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Processing and applying multisensor CubeSat data to map forest fire timing and patterns

University of British Columbia

Distributed systems of small satellites, called CubeSats, are generating a new type of remote sensing data: multispectral imagery with high spatial resolution and near-daily global coverage. Recent studies have shown that this data is valuable for monitoring land cover change. However, in order to achieve widespread application for this purpose, all satellites in the distributed system, or constellation, must acquire imagery with accurate geolocation and consistent radiometric properties. I developed a preprocessing method to automatically co-register and radiometrically normalize a temporally dense series of images using reference imagery. To demonstrate the effectiveness of this approach, I normalized a time series of CubeSat images at both the pixel and the polygon level. Images from the PlanetScope (PS) constellation were used, focusing on a forested area in British Columbia, Canada that was heavily affected by forest fires in 2017. By examining the normalized difference vegetation index (NDVI) before and after the fires, I found that this method allowed simple identification of burned and unburned areas, which was not readily possible without applying the normalization method. After establishing the validity of the preprocessing method, I developed a multi-temporal change detection method which integrates this technique. This methodological approach is resistant to cross-sensor radiometric inconsistencies. I illustrate the effectiveness of the approach using imagery from the PS constellation and the Harmonized Landsat Sentinel-2 (HLS) virtual constellation. The approach is two-fold; first, a bitemporal method is applied exclusively to PS data, and then a multi-temporal method is applied to radiometrically normalized PS and HLS data. I apply this method to generate a forest fire disturbance map at 3.0 m resolution with a sub-weekly time step. A comparison with a more conventional disturbance map from Landsat difference normalized burn ratio shows improved capture of fine-scale spatial heterogeneity in the burn patterns. This method allows for integrated radiometric normalization with high-resolution change mapping at a sub-weekly time step using CubeSat imagery, suitable for fine-scale land cover analysis. These approaches can help fully exploit remote sensing datasets that have high spatiotemporal resolution but contain radiometric inconsistencies in order to quickly identify land cover changes.

Text

2020-03-02

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/73647

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/