Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.9
Journals
Forests
Special Issues
Assessment of Forest Biomass Using Inventory Plots and Modeling
share announcement

Assessment of Forest Biomass Using Inventory Plots and Modeling

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 (28 April 2023) | Viewed by 4864

Special Issue Editors

Dr. Mauricio Zapata Cuartas

E-Mail Website
Guest Editor
Finite Carbon Corporation, Wayne, PA 48184, USA
Interests: forest biometrics; growth and yield modeling; spatial forest planning and management; forest inventory and analysis; data science
Prof. Dr. Bronson P. Bullock

E-Mail Website
Guest Editor
Warnell School of Forestry and Natural Resources, University of Georgia, 180 E Green Street Forestry Building, Office # 4-529, Athens, GA 30602, USA
Interests: forest biometrics; mensuration; measurements; forest inventory; sampling; taper equations; volume and weight equations; stand dynamics; growth and yield models; diameter distributions; timber management
Prof. Dr. Peter L. Marshall

E-Mail Website
Guest Editor
Department of Forest Resources Management, University of British Columbia, Rm 2045, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada
Interests: biometrics; forest measurements; modelling; sampling; stand dynamics

Special Issue Information

Dear Colleagues, 

The accurate assessment of forest biomass has a critical role in the participation of forests in the emission reduction market, and as a key indicator of forest productivity, dynamics and health. A steadily growing interest has sought to reduce cost and improve biomass estimates in woody vegetation areas by combining sophisticated statistical analysis methods and cutting-edge technologies with established forest inventory plots. Many methods are available for biomass estimation, ranging from traditional direct field measurements to indirect methods using remote sensing sources. However, technical challenges and opportunities for more cost-effective biomass assessment reporting remain as modeling and multiscale data sources continue to evolve and advance.

Contributions to this Special Issue are encouraged on all aspects of using forest inventory plots for biomass assessment, including but not limited to: innovative sampling designs, data collection and analysis, model validations and implementation, the pairing of field plot sampling estimations with ancillary information to effectively assess different scales or domains, innovative statistical data modeling, and the evaluation of biomass error prediction (total or by components). This Special Issue aims to provide a selection of contributions to outline the state-of-the-art on quantitative methods using ground forest plot inventories for reliable forest biomass, and to identify gaps where research is needed.

Dr. Mauricio Zapata Cuartas
Prof. Dr. Bronson P. Bullock
Prof. Dr. Peter L. Marshall
Guest Editors

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

  • forest inventory design
  • forest biomass
  • design-based inference
  • biomass error prediction
  • remote sensing
  • forest measurement
  • modeling
  • model-base inference
  • small area estimation

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 5334 KiB  
Article
Stand-Level Biomass and Leaf Trait Models for Young Naturally Regenerated Forests of European Hornbeam
by Bohdan Konôpka, Vlastimil Murgaš, Vladimír Šebeň, Jozef Pajtík and Katarína Merganičová
Forests 2023, 14(6), 1084; https://doi.org/10.3390/f14061084 - 24 May 2023
Viewed by 949
Abstract
European hornbeam (Carpinus betulus L.) is a tree species widely distributed in Europe and the Asian part of the Near East. However, since European hornbeam is not very attractive for commercial purposes, scientific interest in this species has been rather sparse. Our [...] Read more.
European hornbeam (Carpinus betulus L.) is a tree species widely distributed in Europe and the Asian part of the Near East. However, since European hornbeam is not very attractive for commercial purposes, scientific interest in this species has been rather sparse. Our study focused on dense young (up to 10 years old) European hornbeam stands originating from natural regeneration from seeds in Slovakia because in future the importance of this species may increase due to the climate change. We combined previously constructed tree-level biomass models, data on basic leaf traits, i.e., weight and area, and measurements from thirty plots located at ten different sites across Slovakia to construct stand-level allometric relations of the biomass stock in tree components, i.e., leaves, branches, bark, stem under bark and roots, to mean stand diameter at stem base, i.e., at the ground level. Moreover, we calculated and modelled leaf characteristics, namely the specific leaf area (SLA), leaf area ratio (LAR) and leaf area index (LAI), at a stand level. The total tree biomass stock including all tree components ranged between 0.75 and 13.63 kg per m2, out of which the biomass of stem with bark was from 0.31 to 8.46 kg per m2. The biomass models showed that the contribution of roots (omitting those with a diameter under 2 mm) decreased with the increasing mean stand diameter at stem base, whereas the opposite pattern was observed for branches and stem biomass. Further, we found that the mean stand diameter at stem base was a good predictor of both LAR and LAI. The results indicated the high photosynthetic efficiency of European hornbeam leaves per one-sided surface leaf area. Moreover, the growth efficiency (GE), expressed as the biomass increment of woody parts per leaf area unit, of young European hornbeam trees was high. The models proved a close positive linear correlation between LAI and stand biomass stock that may be used for estimating the biomass in young stands from LAI that can be measured using non-destructive terrestrial or aerial methods. The results further indicated that young stands may sequester a non-negligible quantity of carbon; therefore, they should not be omitted from local or country-wide estimates of carbon stocks in forest vegetation. Full article
(This article belongs to the Special Issue Assessment of Forest Biomass Using Inventory Plots and Modeling)
►▼ Show Figures

