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Forests
Special Issues
Advances in Forest Growth and Site Productivity Modeling—Series II
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Advances in Forest Growth and Site Productivity Modeling—Series II

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 January 2024) | Viewed by 3775

Special Issue Editors

Dr. Ram P. Sharma

E-Mail Website
Guest Editor
Institute of Forestry, Tribhuwan University, Kathmandu 44600, Nepal
Interests: forest ecology; forest management; silviculture; forestry modeling; biostatistics; LiDAR; UAV
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangdong Lei

E-Mail Website
Guest Editor
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: forest growth model; multifunctional forest management and planning; the impact of climate change on forests and adaptive forest management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest growth is the keystone ecological process that determines forest structure and function. A forest site is characterized as an interaction of various environmental factors, and site productivity is a quantitative estimate of the potential of a site to produce woods and biomass. Modeling forest growth and site productivity has been an intrinsic part of forestry research for decades, as they contribute to the development of effective forest management plans. An increasing body of literature has shown that the influences of biotic and abiotic factors (climate, stand dynamics, natural disturbances, management practices, etc.) on forest growth and site productivity are substantial, and their action on forest growth is compounded nonlinearly, generating indirect and tipping-point processes. Climate change has already caused a remarkable change in growth, mortality, and site productivity, altering the range of species distributions. Growth and site productivity models developed with the integration of all the interacting factors, including climate, provide high prediction accuracy. Among potential data sources available for growth and site productivity modeling, LiDAR data can be the most accurate, and can be acquired with reasonable cost. Since LiDAR allows for the 3D modeling of individual trees and stands, time-series matrices derived from LiDAR images can be used for growth and site productivity modeling, regardless of the forest types (monospecific or mixed-species; even-aged or uneven-aged). Advances in LiDAR systems alone or in combination with other sensors may be useful in reducing problems associated with the 3D characterization of mixed forests that are structurally more complex and have higher productivity and more stability against climate change than monospecific forests. Models developed with LiDAR data acquired from mixed forests will become more useful to manage these forests.

This Special Issue aims to compile original research articles focusing on the state-of-the-art studies on forest growth and site productivity responses to multiple interacting factors. Researchers may apply various modeling techniques, ranging from parametric to nonparametric techniques and from simpler to complex ones using LiDAR data alone or in combination with ground-based measurements. Critical reviews on the advancement of forest growth and site productivity modeling, as well as those on the validation of conventional growth models against independent data, are suitable for submission. Review articles covering overviews of state-of-the-art growth data acquisition techniques, data processing, forest growth models and their applications are also welcome. Studies based on empirical- and process-based growth modeling using retrospective environmental and dendrometric databases, including data acquired from LiDAR, UAV, dendrochronology, etc., will be considered. This Issue will contribute to the advancement of knowledge on forest growth and yield, helping researchers globally to better understand the patterns of forest growth and site productivity conditions under the influence of various interacting factors. This Special Issue will improve our capacity to understand complex growth and site productivity models, which will provide valuable support in the development of silvicultural strategies and forest management plans under the climate change context. We kindly invite you to read the Special Issue "Advances in Forest Growth and Site Productivity Modeling" at https://www.mdpi.com/journal/forests/special_issues/Forest_Growth

Dr. Ram P. Sharma
Dr. Xiangdong Lei
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

  • tree growth models
  • stand growth models
  • dominant height growth models
  • growth trends
  • climate-sensitive growth models
  • site index
  • site productivity index
  • competition index
  • growth series database
  • empirical growth models
  • processed-based growth models
  • LiDAR time-series data
  • species mixture effects
  • dendrochronology
  • modeling forest biomass
  • carbon dynamics

Published Papers (4 papers)

