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Remote Sens. 2016, 8(7), 474; doi:10.3390/rs8070474

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

1
Department of Physics, The University of the West Indies, Mona, Jamaica
2
Central Great Plains Research Station, USDA-ARS, Akron, CO 80720, USA
3
Department of Life Sciences, The University of the West Indies, Mona, Jamaica
*
Author to whom correspondence should be addressed.
Academic Editors: Sangram Ganguly, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 4 April 2016 / Revised: 13 May 2016 / Accepted: 23 May 2016 / Published: 23 June 2016
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
View Full-Text   |   Download PDF [9910 KB, uploaded 23 June 2016]   |  

Abstract

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. View Full-Text
Keywords: fractional vegetation cover; automated canopy estimation; unsupervised image segmentation; digital photographs fractional vegetation cover; automated canopy estimation; unsupervised image segmentation; digital photographs
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Coy, A.; Rankine, D.; Taylor, M.; Nielsen, D.C.; Cohen, J. Increasing the Accuracy and Automation of Fractional Vegetation Cover Estimation from Digital Photographs. Remote Sens. 2016, 8, 474.

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