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Advances in Forest Growth and Site Productivity Modeling

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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 (30 April 2022) | Viewed by 44704

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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 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, and 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 suitable. Studies based on the empirical- and process-based growth modeling using retrospective environmental and dendrometric databases, including data acquired from LiDAR, UAV, dendrochronology, etc., will be considered. The 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. The Issue will improve our capacity to understand the complex growth and site productivity models, which will be largely supportive for developing silvicultural strategies and forest management plans under the climate change context.

Dr. Ram P. Sharma
Dr. Xiangdong Lei
Guest Editors

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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

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Published Papers (13 papers)

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17 pages, 21992 KiB

Open AccessFeature PaperArticle

About Tree Height Measurement: Theoretical and Practical Issues for Uncertainty Quantification and Mapping

by Samuele De Petris, Filippo Sarvia and Enrico Borgogno-Mondino

Forests 2022, 13(7), 969; https://doi.org/10.3390/f13070969 - 21 Jun 2022

Cited by 5 | Viewed by 4787

Abstract

Forest height is a fundamental parameter in forestry. Tree height is widely used to assess a site’s productivity both in forest ecology research and forest management. Thus, a precise height measure represents a necessary step for the estimation of carbon storage at the local, national, and global scales. In this context, error in height measurement necessarily affects the accuracy of related estimates. Ordinarily, forest height is surveyed by ground sampling adopting hypsometers. The latter suffers from many errors mainly related to the correct tree apex identification (not always well visible in dense stands) and to the measurement process itself. In this work, a statistically based operative method for estimating height measurement uncertainty (

σ_{H}

) was proposed using the variance propagation law. Some simulations were performed involving several combinations of terrain slope, tree height, and survey distances by modelling the

σ_{H}

behaviour and its sensitivity to such parameters. Results proved that

σ_{H}

could vary between 0.5 m and up to 20 m (worst case). Sensitivity analysis shows that terrain slopes and distance poorly affect

σ_{H}

, while angles are the main drivers of height uncertainty. Finally, to give a practical example of such deductions, tree height uncertainty was mapped at the global scale using Google Earth Engine and summarized per forest biomes. Results proved that tropical biomes have higher uncertainty (from 1 m to 4 m) while shrublands and tundra have the lowest (under 1 m). Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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17 pages, 6119 KiB

Open AccessArticle

Effect of Climate on Carbon Storage Growth Models for Three Major Coniferous Plantations in China Based on National Forest Inventory Data

by Lianjin Zhang, Guanghui Lai, Weisheng Zeng, Wentao Zou and Shanjun Yi

Forests 2022, 13(6), 882; https://doi.org/10.3390/f13060882 - 6 Jun 2022

Cited by 3 | Viewed by 2112

Abstract

Forest inventory data (FID) are important resources for understanding the dynamics of forest carbon cycling at regional and global scales. Developing carbon storage growth models and analyzing the difference and climate effect on carbon sequestration capacity have a great importance in practice, which can provide a decision-making basis for promoting high-quality development of forestry and implementing the carbon emission peak and carbon neutralization strategy. Based on the carbon storage dataset of 2680 sample plots from the ninth national forest inventory (NFI) of China, the carbon storage growth models and climate-sensitive variable-parameter carbon storage growth models for three major coniferous plantations (Larix spp., Pinus massoniana, and Pinus tabuliformis) were developed by using weighted nonlinear regression method. The effects of two climate factors (mean annual temperature (

M A T

) and mean annual precipitation (

M A P

)) on carbon storage growth and carbon sequestration capacity were analyzed and compared. The mean prediction error (

M P E

) of carbon storage growth models for three major coniferous plantations was less than 5%, and total relative error (

T R E

) was approximately less than 2% for self- and cross- validation. The maximum current annual increment of carbon storage for P. massoniana, Larix, and P. tabuliformis was 2.29, 1.89, and 1.19 t/(ha·a), respectively, and their corresponding age of inflection point was 9a, 14a, and 30a, respectively. The maximum average increment of carbon storage for P. massoniana, Larix, and P. tabuliformis was 1.85, 1.50, and 0.94 t/(ha·a), respectively, and their corresponding age of quantitative maturity was 16a, 24a, and 53a, respectively. The maximum average increment of carbon storage for the P. massoniana and Larix plantations was approximately 1.97 and 1.60 times, respectively, that of P. tabuliformis plantation. The average increment of carbon storage for the P. massoniana and Larix plantations reduced approximately by 4.5% and 3.8%, respectively, when the MAT decreases by 1 °C. The average increment of carbon storage for the Larix and P.tabuliformis plantations decreased by approximately 6.5% and 3.6%, respectively, when the

M A P

decreases by 100 mm. Our findings suggest that: the carbon sequestration capacity is from highest to lowest in the P. massoniana, Larix, and P. tabuliformis forests.

