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Advances in Forest Growth and Biomass Estimation
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Advances in Forest Growth and Biomass Estimation

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 October 2023) | Viewed by 11923

Special Issue Editor

Dr. Szymon Bijak

E-Mail Website1 Website2
Guest Editor
Department of Dendrometry and Forest Productivity, Warsaw University of Life Sciences - SGGW, Warsaw, Poland
Interests: biomass; dendroecology; tree-rings; site index modeling; soils
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests play various roles in the carbon cycle, either as net emitters or net sinks. Carbon release and accumulation is, hence, a combined result of both natural processes (respiration and oxidation) and human activities (planting, harvesting, fires, and de/reforestation). Forests and their role in the carbon cycle are affected by changing climate conditions. Depending on the circumstances, climate change will either reduce or increase the potential of forests to sequester carbon. However, forest management may have an influence on carbon sequestration by stimulating certain processes or hindering the impact of negative factors. Biomass estimation determines potential carbon emission that could be released into the atmosphere or evaluates the possible amount of carbon sequestered.

As there is a continuous debate regarding the role of forest ecosystems in climate change mitigation, it is essential to properly and effectively estimate carbon storage by forests. Therefore, the principal objective of this Special Issue is to gather and disseminate the latest advances and developments in the field of biomass and carbon storage estimation and modeling in forest ecosystems. We encourage scholars from around the world to submit review papers, original research investigations, and case studies that cover the wide range of issues related to quantifying the possible storage of carbon by forest ecosystems.

Dr. Szymon Bijak
Guest Editor

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

  • biomass assessment methods
  • allometry
  • carbon storage
  • biomass allocation

Published Papers (7 papers)

