pros is used to calculate the rate of spread and direction given one set of three point-based observations of fire arrival time. The function requires that the user specify the time that the fire crossed each point, along with the latitude and longitude of each observational point. This function allows quick input of a dataframe specifying one or many triangles.
A dataframe containing input variables of Time fire front crossed points 1, 2, 3, and latitude/longitude for those same points. Variable names have to be the same as in the following list, but they are case insensitive. The order in which the input variables are entered is not important.
pros allows R users to calculate the rate of spread and direction of a fire across a triangle, given three time measurements and details about the orientation and distance between observational points. The algorithm is based on the description from Simard et al. (1984).
Rate of spread and direction of spread are primary variables of interest when observing wildfire growth over time. Observations might be recorded during normal fire management operations (e.g., by a Fire Behaviour Analyst), during prescribed fire treatments, and during experimental research burns. Rate of spread is especially important for estimating Byram's fireline intensity, fireline intensity = heat constant of fuel × weight of fuel consumed × forward rate of spread (Byram 1959).
Rate of spread is difficult to measure and highly variable in the field. Many techniques were proposed over the years, but most were based on observations collected from a pre-placed reference grid and stopwatch (Curry and Fons 1938; Simard et al. 1982). Early approaches required that observers be in visual contact with the reference grid, but later, thermocouples and dataloggers were employed to measure the onset of the heat pulse at each point.
Simard et al. (1982) proposed calculations for spread based on an equilateral triangle layout. Simard et al. (1984) proposed calculations for spread based on any type of triangle. Both articles also discussed field sampling design and layout, with special attention to the size of the triangles (large enough that the fire traverses the triangle in one to two minutes) and even using triangles of varying size within one field plot (but no triangle larger than one fourth of the site's total area).
The underlying algorithms use trigonometry to solve for rate of spread and direction of spread. One important assumption is that the spread rate and direction is uniform across one triangular plot, and that the fire front is spreading as a straight line; Simard et al. (1982, 1984) acknowledge that these assumption are likely broken to some degree during fire spread events.
The functions require the user to arrange the input dataframe so that each triangle of interest is identified based on a new row in the dataframe. The input format forces the user to identify the triangles, one triangle per row of input dataframe. Very complex arrangements of field plot layouts are possible, and the current version of these functions do not attempt to determine each triangle of interest automatically.
pros returns a dataframe which includes the rate of spread and spread direction. Output units depend on the user’s inputs for distance (typically meters) and time (seconds or minutes).
Tom Schiks, Xianli Wang, Alan Cantin
1. Simard, A.J., Eenigenburg, J.E., Adams, K.B., Nissen, R.L., Deacon, and Deacon, A.G. 1984. A general procedure for sampling and analyzing wildland fire spread.
2. Byram, G.M. 1959. Combustion of forest fuels. In: Davis, K.P. Forest Fire Control and Use. McGraw-Hill, New York.
3. Curry, J.R., and Fons, W.L. 1938. Rate of spread of surface fires in the Ponderosa Pine Type of California. Journal of Agricultural Research 57(4): 239-267.
4. Simard, A.J., Deacon, A.G., and Adams, K.B. 1982. Nondirectional sampling wildland fire spread. Fire Technology: 221-228.
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library(cffdrs) # manual single entry pros.in1 <- data.frame(t(c(2, -79.701027, 43.808872, 50, -79.699650, 43.808833 , 120, -79.700387, 43.809816))) colnames(pros.in1)<-c("T1", "LONG1", "LAT1", "T2", "LONG2", "LAT2", "T3", "LONG3", "LAT3") pros.out1 <- pros(pros.in1) # multiple entries using a dataframe # load the test dataframe for pros data("test_pros") pros(test_pros)
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