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Modeling of Forest Tree and Stand Parameters

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 20217

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Dr. Karol Bronisz

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Department of Forest Management, Dendrometry and Forest Economics, Institute of Forest Sciences, Warsaw University of Life Sciences-SGGW, Warsaw, Poland
Interests: landscape ecology forest ecology; carbon estimation methods; forest biomass models; taper models; site index models; growth and yield models

Special Issue Information

Dear Colleagues,

Regression analysis is one of the most widely used statistical modeling tools. Even today, it is still of upmost importance as it allows one to describe the relationships between the dependent variable and independent variable or variables. This method is widely used in the description and modeling of the features of trees and stands. Single tree and stand regression models that are alike can be used as the final solution, or as a component of more complex tools, such as growth and yield models. In both cases, regression models are a complete tool used in areas such as forest management or forest inventory. They also support the assessment of the role of forests in the context of climate change. Considering the above issues, the purpose of this Special Issue is to support and promote research related to the modeling of trees and stands. In this issue, the publication of research related not only to various regression methods, e.g., ordinary nonlinear least squares, quantile regression, generalized additive models or mixed-effects models, but also different features of trees and stands, e.g., height–diameter models, taper models, mortality models, biomass models or growth and yield models. Moreover, published models may, for example, concern the assessment of the impact of various features, including climatic features, on the growth of trees and stands, assessment of changes in the range of various tree species, the impact of alien tree species or economic aspects in general.

Dr. Karol Bronisz
Guest Editor

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Keywords

regression analysis
features of trees and stands
relationships
statistical tools
growth and yield

Published Papers (11 papers)

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Research

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22 pages, 6523 KiB

Open AccessArticle

Form and Volume of the Stem of Tectona grandis L.f. in the Central-WESTERN Region of Brazil

by Karen Janones da Rocha, César Augusto Guimarães Finger, Cyro Matheus Cometti Favalessa, Sidney Fernando Caldeira and Frederico Dimas Fleig

Forests 2022, 13(11), 1818; https://0-doi-org.brum.beds.ac.uk/10.3390/f13111818 - 31 Oct 2022

Viewed by 1196

Abstract

The international market has recognized the high value of Tectona grandis L.f. plantations, requiring the development of reliable and accurate tools and techniques to quantify forest stocks accurately. In this study, we developed suitable equations to estimate the stem diameters and volume of Tectona grandis trees in the central-western region of Brazil, evaluating the stem form change points (FCPs) and testing the stratification of data as a measure to control their variation. The Schöepfer model was tested in the study of the FCPs of the stem, and single equation, segmented and variable-exponent taper functions were used to describe the stem profile. After the selection of the model for the taper, data stratification in the DBH classes, form parameter “r” and artificial form factor were proposed. The total volumes of each tree were calculated by integrating the Clark III et al. model. The FCPs of the Tectona grandis stems occurred at 28%, 57% and 73% of the total height, corresponding, on average, to the absolute positions of 6.4 m, 13.3 m and 16.5 m. The Clark III et al. equation, without stratification, was the most appropriate equation to estimate the diameters along the stem and the volume of Tectona grandis trees in the central-western region of Brazil. Full article

(This article belongs to the Special Issue Modeling of Forest Tree and Stand Parameters)

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18 pages, 2278 KiB

Open AccessArticle

A Novel Algorithm Based on Geometric Characteristics for Tree Branch Skeleton Extraction from LiDAR Point Cloud

by Jie Yang, Xiaorong Wen, Qiulai Wang, Jin-Sheng Ye, Yanli Zhang and Yuan Sun

Forests 2022, 13(10), 1534; https://0-doi-org.brum.beds.ac.uk/10.3390/f13101534 - 20 Sep 2022

Cited by 4 | Viewed by 1471

Abstract

More accurate tree models, such as branch skeleton, are needed to acquire forest inventory data. Currently available algorithms for constructing a branch skeleton from a LiDAR point cloud have low accuracy with problems such as irrational connection near trunk bifurcation, excessive central deviation and topological errors. Using the C++ and PCL library, a novel algorithm of the incomplete simulation of tree transmitting water and nutrients (ISTTWN), based on geometric characteristics for tree branch skeleton extraction, was developed in this research. The algorithm is an incomplete simulation of tree transmitting water and nutrients. Improvements were made to improve the time and memory consumption. The result show that the ISTTWN algorithm without any improvements is quite time consuming but has consecutive output. After improvement with iteration, the process is faster and has more detailed output. Breakpoint connection is added to recover continuity. The ISTTWN algorithm with improvements can produce a more accurate skeleton and cost less time than a previous algorithm. The superiority and effectiveness of the method are demonstrated, which provides a reference for the subsequent study of tree modeling and a prospect of application in other fields, such as virtual reality, computer games and movie scenes. Full article

