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Forests
Special Issues
Modeling of Biomass Estimation and Stand Parameters in Forests
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Modeling of Biomass Estimation and Stand Parameters in Forests

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 2025 | Viewed by 1279

Special Issue Editor

Prof. Dr. Zhong Zhao

E-Mail Website
Guest Editor
Key Laboratory of Silviculture on the Loess Plateau of State Forestry and Grassland Administration, College of Forestry, Northwest A&F University, Xianyang 712100, China
Interests: forest biomass estimation; forest quality assessment; eco-site classification; forest landscape management

Special Issue Information

Dear Colleagues,

Forest biomass is a key indicator for evaluating forest quality and carbon sequestration potential. Accurate biomass estimation is essential for understanding carbon dynamics, ecosystem productivity, and mitigating climate change. Precise modeling of stand parameters such as stand age, tree height, diameter at breast height, density, leaf area index, etc., offers valuable insights into forest growth patterns, interspecies competition, and potential responses to environmental changes, thus supporting sustainable forest management.

The integration of remote sensing technologies, including LiDAR, drones, and satellite imagery, into forest biomass estimation and stand parameter modeling has significantly enhanced the accuracy and scale of data collection. These technologies enable large-scale, long-term, and precise monitoring, providing critical information for forest dynamics and policymaking. This Special Issue seeks to present cutting-edge research on precise forest biomass estimation, stand parameter modeling, and their broader applications.

Potential topics include, but are not limited to, the following:

  • Modeling forest biomass dynamics under climate change scenarios;
  • Integration of LiDAR and satellite imagery in forest modeling;
  • Applications of artificial intelligence in forest biomass and stand parameter estimation;
  • Integrating remote sensing and field data for enhanced biomass estimation;
  • Policy implications of remote sensing in forest management and conservation.

Prof. Dr. Zhong Zhao
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

  • forest biomass estimation
  • carbon sequestration
  • LiDAR and drones
  • climate change mitigation
  • stand parameter modeling
  • forest dynamics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

17 pages, 9477 KiB  
Article
Semi-Automatic Stand Delineation Based on Very-High-Resolution Orthophotographs and Topographic Features: A Case Study from a Structurally Complex Natural Forest in the Southern USA
by Can Vatandaslar, Pete Bettinger, Krista Merry, Jonathan Stober and Taeyoon Lee
Forests 2025, 16(4), 666; https://doi.org/10.3390/f16040666 - 11 Apr 2025
Viewed by 295
Abstract
In the management of forests, the boundaries of individual units of land containing similar forest resources (e.g., stands) are delineated and used to guide the implementation of management activities. Traditionally, stand boundaries are drawn or digitized by hand; however, work recently has been [...] Read more.
In the management of forests, the boundaries of individual units of land containing similar forest resources (e.g., stands) are delineated and used to guide the implementation of management activities. Traditionally, stand boundaries are drawn or digitized by hand; however, work recently has been conducted to automate the process using aerial imagery or airborne light detection and ranging (LiDAR) data as supporting resources. The work described here applies an object-based image analysis (OBIA) process to aerial imagery and to a landform index database. The size and shape of stands in the outcomes of these applications are then adjusted to conform to the desired product of land managers. These products are then intersected as they each contain information of value in the stand delineation process. The intersected database is then adjusted once again to conform to the desired product of land managers. Conformity of the size and shape of the resulting stand boundaries to a reference database drawn subjectively by hand was low to moderate. Specifically, the overall agreement for spatial and thematic (class names) accuracies was 43.0% and 56.8%, respectively. Nevertheless, the process of automating the stand delineation effort remains promising for achieving an efficient and non-subjective characterization of a structurally complex forested environment. Full article
(This article belongs to the Special Issue Modeling of Biomass Estimation and Stand Parameters in Forests)
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16 pages, 14757 KiB  
Article
Effects of Choosing Different Parameterization Data in Two-Phase Forest Inventories for Standing Stock Estimation
by Ambros Berger and Thomas Gschwantner
Forests 2025, 16(2), 259; https://doi.org/10.3390/f16020259 - 30 Jan 2025
Viewed by 701
Abstract
The demands on national forest inventories to provide detailed information for small geographical regions are rising. Two-phase estimators are often employed to obtain forest resource estimates, yet there is little information on optimal training data selection. This study evaluates the impact of different [...] Read more.
The demands on national forest inventories to provide detailed information for small geographical regions are rising. Two-phase estimators are often employed to obtain forest resource estimates, yet there is little information on optimal training data selection. This study evaluates the impact of different training data on two-phase estimators, with a focus on small area estimators for standing stock and aims to develop guidelines on selecting appropriate training datasets. Linear regression models were parameterized using multiple datasets and subsets based on ecological and administrative boundaries. The models were then applied on varying scales, and their estimates and their confidence intervals were compared to each other as well as to the single-phase, purely terrestrial forest inventory. Results suggest that the different two-phase models generally yield comparable estimates but differ notably from single-phase estimates. Specifically, differences increase in smaller areas and with correspondingly smaller training datasets, suggesting a minimum of 100 data points. To ensure robust estimates, we recommend adapting training sets to local conditions and exercising caution with small training datasets and areas because implausible results may occur. Pooling appropriate datasets is the preferable solution. Full article
(This article belongs to the Special Issue Modeling of Biomass Estimation and Stand Parameters in Forests)
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