Mapping fire scars in a southern African savannah using ... · Mapping ﬁre scars in a southern African savannah using Landsat imagery ... three-band combination is optimal largely

Mapping fire scars in a southern African savannah usingLandsat imagery

A. T. HUDAK*

USFS Rocky Mountain Research Station, 1221 S. Main Street, Moscow,Idaho 83843, USA. tel: 1-208-883-2327; fax: 1-208-883-2318;e-mail: [email protected]

and B. H. BROCKETT

North West Parks & Tourism Board, P.O. Box 1201, Mogwase 0314,South Africa; tel: 27-14-555-5351; fax: 27-14-555-5525;e-mail: [email protected]

(Received 7 August 2002; in final form 15 July 2003 )

Abstract. The spectral, spatial and temporal characteristics of the Landsat datarecord make it appropriate for mapping fire scars. Twenty-two annual fire scarmaps from 1972–2002 were produced from historical Landsat imagery for asemi-arid savannah landscape on the South Africa–Botswana border, centredover Madikwe Game Reserve (MGR) in South Africa. A principal componentstransformation (PCT) helped differentiate the spectral signal of fire scars in eachimage. A simple, nonparametric, supervised classification (parallelepiped) of thePCT data differentiated burned and unburned areas. During most years, fireoccurrences and the percentage of area burned annually were lowest inBotswana, highest in MGR, and intermediate in South Africa outside MGR.These fire scar maps are aiding MGR managers, who are endeavouring torestore a more active fire regime following decades of fire exclusion.

1. Introduction

The multispectral characteristics, spatial extent and grain, and temporal range

and frequency of the Landsat image record make it appropriate for mapping fire

scars in savannah landscapes. Landsats 1, 2 and 3 carried the Multispectral Scanner

(MSS) sensor with four reflectance bands and 57–80 m spatial resolution, while the

subsequent Thematic Mapper (TM) sensor added to Landsats 4 and 5, and the

current Enhanced Thematic Mapper Plus (ETMz) sensor on Landsat 7, have six

reflectance bands with 25–30 m spatial resolution. The continuous Landsat data

record, now spanning over 30 years, can also be used to reconstruct a decadal-scale

fire history, as was first demonstrated by Russell-Smith et al. (1997). Our goal was

to reconstruct the fire history of a semi-arid savannah landscape in southern Africa

for the benefit of rangeland scientists and managers.

While fire scars may be obvious to an image interpreter, mapping the fire scars

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2004 US Government

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160310001632666

*Corresponding author.

INT. J. REMOTE SENSING, 20 AUGUST, 2004,VOL. 25, NO. 16, 3231–3243

can be difficult because automated classification techniques do not possess the

superior pattern recognition capabilities of the human brain. Edwards et al. (1999)

found on-screen manual digitizing to be more accurate than four automated fire

scar mapping techniques. However, the characteristic patchiness of fire scars makes

manually digitizing them extremely tedious, as well as subjective. Visual inter-

pretation is also limited to single bands or three-band (RGB) colour composites.

Several previous fire scar mapping studies using TM imagery have promoted

particular three-band combinations: bands 4, 5 and 7 in Spain (Chuvieco and

Congalton 1988); bands 3, 4 and 5 in Amazonia (Pereira and Setzer 1993); bands 4,

7 and 1 or bands 4, 7 and 2 in Greece (Koutsias and Karteris 1998, 2000). Which

three-band combination is optimal largely depends on the characteristics of the

scene. Given the automated image processing capabilities now widely available, and

lower image costs since the launch of Landsat 7, there is little justification for

limiting image analyses to just three bands.

Rangeland managers need a fire scar mapping strategy that is accurate, efficient,

objective and consistent. We present the methodology developed for mapping the

fire history of a savannah landscape, which varies in land use between three areas of

interest: Madikwe Game Reserve (MGR), private cattle farms in South Africa, and

communal grazing lands in Botswana.