M A T

and

M A P

have different effects on the carbon growth process and carbon sequestration capacity of these plantations. The greatest impact on carbon sequestration capacity was detected in the Larix plantation, followed by the P. massoniana and P. tabuliformis plantations. It is essential to coordinate regional development and employ scientific management strategies to fully develop the maximum carbon sequestration capacity in terms of plantations in China. In the present study, we estimate the carbon storage in major coniferous plantations in China and describe a useful methodology for estimating forest carbon storage at regional and global levels. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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15 pages, 2928 KiB

Open AccessArticle

Nonlinear Mixed-Effects Height to Crown Base Model for Moso Bamboo (Phyllostachys heterocycla (Carr.) Mitford cv. Pubescens) in Eastern China

by Xiao Zhou, Yaxiong Zheng, Fengying Guan, Ram P. Sharma, Xuan Zhang and Yang Zhou

Forests 2022, 13(6), 823; https://doi.org/10.3390/f13060823 - 25 May 2022

Cited by 6 | Viewed by 1931

Abstract

Height to crown base (HCB) is an important variable used as a predictor of forest growth and yield. This study developed a nonlinear, mixed-effects HCB model through inclusion of plot-level random effects using data from 29 sample plots distributed across a state-owned Yixing forest farm in Jiangsu province, eastern China. Among several predictor variables evaluated in the analyses, bamboo height, canopy density, and total basal area of bamboo with a diameter larger than that of the subject bamboo individual contributed significantly to the HCB variations. The inclusion of random effects improved the prediction accuracy of the model significantly, indicating that the HCB variations within and across the sample plots were substantial. The model was localized using four sampling strategies, and the study identified that using two medium-sized bamboos by diameter at breast height per sample plot resulted in the smallest prediction error. This strategy, which would balance both measurement cost and potential error, may be applied to estimate the random effects and localization of the nonlinear mixed-effects HCB model for moso bamboo in eastern China. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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14 pages, 2638 KiB

Open AccessArticle

Site Index Modeling of Larch Using a Mixed-Effects Model across Regional Site Types in Northern China

by Guangshuang Duan, Xiangdong Lei, Xiongqing Zhang and Xianzhao Liu

Forests 2022, 13(5), 815; https://doi.org/10.3390/f13050815 - 23 May 2022

Cited by 10 | Viewed by 2393

Abstract

As the dominant height of the stand at the baseline age, the site index is an important index to evaluate site quality. However, due to the variability of environmental factors, the growth process of the dominant height of the same tree species was variable in different regions which influenced the estimation results of the site index. In this study, a methodology that established site index modeling of larch plantations with site types as a random effect in northern China was proposed. Based on 394 sample plots, nine common base models were developed, and the best model (M8) was selected (R² = 0.5773) as the base model. Moreover, elevation, aspect, and slope position were the main site factors influencing stand dominant height through the random forest method. Then, the three site factors and their combinations (site types) were selected as random effects and simulated by the nonlinear mixed-effects model based on the model M8. The R² values had raised from 0.5773 to 0.8678, and the model with combinations (94 kinds) of three site factors had the best performance (R² = 0.8678). Considering the model accuracy and practical application, the 94 combinations were divided into three groups of site types (3, 5, and 8) by hierarchical clustering. Furthermore, a mixed-effects model considering the random effects of these three groups was established. All the three groups of site types got a better fitting effect (groups 3 R² = 0.8333, groups 5 R² = 0.8616, groups 8 R² = 0.8683), and a better predictive performance (groups 3 R² = 0.8157, groups 5 R² = 0.8464, groups 8 R² = 0.8479 for 20 percent of plots randomly selected per group in the calibration procedure) using the leave-one-out cross-validation approach. Therefore, groups 5 of site types had better applicability and estimation of forest productivity at the regional level and management plan design. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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15 pages, 1605 KiB

