FIRE AND FUEL MAPPING SHOALWATER BAY TRAINING AREA. Brian Tunstall, Neil Powell and Alan Marks

Transcription

1 FIRE AND FUEL MAPPING SHOALWATER BAY TRAINING AREA Brian Tunstall, Neil Powell and Alan Marks Technical Report 10/98 March 1998

2 FIRE AND FUEL MAPPING SHOALWATER BAY TRAINING AREA Brian Tunstall 1, Neil Powell 1, and Alan Marks 1 Environmental Research & Information Consortium Pty Ltd PO Box 179, Deakin West, ACT 2600 INTRODUCTION 2 METHODS 3 FIRE CHARACTERISTICS 3 FIRE MAPPING 3 FUEL CHARACTERISTICS 4 FUEL MEASUREMENT 5 FUEL MAPPING 6 RESULTS 6 FIRE MAPPING 6 FUEL LOADS 7 DISCUSSION 9 FIRE MAPPING 9 FUEL ACCUMULATION RATES 9 CONCLUSIONS 10 REFERENCES 11 CSIRO Land and Water Technical Report 10/98 March 1998 Acknowledgments This study was funded by the Department of Defence 1

3 INTRODUCTION Fire has always been part of the Shoalwater Bay Training Area (SWBTA). Two recently retired land managers had a combined experience of more than 100 years in managing fire in the area. The general characteristics of fire, and the practical limitations to its management, are therefore well known, and have been incorporated into current practices. Fire presents particular management difficulties because of its potentially damaging effects and unpredictability, thus fire has been the subject of research by CSIRO since The background and detail of fire management in SWBTA are given in the Fire Management Plan (Tunstall and Edwards, 1998), which also identifies developments required to improve fire management. The key requirements identified related to the ability to map fuel loads, and hence fire hazard, and to demonstrate the implementation of a fire regime that meets safety and conservation requirements. Assuming uniform rates of fuel production, these requirements can be met through mapping burn patterns. The time since burn indicates the fuel load, while the occurrence of fires, and fire frequencies, are evidence that safety and conservation requirements have been met. The assumption of uniform fuel accumulation is usually inadequate, and is particularly so for an area as diverse as SWBTA. Fuel production and litter decomposition differ greatly among the vegetation types, as do the fuel characteristics. The mapping of fuel loads therefore depends upon knowledge of the rates of production and decomposition of litter for mapped vegetation types, as well as annual maps of burns. Burn patterns have been mapped using visual observation since 1993, but this has proven difficult because of the size of the area, and poor access. The observations have value, but considerable uncertainty exists as to their accuracy. Cost is also significant, in both commitments of personnel, and aircraft charter. The alternative of mapping burn patterns through analysis of satellite imagery was therefore investigated because of likely improvements in accuracy and cost. Mapping patterns of fuel accumulation is made difficult by the many factors to be considered. Accumulation of litter depends on the relative rates of production and decay, which vary with the type of vegetation and seasonal conditions. Fuel type is also of consequence, and this varies with vegetation type, time since burn, and the seasonal conditions. Additionally, the material consumed by fire varies considerably, ranging from only grassy litter to all litter and plant foliage. The uncertainties in mapping potential rates of fuel accumulation in an area as large and diverse as SWBTA are considerable, with errors deriving from the base vegetation map, the limited observations of fuel loads, and the limited ability to track effects of seasonal conditions and past fires. Accepting these limitations, the objectives of this study were to: Map patterns of burns using satellite imagery. To provide information on fuel accumulation rates for mapped vegetation patterns. 2