2. Methods

2.1. Study area

The 250 000 ha study area is centred over South Africa’s MGR and includes

surrounding areas in Botswana and South Africa (figure 1). Climate is semi-arid,

with annual rainfall averaging 520 mm (standard deviation~171 mm); 95% of rain

falls between October and April. Elevation ranges from 872–1474 m, yet the

Figure 1. Study area location.

3232 A. T. Hudak and B. H. Brockett

landscape is generally flat except for rocky ridgelines along MGR’s southern

boundary and roughly parallel through its centre. Soils are heterogeneous and

range from heavy clays to rocky loams; soil type patterns are a major determinant

of vegetation community patterns in this area (Hudak and Wessman 2001).

Communal cattle grazing is the major land use in south-eastern Botswana. In

some areas goats are stocked as well, and fires are discouraged. Dry land cropping

is also practised. Livestock grazing is also the predominant land use in South

African savannahs (Hoffman 1997). Grazing lands in South Africa are mostly

under private tenure, and most cattle ranchers actively exclude fire. There is a

limited amount of irrigation and dry land cropping surrounding MGR. In MGR

(established 1991), wild herbivores have been reintroduced on former cattle farms,

and a prescribed fire management programme has been implemented, since 1992.

Fires in MGR are ignited to maintain or enhance vegetation heterogeneity, reduce

woody plant encroachment, ensure fodder for large herbivores, and increase game

visibility for tourists. A long-term goal is to restore a more active fire regime

following decades of fire exclusion, caused by a lack of anthropogenic ignitions and

a chronic depletion of fine fuels due to cattle grazing (Hudak 1999).

2.2. Image acquisition

We made every effort to obtain Landsat image records of all fires occurring

since the launch of Landsat 1 in 1972. This amounted to 36 images documenting

most fires occurring in 22 of 31 years from 1972–2002 (table 1). Years in which we

could not obtain data were 1974, 1975–1978 and 1981–1985. All MSS and ETMz

images, and the 17 November 1986 and 21 June 1996 TM images, were full scenes

from which we subset our area of interest. The remaining TM images listed in

table 1 were 50 km650 km subscenes. The majority of images were acquired late in

the calendar year, or at the end of the dry season, which is when most fires occur.

2.3. Image georegistration

All data were obtained in digital format except the 17 November 1980 data;

these four MSS bands were only available as positive transparencies. Each was

scanned with a photogrammetric-quality scanner to produce digital output of

y21 m resolution, which exceeded the ground instantaneous field of view (GIFOV)

of MSS and thus precluded any loss in resolution. Bands 4, 5 and 6 were

coregistered to band 7 using image tie-points generated through an automated

routine (Kennedy and Cohen in press). The cumulative RMS registration error

using a third order polynomial transformation was v10 m. The coregistered bands

were combined into a four-band image stack for further digital processing.

This same automated routine was used to generate image tie-points for

coregistering the 36 Landsat images (table 1). The 15 m panchromatic band from

the 11 August 2000 ETMz scene was chosen as the base image for coregistration

because the area burned at this time was minimal (fire scars introduce landscape

patterns that influence the routine), and it fully covered the study area (the south-

eastern corner was cut off from some scenes because the study area occurred along

the eastern edge of Landsat path/row 172/77). In the few cases where the automated

routine failed to find a sufficient number or distribution of points, more image tie-

points were added manually. We used first order polynomial transformations with

cumulative RMS error thresholds of v10 m for ETMz and TM and v20 m for

MSS. All image coregistrations were performed in ERDAS Imagine 8.5.

Savannah fire scar mapping from Landsat imagery 3233

2.4. Image classification

All reflectance bands (four for MSS; six for TM and ETMz) in each image

were transformed to unstandardized principal components and included in a

supervised classification. The advantages of using the principal components

transformation (PCT) for mapping fire scars were first presented by Richards

(1984) and later by Milne (1986). In our analysis, we tested the standardized PCT,

which produced less noisy higher-order components relative to the unstandardized

PCT (Singh and Harrison 1985, Fung and LeDrew 1987). The standardized PCT

appeared to decrease omission errors in the subsequent fire scar classifications butat the cost of increased commission errors. Since we preferred to map fire scars

conservatively (i.e. minimize commission errors), we decided to use unstandardized

PCTs throughout our analysis. The unstandardized PCT did an excellent job of

extracting fire scars from earlier in the dry season, and discriminating dark soils and

topographic shadows from fire scars (more so with TM and ETMz than MSS).