Open AccessArticle

Compatible Biomass Model of Moso Bamboo with Measurement Error

by Xiao Zhou, Yaxiong Zheng, Fengying Guan, Xiao Xiao, Xuan Zhang and Chengji Li

Forests 2022, 13(5), 774; https://doi.org/10.3390/f13050774 - 17 May 2022

Cited by 6 | Viewed by 2057

Abstract

Moso bamboo is characterized by its fast growth and high yield and is important as a carbon sink species. Therefore, understanding the biomass distribution of its components is crucial. Based on the measured individual biomass data of 66 Phyllostachys heterocycla cv. Pubescens plants in the Yixing state-owned forest in Jiangsu Province, nonlinear simultaneous equations with measurement errors were constructed using nonlinear error-in-variable models (NEIVM) (one step, two step) and nonlinear seemingly unrelated regression (NSUR). Variables affecting biomass were evaluated, including diameter at breast height (DBH), bamboo height (H), height to crown base (HCB), node length at DBH (NL), base diameter (BD), and bamboo age (A). DBH, H, and HCB had significant effects on the biomass of each component. They were used to construct a one-predictor system using DBH, a two-predictor system using DBH and H, and a three-predictor system using DBH, H, and HCB. Regardless of the number of variables used, the fitting accuracy of the NEIVM one-step method exceeded that of the two-step method, and that of NEIVM exceeded that of NSUR estimation. As a system using three predictive variables is better than other systems, we recommend using the one-step NEIVM method for Moso bamboo biomass estimation. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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18 pages, 1694 KiB

Open AccessArticle

An Approach to Estimate Individual Tree Ages Based on Time Series Diameter Data—A Test Case for Three Subtropical Tree Species in China

by Yiru Zhang, Haikui Li, Xiaohong Zhang, Yuancai Lei, Jinjin Huang and Xiaotong Liu

Forests 2022, 13(4), 614; https://doi.org/10.3390/f13040614 - 14 Apr 2022

Cited by 2 | Viewed by 3450

Abstract

Accurate knowledge of individual tree ages is critical for forestry and ecological research. However, previous methods suffer from flaws such as tree damage, low efficiency, or ignoring autocorrelation among residuals. In this paper, an approach for estimating the ages of individual trees is proposed based on the diameter series of Cinnamomum camphora (Cinnamomum camphora (L.) Presl), Schima superba (Schima superba Gardn. et Champ.), and Liquidambar formosana (Liquidambar formosana Hance). Diameter series were obtained by stem analysis. Panel data contains more information, more variability, and more efficiency than pure time series data or cross-sectional data, which is why diameter series at stump and breast heights were chosen to form the panel data. After choosing a base growth equation, a constraint was added to the equation to improve stability. The difference method was used to reduce autocorrelation and the parameter classification method was used to improve model suitability. Finally, the diameter increment equation of parameter a-classification was developed. The mean errors of estimated ages based on the panel data at breast height for C. camphora, S. superba, and L. formosana were 0.47, 2.46, and −0.56 years and the root mean square errors were 2.04, 3.15 and 2.47 years, respectively. For C. camphora and L. formosana, the estimated accuracy based on the panel data was higher at breast height than at stump height. This approach to estimating individual tree ages is highly accurate and reliable, and provides a feasible way to obtain tree ages by field measurement. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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15 pages, 1480 KiB