(This article belongs to the Special Issue Modeling of Forest Tree and Stand Parameters)

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16 pages, 1463 KiB

Open AccessArticle

Evaluating the Impacts of Flying Height and Forward Overlap on Tree Height Estimates Using Unmanned Aerial Systems

by Heather Grybas and Russell G. Congalton

Forests 2022, 13(9), 1462; https://0-doi-org.brum.beds.ac.uk/10.3390/f13091462 - 11 Sep 2022

Cited by 2 | Viewed by 1303

Abstract

Unmanned aerial systems (UASs) and structure-from-motion (SfM) image processing are promising tools for sustainable forest management as they allow for the generation of photogrammetrically derived point clouds from UAS images that can be used to estimate forest structure, for a fraction of the cost of LiDAR. The SfM process and the quality of products produced, however, are sensitive to the chosen flight parameters. An understanding of the effect flight parameter choice has on accuracy will improve the operational feasibility of UASs in forestry. This study investigated the change in the plot-level accuracy of top-of-canopy height (TCH) across three levels of flying height (80 m, 100 m, and 120 m) and four levels of forward overlap (80%, 85%, 90%, and 95%). A SenseFly eBee X with an Aeria X DSLR camera was used to collect the UAS imagery which was then run through the SfM process to derive photogrammetric point clouds. Estimates of TCH were extracted for all combinations of flying height and forward overlap and compared to TCH estimated from ground data. A generalized linear model was used to statistically assess the effect of parameter choice on accuracy. The RMSE (root-mean-square error) of the TCH estimates (RMSE_TCH) ranged between 1.75 m (RMSE_TCH % = 5.94%) and 3.20m (RMSE_TCH % = 10.1%) across all missions. Flying height was found to have no significant effect on RMSE_TCH, while increasing forward overlap was found to significantly decrease the RMSE_TCH; however, the estimated decrease was minor at 4 mm per 1% increase in forward overlap. The results of this study suggest users can fly higher and with lower levels of overlap without sacrificing accuracy, which can have substantial time-saving benefits both in the field collecting the data and in the office processing the data. Full article

(This article belongs to the Special Issue Modeling of Forest Tree and Stand Parameters)

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20 pages, 4098 KiB

Open AccessArticle

Machine Learning: Crown Diameter Predictive Modeling for Open-Grown Trees in the Cerrado Biome, Brazil

by Gabriel Fernandes Bueno, Emanuel Arnoni Costa, César Augusto Guimarães Finger, Veraldo Liesenberg and Polyanna da Conceição Bispo

Forests 2022, 13(8), 1295; https://0-doi-org.brum.beds.ac.uk/10.3390/f13081295 - 15 Aug 2022

Cited by 3 | Viewed by 1777

Abstract

The Brazilian Cerrado biome is a hotspot due to its ecological importance and high diversity of fauna and flora. We aimed to develop statistical models to predict the crown diameter of open-growing trees using several forest attributes. Potential crown diameter trends in the measured trees were determined by quantile regression. Crown diameter models were developed by regression analyses, artificial neural networks, support vector machine, and random forest techniques. We evaluated 200 trees characterized into 60 species belonging to 30 botanical families. Our equation for potential crown diameter predicts the derived basal area, number of trees, and the necessary growth space of crown diameter at breast height. Artificial neural networks (with the following validation statistics: R² = 0.90, RMSE = 1.21, MAE = 0.93, and MAPE = 16.25) predicted crown diameter more accurately than the other evaluated techniques. Modeling crown diameter via machine learning represents an important step toward the assessment of crown dynamics by species and can support the decision making of silvicultural practices and other related activities in several rural properties within the Cerrado biome. Full article

(This article belongs to the Special Issue Modeling of Forest Tree and Stand Parameters)

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15 pages, 2873 KiB

Open AccessArticle

Climatic and Topographic Variables Improve Estimation Accuracy of Patula Pine Forest Site Productivity in Southern Mexico

by Adan Nava-Nava, Wenceslao Santiago-García, Gerónimo Quiñonez-Barraza, Héctor Manuel de los Santos-Posadas, José René Valdez-Lazalde and Gregorio Ángeles-Pérez