We also tested the Kauth-Thomas transformation and found that the PCT more

Table 1. Landsat images (36) used to compile the 1972–2002 fire history.

Satellite Sensor Acquisition date

Landsat 1 MSS 30 September 1972Landsat 1 MSS 29 December 1972Landsat 1 MSS 13 October 1973Landsat 2 MSS 06 September 1975Landsat 2 MSS 09 October 1979Landsat 2 MSS 17 November 1980Landsat 5 TM 17 November 1986Landsat 5 MSS 04 November 1987Landsat 5 MSS 22 November 1988Landsat 5 MSS 22 September 1989Landsat 5 TM 22 September 1989Landsat 5 TM 05 June 1990Landsat 5 TM 28 November 1990Landsat 5 TM 04 March 1991Landsat 5 TM 15 November 1991Landsat 5 TM 06 March 1992Landsat 5 TM 16 October 1992Landsat 5 TM 17 September 1993Landsat 5 TM 20 September 1994Landsat 5 TM 09 October 1995Landsat 5 TM 21 June 1996Landsat 5 TM 11 October 1996Landsat 5 TM 28 September 1997Landsat 5 TM 18 January 1998Landsat 5 TM 15 September 1998Landsat 5 TM 26 March 1999Landsat 7 ETMz 12 October 1999Landsat 7 ETMz 13 November 1999Landsat 7 ETMz 11 August 2000Landsat 7 ETMz 27 August 2000Landsat 7 ETMz 28 September 2000Landsat 7 ETMz 07 March 2001Landsat 7 ETMz 01 October 2001Landsat 7 ETMz 20 December 2001Landsat 7 ETMz 18 September 2002Landsat 7 ETMz 21 November 2002


closely captured the fire scar patterns, in contrast to Patterson and Yool (1998). For

mapping a dichotomous dependent variable such as fire presence/absence, logistic

regression modelling (Koutsias and Karteris 1998, 2000) is a viable alternative,

which we did not test in this study.

While conducting preliminary analyses, we found that classification accuracies

of the PCT data consistently exceeded those of the raw data, using unsupervised,

parallelepiped, minimum distance, Mahalanobis distance, or maximum likelihood

classifiers. We chose the parallelepiped classifier, which labels pixels having spectra

within a user-defined number of standard deviations from the training data mean

spectra. Unlike the other supervised classifiers just mentioned, the parallelepiped

classifier is nonparametric and does not force labels on all pixels in the image

(Richards 1993). This worked to our advantage because we were only interested in

classifying burned pixels. In some cases we needed to train two burn classes to

capture spectral variability between soil types, in fire severity, or in time elapsed

since burning. High correlations between classes can lead to considerable overlap

between parallelepipeds, but this was not a problem for our application since we

merged the two burn classes into one burn rule image. While we also trained

spectrally distinct land covers such as an open water reservoir and several irrigated

crop circles, these parallelepipeds did not overlap with the burn classes in feature

space and thus did not influence the burn rule images.

There were two subjective elements of the parallelepiped classifier. One was

defining the training sites; the maximum number of pixels included in a training site

was 2500. The other was determining the optimal number of standard deviations

from the training data means for accurate classification. We used 2–5 standard

deviations, choosing whichever produced the most accurate classification. Larger

standard deviation values were required in images where larger areas burned.

Classification accuracy was assessed by comparison to various colour composite

displays of the raw and PCT bands, and tweaking the training sites or standard

deviation settings until the classification was visually acceptable. Errors of omission

were allowed before errors of commission in an effort to conservatively map the fire

scars.

