Example of Fuel Moisture Content Mapping & Background Information
Los Angeles, CA, Landsat (7 or 8) NDWI-Based Shrub Fuel Moisture Content (FMC) Percent Mapping
See Legend Below Map for Classification Information. Landsat 30 x 30 meters pixels (amount listed above maps) were converted to a 450 x 450 meters with a block average to increase visibility on maps. The amount of pixels vary, in large part, due to changing solar elevation, but also for other reason explained below. Maps can be developed in different areas with different vegetation.
Landsat 8; 10/15/2017; Average FMC = 74.54 (278,893 pixels)
(Real-Time [RT] Tier Image)
Legend; FMC
Gray areas are major recent burns and were not included (Sand Fire to west and San Gabriel Complex Fire to east).
FMC Amount & Corresponding Fire Danger (see Classification Background Section below) - Extreme: < 77; High: 77 - 100; Moderate: 100 – 125; Low: > 125. (Dennison et al. [2008] research, included below, shows that 48 of 59 fires occurred below an interpolated live fuel moisture value of 95%.) North is ↑. For scale, map is about 98 miles east-west and 48 miles north-south. Landsat 30 x 30 meter pixels were converted to 450 x 450 meters on map for greater visibility with a block average function in GIS (the value of a 450 x 450 meter pixel is the average of the original 30 x 30 meter pixel centroids that falls within the block extent).
Landsat 8; 6/25/2017; Average FMC = 86.30 (all values in the 77-100 range; 699,075 pixels)
(Real-Time [RT] Tier Image)
Legend; FMC
Gray areas are major recent burns and were not included (Sand Fire to west and San Gabriel Complex Fire to east).
FMC Amount & Corresponding Fire Danger (see Classification Background Section below) - Extreme: < 77; High: 77 - 100; Moderate: 100 – 125; Low: > 125. (Dennison et al. [2008] research, included below, shows that 48 of 59 fires occurred below an interpolated live fuel moisture value of 95%.)
Landsat 8; 6/9/2017; Average FMC = 99.96 (682,734 pixels)
Legend; FMC
Gray areas are major recent burns and were not included (Sand Fire to west and San Gabriel Complex Fire to east).
FMC Amount & Corresponding Fire Danger (see Classification Background Section below) - Extreme: < 77; High: 77 - 100; Moderate: 100 – 125; Low: > 125. (Dennison et al. [2008] research, included below, shows that 48 of 59 fires occurred below an interpolated live fuel moisture value of 95%.)
Landsat 8; 4/22/2017; Average FMC = 122.38; (561,260 pixels)
Legend; FMC
Gray areas are major recent burns and were not included (Sand Fire to west and San Gabriel Complex Fire to east).
FMC Amount & Corresponding Fire Danger (see Classification Background Section below) - Extreme: < 77; High: 77 - 100; Moderate: 100 – 125; Low: > 125. (Dennison et al. [2008] research, included below, shows that 48 of 59 fires occurred below an interpolated live fuel moisture value of 95%.)
Landsat 7; 1/8/2017; Average FMC = 86.10; (based on 58,350 pixels)
Legend; FMC
Gray areas are major recent burns and were not included (Sand Fire to west and San Gabriel Complex Fire to east).
FMC Amount & Corresponding Fire Danger (see Classification Background Section below) - Extreme: < 77; High: 77 - 100; Moderate: 100 – 125; Low: > 125. (Dennison et al. [2008] research, included below, shows that 48 of 59 fires occurred below an interpolated live fuel moisture value of 95%.)
Landsat 8; 11/29/16; Average FMC = 69.42; (96,459 pixels)
(Most Pixels are < FMC 77 percent [Extreme Fire Danger, especially when combined with hot and dry weather conditions])
Legend; FMC
Gray areas are major recent burns and were not included (Sand Fire to west and San Gabriel Complex Fire to east).
FMC Amount & Corresponding Fire Danger (see Classification Background Section below) - Extreme: < 77; High: 77 - 100; Moderate: 100 – 125; Low: > 125. (Dennison et al. [2008] research, included below, shows that 48 of 59 fires occurred below an interpolated live fuel moisture value of 95%.)
Landsat 8; 11/13/16; Average FMC = 63.01; (147,929 pixels)
(Most Pixels are < FMC 77 percent [Extreme Fire Danger, especially when combined with hot and dry weather conditions])
Legend; FMC
Gray areas are major recent burns and were not included (Sand Fire to west and San Gabriel Complex Fire to east).
