Patterns in height-diameter relationships for selected tree species and sites in eastern Texas

Fulton, Mark R.

doi:10.1139/cjfr-29-9-1445

Cited by 9 publications

(16 citation statements)

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“…Therefore compartments of low and high values for the random effect are of the same magnitude. The variability of v i reflects the variability of the relationship between total tree height and DBH and, intuitively, derives mainly from differences in water availability, soil richness (Fulton, 1999) and the time and intensity of thinning (Zhang et al, 1997).…”

Section: Variogram Analysis For the Random Stand Effect And Stand Denmentioning

confidence: 99%

“…The localisation of the H/D curve has been made with the use of other stand variables (Lappi, 1991(Lappi, , 1997 or according to the site or region where the curve is going to be used (Fulton, 1999;Huang et al, 2000). These approaches to H/D modelling are based on the assumption that site conditions and previous silvicultural practices influence the relationship between total tree height and DBH.…”

Section: Introductionmentioning

confidence: 99%

“…In the models that use stand variables for prediction, there does not seem to be a single or universal variable to explain variation in stand-specific parameters, since these depend on the investigator's intuition, model Forest Ecology and Management 195 (2004) [221][222][223][224][225][226][227][228][229][230][231][232][233][234][235] specification, site productivity (Fulton, 1999), and thinning regime (Zhang et al, 1997). Fang et al (1998), for example, used percentiles of the diameter distribution; Zakrzewski and Bella (1988) found that the quadratic mean diameter and height are the best explanatory variables, while Parresol (1992) used the basal area per hectare.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Geostatistical prediction of height/diameter models

Nanos¹,

Calama²,

Montero³

et al. 2004

Forest Ecology and Management

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Section: Variogram Analysis For the Random Stand Effect And Stand Denmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Geostatistical prediction of height/diameter models

Nanos¹,

Calama²,

Montero³

et al. 2004

Forest Ecology and Management

View full text Add to dashboard Cite

“…In contrast to fixed-effects modeling, some researchers developed discrete models to express the variance of height-diameter relationships among different geographical or ecological regions (Fulton 1999;Huang et al 2000;Zhang et al 2002;Li and Fa 2011;Li and Zhao 2013). They thought that maximum height in different regions was variable, but that the rate of height growth was not.…”

Section: Introductionmentioning

confidence: 99%

“…They thought that maximum height in different regions was variable, but that the rate of height growth was not. Fulton (1999) developed a Richard model with three asymptote parameters in terms of three maximum heights performed in three regions. However, Li and Fa (2011) insisted that height-diameter models for different sites were difficult to build, taking into account that it is impossible to completely survey the site of a specific tree species in any country in the world.…”

Section: Introductionmentioning

confidence: 99%

Improving the accuracy of the height–diameter equation using the classified factors method

Wang

Yue

et al. 2015

Environ Earth Sci

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The height-diameter relationship model is crucial in the estimation of forest stand volume, biomass and carbon storage, etc. To improve prediction accuracy, the classified factors method for height-diameter relationship modeling was developed based on the classified height method. The data set contained 959 tree samples obtained from 28 plots measured by topographic factors (aspect, slope position and altitude). The Chapman-Richards equation was used to build models. Classification methods have improved fitting performance and prediction accuracy compared to the classical method. The classified height method has the best fitting performance because it has the highest determination coefficient (R 2 = 0.927) and the lowest root mean square error (RMSE = 1.548), whereas the classified factors method has the highest prediction accuracy because it has the lowest mean absolute error (MAE = 1.137) and mean relative error (MRE = 0.109).

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A spatial-explicit dynamic vegetation model that couples carbon, water, and nitrogen processes for arid and semiarid ecosystems

Zhang

Chen

et al. 2012

J. Arid Land

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Arid and semiarid ecosystems, or dryland, are important to global biogeochemical cycles. Dryland's community structure and vegetation dynamics as well as biogeochemical cycles are sensitive to changes in climate and atmospheric composition. Vegetation dynamic models has been applied in global change studies, but the complex interactions among the carbon (C), water, and nitrogen (N) cycles have not been adequately addressed in the current models. In this study, a process-based vegetation dynamic model was developed to study the responses of dryland ecosystems to environmental changes, emphasizing on the interactions among the C, water, and N processes. To address the interactions between the C and water processes, it not only considers the effects of annual precipitation on vegetation distribution and soil moisture on organic matter (SOM) decomposition, but also explicitly models root competition for water and the water compensation processes. To address the interactions between C and N processes, it models the soil inorganic mater processes, such as N mineralization/immobilization, denitrification/nitrification, and N leaching, as well as the root competition for soil N. The model was parameterized for major plant functional types and evaluated against field observations.

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Patterns in height-diameter relationships for selected tree species and sites in eastern Texas

Cited by 9 publications

References 0 publications

Geostatistical prediction of height/diameter models

Geostatistical prediction of height/diameter models

Improving the accuracy of the height–diameter equation using the classified factors method

A spatial-explicit dynamic vegetation model that couples carbon, water, and nitrogen processes for arid and semiarid ecosystems

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