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Forest Biometrics, Inventory, and Modelling of Growth and Yield: 2nd...
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Forest Biometrics, Inventory, and Modelling of Growth and Yield: 2nd Edition

A Special Issue of Forests (ISSN 1999-4907) belonging to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 2865

Editors

Prof. Dr. Antonio Carlos Ferraz Filho

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Guest Editor
Campus Professora Cinobelina Elvas, Universidade Federal do Piauí, Bom Jesus 64900-000, Piauí, Brazil
Interests: African mahogany; clear wood production; diameter distributions; dominant height; Eucalyptus plantations; fast growing plantations; forest biomass; forest economy; forest inventory; forest management; forest regeneration; growing space; growth and yield models; height-diameter equations; high-value timber species; horizontal structure; individual tree models; Khaya grandifoliola; leaf area removal; native forest; probability density functions; pruning; reforestation; seasonally dry tropical forests; sampling intensity; site index; solid wood products; species selection; stand density management; thinning; volume equations
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Emanuel José Gomes De Araújo

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Guest Editor
Silviculture Department, Universidade Federal Rural do Rio de Janeiro, Seropédica, Rio de Janeiro 23890-000, Brazil
Interests: Amazonian timber species; commercial timber; diametric structure; forest management; forestry production, Geographic Information System; geoprocessing; inventory accuracy; merchantable volume; modelling; native woods;· national forest inventory; productive capacity; sensors; spatial dependence; suitability maps; uneven-aged forest; spatial distribution; volume equations
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chris Cieszewski

E-Mail Website
Guest Editor
Fiber Supply Assessment, WSFNR, University of Georgia, Athens, GA 30602, USA
Interests: ADA, Algebraic Difference Approach; GADA, or Generalized Algebraic Difference Approach; self-referencing functions; self-referencing models; implicit equations; dynamic equations; projection equations; projection models; base-age invariance; path invariance; indifference under reparametrization parameter estimation; model conditioning; well-behaved model; pooled cross-sectional and longitudinal data models; site models, site index models; site-height-age models; anamorhism; polymorhism; complex polymorphism; fixed-effects vs. mixed-effects parameter estimation of self-referencing models; subject specific parameter estimation; variable parameter models
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andressa Ribeiro

E-Mail Website
Guest Editor
Campus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus 64900-000, PI, Brazil
Interests: forest resources; forest management; silviculture

Special Issue Information

Dear Colleagues,

We present the second edition of the Special Issue “Forest Biometrics, Inventory, and Modelling of Growth and Yield” (https://www.mdpi.com/journal/forests/special_issues/6ZQQIP52S9), following on from the success of the first edition, which comprised 10 articles. We look forward to receiving more high-quality contributions in this edition.

Data gathering procedures applied in forest trees and stands constitute a fundamental step relevant to the knowledge and sustainable use of these important resources. In an age where the well-being of humanity is in serious danger due to the high reliance on fossil fuels and derived products, solutions based on renewable sources are highly desired. The natural variability in forests requires significant scientific advances in all areas pertaining to the knowledge of current standing stocks and how these stocks will change in the future. Thus, this Special Issue welcomes all studies (i.e., review and research articles) that bring new data and methods about the following: i) forest biometrics; ii) forest inventory procedures; iii) the modeling of forest growth and yield. We welcome studies conducted on all types of trees (e.g., urban, isolated, in rural integration) and forests (e.g., natural, planted, productive, protective), and we particularly encourage studies from under-reported areas, such as tropical forests located in low-income countries.

Prof. Dr. Antonio Carlos Ferraz Filho
Prof. Dr. Emanuel José Gomes De Araújo
Prof. Dr. Chris Cieszewski
Prof. Dr. Andressa Ribeiro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mensuration
  • growth dynamics
  • forest management
  • forest inventory
  • data collection
  • remote sensing
  • silviculture
  • statistical methods
  • resource assessment

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Related Special Issue

Published Papers (2 papers)

