Modeling Aboveground Forest Biomass: New Developments

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: 30 June 2024 | Viewed by 11527

Special Issue Editors


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Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
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

Published Papers (4 papers)

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Research

20 pages, 5152 KiB  
Article
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 - 05 Apr 2024
Viewed by 1210
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 [...] Read more.
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  
Article
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 769
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 [...] Read more.
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  
Article
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 988
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 [...] Read more.
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  
Article
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 - 03 Aug 2023
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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 [...] Read more.
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 = 0.0199DBH2.1887H0.5825, for DDF was C = 0.0145DBH2.1435H0.748, for DEF was C = 0.0167DBH2.1423H0.7070, and the general equation for all species/wood density groups was C = 0.017543DBH2.1625H0.6614, 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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