4 METHODS Fire Characteristics The characteristics of fires determine the likely success in mapping burns using satellite imagery. Analysis is simple where fire destroys all vegetation because of the gross change in land cover, and because such fires usually occur under dry conditions when image acquisition is reliable and evidence of burns persists. However, most fires in SWBTA are lit when the soil is moist, resulting in patchy burns that only slightly modify the vegetation. Tree canopies often remain intact, limiting spectral change to the removal of ground layer vegetation As fires in SWBTA generally occur at the beginning of the growing season, the changes due to fire usually rapidly diminish through regrowth, particularly when the impact of fire is limited to ground vegetation. Favourable temperatures and soil moisture promote growth, thereby limiting the persistence of any change, and this is of particular consequence because of the difficulties in obtaining cloud free imagery. Burn patterns can always be detected if suitable imagery is available immediately following a fire, but they may remain undetected given delays in imagery acquisition. The significance of the length of delay in obtaining satellite imagery depends on the severity of the fire and vegetation type. Fire scars can be mapped several years after occurrence with some vegetation types, but may be difficult to determine after several months in others. The fire season in SWBTA starts in late July or early August when fuel becomes dry due to increasing temperatures and lack of rain. Early season fires generally burn at a low intensity because the fuel remains moist, but high fire intensities can occur in August on the occasional days with moderate to strong westerly winds. The fire season continues until around the end of the calendar year, with the likelihood of fire during this period varying depending on rainfall. August and September are traditionally the driest months, and therefore generally pose the highest fire risk. However, with low rainfall, the fire risk increases with increasing ambient temperatures into January. Fires during summer generally burn at high intensities over extensive areas, and cause considerable change to vegetation, but can still be difficult to map due to the un-availability of cloud free imagery following the fires. Few cloud free images exist for SWBTA, and these mainly cover the winter period of June and July. Fire Mapping The methods used for mapping burns from satellite imagery are based on those developed for the Mt Bundey Training Area (MBTA) in the Northern Territory (Tunstall, 1995). This used multispectral Landsat TM imagery acquired at four times during the fire season, with the first image being free of fire scars. The analysis focused on detecting changes between acquisition dates because of the uncertainties in identifying burnt areas through spectral characteristics alone. Differences in vegetation type, and the extent of change due to fire, result in burnt areas having a wide range of reflectance characteristics. Satellite imagery is highly sensitive in detecting change but need not identify the cause as change can occur for a number of reasons. The results for Mt Bundey indicated that change due to regrowth following burning enhanced the discrimination of change due to fire. Band 4 was found to contain most information on burns, and this band responds mainly to changes in green biomass. Fire scars were best mapped by observing changes in Band 4 between image acquisitions, with observations from more than one interval being required to 3

5 determine the cause of change. Because of these results, and the cost benefits associated with the acquisition and processing of a single band, this analysis was restricted to Band 4 of Landsat TM. The dates of acquisition are given in Table 1. The TNTMIPS GIS was used for all processing unless otherwise specified. The images were registered to a multi-band Landsat TM image georeferenced to an RMS accuracy better than 10 m using ground points obtained with differential GPS. The georeferencing was therefore more accurate than would be obtained using a map, and the registration to a common image minimised changes arising due to errors in the registration between images. Given the position of SWBTA relative to the path of the satellite, an image for SWBTA is obtained by mosaicking data from two adjacent passes, where the passes are a minimum of 8 days apart. In mosaicking the images, spectral differences between acquisitions were reduced using histogram matching following masking of water and cloud. The Band 4 images were decorrelated within years to remove common information, and then differenced between successive times to highlight changes. Boundaries of burns were digitised from the screen to produce vector files by reference to the processed and raw images. The Environment Officer and Land Managers for SWBTA also produced maps of burn patterns using ground and aerial observations. At least one flight was scheduled during each fire season to record fire scars, and opportunistic observations were also made during flights conducted for other purposes. This information was compiled in the ArcView GIS. Table 1 Acquisition dates for the LANDSAT TM imagery. These were the only image pairs regarded as having sufficiently low levels of cloud cover. Band 4 only was acquired, except for the May 94 images, which contained all bands and was cloud free. Path 92 Path June 21 June 30 July 8 August May 7 May 6 November 17 December July 13 July 6 September 30 August May May 96 Fuel characteristics Most of SWBTA is subject to fire, but spatial differences exist in the timing and frequency of burns that relate to differences in fuel type, and rates of accumulation. Fires propagated by grassy fuels will burn early in the season, fires propagated by tree and shrub litter tend to burn mid season, while those dependant on the desiccation of live shrub material usually occur late in the season. Grassy fuels are replaced most rapidly, hence grasslands have the highest fire frequency. Accumulation of fuels is slowest in heath vegetation because of the low fertility. Accumulation of shrub and leaf litter in woodlands and forests is generally intermediate between woodlands and heath, but varies depending on the relative rates of production and decay. 4

