Generalized nonlinear height–diameter models for a &lt;i&gt;Cryptomeria fortunei&lt;/i&gt; plantation in the Pingba region of Guizhou Province, China

Abstract. The relationship between height and diameter (H-D) is an important component in forest growth and yield models, and a better understanding of the relationship will improve forest monitoring, management, and biomass estimation. Sixteen nonlinear growth functions were fitted to H-D data for 1261 trees from a Cryptomeria fortunei plantation in the Pingba region of Guizhou Province, China. Of the 1261 trees, 80 % were randomly selected for model calibration, while the remaining trees were reserved for model validation. All models were evaluated and compared by means of multiple-model performance criteria. Although all 16 models showed a good fit to the dataset and each of them accounted for more than 75 % of the total variation in height, a large difference in asymptotic estimates was observed. The Chapman–Richards, Weibull, and Näslund models were recommended for C. fortunei plantations, due to their satisfactory height prediction and biological interpretability.

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Features and Partial Derivatives of Bertalanffy-Richards Growth Model in Forestry

Nonlinear Analysis Modelling and Control ◽

10.15388/na.2004.9.1.15171 ◽

2004 ◽

Vol 9 (1) ◽

pp. 65-73 ◽

Cited By ~ 21

Author(s):

Y. C. Lei ◽

S. Y. Zhang

Keyword(s):

Growth Model ◽

Growth Models ◽

Nonlinear Models ◽

Forest Growth ◽

Growth And Yield ◽

Partial Derivatives ◽

Nonlinear Growth ◽

Derivatives Of ◽

Allometric Parameter ◽

Richards Growth Model

The Bertalanffy-Richards growth model is employed more than any other models for forest growth and yield modelling. However, its features have not completely been recognised. As a result, misunderstanding of the model still appears in some papers published in forest journals. A study by [1] is cited here as an evidence of the misunderstanding. This paper tries to explain different features of the Bertalanffy-Richards growth model based on the different conditions of the allometric parameter and introduces an assessment software to easily get the partial derivatives with respect to each parameter when more complex techniques (e.g., the Marquardt method) are employed to estimate parameters of any nonlinear models. This paper indicates that [1] study appears some unreasonable evidences of nonlinear growth models from a forestry perspective.

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Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽

10.3390/w13010037 ◽

2020 ◽

Vol 13 (1) ◽

pp. 37

Author(s):

Tomás de Figueiredo ◽

Ana Caroline Royer ◽

Felícia Fonseca ◽

Fabiana Costa de Araújo Schütz ◽

Zulimar Hernández

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Regression Models ◽

Meteorological Data ◽

Water Storage ◽

Model Performance ◽

Soil Water Balance ◽

Soil Water Storage ◽

The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

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A Blind Spot in the Use of the Weibull Function for Modeling Diameter Distributions

Forest Science ◽

10.1093/forsci/fxaa042 ◽

2020 ◽

Author(s):

Adrian Norman Goodwin

Keyword(s):

Lower Bound ◽

Blind Spot ◽

Forest Growth ◽

Growth And Yield ◽

Diameter Distribution ◽

Absolute Difference ◽

Weibull Function ◽

Distribution Models ◽

Difference Statistic ◽

Diameter Distributions

Abstract Diameter distribution models based on probability density functions are integral to many forest growth and yield systems, where they are used to estimate product volumes within diameter classes. The three-parameter Weibull function with a constrained nonnegative lower bound is commonly used because of its flexibility and ease of fitting. This study compared Weibull and reverse Weibull functions with and without a lower bound constraint and left-hand truncation, across three large unthinned plantation cohorts in which 81% of plots had negatively skewed diameter distributions. Near-optimal lower bounds for the unconstrained Weibull function were negative for negatively skewed data, and the left-truncated Weibull using these bounds was 14.2% more accurate than the constrained Weibull, based on the Kolmogorov-Smirnov statistic. The truncated reverse Weibull fit dominant tree distributions 23.7% more accurately than the constrained Weibull, based on a mean absolute difference statistic. This work indicates that a blind spot may have developed in plantation growth modeling systems deploying constrained Weibull functions, and that left-truncation of unconstrained functions could substantially improve model accuracy for negatively skewed distributions.

