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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 (31 December 2024) | Viewed by 8672

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Prof. Dr. Antonio Carlos Ferraz Filho

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Departamento de Engenharia, Campus Professora Cinobelina Elvas, Universidade Federal do Piauí, Bom Jesus 64900-000, PI, 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

Prof. Dr. Emanuel José Gomes De Araújo

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Instituto de Florestas, 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

Prof. Dr. Chris Cieszewski

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Fiber Supply Assessment, WSFNR, University of Georgia, Athens, GA 30602, USA
Interests: ADA (algebraic difference approach); GADA (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; anamorphism; polymorphism; complex polymorphism; fixed-effects vs. mixed-effects parameter estimation of self-referencing models; subject specific parameter estimation; variable parameter models

Special Issue Information

Dear Colleagues,

Data-gathering procedures applied in forest trees and stands constitute a fundamental step pertaining to the knowledge and sustainable use of these important resources. In an age where the well-being of humanity is in serious check due to the high reliance in fossil derived fuel and products, solutions based in renewable sources are highly desired. The natural variability of forests will require 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: i) forest biometrics; ii) forest inventory procedures; iii) modeling of forest growth and yield. We welcome studies conducted in 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 underreported 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
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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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Published Papers (9 papers)

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Research

27 pages, 3060 KiB

Open AccessArticle

Carbon Sequestration Estimates for Minor Exotic Softwood Species for Use in New Zealand’s Emissions Trading Scheme

by Michael S. Watt, Mark O. Kimberley, Benjamin S. C. Steer and Micah N. Scholer

Forests 2025, 16(4), 598; https://doi.org/10.3390/f16040598 - 28 Mar 2025

Viewed by 178

Abstract

New Zealand’s Emissions Trading Scheme (ETS) allows growers to receive payments through the accumulation of carbon units for increased carbon stock. For forests < 100 ha, growers rely on pre-formulated lookup tables (LUTs) to estimate changes in carbon stock by age. Currently, minor exotic softwood species, which are predominantly redwood and cypresses, are covered by a general Exotic Softwoods LUT. However, this table has been found to significantly underestimate carbon sequestration for these species. Using a combination of growth models and productivity surfaces, the objective of this study was to provide draft updates for the Exotic Softwoods LUT based on redwood, and two key cypresses (Cupressus lusitanica and C. macrocarpa), at different scales (national, Island level, regional), and to identify the most appropriate scale for a revised LUT. For cypress species, carbon predictions were made using C. lusitanica for the North Island and C. macrocarpa for the South Island, as these are the preferred species for each island. Variation in redwood carbon among New Zealand’s nine regions ranged over two-fold at ages 30 (390–847 tonnes CO₂ ha⁻¹) and 50 (926–1956 tonnes CO₂ ha⁻¹) and carbon was much higher within the North Island than the South Island. Predicted carbon for cypresses was higher within the North Island than the South Island at all ages and varied across regions, by 38% at age 30 (610–840 tonnes CO₂ ha⁻¹) and 12% at age 50 (1019–1146 tonnes CO₂ ha⁻¹). These findings suggest that a separate LUT for redwood is warranted, and that cypress species could serve as the default species for a revised Exotic Softwoods LUT. They also suggest that regional tables should be considered for both redwood and cypresses. However, the government may consider factors other than the technical considerations outlined here when updating the LUTs. Full article

(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield)

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15 pages, 3405 KiB

Open AccessArticle

Spatiotemporal Patterns and Interconnections of Forest Biomass and Economic Density in the Yellow River Basin, China

by Yaopeng Hu, Jiahui Zhai, Qingjun Wu, Xuanqin Yang, Yaquan Dou and Xiaodi Zhao

Forests 2025, 16(2), 358; https://doi.org/10.3390/f16020358 - 17 Feb 2025

Viewed by 314

Abstract

Forests are among the most diverse ecosystems on the planet, and their biomass serves as a key measure for assessing the biological productivity and carbon cycle of terrestrial forest ecosystems. Recognizing the factors that impact forest ecosystems is essential for assessing their health and developing effective conservation strategies to preserve species diversity and ecological equilibrium. This study considered forest biomass as the explained variable, economic density as the explanatory variable, and human activities, land use, and forestland protection as the control variables. Panel data encompassing 448 counties within the Yellow River Basin (YRB) for the years 2008, 2013, and 2018 were utilized as inputs for ArcGIS spatial analysis and two-way fixed-effects modeling. This approach aimed to evaluate the impact of socio-economic factors on forest biomass. The findings indicate that, (1) from both temporal and spatial viewpoints, the distribution of forest biomass in the upper reaches of the Yellow River demonstrated an improvement over the period from 2008 to 2018. Notably, in 2013, there was a significant reduction in the forest biomass distribution in the middle and lower sections, although the levels remained substantially above the average for those regions. Throughout the period from 2008 to 2018, the overall forest biomass within the YRB displayed a spatial distribution pattern, with elevated levels observed in the western areas and diminished levels in the eastern regions. (2) A one-unit increase in economic density led to a 1.002% increase in forest biomass. In the YRB, a positive correlation was observed between the economic density and forest biomass, especially in the middle and lower reaches of the river. (3) In the upstream region, forest biomass was strongly negatively correlated with cultivated land but significantly positively correlated with forest land protection. In the middle reaches, although population growth and arable land expansion led to a decrease in forest biomass, primary industry development and urbanization promoted forest biomass growth. The development of primary industries other than planting, such as the forestry industry, can contribute to the forest biomass. Moreover, in the downstream area, a strong negative correlation was observed between the number of permanent residents and forest biomass. We recommend modifications to human activities to enhance the forest biomass and the preserve forest ecosystem stability. Full article