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Research

18 pages, 2638 KiB  
Article
Predicting Individual Tree Mortality of Larix gmelinii var. Principis-rupprechtii in Temperate Forests Using Machine Learning Methods
by Zhaohui Yang, Guangshuang Duan, Ram P. Sharma, Wei Peng, Lai Zhou, Yaru Fan and Mengtao Zhang
Forests 2024, 15(2), 374; https://doi.org/10.3390/f15020374 - 17 Feb 2024
Viewed by 670
Abstract
Accurate prediction of individual tree mortality is essential for informed decision making in forestry. In this study, we proposed machine learning models to forecast individual tree mortality within the temperate Larix gmelinii var. principis-rupprechtii forests in Northern China. Eight distinct machine learning techniques [...] Read more.
Accurate prediction of individual tree mortality is essential for informed decision making in forestry. In this study, we proposed machine learning models to forecast individual tree mortality within the temperate Larix gmelinii var. principis-rupprechtii forests in Northern China. Eight distinct machine learning techniques including random forest, logistic regression, artificial neural network, generalized additive model, support vector machine, gradient boosting machine, k-nearest neighbors, and naive Bayes models were employed, to construct an ensemble learning model based on comprehensive dataset from this specific ecosystem. The random forest model emerged as the most accurate, demonstrating 92.9% accuracy and 92.8% sensitivity, making it the best model among those tested. We identified key variables impacting tree mortality, and the results showed that a basal area larger than the target trees (BAL), a diameter at 130 cm (DBH), a basal area (BA), an elevation, a slope, NH4-N, soil moisture, crown density, and the soil’s available phosphorus are important variables in the Larix Principis-rupprechtii individual mortality model. The variable importance calculation results showed that BAL is the most important variable with an importance value of 1.0 in a random forest individual tree mortality model. By analyzing the complex relationships of individual tree factors, stand factors, environmental, and soil factors, our model aids in decision making for temperate Larix gmelinii var. principis-rupprechtii forest conservation. Full article
(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling—Series II)
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18 pages, 4050 KiB  
Article
The Effect of Age on the Evolution of the Stem Profile and Heartwood Proportion of Teak Clonal Trees in the Brazilian Amazon
by Mario Lima dos Santos, Eder Pereira Miguel, Leonardo Job Biali, Hallefy Junio de Souza, Cassio Rafael Costa dos Santos and Eraldo Aparecido Trondoli Matricardi
Forests 2023, 14(10), 1962; https://doi.org/10.3390/f14101962 - 28 Sep 2023
Viewed by 798
Abstract
Stem profile modeling is crucial in the forestry sector, particularly for commercially valuable species like teak (Tectona grandis Linn F.), whose value depends on its stem dimensions, heartwood proportion, and age. We proposed a nonlinear mixed-effect model to describe the evolution of [...] Read more.
Stem profile modeling is crucial in the forestry sector, particularly for commercially valuable species like teak (Tectona grandis Linn F.), whose value depends on its stem dimensions, heartwood proportion, and age. We proposed a nonlinear mixed-effect model to describe the evolution of the stem and heartwood profiles of clonal teak trees with ages between 4 and 12 years in the Brazilian Amazon. Tapering models were used to estimate the bark, bark-free, and heartwood diameters. Dummy variables were included in each tapering model to estimate each type of diameter and enable compatibility. We used mixed models with age as a random effect in order to improve the accuracy. The Demaerschalk model provided the most accurate and compatible estimates for all three types of stem diameter. Also, age as a random effect significantly improved the model’s accuracy by 7.2%. We observed a progressive increase in the heartwood proportion (14% to 34%) with advancing age, while the proportions of bark (23% to 20%) and sapwood (63% to 45%) showed inverse behavior. The growth rate of the heartwood differed from that of the bark volume, emphasizing the importance of considering the age of heartwood maximization when determining the cutting cycle of the species. Full article
(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling—Series II)
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23 pages, 5145 KiB  
Article
Sampling Estimation and Optimization of Typical Forest Biomass Based on Sequential Gaussian Conditional Simulation
by Shaolong Luo, Li Xu, Jinge Yu, Wenwu Zhou, Zhengdao Yang, Shuwei Wang, Chaosheng Guo, Yingqun Gao, Jinnan Xiao and Qingtai Shu
Forests 2023, 14(9), 1792; https://doi.org/10.3390/f14091792 - 02 Sep 2023
Viewed by 865
Abstract