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Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽

10.3390/atmos12010012 ◽

2020 ◽

Vol 12 (1) ◽

pp. 12

Author(s):

Yulia Ivanova ◽

Anton Kovalev ◽

Vlad Soukhovolsky

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Principal Component ◽

Stem Growth ◽

Forest Growth ◽

Parabolic Approximation ◽

New Approach ◽

The Relationship ◽

Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

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Mixed Regional Shifts in Conifer Productivity under 21st-Century Climate Projections in Canada’s Northeastern Boreal Forest

Forests ◽

10.3390/f12020248 ◽

2021 ◽

Vol 12 (2) ◽

pp. 248

Author(s):

Tyler Searls ◽

James Steenberg ◽

Xinbiao Zhu ◽

Charles P.-A. Bourque ◽

Fan-Rui Meng

Keyword(s):

Climate Change ◽

Model Validation ◽

21St Century ◽

Black Spruce ◽

Abies Balsamea ◽

Acer Rubrum ◽

Forest Growth ◽

Balsam Fir ◽

Growth And Yield ◽

Climate Projections

Models of forest growth and yield (G&Y) are a key component in long-term strategic forest management plans. Models leveraging the industry-standard “empirical” approach to G&Y are frequently underpinned by an assumption of historical consistency in climatic growing conditions. This assumption is problematic as forest managers look to obtain reliable growth predictions under the changing climate of the 21st century. Consequently, there is a pressing need for G&Y modelling approaches that can be more robustly applied under the influence of climate change. In this study we utilized an established forest gap model (JABOWA-3) to simulate G&Y between 2020 and 2100 under Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5 in the Canadian province of Newfoundland and Labrador (NL). Simulations were completed using the province’s permanent sample plot data and surface-fitted climatic datasets. Through model validation, we found simulated basal area (BA) aligned with observed BA for the major conifer species components of NL’s forests, including black spruce [Picea mariana (Mill.) Britton et al.] and balsam fir [Abies balsamea (L.) Mill]. Model validation was not as robust for the less abundant species components of NL (e.g., Acer rubrum L. 1753, Populus tremuloides Michx., and Picea glauca (Moench) Voss). Our simulations generally indicate that projected climatic changes may modestly increase black spruce and balsam fir productivity in the more northerly growing environments within NL. In contrast, we found productivity of these same species to only be maintained, and in some instances even decline, toward NL’s southerly extents. These generalizations are moderated by species, RCP, and geographic parameters. Growth modifiers were also prepared to render empirical G&Y projections more robust for use under periods of climate change.

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Ontario's forest growth and yield modelling program: Advances resulting from the Forestry Research Partnership

The Forestry Chronicle ◽

10.5558/tfc84694-5 ◽

2008 ◽

Vol 84 (5) ◽

pp. 694-703 ◽

Cited By ~ 14

Author(s):

Mahadev Sharma ◽

John Parton ◽

Murray Woods ◽

Peter Newton ◽

Margaret Penner ◽

...

Keyword(s):

