Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application

An accurate survey of field vegetation information facilitates the evaluation of ecosystems and the improvement of remote sensing models. Extracting fractional vegetation cover (FVC) information using aerial images is one of the important areas of unmanned aerial vehicles. However, for a field with diverse vegetation species and a complex surface environment, FVC estimation still has difficulty guaranteeing accuracy. A segmented FVC calculation method based on a thresholding algorithm is proposed to improve the accuracy and speed of FVC estimation. The FVC estimation models were analyzed by randomly selected sample images using four vegetation indices: excess green, excess green minus excess red index, green leaf index, and red green blue vegetation index (RGBVI). The results showed that the empirical model method performed poorly (validating R 2 = 0.655 to 0.768). The isodata and triangle thresholding algorithms were introduced for vegetation segmentation, and their accuracy was analyzed. The results showed that the correlation between FVC estimation under RGBVI was the highest, and the triangle and isodata thresholding algorithms were complementary in terms of vegetation recognition accuracy, based on which a segmentation method of FVC calculation combining triangle and isodata algorithms was proposed. After testing, the accuracy of the improved FVC calculation method is higher than 90%, and the vegetation recognition accuracy is improved to more than 80%. This study is a positive guide to using digital cameras in field surveys.