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Forests
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Estimating and Modeling Aboveground and Belowground Biomass
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Estimating and Modeling Aboveground and Belowground Biomass

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 (20 August 2023) | Viewed by 7294

Special Issue Editor

Dr. Yuancai Lei

E-Mail Website
Guest Editor
Institute of Forest Resource Information Techniques (IFRIT), Chinese Academy of Forestry, Beijing 100091, China
Interests: forest biomass or carbon estimation; growth and yield models; sampling; forest management

Special Issue Information

Dear Colleagues,

Accurate above-ground and below-ground biomass measurements, estimates, and analyses are critical components in quantifying forest carbon stocks and sequestration rates and assessing potential impacts due to climate change. To this end, biomass equations and estimated approaches at tree, stand, and forest levels have been and will remain a key component of future carbon measurements and estimation.

This Special Issue on “Estimating and modeling above ground and belowground biomass” aims to present the state-of-the-art developments and best practices in forest biomass and carbon estimation. The topics will include sampling and estimation methods for forest biomass and carbon; addressing forest biomass to forest carbon link; addressing forest biomass to forest silviculture or management, tree species diversity, and forest classification structures and approaches and challenges in integrating data from various sources to improve the accuracy at local, regional, national, and global scale.

Dr. Yuancai Lei
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 estimation
  • forest management
  • model approach
  • uncertainty
  • carbon
  • sampling error

Published Papers (4 papers)

