15arspc Submission 202

8/8/2019 15arspc Submission 202

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MONITORING THE 2009 VICTORIAN BUSHFIRES WITH ALOSPALSAR MULTI-TEMPORAL AND COHERENCE IMAGES

Hai Tung Chu, Xiaojing Li, Linlin Ge and Kui Zhang

School of Surveying and Spatial Information SystemsThe University of New South Wales, Sydney,

NSW 2052 AustraliaPh. +61 2 93854174, Fax. +61 2 9313 7493

[email protected]

ABSTRACTThe Victorian bushfires in February 2009 were one of the most terrifying recentnatural disasters in Australia. Synthetic Aperture Radar (SAR) is an allweatherremote observation system which can penetrate through cloud and smoke to

image the Earths surface at anytime. Hence, it is an ideal tool for changedetection, and in particular for fire monitoring.

This paper describes the use of ALOS/PALSAR multi-temporal images andinterferometric coherence data to study the 2009 Victorian Bushfires. Two areaswhere several ALOS PALSAR images had been acquired before, during andafter the fire period were selected for this study.

Landsat TM images acquired for a similar period over the same areas wereused for validation purposes. Various techniques were applied to detect burntareas, including backscatter intensity difference, coherence analysis andPrincipal Component Analysis. However, none of these methods could clearlylocate the fire-affected regions on their own. A combined approach wassignificantly better. Burnt scars extraction was carried out using MaximumLikelihood, Artificial Neural Network, and Support Vector Machine classificationtechniques, and the results were compared to identify the best classifier thatproduced the most accurate fire damage map.

1. INTRODUCTIONIn 2009 Australians had experienced one of the historically worst naturaldisasters, namely The Victorian Bushfires. The impacts of this disaster werecatastrophic. A total of 173 lives has been lost, properties were damaged. Largeareas of natural forests were severely destroyed. These facts has emphasizedneeds for bushfire detection and monitoring. Optical satellite imagery such asLandsat TM, SPOT, MODIS has been used extensively for forest fire mappingand often gave good results. However, the optical system can only operate atday time and may be obstructed by clouds and smokes. Consequently, in manycases it is not possible to observe the ground surface. In contrast, the SyntheticAperture Radar (SAR) systems use their own source of energy at microwavewavelengths to image Earths surfaces and therefore is capable to obtainimages at anytime with minimal impacts from clouds and smokes. For these


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reasons, SAR images are very appropriate for forest fire detection andmonitoring.

Uses of SAR data for forest fire detection and burnt areas mapping has beenreported by many researchers (Bourgeau-Chavez et al. 2001, Gimeno et al.2004, Siegert and Hoffmann 1998, Siegert and Ruecker 2000, Takeuchi andYamada 2002, Almeida-Filho et al. 2007 and Almeida-Filho et al. 2009). Thesestudies mainly relied on differences in backscatter intensities between beforeand after fire images to detect and map burnt areas. Siegert and Rueker (2000),used multitemporal ERS-2 SAR images for identification of burnt scars overstudy site in East Kalimantan, Indonesia. They found that backscatterdecreased by 2 -5 dB in fire impacted areas while there were only slightlychange in backscatter (less than 0.5 dB) for non-fire impacted areas. Gimeno etal. (2004), reported that the changes of backscatter between forested and fire-disturbed zones was up to + 8 dB in ERS time series data for Mediterraneanforest regions. However, results of work carried out by Takeuchi and Yamada(2002) showed a little decrease in backscatter of JERS-1 SAR data after fire but

difference was not large enough to identify and extract fire affected areas.Siegert and Hoffmann (1998), described two type of forested fire damage areascan be detected including severe damage with completed burning of vegetationand medium damage with parts of burnt vegetation. The first case related tostrong decrease in backscatters while the later represents areas with littlechange in backscatters.

Applications of interferometric coherence SAR data for forest fire damagemonitoring has been undertaken by Takeuhi and Yamada (2002). Resultsshowed a significant increase in coherence in damaged areas after the fireallowing better interpretation and extraction of burnt areas compared to uses ofbackscatter data. Antikidis et al. 1998 evaluated capabilities of interferometricTandem ERS SAR data for mapping deforestation areas in Indonesia. It isfound that there were clear changes in coherences between images acquiredbefore and after the fire event.