Figure 2 illustrates the image classification methodology just described,

performed using RSI ENVI 3.4. Fire scars that are visible in most of the raw

bands are visible in fewer PCT bands, but more distinctly (figure 2). Large fire scars

can comprise a large proportion of the total spectral variance, and therefore often

appear in PC2 or PC3. Figure 3 shows the greater differentiation between burned

and unburned training spectra that is possible after applying the PCT and is

intended as a graphical explanation for the more accurate classification map that

can be produced from PCT data rather than raw data (figure 2). The spectral

signatures of the two burned training sites are much less similar to the spectral

signature of the unburned training site in the case of the PCT data than in the case

of the raw data (figure 3). There is little difference in PC6 values between classes,

because little meaningful information remains in PC6. However, fire scar patterns

were sometimes detectable in the highest PC (particularly PC4 from the MSS

image PCTs), so for consistency all PCT bands were included in all image

classifications. Inclusion of the noisy highest PCs did not concern us greatly because

random, single-pixel classifications would be filtered from the subsequent fire scar

maps.


2.5. Annual burn mapping

Burn rule images produced from individual images were aggregated into yearly

composite maps. We had both a full MSS scene and a TM subscene on 22

September 1989, so when aggregating these, we gave precedence to the higher-

resolution TM burn rule image across the common portion of the study area. A

363 majority filter was applied to each annual burn map while also resampling to a

resolution of 1 ha. This eliminated salt and pepper from the maps along with the

Figure 2. Schematic illustrating the image processing methodology followed for mapping firescars using, in this case, the 28 September 2000 ETMz image. Classification of the rawdata was rejected in favour of classifying the PCT data because the latter proved muchmore accurate for distinguishing burned from unburned areas. Two burn classes wereneeded in this example to encompass variability in fire dates and soil types.


discrepancy between the spatial resolutions of MSS and TM or ETMz. The

mapping resolution of 1 ha was chosen for its convenience as a management unit.

We produced a map of 1972–2002 fire occurrences by combining the 22 annual

burn maps and then applying a 363 median filter, for the purpose of making a

more interpretable map for land managers.

Our only deviations from this fire scar mapping methodology were due to data

unavailability soon after two significant fires were known to have occurred. In

December of 1990 and 1998, large fires occurred that were undocumented with a

post-burn image prior to the onset of seasonal rains and the masking effect of

subsequent vegetation growth. However, we successfully retrieved these fires scars

using unstandardized, multi-temporal PCTs of pre- and post-burn images. For the

December 1990 fires, we combined 5 June 1990 pre-burn and 4 March 1991 post-

burn images into a 12-band stack and applied a PCT. We did the same for the

December 1998 fires, using 15 September 1998 pre-burn and 26 March 1999 post-

burn images. The fire scars were apparent in PC10 in 1990 and PC9 in 1998. (We

also tested standardized multi-temporal PCTs on our 1990 and 1998 multi-temporal

datasets and, in both cases, they failed to reveal the December 1990 or 1998 fires in

any of the PC bands. Thus we consistently used unstandardized PCTs throughout

our analysis.) We inspected these PC bands along with their respective histograms,

identified brightness thresholds that best separated burned from unburned areas,

and manually digitized the perimeter of the fire scars before adding them to the

1990 or 1998 composite maps. This thresholding step was important for not

misclassifying islands of unburned vegetation within the larger fire scars, as often

happens with manual or vector methods of fire scar delineation (Edwards et al.

1999).

Some ancillary field information assisted in mapping fire scars in several years.

Fire scar perimeters in 1993, 1994 and 1998–2000 were drawn on 1 : 50 000

topocadastral maps. Fire scar perimeters in 1997, 2001 and 2002 were delineated in

the field with a global positioning system (GPS). On 4–5 August 2002, fire presence

was mapped along parallel transects 660 m apart in western MGR. The 246 GPS

positions were not differentially corrected.

Figure 3. Comparison of training site spectra from the (a) raw and (b) PC-transformed 28September 2000 ETMz image. Colours match the classes mapped in figure 2.


3. Results

3.1. Annual burn maps

Throughout most of the recorded history, fires have been less frequent in Botswana

compared to South Africa, especially in MGR (figure 4, table 2). Since 1989, but with the

notable exception of 2002, most fire activity has been within MGR.The map of 1972–2002 fire occurrences shows that MGR had the highest fire

frequency, Botswana had the lowest fire frequency, and South Africa outside MGR

had an intermediate fire frequency (figure 5, table 3).