(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield)

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19 pages, 12245 KiB

Open AccessArticle

Development of Full Growth Cycle Crown Width Models for Chinese Fir (Cunninghamia lanceolata) in Southern China

by Zheyuan Wu, Dongbo Xie, Ziyang Liu, Linyan Feng, Qiaolin Ye, Jinsheng Ye, Qiulai Wang, Xingyong Liao, Yongjun Wang, Ram P. Sharma and Liyong Fu

Forests 2025, 16(2), 353; https://doi.org/10.3390/f16020353 - 16 Feb 2025

Viewed by 492

Abstract

This study focused on 16,101 Cunninghamia lanceolata trees across 133 plots in seven cities of Guangdong Province, China, to develop a comprehensive full growth cycle crown width (CW) model. We systematically analyzed the dynamic characteristics of CW and its multi-scale influencing mechanisms. A binary basic model, with the diameter at breast height (DBH) and height (H) as core predictor variables, effectively reflected tree growth patterns. The inclusion of age groups as dummy variables allowed the model to capture the dynamic changes in CW across different growth stages. Furthermore, the incorporation of a nested two-level nonlinear mixed-effects (NLME) model, accounting for random effects from the forest block- and sample plot-level effects, significantly improved the precision and applicability of the final model (R² = 0.731, RMSE = 0.491). This model quantified both macro- and micro-level effects of region and plot on CW. Our findings showed that the two-level NLME model, incorporating tree age groups, optimally accounted for environmental heterogeneity and tree growth cycles, resulting in the best-fitting statistics. The proposed full growth cycle CW model effectively enhanced the model’s efficiency and predictive accuracy for Cunninghamia lanceolata, providing scientific support for the sustainable management and dynamic monitoring of plantation forests. Full article

(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield)

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18 pages, 4437 KiB

Open AccessArticle

Uncertainty Analysis of Remote Sensing Estimation of Chinese Fir (Cunninghamia lanceolata) Aboveground Biomass in Southern China

by Yaopeng Hu, Liyong Fu, Bo Qiu, Dongbo Xie, Zheyuan Wu, Yuancai Lei, Jinsheng Ye and Qiulai Wang

Forests 2025, 16(2), 230; https://doi.org/10.3390/f16020230 - 25 Jan 2025

Viewed by 876

Abstract

Forest aboveground biomass (AGB) is not only the basis for forest carbon stock research, but also an important parameter for assessing the forest carbon cycle and ecological functions of forests. However, there are various uncertainties in the estimation process, limiting the accuracy of AGB estimation. Therefore, we extracted the spectral features, vegetation indices and texture factors from remote sensing images based on the field data and Landsat 8 OLI remote sensing images in Southern China to quantify the uncertainties. Then, we established three AGB estimation models, including K Nearest Neighbor Regression (KNN), Gradient Boosted Regression Tree (GBRT) and Random Forest (RF). Uncertainties at the plot scale and models were measured by using error equations to analyze the influences of uncertainties at different scales on AGB estimation. Results were as follows: (1) The R² of the per-tree biomass model for Cunninghamia lanceolata was 0.970, while the uncertainty of the residual and parameters for per-tree biomass model was 4.62% and 4.81%, respectively; and the uncertainty transferred to the plot scale was 3.23%. (2) The estimation methods had the most significant effects on the remote sensing models. RF was more accurate than other two methods, and had the highest accuracy (R² = 0.867, RMSE = 19.325 t/ha) and lowest uncertainty (5.93%), which outperformed both the KNN and GBRT models (KNN: R² = 0.368, RMSE = 42.314 t/ha, uncertainty = 14.88%; GBRT: R² = 0.636, RMSE = 32.056 t/ha, uncertainty = 6.3%). Compared to KNN and GBRT, the R² of RF was enhanced by 0.499 and 0.231, while the uncertainty was decreased by 8.95% and 0.37%, respectively. The uncertainty associated with the scale of remote sensing models remains the primary source of uncertainty when compared to the plot scale. On the remote sensing scale, RF is the model with the best estimation effect. This study examines the impact of both plot-scale and remote sensing model-scale methodologies on the estimation of AGB for Cunninghamia lanceolata. The findings aim to offer valuable insights and considerations for enhancing the accuracy of AGB estimations. Full article

