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Related Concept Videos

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Softwoods and hardwoods, derived from different types of trees, are distinguished by their leaf structures and cellular compositions, each serving unique purposes in construction and manufacturing. Softwoods come from cone-bearing trees with needle-like leaves and are predominantly composed of longitudinal cells called tracheids and a smaller proportion of radial cells known as rays. Due to their cellular structure, softwoods are commonly used in construction for structural frames, sheathing,...
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Updated: Sep 4, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Assessing scale-dependent effects on Forest biomass productivity based on machine learning.

Jingyuan He1, Chunyu Fan1, Yan Geng1

  • 1Research Center of Forest Management Engineering of State Forestry Administration Beijing Forestry University Beijing China.

Ecology and Evolution
|July 18, 2022
PubMed
Summary
This summary is machine-generated.

Estimating forest above-ground biomass (AGB) productivity is crucial. Our study found that model accuracy improved with scale, identifying a minimum sampling area of 140x140m for reliable biomass productivity modeling.

Keywords:
above‐ground biomassproductivityrandom Forest algorithmrandom spatial samplingscale dependence

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Area of Science:

  • Forest Ecology
  • Biomass Estimation
  • Spatial Modeling

Background:

  • Accurate estimation of forest above-ground biomass (AGB) productivity is fundamental to ecological research.
  • Understanding the drivers of AGB productivity variation is essential for effective forest management and assessment.

Purpose of the Study:

  • To model forest AGB productivity using Random Forest (RF) algorithm.
  • To investigate how scale influences the importance of explanatory variables (topography, species diversity, stand structure, stand density) on AGB productivity.
  • To determine the minimum sampling area required for accurate biomass productivity modeling in Northeastern China.

Main Methods:

  • Utilized a 30-ha permanent field plot in Northeastern China.
  • Employed the Random Forest (RF) algorithm to model AGB productivity.
  • Analyzed data across varying area scales (10m to 200m grain size).

Main Results:

  • Model accuracy for AGB productivity estimation improved as the grain size increased from 10m to 200m.
  • The relative importance of explanatory variables driving biomass productivity varied significantly with scale.
  • Identified a minimum sampling area of 140m x 140m for reliable biomass productivity modeling in the study region.

Conclusions:

  • The relationship between environmental factors and AGB productivity is scale-dependent.
  • The Random Forest algorithm effectively models AGB productivity, with accuracy improving at larger spatial scales.
  • Findings provide critical insights for optimizing forest assessment methodologies and sampling strategies.