3.2. Validation

A 26 September 2000 Systeme Probatoire de l’Observation de la Terre (SPOT)

Multispectral (XS) image (20 m GIFOV) was used to assess the accuracy of our 28

September 2000 fire scar classification (figure 2), using a slight variation of the dot

grid technique (Avery 1977). After coregistering the SPOT XS image, we overlaid a

3 km63 km grid on it and the 28 September 2000 fire scar map. Based on a visual

inspection of the SPOT image, we labelled each grid intersection point as burned or

unburned, and compared these results with the fire scar map. Out of a total of 396

validation points, 366 were correctly classified as unburned (92.42%), 26 were

correctly classified as burned (6.57%), 4 were incorrectly classified as unburned

(1.01%), and 0 were incorrectly classified as burned (0.00%). The four omission

errors occurred in very patchy burn areas.

Some fires in MGR in 2000 and 2001 were ignited as part of the Southern

African Fire–Atmosphere Research Initiative (SAFARI 2000) project. We

compared an independently-produced fire severity map (Landmann unpublished)

of the 2000 SAFARI fires to our 2000 annual burn map, and found the fire scar

perimeters virtually indistinguishable. Furthermore, we plotted several fires ignited

by SAFARI 2000 researchers in 2000 (n~9) and 2001 (n~3) over our 2000 and

2001 annual burn maps, respectively; all 12 ignitions were consistently located on

the northern edges of the fire scars, on days with northerly winds.

The GPS positions of fire presence gathered from a helicopter over three

prescribed burns in MGR provided rigorous validation of omission errors in the 18

September 2002 image classification. More omission errors occurred where fires

were patchy, had occurred on dark soils, or where more time had elapsed since the

fire. Majority filtering and aggregation of the 30 m image classification to the 100 m

burn map created three new omission errors while eliminating three others, so

overall accuracy did not change from 90% (table 4).

4. DiscussionThe historical imagery was often our only record of past fires. Only a few fire

scar classifications could be properly assessed for accuracy with independent

ancillary data, which were only available within MGR. While valuable for guiding

the supervised classifications, the field maps of fire scars were inaccurate at a finer

scale because they failed to capture the patchy nature of fires. The overall scarcity

of independent validation information contributed greatly to our motivation to

develop a consistent fire scar mapping methodology for all years, since then we

could argue that the maps produced in years when we lacked validation

information were produced in the same manner as maps produced in years when

we did have independent information. This strategy was employed by Russell-Smith

et al. (1997); however, they could only ground-truth two years (1993 and 1994) of a


Figure 4. Annual burn maps, 1972–2002. Each map is a composite of 1, 2, 3 or 4 individualburn rule images, depending on source data availability (table 1).


1980–1994 fire history reconstruction of Kakadu National Park, northern

Australia. Furthermore, their fire scar analysis was a completely manual

interpretation of photographic, not digital, MSS products. We therefore consider

our automated analysis a significant improvement upon their methodology.

A PCT of the raw image bands improved our fire scar classification accuracy

because it greatly reduced or eliminated overlap between burned and unburned

training statistics. We were satisfied with our fire scar mapping strategy because it

met our desired criteria: accuracy, efficiency, objectivity and consistency.

Automated approaches to determining burn/unburn thresholds would have been

desirable for their efficiency, objectivity and consistency, but are too inaccurate

across the wide range of sensor, radiometry, atmosphere and scene conditions

existing in long image time series. We therefore determined these thresholds

visually, like Salvador et al. (2000).While our approach is suitable for mapping fire frequency, producing accurate

maps of other characteristics of fire regimes, particularly fire severity, remains more

of a challenge (Russell-Smith et al. 1998). Multi-temporal PCTs (Hudak et al. 1998)

Table 2. Hectares burned (% of area), 1972–2002, inside and outside MGR in South Africa,in Botswana, and in total. Only the 50 km650 km central portion of the60 km660 km study area was tabulated because the spatial extent of the sourceimagery varied slightly between years.

YearS. Africa, MGR

(60 452 ha)S. Africa, outsideMGR (140 202 ha)

Botswana(49 346 ha) Total (250 000 ha)

1972 481 (0.796%) 3 267 (2.330%) 1 707 (3.459%) 5 455 (2.182%)1973 1 039 (1.719%) 2 931 (2.091%) 308 (0.624%) 4 278 (1.711%)1974 NA NA NA NA1975 3 (0.005%) 715 (0.510%) 49 (0.099%) 767 (0.307%)1976 NA NA NA NA1977 NA NA NA NA1978 NA NA NA NA1979 1 377 (2.278%) 1 824 (1.301%) 60 (0.122%) 3 261 (1.304%)1980 519 (0.859%) 1 209 (0.862%) 10 (0.020%) 1 738 (0.695%)1981 NA NA NA NA1982 NA NA NA NA1983 NA NA NA NA1984 NA NA NA NA1985 NA NA NA NA1986 0 (0.000%) 233 (0.166%) 4 (0.008%) 237 (0.095%)1987 2 810 (4.648%) 1 497 (1.068%) 264 (0.535%) 4 571 (1.828%)1988 0 (0.000%) 59 (0.042%) 0 (0.000%) 59 (0.024%)1989 17 796 (29.438%) 1 424 (1.016%) 90 (0.182%) 19 310 (7.724%)1990 7 214 (11.933%) 3 812 (2.719%) 88 (0.178%) 11 114 (4.446%)1991 0 (0.000%) 477 (0.340%) 34 (0.069%) 511 (0.204%)1992 5 805 (9.603%) 1 448 (1.033%) 85 (0.172%) 7 338 (2.935%)1993 9 858 (16.307%) 508 (0.362%) 8 (0.016%) 10 374 (4.150%)1994 12 342 (20.416%) 925 (0.660%) 0 (0.000%) 13 267 (5.307%)1995 380 (0.629%) 46 (0.033%) 7 (0.014%) 433 (0.173%)1996 30 422 (50.324%) 12 251 (8.738%) 226 (0.458%) 42 899 (17.160%)1997 343 (0.567%) 243 (0.173%) 5 (0.010%) 591 ( 0.236%)1998 8 043 (13.305%) 6 491 (4.630%) 254 (0.515%) 14 788 (5.915%)1999 4 748 (7.854%) 8 075 (5.760%) 522 (1.058%) 13 345 (5.338%)2000 18 975 (31.389%) 6 862 (4.894%) 247 (0.501%) 26 084 (10.434%)2001 12 081 (19.984%) 3 462 (2.469%) 150 (0.304%) 15 693 (6.277%)2002 12 162 (20.118%) 24 178 (17.245%) 589 (1.194%) 33 929 (14.772%)


or multi-temporal Kauth-Thomas transformations (Rogan and Yool 2001) may

produce suitable fire severity maps if both pre- and post-burn images are available.

Fire severity maps are more dependent on robust field validation (White et al. 1996)

than maps of fire presence/absence like ours.

5. Conclusion

The scope of this paper has been limited to our fire scar mapping methodology,

but the motivation to develop it stems from our goal of maintaining a fire history

Figure 5. Map of 1972–2002 fire occurrences, produced by compositing the annual burn mapsportrayed in figure 4.

Table 3. Total hectares (% of area) of each fire occurrence level mapped in figure 5. Only the50 km650 km central portion of the 60 km660 km study area was tabulated becausethe spatial extent of the source imagery varied slightly between years.

