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Outline to Use SLFMI Raster to Assess Live Fuel Moisture in Shrubs

1) Have GIS software installed on computer. You can download free QGIS (Version 3) from this website here.

2) Download compressed folders below. Each folder include one S2FMI raster, as well as the corresponding Sentinel-2 SLC (scene classification) and TCI (true color imager) layer. Before analyzing the SLFMI raster, make sure to check the SLC and TCI layers to get an idea of the clearness of the atmosphere (more about that in next step).

Sentinel-2 SLC Classification

(raster will be classified as grayscale when opening)

3) For accurate assessment of shrub pixels, analyze S2FMI pixels values for the same pixels over time along with high resolution background imagery (Google and Bing imagery can both be loaded for free as continuous background imagery in free QGIS). All raster have 20-meter resolution, which is too coarse to distinguish shrubs from other vegetation or from soil. S2FMI shrub values range from about 2.0 to 8.0 (depends largely on type and density of vegetation, as well as the amount and color of soil that may visible); pixel values much outside of the 2.0 to 8.0 range include other vegetation, shaded areas, or other surfaces. Avoid riparian vegetation and areas, as well as any type of tree or tree shadow. Also, Sentinel-2 pixels may have about half a pixel horizontal positioning error, so keep at least a one pixel boundary between pixel you use and non-shrub surfaces. Also, you need to make sure to avoid a recently burned area, the imagery may not be recent enough to show a recent burned area.

Pixels analyzed need to be in sunlight at the time of Sentinel-2 imaging all season long so change over time for the same pixels can be assessed. Use both the TCI and SLC layer to check for clouds (clouds are not visible enough by just viewing the TCI; also make sure a surface with a pixels extent is not in a cloud shadow). It is a good idea to avoid a surface within about 4 kilometers of a cloud, even if the SLC file shows the surface being clear. You can decipher shadows, for objects such as trees and clouds, by viewing the TCI and just using common sense.

In mountainous areas in particular (because of significantly changing and abrupt topography), sunlit surfaces are in large part a function of solar azimuth and solar elevation. It is more difficult than it would seem to distinguish sunlit vegetation in mountains from vegetation in shadow by solely viewing a satellite image. For areas in California, you can download a corresponding hillshade tile to help to help locate surfaces in sunlight (hillshade tile have been developed based on the average Sentinel-2 solar azimuth and the lowest Sentinel-2 solar elevation from the beginning of spring to the beginning of fall). To have an image specific hillshade value, download the corresponding elevation raster and create a hillshade in GIS with the image-specific solar azimuth and solar elevation (available on the Copernicus website). California elevation rasters can be downloaded from this website.

Below, is an example of 20-meter pixel boundaries (orange) that represent sunlit shrub areas from spring to fall with Google high resolution imagery. Try to use pixels with the least amount of soil you can, though it may not be possible to find imagery of shrubs in a particular areas without some soil visible.

4) A way to use the data in QGIS (and similarly in other GIS software) to analyze and compare S2FMI values, is to have the high resolution imagery on the top and checked in the Layers window (so it is the visible layer). If you then click the Identify Features tool, whatever layer that is shaded in the Layers window (click on layer name to shade it), will have the value appear in the Identify Results window. You can have many S2FMI rasters in the Layers window under the visible high resolution imagery, then click on the same shrub area but have different S2FMI layers shaded to compare fuel moisture change over time.

S2FMI pixel values are in their native floating point format ranging from about 2.0 to 8.0. These values can be correlated to FM values for a particular area, but not for the whole scene. This is so because vegetation with the same fuel moisture can have different reflectance (depending on vegetation type, density, as well as amount and color soil visible). For actual fuel moisture amounts to be mapped for a particular area, a predictive model would need to be made for a particular area, then a S2FMI values could be used to predict a fuel moisture value.

However, there is a way to estimate if fuel moisture has decreased to dangerous levels, or is within danger levels. To do this, determine dates that you know fuel moisture corresponds to elevated and extreme fire danger levels. Contact GIS Ag Maps and we will upload the Sentinel-2-based S2FMI raster to the website for you to download. For example, for Chamise Chaparral the elevated and extreme live fuel moisture levels can be interpreted as 100 and 77 percent, respectively (Dennison et al, 2008; PDF on this website).

Figure 2 (Dennison, et al. 2008)

In the United States, a date for a particular fuel moisture level can be determined or closely estimated from the following website: National Fuel Moisture Database (offsite page; open in new tab). These can be baseline values for later seasons to determine if fuel moisture levels have dropped to elevated or extreme danger. Simply compare raster values with the Identify Features tool, or subtract a baseline S2FMI raster from a S2FMI raster with the Raster Calculator, and if the new raster shows negative pixels values, values are lower than the baseline values and have decreases to elevated or extreme danger. An example of Elevated and Extreme Fire Danger rasters are included in Tile 18 downloads (keep in mind though that within a Sentinel-2 scene, FM values can vary; so an Elevated or Extreme Danger Level in one area of the imager may not be in another).

Reference

Dennison P.E., Moritz M.A., Taylor R.S.  2008.  Evaluating predictive models of critical live fuel moisture in the Santa Monica Mountains, California. International Journal of Wildland Fire 17, 18–27.