Automated estimation of foliage cover in forest understorey from digital nadir images

Abstract

1.Understorey vegetation can contribute significantly to the foliage cover and gas exchange of forest and impact the accuracy of remotely-sensed vegetation indices. Visual assessment is a rapid means of estimating cover, but its drawbacks include bias and inconsistency between observers and observation periods and the inability of observers to distinguish between cover intervals smaller than 10%. The accuracy of visual assessment is unlikely to be able to reliably detect changes smaller than 10%. Hence, there is a need for rapid and accurate alternatives for estimating understorey cover in forests. 2.Nadir (downward facing) photography is an alternative method that has been applied successfully in agriculture but not tested in forests. Detection of green foliage in images of forest understorey is far more challenging than in images of agricultural crops owing to the heterogeneity of the vegetation, the background and the lighting conditions. We tested several image analysis approaches to classifying and quantifying foliage cover in images taken from a height of 3-4m above the ground using a digital SLR camera on an extendible pole. To reduce the impact of lighting variations on image processing, we converted red, green and blue (RGB) digital numbers from the RGB image to green leaf algorithm (GLA) values and to the CIE L*a*b* colour model prior to analysis. 3.The most successful classification method (LAB2) utilised the GLA, a* and b* values of each pixel to classify green vegetation using a minimum-distance-to-means classifier. A histogram-corner detection method (Rosin) was superior to other methods when cover was <10%. Between-class variance methods were the least accurate methods. Owing to the spectral complexity of the forest floor, it was necessary to filter noise from the classified images. Further work is needed to separate shades of gray from hues of green in images with sparse cover and coarse woody debris. 4.Synthesis and Applications. We propose that the LAB2 method be used to quantify foliage cover in nadir images of understorey with cover >10% but that the Rosin method be used for cover <10%. The automated methodology that we have developed yields estimates of foliage cover in forest understorey from digital photography that are rapid, objective and at least as accurate as visual assessment (i.e. within 5%). User intervention is limited to quality control. The improved method could be extended to indirect estimation of leaf area index for studies of forest water balance and productivity. © 2011 The Authors. Methods in Ecology and Evolution © 2011 British Ecological Society.