CANAPI: canopy analysis with panchromatic imagery

Abstract

The validation of remotely sensed canopy structural parameters derived from moderate resolution imaging is a perennial problem because it is very expensive to undertake field measurements at scales of 250 m and above. High-resolution imaging and airborne light detection and ranging (lidar) systems are widely used sources of reference data, with the former used to delineate crowns and the latter to estimate tree heights and other statistics. A simple yet effective automated method that provides mapped tree crown cover, radii and height estimates from high-resolution panchromatic images of large dimensions – CANopy Analysis from Panchromatic Imagery (CANAPI) – is presented, together with comparisons with QuickBird 0.6 m spatial resolution imagery, field inventory data and lidar canopy height estimates from the NASA Laser Vegetation Imaging Sensor (LVIS) for forest sites in the Sierra National Forest in California. The method was developed as an ImageJ macro using simple image processing functions and is easily extended. It has some limitations but is likely to be useful in analysing open and semiopen forest and shrub canopies where the illumination is oblique.

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CANAPI: canopy analysis with panchromatic imagery

Acknowledgements

I am grateful to the editor and three anonymous reviewers for their valuable comments on the manuscript. This work was supported by NASA Award NNX08AE71G (program manager Diane E. Wickland) and JPL subcontract #1365499 (MISR PI: David Diner; Technical Manager: Earl Hansen). I thank Xiaohong Chopping, Crystal Schaaf, Alan Strahler and Michael Palace for constructive comments; Wayne Rasband (U.S. National Institutes of Health) for developing and maintaining the ImageJ package (http://rsb.info.nih.gov/ij); and Malcolm North and Jiquan Chen for providing the TEE database (http://teakettle.ucdavis.edu/index.htm). Data sets were provided by the LVIS team in the Laser Remote Sensing Branch at NASA Goddard Space Flight Center with support from the University of Maryland, College Park.