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Research

20 pages, 4115 KiB  
Article
Research on Estimating and Evaluating Subtropical Forest Carbon Stocks by Combining Multi-Payload High-Resolution Satellite Data
by Yisha Du, Donghua Chen, Hu Li, Congfang Liu, Saisai Liu, Naiming Zhang, Jingwei Fan and Deting Jiang
Forests 2023, 14(12), 2388; https://doi.org/10.3390/f14122388 - 07 Dec 2023
Viewed by 903
Abstract
Forest carbon stock is an important indicator reflecting the structure of forest ecosystems and forest quality, and an important parameter for evaluating the carbon sequestration capacity and carbon balance of forests. It is of great significance to study forest carbon stock in the [...] Read more.
Forest carbon stock is an important indicator reflecting the structure of forest ecosystems and forest quality, and an important parameter for evaluating the carbon sequestration capacity and carbon balance of forests. It is of great significance to study forest carbon stock in the context of current global climate change. To explore the application ability of multi-loaded, high-resolution satellite data in the estimation of subtropical forest carbon stock, this paper takes Huangfu Mountain National Forest Park in Chuzhou City as the study area, extracts remote sensing features such as spectral features, texture features, backscattering coefficient, and other remote sensing features based on multi-loaded, high-resolution satellite data, and carries out correlation analyses with the carbon stock of different species of trees and different age groups of forests. Regression models for different tree species were established for different data sources, and the optimal modeling factors for multi-species were determined. Then, three algorithms, namely, multiple stepwise regression, random forest, and gradient-enhanced decision tree, were used to estimate carbon stocks of multi-species, and the predictive ability of different estimation models on carbon stocks was analyzed using the coefficient of determination (R2) and the root mean square error (RMSE) as indexes. The following conclusions were drawn: for the feature factors, the texture features of the GF-2 image, the new red edge index of the GF-6 image, the radar intensity coefficient sigma, and radar brightness coefficient beta of the GF-3 image have the best correlation with the carbon stock; for the algorithms, the random forest and gradient-boosting decision tree have the better effect of fitting and predicting the carbon stock of multi-tree species, among which gradient-boosting decision tree has the best effect, with an R2 of 0.902 and an RMSE of 10.261 t/ha. In summary, the combination of GF-2, GF-3, and GF-6 satellite data and gradient-boosting decision tree obtains the most accurate estimation results when estimating forest carbon stocks of complex tree species; multi-load, high-resolution satellite data can be used in the inversion of subtropical forest parameters to estimate the carbon stocks of subtropical forests. The multi-loaded, high-resolution satellite data have great potential for application in the field of subtropical forest parameter inversion. Full article
(This article belongs to the Special Issue Advances in Forest Growth and Biomass Estimation)
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30 pages, 2502 KiB  
Article
Simulating Sustainable Forest Management Practices Using Crown Attributes: Insights for Araucaria angustifolia Trees in Southern Brazil
by César Augusto Guimarães Finger, Emanuel Arnoni Costa, André Felipe Hess, Veraldo Liesenberg and Polyanna da Conceição Bispo
Forests 2023, 14(7), 1285; https://doi.org/10.3390/f14071285 - 21 Jun 2023
Viewed by 950
Abstract
Araucaria angustifolia (Bertol.) Kuntze, commonly known as Brazilian pine, is a significant tree species in the Brazilian flora that once covered an area of 200,000 km2 in the Southern region. During the 1970s, high-quality timber logs from this conifer became the primary [...] Read more.
Araucaria angustifolia (Bertol.) Kuntze, commonly known as Brazilian pine, is a significant tree species in the Brazilian flora that once covered an area of 200,000 km2 in the Southern region. During the 1970s, high-quality timber logs from this conifer became the primary export product of the country. However, the species is endangered due to uncontrolled exploitation and is subject to a harvesting ban. It is crucial, therefore, to explore sustainable cultivation methods for this species, which necessitates urgent research and scientific insights. In this study, we present a simulation of a management strategy for in situ conservation by manipulating growth space and crown size dynamics. Forest inventory data and mixed forest regression equations were employed to describe the horizontal dimensions of average and maximum potential crown growth, resulting in two management scenarios. The results presented in management diagrams show that both approaches required logging numerous trees to ensure adequate space for healthy tree growth and provide soil coverage and forest protection. Therefore, the absence of effective forest management initiatives for Araucaria forests may have further implications for the structure, production, conservation, and overall development. To address these challenges, we propose two hypotheses: firstly, that tree diameter depends on crown dimensions, which are in turn influenced by tree growth space, and, secondly, that crown dimensions serve as a reliable indicator of existing competition and can be utilized to simulate forest management practices. We urge that implementing sustainable forest management initiatives for Araucaria angustifolia at selected locations can contribute to expanding natural forest areas, mitigate deterioration caused by high competition, discourage illegal logging, and prevent overexploitation of their edible seeds, which hinders regeneration. Our results underscore the significant implications of the lack of forest management initiatives in rural properties, potentially resulting in irreversible deterioration. The exact consequences of this deterioration remain unclear, emphasizing the need for further studies to understand its eventual effects on the growth reaction of trees of different diameters, ages, and crown conditions after the liberation of their crowns. Full article
