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Forests
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New Theories and Methods in Tree and Stand Measurement and...
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New Theories and Methods in Tree and Stand Measurement 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 (25 October 2023) | Viewed by 2928

Special Issue Editors

Prof. Dr. Huiru Zhang

E-Mail Website
Guest Editor
Experimental Centre of Forestry in North China, Chinese Academy of Forestry, Beijing, China
Interests: sustainable forest management; forest monitoring and modelling; forest harvest optimization decisions
Prof. Dr. Liyong Fu

E-Mail Website
Guest Editor
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China
Interests: forest measurement; forest growth and yield modeling; forest biometrics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guangxing Wang

E-Mail Website
Guest Editor
Department of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL, USA
Interests: remote sensing; GIS; spatial statistics and their applications to geography; natural and environmental resources with the specific areas; land use and land cover change detection; sampling design; forest inventory and forest growth modelling; forest carbon sequestration modeling and mapping; environmental dynamic modeling and quality assessment; quality assessment and spatial uncertainty analysis of remote sensing and GIS products
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest measurement and growth simulation, as a fundamental tool for studying the response of various typical forest types to planned management, has become a hot field that countries are competing to develop as it can significantly shorten research cycles and reduce research costs. In recent years, with the development of Unmanned Aerial Vehicle (UAV) photogrammetry techniques and sensors, modern statistical methods (e.g. Mixed Effect Model, Errors-in-Variables Model, Simultaneous Equation Model, Quantile Regression Model, Generalize Linear Model, Geographically Weighted Regression, etc.) and computer simulation techniques, forest measurement and growth simulation studies have made great progress and have improved greatly in terms of efficiency and effectiveness.

This special issue will provide researchers with an overview of the general research progress and trends in the field, present the results and application effects of forest measurement and growth simulation studies based on different measurement methods and modern statistical methods, promote direct learning and communication among researchers, and also provide a practical reference for forest managers. The scope of the call for articles for the special issue includes, but is not limited to the following.

  • Tree Measurement, Data Acquisition, and Processing Methods of UAV;
  • Forest Growth and Yield Models Based on Machine Learning;
  • Random Growth and Forest Yield Models of Trees and Stands;
  • Models of Tree Crown Structure, Stem Form, Timber Quality and Mechanisms;
  • Prediction Modeling of Stand, Forest Resources, Biomass and Carbon Storage at Different Scales.

Prof. Dr. Huiru Zhang
Prof. Dr. Liyong Fu
Prof. Dr. Guangxing Wang
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

  • UAV tree measurement
  • mixed effect model
  • errors-in-variables model
  • machine learning model
  • tree growth and contour model
  • prediction model of biomass and carbon storage

Published Papers (3 papers)

