Calibrated tree counting on remotely sensed images of planted forests

Abstract

The development of robust and accurate methods for counting trees from remotely sensed data could provide substantial cost savings in forest inventory. A new methodology that provides a framework for calibrating tree detection algorithms to obtain accurate tree counts for even-aged stands is described. The methodology was evaluated using two tree detection algorithms and two operators using airborne laser scanning (ALS) and orthophotograph images for four Pinus radiata D.Don stands ranging in age between 5 and 32 years with stand densities ranging between 204 and 826 stems ha⁻¹. For application of the methodology to ALS images the error of estimate on the total count was 4.7% when calibration counts from actual ground plots were used and 10.5% when calibration counts from virtual plots on the image were used. For orthophotographs, the error of estimate was 6.1% using ground calibration plots and 24.3% using calibration counts from virtual plots. The described methodology was shown to be robust to variations in the process from the two operators and two algorithms evaluated. The measure of accuracy determined using the methodology can be used to provide an objective basis for evaluating a wide range of tree counting and detection processes in future research.

Acknowledgements

Timberlands Limited provided lidar and orthophotographs and permitted access to their forest for measurement of ground plots, carried out by Rodrigo Osorio and Pat Hodgkiss. The TIMBRS software was provided and supported by Darius Culvenor, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and was used by Marika Fritzsche and Chris Goulding to carry out initial evaluations of tree counting on orthophotographs. Lania Holt also provided technical input to the study. Thanks are due to reviewers for their valuable input to this article.

Additional information

Funding

This research was funded by Future Forests Research Ltd.