(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield)

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25 pages, 4556 KiB

Open AccessArticle

Bark Biometry Along the Stem for Three Commercial Tree Species in Romania

by Maria Magdalena Vasilescu

Forests 2024, 15(12), 2264; https://doi.org/10.3390/f15122264 - 23 Dec 2024

Viewed by 701

Abstract

In general, bark serves a protective role for trees and is genetically determined. The quantification of bark based on biometric characteristics is linked to studies on the distribution of forest species across the globe and vegetation fires. In Romania, on the other hand, the improvement of the wood traceability system requires an increase in the accuracy of the estimation of the biometric characteristics of bark and, implicitly, of the volume of wood under the bark. The aim of this study was to develop more precise models for predicting bark thickness along the stem of three key Romanian species, taking into account a comprehensive range of models and stem sections, including those with a diameter over bark smaller than 8 cm, which have been excluded in previous studies. The study is based on two datasets, one containing the national measurements of three commercially valuable forest species, i.e., Norway spruce (Picea abies (L.) Karst), European beech (Fagus sylvatica L.), and pedunculate oak (Quercus robur L.) from 12,186 trees, and a second dataset containing the measurements from 61 logs of the same species at a specific forest site. A set of seven double bark thickness (DBT) estimation models with stem diameter over bark (DOB), DOB and total tree height (H), DOB and relative height along the stem (h/H), and diameter over bark at breast height (DBH) and DOB as predictors were used. The DBT models were evaluated using the coefficient of determination (R²), mean absolute error (MAE), root mean squared error (RMSE), the Akaike information criterion (AIC), and the Bayesian information criterion (BIC). This led to the selection of two more accurate models, Model 2 (based on a third-degree polynomial) and Model 3 (based on a logarithmic function), with DOB as the predictor. Relative double bark thickness (RDBT) and proportion of bark area (PBA) were also estimated using a sixth-degree polynomial and relative height as a predictor variable after stratifying the data by DBH classes to reduce variability. The results of this study indicate that there is a need to complete the database, for all three forest species of commercial value in Romania especially for large trees with DBH greater than 60–70 cm. The models obtained for PBA are of great use to the industry and the economy, in particular in the context of the traceability of wood. This is due to the fact that PBA can be equated with the proportion of bark volume (PBV), which describes the variation in the proportion of bark in the volume of the wood assortments along the stem. For a given DBH, PBA and PBV demonstrate minimal variability in sections from the tree’s base to a relative height of 0.6; however, a pronounced increase is observed at crown level in sections above relative heights of 0.8. Full article

(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield)

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13 pages, 2965 KiB

Open AccessArticle

Genetic Parameters Estimated in the Early Growth of Dimorphandra mollis Benth. Progenies

by Kennedy de Paiva Porfírio, Andressa Ribeiro, Séfora Gil Gomes de Farias, Thais Santiago de Sousa, Diego Felipe Ciccheto, Priscila Alves Barroso, Fabio Sandro dos Santos, Dandara Yasmim Bonfim de Oliveira Silva and Antonio Carlos Ferraz Filho

Forests 2024, 15(7), 1184; https://doi.org/10.3390/f15071184 - 9 Jul 2024

Viewed by 1126

Abstract

The extractivism of Dimorphandra mollis Benth., which is a native tree from the Brazilian Cerrado biome, popularly known as fava d’anta, combined with the reduction in native vegetation area in the country over the years may result in a decrease in the specie’s natural populations. The objective of this study was to estimate the quantitative genetic parameters in nursery, hardening, and field phases, based on a progeny test. The experimental design adopted was randomized blocks (six blocks for the nursery and hardening phases, and four blocks for the field phase with 5 plants/plot and 72 mother trees), with evaluations of the collar diameter and seedling height at 30, 90, 150, 480, and 570 days after sowing, between the production and planting phases. Among the coefficients of variance, the phenotypic and additive ones showed the highest values. Heritabilities for height ranged from moderate to high (0.15 to 0.43), indicating good genetic control of the traits, high potential for selection, and possibility of genetic gains. The genetic divergence of the progenies resulted in division into five groups, confirming the existence of genetic variability among the evaluated progenies and the potential for conservation and breeding programs. Full article

(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield)

