Forests

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26 pages, 9302 KiB

Open AccessArticle

Application of Machine Learning for Aboveground Biomass Modeling in Tropical and Temperate Forests from Airborne Hyperspectral Imagery

by Patrick Osei Darko, Samy Metari, J. Pablo Arroyo-Mora, Matthew E. Fagan and Margaret Kalacska

Forests 2025, 16(3), 477; https://doi.org/10.3390/f16030477 - 8 Mar 2025

Viewed by 445

Abstract

Accurate operational methods used to measure, verify, and report changes in biomass at large spatial scales are required to support conservation initiatives. In this study, we demonstrate that machine learning can be used to model aboveground biomass (AGB) in both tropical and temperate [...] Read more.

Accurate operational methods used to measure, verify, and report changes in biomass at large spatial scales are required to support conservation initiatives. In this study, we demonstrate that machine learning can be used to model aboveground biomass (AGB) in both tropical and temperate forest ecosystems when provided with a sufficiently large training dataset. Using wavelet-transformed airborne hyperspectral imagery, we trained a shallow neural network (SNN) to model AGB. An existing global AGB map developed as part of the European Space Agency’s DUE GlobBiomass project served as the training data for all study sites. At the temperate site, we also trained the model on airborne-LiDAR-derived AGB. In comparison, for all study sites, we also trained a separate deep convolutional neural network (3D-CNN) with the hyperspectral imagery. Our results show that extracting both spatial and spectral features with the 3D-CNN produced the lowest RMSE across all study sites. For example, at the tropical forest site the Tortuguero conservation area, with the 3D-CNN, an RMSE of 21.12 Mg/ha (R² of 0.94) was reached in comparison to the SNN model, which had an RMSE of 43.47 Mg/ha (R² 0.72), accounting for a ~50% reduction in prediction uncertainty. The 3D-CNN models developed for the other tropical and temperate sites produced similar results, with a range in RMSE of 13.5 Mg/ha–31.18 Mg/ha. In the future, as sufficiently large field-based datasets become available (e.g., the national forest inventory), a 3D-CNN approach could help to reduce the uncertainty between hyperspectral reflectance and forest biomass estimates across tropical and temperate bioclimatic domains. Full article

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

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24 pages, 5117 KiB

Open AccessArticle

Estimation of Aboveground Biomass of Picea schrenkiana Forests Considering Vertical Zonality and Stand Age

by Guohui Zhang, Donghua Chen, Hu Li, Minmin Pei, Qihang Zhen, Jian Zheng, Haiping Zhao, Yingmei Hu and Jingwei Fan

Forests 2025, 16(3), 445; https://doi.org/10.3390/f16030445 - 1 Mar 2025

Viewed by 618

Abstract

The aboveground biomass (AGB) of forests reflects the productivity and carbon-storage capacity of the forest ecosystem. Although AGB estimation techniques have become increasingly sophisticated, the relationships between AGB, spatial distribution, and growth stages still require further exploration. In this study, the Picea schrenkiana [...] Read more.

The aboveground biomass (AGB) of forests reflects the productivity and carbon-storage capacity of the forest ecosystem. Although AGB estimation techniques have become increasingly sophisticated, the relationships between AGB, spatial distribution, and growth stages still require further exploration. In this study, the Picea schrenkiana (Picea schrenkiana var. tianschanica) forest area in the Kashi River Basin of the Ili River Valley in the western Tianshan Mountains was selected as the research area. Based on forest resources inventory data, Gaofen-1 (GF-1), Gaofen-6 (GF-6), Gaofen-3 (GF-3) Polarimetric Synthetic Aperture Radar (PolSAR), and DEM data, we classified the Picea schrenkiana forests in the study area into three cases: the Whole Forest without vertical zonation and stand age, Vertical Zonality Classification without considering stand age, and Stand-Age Classification without considering vertical zonality. Then, for each case, we used eXtreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Residual Networks (ResNet), respectively, to estimate the AGB of forests in the study area. The results show that: (1) The integration of multi-source remote-sensing data and the ResNet can effectively improve the remote-sensing estimation accuracy of the AGB of Picea schrenkiana. (2) Furthermore, classification by vertical zonality and stand ages can reduce the problems of low-value overestimation and high-value underestimation to a certain extent. Full article

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

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32 pages, 34511 KiB

Open AccessArticle

Assessing Above-Ground Biomass Dynamics and Carbon Sequestration Potential Using Machine Learning and Spaceborne LiDAR in Hilly Conifer Forests of Mansehra District, Pakistan

by Muhammad Imran, Guanhua Zhou, Guifei Jing, Chongbin Xu, Yumin Tan, Rana Ahmad Faraz Ishaq, Muhammad Kamran Lodhi, Maimoona Yasinzai, Ubaid Akbar and Anwar Ali

Forests 2025, 16(2), 330; https://doi.org/10.3390/f16020330 - 13 Feb 2025

Viewed by 735

Abstract

Consistent and accurate data on forest biomass and carbon dynamics are essential for optimizing carbon sequestration, advancing sustainable management, and developing natural climate solutions in various forest ecosystems. This study quantifies the forest biomass in designated forests based on GEDI LiDAR datasets with [...] Read more.