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Research

15 pages, 2687 KB  
Article
Form Factor Variability in Khaya grandifoliola Trees in Brazil: Implications for Accurate Volume Estimation
by Andressa Ribeiro, Rafaella Carvalho Mayrinck, Ximena Mendes de Oliveira, Carolina Souza Jarochinski Dellu, José Lucas Vieira Pinheiro, Kennedy Paiva Porfírio, Maurício Sangiogo and Antonio Carlos Ferraz Filho
Forests 2026, 17(2), 237; https://doi.org/10.3390/f17020237 - 10 Feb 2026
Cited by 1 | Viewed by 1352
Abstract
Form factor is a key parameter for describing tree taper and provides a simple yet effective method for estimating wood volume. However, applying a single form factor across all tree sizes and ages may lead to substantial errors in volume estimation. This study [...] Read more.
Form factor is a key parameter for describing tree taper and provides a simple yet effective method for estimating wood volume. However, applying a single form factor across all tree sizes and ages may lead to substantial errors in volume estimation. This study aimed to determine form factors and their ability to estimate the wood volume of Khaya grandifoliola trees across a wide range of ages (1 to 18 years) and diameters at breast height (2 to 92 cm). Using an electronic dendrometer, a total of 733 trees were scaled across Brazil to derive total and stem form factors. Wood volume was computed using Smalian’s formula, and form factors were determined and stratified for 10 diameter and 7 age classes. The form factor values ranged from 0.30 to 1.75. Mean total and stem form factors were 0.42 and 0.83, respectively, when grouped by diameter class, and increased to 0.50 and 0.93 when grouped by age class. Results revealed a consistent decrease in form factor with increasing diameter and age, indicating a gradual change in tree shape as trees mature. These findings highlight that using diameter-class specific form factors enhances the accuracy of volume estimation while maintaining the easiness of traditional methods. Full article
(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield: 2nd Edition)
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23 pages, 7244 KB  
Article
Individual-Tree Crown Width Prediction for Natural Mixed Forests in Northern China Using Deep Neural Network and Height Threshold Method
by Lai Zhou, Xiaofang Cheng, Shaoyu Liu, Chunxin He, Wei Peng and Mengtao Zhang
Forests 2025, 16(12), 1778; https://doi.org/10.3390/f16121778 - 26 Nov 2025
Cited by 3 | Viewed by 934
Abstract
Crown width (CW) is a critical metric for characterizing tree-canopy dimensions; however, its direct measurement remains labor-intensive and is often impractical in inaccessible crowns. Consequently, CW is frequently derived from projections, which are susceptible to multiple sources of imprecision, including canopy density, crown [...] Read more.
Crown width (CW) is a critical metric for characterizing tree-canopy dimensions; however, its direct measurement remains labor-intensive and is often impractical in inaccessible crowns. Consequently, CW is frequently derived from projections, which are susceptible to multiple sources of imprecision, including canopy density, crown irregularity, terrain heterogeneity, and the observer’s vantage point, especially in structurally complex natural forests. While deep neural network (DNN) models show substantial potential for CW prediction, their performance in heterogeneous forests remains uncertain. We developed DNN models integrated with a Height Threshold Method (HTM) to predict individual-tree CW in the natural mixed forests of Northern China, dominated by Larix principis-rupprechtii and Picea asperata. Our study further compared the relative importance of feature engineering versus model architectural complexity in predictive accuracy and identified the key ecological variables governing CW. The model performance was evaluated through the coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Field surveys of 34 representative sample plots produced 1884 individual-tree records. The main results were as follows: (1) all DNNs avoided overfitting, and were statistical stable under ten-fold cross-validation; (2) the optimized DNN3-2 model (tuned hidden layer count, neurons/hidden layer, L2 regularization, and dropout) achieved peak performance, explaining 69% of CW variance with residuals with stable variance and excellent coverage properties; (3) tree size, neighborhood competition, species identity, and site quality were the most important predictors; and (4) stand parameters calculated from competitive neighborhoods defined by the HTM, particularly mean stand crowding, Simpson’s index (1-D), and Shannon’s index (H′), significantly improved prediction accuracy. By integrating DNN with the HTM, our approach allows for accurate prediction of individual-tree CW in natural mixed forests of Northern China, dominated by Larix principis-rupprechtii and Picea asperata. Full article
(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield: 2nd Edition)
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