Remote Sens. 2013, 5(5), 2200-2218; doi:10.3390/rs5052200
Article

Exploring the Potential for Automatic Extraction of Vegetation Phenological Metrics from Traffic Webcams

1 Electrical Systems and Optics Division, Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UK 2 School of Geography, University of Nottingham, University Park, Nottingham NG7 2RD, UK
* Author to whom correspondence should be addressed.
Received: 14 March 2013; in revised form: 2 May 2013 / Accepted: 2 May 2013 / Published: 10 May 2013
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Abstract: Phenological metrics are of potential value as direct indicators of climate change. Usually they are obtained via either satellite imaging or ground based manual measurements; both are bespoke and therefore costly and have problems associated with scale and quality. An increase in the use of camera networks for monitoring infrastructure offers a means of obtaining images for use in phenological studies, where the only necessary outlay would be for data transfer, storage, processing and display. Here a pilot study is described that uses image data from a traffic monitoring network to demonstrate that it is possible to obtain usable information from the data captured. There are several challenges in using this network of cameras for automatic extraction of phenological metrics, not least, the low quality of the images and frequent camera motion. Although questions remain to be answered concerning the optimal employment of these cameras, this work illustrates that, in principle, image data from camera networks such as these could be used as a means of tracking environmental change in a low cost, highly automated and scalable manner that would require little human involvement.
Keywords: phenology; traffic camera; network; image processing; automation; climate change

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MDPI and ACS Style

Morris, D.E.; Boyd, D.S.; Crowe, J.A.; Johnson, C.S.; Smith, K.L. Exploring the Potential for Automatic Extraction of Vegetation Phenological Metrics from Traffic Webcams. Remote Sens. 2013, 5, 2200-2218.

AMA Style

Morris DE, Boyd DS, Crowe JA, Johnson CS, Smith KL. Exploring the Potential for Automatic Extraction of Vegetation Phenological Metrics from Traffic Webcams. Remote Sensing. 2013; 5(5):2200-2218.

Chicago/Turabian Style

Morris, David E.; Boyd, Doreen S.; Crowe, John A.; Johnson, Caroline S.; Smith, Karon L. 2013. "Exploring the Potential for Automatic Extraction of Vegetation Phenological Metrics from Traffic Webcams." Remote Sens. 5, no. 5: 2200-2218.

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