15arspc submission 20

8/8/2019 15arspc Submission 20

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LAND COVER CHANGE DETECTION USING LANDSAT TMIMAGERY OF THE 2009 VICTORIAN BUSHFIRES

Li Guo 1,a , Linlin Ge 1,b and Xiaojing Li 1,c

1Geodesy and Earth Observing Systems Group (GEOS)School of Surveying and Spatial Information Systems (SSIS)

University of New South Wales (UNSW), Sydney, NSW 2052, AustraliaPhone: +61 2 9385 4201Fax: +61 2 9313 7493

Email: [email protected] ; b [email protected] ; [email protected]

Abstract

The 2009 Victorian bushfires, also called the Black Saturday bushfires, ignitedacross the Australian state of Victoria on Saturday 7 th February 2009, resultingin Australias highest ever loss of life from a bushfire. According to the VictorianPolice, the bushfires caused at least 173 known deaths of people and 414people injured. The use of multispectral Landsat Thematic Mapper (TM) datawas able to detect and analyse the land cover changes caused by the bushfiresin a rapid and cost-effective way. Many digital change detection techniques,such as image differencing and post-classification have been widely used forthis purpose. The objective of this research was to quantify land cover changesby using two Landsat TM data, one pre-fire and one post-fire, acquired on 1 st March 2008 and 21 st April 2009, respectively, and to examine the strength andweakness of different methods. Change detection results derived from eachmethod were assessed for accuracy against ground survey.

Keywords: Bushfires, land cover change, Landsat TM, Image differencing,Post-classification comparison

1. Introduction

Australia was frequently ravaged by bushfires during the warmer months of theyear. This is because that the warm and dry conditions intensify the probabilityof fire. Especially, the southeast state of Victoria was fire prone owing to thedense population, rugged terrain and well suited fire conditions in comparison ofother states (Beringer, 2000), as evident with the most notorious bushfire of2009 Victorian bushfires. According to the Victoria Police (2009), the bushfirescaused at least 173 known deaths of people and 414 people were listed asinjured at 14 th February 2009. And many towns which located in the north-eastof the state capital Melbourne, such as Kinglake and Marysville, were badlydamaged or almost completely destroyed (ABC News, 2009). These enormous
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figures led to a heated discussion about how to alleviate the devastatingconsequences of the bushfires.

Due to the hazardous nature of bushfires, fire fighters ideally require enoughinformation about bushfires to ensure to control them in time and minimise thepotential risk to communities and properties. However in bushfire cases, thistask has proven to be difficult due to the factors such as lack of accessibility andlarge scales assessment (Graml & Wigley, 2008). For instance, traditional fieldsurvey techniques such as using total station and GPS were accurate, it seemsto be high risk for field survey in fire case. Furthermore, it needed to use moreworking hours to collect the data, so traditional regional mapping was basicallytime-consuming to be applied for the large size assessment of bushfires(Paradzayi et. al, 2008). But the online inventory was demanded for the maps ofdamaged area. Therefore, satellite remote sensing was introduced to overcomethe problems above.

In addition, the Landsat TM program for Earth observation has provided thevaluable information about the Earths surface characteristics over the pastthree decades. Before the 2009 Victorian bushfires, a few studies using LandsatTM data have been already reported to efficiently evaluate the devastateddestruction in the bushfire case. Tupper (2000) analysed that two consecutiveoverpass Landsat TM imagery can be used to quantify agricultural loss in 2007Southern NSW bushfires. Turner et. al (1994) and White et. al (1996) also usedthe Landsat TM imagery to assess the burned patterns in 1988 YellowstoneNational Park and Glacier National Park, respectively. Thus, the 2009 Victorianbushfires detection will be examined by means of multispectral Landsat TMdata.