Forests 2022, 13(8), 1277; https://0-doi-org.brum.beds.ac.uk/10.3390/f13081277 - 12 Aug 2022

Cited by 3 | Viewed by 1888

Abstract

Sustainable forest management requires accurate biometric tools to estimate forest site quality. This is particularly relevant for prescribing adequate silvicultural treatments of forest management planning. The aim of this research was to incorporate topographic and climatic variables into dominant height growth models of patula pine stands to improve the estimation of forest stand productivity. Three generalized algebraic difference approach (GADA) models were fit to a dataset from 66 permanent sampling plots, with six re-measurements and 77 temporary inventory sampling plots established on forest stands of patula pine. The nested iterative approach was used to fit the GADA models, and goodness-of-fit statistics such as the root mean square error, Akaike’s Information Criterion, and Bias were used to assess their performance. A Hossfeld IV GADA equation type that includes altitude, slope percentage, mean annual precipitation, and mean annual minimum temperature produced the best fit and estimation. Forest site productivity was negatively affected by altitude, while increasing the mean annual minimum temperature suggested the fastest-growing rates for dominant tree height. Full article

(This article belongs to the Special Issue Modeling of Forest Tree and Stand Parameters)

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20 pages, 2827 KiB

Open AccessArticle

Individual Tree Basal Area Increment Models for Brazilian Pine (Araucaria angustifolia) Using Artificial Neural Networks

by Lorena Oliveira Barbosa, Emanuel Arnoni Costa, Cristine Tagliapietra Schons, César Augusto Guimarães Finger, Veraldo Liesenberg and Polyanna da Conceição Bispo

Forests 2022, 13(7), 1108; https://0-doi-org.brum.beds.ac.uk/10.3390/f13071108 - 15 Jul 2022

Cited by 6 | Viewed by 1637

Abstract

This research aimed to develop statistical models to predict basal area increment (BAI) for Araucaria angustifolia using Artificial Neural Networks (ANNs). Tree species were measured for their biometric variables and identified at the species level. The data were subdivided into three groups: (1) intraspecific competition with A. angustifolia; (2) the first group of species that causes interspecific competition with A. angustifolia; and (3) the second group of species that causes interspecific competition with A. angustifolia. We calculated both the dependent and independent distance and the described competition indices, considering the impact of group stratification. Multi-layer Perceptron (MLP) ANN was structured for modeling. The main results were that: (i) the input variables size and competition were the most significant, allowing us to explain up to 77% of the A. angustifolia BAI variations; (ii) the spatialization of the competing trees contributed significantly to the representation of the competitive status; (iii) the separate variables for each competition group improved the performance of the models; and (iv) besides the intraspecific competition, the interspecific competition also proved to be important to consider. The ANN developed showed precision and generalization, suggesting it could describe the increment of a species common in native forests in Southern Brazil and with potential for upcoming forest management initiatives. Full article

(This article belongs to the Special Issue Modeling of Forest Tree and Stand Parameters)

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15 pages, 4285 KiB

Open AccessArticle

Configuration of the Deep Neural Network Hyperparameters for the Hypsometric Modeling of the Guazuma crinita Mart. in the Peruvian Amazon

by Gianmarco Goycochea Casas, Duberlí Geomar Elera Gonzáles, Juan Rodrigo Baselly Villanueva, Leonardo Pereira Fardin and Hélio Garcia Leite

Forests 2022, 13(5), 697; https://0-doi-org.brum.beds.ac.uk/10.3390/f13050697 - 29 Apr 2022

Cited by 5 | Viewed by 1992

Abstract

The Guazuma crinita Mart. is a dominant species of great economic importance for the inhabitants of the Peruvian Amazon, standing out for its rapid growth and being harvested at an early age. Understanding its vertical growth is a challenge that researchers have continued to study using different hypsometric modeling techniques. Currently, machine learning techniques, especially artificial neural networks, have revolutionized modeling for forest management, obtaining more accurate predictions; it is because we understand that it is of the utmost importance to adapt, evaluate and apply these methods in this species for large areas. The objective of this study was to build and evaluate the efficiency of the use of a deep neural network for the prediction of the total height of Guazuma crinita Mart. from a large-scale continuous forest inventory. To do this, we explore different configurations of the hidden layer hyperparameters and define the variables according to the function HT = f(x) where HT is the total height as the output variable and x is the input variable(s). Under this criterion, we established three HT relationships: based on the diameter at breast height (DBH), (i) HT = f(DBH); based on DBH and Age, (ii) HT = f(DBH, Age) and based on DBH, Age and Agroclimatic variables, (iii) HT = f(DBH, Age, Agroclimatology), respectively. In total, 24 different configuration models were established for each function, concluding that the deep artificial neural network technique presents a satisfactory performance for the predictions of the total height of Guazuma crinita Mart. for modeling large areas, being the function based on DBH, Age and agroclimatic variables, with a performance validation of RMSE = 0.70, MAE = 0.50, bias% = −0.09 and VAR = 0.49, showed better accuracy than the others. Full article

