as much as 100 percent. The generated data revealed that depending on available information, that bias can still be present after correction. In addition to generated data we corrected the site index of Douglas-fir and ponderosa pine in Oregon USA, tree volume of 27 species from Romania, stand merchantable volume for longleaf pine in Louisiana and East Texas USA, and canopy fuel weight in Washington USA. Using only the available information, the unbiased back-transformed estimates can change from <= 1 percent (i.e. the site index and canopy fuel weight) to >= 1/3 (tree and stand volume). |
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article
A Scalar Measure Tracing Tree Species Composition In Space Or Time |
Bogdan M.Strimbu; Mihaela Paun; Cristian Montes; Sorin C.Popescu | Physica A: Statistical Mechanics And Its Applications, 2018 | |
AbstractThe tree species composition of a forest ecosystem is commonly represented with weights that measure the importance of one species with respect to the other species. Inclusion of weight in practical applications is difficult because of the inherent multidimensional perspective on composition. Scalar indices overcome the multidimensional challenges, and, consequently, are commonly present in complex ecosystem modeling. However, scalar indices face two major issues, namely non-uniqueness and non-measurability, which limit their ability to be generalized. The objective of this study is to identify the conditions for developing a univariate true measure of composition from weights. We argue that six conditions define a scalar measure of species mixture: (1) usefulness, (2) all species have equal importance, (3) all individuals have the same importance, (4) the measurements expressing importance of an individual are consistent and appropriate, (5) the function measuring composition is invertible, and (6) the function is a true-measure. We support our argument by formally proving all the conditions. To illustrate the applicability of the scalar measure we develop a rectilinear-based measure, and apply it in yield modeling and assessment of ecosystem dynamics. (C) 2018 Elsevier B.V. All rights reserved. |
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