(This article belongs to the Special Issue Advances in Forest Growth and Biomass Estimation)
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18 pages, 5317 KiB  
Article
Application of MaxEnt Model in Biomass Estimation: An Example of Spruce Forest in the Tianshan Mountains of the Central-Western Part of Xinjiang, China
by Xue Ding, Zhonglin Xu and Yao Wang
Forests 2023, 14(5), 953; https://doi.org/10.3390/f14050953 - 05 May 2023
Cited by 3 | Viewed by 1738
Abstract
Accurately estimating the above-ground biomass (AGB) of spruce forests and analyzing their spatial patterns are critical for quantifying forest carbon stocks and assessing regional climate conditions in China’s drylands, with significant implications for the sustainable management and conservation of forest ecosystems in the [...] Read more.
Accurately estimating the above-ground biomass (AGB) of spruce forests and analyzing their spatial patterns are critical for quantifying forest carbon stocks and assessing regional climate conditions in China’s drylands, with significant implications for the sustainable management and conservation of forest ecosystems in the Tianshan Mountains. The K-Means clustering algorithm was used to divide 144 measured AGB samples into four AGB classes, combined with remote sensing data from Landsat products, 19 bioclimatic variables, 3 topographical variables, and 3 soil variables to generate probability distributions of four AGB classes using the MaxEnt model. Finally, the spatial distribution of AGB was mapped using the mathematical formulae available in the GIS software. Results indicate that (1) the area under the receiver operating characteristic curve (AUC-ROC) of the AGB models for all classes exceeded 0.8, indicating satisfactory model accuracy; (2) the dominant factors affecting the distribution of different AGB classes varied. The primary dominant factors for the first–fourth AGB classes model were altitude (20.4%), precipitation of warmest quarter (Bio18, 15.7%), annual mean temperature (Bio1, 50.5%), and red band (Band4, 26.7%), respectively, and the response curves indicated that the third AGB model was more tolerant of elevation than the first and second AGB classes; (3) the AGB has a spatial distribution pattern of being higher in the west and low in the east, with a “single-peaked” pattern in terms of latitude, and the average AGB of pixels was 680.92 t·hm−2; (4) the correlation coefficient between measured and predicted AGB is 0.613 (p < 0.05), with the average uncertainty of AGB estimation at 39.32%. This study provides valuable insights into the spatial patterns and drivers of AGB in spruce forests in the Tianshan Mountains, which can inform effective forest management and conservation strategies. Full article
(This article belongs to the Special Issue Advances in Forest Growth and Biomass Estimation)
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11 pages, 2653 KiB  
Article
Allometric Models to Estimate the Biomass of Tree Seedlings from Dry Evergreen Forest in Thailand
by Sangsuree Thippawan, Kanisorn Chowtiwuttakorn, Nanthachai Pongpattananurak and Ekaphan Kraichak
Forests 2023, 14(4), 725; https://doi.org/10.3390/f14040725 - 01 Apr 2023
Viewed by 1653
Abstract
Seedlings are an important stage for plant populations, as the abundance and rigor of seedlings can indicate a changing forest structure in the future. Studying the different traits of the seedling can represent how the plant grows. Biomass is one of the traits [...] Read more.
Seedlings are an important stage for plant populations, as the abundance and rigor of seedlings can indicate a changing forest structure in the future. Studying the different traits of the seedling can represent how the plant grows. Biomass is one of the traits that can represent the plant’s performance and many other growth processes of the seedling. Several allometric equations have been developed to estimate tree biomass. However, allometric equations for the biomass of seedlings remains poorly studied, especially those from the tropics. The objective of this research is to create and develop a model that can be used to predict the biomass of seedlings, including total biomass, aboveground biomass, and belowground biomass, from root collar diameter, shoot height, main stem length, and wood density from 205 two-year-old seedlings from twenty tree species found in dry evergreen forest in Huai Kha Khaeng Wildlife Sanctuary, Uthai Thani, Thailand. The results showed that the root collar diameter, shoot height, and wood density could be used to create a model to best predict the seedling biomass. This model should be tested with other seedlings in the wild and other datasets to evaluate the performance of the model. To our knowledge, this study is among the first to provide the first allometry for seedlings in tropical dry evergreen forest. The results from this study will allow ecologists to monitor and examine the growth of the seedlings at all stages of life in dynamic tropical environments in the future. Full article
(This article belongs to the Special Issue Advances in Forest Growth and Biomass Estimation)
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21 pages, 4468 KiB  
Article
Validation of Forest Vegetation Simulator Model Finds Overprediction of Carbon Growth in California
by Claudia Herbert, Jeremy S. Fried and Van Butsic
Forests 2023, 14(3), 604; https://doi.org/10.3390/f14030604 - 17 Mar 2023
Cited by 2 | Viewed by 1975
Abstract
Using regression-based, bootstrapped equivalence tests, and remeasured inventory plot data from thousands of plots across California, we found that the Forest Vegetation Simulator (FVS), as typically used out-of-the-box, overpredicts carbon sequestration in live trees that remain alive ten years later by 27%, on [...] Read more.