6 Differences in decay rates for different litter types result in grassy fuel loads peaking first, reaching a maximum in around four years. Grassy fuel loads may then decline because of declining production by the grasses. Data are not available for SWBTA on the time required to achieve peak fuel loads for leaf from trees and shrubs, but 8 years would be a reasonable estimate, bearing in mind that this would vary with the vegetation type and seasonal conditions. The breakdown rates for logs and branches are unknown as these are almost invariably burnt, but this is of little consequence in fire management as this material does not affect the propagation of fires. Fuel accumulation is sporadic throughout the year through being linked with the growth patterns of plants. The development of grassy fuel depends on the production and senescence of leaves, and hence exhibits an annual cycle related to the phenology of the species, where this can be modified by frosts and droughts. The occurrence of frosts is of particular consequence in determining the condition of grassy fuel at the end of winter, and strongly determines the timing for the commencement, and the severity, of the fire season. Woody species, such as shrubs and trees, mostly shed old leaves in association with new growth. Native woody species generally hold leaves from 2 to 4 years, and synchronise leaf fall and growth to conserve nutrients. Tree and shrub litter therefore mainly accumulates under moist conditions when fire is unlikely, however, the conditions that promote grass growth also promote accession of tree and shrub litter, creating a strong, but temporally offset association between the quality of the growing season and fire risk. Accumulation of grass, tree and shrub litter also occurs due to drought as well as growth, as few species sustain a full canopy when conditions become extremely adverse. This accumulation of fuel is associated with conditions of high fire risk, when fires usually consume all leafy material because of the low moisture contents and the resulting high fire intensities. The fuel available to support fire therefore depends upon the prevailing environmental conditions, as well as vegetative growth and senescence. Fuel Measurement The land cover map derived from Landsat TM imagery by CSIRO (Tunstall et al, 1998) was used as basis for sampling fuel accumulation. The map was derived by classifying multispectral Landsat TM imagery georeferenced to an RMS error of 10 m through use of differential GPS, with mosaicking of data from adjacent passes being conducted as described above. Ideally, fuel loads should be measured over many years in the different vegetation types throughout SWBTA, and be related to time since burning. This was impractical for this study because of the uncertainties as to the time since burning, and the level of resources needed to take the necessary measurements. Sampling therefore focused on sites having one years fuel accumulation. Some sites with several years accumulation were also sampled, as were sites where the fuel load was considered to be at a maximum. The 136 sample sites were located close to roads or tracks for access, and because such areas are most frequently burnt. Measurements were obtained just prior to the fire season, with the sampling being conducted in two successive years. The location, general vegetation type, and estimated period of fuel accumulation were recorded for each site, with an inferred maximum fuel being assigned an accumulation period of 10 years. 5

