Development of a Rapid and Precise Method of Digital Image Analysis to Quantify Canopy Density and Structural Complexity

Abstract

Estimation of canopy density is necessary for ecological research and woodland management. However, traditional manual methods are time consuming and subject to interobserver variability, while existing photographic methods usually require expensive fish-eye lenses and complex analysis. Here we introduce and test a new method of digital image analysis, CanopyDigi. This allows user-defined threshold to polarise the 256 grey shades of a standard monochrome bitmap into dark “canopy” and light “sky” pixels (the threshold being selected using false-colour images to ensure its suitability). Canopy density data are calculated automatically and rapidly, and, unlike many other common methods, aggregation data are obtainable using Morisita’s index to differentiate closed (diffuse light) and open (direct light) canopies. Results were highly repeatable in both homogeneous and heterogeneous woodland. Estimates correlated strongly with existing (nondigital) canopy techniques, but quicker and with significantly lower interobserver variability (CV = 3.74% versus 20.73%). We conclude that our new method is an inexpensive and precise technique for quantifying canopy density and aggregation.