Tree Species ◽

Stand Density ◽

Forest Growth ◽

Growth And Yield ◽

Research Partnership ◽

Wood Supply ◽

Permanent Sample Plots ◽

Forestry Research ◽

Research Activities ◽

Forest Growth And Yield

The province of Ontario holds approximately 70.2 million hectares of forests: about 17% of Canada’s and 2% of the world’s forests. Approximately 21 million hectares are managed as commercial forests, with an annual harvest in the early part of the decade approaching 200 000 ha. Yield tables developed by Walter Plonski in the 1950s provide the basis for most wood supply calculations and growth projections in Ontario. However, due to changes in legislation, policy, and the planning process, they no longer fully meet the needs of resource managers. Furthermore, Plonski`s tables are not appropriate for the range of silvicultural options now practised in Ontario. In October 1999, the Canadian Ecology Centre- Forestry Research Partnership (CEC-FRP) was formed and initiated a series of projects that collectively aimed at characterizing, quantifying and ultimately increasing the economically available wood supply. Comprehensive, defensible, and reliable forecasts of forest growth and yield were identified as key knowledge gaps. The CEC-FRP, with support from the broader science community and forest industry, initiated several new research activities to address these needs, the results of which are outlined briefly in this paper. We describe new stand level models (e.g., benchmark yield curves, FVS Ontario, stand density management diagrams) that were developed using data collected from permanent sample plots and permanent growth plots established and remeasured during the past 5 decades. Similarly, we discuss new height–diameter equations developed for 8 major commercial tree species that specifically account for stand density. As well, we introduce a CEC-FRP-supported project aimed at developing new taper equations for plantation grown jack pine and black spruce trees established at varying densities. Furthermore, we provide an overview of various projects undertaken to explore measures of site productivity. Available growth intercept and site index equations are being evaluated and new equations are being developed for major commercial tree species as needed. We illustrate how these efforts are advancing Ontario’s growth and yield program and supporting the CEC-FRP in achieving its objective of increasing the supply of fibre by 10% in 10 years while maintaining forest sustainability. Key words: permanent sample plots (PSPs), permanent growth plots (PGPs), normal yield tables, sustainable forest management, NEBIE plot network, forest inventory, Forest Vegetation Simulator

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Derivation of S-Curve from Oscillatory Hydrograph Using Digital Filter

Water ◽

10.3390/w13111456 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1456

Author(s):

Kee-Won Seong ◽

Jang Hyun Sung

Keyword(s):

Digital Filter ◽

Model Performance ◽

Performance Criteria ◽

Rainfall Runoff ◽

Small Watershed ◽

Unit Hydrograph ◽

Time To Peak ◽

S Curve ◽

And Performance ◽

Smooth Data

An oscillatory S-curve causes unexpected fluctuations in a unit hydrograph (UH) of desired duration or an instantaneous UH (IUH) that may affect the constraints for hydrologic stability. On the other hand, the Savitzky–Golay smoothing and differentiation filter (SG filter) is a digital filter known to smooth data without distorting the signal tendency. The present study proposes a method based on the SG filter to cope with oscillatory S-curves. Compared to previous conventional methods, the application of the SG filter to an S-curve was shown to drastically reduce the oscillation problems on the UH and IUH. In this method, the SG filter parameters are selected to give the minimum influence on smoothing and differentiation. Based on runoff reproduction results and performance criteria, it appears that the SG filter performed both smoothing and differentiation without the remarkable variation of hydrograph properties such as peak or time-to peak. The IUH, UH, and S-curve were estimated using storm data from two watersheds. The reproduced runoffs showed high levels of model performance criteria. In addition, the analyses of two other watersheds revealed that small watershed areas may experience scale problems. The proposed method is believed to be valuable when error-prone data are involved in analyzing the linear rainfall–runoff relationship.

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Strengthening the Capacity on Geospatial Information Technology and Earth Observation Applications

Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region ◽

10.1007/978-3-030-73569-2_14 ◽

2021 ◽

pp. 269-289

Author(s):

Rajesh Bahadur Thapa ◽

Poonam Tripathi ◽

Mir A. Matin ◽

Birendra Bajracharya ◽

Betzy E. Hernandez Sandoval

Keyword(s):

Decision Making ◽

Information Technology ◽

Carbon Balance ◽

Anthropogenic Impacts ◽

Earth Observation ◽

Forest Monitoring ◽

Biomass Estimation ◽

Geospatial Information ◽

Significant Opportunity ◽

Support Decision Making

AbstractThe innovative transformation in geospatial information technology (GIT) and Earth observation (EO) data provides a significant opportunity to study the Earth’s environment and enables an advanced understanding of natural and anthropogenic impacts on ecosystems at the local, regional, and global levels (Thapa et al. in Carbon Balance Manag 10(23):1–13, 2015; Flores et al. in SAR handbook: comprehensive methodologies for forest monitoring and biomass estimation. NASA Publication, 2019; Leibrand et al. in Front Environ Sci 7:123, 2019; Chap. 10.1007/978-3-030-73569-2_1). The major advantages of these technologies can be briefly categorized into five broad areas: multidisciplinary; innovative and emerging; providing platforms for analysis, modelling, and visualization; capability to support decision-making; and impact on policies.

