NOTE: I wrote the bulk of this post in July, but shelved it due to some pending layer changes.  With the western US currently on fire, it seemed like a good time to finally wrap it up, although some of the references are no longer timely.

Here in Truckee, smoke from the Detwiler fire – 100 miles away – drifted in two nights ago.  A good reminder that I’ve been meaning to do a post on wildland fire information, using both CalTopo and other tools.  Whether there’s a fire burning near your house or, like me, you’re just trying to dodge smoke in order to exercise, getting accurate information can be a frustrating experience if you don’t know where to look.

Not to pick too heavily on the mainstream media, but if I were trying to learn about the fire currently blowing smoke over the Sierra, the top Google News result is a recently published LA Times story:

The headline is a bit sensational – the exact quote in the article is that weather satellites spotted “explosive fire behavior” – but that’s the same as it ever was.  Despite the article’s many small but not terribly useful details, such as firefighters tackled 2- to 4-foot flames, the core information is wanting.  The fire perimeter map is over a day old, and their included GOES satellite image is indecipherable to anyone who doesn’t know what they’re looking at, myself included:

So, where I do go for information?

First stop is a set of “real time” (technically, several per day) CalTopo layers sourced from the two MODIS satellites (named Aqua and Terra) and VIIRS.  Current visible-spectrum imagery from the satellites is available through the standard layer dropdown, in both standard and relief-shaded versions.

Generally one of the satellite passes has better resolution and color quality than the others, and the current day’s imagery is often not available until late in the afternoon.  Here’s yesterday’s Terra pass over the Detwiler fire:

As a side note, even smaller fires and controlled burns often produce smoke that’s visible to the satellites.  Something to keep in mind the next time the news excitedly proclaims that a fire is now VISIBLE FROM SPACE, along with a MODIS capture like the one above.

The MODIS satellites also capture infrared imagery, which is processed and used for hot spot detection.  Generally hot spot data comes from a series of midday and overnight passes, capturing fire behavior at two points in the diurnal cycle.  These hotspot detections form the core of CalTopo’s current fire activity layer:

Each dot represents a hotspot detection.  Color indicates age – red is detections less than 12 hours old, orange is 12-24 hours old, and yellow is 24-48 hour sold.

MODIS data is stitched together from a number of satellite passes, with each pass covering a long and narrow swath of the planet.  The image resolution is nominally around 1 kilometer per pixel (ie each pixel in the MODIS image represents 1 square kilometer), however the resolution drops as you move towards the edge of the pass.   As a result, some hotspots have a larger error radius than others.  Zooming in on the map shows that the error radius of each hotspot as a semi-transparent circle.

Technically, the pixels are in a rectangular grid, and each hotspot detection comes with separate X and Y error values. Due to the complexities of properly mapping this grid, I average the X and Y values into a single, circular error radius:

In the above image, the older (orange) points have a much larger error radius than the newer (red) ones.  All we can know is that there is a hotspot somewhere inside each circle; the entirety of the orange circles in particular is probably not on fire.  It’s also worth noting that because the MODIS satellite is generally taking a semi oblique image, rather than looking straight down, terrain with heavy vertical relief can throw the estimated locations off – a single detection on the far side of a steep river canyon may be an erroneous interpolation and not actually indicate that the fire has crossed over.

Zooming in more shows not only the time of the detection, but the temperature, radiated power, and confidence that the detection is actually a fire.  While it can be fun to geek out on, I’m certainly not qualified to make any useful predictions based on that information.  If anyone reading this is, please chime in.

Infrared hotspot data is also available from VIIRS, and I’m looking at adding that along with other data sources such as GOES, so the accuracy of the fire activity layer may improve for next season.  Both MODIS and VIIRS hotspot detection data is available straight from the source at https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/active-fire-data.

The current fire activity layer also includes smoke information from the NOAA hazard mapping system (HMS), typically updated every several hours.

MODIS is a good rough indication of a fire’s activity, but it’s not perfect.  In the US, a fire’s incident management team keeps track of the actual fire perimeter using a combination of aircraft observations, including nighttime IR flights, and GPS tracks recorded by teams on the ground.  These fire perimeters are typically uploaded once a day to GeoMAC, and then pulled into the CalTopo fire activity layer as crosshatched yellow polygons:

Detwiler perimeter, a little hard to see through the smoke data also included in the fire activity layer.

CalTopo also sources historic fire perimeters from GeoMAC for the fire history layer.  Wildland firefighters might scoff at the notion of calling 15 years of perimeter data “historical”, since they use decades’ worth of data to analyze fire behavior in a given location, but I have yet to find a better nationwide source.

Although not guaranteed, the fuel load in recently burned areas will generally be lower than in areas that haven’t burned for a while.  Old fires may also be bounded by existing fuel breaks that can help stop the spread of a new fire.  Because of these factors, it can be useful to look at existing fire perimeters when trying to figure out how and where a fire might spread:

This part of the Shasta-Trinity has burned a bit

As a reminder, subscribers can pull these layers (along with all of CalTopo’s other layers) into Google Earth via super ovlerays for 3-D viewing:

If you want to step outside the CalTopo bubble, one of the first questions to ask is “who is responsible for fighting the fire?”  Once a fire gets large enough this is generally available from the news, but it’s also helpful to look at the CalTopo land management layer to figure out whether a fire is on federal or non-federal land.  This is a good first-order approximation, but keep in mind that fires on non-federal land could be the responsibility of either a state or local agency.  Agencies will also swap responsibility for some areas depending on who has nearby resources, so responsibility areas do not map 1:1 with land ownership.

Detwiler fire approaching, but not yet on, Forest Service land

For fires on federal land, my first stop is generally InciWeb, which has all kinds of good information:

InciWeb home page / incident list

Each incident page has a number of easy to miss tabs near the top.  The news articles (aka press releases) contain dry but substantive and useful information, and the maps tab contains links to the actual incident maps.  Generally these are updated daily as part of the incident action plan (IAP), and not only show the fire perimeter, but break it down into established fire line and uncontrolled fire edge.  Coming from a SAR background where incident data is often restricted due to law enforcement concerns, the ready availability of fire information is refreshing:

Southern edge of the Whittier fire

If InciWeb  doesn’t have the fire listed, or if you want to dig really deep into the details, my next stop is the national interagency fire center FTP site at http://ftp.nifc.gov/incident_specific_data/.  It takes a little digging (based on both location and jurisdiction) to find a specific fire, but the NIFC server generally has more detail than inciweb, and at least in California, details for state-managed fires.