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Modeling Aboveground Forest Biomass: New Developments

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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: 31 January 2025 | Viewed by 15217

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Dr. Ana Cristina Gonçalves

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MED—Mediterranean Institute for Agriculture, Environment and Development & CHANGE—Global Change and Sustainability Institute, Instituto de Investigação e Formação Avançada, Departamento de Engenharia Rural, Escola de Ciências e Tecnologia, Universidade de Évora, Apartado 94, 7002-544 Évora, Portugal
Interests: forestry; silviculture; modeling; biomass; stand structure
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Prof. Dr. Teresa Fidalgo Fonseca

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Department of Forestry Sciences and Landscape Architecture (CIFAP), University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal Forest Research Centre (CEF), School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal
Interests: silviculture; forest management, biometrics; forest inventory; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest biomass modelling is crucial to its monitoring and storage. However, biomass in stands and forests varies according to the species, stand structure and site. Biomass models can be developed using data obtained destructive sampling, forest inventory, remote sensing and ancillary. There is a wide range of data science methods and techniques currently applied in order to fit the models and evaluate their uncertainties. Biomass models can be utilized in order to produce management alternatives. This Special Issue aims to offer an overview of the various data sets and modelling methods currently employed to develop biomass functions, as well as their applicability at both the tree and area levels.

Potential topics include, but are not limited to, the following:

biomass models at tree level;
biomass models at stand level;
data sets used in biomass modelling;
data science methods and techniques used in biomass modelling;
model performances and uncertainties.
development of management alternatives with biomass models

Prof. Ana Cristina Gonçalves
Prof. Dr. Teresa Fidalgo Fonseca
Guest Editors

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Keywords

biomass
modelling
data sets
methods
uncertainties
data science

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Published Papers (6 papers)

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Research

22 pages, 4627 KiB

Open AccessArticle

Thinning Effects on Aboveground Biomass Increments in Both the Overstory and Understory of Masson Pine Forests

by Feng Liu, Xiaolin Liu, Mengyuan Zeng, Jianjun Li and Chang Tan

Forests 2024, 15(7), 1080; https://doi.org/10.3390/f15071080 - 21 Jun 2024

Viewed by 695

Abstract

Masson pine (Pinus massoniana Lamb.) is a tree species that is widely distributed throughout southern China and holds significant economic and ecological value. The main objective of our study was to assess the effects of thinning on aboveground biomass increments and tree diversity in both the overstory and understory. Additionally, the underlying factors and mechanisms responsible for driving changes in biomass increment were analyzed. Four different thinning treatments (control, light thinning, moderate thinning, and heavy thinning) were implemented in 214 plots (~1800 tree ha⁻¹) in three Masson pine forests in Hunan Province, China. A robustly designed experiment was used with over six years of repeated measurements. The differences in biomass increment and tree diversity among the different treatments were compared using repeated measures ANOVAs. The Mantel test was used to determine environmental metrics correlated with biomass increments across tree strata. Structural equation modeling was utilized to explore the multivariate relationships among site environment, tree diversity, and post-treatment biomass increment. The results indicated that thinning overall increased biomass increment, the Shannon index, and the Gini index, while decreasing the Dominance index over time. Moderate thinning (25%–35% of trees removed) was found to promote overstory biomass increment to 9.72 Mg·ha⁻¹·a⁻¹ and understory biomass increment to 1.43 Mg·ha⁻¹·a⁻¹ six years post-thinning, which is significantly higher than that of other treatments. Environmental metrics such as light intensity, soil organic matter, and other soil physiochemical properties were positively correlated with biomass increments, and their effects on the overstory and understory differed. Structural equation modeling revealed that thinning treatments, environmental metrics, tree diversity, and their interactions could be the main drivers for biomass increments across tree strata. Specifically, thinning treatments, light intensity, and tree size diversity (Gini index) had significant effects on overstory biomass increment, while understory species richness (Shannon index) and soil organic matter affected understory biomass increment. In conclusion, moderate thinning is an effective silvicultural treatment for stimulating biomass increments of both the overstory and understory in Masson pine forests in southern China if a middle period (e.g., six years) is considered. Some factors, such as species richness, tree size diversity, and environmental metrics (e.g., light and soil), are suggested for consideration to improve the efficiency of thinning. Full article

(This article belongs to the Special Issue Modeling Aboveground Forest Biomass: New Developments)

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18 pages, 2140 KiB

Open AccessArticle

Construction of Additive Allometric Biomass Models for Young Trees of Two Dominate Species in Beijing, China

by Shan Wang, Zhongke Feng, Zhichao Wang, Lili Hu, Tiantian Ma, Xuanhan Yang, Hening Fu and Jinshan Li