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Research

16 pages, 15046 KiB  
Article
Evaluating Forest Site Quality Using the Biomass Potential Productivity Approach
by Xingrong Yan, Linyan Feng, Ram P. Sharma, Guangshuang Duan, Lifeng Pang, Liyong Fu and Jinping Guo
Forests 2024, 15(1), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/f15010023 - 21 Dec 2023
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Abstract
Biomass productivity is of great significance for the evaluation of forest quality, which is important for the improvement of forest management. We propose the computational methods of biomass potential productivity (BPP) and biomass realistic productivity (BRP), both of which provide reliable practical guides [...] Read more.
Biomass productivity is of great significance for the evaluation of forest quality, which is important for the improvement of forest management. We propose the computational methods of biomass potential productivity (BPP) and biomass realistic productivity (BRP), both of which provide reliable practical guides for predicting forest growth under multi-aged, multi-species, and multi-layered canopy conditions. We used 2222 national forest inventory plots that were measured in four consecutive periods in the Jilin Province for this purpose. We analyzed and verified the computational methods of BPP based on the BRP and evaluated its practical significance. The results showed that growth models of the stand height, stand basal area, and stand biomass of four forest types (pure larch forest, larch broadleaf mixed forest, Mongolian oak pure forest, and Mongolian oak broadleaf mixed forest) fit adequately, BPP was greater than BRP, and this difference decreased with an increasing stand age, suggesting that the potential productivity of the middle-aged and young forest was higher than that of the mature forest, although the difference is minimal. In addition, the realistic productivity of stands with better site quality was close to the potential productivity, which is consistent with the biological significance of the potential productivity of the biomass. The degree of difference between the potential productivity of the biomass and the realistic productivity of biomass also decreases with the decline in site quality, and it can be termed as the potentially improved stand biomass. The BPP model was able to perform well in both the pure and mixed forests. The BRP not only verifies the rationality of the BPP but can be also used to quantify the forest site quality, which is helpful for evaluating forest growth and informed decision making in forestry. Full article
(This article belongs to the Special Issue New Theories and Methods in Tree and Stand Measurement and Modeling)
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15 pages, 1043 KiB  
Article
Individual Tree Height Increment Model for Quercus mongolica Secondary Forest in the Northeastern China Using Generalized Nonlinear Two-Level Mixed-Effects Model
by Xuefan Hu, Yingshan Jin, Xiaohong Zhang and Huiru Zhang
Forests 2023, 14(11), 2162; https://0-doi-org.brum.beds.ac.uk/10.3390/f14112162 - 30 Oct 2023
Viewed by 690
Abstract
(1) Background: Mongolian oak secondary forest is widely distributed in the northeast of China, and most of these forests are formed after the overcutting of broad-leaved Pinus koraiensis mixed forest. Most of the forest productivity is low and the ecological function is degraded, [...] Read more.
(1) Background: Mongolian oak secondary forest is widely distributed in the northeast of China, and most of these forests are formed after the overcutting of broad-leaved Pinus koraiensis mixed forest. Most of the forest productivity is low and the ecological function is degraded, due to insufficient understanding of Mongolian oak and lack of scientific management. Deepening the research on exploring reasonable management measures of Mongolian oak secondary forest to an improved stand status is the basis for improving its quality and promoting its forward succession process. (2) Methods: Twelve permanent plots with an area of 1 ha were established in the Mongolian oak secondary forest on Tazigou forest farm in Wangqing, Jilin Province of northeastern China. The response of tree height increment of Mongolian oak secondary forest is studied based on the survey data of 2013 and 2018. Two-level nonlinear mixed-effects models were constructed to predict the height of a single tree using sample plots and tree species as random effects, combined with a variety of tree size factors, site factors, and competitive factors as independent variables. (3) Results: The significant factors related to the height increment of Mongolian oak secondary forest are the initial diameter at breast height as the size of the tree itself (DBH), height (H), crown height ratio (CR), and site productivity index reflecting site quality (SPI). The distance-dependent and distance-independent competition indexes have no significant effect on tree height increment. The fitting accuracy of the two-level mixed-effects model that introduces plots and tree species as random effects has been greatly improved (coefficient of determination R2 increased by 51.8%). The prediction results show that the two trees with the largest DBH have the strongest prediction ability. (4) Conclusions: The generalized nonlinear two-level mixed-effects model constructed in this study can describe the height increment of an individual tree in the Mongolian oak secondary forest. Two sample trees, namely the two largest trees in each sub-plot, were applied for estimating the random effects when both measurement cost and potential errors of prediction were balanced. Full article
(This article belongs to the Special Issue New Theories and Methods in Tree and Stand Measurement and Modeling)
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18 pages, 3387 KiB  
Article
Using Functional Traits to Improve Estimates of Height–Diameter Allometry in a Temperate Mixed Forest
by Huanran Gao, Keda Cui, Klaus von Gadow and Xinjie Wang
Forests 2023, 14(8), 1604; https://0-doi-org.brum.beds.ac.uk/10.3390/f14081604 - 09 Aug 2023
Viewed by 1013
Abstract
Accurate estimates of tree height (H) are critical for forest productivity and carbon stock assessments. Based on an extensive dataset, we developed a set of generalized mixed-effects height–DBH (H–D) models in a typical natural mixed forest in Northeastern China, adding species functional traits [...] Read more.
Accurate estimates of tree height (H) are critical for forest productivity and carbon stock assessments. Based on an extensive dataset, we developed a set of generalized mixed-effects height–DBH (H–D) models in a typical natural mixed forest in Northeastern China, adding species functional traits to the H–D base model. Functional traits encompass diverse leaf economic spectrum features as well as maximum tree height and wood density, which characterize the ability of a plant to acquire resources and resist external disturbances. Beyond this, we defined expanded variables at different levels and combined them to form a new model, which provided satisfactory estimates. The results show that functional traits can significantly affect the H–D ratio and improve estimations of allometric relationships. Generalized mixed-effects models with multilevel combinations of expanded variables could improve the prediction accuracy of tree height. There was an 82.42% improvement in the accuracy of carbon stock estimates for the studied zone using our model predictions. This study introduces commonly used functional traits into the H–D model, providing an important reference for forest growth and harvest models. Full article
(This article belongs to the Special Issue New Theories and Methods in Tree and Stand Measurement and Modeling)
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