A novel method for extracting green fractional vegetation cover from digital images

Abstract

AbstractQuestionAlthough digital photography is an efficient and objective means of extracting green fractional vegetation cover (FVC), it lacks automation and classification accuracy. How can green FVC be extracted from digital images in an accurate and automated method?MethodsSeveral colour spaces were compared on the basis of a separability index, and CIE L*a*b* was shown to be optimal for the tested colour spaces. Thus, all image processing was performed in CIE L*a*b* colour space. Gaussian models were used to fit the green vegetation and background distributions of the a* component. Three strategies (T0, T1 and T2 thresholding method) were tested to select the optimal thresholds for segmenting the image into green vegetation and non‐green vegetation. The a* components of the images were then segmented and the green FVC extracted.ResultsThe FVC extracted using T0, T1, and T2 thresholding methods were evaluated with simulated images, and cross‐validated with FVC extracted with supervised classification methods. The results show that FVC extracted with T0, T1 and T2 thresholding methods are similar to those estimated with supervised classification methods. The mean errors associated with the FVC values provided in our approach and supervised classification are less than 0.035. In a test with simulated data, our method performed better than the supervised classification method.ConclusionsMethods presented in this paper were demonstrated to be feasible and applicable for automatically and accurately extracting FVC of several green vegetation types with varying background and shadow conditions. However, our algorithm design assumes a Gaussian distribution for both vegetated and non‐vegetated portions of a digital image; moreover, the impact of view angle on the FVC extraction from digital images must also be considered.