Consistent and accurate data on forest biomass and carbon dynamics are essential for optimizing carbon sequestration, advancing sustainable management, and developing natural climate solutions in various forest ecosystems. This study quantifies the forest biomass in designated forests based on GEDI LiDAR datasets with a unique compartment-level monitoring of unexplored hilly areas of Mansehra. The integration of multisource explanatory variables, employing machine learning models, adds further innovation to the study of reliable above ground biomass (AGB) estimation. Integrating Landsat-9 vegetation indices with ancillary datasets improved forest biomass estimation, with the random forest algorithm yielding the best performance (R² = 0.86, RMSE = 28.03 Mg/ha, and MAE = 19.54 Mg/ha). Validation with field data on a point-to-point basis estimated a mean above-ground biomass (AGB) of 224.61 Mg/ha, closely aligning with the mean ground measurement of 208.13 Mg/ha (R² = 0.71). The overall mean AGB model estimated a forest biomass of 189.42 Mg/ha in the designated moist temperate forests of the study area. A critical deficit in the carbon sequestration potential was analysed, with the estimated AGB in 2022, at 19.94 thousand tons, with a deficit of 0.83 thousand tons to nullify CO₂ emissions (20.77 thousand tons). This study proposes improved AGB estimation reliability and offers insights into the CO₂ sequestration potential, suggesting a policy shift for sustainable decision-making and climate change mitigation policies. Full article

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

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28 pages, 2179 KiB

Open AccessArticle

Modeling Forest Regeneration Dynamics: Estimating Regeneration, Growth, and Mortality Rates in Lithuanian Forests

by Robertas Damaševičius and Rytis Maskeliūnas

Forests 2025, 16(2), 192; https://doi.org/10.3390/f16020192 - 21 Jan 2025

Viewed by 1142

Abstract

This study presents a novel approach to analyzing forest regeneration dynamics by integrating a Markov chain model with Multivariate Time Series (MTY) decomposition. The probabilistic tracking of age-class transitions was combined with the decomposition of regeneration rates into trend, seasonal, and irregular components, [...] Read more.

This study presents a novel approach to analyzing forest regeneration dynamics by integrating a Markov chain model with Multivariate Time Series (MTY) decomposition. The probabilistic tracking of age-class transitions was combined with the decomposition of regeneration rates into trend, seasonal, and irregular components, unlike traditional deterministic models, capturing the variability and uncertainties inherent in forest ecosystems, offering a more nuanced understanding of how Scots pine (Pinus sylvestris L.) and other tree species evolve under different management and climate scenarios. Using 20 years of empirical data from the Lithuanian National Forest Inventory, the study evaluates key growth and mortality parameters for Scots pine, Spruce (Picea abies), Birch (Betula pendula), and Aspen (Populus tremula). The model for Scots pine showed a 79.6% probability of advancing from the 1–10 age class to the 11–20 age class, with subsequent transitions of 82.9% and 84.1% for older age classes. The model for Birch shown a strong early growth rate, with an 84% chance of transitioning to the next age class, while the model for Aspen indicated strong slowdown after 31 years. The model indicated moderate early growth for Spruce with a high transition in later stages, highlighting its resilience in mature forest ecosystems. Sensitivity analysis revealed that while higher growth rates can prolong forest stand longevity, mortality rates above 0.33 severely compromise stand viability. The Hotelling

T^{2}

control chart identified critical deviations in forest dynamics, particularly in years 13 and 19, suggesting periods of environmental stress. The model offers actionable insights for sustainable forest management, emphasizing the importance of species-specific strategies, adaptive interventions, and the integration of climate change resilience into long-term forest planning. Full article

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

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21 pages, 9860 KiB

Open AccessArticle

Uncertainty Analysis of Forest Aboveground Carbon Stock Estimation Combining Sentinel-1 and Sentinel-2 Images

by Bo Qiu, Sha Li, Jun Cao, Jialong Zhang, Kun Yang, Kai Luo, Kai Huang and Xinzhou Jiang

Forests 2024, 15(12), 2134; https://doi.org/10.3390/f15122134 - 2 Dec 2024

Cited by 1 | Viewed by 1118

Abstract

Accurate estimation of forest aboveground carbon stock (AGC) is essential for understanding carbon accounting and climate change. In previous studies, the extracted factors, such as spectral textures, vegetation indices, and textural features, were used to estimate the AGC. However, few studies examined how [...] Read more.