Open AccessArticle

Developing Tree Mortality Models Using Bayesian Modeling Approach

by Lu Xie, Xingjing Chen, Xiao Zhou, Ram P. Sharma and Jianjun Li

Forests 2022, 13(4), 604; https://doi.org/10.3390/f13040604 - 12 Apr 2022

Cited by 7 | Viewed by 2451

Abstract

The forest mortality models developed so far have ignored the effects of spatial correlations and climate, which lead to the substantial bias in the mortality prediction. This study thus developed the tree mortality models for Prince Rupprecht larch (Larix gmelinii subsp. principis-rupprechtii), one of the most important tree species in northern China, by taking those effects into account. In addition to these factors, our models include both the tree—and stand—level variables, the information of which was collated from the temporary sample plots laid out across the larch forests. We applied the Bayesian modeling, which is the novel approach to build the multi-level tree mortality models. We compared the performance of the models constructed through the combination of selected predictor variables and explored their corresponding effects on the individual tree mortality. The models precisely predicted mortality at the three ecological scales (individual, stand, and region). The model at the levels of both the sample plot and stand with different site condition (block) outperformed the other model forms (model at block level alone and fixed effects model), describing significantly larger mortality variations, and accounted for multiple sources of the unobserved heterogeneities. Results showed that the sum of the squared diameter was larger than the estimated diameter, and the mean annual precipitation significantly positively correlated with tree mortality, while the ratio of the diameter to the average of the squared diameter, the stand arithmetic mean diameter, and the mean of the difference of temperature was significantly negatively correlated. Our results will have significant implications in identifying various factors, including climate, that could have large influence on tree mortality and precisely predict tree mortality at different scales. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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16 pages, 5501 KiB

Open AccessArticle

Climate Change Effects on Height–Diameter Allometric Relationship Vary with Tree Species and Size for Larch Plantations in Northern and Northeastern China

by Qigang Xu, Xiangdong Lei, Hao Zang and Weisheng Zeng

Forests 2022, 13(3), 468; https://doi.org/10.3390/f13030468 - 17 Mar 2022

Cited by 6 | Viewed by 2469

Abstract

Tree height–diameter relationship is very important in forest investigation, describing forest structure and estimating carbon storage. Climate change may modify the relationship. However, our understanding of the effects of climate change on the height–diameter allometric relationship is still limited at large scales. In this study, we explored how climate change effects on the relationship varied with tree species and size for larch plantations in northern and northeastern China. Based on the repeated measurement data of 535 plots from the 6th to 8th national forest inventory of China, climate-sensitive tree height–diameter models of larch plantations in north and northeast China were developed using two-level nonlinear mixed effect (NLME) method. The final model was used to analyze the height–diameter relationship of different larch species under RCP2.6, RCP 4.5, and RCP8.5 climate change scenarios from 2010 to 2100. The adjusted coefficient of determination

R_{a d j}^{2}

, mean absolute error (MAE) and root mean squared error (RMSE) of the NLME models for calibration data were 0.92, 0.76 m and 1.06 m, respectively. The inclusion of climate variables mean annual temperature (MAT) and Hargreaves climatic moisture deficit (CMD) with random effects was able to increase

R_{a d j}^{2}

by 19.5% and reduce the AIC (Akaike’s information criterion), MAE and RMSE by 22.2%, 44.5% and 41.8%, respectively. The climate sensitivity of larch species was ranked as L. gmelinii > the unidentified species group > L. principis > L. kaempferi > L. olgensis under RCP4.5, but L. gmelinii > L. principis > the unidentified species group > L. olgensis > L. kaempferi under RCP2.6 and RCP8.5. Large trees were more sensitive to climate change than small trees. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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13 pages, 2473 KiB

Open AccessArticle

The Effect of Stand Density, Biodiversity, and Spatial Structure on Stand Basal Area Increment in Natural Spruce-Fir-Broadleaf Mixed Forests

by Di Liu, Chaofan Zhou, Xiao He, Xiaohong Zhang, Linyan Feng and Huiru Zhang

Forests 2022, 13(2), 162; https://doi.org/10.3390/f13020162 - 21 Jan 2022

Cited by 11 | Viewed by 3590

Abstract

Forest trees exhibit a large variation in the basal area increment (BAI), and the variation is attributed to the stand density, biodiversity, and stand spatial structure. Studying and quantifying the effect of these above variables on tree growth is vital for future forest management. However, the stand spatial structure based on neighboring trees has rarely been considered, especially in the mixed forests. This study adopted the random-forest (RF) algorithm to model and interpret BAI based on stand density, biodiversity, and spatial structure. Fourteen independent variables, including two stand density predictors, four biodiversity predictors, and eight spatial structure predictors, were evaluated. The RF model was trained for the whole stand, three tree species groups (gap, neutral, and shade_tolerant), and two tree species (spruce and fir). A 10-fold blocked cross-validation was then used to optimize the hyper-parameters and evaluate the models. The squared correlation coefficients (R²) for the six groups were 0.233 for the whole stand, 0.575 for fir, 0.609 for shade_tolerant, 0.622 for neutral, 0.722 for gap, and 0.730 for spruce. The Stand Density Index (SDI) was the most-important predictor, suggesting that BAI is primarily restricted by competition. BAI and species biodiversity were positively correlated for the whole stand. The stands were expected to be randomly distributed based on the relationship between the uniform angle index (W) and growth. The relationship between dominance (U) and BAI indicated that small trees should be planted around the light-demanding tree species and vice versa. Of note, these findings emphasize the need to consider the three types of variables in mixed forests, especially the spatial structure factors. This study may help make significant advances in species composition, spatial arrangement, and the sustainable development of mixed forests. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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23 pages, 2657 KiB

