We are seeing tree mortality increase in western U.S. forests and die-off events around the world caused by serve or interacting disturbances like logging, drought, wildfire and pine beetle infestation. Limiting our knowledge of how forests respond is a lack of data on functional vegetation states at the tree or stand level over long periods of time and broad regions. Moderate resolution satellite imagery can provide changes in percent forest cover but cannot resolve vegetation state changes (e.g. from conifer to deciduous forest). The high resolution of Planet’s Dove imaging technologies may provide an opportunity to capture response at fine scales. We aim to integrate Planet’s constellation of satellites with Landsat imagery to create a multi-scale network for forest monitoring. However, the uncalibrated nature of these systems and the variability of sensorcharacteristics across the constellation make this problematic. We conducted a limited investigation of radiometric and thematic data methods for linking vegetation properties across spatial scales from 3 to 30 meters. The greatest challenge arises from the variation in Dove sensor radiometric response (roughly +/- 10%) across the constellation and optical cross talk associated with their broad, overlapping Bayer filter response. Applying a spectral band adjustment factor to improve radiometric correlation requires knowledge of the actual spectral response of the sensors which is not readily available. Using a K-means clustering algorithm to bridge scales and minimize sensor differences had mixed results for low reflectance scene components – perhaps again the result of cross-talk between Dove sensor spectral bands.