Increasing the Accuracy and Automation of Fractional Vegetation Cover Estimation from Digital Photographs

Remote Sensing

David C. Nielsen¹  Jane Cohen²  Michael Taylor³  André Coy³  Dale Rankine³ 

[1] Central Great Plains Research Station, USDA-ARS, Akron, CO 80720, USA;Department of Life Sciences, The University of the West Indies, Mona, Jamaica;Department of Physics, The University of the West Indies, Mona, Jamaica;

关键词: fractional vegetation cover;    automated canopy estimation;    unsupervised image segmentation;    digital photographs;   

DOI  :  10.3390/rs8070474

来源: DOAJ

【 摘 要 】

The use of automated methods to estimate fractional vegetation cover (FVC) from digital photographs has increased in recent years given its potential to produce accurate, fast and inexpensive FVC measurements. Wide acceptance has been delayed because of the limitations in accuracy, speed, automation and generalization of these methods. This work introduces a novel technique, the Automated Canopy Estimator (ACE) that overcomes many of these challenges to produce accurate estimates of fractional vegetation cover using an unsupervised segmentation process. ACE is shown to outperform nine other segmentation algorithms, consisting of both threshold-based and machine learning approaches, in the segmentation of photographs of four different crops (oat, corn, rapeseed and flax) with an overall accuracy of 89.6%. ACE is similarly accurate (88.7%) when applied to remotely sensed corn, producing FVC estimates that are strongly correlated with ground truth values.

【 授权许可】

Unknown