Figure 1

19 pages, 4812 KiB  
Article
Prediction of Regional Forest Biomass Using Machine Learning: A Case Study of Beijing, China
by Jincheng Liu, Chengyu Yue, Chenyang Pei, Xuejian Li and Qingfeng Zhang
Forests 2023, 14(5), 1008; https://doi.org/10.3390/f14051008 - 13 May 2023
Cited by 2 | Viewed by 1672
Abstract
Dynamic changes in forest biomass are closely related to the carbon cycle, climate change, forest productivity and biodiversity. However, most previous studies mainly focused on the calculation of current forest biomass, and only a few studies attempted to predict future dynamic changes in [...] Read more.
Dynamic changes in forest biomass are closely related to the carbon cycle, climate change, forest productivity and biodiversity. However, most previous studies mainly focused on the calculation of current forest biomass, and only a few studies attempted to predict future dynamic changes in forest biomass which obtained uncertain results. Therefore, this study comprehensively considered the effects of multi-stage continuous survey data of forest permanent sample plots, site condition factors and corresponding meteorological factors using Beijing as an example. The geographic detector method was used to screen the key interfering factors that affect the growth of forest biomass. Then, based on the back-propagation artificial neural network (BP-ANN) and support vector machine (SVM) learning methods, 80% of the sample data were extracted to train the model, and thereby verify the prediction accuracy of different modeling methods using different training samples. The results showed that the forest biomass prediction models based on both the machine learning algorithms had good fitting accuracy, and there was no significant difference in the prediction results between the two models. However, the SVM model was better than the BP-ANN. While the BP-ANN model provided more volatile predictions, and the accuracy was above 80%, the prediction results of the SVM model were relatively stable, and the accuracy was above 90%. This study not only provides good technical support for the scientific estimation of regional forest biomass in the future, but also offers reliable basic data for sustainable forest management, planning decisions, forest carbon sequestration and sustainable development. Full article
(This article belongs to the Special Issue Assessment of Forest Biomass Using Inventory Plots and Modeling)
►▼ Show Figures

Figure 1

14 pages, 2108 KiB  
Article
Using Tree Height, Crown Area and Stand-Level Parameters to Estimate Tree Diameter, Volume, and Biomass of Pinus radiata, Eucalyptus globulus and Eucalyptus nitens
by Carlos A. Gonzalez-Benecke, M. Paulina Fernández, Jorge Gayoso, Matias Pincheira and Maxwell G. Wightman
Forests 2022, 13(12), 2043; https://doi.org/10.3390/f13122043 - 01 Dec 2022
Cited by 3 | Viewed by 1675
Abstract
Accurate estimates of tree diameter, height, volume, and biomass are important for numerous economic and ecological applications. In this study, we report exponential equations to predict tree DBH (cm), stem volume over bark (VOB, m3), and total above-stump biomass (TASB, kg) [...] Read more.
Accurate estimates of tree diameter, height, volume, and biomass are important for numerous economic and ecological applications. In this study, we report exponential equations to predict tree DBH (cm), stem volume over bark (VOB, m3), and total above-stump biomass (TASB, kg) using three varying levels of input data for Pinus radiata D. Don, Eucalyptus globulus Labill., and Eucalyptus nitens (H.Deane & Maiden) Maiden planted trees. The three sets of input data included: (1) tree height (HT, m), (2) tree HT and ground projected living crown area (CA, m2), and (3) tree HT, CA, and additional stand parameters. The analysis was performed using a large dataset covering the range of distribution of the species in central Chile and included stands of varying ages and planting densities. The first set of equations using only HT were satisfactory with Adj-R2 values ranging from 0.78 to 0.98 across all species and variables. For all three species, estimation of DBH, VOB, and TASB as a function of HT improved when CA was added as an additional independent variable, increasing Adj-R2 and reducing RMSE. The inclusion of stand variables, such as age and stand density, also resulted in further improvement in model performance. The models reported in this study are a robust alternative for DBH, VOB, and TASB estimations on planted stands across a wide range of ages and densities, when height and CA are known, especially when input data are derived from remote sensing techniques. Full article
(This article belongs to the Special Issue Assessment of Forest Biomass Using Inventory Plots and Modeling)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop