Related Experiment Video
Updated: Oct 13, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
Published on: August 29, 2019
Informed cover measurement: Guidelines and error for point-intercept approaches
Taly Dawn Drezner1, Zvi Drezner2
1Department of Physical Sciences, College of Southern Nevada 6375 West Charleston Boulevard Las Vegas Nevada 89146 USA.
Premise:
The point-intercept method is one of the most commonly used approaches to measure species cover in ecosystems worldwide. In this approach, multiple points are sampled for presence/absence of a species, and the number of present points divided by the total number of sampled points provides an estimate of percent cover. Our purpose is to mathematically analyze the accuracy of the point-intercept approach and establish guidelines for its use.
Methods:
We developed formulas that analyze the point-intercept method and confirmed their effectiveness using simulations.
Results:
We find that a point-intercept spacing of at least 80% of the largest plant diameter provides the most reliable results. We present a user-friendly spreadsheet that calculates the number of intercepts needed for fieldwork, as well as the standard deviation, expected deviation, and confidence interval of the collected data.
Discussion:
We provide a variety of guidelines for establishing field protocols based on our results, including dealing with rare species and combining results for multiple species. Quadrat characteristics (intercept spacing, number of point intercepts) can now be easily calculated to guide research design prior to fieldwork; after fieldwork is complete, the accuracy of this technique can (and should) be reported in all future ecological studies in which it is used.
Related Concept Videos
Design Example: Measuring Distance Between Two Points with Obstructions
Common Leveling Mistakes and Errors
Distance Corrections
Areas Within Irregular Boundaries
Errors and Mistakes in Surveying
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

