General Pyromorphology: The Spatiotemporal Dimensions of Fire at the Scale of Planet, Landscape and Species

4.1.3 Igne natura renovatur integra [through fire, nature is reborn][31]

“Fire is an expression of life on Earth and an index of life’s history.” Glikson and Groves, 2016.

Fusain deposits indelibly mark wildfires within the paleolandscape, and though much fragmented, nonetheless collectively constitute a record that is sufficiently rich as to provide a general understanding of the coevolution of fire and biota. For example, fossil data reveals that during the dry seasons of the Devonian [395 mya], progymnosperm forests persisted in conditions not unlike those of some Northern Hemisphere coniferous forests today (Cressler, 2001; Scheckler, 2001; Algeo et al, 2001; Pausas and Keeley, 2009). Then, as now, some forests experienced frequent, but relatively low intensity surface fires [32], which burned through dry shrubs and plant litter in the understory (Rimmer et al, 2015). Conversely, during the Carboniferous (345 mya), wetland forests dominated by the now extinct tree species Lepidodendron [33], together with relatively diverse flora including tree ferns [Fig. 14], coexisted with much longer fire intervals of between 105 and 1585 years (Falcon- Lang, 2000; Pausas and Keeley, 2009). However, in contrast to the surface fires of the progymnosperm forests of some 50 million years earlier, the Lepidodendron forests of the Carboniferous were subject to intense fires that would have engulfed large swathes of trees from ground to crown. As with the low intensity fires of the former, the high intensity fires of the latter evidence a symbiosis of biota and fire that, despite many millions of years of evolution, remains to the present day.

Whereas, historically, paleoecologists believed that plant evolution was tied only to climatological and alluvial shifts, a now sizeable body of data suggests that fire plays a pivotal role (He and Lamont, 2017; Pausas and Keeley, 2009). The intensity, frequency and behaviour of wildfires in and across landscapes vary greatly. ‘Fire regime’ is a term used to describe the relationship between a given ecosystem and variables including temporal nature [seasonality, frequency, and speed of spread], fuel type [ground, surface, and crown], spatial pattern [size and composition], and the scale and the nature of impact of the fire (Platt et al, 2015; Bowman et al, 2009; Heyerdahl, 1997; Agee, 1993, 1998; Heinselman, 1981). The primary variables used to classify fire regimes are fire frequency and severity (Heinselman 1973, 1978, 1981; Morgan et al, 1996, 2001; Hardy et al, 1998a, 1998b, 2000; Brown and Smith, 2000). More generally, an ecosystem’s affiliated fire regime is defined by the estimated frequency the data suggests fire usually returns to a given site (Jensen and McPherson, 2008), which can be expressed as its ‘mean fire-return interval’ [MFRI] (LANDFIRE, n.d). MFRI is not the only method employed to express the temporal- spatial relationship between fire and landscape. ‘Fire rotation period’ (Heinselman, 1973) is another commonly used term, as is ‘fire cycle’. But, whereas MFRI expresses spatial patterns within the landscape, fire rotation period and fire cycle represent the duration it would take for an equivocally sized area to burn (Morgan et al, 2001; Heinselman, 1973; Agee, 1993). For the purposes of this thesis the MFRI is the more relevant of the methods, because, as will become apparent in the case studies, fire spreads not uniformly within and across the landscape, but instead forms distinct broad-scale spatial patterns comprised burned and unburned patches of biota. The term ‘mosaic’ is used to describe this heterogeneous feature born of the behaviour of wildfires in landscapes. (Malamud et al, 2004; Turner and Romme, 1994).