2. Study area and data set

2.1 Study area

The study of bushfire detection was conducted at the state of Victoria locatedon the southeastern coast of Australia between 37 9 ' 0" and 38 5 '0"S latitudeand 145 2'0" and 146 12 '0"E longitude (Fig. 1).This region had beenrecognised as one of the most devastated areas in the 2009 Victorian bushfires,and many towns such as Kinglake and Marysville were totally damaged. Thetown of Kinglake was the worst impacted area, with a total of 120 deaths andmore than 1,200 homes destroyed. In addition, all but 14 of over 400 buildingsdestroyed were confirmed in the Marysville area (ABC News, 2009). Thesedevastating consequences of the bushfires point out that the recognition of landcover change caused by the 2009 Victorian bushfires is necessary andimportant to improve the understanding of specific Earths su rface changeinformation for this area.


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Figure 1 Selected bushfire detection area and the subset of Landsat TM false colorimage.

2.2 Data set

Two TM data from Landsat 5, one pre-fire and one post-fire, acquired on 1 st

March 2008 and 21st

April 2009, respectively, were used in this research for theinvestigation of land cover change caused by the 2009 Victorian bushfires. Thespecific scene information about these two Landsat TM images can be found inthe following Table 1. The image pair was acquired in the same season in orderto minimise the impacts of seasonal differences of vegetation. In addition, theoperational control of Landsat 5 and its entire data archive were administeredby the U.S Geological Survey (USGS) (Chander et. al., 2004). So all Landsat 5images used in this research were downloaded from USGS website andbelonged to USGS.

Table 1 Scene information of Landsat TM images acquired on 1st

March 2008 and 21st

April 2009.

AcquisitionDate

Path/Row Sunselevation/Suns

azimuth angles

Datum Map Projection

1st March 2008 92/86 43.68/58.95 WGS84 UTM

21 st April 2009 92/86 29.91/41.62 WGS84 UTM


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3. MethodsFigure 2 illustrates the detail analysis of bushfire detection methods used in thisstudy, and describes in the following sections.

Figure 2 Flow diagram of bushfire detection framework.

3.1 Data pre-processing

Although the change detection techniques are different on algorithms, allmethods deal with multi-temporal images that are acquired in different datesand have different sun angles (Cheng et. al., 2004). It can be more difficult toquantify changes on multi-temporal data that have different illuminations, andLandsat TM images acquired on different dates have the problem of image shift.Consequently, this study will firstly take the original 1 st March 2008 and 21 st April 2009 images as the reference image and subject image, respectively, toperform the image to image registration and radiometric normalisation beforeconducting the change detection analysis.


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3.2 Change detection approachesTwo change detection techniques including image differencing and post-

classification were used in this research to determine the changes haveoccurred by the 2009 Victorian bushfires.

3.2.1 Image differencingImage differencing is a technique t hat refers to subtracting a pixels DigitalNumber (DN) on one date from corresponding pixels DN on the second date,and produce a residual image which represents the change between two dates(Mas, 1999). The subtraction results in positive and negative values reflect theareas of change and zero value reflects no change (Sohl, 1999). This method isthe most widely used digital change detection algorithms, because it isstraightforwardness and easily to implement (Sunar, 1998). But this approach

cannot provide a detailed change matrix, and requires selection of thresholds.

In addition, bushfires may make the changes of land surfaces by reducing thegreenness of the vegetation and altering both aboveground and belowgroundmoisture and exposing soil. Patterson and Yool (1998) claimed that thevegetation removal, soil exposure and the moisture content change of theEarths surface can be detected by different remotely sensed indices. Thus,remotely sensed indices such as Normalise Burn Ratio (NBR), NDVI(Normalised Difference Vegetation Index) and MNDWI (Modified NormalisedDifference Water Index) were integrated in the image differencing approach fordiscriminating between the bushfire areas and non-bushfire areas in this study.

3.2.2 Post-classification comparisonIn the post-classification approach, images belonged to the different dates arefirstly classified and labelled individually. Later, the classification results arecompared directly and the change areas are extracted (Singh, 1989 & Suzanchi,2006). Both supervised and unsupervised classification methods can be used inthis approach. Individual classification of two images minimises the problem ofnormalising for atmospheric and sensor differences between two dates(Suzanchi, 2006). Unfortunately, the errors in the individual classification ofeach image are reflected in the final change detection (Teng et. al, 2008).Therefore, this method requires individual classification of images should be asexact as possible. And Maximum Likelihood Classification (MLC) was adoptedto generate the land cover change map in this study.