Forests 2024, 15(6), 991; https://doi.org/10.3390/f15060991 - 5 Jun 2024

Viewed by 495

Abstract

The traditional volume-derived biomass method is limited because it does not fully consider the carbon sink of young trees, which leads to the underestimation of the carbon sink capacity of a forest ecosystem. Therefore, there is an urgent need to establish an allometric biomass model of young trees to provide a quantitative basis for accurately estimating the carbon storage and carbon sink of young trees. The destructive data that were used in this study included the biomass of the young trees of the two dominant species (Betula pendula subsp. mandshurica (Regel) Ashburner & McAll and Populus × tomentosa Carrière) in China, which was composed of the aboveground biomass (B_a), belowground biomass (B_b), and total biomass (B_t). Univariate and bivariate dimensions were selected and five candidate biomass models were independently tested. Two additive allometric biomass model systems of young trees were established using the proportional function control method and algebraic sum control method, respectively. We found that the logistic function was the most suitable for explaining the allometric growth relationship between the B_a, B_t, and diameter at breast height (D) of young trees; the power function was the most suitable for explaining the allometric growth relationship between the B_b and D of young trees. When compared with the independent fitting model, the two additive allometric biomass model systems provide additive biomass prediction which reflects the conditions in reality. The accuracy of the B_t models and B_a models was higher, while the accuracy of the B_b models was lower. In terms of the two dimensions—univariate and bivariate, we found that the bivariate additive allometric biomass model system was more accurate. In the univariate dimension, the proportional function control method was superior to the algebraic sum control method. In the bivariate dimension, the algebraic sum control method was superior to the proportional function control method. The additive allometric biomass models provide a reliable basis for estimating the biomass of young trees and realizing the additivity of the biomass components, which has broad application prospects, such as the monitoring of carbon stocks and carbon sink evaluation. Full article

(This article belongs to the Special Issue Modeling Aboveground Forest Biomass: New Developments)

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20 pages, 5152 KiB

Open AccessArticle

Driving Factors and Spatial Distribution of Aboveground Biomass in the Managed Forest in the Terai Region of Nepal

by Yam Bahadur KC, Qijing Liu, Pradip Saud, Chang Xu, Damodar Gaire and Hari Adhikari

Forests 2024, 15(4), 663; https://doi.org/10.3390/f15040663 - 5 Apr 2024

Cited by 2 | Viewed by 2047

Abstract

Above-ground biomass (AGB) is affected by numerous factors, including topography, climate, land use, or tree/forest attributes. Investigating the distribution and driving factors of AGB within the managed forests in Nepal is crucial for developing effective strategies for climate change mitigation, and sustainable forest management and conservation. A total of 110 field plots (circular 0.02 ha plots with a 9 m radius), and airborne laser scanning (ALS)-light detection and ranging (LiDAR) data were collected in 2021. The random forest (RF) model was employed to predict the AGB at a 30 m × 30 m resolution based on 32 LiDAR metrics derived from ALS returns. The study assessed the relationships between the AGB distribution and nine independent variables using statistical techniques like the random forest model and partial dependence plots. Results showed that the mean value of the estimated AGB was 120 tons/ha, ranging from 0 to 446.42 tons/ha. AGB showed higher values in the northeast and southeast regions, gradually decreasing towards the northwest. Land use land cover, mean annual temperature, and mean annual precipitation were identified as the primary factors influencing the variability in AGB distribution, accounting for 64% of the variability. Elevation, slope, and distance from rivers were positively correlated with AGB, while proximity to roads had a negative correlation. The increase in precipitation and temperature contributed to the initial rise in AGB, but beyond a certain lag, these variables led to a decline in AGB. This study showed the efficiency of the random forest model and partial dependence plots in examining the relationship between the AGB and its driving factors within managed forests. The study highlights the importance of understanding the AGB driving factors and utilizing LiDAR data for informed decisions regarding the region’s sustainable forest management and climate change mitigation efforts. Full article

(This article belongs to the Special Issue Modeling Aboveground Forest Biomass: New Developments)

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18 pages, 2444 KiB

Open AccessArticle

Tree Biomass Modeling Based on the Exploration of Regression and Artificial Neural Networks Approaches

by Şerife Kalkanlı Genç, Maria J. Diamantopoulou and Ramazan Özçelik

Forests 2023, 14(12), 2429; https://doi.org/10.3390/f14122429 - 13 Dec 2023

Viewed by 1065

Abstract

Understanding the dynamics of tree biomass is a significant factor in forest ecosystems, and accurate quantitative knowledge of its development provides support for the optimization of forest management. This work aimed to employ innovative practices in tree biomass modeling, artificial neural network approaches along with the least-squares regression methodology, in order to construct reliable and accurate estimation and prediction models that contribute to solving the emerging problems in the field of sustainable forest management. Based on this aim, different modeling strategies were developed and explored. The nonlinear seemingly unrelated regression (NSUR) methodology, the generalized regression (GRNN), the resilient propagation (RPNN) and the Bayesian regularization (BRNN) artificial neural network algorithms were utilized for the construction of reliable biomass models to attain the most accurate and reliable tree biomass components and total tree biomass estimations. The work showed that GRNN models provided a significantly better performance compared with the other modeling methodologies tested. Considering the non-parametric nature of the GRNN neural network algorithm, the fact that it was designed for nonlinear regression-type problems capable of dealing with small datasets, this modeling approach warrants consideration as an effective alternative to nonlinear regression or to other neural network approaches to the field of tree biomass modeling. Full article