“A fire is to a regime as a storm is to a climate” (Pyne, 2012, p.17), and an ecosystem is as tightly coupled to its fire regime, as it is to seasonal weather patterns. In turn, the many species of which an ecosystem is comprised are not adapted to fire in a generic sense, but, as with other environmental factors, including precipitation, humidity, solar and electromagnetic radiation, are adapted to one of several fire regimes, and to their affiliated mean fire return intervals (Swetnam and Baisin, 1996, Swetnam, 1993), suggesting that fire plays an integral role in selection for evolutionary fitness (He and Lamont, 2017; Hunter, 1990). The spatiotemporal dimensions of seasonality are governed by the configuration of continents, oceans, and atmosphere. Global seasonal variability in precipitation has been evident since the Carboniferous, becoming a primary determinant of fire regimes in the Paleogene, 25 mya (He and Lamont). Regional precipitation patterns dictate the possible sum and the general state of the biomass of an ecosystem. In the western United States this is evidenced by the impact of the jet stream, and more specifically, by the positioning of its northern and southern branches, each of which manifest very different weather conditions (Dettinger et al, 1998). The configuration of the wet/dry periods the jet stream [Fig. 15] creates is indicative of when, where and why wildfires will likely occur (Weisberg and Swanson, 2003; Grissino-Mayer and Swetnam, 2000; Morgan et al, 2001; Heyerdahl, 1997; Turner et al, 1994). Whereupon precipitation and/or humidity exceeds a certain level fire extent will be zero (Schroeder and Buck, 1970), because fire cannot burn whereupon fuel moisture surmounts the moisture of extinction [34], which averages somewhere between 12 – 40% of dry weight (Heyerdahl, 1997; Albini, 1976). Wind speed and direction plays a pivotal role in fire behaviour. High and low pressure systems are essentially a balancing act that develops due to temperature differentials in the atmosphere. High- pressure systems [anticyclones] create higher daytime temperatures, but lower wind speeds, and generally more settled conditions, and low-pressure systems [cyclones] vice versa. In both instances, wind speeds reflect the pressure gradient and the centrifugal forces [35] within the system. In the Northern hemisphere high-pressure systems flow clockwise, low-pressure systems flow counter-clockwise, and both their winds flow roughly parallel to the isobars (Met Office, 2013). Historically, the jet stream’s behaviour has manifested weather patterns that were particularly conducive to fires in the spring in the southwestern United States (Jensen and McPherson, 2008; Agee, 1998; Swetnam and Betancourt, 1990), and in mid-to-late summer in the Pacific Northwest (Agee, 1998; Wright, 1996). Regionally, cold fronts [36] are preceded by southeasterly to southwesterly winds, and are followed by westerly to northwesterly winds that bring cooler air flowing at a rate of 10-20 mph (Schneider and Breedlove, n.d). Whereupon atmospheric convection and moisture levels are sufficiently high a thunderstorm will ensue, and with it the sparks that may ignite the biomass below. In the event that a wildfire does start, its duration and direction may be influenced by Foehn winds, which known by local names, such as the Santa Anna and Mono in California, result from stable, high pressure air rising across and down the lee slopes of mountain ranges (Ibidem). More generally, unstable air manifests hotter wildfires that burn with greater intensity, and that are more likely to create blow-ups, which are sudden increases in both the rate of spread and intensity of the fire. In contract, stable air, and in particular the thermal belt that often rests along the mid-portion of mountainous slopes, can provide just the right combination of temperature invariance and low humidity to enable wildfires to actively burn through the night (Ibidem) [Fig. 16].

Solar radiation is a further systemic variable that is highly indicative of both wildfire probability and behaviour upon ignition. The spatiotemporal distribution of the Sun’s radiant energy is highly varied and dependent upon factors including solar irradiance [37] [i.e. the Sun’s rotation] (NASA, 2015); the gaseous and particulate composition of the atmosphere [i.e. aerosol levels] (NASA, 2017; Winckler, 2010), weather conditions [i.e. cloud cover] (Matusuzko, 2011), and landscape composition and type. Different species absorb solar radiation at different rates and in the case of plants the sum thereof is proportionate to the rate at which a species converts the radiation through the process of photosynthesis. In turn, ecosystems, and the biotic assemblages therein, greatly vary in their affiliated radiation absorbance/reflectance levels, as in turn do the microclimates that result therefrom (Chen et al, 1999). Biomass in direct sunlight can be as much as 10°C warmer than that which is in dense shade, which results in the former being easier to ignite than the latter (Schneider and Breedlove, n.d). In and of itself, at most, solar radiation can heat a surface to just over 71°C, which in the case of woody fuels is just over half of the minimum [204 - 371°C] required to create the combustion thereof (Ibid). Furthermore, studies evidence that, in some plant species, transpiration [38] is coupled with air temperature, wherein the higher the latter the greater the extent of the former (Crawford et al, 2012). Consequently, solar radiation simultaneously impacts upon surface air temperature, fuel moisture, and relative humidity [39], which collectively shape the combustibility of an environment. Thus, the “rhythms” of fire regimes (Pyne, 2012, p.25), and in more ways than one, a wildfire’s front [40] “links the atmosphere, biosphere, and hydrosphere via the release of heat, gases [notably water vapour], and matter” (Bowman et al, 2009, p.484), together with the pedosphere [41], therein might be conceived as ‘the point of convergence’ [42] of the Earth’s systems [Fig. 17].