Fires S. Africa, MGR S. Africa, outside MGR Botswana Total

8 94 (0.156%) 0 (0.000%) 0 (0.000%) 94 (0.038%)7 570 (0.943%) 13 (0.009%) 0 (0.000%) 583 (0.233%)6 2 469 (4.084%) 81 (0.058%) 0 (0.000%) 2 550 (1.020%)5 5 708 (9.442%) 409 (0.292%) 0 (0.000%) 6 117 (2.447%)4 7 387 (12.220%) 2 903 (2.071%) 7 (0.014%) 10 297 (4.119%)3 10 062 (16.645%) 5 441 (3.881%) 95 (0.192%) 15 598 (6.239%)2 13 005 (21.513%) 10 122 (7.220%) 642 (1.301%) 23 769 (9.508%)1 12 259 (20.279%) 29 483 (21.029%) 2 672 (5.415%) 44 414 (17.766%)0 8 898 (14.719%) 91 750 (65.441%) 45 930 (93.078%) 146 578 (58.631%)

Total 60 452 (100.000%) 140 202 (100.000%) 49 346 (100.000%) 250 000 (100.000%)


for this semi-arid savannah landscape. The contrasting land management practices

within this study area make it useful for measuring the influence of human land use

practices on landscape heterogeneity (Hudak et al. in press). This fire history record

will serve as a valuable baseline dataset for future research to explore interactions

between land use, fire process, vegetation pattern, landscape structure and climate.

More immediately, these fire scar maps have practical utility for MGR managers

who apply fires to meet several objectives: control woody plant encroachment,

promote vegetation diversity, and improve habitat for game and game-viewing. Our

fire scar mapping methodology can be used to map future fires and continually

update this baseline fire history, for the benefit of science and management. It is

also sufficiently generalized and flexible to test in other savannah landscapes.

Acknowledgments

This research was assisted by a grant from the NSF Biocomplexity in the

Environment Program, Dynamics of Coupled Natural and Human Systems (DEB-

0119827). North West Parks and Tourism Board in South Africa purchased most

images used in this study. An EPA STAR Graduate Fellowship funded the 21 June

1996 image purchase. We thank Carol Wessman, who purchased the 29 December

1972 and 13 October 1973 MSS images off a NASA research grant, and Paul

Swetik for reading these image files from 8 mm tapes in an obsolete format. Tobias

Landmann shared the 11 and 27 August 2000 ETMz images along with a 2000

map of prescribed fire severity from the SAFARI project. Landmann and Tanja

Kraus also provided ignition locations of the 2000–2001 SAFARI project fires. We

thank Jule Caylor for having the four 17 November 1980 MSS transparencies

scanned into digital data at the USFS Remote Sensing Applications Center in Salt

Lake City. The South African Weather Service provided rainfall data. Dr Greg

Stuart-Hill documented 1989–1992 fire events through local interviews conducted in

August 1992. MGR field staff mapped fire scars post 1992, Ray Schaller furnished

field maps of burning blocks for validating our 1998 and 2000–2002 maps, and both

Ray and Michele Hofmeyr provided MGR boundary layers. We also thank Jeff

Evans for graphics assistance and Kate Schneider and two anonymous reviewers

for helpful comments.

References

AVERY, T. E., 1977, Interpretation of Aerial Photographs, 3rd edn (Minneapolis, MN, USA:Burgess Publishing).

CHUVIECO, E., and CONGALTON, R., 1988, Mapping and inventory of forest fires from digitalprocessing of TM data. Geocarto International, 4, 41–53.

EDWARDS, A., ALLAN, G., YATES, C., HEMPEL, C., and RYAN, P., 1999, A comparative

Table 4. Validation of three 2002 fire scars in western Madikwe Game Reserve classifiedfrom a 30 m, 18 September 2002 ETMz image and then mapped at 100 m resolution.

Fire initiationdate

30 m classification 100 m map

Correct (%)Omissionerrors (%) Correct (%)

Omissionerrors (%)

29 May 2002 31 (96.875%) 1 (3.125%) 31 (96.875%) 1 (3.125%)13 June 2002 90 (84.112%) 17 (15.888%) 87 (81.308%) 20 (18.692%)3 August 2002 100 (93.458%) 7 (6.542%) 103 (96.262%) 4 (3.738%)

Total 221 (89.837%) 25 (10.163%) 221 (89.837%) 25 (10.163%)


assessment of fire mapping techniques and user interpretations using Landsatimagery. Proceedings of the Australian Bushfire Conference, Albury, Australia.

FUNG, T., and LEDREW, E., 1987, Application of principal components analysis to changedetection. Photogrammetric Engineering and Remote Sensing, 53, 1649–1658.