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Research

22 pages, 4067 KiB  
Article
Individual Tree Aboveground Biomass Estimation Based on UAV Stereo Images in a Eucalyptus Plantation
by Yao Liu, Peng Lei, Qixu You, Xu Tang, Xin Lai, Jianjun Chen and Haotian You
Forests 2023, 14(9), 1748; https://doi.org/10.3390/f14091748 - 29 Aug 2023
Viewed by 1080
Abstract
As one of the three fastest-growing tree species in the world, eucalyptus grows rapidly, with a monthly growth rate of up to 1 m and a maximum annual growth rate of up to 10 m. Therefore, ways to accurately and quickly obtain the [...] Read more.
As one of the three fastest-growing tree species in the world, eucalyptus grows rapidly, with a monthly growth rate of up to 1 m and a maximum annual growth rate of up to 10 m. Therefore, ways to accurately and quickly obtain the aboveground biomass (AGB) of eucalyptus in different growth stages at a low cost are the foundation of achieving eucalyptus growth-change monitoring and precise management. Although Light Detection and Ranging (LiDAR) can achieve high-accuracy estimations of individual eucalyptus tree biomasses, the cost of data acquisition is relatively high. While the AGB estimation accuracy of high-resolution images may be affected by a lack of forest vertical structural information, stereo images obtained using unmanned aerial vehicles (UAVs) can not only provide horizontal structural information but also vertical structural information through derived point data, demonstrating strong application potential in estimating the biomass of eucalyptus plantations. To explore the potential of UAV stereo images for estimating the AGB of individual eucalyptus trees and further investigate the impact of stereo-image-derived features on the construction of biomass models, in this study, UAVs equipped with consumer-grade cameras were used to obtain multitemporal stereo images. Different features, such as spectral features, texture, tree height, and crown area, were extracted to estimate the AGB of individual eucalyptus trees of five different ages with three algorithms. The different features extracted based on the UAV images had different effects on estimating AGB in individual eucalyptus trees. By estimating eucalyptus AGB using only spectrum features, we found that tree height had the greatest impact, with its R2 value increasing by 0.28, followed by forest age. Other features, such as spectrum, texture, and crown area, had relatively small effects. For the three algorithms, the estimation accuracy of the CatBoost algorithm was the highest, with an R2 ranging from 0.65 to 0.90, and the normalized root-mean-square error (NRMSE) ranged from 0.08 to 0.15. This was followed by the random forest algorithm. The ridge regression algorithm had the lowest accuracy, with an R2 ranging from 0.34 to 0.82 and an NRMSE value ranging from 0.11 to 0.21. The AGB model that we established with forest age, TH, crown area, and HOM-B feature variables using the CatBoost algorithm had the best estimation accuracy, with an R2 of 0.90 and an NRMSE of 0.08. The results indicated that accurately estimating the AGB of individual eucalyptus trees can be achieved based on stereo images obtained using UAVs equipped with affordable, consumer-grade cameras. This paper can provide methodological references and technical support for estimating forest biomass, carbon storage, and other structural parameters based on UAV images. Full article
(This article belongs to the Special Issue Estimating and Modeling Aboveground and Belowground Biomass)
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11 pages, 1000 KiB  
Article
Quercus suber Allometry in the West Mediterranean Basin
by Catarina Jorge, Margarida Tomé, Ricardo Ruiz-Peinado, Lobna Zribi and Joana Amaral Paulo
Forests 2023, 14(3), 649; https://doi.org/10.3390/f14030649 - 22 Mar 2023
Cited by 1 | Viewed by 2061
Abstract
The necessity for accurate biomass estimates is greater than ever for the sustainable management of forest resources, which is an increasingly pressing matter due to climate change. The most used method to estimate biomass for operational purposes is through allometric equations. Typically, each [...] Read more.
The necessity for accurate biomass estimates is greater than ever for the sustainable management of forest resources, which is an increasingly pressing matter due to climate change. The most used method to estimate biomass for operational purposes is through allometric equations. Typically, each country develops their own models to be applied at the local scale because it is more convenient. But, for Quercus suber, a joint regional model can be more beneficial, since the species is distributed across the Mediterranean and is challenging to account for due to felling limitations and the nature of mature cork biomass itself. We found that these characteristics are reflected in the biomass datasets and compatibility was, perhaps, the largest impediment to such a model. The use of dummy variables to differentiate between countries, as well as compromises in the limits of biomass compartments, allowed us to develop two joint models to estimate aboveground biomass in Portugal, Spain and Tunisia. One model as a function of diameter and another as a function of diameter and total tree height. In addition, we developed a separate model for roots (modelling efficiency of fitting = 0.89), since it was not possible to assure additivity of the whole tree. All coefficients were estimated using Seemingly Unrelated Regressions (SUR) and model fitting assured additivity in the aboveground compartments—leaves and woody biomass (modelling efficiency of fitting = 0.89 and 0.93, respectively). This work proves that it is possible to have a biologically sound and efficient model for the three countries, despite differences in the observed allometric patterns. Full article
(This article belongs to the Special Issue Estimating and Modeling Aboveground and Belowground Biomass)
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20 pages, 3748 KiB  
Article
Error Analysis on the Five Stand Biomass Growth Estimation Methods for a Sub-Alpine Natural Pine Forest in Yunnan, Southwestern China
by Guoqi Chen, Xilin Zhang, Chunxiao Liu, Chang Liu, Hui Xu and Guanglong Ou
Forests 2022, 13(10), 1637; https://doi.org/10.3390/f13101637 - 06 Oct 2022
Cited by 2 | Viewed by 1659
Abstract
Forest biomass measurement or estimation is critical for forest monitoring at the stand scale, but errors among different estimations in stand investigation are unclear. Thus, the Pinus densata natural forest in Shangri-La City, southwestern China, was selected as the research object to investigate [...] Read more.
Forest biomass measurement or estimation is critical for forest monitoring at the stand scale, but errors among different estimations in stand investigation are unclear. Thus, the Pinus densata natural forest in Shangri-La City, southwestern China, was selected as the research object to investigate the biomass of 84 plots and 100 samples of P. densata. The stand biomass was calculated using five methods: stand biomass growth with age (SBA), stem biomass combined with the biomass expansion factors (SB+BEF), stand volume combined with biomass conversion and expansion factors (SV+BCEF), individual tree biomass combined with stand diameter structure (IB+SDS), and individual tree biomass combined with stand density (IB+SD). The estimation errors of the five methods were then analyzed. The results showed that the suitable methods for estimating stand biomass are SB+BEF, M+BCEF, and IB+SDS. When using these three methods (SB+BEF, SV+BCEF, and IB+SDS) to estimate the biomass of different components, wood biomass estimation using SB+BEF is unsuitable, and root biomass estimation employing the IB+SDS method was not preferred. The SV+BCEF method was better for biomass estimation. Except for the branches, the mean relative error (MRE) of the other components presented minor errors in the estimation, while MRE was lower than other components in the range from −0.11%–28.93%. The SB+BEF was more appealing for branches biomass estimation, and its MRE is only 0.31% lower than SV+BCEF. The stand biomass strongly correlated with BEF, BCEF, stand structure, stand age, and other factors. Hence, the stand biomass growth model system established in this study effectively predicted the stand biomass dynamics and provided a theoretical basis and practical support for accurately estimating forest biomass growth. Full article
(This article belongs to the Special Issue Estimating and Modeling Aboveground and Belowground Biomass)
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18 pages, 3791 KiB  
Article
A Method for Estimating Forest Aboveground Biomass at the Plot Scale Combining the Horizontal Distribution Model of Biomass and Sampling Technique
by Chi Lu, Hui Xu, Jialong Zhang, Aiyun Wang, Heng Wu, Rui Bao and Guanglong Ou
Forests 2022, 13(10), 1612; https://doi.org/10.3390/f13101612 - 02 Oct 2022
Cited by 3 | Viewed by 1845
Abstract
Accurate estimation of small-scale forest biomass is a prerequisite and basis for trading forest carbon sinks and optimizing the allocation of forestry resources. This study aims to develop a plot-scale methodology for estimating aboveground biomass (AGB) that combines a biomass horizontal distribution model [...] Read more.
Accurate estimation of small-scale forest biomass is a prerequisite and basis for trading forest carbon sinks and optimizing the allocation of forestry resources. This study aims to develop a plot-scale methodology for estimating aboveground biomass (AGB) that combines a biomass horizontal distribution model (HDM) and sampling techniques to improve efficiency, reduce costs and provide the reliability of estimation for biomass. Simao pine (Pinus kesiya var. langbianensis) from Pu’er City, Yunnan Province, was used as the research subject in this study. A canopy profile model (CPM) was constructed based on data from branch analysis and transformed into a canopy biomass HDM. The horizontal distribution of AGB within the sample plots was simulated using the HDM based on the data from the per-wood survey and compared with the results from the location distribution model (LDM) simulation. AGB sampling estimations were carried out separately by combining different sampling methods with the AGB distribution of sample plot simulated by different biomass distribution models. The sampling effectiveness of all sampling schemes was compared and analyzed, and the best plan for the sampling estimation of AGB in plot-scale forests was optimized. The results are as follows: the power function model is the best model for constructing the CPM of the Simao pine in this study; with visual comparison and the analysis of the coefficient of variation, the AGB simulated by HDM has a larger and more continuous distribution than that simulated by LDM, which is closer to the actual distribution; HDM-based sampling plans have smaller sample sizes and sampling ratios than LDM-based ones; and lastly, the stratified sampling method (STS)-HDM-6 plan has the best sampling efficiency with a minimum sample size of 10 and a minimum sampling ratio of 15%. The result illustrates the potential of the method for estimating plot-scale forest AGB by combining HDM with sampling techniques to reduce costs and increase estimation efficiency effectively. Full article
(This article belongs to the Special Issue Estimating and Modeling Aboveground and Belowground Biomass)
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