4.1.4 Fire as Autopoietic Process: Feedback and Fluctuation in Fire Frameworks

“Our models attribute all morphogenesis to a conflict, to a battle between two or several attractions” Thom, 1972.

Whereupon viewing fire regimes through the lens of complexity, collectively, the distinct variants become the schemata, that being the term applied when describing the “patterns of behaviour and conceptual frameworks” within a complex adaptive system of which immediate behavioural changes are driven by feedback (Levin, 1999). Multiple studies have explored biomes as non-equilibrium systems (Archibald et al, 2013; Agee, 1998; Turner and Romme, 1994; Baker, 1989), some observing that within landscapes “temporal scale-free behaviour is evident in power spectra of fluctuations that obey power laws” (Bolliger et al, 2003, p.541), and this extends not merely to fire regimes (Green et al, 1990; Malamud et al, 1998; Song et al, 2001) but to evolutionary ecology (Bak and Sneppen, 1993; Sole et 1999), and, within the abiotic realm, to earthquakes (Gutenberg and Richter, 1954) and avalanches in sand piles (Bak et al, 1987, 1994, 1996), amongst other natural phenomena. Power-laws are evidenced in several studies of historical data sets of the spatiotemporal patterns of fire-prone landscapes (Malamud et al, 2005; Mortiz et al 2004); indicating common mechanisms are at play within these ecosystems. For example, upon analysis of a high-resolution data set of 88,916 wildfires in the conterminous United States in the period 1970-2000, a team led by Bruce Malamud of the Environmental Monitoring and Modelling Research Group at King’s College London found “robust” frequency- area power-law behaviour in 18 ecologically distinct regions (Malamud et al, 2005, p.4694). Their study also identified that “the ratio of the number of large to small wildfires decreases from east to west” (Ibidem, p.4697). Previous studies have suggested that the degree of connectivity between the biotic and abiotic units [i.e. density and configuration of biomass] is a primary factor in defining spatiotemporal patterns, with pre-existing conditions influencing the legacies left by fire disturbance (Bolliger et al, 2003). However, while correlating with the spatiotemporal pattern as would be expected given the regional climatic variations, Malamud et al acknowledged that, both spatially and temporally, so many are the variables within the ecoregions studied that multiple feedback mechanisms are likely impacting on wildfire behaviour.

Feedback mechanisms within the schemata of fire regimes vary in both speed and strength. The term slow variable describes that which, relative to the fast variable, usually shapes the system parameters considerably more slowly (Walker et al, 2012; Gunderson et al, 2002, 2010; Ludwig et al, 1978). During the Devonian and the Carboniferous, the climate and atmospheric composition were slow variables at play in fire regime schemata, whereas the weather was a fast variable. For example, during the Devonian Earth’s biomass expanded 800-fold (Borzenkova and Turchinovich, 2009) which set in motion a steep decline in atmospheric CO2 (Vecoli et al, 2010), coupled with a sharp rise in atmospheric O2. Consequently, the atmospheric conditions were comparatively favourable for fire to those of the early Triassic [252 mya], the latter a period in which atmospheric O2 fell as low as 12%. However, while the fossil record evidences a general correlation between atmospheric O2 levels and wildfire activity, fusain deposits from the Triassic period speak to not one, but several variables at play within the wildfire schemata [Fig. 18] (Abdullah et al, 2012; Uhl et al, 2008; Berner et al. 2003). Whereupon contemplating what other variables may have contributed to the sharp decline in wildfire in the period, the Permian-Triassic extinction [P-Tr] event [c. 252 mya] may provide some answers. The P-Tr, which is colloquially referred to as the ‘Great Dying’, resulted in a dramatic drop in both species populations, and the distribution thereof (Shen et al, 2011; Benton, 2005). While the fossil records suggest that flora weathered the P-Tr relatively well, with some 50% of plant families surviving (McElwain and Punyasena, 2007), given the immensity of the faunal species that went extinct, we might reasonably assume that the P-Tr significantly impacted upon the functioning of early Triassic forest systems. For example, studies of diverse living ecosystems strongly support the statement that faunal assemblages play a central role in shaping plant communities, including both population and distribution of species [43] (Ripple, 2014; Cromsigt and te Beest, 2014; Haynes, 2012; Wallace, 2004; Levin, 1999; Folke, Holling and Perrings, 1996).