(This article belongs to the Special Issue Modeling of Forest Tree and Stand Parameters)

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15 pages, 1920 KiB

Open AccessArticle

Estimation of Aboveground Vegetation Water Storage in Natural Forests in Jiuzhaigou National Nature Reserve of China Using Machine Learning and the Combination of Landsat 8 and Sentinel-2 Data

by Xiangshan Zhou, Wunian Yang, Ke Luo and Xiaolu Tang

Forests 2022, 13(4), 507; https://0-doi-org.brum.beds.ac.uk/10.3390/f13040507 - 25 Mar 2022

Cited by 1 | Viewed by 1999

Abstract

Aboveground vegetation water storage (AVWS) is a fundamental ecological parameter of terrestrial ecosystems which participates in plant metabolism, nutrient and sugar transport, and maintains the integrity of the hydraulic system of the plant. The Jiuzhaigou National Nature Reserve (JNNR) is located in the Eastern Tibet Plateau and it is very sensitive to climate change. However, a regional estimate of the AVWS based on observations is still lacking in the JNNR and improving the model accuracy in such mountainous areas is challenging. Therefore, in this study, we combined the Landsat 8 and Sentinel-2 data to estimate AVWS using multivariate adaptive regression splines (MARS), random forest (RF) and extreme gradient boosting (XGBoost) with the linkage of 54 field observations in the JNNR. The results showed that AVWS varied among different forest types. The coniferous forests had the highest AVWS (212.29 ± 84.43 Mg ha⁻¹), followed by mixed forests (166.29 ± 72.73 Mg ha⁻¹) and broadleaf forests (142.60 ± 46.36 Mg ha⁻¹). The average AVWS was 171.2 Mg ha⁻¹. Regardless of the modelling approaches, both Sentinel-2 and Landsat 8 successfully estimated AVWS separately. Prediction accuracy of AVWS was improved by combining Landsat 8 and Sentinel-2 images. Among the three machine learning approaches, the XGBoost model performed best with a model efficiency of 0.57 and root mean square error of 48 Mg ha⁻¹. Predicted AVWS using XGBoost showed a strong spatial pattern of across the study area. The total AVWS was 5.24 × 10⁶ Mg with 67.2% coming from conifer forests. The results highlight the potential of improving the accuracy of AVWS estimation by integrating different optical images and using machine learning approaches in mountainous areas. Full article

(This article belongs to the Special Issue Modeling of Forest Tree and Stand Parameters)

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14 pages, 4827 KiB

Open AccessArticle

Nonlinear Quantile Mixed-Effects Models for Prediction of the Maximum Crown Width of Fagus sylvatica L., Pinus nigra Arn. and Pinus brutia Ten.

by Dimitrios I. Raptis, Vassiliki Kazana, Stavros Kechagioglou, Angelos Kazaklis, Christos Stamatiou, Dimitra Papadopoulou and Thekla Tsitsoni

Forests 2022, 13(4), 499; https://0-doi-org.brum.beds.ac.uk/10.3390/f13040499 - 23 Mar 2022

Cited by 3 | Viewed by 1758

Abstract

In the current study, a novel approach combining quantile regression with nonlinear mixed-effects (QR-NLME) modeling was applied to predict the maximum crown width (cw_max) of three economically important forest species—the European beech (Fagus sylvatica L.), the black pine (Pinus nigra Arn.), and the Calabrian pine (Pinus brutia Ten.) at tree level. A power QR-NLME model was fitted first to a dataset including 1414 European beech trees obtained from 29 randomly distributed sample plots, 770 black pine trees from 25 sample plots, and 1880 Calabrian pine trees from 41 sample plots in Greece, to predict the cw_max at tree level. Additionally, a nonlinear mixed-effects model (NLME) was fitted to the same dataset to predict the average crown width at tree level for all species. In the second stage, the crown competition factor (CCF) was estimated based on the population average response of the cw_max predictions. The proposed approach presented sound results when compared with the outcomes of relevant models from other regions fitted to open-grown tree data, and therefore, it can be well implemented on clustered data structures, in cases of absence of open-grown tree data. Full article