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Carbon and Nitrogen Stable Isotope Abundances and Soil Stoichiometry of Zanthoxylum Planispinum Var. Dintanensis Plantations at Different Ages

10.21203/rs.3.rs-838535/v1 ◽

2021 ◽

Author(s):

Yanghua Yu ◽

Yingu Wu ◽

Yanping Song ◽

Yitong Li

Keyword(s):

Stable Isotope ◽

Leaf Litter ◽

Microbial Biomass Carbon ◽

Guizhou Province ◽

Soil C ◽

Soil Microbial ◽

Plantation Age ◽

Soil Stoichiometry ◽

C And N ◽

The Relationship

Abstract Background and aimsUnderstanding the relationship between carbon, nitrogen and their stable isotope 13C, 15N and soil stoichiometry may assist to reveal the distribution pattern and stability mechanism of nutrient elements in karst ecosystem.MethodsFour plantations of Zanthoxylum planispinum var. dintanensis (5–7, 10–12, 20–22 and 30–32 years) in the karst plateau gorge area of Guizhou Province, China, were selected as the research objects to clarify the variation characteristics and interaction effects of leaf, litter, soil C, N and their isotopes with plantation age, and to explore the relationship between soil stoichiometry and the 13C, 15N of Zanthoxylum planispinum var. dintanensis plantation.Results(1) the 13C in leaf, litter and soil were − 28.04‰±0.59‰, -26.85‰±0.67‰ and − 19.39‰±1.37‰, respectively, correspondingly, the contents of 15N were 2.01‰±0.99‰, 2.91‰±1.32‰ and 3.29‰±0.69‰, respectively. The contents of the 13C and 15N can be rank ordered as soil > litter > leaf; (2) with the increase of plantation age, the soil 13C decreased; the leaf and litter 15N increased first then decreased; the litter 13C and soil 15N did not vary significantly; (3) the litter layer positively correlated to soil 13C, and negatively correlated to 15N; (4) redundancy analysis showed that soil microbial biomass carbon (MBC) and bacteria/fungi (BAC/FUN) were the dominant factors affecting C and N isotope natural abundances.ConclusionsThis study indicated that the species and acidity of soil microbial can affect the C and N isotope natural abundance.

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How random are predictions of forest growth? The importance of weather variability

Canadian Journal of Forest Research ◽

10.1139/cjfr-2019-0366 ◽

2020 ◽

Author(s):

Joanna Horemans ◽

Olga Vindušková ◽

Gaby Deckmyn

Keyword(s):

Climate Change ◽

Uncertainty Analysis ◽

Climate Models ◽

Global Climate ◽

Basal Area ◽

Random Order ◽

Forest Growth ◽

Global Climate Models ◽

Growth And Yield ◽

Weather Variability

Quantifying the output uncertainty and tracking down its origins is key to interpreting the results of model studies. We perform such an uncertainty analysis on the predictions of forest growth and yield under climate change. We specifically focus on the effect of the inter-annual climate variability. For that, the climate years in the model input (daily resolution) were randomly shuffled within each 5-year period. In total, 540 simulations (10 parameter sets, 9 climate shuffles, 3 global climate models and 2 mitigation scenarios), were made for one growing cycle (80 years) of a Scots pine forest growing in Peitz (Germany). Our results show that, besides the important effect of the parameter set, the random order of climate years can significantly change results such as basal area and produced volume, and the response of these to climate change. We stress that the effect of weather variability should be included in the design of impact model ensembles, and the accompanying uncertainty analysis. We further suggest presenting model results as likelihoods to allow risk assessment. For example, in our study the likelihood of a decrease in basal area of >10% with no mitigation was 20.4%, while the likelihood of an increase >10% was 34.4%.

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