Applying the logic of Occam’s Razor [44], a reduction in faunal diversity may equate to a reduction in floral diversity, therein the available fuel source [hydrocarbons], which in turn led to a decline in the number, and possibly the size, of wildfires. Fossil records evidence that as species populations declined at the end of the P-Tr, soil erosion was rampant (Sephton et al, 2005), as was fungal virulence (Visscher et al, 2011), both of which are linked to deforestation. The former, soil erosion, is suggestive of fires so intense as to burn sufficient vegetation as could cause soil to lose its structural integrity [i.e. loss of root systems], therein easily displaced by heavy precipitation. The latter, fungal virulence, suggests that forests were comprised of sizeable quantities of dead and decaying trees [i.e. tinder-dry fuel] most likely incurred by the rapid shift from a wet to arid climate, for which some species were not adapted. Therein, the above hypothesis works within the wider framework of our evolving understanding of the relationship between biota and fire.

Historically, landscape topography is a generally slow variable within fire regimes. Studies evidence that the location, size, elevation, aspect [exposure to solar radiation], and form of mountains, slopes, and water bodies have a bearing on the spatiotemporal dimensions of wildfires (Wallace et al, 2004; Heyerdahl, 1997), impacting on fire’s rate of spread, direction, and intensity (Schneider and Breedlove, n.d). Whereas landscape gradient influences the speed, and in some instances, the direction of a wildfire front, lakes and wide-rivers act as boundaries that can either slow or stop a wildfire front from progressing in a particular direction. A wildfire’s rate of spread increases on uphill gradients, which is due to the upward motion of the flame front in combination with the processes of convective and radiant heat transfer preheating the fuels above (Ibidem), thus priming them for combustion. The rate of spread is relative to the gradient: “the steeper the slope, the faster the fire burns” (Ibidem, p.8). A wildfire’s rate of spread will generally slow on a downhill gradient, however, falling and rolling burning debris will often ignite one, if not several, secondary fires below the main fire (Ibidem). Landscape configurations known to compound a fire’s rate of spread and intensity include: Box Canyons, that in similar fashion to a tower block juxtapose steep walls against a level base, of which the thermodynamical consequence is ‘the chimney effect’ [strong updrafts], therein a rapid rate of spread, this being a behaviour also affiliated to other topographic features with steep walls, including both narrow and wide canyons (Ibidem).

While topographic features are, ultimately, impermanent within the landscape, most often they remain in situ for lengthy periods of time, thus usually constitute a slow - sometimes extremely so - variable. For example, tectonic reorganisation within the landscape has been attributed as one of the causations of a wide-scale reduction in the number of coal-forming forests during the Permian [45] [299-251 mya], which is cited as a possible driver in the decline of wildfires in the period (Uhl et al, 2008). However, whereupon reducing the temporal scale from many millions of years down to decades, we find topographic shifts reducing from the global to the local in scale. In living forests causations for topographic shifts include geological failures, such as landslides and earthquakes, volcanic eruptions and the secondary impacts thereof, such as lahars and debris flows, and anthropogenic activities, including mining and road building. Thus, in some instances, landscape topography can become a fast, not a slow variable in the fire regime schemata, and particularly when a natural hazard has recently occurred.

