Vegetation coverage extraction model of winter wheat based on pixel dichotomy

Objective  To quickly and accurately extract vegetation coverage information of winter wheat in turning green period.

Method  The field visible light images of winter wheat were obtained by UAV, and four common visible light vegetation indices were extracted. According to the principle of pixel dichotomy model, the vegetation coverage extraction models were established based on visible-band difference vegetation index(VDVI), excess green (EXG), normalized green-blue difference index (NGBDI) and normalized green-red difference index (NGRDI) respectively. The accuracies of four models were verified using the support vector machine (SVM) supervised classification results as the truth values.

Result  The VDVI vegetation coverage extraction model had the highest accuracy and the best extraction effect to extract vegetation coverage among the four models. Compared with the supervised classification results, the extraction errors (E_F) of four vegetation coverage extraction models were 3.36%, 15.68%, 8.74% and 15.46% respectively, the values of R² were 0.946 1, 0.934 4, 0.695 3 and 0.746 0 respectively, and the values of root mean square error (RMSE) were 0.021 9, 0.059 5, 0.042 0 and 0.055 9 respectively.

Conclusion  The vegetation coverage extraction model based on visible vegetation index and pixel dichotomy has realized accurate and rapid extraction of vegetation coverage of winter wheat in turning green period, which provides a new way to extract vegetation coverage and a reference for UAV remote sensing monitoring vegetation coverage information.