In summary it appears that this method can provide a cost-effective and repeatable method for calculating emergent plant cover across very large assets, or many assets spread over a large area. Although the use of compressed imagery may mean that the classifications should not be used as absolute delineation of the plant extents, it is likely this method could be used to quickly estimate plant cover and observe trends in plant cover over time using a simple formula rather than ‘black-box’ machine learning classifications. Further optimizations could likely be achieved by different approaches of normalising the jpeg data, as well as further trial and error of the minimum NDVI value used to indicate that vegetation is present.
This data could be used to help inform where more detailed site based assessments may be required, and help answer questions like “where is wetland plant cover very high or very low?”, “where is plant cover in sediment basins too high?”, or “which sites are seeing the biggest gains or losses in vegetation over time?”. This method has some additional considerations which would also apply to other methods of remote sensing that include: the need to understand water levels and seasonality of plant species at the time of imaging and also checking if tree canopies or algal blooms are influencing the results. It can also not indicate where the vegetation cover is actually nuisance species rather than desirable ones, which is best done via ground truthing alongside this sort of analysis.
Next step: to capture some super high resolution multispectral imagery via drone survey, and compare those results…