Comparative to the weather, a fire regime’s biota is a slow variable, in so far as its mass [i.e. fuel load] needs to have reached a critical threshold to sustain a fire within a landscape, this being a process that at the minimum takes several months or more (Heyerdahl, 1997). However, whereupon one changes the temporal scale from months, years, decades, or centuries to millennia, the biota becomes a comparatively fast variable compared to the climate and atmospheric composition. Furthermore, as will become evident in the case studies to follow, feedback between the variables of fire regimes, and in particular between wildfire and biota, creates not only spatial complexity, but also temporal memory effects (Malamud, 2005). A great many species indigenous to fire-prone regions exhibit lifecycles that are synchronous to fire regimes [Fig. 19], of which examples will be given below. Indeed, some species even exhibit mechanisms that propagate fire by means of creating conditions that are conducive to their successful reproduction, thus shape pre and post-fire conditions in more or less equal measure. The interplay between the biotic and the abiotic is sufficiently intimate as to constitute a synecological unit akin to the many that have been observed both in and between floral and faunal species groups, such as angiosperms [flowering plants] and pollinators. Therein, one might plausibly argue that environmental management, and no less so than in the case of fire regimes, is not the exclusive domain of humanity; biota exhibit many, varied, and often subtle ways of influencing the environment, be it through reproductive processes, or other means, including, but not limited to allelopathy [46], epiphytism [47], and nitrogen [48] and carbon fixation [49]. However, whereas some, but not all interspecies relationships are to the benefit of one, but not the other [i.e. parasitism], the relationship between fire-adapted species and their affiliated fire regimes are not merely symbiotic, but mutualistic [50]; an interdependent symbiosis. Thus, one might argue that fire ecology extends the concept of autopoiesis [51] (Maturana and Varela, 1973) beyond the realm of the living, to encompass both the life and the physical sciences: the natural sciences. But, the spectrum of the parameters of the autopoietic processes within the synecological unit that the biotic and abiotic components of fire regimes collectively comprise are yet to be firmly established (Bolliger et al, 2003; Malamud, 2005).

4.1.5 Herding Biochemical Cats: Quantifying the Qualitative Properties of Fire

“If flame were hiding in forests ready-made, Not for one moment could the fires be hid, But everywhere they’d burn the woods, turn trees to ashes” Lucretius, circa. 56 BC.

Wildfire is inherently heterogeneous in character (Lertzman et al, 1998), and with the possible exception of volcanic eruptions, perhaps the foremost so within the category of geological and meteorological hazards. Whereas some categories of hazardous natural phenomenon are essentially mechanical in nature (i.e. geological structural failures such as earthquakes and landslides), therein confined to problems of an essentially Newtonian quality, as biochemical events, wildfires embody an Einsteinian dynamic. In strength, wildfires are no less variable than in any other respect. Several are the definitions for fire severity, however, at the time of writing, the foremost useful definition for the purposes of this thesis is that of USDA Forest Service biologist Colin Hardy and colleagues, whom in recognition of the coevolution of fire and biota describe fire severity as “the intensity of the fire as it affects the bio- geochemical environment” (Hardy, 2005, p.5 in reference to Hardy et al, 1998, 2001). This description is concordant with that of several other parties whose research examines the relationship between biota and fire (Ryan and Noste, 1985; Morgan et al, 2001).

A wide variety of variables are used to gage fire severity, including flame length, fire size, resistance to control, rate of spread, and fuel consumed, amongst others (Hardy, 2005). Generally, mortality in overstory biomass is considered the primary indicator of fire severity (Agee, 1993; Morgan et al, 1996). However, whereupon assessing fire severity within wildlands, fire ecologists examine the percentage loss in organic biomass (Keeley, 2009; Lenihan et al, 1998), including soil matter and changes thereto [i.e. depth of heat penetration and impacts thereof] (Wells et al, 1979; Ryan and Noste, 1985), floral and faunal mortality rates (Chappell and Agee, 1996; Larson and Franklin, 2005), spatiotemporal shifts in the patterning of flora [i.e. species richness and invasive plant populations] [Keeley et al, 2005; Wang and Kemball, 2003; Ryan, 2002; Turner et al, 1999; Whelan, 1995; Lea and Morgan, 1993; Morgan and Neuenschwander, 1988; Ryan and Noste, 1985), together with the fire’s general behaviour (Turner et al, 1994). Fire severity varies across the several qualitatively different fire regimes, as does fire intensity, which a quantitative measure describes “the physical combustion process of energy release from organic matter” (Keeley, 2009, p.116). Fire intensity, like fire severity, is subject to several interpretations. However, its use in this thesis is aligned to that of the seminal mathematical firespread model (Rothermel, 1972), which today remains the cornerstone of fire behaviour metrics, for which “the energy per unit volume is multiplied by the velocity at which the energy is moving” (Keeley, 2009, p.117). A variation on fire intensity is fireline intensity, which quantifies the rate of heat transfer at the flame front (Byram, 1959). Studies evidence a coupling of fireline intensity and flame length in forest and shrubland fires (Keeley, 2009; Fernandes et al, 2000; Johnson, 1992; Andrews and Rothermel, 1982). However, the metrics affiliated to an ecosystem’s historical natural fire regime [52] (Hardy et al, 2001) are indicative, but not prescriptive in their affiliation, acting as a guide, not a hard and fast rule. Within the context of fire ecology, as with frequency and severity, changes in fire intensity promotes or excludes particular species, thus tends alter the composition of biotic assemblages (Archibald, 2013).