The traditional classical sampling statistics method ignores the spatial location relationship of survey samples, which leads to many problems. This study aimed to propose a spatial sampling method for sampling estimation and optimization of forest biomass, achieving a more efficient and effective monitoring [...] Read more.
The traditional classical sampling statistics method ignores the spatial location relationship of survey samples, which leads to many problems. This study aimed to propose a spatial sampling method for sampling estimation and optimization of forest biomass, achieving a more efficient and effective monitoring system. In this paper, we used Sequential Gaussian Conditional Simulation (SGCS) to obtain the biomass of four typical forest types in Shangri-La, Yunnan Province, China. In addition, we adopted a geostatistical sampling method for sample point layout and optimization to achieve the purpose of improving sampling efficiency and accuracy, and compared with the traditional sampling method. The main results showed that (1) the Gaussian model, exponential model, and spherical model were used to analyze the variogram of the four typical forests biomass, among which the exponential model had the best fitting effect (R2 = 0.571, RSS = 0.019). The range of the exponential model was 8700 m, and the nugget coefficient (C0/(C0 + C)) was 11.67%, which showed that the exponential model could be used to analyze the variogram of forest biomass. (2) The coefficient of variation (CV) based on 323 biomass field plots was 0.706, and the CV based on SGCS was 0.366. In addition, the Overall Estimate Consistency (OEC) of the simulation result was 0.871, which can be used for comparative analysis of traditional and spatial sampling. (3) Based on the result of SGCS, with 95% reliability, the sample size of traditional equidistant sampling (ES) was 191, and the sampling accuracy was 95.16%. But, the spatial sampling method based on the variation scale needed 92 samples, and the sampling accuracy was 93.12%. On the premise of satisfying sampling accuracy, spatial sampling efficiency was better than traditional ES. (4) The accuracy of stratified sampling (SS) of four typical forest areas based on 191 samples was 97.46%. However, the sampling accuracy of the biomass variance stratified space based on the SGCS was 93.89%, and the sample size was 52. Under the premise of satisfying the sampling accuracy, the sampling efficiency was obviously better than the traditional SS. Therefore, we can obtain the conclusion that the spatial sampling method is superior to the traditional sampling method, as it can reduce sampling costs and solve the problem of sample redundancy in traditional sampling, improving the sampling efficiency and accuracy, which can be used for sampling estimation of forest biomass. Full article
(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling—Series II)
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16 pages, 3242 KiB  
Article
Developing Growth and Harvest Prediction Models for Mixed Coniferous and Broad-Leaved Forests at Different Ages
by Weiping Hua, Xin Pan, Dehuang Zhu, Chengzhen Wu, Shangping Chi, Chongyang Zhuang, Xidian Jiang, Jing Liu and Jianwei Wu
Forests 2023, 14(7), 1416; https://doi.org/10.3390/f14071416 - 11 Jul 2023
Cited by 1 | Viewed by 776
Abstract
In order to clarify the combined impact of tree species composition, site quality, and stand age on the growth and harvest of mixed forests, the prediction models of average DBH and stand volume for mixed forests were established, respectively. The interval period and [...] Read more.
In order to clarify the combined impact of tree species composition, site quality, and stand age on the growth and harvest of mixed forests, the prediction models of average DBH and stand volume for mixed forests were established, respectively. The interval period and tree species composition coefficient (TSCC) were considered as independent variables. These models were then optimized by using the particle swarm optimization algorithm for reparameterization and evaluating their applicability. It was found that after introducing the site quality grade and TSCC, the average stand height prediction model showed a better fitting result. The fit accuracy of the average DBH prediction model and the stand volume prediction model were both improved with the help of the TSCC, mainly because the tree species composition affects the growth rate of the average stand height and average DBH and the maximum growth rate of the stand volume. The degree of the impact can be sorted as Cunninghamia lanceolata > Pinus massoniana > hard broad-leaved tree species (group). Overall, the established growth and harvest prediction models for mixed forests with the interval period and TSCC as independent variables have high fit accuracy and applicability. Full article
(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling—Series II)
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