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Related Concept Videos

Survival Tree01:19

Survival Tree

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.
 Building a Survival Tree
Constructing a survival tree begins...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

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Related Experiment Video

Updated: Jul 5, 2026

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
09:04

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

[Estimation of forest canopy closure by using partial least square regression].

Xiao-Ming Du1, Ti-Jiu Cai, Cun-Yong Ju

  • 1College of Forestry, Northeast Forestry University, Harbin 150040, China. duxiaoming800@sina.com

Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
|May 10, 2008
PubMed
Summary

This study explored using the Bootstrap approach and partial least square (PLS) regression for estimating forest canopy closure. Both methods achieved similar accuracy, around 5% deviation, highlighting the importance of local factors.

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Last Updated: Jul 5, 2026

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
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Published on: August 29, 2019

Computer Vision-Based Biomass Estimation for Invasive Plants
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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Area of Science:

  • Forestry science
  • Remote sensing applications
  • Statistical modeling in ecology

Context:

  • Accurate forest canopy closure estimation is vital for sustainable forest management and ecological monitoring.
  • Traditional methods often struggle with variability across different forest types and terrains.
  • Integrating remote sensing with advanced statistical techniques offers a promising avenue for improved forest assessment.

Purpose:

  • To evaluate the feasibility of the Bootstrap approach for optimal variable selection in forest canopy closure estimation.
  • To compare the performance of a partial least square (PLS) regression model using all variables versus optimally selected variables.
  • To investigate factors influencing the selection of optimal variables for canopy closure modeling.

Summary:

  • The study utilized remote sensing and forest inventory data to build a forest canopy closure estimation model.
  • Partial least square (PLS) regression was employed, with and without Bootstrap-selected optimal variables.
  • Both modeling approaches yielded comparable results, with a relative deviation of approximately 5% in forest canopy closure estimation.

Impact:

  • The findings demonstrate the robustness of PLS regression for forest canopy closure estimation, irrespective of variable selection method.
  • Identified optimal variables varied significantly compared to other regions, underscoring the influence of local vegetation and terrain.
  • This research contributes to refining variable selection strategies for remote sensing-based forest monitoring in diverse geographical contexts.