3.3 Accuracy assessmentThe accuracy assessment was implemented by the error matrix and Kappastatistic, which were considered as the standard descriptive and discrete


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multivariate statistics, respectively, in the remote sensing field (Congalton,2004).

3.3.1 Error Matrix

Error matrix also can be called as confusion matrix. It is a square array ofnumbers defined in rows and columns. These rows and columns express thenumber of sample units such as pixels, clusters of pixels or polygons, which areassigned to a particular category relative to the actual category as indicated bythe reference data (Congalton, 2004). Generally, the columns represent thereference data and the rows indicate the results generated from the differentclassification approaches. In addition, several statistical accuracies can begenerated from this matrix such as user accuracy, producer accuracy, overallaccuracy and Kappa coefficient (Foody, 2002).

3.3.2 Kappa StatisticKappa can be used as another measurement for accuracy assessment. It is anindex that depends on the classification accuracy after adjustment for changeagreement, and measures the relationship of beyond the change agreement toexpected disagreement (Fitzgerald & Lees, 1994). The Kappa coefficient can beabbreviated as KHAT. Kappa values range from -1 to +1. If the value of KHAT ishigher, it means that the agreement is stronger. For instance, if the Kappa valueis 1, there is perfect agreement. In contrast, if the KHAT is 0, this means noagreement. According to the Landis and Koch (1977), the possible ranges forKHAT can be categorised into three groups: a value greater than 0.80 (i.e., 80%)

indicates strong agreement; a value between 0.40 and 0.80 (i.e., 40% to 80%)represents moderate agreement; and a value below 0.40 or 40% representspoor agreement.

4 Results and discussion

4.1 Image differencingDuring the image differencing process, different types of remotely sensedindices were firstly implemented (Fig. 3). Subsequently, each differencing resultgenerated from different remotely sensed indices was stacked in order to formthe color composite (Fig. 4). Furthermore, the color composite contained threedifferent surface information including the surface change of brightness, surfacechange of greenness and surface change of wetness (Fig. 5). Finally, MaximumLikelihood Classification (MLC) was adopted to generate the land cover changemap for color composite (Fig. 6).


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Figure 3 Remotely sensed index of: 1)1 stMarch2008NBR; 2)21 stApril 2009NBR;3)1 stMarch2008NDVI; 4)21 stApril2009NDVI; 5)1 stMarch2008MNDWI;

6)21 stApril2009MNDWI.


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Figure 4 Index differencing of: 1)NBR; 2)NDVI; 3)MNDWI.


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Figure 5 Color composite of NBR differencing, NDVI differencing and MNDWIdifferencing.

Figure 6 Result of image differencing.


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4.2 Post-classification comparisonLand cover classification of each Landsat TM image was implemented by usingMaximum Likelihood Classification, in which six classes were selected includingforest, grassland, bared land, water body, urban area and residential area (Fig.7). Then, the land cover change caused by the 2009 Victorian bushfires wasderived by comparing two individual classified images pre- and post- bushfires(Fig. 8). Since all of the vegetation samples were classified as the forest orgrassland before the 2009 Victorian bushfires. Therefore, if a pixel changesfrom the forest or grassland to the bared land, it would be classified as theburned area. On the other hand, it would be defined as the unburned areas if itis unchanged.

Figure 7 Image classification result of: 1)1 st March 2008 Maximum LikelihoodClassification; 2)21 st April 2009 Maximum Likelihood Classification.


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Figure 8 Result of post-classification comparison

4.3 Accuracy assessment

The reference data for damaged areas caused by the 2009 Victorian bushfiresin this case study area was extracted by comparing the ground truth dataprovided by the Victorian Country Fire Authority (CFA) and visual interpretationbased on original Landsat TM imagery. In total, the burned areas and unburnedareas were about 1,682.36 Km (i.e. 1,869,291 pixels) and 12,529.18 Km(13,921,309 pixels), respectively.