(This article belongs to the Special Issue Modeling of Forest Tree and Stand Parameters)

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22 pages, 3719 KiB

Open AccessArticle

Crown Profile Modeling and Prediction Based on Ensemble Learning

by Yuling Chen, Chen Dong and Baoguo Wu

Forests 2022, 13(3), 410; https://0-doi-org.brum.beds.ac.uk/10.3390/f13030410 - 03 Mar 2022

Cited by 6 | Viewed by 2190

Abstract

Improving prediction accuracy is a prominent modeling issue in relation to forest simulations, and ensemble learning is a new effective method for improving the precision of crown profile model simulations in order to overcome the disadvantages of statistical modeling. Background: Ensemble learning (a machine learning paradigm in which multiple learners are trained to achieve better performance) has strong nonlinear problem learning ability and flexibility in terms of analyzing longitudinal data, and it remains rarely explored so far in the field of crown profile modeling forest science. In this study, we explored the application of ensemble learning to the modeling and prediction of crown profiles. Methods: We evaluated the performance of ensemble learning procedures and marginal model in modeling crown profile using the crown profile database from China fir plantations in Fujian, in southern China. Results: The ensemble learning approach for the crown profile model appeared to have better performance and higher efficiency (R² > 0.9). The crown equation model 18 showed an intermediate performance in its estimation, whereas GBDT (MAE = 0.3250, MSE = 0.2450) appeared to have the best performance and higher efficiency. Conclusions: The ensemble learning method can combine the advantages of multiple learners and has higher model accuracy, robustness and overall induction ability, and is thus an effective technique for crown profile modeling and prediction. Full article

(This article belongs to the Special Issue Modeling of Forest Tree and Stand Parameters)

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Review

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12 pages, 3264 KiB

Open AccessReview

Forest Biometric Systems in Mexico: A Systematic Review of Available Models

by Jorge Omar López-Martínez, Benedicto Vargas-Larreta, Edgar J. González, José Javier Corral-Rivas, Oscar A. Aguirre-Calderón, Eduardo J. Treviño-Garza, Héctor M. De los Santos-Posadas, Martin Martínez-Salvador, Francisco J. Zamudio-Sánchez and Cristóbal Gerardo Aguirre-Calderón

Forests 2022, 13(5), 649; https://0-doi-org.brum.beds.ac.uk/10.3390/f13050649 - 22 Apr 2022

Cited by 1 | Viewed by 2048

Abstract

Biometric systems are the basis of forest management and consist of a set of equations that describe the relationships between forest attributes and dendrometric variables. A systematic review of the state of the art of biometric systems in Mexico was carried out by a Mexican consortium (10 researchers), covering a period of 50 years ca (1970–2019), using the main scientific literature delivered by a systematic search (WoS, Scopus, Scielo, Redalyc) and a targeted search (theses, technical reports, etc.). A single selection criterion was established for the inclusion of information in the analysis: the document had to present at least one of the equations of interest. We found 376 documents containing 2524 equations for volume (69%), diameter (11%), height (9%) and site index (11%). These equations were developed for forest species mainly from temperate regions (88%), such as pine (66%) and oak (9%). Consequently, the Mexican states with the highest number of equations were Durango (28%), Chihuahua (17%), Hidalgo (13%) and Oaxaca (8%). Although large, the number of equations identified concentrated on a relatively small number of models: Schumacher & Hall and Fang et al. for volume; Chapman-Richards and Schumacher for site index and diameter; and Chapman-Richards and the allometric equation for height. An analysis of model fit, measured through R², showed that, on average, the volume, diameter and site index models show high fit (R² = 0.96), although this pattern was more consistent in the volume models. Publication bias was evaluated by means of a funnel plot analysis, with no apparent bias identified. A limitation of our study is that the information obtained is not updated to the present year; however, the 50-year trends allow us to assume that no recent significant changes in the patterns exist. Finally, we highlight the need to assess the predictive ability of the models to ensure accurate estimates to support better forest management decisions. Full article

(This article belongs to the Special Issue Modeling of Forest Tree and Stand Parameters)

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