Using regression-based, bootstrapped equivalence tests, and remeasured inventory plot data from thousands of plots across California, we found that the Forest Vegetation Simulator (FVS), as typically used out-of-the-box, overpredicts carbon sequestration in live trees that remain alive ten years later by 27%, on average. We found FVS growth prediction sensitive to forest type and FVS variant, with the largest overpredictions occurring in stands within the North Coast variant, growing on the lowest site class, having ages that are unknown or between 50 and 100 years, and that are within governmentally designated reserved areas or on national forests. Direction and magnitude of errors are related to the stand attributes; these relationships point the way towards opportunities to improve the underlying growth models or calibrate the system to improve prediction accuracy. Our findings suggest that forest managers relying on out-of-the-box FVS growth models to forecast carbon sequestration implications of their management of California forests will obtain estimates that overstate the carbon that can be sequestered under light-touch or caretaker management, potentially leading to management decisions that fail to deliver the expected carbon sequestration benefits—a failure that could take a long time to recognize. Full article
(This article belongs to the Special Issue Advances in Forest Growth and Biomass Estimation)
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21 pages, 6502 KiB  
Article
Predicting Growth of Individual Trees Directly and Indirectly Using 20-Year Bitemporal Airborne Laser Scanning Point Cloud Data
by Valtteri Soininen, Antero Kukko, Xiaowei Yu, Harri Kaartinen, Ville Luoma, Otto Saikkonen, Markus Holopainen, Leena Matikainen, Matti Lehtomäki and Juha Hyyppä
Forests 2022, 13(12), 2040; https://doi.org/10.3390/f13122040 - 30 Nov 2022
Cited by 1 | Viewed by 2736
Abstract
Reviewing forest carbon sinks is of the utmost importance in efforts to control climate change. This study focuses on reporting the 20-year boreal forest growth values acquired with airborne laser scanning (ALS). The growth was examined on the Kalkkinen research site in southern [...] Read more.
Reviewing forest carbon sinks is of the utmost importance in efforts to control climate change. This study focuses on reporting the 20-year boreal forest growth values acquired with airborne laser scanning (ALS). The growth was examined on the Kalkkinen research site in southern Finland as a continuation of several earlier growth studies performed in the same area. The data for the study were gathered with three totally different airborne laser scanning systems, namely using Toposys-I Falcon in June 2000 and Riegl VUX-1HA and miniVUX-3UAV in June 2021 with approximate point densities of 11, 1360, and 460 points/m2, respectively. The ALS point cloud was preprocessed to identify individual trees, from each of which different features were extracted either for direct or indirect growth measurement. In the direct method, the growth value is predicted based on differences of features, whereas in the indirect method, the growth value is obtained by subtracting the results of two independent predictions of different years. The growth in individual tree attributes, such as growth in height, diameter at breast height (DBH), and stem volume, were calculated for direct estimation. Field reference campaigns were performed in the summer of 2001 and in November 2021 to validate the obtained growth values. The study showed that long-term series growth of height, DBH, and stem volume are possible to record with a high-to-moderate coefficient of determination (R2) of 0.90, 0.48, and 0.45 in the best-case scenarios. The respective root-mean-squared errors (RMSE) values were 0.98 m, 0.02 m, and 0.17 m3, and the biases were −0.06 m, 0.00 m, and 0.17 m3. The direct method produced better metrics in terms of RMSE-% and bias, but the indirect method produced better best-fit lines. Additionally, the mean growth values for height, diameter, and stem volume intervals were compared, and they are presumed to be usable even for forest modelling. Full article
(This article belongs to the Special Issue Advances in Forest Growth and Biomass Estimation)
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17 pages, 3092 KiB  
Article
A Comparison of Models of Stand Volume in Spruce-Fir Mixed Forest in Northeast China
by Jiarong Liu, Jingyuan He, Lei Chai, Xun Zhong, Bo Jia and Xinjie Wang
Forests 2022, 13(7), 1117; https://doi.org/10.3390/f13071117 - 15 Jul 2022
Viewed by 1219
Abstract
Based on a multiple linear regression model, random forest algorithm and generalized additive model, a stand volume model was constructed to provide a theoretical basis for sustainable management. A total of 224 fixed plots in the Jingouling forest farm, Wangqing County, Jilin Province, [...] Read more.
Based on a multiple linear regression model, random forest algorithm and generalized additive model, a stand volume model was constructed to provide a theoretical basis for sustainable management. A total of 224 fixed plots in the Jingouling forest farm, Wangqing County, Jilin Province, were used as data sources. Specifically, 157 plots were used as training data, and 77 plots were used as test data. The effects of stand structure variables, topography variables, cutting variables, diversity variables and climate variables on stand volume were analyzed. The random forest algorithm explained 95.51% of the stand volume, and the generalized additive model explained 95.45% of the stand volume. Stand structure variables and topography variables had more influence on the stand volume of spruce-fir than other variables. Among the diversity variables, the evenness index, Shannon index and Simpson index had a relatively greater impact on the stand volume. The cutting times and the intensity of the first cutting had a direct relationship with stand volume. The influence of climate variables on the stand volume was relatively small in the study area. Full article
(This article belongs to the Special Issue Advances in Forest Growth and Biomass Estimation)
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