The image differencing method integrated the remotely sensed indices includingNBR, NDVI and MNDWI offers the best bushfire detection result, which can be

found in the following Table 2. It generates the KHAT value about 84.44%,indicating a strong a greement. Furthermore, its omission error, such as theactual bushfire pixels are not detected, and commission error, such as thechange pixels but not belong to bushfires, are 14.07% and 13.38%, respectively(Tab. 4). On the other hand, the approach of post-classification comparisonprovides the lowest KHAT value (i.e. 59.84%), which represents moderateagreement (Tab. 3). And the omission error and commission error are 15.96%and 46.15%, respectively (Tab. 4).


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Table 2 Error matrix for image differencing method derived from the NBR, NDVI andMNDWI (Unit: pixel).

Burned areas Unburnedareas

Row total Usersaccuracy

Burned areas 1606236 248191 1854427 86.62%

Unburnedareas

263055 13673118 13936173 98.11%

Column total 1869291 13921309 15790600

Produ cersaccuracy

85.93% 98.22%

Overall accuracy = 96.76%, KHAT = 84.44%

Table 3 Error matrix for post-classification comparison (Unit: pixel).

Burned areas Unburnedareas

Row total Usersaccuracy

Burned areas 1571027 1346617 2917644 53.85%

Unburnedareas

298264 12574692 12872956 97.68%

Column total 1869291 13921309 15790600

Producers

accuracy84.04% 90.33%

Overall accuracy = 89.58%, KHAT = 59.84%

Table 4 Bushfire detection performance acquired from error matrices using differentchange detection techniques.

Change DetectionTechniques

Omission Error(%)

CommissionError(%)

KHAT(%)

Imagedifferencing

14.07 13.38 84.44

Post-classification

15.96 46.15 59.84


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4.4 Comparison of the results of two different change detectiontechniquesAlthough each of these change detection techniques could individuallycontribute to the bushfire detection, it can be found from this study that theimage differencing approach integrated the three remotely sensed indicesincluding NBR, NDVI and MNDWI, represents the better result than the post-classification comparison technique. This is because that the accuracy of thepost-classification comparison highly relays on the accuracy of the initialclassification, which is especially controversial in that it always pose a seriouserror to the final result of post-classification comparison (Teng et. al, 2008). Andthe mis-classification errors in the original images always make the results,which are obtained using post-classification comparison, are judgedunsatisfactory (Coppin et. al, 2004). For instance, most of bared lands weremisclassified as either residential areas or grasslands. The reason is that thelow spatial resolution of Landsat TM imagery results in low capability of spectralseparation between different classes.

In addition, the accuracy of post-classification comparison technique was alsorestricted by using the pixel-based classification methods such as MLC. Thereason is that the pixel-based classifiers only make use of the spectralinformation or the value of pixel itself (Richards & Jia, 1998). The resultsobtained from MLC were unsatisfactory, particularly, in the case of mapping thebared lands. This is because that the spectral profile of bared lands alwaysmixed up with the grasslands and residential areas. For example, many baredlands could be found to be spectral similar with residential areas or grasslands.

5. ConclusionsThe profound and serious consequences caused by the 2009 Victorianbushfires result in the assessment of bushfires become critically significant. Inorder to minimise the bushfires negative impacts on society, an efficient andreliable bushfire detection system was proposed to assess the devastatedeffects of the 2009 Victorian bushfires. It is possible to utilise the repetitivecapability of satellite remote sensing imagery to identify the location of change

to the Earths surface and integrate the different remotely sensed indices. Theresults confirm that the procedure can offer essential spatial information forbushfire assessment.

Acknowledgements

This research was strongly supported by the Geodesy and Earth ObservingSystems Group (GEOS) of the School of Surveying and Spatial InformationSystems (SSIS) in the University of New South Wales (UNSW), and thanks theVictorian Country Fire Authority (CFA) for providing the ground truth data.


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15arspc submission 20

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