Across the conterminous United States, Remote Automatic Weather Stations [RAWS] continuously monitor meteorological variables including air temperature, dew point, pressure, wind speed and direction, relative humidity, precipitation, solar radiation, fuel temperature and fuel moisture at 1,731 sites (USGS, 2016). First introduced by the USDA Forest Service in 1976, and today monitored by the National Interagency Fire Centre [NIFC], together with data live-streaming from satellites and other terrestrial and space information and communication technology [ICT] devices, RAWS enable the provision of daily fire weather [53] updates to an array of local, and national government agencies. The Forest Service utilise RAWS data to construct wildfire indices that act as ballpark guides on event probabilities. Developed in 1972, revised in 1978, and subject to modifications thereafter (Deeming et al; 1977; Bradshaw et al, 1983), the National Fire Danger Rating System [NFDRS] works in much the same way as many other hazard classification systems, such as the Volcanic Explosivity Index [VEI] and the Saffir-Simpson Hurricane Wind Scale (Schott et al. 2012), ranking event probabilities by number.

Physics-based nonlinear dynamic equations generate numerical values for NFDRS indices including Spread Component [SI] (Schoenberg et al, 2007), which, highly variable, and constructed from data including fuel moisture, relative humidity, wind speed/direction, and topography (slope class), (sensu Rothermel, 1972) is calculated “with the fuel elements weighted by surface area” (Bradshaw et al, 1983, p.23); Energy Release Component [ERC] (Schoenberg et al, 2007), which utilises readings of fuel moisture to estimate the available energy [i.e. heat release per unit at the fire front], therein potential fire intensity (Bradshaw et al, 1983); and Ignition Component, which spanning 0-100, and calculated from readings of fuel moisture together with the temperature of the receptive fine fuels, ranks the probability of a firebrand igniting a fire in a fine fuel complex [54] (NIFC, n.d). Further NFDRS indices include the Haines Index, which ranked 1-6, translates atmospheric stability and moisture content readings into probabilities for plume-dominated fires [55]; Lightening Activity Level [LAL], also ranked 1-6, which interprets storm data to give estimates of cloud-to- ground lightning strikes; and the Keetch-Byram Drought Index [KBDI], which ranked 0-800, and customised to individual geographic regions, indicates the moisture content within the topsoil [upper 8 inches], based on recent precipitation measurements as they relate to annual rainfall patterns (Ibidem).

The Burning Index [BI] is the foremost commonly used of the several NFDRS indices (Schoenberg et al, 2007). Ranked 0-110, the BI number is a function of SC and ERC values, and is indicative of fire intensity; its value divided by 10 equal to flame length [FL] in feet at the flame front (Pyne et al, 1996; Andrews and Bradshaw, 1997; Mesonet, 2016). For example, a BI number of 0-28 equates to a flame length of up to 2.8 feet, therein a low intensity fire. Whereas a BI number of 110 equates to a flame length of 10.10 feet, therein a high intensity fire [Fig. 20]. However, invaluable though composite indices are in providing a bird’s eye view of fire-prone landscapes, and the condition of the biomass and weather systems thereof, they alone cannot fully quantify how and why a wildland fire will become manifest therein (Cocke et al, 2005). For example, LANDSAT’s Normalized Burn Ratio [dNBR] (USGS, 2004) is used to monitor fire severity and ecosystem response in regions including California’s chaparral shrublands. Analysis of the region’s ecosystemic response to the wildfires of late October 2003, which, one of the case studies used in this thesis, burned through 200,000 ha, revealed that the dNBR that was attributed to the event by USGS’ EROS data center did not in fact correlate with the real-world results (Keeley 2009; Keeley et al, 2008). Therein, in and of itself, remote-sensing data has “limited predictive ability” in projecting the ecosystem impacts of wildland fire, hence requires coupling with other methods including field studies (Keeley, 2009).

>Continue to Chapter 4 [part II] here.