HOFFMAN, M. T., 1997, Human impacts on vegetation. In Vegetation of Southern Africa,edited by R. M. Cowling et al. (New York, NY, USA: Cambridge University Press),pp. 507–534.

HUDAK, A. T., 1999, Rangeland mismanagement in South Africa: failure to apply ecologicalknowledge. Human Ecology, 27, 55–78.

HUDAK, A. T., and WESSMAN, C. A., 2001, Textural analysis of high resolution imagery toquantify bush encroachment in Madikwe Game Reserve, South Africa, 1955–1996.International Journal of Remote Sensing, 22, 2731–2740.

HUDAK, A. T., BROCKETT, B. H., and WESSMAN, C. A., 1998, Fire scar mapping in asouthern African savanna. Proceedings of the International Geoscience and RemoteSensing Symposium, Seattle, WA, USA, 6–10 July 1998, pp. 1608–1610.

HUDAK, A. T., FAIRBANKS, D. H. K., and BROCKETT, B. H. Trends in fire patterns in asouthern African savanna under alternative land use practices. Agriculture,Ecosystems & Environment, in press.

KENNEDY, R. E., and COHEN, W. B., 2003, Automated designation of tie-points for image-to-image registration. International Journal of Remote Sensing, 24, 3467–3490.

KOUTSIAS, N., and KARTERIS, M., 1998, Logistic regression modeling of multitemporalThematic Mapper data for burned area mapping. International Journal of RemoteSensing, 19, 3499–3514.

KOUTSIAS, N., and KARTERIS, M., 2000, Burned area mapping using logistic regressionmodeling of a single post-fire Landsat-5 Thematic Mapper image. InternationalJournal of Remote Sensing, 21, 673–687.

MILNE, A. K., 1986, The use of remote sensing in mapping and monitoring vegetationalchange associated with bushfire events in eastern Australia. Geocarto International, 1,25–32.

PATTERSON, M. W., and YOOL, S. R., 1998, Mapping fire-induced vegetation mortality usingLandsat Thematic Mapper data: a comparison of linear transformation techniques.Remote Sensing of Environment, 65, 132–142.

PEREIRA, M. C., and SETZER, A. W., 1993, Spectral characteristics of fire scars in Landsat-5TM images of Amazonia. International Journal of Remote Sensing, 14, 2061–2078.

RICHARDS, J. A., 1984, Thematic mapping from multitemporal image data using theprincipal components transformation. Remote Sensing of Environment, 16, 35–46.

RICHARDS, J. A., 1993, Remote Sensing Digital Image Analysis: An Introduction, 2nd edn(New York, NY, USA: Springer-Verlag), pp. 192–193.

ROGAN, J., and YOOL, S. R., 2001, Mapping fire-induced vegetation depletion in thePeloncillo Mountains, Arizona and New Mexico. International Journal of RemoteSensing, 22, 3101–3122.

RUSSELL-SMITH, J., RYAN, P. G., and DURIEU, R., 1997, A Landsat MSS-derived fire historyof Kakadu National Park, monsoonal northern Australia, 1980–94: seasonal extent,frequency and patchiness. Journal of Applied Ecology, 34, 748–766.

RUSSELL-SMITH, J., RYAN, P. G., KLESSA, D., WAIGHT, G., and HARWOOD, R., 1998, Fireregimes, fire-sensitive vegetation and fire management of the sandstone ArnhemPlateau, monsoonal northern Australia. Journal of Applied Ecology, 35, 829–846.

SALVADOR, R., VALERIANO, J., PONS, X., and DIAZ-DELGADO, R., 2000, A semi-automaticmethodology to detect fire scars in shrubs and evergreen forest with Landsat MSStime series. International Journal of Remote Sensing, 21, 655–671.

SINGH, A., and HARRISON, A., 1985, Standardized principal components. InternationalJournal of Remote Sensing, 6, 883–896.

WHITE, J. D., RYAN, K. C., KEY, C. C., and RUNNING, S. W., 1996, Remote sensing offorest fire severity and vegetation recovery. International Journal of Wildland Fire, 6,125–136.


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