FMC Amount & Corresponding Fire Danger (see Classification Background Section below) - Extreme: < 77; High: 77 - 100; Moderate: 100 – 125; Low: > 125. (Dennison et al. [2008] research, included below, shows that 48 of 59 fires occurred below an interpolated live fuel moisture value of 95%.)
4/3/16 Landsat 8 NDWI-Based Fuel Moisture Content (FMC) Map
(Average Landsat 8 NDWI FMC = 119.79; 464,365 pixels)
Legend; FMC
Data Background
Values are based on Landsat-derived Normalized Difference Water Index (NDWI) ([NIR - SWIR] / [NIR + SWIR]; [Band 5 - Band 6] / [Band 5 + Band 6]). Landsat 7 or 8 imagery was downloaded from GLOVIS and converted to surface reflectance with the Landsat 8 GIS Ag Maps DOS SR method (based on Chavez [1996 and 1988] using NIR2 scatter as is explained in the Tutorial (can be accessed at the top of the page). For Landsat 7, LEDAPS surface reflectance is used. Landat 7 and 8 have different enough sensors, that different equations to convert NDWI values to FMC needed to be developed in order that values from the different satellite sensors would fit together well enough.
NDWI has been correlated to live shrub moisture (LFM) in Southern California and resulted in meaningfully high correlation levels (Dennison et al., 2005) and LFM and been shown to be highly correlated to wildfires within the extent for the mapping here (Dennison et al., 2008). (NDWI is described in detail on this website.) Dennison et al. applied MODIS imagery, which has a SWIR bandwidth on a different spectral signature plateau than Landsat. Chuvieco et al. (2002) correlated Landsat bands to shrubland LFM in Spain and found that Landsat SWIR was the most sensitive to changes in moisture content.
Landcover data was downloaded from the National Map Viewer and spatial data representing shrubland was used (Landcover Code 52). Elevation data with a similar spatial extent was also downloaded from the National Map Viewer and was used in GIS to extract shrub pixels that were in very direct sunlight - sunlight characteristics on the same mountainous surface change over the season as the the solar elevation and azimuth change. The sunlit shrubland pixels were used to extract corresponding Landsat 8 NDWI values. The NDWI values for numerous scenes were associated with US Forest Service FMC (percent water weight) values from many sites (hand measured) throughout the extent (publicly available by the United States Forest Service Wildland Fire Assessment System [USFS-WFAS] at: http://www.wfas.net/index.php/national-fuel-moisture-database-moisture-drought-103) and a linear regression equation was developed to correlate with NDWI values and predict FMC. Pixels representing clouds, shadows, burn areas, live fire and smoke if applicable, are removed. A series of geostatistical processing steps is applied to the NDWI/FMC values to remove heavy noise and erroneous values inherent in the data. Landsat pixels are modified to 450 x 450 meter resolution with a block average for greater visibility on the map (with this step, the value of a 450 x 450 meter pixel is the average of the original 30 x 30 meter pixel centroids that falls within the block extent; there is a varying amount of pixel centers that fall within the 450 x 450 extent).
Classification Background
Classification is based on Dennison et al. (2008) research between live fuel moisture (LFM) and wildfire in Santa Monica Mountains (area in extent above) shrubland. LFM = ([Fresh Weight - Dry Weight]/Dry Weight) x 100. Map classification is based on graphs below, and is as follows (LFM and fire danger): 0 to 77, Extreme; 77 to 100, High; 100 to 125, Moderate; > 125 , Low.
![Live Fuel Moisture versus Area Burned plot (Dennison et al. [2008])](http://www.gisagmaps.com/storage/classification_graphic.jpg?__SQUARESPACE_CACHEVERSION=1468851218902)
Reference
Chavez, P.S., Jr. 1996. Image-based atmospheric corrections–revisited and improved. Photogrammetric Engineering and Remote Sensing 62(9), pp.1025-1036.
Chavez, P.S., Jr. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24: pp.459-479.
Chuvieco E, Riano D, Aguado I, Cocero D. 2002. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment. International Journal of Remote Sensing 23, 2145–2162.
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.
Dennison PE, Roberts D.A., Peterson S.H., Rechel J. 2005. Use of Normalized Water Index for monitoring live fuel moisture. International Journal of Remote Sensing 26, 1035–1042.