(This article belongs to the Special Issue Modeling Aboveground Forest Biomass: New Developments)

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30 pages, 11579 KiB

Open AccessArticle

Thinning Combined with Prescribed Burn Created Spatially Heterogeneous Overstory Structures in Contemporary Dry Forests: A Comparison Using LiDAR (2016) and Field Inventory (1934) Data

by Sushil Nepal, Bianca N. I. Eskelson, Martin W. Ritchie and Sarah E. Gergel

Forests 2023, 14(10), 2096; https://doi.org/10.3390/f14102096 - 19 Oct 2023

Viewed by 1186

Abstract

Restoring current ponderosa pine (Pinus ponderosa Dougl. Ex P. and C. Laws)-dominated forests (also known as “dry forests”) to spatially resilient stand structures requires an adequate understanding of the overstory spatial variation of forests least impacted by Euro-American settlers (also known as “reference conditions”) and how much contemporary forests (2016) deviate from reference conditions. Because of increased tree density, dry forests are more spatially homogeneous in contemporary conditions compared to reference conditions, forests minimally impacted by Euro-American settlers. Little information is available that can be used by managers to accurately depict the spatial variation of reference conditions and the differences between reference and contemporary conditions. Especially, forest managers need this information as they are continuously designing management treatments to promote contemporary dry forest resiliency against fire, disease, and insects. To fill this knowledge gap, our study utilized field inventory data from reference conditions (1934) along with light detection and ranging and ground-truthing data from contemporary conditions (2016) associated with various research units of Blacks Mountain Experimental Forest, California, USA. Our results showed that in reference conditions, above-ground biomass—a component of overstory stand structure—was more spatially heterogeneous compared to contemporary forests. Based on semivariogram analyses, the 1934 conditions exhibited spatial variation at a spatial scale < 50 m and showed spatial autocorrelation at shorter ranges (150–200 m) compared to those observed in contemporary conditions (>250 m). In contemporary conditions, prescribed burn with high structural diversity treatment enhanced spatial heterogeneity as indicated by a greater number of peaks in the correlograms compared to the low structural diversity treatment. High structural diversity treatment units exhibited small patches of above-ground biomass at shorter ranges (~120 to 440 m) compared to the low structural diversity treatment units (~165 to 599 m). Understanding how spatial variation in contemporary conditions deviates from reference conditions and identifying specific management treatments that can be used to restore spatial variation observed in reference conditions will help managers to promote spatial variation in stand structure that has been resilient to wildfire, insects, and disease. Full article

(This article belongs to the Special Issue Modeling Aboveground Forest Biomass: New Developments)

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15 pages, 4679 KiB

Open AccessArticle

Formulating Equations for Estimating Forest Stand Carbon Stock for Various Tree Species Groups in Northern Thailand

by Khwanchai Duangsathaporn, Narapong Sangram, Yenemurwon Omule, Patsi Prasomsin, Kritsadapan Palakit and Pichit Lumyai

Forests 2023, 14(8), 1584; https://doi.org/10.3390/f14081584 - 3 Aug 2023

Viewed by 4744

Abstract

Through this study, we established equations for estimating the standing tree carbon stock based on 24 tree species in multiple size classes in a case study at the Ngao Demonstration Forest (NDF) in northern Thailand. Four hundred thirty-nine wood samples from trees in mixed deciduous forest (MDF), dry dipterocarp forest (DDF), and dry evergreen forest (DEF) were collected using non-destructive methods to estimate aboveground carbon equations through statistical regression. The equations were established based on four criteria: (1) the coefficient of determination (R²), (2) standard error of estimate (SE), (3) F-value, and (4) significant value (p-value, α ≤ 0.05). The aboveground carbon stock (C) equations for standing trees in the MDF was C = 0.0199DBH^2.1887H^0.5825, for DDF was C = 0.0145DBH^2.1435H^0.748, for DEF was C = 0.0167DBH^2.1423H^0.7070, and the general equation for all species/wood density groups was C = 0.017543DBH^2.1625H^0.6614, where DBH is tree diameter at breast height, and H is tree total height. The aboveground carbon stock in the DDF, MDF, and DEF was 142, 53.02, and 12 tons/ha, respectively, and the estimated aboveground carbon stock in the Mae Huad sector at the NDF was 61 tons/ha. Full article

(This article belongs to the Special Issue Modeling Aboveground Forest Biomass: New Developments)

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