Web Cameras in Automatic Autumn Colour Monitoring

Abstract

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.

Multi-Scale Sensor Fusion With an Online Application: Integrating GOES, MODIS, and Webcam Imagery for Environmental Monitoring

“…Automatic camera adjustments (e.g., white balance) due to variable lighting conditions can make image standardization difficult, but can be addressed by changing camera settings or by directly utilizing uncompressed raw format images for which these metadata are included [36]. Supplementary techniques include using fixed color standards in the field of view and calculating RGB relative channel brightness [2], spectral angle calculations with K-means cluster analysis [37], and thresholding after transformation to hue, saturation, and luminance color space [38].…”

“…Camera jitter and slight shifts in directional views complicates pixel registration across images, but can be addressed with calculations based on invariant targets. Sun glint can lead to saturation, which has resulted in some webcam phenology studies restricting analysis to overcast days [37]. Automatic camera adjustments (e.g., white balance) due to variable lighting conditions can make image standardization difficult, but can be addressed by changing camera settings or by directly utilizing uncompressed raw format images for which these metadata are included [36].…”

Outdoor Webcams as Geospatial Sensor Networks: Challenges, Issues and Opportunities

“…These include quantifying the increases in forested extent for an alpine treeline ecotone (Roush et al 2007), monitoring deciduous autumnal color change (Astola et al 2008), calculating normalized snow indices from ground-based cameras (Hinkler et al 2002), and comparing the 'green-up' signal to canopy photosynthesis data (Richardson et al 2007). Digital cameras have been used for fog monitoring and visibility estimates (Baumer et al 2008;Hautiere et al 2006) and for tracking cloud diurnal and seasonal patterns (Bendix et al 2008).…”

“…In some applications, certain lighting conditions result in data loss, e.g. in the plant phenology study by Astola et al (2008) only images from overcast days were used because specular reflection on sunny days resulted in image sensor saturation. Insolation and atmospheric conditions can be further complications for cameras that automatically adjust exposure time for ad hoc lighting conditions.…”

Re-photography and Environment Monitoring Using a Social Sensor Network

“…The sensing technologies available to climatologists today are many; however the visual spectrum still has an important role to play in environmental observations. One good example is the monitoring of subtle vegetation colour changes over time which can signify variations in the onset of Autumn [1]. Furthermore, due to cloud cover and atmospheric attenuation, the planet's surface can rarely be visually observed with complete clarity from space.…”