Integration of multi-scale optical data for vegetation monitoring

Optical sensors are used for monitoring vegetation at different levels of the spatial and temporal dimensions. Adding the spectral or electromagnetic dimension, inherent to this type of data, increases the difficulty of comparing results from different measuring methods at different scales. An increasingly common example is how to compare data collected at one or more ground stations to data collected by global coverage satellites. Our solution to this challenge begins with an integrated approach to storing and querying optical data and key derived products. Uniformly dealing with aggregation and interpolation operations, for instance, allows for easy comparisons between different data sets. This, in turn, enables the assessment of the comparison methods themselves, helping delineate effective comparison strategies. Documentation through metadata helps identify data sources, acquisition methods and associated limitations. Metadata is also key in generating provenance information about data processing workflows that lead to derived products. Our approach aims at simplifying the initial steps of optical data analysis by providing common processing options to integrated data sets. Examples include helping compare measurements from broadband sensors at ground stations to multi-spectral sensors in satellites; or allowing direct comparison of ground hyper-spectral measurements within similar and changing scales; or even compare responses from ground stations at a variety of ecosystems according to different models. The solution is being tested with optical data for a variety of Canadian sites.