Open AccessArticle

Simultaneous Compatible System of Models of Height, Crown Length, and Height to Crown Base for Natural Secondary Forests of Northeast China

by Zeyu Zhou, Liyong Fu, Chaofan Zhou, Ram P. Sharma and Huiru Zhang

Forests 2022, 13(2), 148; https://doi.org/10.3390/f13020148 - 19 Jan 2022

Cited by 7 | Viewed by 1700

Abstract

Individual trees are characterized by various sizes and forms, such as diameter at breast height, total height (H), height to crown base (HCB), crown length (CL), crown width, and crown and stem forms. Tree characteristics are strongly related to each other, and studying their relationships is very important. The knowledge of the compatibility and additivity properties of the major tree characteristics, such as H, CL, and HCB, is essential for informed decision-making in forestry. H can be used to represent site quality and CL represents biomass and photosynthesis of crown, which is the performance of individual tree vigor and light interception, and the longer the crown length (or shorter HCB) is, the more vigorous the tree would be. However, none of the studies have uncovered their inherent relationships quantitatively. This study attempts to explore such relationships through the application of appropriate modeling approaches. We applied seemingly unrelated regression, such as nonlinear seemingly unrelated regression (NSUR), which is commonly used for exploring the compatibility and additivity properties of the variables, for the proposes. The NSUR involves the variance and covariance matrices of the sub-models that are used for the interpretation of the correlations among the variables of interest. The data set acquired from Mongolian oak forest and spruce-fir forest in the Jingouling forest farm of the Wangqing Forest Bureau in the Northeast of China were used to construct two types of model systems: a compatible model system (the model system of H, CL, and HCB can be estimated simultaneously) and an additive model system (the sum of HCB and CL is H, the form of the H sub-model equals the sum of the HCB and CL sub-models) from the individual models of H, CL, and HCB. Among the various tree-level and stand-level variables evaluated, D (diameter at breast), Dg (quadratic mean diameter), DT (dominant diameter), CW (crown width), SDI (stand density index), and BAS (basal area of stand) contributed significantly highly to the variations of the response of the variables of interest in the model systems. Modeling results showed the existence of the compatibility and additivity of H, CL, and HCB simultaneously. The additive model system exhibited better fitting performance on H and HCB but poorer fitting on CL compared with the simultaneous model system, indicating that the performance of the additive model system could be higher than that of the simultaneous model system. Model tests against the validation data set also confirmed such results. This study contributes a novel approach to solving the compatibility and additivity of the problems of H, CL, and HCB models through the application of the robust estimating method, NSUR. The results and algorithm presented will be useful for constructing similar compatible and additive model systems of multiple tree-level models for other tree species. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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17 pages, 4548 KiB

Open AccessArticle

A Nonlinear Mixed-Effects Height-Diameter Model with Interaction Effects of Stand Density and Site Index for Larix olgensis in Northeast China

by Xiaofang Zhang, Liyong Fu, Ram P. Sharma, Xiao He, Huiru Zhang, Linyan Feng and Zeyu Zhou

