Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 154

improved, mapping expertise increased, and extensive spatial ecological data sets
became available, most of today's fuel-mapping efforts integrate these multiple
technologies to get the best possible fuel maps (Keane et al. 2001 ). Therefore, these
approaches should not be considered methods per se , but rather a set of general
strategies to map fuels.
Several analysis methods were not included as approaches in this chapter
because they are used across most of the four mapping approaches. The most
important and most commonly used analysis method is statistical modeling, where
advanced statistical techniques, such as multiple regression analysis, generalized
linear modeling, and regression trees, are used with field and spatial data to create
empirical models that are then employed to build fuels maps (Miller et al. 2003 ).
Another exciting branch of spatial analysis is the integration of expert knowledge
into numerical analysis to develop fuel maps (Keane and Reeves 2011); the vast
knowledge and expertise of fire professionals can be used to develop and test fuel
maps using a wide variety of computing technology, such as expert systems, neural
networks, and artificial intelligence (Krivtsov et al. 2009 ).

9.3.1 Field Assessment

Field assessments involve traversing a landscape on the ground and recording fuel
conditions using data recorders, notebooks, or paper maps (Arroyo et al. 2008 ).
Conditions in the field are assessed using a diversity of methods that include actual
sampling of the fuel (Chap. 8), recording a category in a fuel classification cat-
egory (Chap. 7), or describing the fuel type using vegetation, disturbance, and site
characteristics. The observed conditions are then assigned to polygons on a photo
or map. Few fuel maps were created using this approach, and of those that were,
they were mostly for fine-scale, small-area projects. The exception was Hornby
( 1936 ), who remarkably mapped more than 6 million ha in the northern Rocky
Mountains using more than 90 Civilian Conservation Corps (CCC) workers. These
crews walked, rode, or drove through national forests in Montana and Idaho of the
USA and described fuel conditions by coloring polygons on maps. But, instead of
actually recording fuels loadings, the CCC crews mapped two categorical fire be-
havior descriptors that were inferred from the fuel conditions: resistance to control
and rate of fire spread. The fuel classification used by Hornby ( 1936 ) was only
useful for one fire management purpose, suppressing wildfires. Many employed the
Hornby ( 1935 ) methods to other parts of the country (Abell 1937 ; Banks and Frayer
1966 ; New Jersey Department of Conservation and Development 1942 ) (Chap. 1).
The primary advantage of the field survey strategy is that fuels are mapped from
actual conditions observed on the ground (Table 9.1 ). Mapping error is limited to
erroneous fuel-type assessments or improper stand delineations on paper maps and
no error is introduced from inappropriate statistical modeling or data analysis. Fuel
assignments can be subjectively adjusted based on the observers' knowledge of the
fuel complex, of how fire burns the fuel complex, and of how fire behavior models