Web Cameras In Automatic Autumn Colour Monitoring

• Published in: IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium
• Main Authors: Astola, Heikki, Molinier, Matthieu, Mikkola, Tapani, Kubin, Eero
• Format: Other Non-Article Part of Journal/Newspaper
• Language: English
• Published: IEEE Institute of Electrical and Electronic Engineers 2008
• Subjects: change detection; SAR; dual-polarisation; TerraSAR-X; clearcuts; Enontekiö; Oulanka
• Online Access: https://cris.vtt.fi/en/publications/910dd564-df53-42cd-aae2-df9f10080431; https://doi.org/10.1109/IGARSS.2008.4779476

The objective of ForSe - Season Monitoring study was to develop an automatic method to analyze web-camera images of nature. As the outcome the image analysis produces indices that indicate the seasonal development stage of the forest (e.g. degree of autumn colour of deciduous trees). IP web-cameras of a pilot camera network were programmed to take one image in 15 minute interval on daylight hours during autumn period. One camera was used as a source of the training data (Enontekiö), and one for testing data (Oulanka). The image data was preprocessed to reduce noise and to and spectral angle feature was calculated to compensate the illumination variations between consequential images and within a single image. Selected areas of the training site camera images of autumn season were classified into six classes describing the seasonal status of the leaves (green, light green, yellow, red, brown, fallen). The spectral angle features were calculated for these areas and clustered by K-means into 30 clusters. Class labels were assigned to the cluster centres using k-NN method (k=5). To see the progress of a certain colour class in the time series of images of a test site camera, the classified pixels within selected regions of interest (ROI) were used to produce a continuous season colour index (SCI). The behaviour of the index was compared with a reference classification supplied by phenology experts from Finnish Forest Research Institute (Metla). The results were well in line with the reference classification, and show that the implemented processing chain can be used to obtain a numerical index describing the seasonal status of deciduous leaves' colour.