Modeling the Dominant Height of Larix principis-rupprechtii in Northern China—A Study for Guandi Mountain, Shanxi Province DOI Open Access
Yunxiang Zhang, Xiao‐Hua Zhou,

Jinping Guo

et al.

Forests, Journal Year: 2022, Volume and Issue: 13(10), P. 1592 - 1592

Published: Sept. 29, 2022

An accurate estimate of the site index is essential for informing decision-making in forestry. In this study, we developed (SI) models using stem analysis data to and dominant height growth Larix gmelinii var. principis-rupprechtii northern China. The included 5122 height–age pairs from 75 trees 29 temporary sample plots (TSPs). Nine commonly used functions were parameterized modeling method, which accounts heterogeneous variance autocorrelation time-series introduces plot-level random effects model. results show that Duplat Tran-Ha I model with described largest proportion variation. This accurately evaluated quality predicted tree natural forests Guandi Mountain region. As an important supplement improving methods evaluation, may serve as a fundamental tool scientific management larch forests. research can inform evaluation predict forest area well provide theoretical basis at similar sites.

Language: Английский

Predicting Individual Tree Mortality of Larix gmelinii var. Principis-rupprechtii in Temperate Forests Using Machine Learning Methods DOI Open Access

Zhaohui Yang,

Guangshuang Duan, Ram P. Sharma

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(2), P. 374 - 374

Published: Feb. 17, 2024

Accurate prediction of individual tree mortality is essential for informed decision making in forestry. In this study, we proposed machine learning models to forecast within the temperate Larix gmelinii var. principis-rupprechtii forests Northern China. Eight distinct techniques including random forest, logistic regression, artificial neural network, generalized additive model, support vector machine, gradient boosting k-nearest neighbors, and naive Bayes were employed, construct an ensemble model based on comprehensive dataset from specific ecosystem. The forest emerged as most accurate, demonstrating 92.9% accuracy 92.8% sensitivity, it best among those tested. We identified key variables impacting mortality, results showed that a basal area larger than target trees (BAL), diameter at 130 cm (DBH), (BA), elevation, slope, NH4-N, soil moisture, crown density, soil’s available phosphorus are important Principis-rupprechtii model. variable importance calculation BAL with value 1.0 By analyzing complex relationships factors, stand environmental, our aids conservation.

Language: Английский

Citations

1
Modeling stand mortality of Chinese fir plantations in subtropical China using mixed-effects zero-inflated negative binomial models DOI
Jun Liu, Xunzhi Ouyang, Ping Pan

et al.

Forest Ecology and Management, Journal Year: 2024, Volume and Issue: 565, P. 122016 - 122016

Published: June 10, 2024

Language: Английский

Citations

1
A climate sensitive nonlinear mixed-effects height to crown base model: a study focuses on Phyllostachys pubescens DOI
Xiao Zhou, Xuan Zhang, Zhen Li

et al.

Trees, Journal Year: 2024, Volume and Issue: 38(4), P. 849 - 862

Published: June 18, 2024

Language: Английский

Citations

1
Modeling the influence of competition, climate, soil, and their interaction on height to crown base for Korean pine plantations in Northeast China DOI

Yunfei Yan,

Junjie Wang, Suoming Liu

et al.

European Journal of Forest Research, Journal Year: 2024, Volume and Issue: 143(6), P. 1627 - 1640

Published: July 9, 2024

Language: Английский

Citations

1
Modeling Degraded Bamboo Shoots in Southeast China DOI Open Access
Xiao Zhou,

Fengying Guan,

Shaohui Fan

et al.

Forests, Journal Year: 2022, Volume and Issue: 13(9), P. 1482 - 1482

Published: Sept. 14, 2022

Degraded bamboo shoots (DBS) constitute an important variable in the carbon fixation of forests. DBS are useful for informed decision making Despite their importance, studies on limited. In this study, we aimed to develop models describe variations. By using data from 64 plots Yixing forest farm Jiangsu Province, China, a mixed-effects model was constructed, including block-level random effects. We evaluated potential impact several variables DBS. The number (NBS), mean height crown base (MHCB), hydrolytic nitrogen (HN), and available potassium (AK) significantly contributed model. introducing effect logistic model, fitting statistics were improved. showed that there increased stands with decreased MHCB AK, whereas decreasing NBS HN. application K fertilizer reduced during emergence stage. adjusting these factors, forests can be reduced, which provides theoretical basis increasing biomass It also provide studying sink characteristics help formulate more effective management plans.

Language: Английский

Citations

4
Constructing two-level nonlinear mixed-effects crown width models for Moso bamboo in China DOI Creative Commons
Xiao‐Hua Zhou, Zhen Li, Liyang Liu

et al.