7 Ground fuel loads were determined by harvesting all organic matter within 1 m 2 quadrats, with the material being stratified into grass leaf, tree and shrub leaf, and twigs. Green and dry grass material was not separated, and locations with large branches or logs were avoided. The material was oven dried, and weighed. Fuel Mapping Two separate land cover maps were derived from Landsat TM imagery for SWBTA, the first by CSIRO, the second by Environmental Information Resources Consortium Pty Ltd (ERIC), but by the same person using the same techniques. The scenes used were all cloud-free but all contained fire scars, making the results unreliable for burnt areas. The second image processed provided higher discrimination than the first because of the higher sun angle and intensity of illumination, but it contained considerable areas of fire scars. Vector polygons were produced delineating the fire scars in both classified images, allowing burnt areas to be masked in the second image, and hence replaced with information from unburnt areas from the first. The degree of spatial coincidence between classes was established numerically, restricting the comparison to areas that were unburnt in both images. For sand dunes, this evaluation was further restricted to areas of sand dune. The number of land cover classes in the final image was 47, which included water. Relationships between vegetation types and land cover classes were determined through field sampling for 156 sites. The sampling was stratified according to land cover classes and patterns of parent material determined through analysis of airborne gamma radiation data. The vegetation map was derived by assigning labels according to land cover class and parent material. The limited number of fuel samples, differences in the fuel accumulation period, and the large number of vegetation types prevented statistical analysis of relationships between measured fuel loads and the mapped vegetation classes. Indeed, not all vegetation types were sampled. The fuel loads ascribed to classes in the vegetation map therefore reflect a best judgement of the results. This judgement was simple for several major mapped classes where the vegetation types and fuel characteristics were consistent, but was uncertain for classes containing high variation in the amount and composition of fuel. A map of fuel accumulation rates was obtained by applying these estimates to the vegetation map. RESULTS Fire Mapping Maps produced from the satellite imagery identifying the annual patterns of burns are given in Figures 1, 2, 3 for 1993, 1994, and 1995 respectively. Equivalent maps produced from ground observation are given in Figs 4, 5, 6. The percentage of the 2700 km 2 land area of SWBTA recorded as being burnt in these maps is given in Table 2. It is apparent that major discrepancies exist between the different forms of observation. The burn patterns can be mapped at higher spatial resolution and accuracy using satellite imagery than visual observation, but both methods miss major burns. To obtain a best estimate of the area burnt each year the results from the two methods were combined. The combined maps 6

8 are given in Figs 7, 8, 9, and the percentage of SWBTA estimated as being burnt each year in Table 2. Some of the reasons for the discrepancies between mapping techniques can be identified by reference to the maps of burns. Range staff did not record some fires, and their estimates of the extent of burns were usually approximate. However, regrowth prevented the discrimination of some burns in the satellite imagery, and reduced the accuracy of determination of extents, while some were missed because of the inability to obtain cloud free imagery after the fires. Table 2 The percentage of the SWBTA ground area burnt each year as estimated using satellite imagery and ground observations. Satellite Ground Combined Fuel Loads Relationships between fuel loads and vegetation with environmental regions of SWBTA are given by Tunstall and Edwards (1998). The characteristics of fuel load for the mapped vegetation classes are given in Table 3. The areal expression of these characteristics is given in Figures. 10 and 11, where Figure 10 indicates relative levels of accumulation of grassy fuels, and Figure 11 the relative levels of accumulation of all ground fuel. Rates of grassy fuel accumulation are highest for grasslands, then woodlands, and these predominantly occur in the western areas of SWBTA. Rates of accumulation for total ground fuel are most strongly related to fertility, hence rates are slowest for the sand dunes. Table 2 Areas for the different categories of fuel accumulation rates. Grass Total

9 Table 3 Mapped vegetation categories, and relative fuel accumulation rates for grass and total ground litter. 1= low to 5 = high; 6 = 0, 7 = burnt, 8 = water. Class Description Fuel Grass Total 1 Grassland Open Paperbark Woodland Paperbark Woodland Eucalypt / Paperbark Woodland Eucalypt / Paperbark Forest Open Eucalypt / Paperbark Forest Sparse Eucalypt Paperbark Woodland Sparse Eucalypt Woodland Open Eucalypt Woodland Eucalypt Woodland Dense Eucalypt Woodland Creekline Eucalypt Woodland Sparse Eucalypt Forest Open Eucalypt Forest Eucalypt Forest Dense Eucalypt Forest Very Dense Eucalypt Forest Broadleaf Forest Araucaria Forest Pine Plantation Dense Eucalypt Heath Heath Open Heath Low / Sparse Heath Acacia Shrubland Swamp Heath Sedge Swamp Paperbark Swamp Paperbark Forest Dense Mangrove Open Mangrove Samphire Marine Couch Mudflat Sand Bare Disturbed Burnt Water 8 8 8