Accurate estimation of forest aboveground carbon stock (AGC) is essential for understanding carbon accounting and climate change. In previous studies, the extracted factors, such as spectral textures, vegetation indices, and textural features, were used to estimate the AGC. However, few studies examined how different factors affect estimation accuracy in detail. Meanwhile, there are also many uncertainties in the collection and processing of the field data. To quantify the various uncertainties in the process of AGC estimation, we used the random forest (RF) to establish estimation models based on field data and Sentinel-1/2 images in Shangri-La. The models included the band information model (BIM), the vegetation index model (VIM), the texture information model (TIM), the Sentinel-2 factor model (S-2M), and the Sentinel-1/2 factor model (S-1/2M). Then, uncertainties resulting from the plot scale and estimation models were calculated using error equations. Our goal is to analyze the influence of different factors on AGC estimation and to assess the uncertainty of plot scale and estimation models quantitatively. The results showed that (1) the uncertainty of the measurement was 3.02%, while the error of the monocarbon stock model was the main uncertainty at the plot scale, which was 9.09%; (2) the BIM had the lowest accuracy (R² = 0.551) and the highest total uncertainty (22.29%); by gradually introducing different factors in the process of modeling, the accuracies improved significantly (VIM: R² = 0.688, TIM: R² = 0.715, S-2M: R² = 0.826), and the total uncertainty decreased to some extent (VIM: 14.12%, TIM: 12.56%, S-2M: 10.79%); (3) the S-1/2M with the introduction of Sentinel-1 synthetic aperture radar (SAR) data has the highest accuracy (R² = 0.872) and the lowest total uncertainty (8.43%). The inaccuracy of spectral features is highest, followed by vegetation indices, while textural features have the lowest inaccuracy. Uncertainty in the remote-sensing-based estimation model remains a significant source of uncertainty compared to the plot scale. Even though the uncertainty at the plot scale is relatively small, this error should not be ignored. The uncertainty in the estimation process could be further reduced by improving the precision of the measurement and the fitting of the monocarbon stock estimation model. Full article

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

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20 pages, 11745 KiB

Open AccessArticle

Biomass Prediction Using Sentinel-2 Imagery and an Artificial Neural Network in the Amazon/Cerrado Transition Region

by Luana Duarte de Faria, Eraldo Aparecido Trondoli Matricardi, Beatriz Schwantes Marimon, Eder Pereira Miguel, Ben Hur Marimon Junior, Edmar Almeida de Oliveira, Nayane Cristina Candido dos Santos Prestes and Osmar Luiz Ferreira de Carvalho

Forests 2024, 15(9), 1599; https://doi.org/10.3390/f15091599 - 11 Sep 2024

Cited by 2 | Viewed by 1390

Abstract

The ecotone zone, located between the Cerrado and Amazon biomes, has been under intensive anthropogenic pressures due to the expansion of commodity agriculture and extensive cattle ranching. This has led to habitat loss, reducing biodiversity, depleting biomass, and increasing CO₂ emissions. In [...] Read more.

The ecotone zone, located between the Cerrado and Amazon biomes, has been under intensive anthropogenic pressures due to the expansion of commodity agriculture and extensive cattle ranching. This has led to habitat loss, reducing biodiversity, depleting biomass, and increasing CO₂ emissions. In this study, we employed an artificial neural network, field data, and remote sensing techniques to develop a model for estimating biomass in the remaining native vegetation within an 18,864 km² ecotone region between the Amazon and Cerrado biomes in the state of Mato Grosso, Brazil. We utilized field data from a plant ecology laboratory and vegetation indices from Sentinel-2 satellite imagery and trained artificial neural networks to estimate aboveground biomass (AGB) in the study area. The optimal network was chosen based on graphical analysis, mean estimation errors, and correlation coefficients. We validated our chosen network using both a Student’s t-test and the aggregated difference. Our results using an artificial neural network, in combination with vegetation indices such as AFRI (Aerosol Free Vegetation Index), EVI (Enhanced Vegetation Index), and GNDVI (Green Normalized Difference Vegetation Index), which show an accurate estimation of aboveground forest biomass (Root Mean Square Error (RMSE) of 15.92%), can bolster efforts to assess biomass and carbon stocks. Our study results can support the definition of environmental conservation priorities and help set parameters for payment for ecosystem services in environmentally sensitive tropical regions. Full article

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

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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

Cited by 1 | Viewed by 1402

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 [...] Read more.

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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17 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 1059

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 [...] Read more.

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 3 | Viewed by 2900

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

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

Cited by 2 | Viewed by 1676

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

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 1579

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

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

Cited by 2 | Viewed by 6191

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 = 0.0199DBH^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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