Forests 2021, 12(11), 1460; https://doi.org/10.3390/f12111460 - 26 Oct 2021

Cited by 9 | Viewed by 6380

Abstract

Tree height is a basic input variable in various forest models, such as growth and yield models, biomass models, and carbon budget models, which serve as very important tools for the informed decision-making in forestry. The height-diameter model is the most important component of the growth and yield models and forest simulators. We developed the nonlinear mixed-effects height-diameter model with the interaction effects of stand density and site index introduced using data from 765 Larix olgensis trees in Jingouling forest farm of the Wangqing Forest Bureau in northeast China. Among the various basic versatile functions evaluated, a simple exponential growth function fitted the data adequately well, and this was then expanded through the introduction of the variables describing the interaction effects of the stand density and site index on the height-diameter relationship. Sample plot-level random effects were included into this model through mixed-effects modeling. The results showed that the random effect of the stand density on the height-diameter relationship was substantially different at different classes of the site index, and the random effect of the site index was different for the different stand density classes. The nonlinear mixed-effects (NLME) height-diameter model coping with the interaction effects of the stand density and site index had a better performance than those of the NLME models with the random effect of the single variable of stand density or site index. To conclude, the inclusion of the interaction effects of stand density and site index could significantly improve the prediction accuracy of the height-diameter model for Larix olgensis Henry. The proposed model with the interactive random effects included can be applied for the accurate prediction of Larix olgensis tree height in northeast China. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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21 pages, 13078 KiB

Open AccessArticle

A Combination of Biotic and Abiotic Factors and Diversity Determine Productivity in Natural Deciduous Forests

by Mahmoud Bayat, Pete Bettinger, Sahar Heidari, Seyedeh Kosar Hamidi and Abolfazl Jaafari

Forests 2021, 12(11), 1450; https://doi.org/10.3390/f12111450 - 25 Oct 2021

Cited by 18 | Viewed by 3802

Abstract

The relative importance of different biotic and abiotic variables for estimating forest productivity remains unclear for many forest ecosystems around the world, and it is hypothesized that forest productivity could also be estimated by local biodiversity factors. Using a large dataset from 258 forest monitoring permanent sample plots distributed across uneven-aged and mixed forests in northern Iran, we tested the relationship between tree species diversity and forest productivity and examined whether several factors (solar radiation, topographic wetness index, wind velocity, seasonal air temperature, basal area, tree density, basal area in largest trees) had an effect on productivity. In our study, productivity was defined as the mean annual increment of the stem volume of a forest stand in m³ ha⁻¹ year⁻¹. Plot estimates of tree volume growth were based on averaged plot measurements of volume increment over a 9-year growing period. We investigated relationships between productivity and tree species diversity using parametric models and two artificial neural network models, namely the multilayer perceptron (MLP) and radial basis function networks. The artificial neural network (ANN) of the MLP type had good ability in prediction and estimation of productivity in our forests. With respect to species richness, Model 4, which had 10 inputs, 6 hidden layers and 1 output, had the highest R² (0.94) and the lowest RMSE (0.75) and was selected as the best species richness predictor model. With respect to forest productivity, MLP Model 2 with 10 inputs, 12 hidden layers and 1 output had R² and RMSE of 0.34 and 0.42, respectively, representing the best model. Both of these used a logistic function. According to a sensitivity analysis, diversity had significant and positive effects on productivity in species-rich broadleaved forests (approximately 31%), and the effects of biotic and abiotic factors were also important (29% and 40%, respectively). The artificial neural network based on the MLP was found to be superior for modeling productivity–diversity relationships. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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Review

Jump to: Research

18 pages, 1809 KiB

Open AccessReview

A Review of General Methods for Quantifying and Estimating Urban Trees and Biomass

by Mingxia Yang, Xiaolu Zhou, Zelin Liu, Peng Li, Jiayi Tang, Binggeng Xie and Changhui Peng

Forests 2022, 13(4), 616; https://doi.org/10.3390/f13040616 - 15 Apr 2022

Cited by 24 | Viewed by 5221

Abstract

Understanding the biomass, characteristics, and carbon sequestration of urban forests is crucial for maintaining and improving the quality of life and ensuring sustainable urban planning. Approaches to urban forest management have been incorporated into interdisciplinary, multifunctional, and technical efforts. In this review, we evaluate recent developments in urban forest research methods, compare the accuracy and efficiency of different methods, and identify emerging themes in urban forest assessment. This review focuses on urban forest biomass estimation and individual tree feature detection, showing that the rapid development of remote sensing technology and applications in recent years has greatly benefited the study of forest dynamics. Included in the review are light detection and ranging-based techniques for estimating urban forest biomass, deep learning algorithms that can extract tree crowns and identify tree species, methods for measuring large canopies using unmanned aerial vehicles to estimate forest structure, and approaches for capturing street tree information using street view images. Conventional methods based on field measurements are highly beneficial for accurately recording species-specific characteristics. There is an urgent need to combine multi-scale and spatiotemporal methods to improve urban forest detection at different scales. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling)

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