- freely available
Exploring the Potential for Automatic Extraction of Vegetation Phenological Metrics from Traffic Webcams
AbstractPhenological 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.
- Supplementary File 1:
Note (ZIP, 761 KB)
Share & Cite This Article
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.View more citation formats
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.
Notes: Multiple requests from the same IP address are counted as one view.