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 14

Published: Feb. 16, 2023

Bamboo crown width (CW) is a reliable index for evaluating growth, yield, health and vitality of bamboo, light capture ability carbon fixation efficiency bamboo forests. Based on statistical results produced from fitting the eight basic growth functions using data 1374 Phyllostachys pubescens in Yixing, Jiangsu Province, China, this study identified most suitable function (logistic function) to construct two-level mixed effects (NLME) CW model with forest block sample plot-level included as random model. Four methods selecting bamboos per plot (largest medium-sized smallest randomly selected bamboos) sizes (1–8 plot) were evaluated calibrate our NLME Using diameter at breast height (DBH), base (HCB), arithmetic mean (MDBH), (H) predictor variables, best fit statistics (Max R 2 , min RMSE, TRE). This was further improved by introducing two levels. The showed positive correlation HCB DBH negative H. poles used estimate provided satisfactory compromise regarding measurement cost, efficiency, prediction accuracy. presented may guide effective management estimation

Language: Английский

Citations

2
Modelling tree diameter of less commonly planted tree species in New Zealand using a machine learning approach DOI
Yue Lin, Serajis Salekin, Dean F. Meason

et al.

Forestry An International Journal of Forest Research, Journal Year: 2022, Volume and Issue: 96(1), P. 87 - 103

Published: Sept. 19, 2022

Abstract A better understanding of forest growth and dynamics in a changing environment can aid sustainable management. Forest data are typically captured by inventorying large network sample plots. Analysing these inventory datasets to make precise forecasts on be challenging as they often consist unbalanced, repeated measures collected across geographic areas with corresponding environmental gradients. In addition, such rarely available for less commonly planted tree species, incomplete even more unbalanced. Conventional statistical approaches not able deal identify the different factors that interactively affect growth. Machine learning offer potential overcome some challenges modelling complex response climatic factors, unbalanced data. this study, we employed widely used machine algorithm (random forests) model individual diameter at breast height (DBH, 1.4 m) age, stocking, site following five species groups New Zealand: Cupressus lusitanica (North Island); macrocarpa (South Eucalyptus nitens; Sequoia sempervirens; Podocarpus totara; Leptospermum scoparium. Data build models were extracted combined from three national level databases, included stand variables, information about sites climate features. The random predict DBH high precision five-tree (R2 > 0.72 root-mean-square error ranged 2.79–11.42 cm). Furthermore, interpretable allowed us explore effects site, To our knowledge, is first attempt utilize common Zealand. This approach forecast carbon sequestration help understand how types affected climate.

Language: Английский

Citations

3
A Climate-Sensitive Mixed-Effects Individual Tree Mortality Model for Masson Pine in Hunan Province, South–Central China DOI Creative Commons
Ni Yan, Youjun He, Keyi Chen

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(9), P. 1543 - 1543

Published: Sept. 1, 2024

Accurately assessing tree mortality probability in the context of global climate changes is important for formulating scientific and reasonable forest management scenarios. In this study, we developed a climate-sensitive individual model Masson pine using data from seventh (2004), eighth (2009), ninth (2014) Chinese National Forest Inventory (CNFI) Hunan Province, South–Central China. A generalized linear mixed-effects with plots as random effects based on logistic regression was applied. Additionally, hierarchical partitioning analysis used to disentangle relative contributions variables. Among various candidate predictors, diameter (DBH), Gini coefficient (GC), sum basal area all trees larger than subject (BAL), mean coldest monthly temperature (MCMT), summer (May–September) precipitation (MSP) contributed significantly mortality. The contribution variables (MCMT MSP) 44.78%, size (DBH, 32.74%), competition (BAL, 16.09%), structure (GC, 6.39%). validation results independent showed that performed well suggested an influencing mechanism mortality, which could improve accuracy decisions under changing climate.

Language: Английский

Citations

0
Response of bamboo canopy density to terrain, soil and stand factors DOI
Xiao Zhou, Xuan Zhang, Ram P. Sharma

et al.

Trees, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 29, 2024

Language: Английский

Citations

0
A Study on the Growth Model of Natural Forests in Southern China Under Climate Change: Application of Transition Matrix Model DOI Open Access
Xin Meng,

Zhengrui Ma,

Ying Xia

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 1947 - 1947

Published: Nov. 5, 2024

This study establishes a climate-sensitive transition matrix growth model and predicts forest under different carbon emission scenarios (representative concentration pathways RCP2.6, RCP4.5, RCP8.5) over the next 40 years. Data from Eighth (2013) Ninth (2019) National Forest Resource Inventories in Chongqing climate data Climate AP are utilized. The is used to predict compare number of trees, basal area, stock volume scenarios. results show that has high accuracy. relationships between variables growth, mortality, recruitment correspond natural succession growth. Although do not differ significantly for scenarios, sufficient seedling regeneration large-diameter trees. process aligns with succession, pioneer species being replaced by climax species. proposed fills gap models secondary forests an accurate method predicting can be long-term prediction stands understand future trends provide reliable references management. predicted harvesting intensities determine optimal intensity guide management City. this help formulate targeted policies deal more effectively change promote sustainable health.

Language: Английский

Citations

0