Tree canopy closure is often a desired metric in ecological applications of spectral remote sensing. There are numerous models and field protocols for estimating this variable, many of which are specialized or may have poor accuracies. Specialized instruments are also available but they may be cost prohibitive for small programs. An expedient alternative is the use of in-situ handheld digital photography to estimate canopy closure. This approach is cost effective while maintaining accuracy. The objective of this study was to develop and test an efficient field protocol for determining tree canopy closure from zenith-looking and oblique digital photographs.
Investigators created a custom software package that uses Euclidean distance to cluster pixels into sky and non-sky categories. The percentages of sky and tree canopy are calculated from the clusters. Acquisition protocols were tested using JPEG photographs taken at multiple upward viewing angles and along transects within an open stand of loblolly pine trees and a grove of broadleaf-deciduous trees. JPEG lossy compression introduced minimal error but provided an appropriate trade-off given limited camera storage capacity and the large number of photographs required to meet the field protocol. This is in contrast to relatively larger error introduced by other commonly employed measurement techniques such as using gridded template methods and canopy approximations calculated by tree diameter measurements.
Experiment results demonstrated the viability of combining image classification software with ground-level digital photographs to produce fast and economical tree canopy closure approximations.