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18 pages, 2067 KiB

Open AccessArticle

Evaluation of Different Modeling Approaches for Estimating Total Bole Volume of Hispaniolan Pine (Pinus occidentalis Swartz) in Different Ecological Zones

by Santiago W. Bueno-López, Luis R. Caraballo-Rojas and Juan G. Torres-Herrera

Forests 2024, 15(6), 1052; https://doi.org/10.3390/f15061052 - 18 Jun 2024

Cited by 1 | Viewed by 807

Abstract

Pinus occidentalis (Swartz) is the primary timber species in the Dominican Republic (DR). Despite its economic importance, studies conducted on this species are scarce, making it difficult to estimate current inventory levels. This study aims to enhance the accuracy of estimating the total bole volume of P. occidentalis in different ecological zones (EZs) within La Sierra, evaluating and comparing two established volume equations—combined variable (CV) and Schumacher and Hall (S&H) across nine modeling variants. An indicator variables analysis determined the necessity of distinct equations for two EZs. Fitting included both linear and nonlinear models. Our comprehensive statistical analysis included goodness-of-fit metrics to evaluate each model variant’s performance rigorously. The second modeling variant (SH02) for the SH equation was most effective in the Dry Ecological Zone, showing superior performance in both the fitting and validation phases. Similarly, the third modeling variant (SH03) for the SH equation emerged as the best fit for the Combined Intermediate and Humid Ecological Zones, achieving the lowest overall ranking sum among tested variants. SH02 and SH03 provide reliable and precise volume estimations, allowing for the optimization of forestry management practices for P. occidentalis trees. The SH models outperformed the CV model variants’ consistency in parameter estimation. This tailored approach ensures more accurate volume predictions, which is crucial for sustainable management and conservation efforts. Full article

(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield)

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16 pages, 1394 KiB

Open AccessArticle

A Comparison of Probability Density Functions Fitted by Moments and Maximum Likelihood Estimation Methods Used for Diameter Distribution Estimation

by Jose Javier Gorgoso-Varela, Segun M. Adedapo and Friday N. Ogana

Forests 2024, 15(3), 425; https://doi.org/10.3390/f15030425 - 22 Feb 2024

Cited by 3 | Viewed by 1476

Abstract

Modeling diameter distribution is a crucial aspect of forest management, requiring the selection of an appropriate probability density function or cumulative distribution function along with a fitting method. This study compared the suitability of eight probability density functions—A Charlier, beta, generalized beta, gamma, Gumbel, Johnson’s SB, and Weibull (two- and three-parameter)—fitted using both derivative methods (Moments) fitted in SAS/STAT^TM and optimization methods (MLE) fitted with the ‘optim’ function in R for diameter distribution estimation in forest stands. The A Charlier and Gumbel functions were used for the first time in this type of comparison. The data were derived from 167 permanent sample plots in an Atlantic forest (Quercus robur) and 59 temporary sample plots in tropical forests (Tectona grandis). Fit quality was assessed using various indices, including Kolmogorov–Smirnov, Cramér–von Mises, mean absolute error, bias, and mean squared error. The results indicated that Johnson’s SB function was more suitable for describing the diameter distribution of the stands. Johnson’s SB, three-parameter Weibull, and generalized beta consistently performed well across different fitting methods, while the fits produced by gamma, Gumbel, and two-parameter Weibull were of poor quality. Full article

(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield)

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19 pages, 5571 KiB

Open AccessArticle

Improving Volume and Biomass Equations for Pinus oocarpa in Nicaragua

by Luis Alberto Valerio Hernández, Walter Antonio Campos Vanegas, Luis Enrique Cruz Tórrez, José Adolfo Peña Ortiz and Benedicto Vargas-Larreta

Forests 2024, 15(2), 309; https://doi.org/10.3390/f15020309 - 6 Feb 2024

Cited by 2 | Viewed by 1686

Abstract

We present a new set of equations for tree level volume and aboveground biomass estimation for ocote pine (Pinus oocarpa Schiede ex Schltdl). These equation systems are the first developed for this species in Nicaragua. The first system includes a taper function, a merchantable volume equation, and volume equations for stem, coarse branches, and whole trees. The second system estimates whole tree and individual tree component biomass (stem wood, bark, branches, and needles). Data from 112 sampled trees were used for models’ development. Seemingly Unrelated Iterative Regression and the Generalized Method of Moments were used to simultaneously fit the volume and biomass equations systems, respectively; both methods ensure additivity and compatibility between equations. Weighted regression and a second-order continuous autoregressive error structure were used to correct heteroscedasticity and autocorrelation within the hierarchical dataset. The predictive power of the new proposed equations is higher than the currently used models for P. oocarpa in the country. These equation systems represent a scientific advancement that will enhance forest inventories, optimize timber management of the species, and facilitate accurate monitoring of forest carbon dynamics. Additionally, the new equations will contribute to a more precise accounting of CO₂ emissions from the country’s forestry sector. Full article

(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield)

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