10 DISCUSSION Fire Mapping The area of SWBTA burnt each year is significant, but is always less than 50%. However, the fire frequency varies greatly. Areas around roads and boundaries are burnt frequently, while many areas are rarely burnt. Low fire frequencies generally occur in hills and sand dunes. The pattern of burning across years accords with prior unpublished observations that a biennial pattern of burning occurs in SWBTA. The proportion of the area burnt alternates between 20% and 50% due to the pattern of accumulation of grassy fuel. The production of grass in one year is usually sufficient to support fire, but the green material must cure to be effective in promoting fire. That is, a second winter period following burning is generally required to achieve sufficient grassy fuel for the conduct of burns. The observations on burn patterns given here are useful in identifying the areas usually burnt and the proportion of SWBTA burnt each year. They also provide an indication of areas likely to have long periods of fuel accumulation. However, neither procedure alone provided the reliability in detecting and mapping burns needed for accurate prediction of fuel loads across the entire area. Only the combined information would be regarded as providing a reliable indication of the patterns of burns. The land cover classifications produced using all Landsat TM bands appeared to provide the highest discrimination of burn patterns, which contrasts with the results for the MBTA. The extent of burns still had to be manually digitised because only small patches within burnt areas uniquely identified burns, but the ability to highlight these burnt patches through classification facilitated this operation. The above suggests that for SWBTA fire scars would best be mapped through classification of multi-band Landsat TM imagery. However, this observation requires qualification, as the classification was facilitated by the cloud free imagery. The greatest difficulty in resolving fire scars using Landsat TM imagery relates to the ability to obtain suitable imagery when required, and this is primarily determined by the occurrence of cloud. The ground observations are inaccurate in identifying the extent of burns, but have particular value when made in association with burning conducted to develop fire breaks. Firebreaks generally only have to be around 100 m wide to be effective, and so knowledge of the occurrence of fire provides useful information. However, supplementary observations are needed to support decisions on conservation and the conduct of hazard reduction burns. Fuel accumulation rates The information on fuel accumulation rates is more deficient than for the patterns of burns, but the maps of fuel accumulation rates provide more detail than previously available (Tunstall and Edwards, 1998). While this represents an improvement in the information available, determination of the level of benefit provided by these improvements is difficult. Fine spatial detail is hard to apply to management because local differences in fuel load seldom block the passage of burns, and fires tend to be widespread. Detailed spatial information could be used in modeling using GIS, but not with the current approach to fire management. The current approach divides SWBTA into environmentally homogeneous areas of a size appropriate to fire management, and detailed patterns are only addressed where they can block the passage of fire. 9

11 Conclusions More work is required to determine the most cost-effective means of mapping patterns of burns in SWBTA. Fires represent the most widespread and regular environmental impact on the training area, and are a significant threat to safety. There is therefore a need to implement appropriate management practices, and show the achievement of desired outcomes. Continuation of ground observations will be required until reliable results can be obtained using alternative procedures, but the procedures must be cost effective. Use of remote sensing is therefore essential, but questions remain as to the best form of imagery and processing. Satellite imagery provides the most cost-effective mapping, and can reliably map fire scars provided images are available when required; however, cloud makes image acquisition unreliable for SWBTA, particularly for coastal areas. Use of airborne imagery can circumvent this limitation, but costs would currently be prohibitive for the routine observations required for monitoring. The main alternative is to examine the application of image sources other than Landsat TM to increase the potential acquisition times. However, the most effective bands and processing procedures will have to be determined, as it appears that the characteristics of the vegetation in SWBTA are sufficiently different from elsewhere to produce what appears to be a unique response to fire. One possibility yet to be explored is the use of Synthetic Aperture Radar (SAR) interferometry. Current applications have been directed towards detecting small changes in elevation, the reliability of this measurement depending on maintaining the reflective characteristics of the observed surface. As changes in vegetation affect the reflective characteristics, SAR could potentially be used to identify changes in vegetation where the elevation has remained constant. Use of radar has the advantage of largely being unaffected by cloud. While many deficiencies can be identified in the information on fuel accumulation rates, the available information is useful and could be used in managing the area. Further observations could be readily obtained if management called for them. 10

12 REFERENCES Tunstall, B. R. (1995). Land condition monitoring, Mt Bundey Training Area. CSIRO Aust., Division of Water Resources, Consultancy Report 95/20. Tunstall, B. R., and Edwards, J. M. (1998). Fire Management Plan: Shoalwater Bay Training Area. CSIRO Aust. Land and Water. Technical Report 5/98. 11

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