The objective of this study is to analyze capabilities of multi-temporal SAR data,particularly ALOS/PALSAR images to identify and extract burnt areas during theVictorian bushfire in February 2009. The combination of SAR backscatterintensity and interferometric coherence data is also a goal of the research.

2. STUDY AREAS AND DATA USEDTwo test sites have been selected for this study.

Site 1 locates in far north-east of the city of Melbourne with a distance of 200km. The study area is covered by one full scene of ALOS/PALSAR image (track378, frame 643) with several suburbs, such as Bright, Mt Beauty and Omeo.The main land cover types are forests, grass and bare grounds.

The 2 nd test site covers small part of the city of Melbourne and its northernregions including Kinglake, Marysviles, Broadford, Yea, Kilmore suburbs. Theextension of the study area is also approximately to a full scene of theALOS/PALSAR images (track 383, frame 643). The main land cover classesare forests, grass and urban residence.


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Table 1: ALOS/PALSAR images for the 1 st study area

Satellite/Sensor Track - Frame Acquisition dates Polarization Orbit

ALOS/PALSAR 378_643

30/12/2008 HH Descending



Table 2: Interferometric pairs over 1 st study area

Satellite/Sensor Track-Frame

Date: Master Date: Slave Baselinelength (m)

Observationinterval (days)

ALOS/PALSAR 378_643

30/12/2008 14/02/2009 270 46

30/12/2008 02/07/2009 947 186

14/02/2009 02/07/2009 680 138

Table 3: ALOS/PALSAR images for the 2 nd study area

Satellite/Sensor Track - Frame Acquisition dates Polarization Orbit

ALOS/PALSAR 382_643





Table 4: Interferometric pairs over 2 nd study area

Satellite/Sensor Track-Frame

Date:Master

Date: Slave Baselinelength (m)

Observationinterval(days)

ALOS/PALSAR 378_643

06/12/2008 21/01/2009 304 46

06/12/2008 08/03/2009 878 92

06/12/2008 24/07/2009 1510 230

21/01/2009 08/03/2009 579 46

21/01/2009 24/07/2009 1196 184

08/03/2009 24/07/2009 626 138

Site 1: Landsat 5 TM acquired on 24/01/2009 and 25/02/2009.

Site 2: Landsat 5 TM acquired on 31/01/2009 and 16/12/2009

These images were acquired before and just after the bushfire and exploited forvalidations.


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3. METHODOLOGYAll of ALOS/PALSAR satellite images including intensities and coherence datawere registered to the map coordinate system (UTM projection, WGS84 datum)with 30m spatial resolution. Speckle noises in the PALSAR images were filtered

using the Enhanced Frost Filter with the 5x5 window size.SAR backscattered values are converted to decibel (dB) by the equation (1)

below:

Db = 10 * log 10(DN2) + CF (1)

Where Db, DN are magnitude values and CF is Calibration Factor provided byJapan Aerospace Exploration Agency (JAXA).

The Temporal Backscatter Changed images was generated by the followingequation ( Chu and Ge, 2010).

TBchange = Max(Db 1, Db 2,.., Db n) Min(Db 1, Db 2 , Db n) (2)

Where: TB change is Temporal Backscatter Change images.

Db 1, Db 2 Db n are pixel backscattered values in SAR images.

Max and Min are the functions to get the largest and smallest

pixels values within all applied images.

The TB change image is in fact a modification of the intensity differencingtechnique. It allows picking up the biggest changes of pixel values betweenmulti-temporal images, consequently, it will enhance change features. This

image is very useful for change detection, including changes subjected to fires.As mentioned earlier, interferometric coherence between pairs of SAR imagesare valuable for burnt monitoring. The coherence images measures correlationbetween two images. In the coherence images stable objects appear bright withhigh coherence value while changed objects appear darker with low coherencevalue. So, it is expected that the coherence of pixel in fire areas will be low orreduced compared to non-change areas. Moreover, coherence of pixel in burntarea in a pair of across fire SAR images would be lower than coherence in apair of before fire or after fires images. In this study the coherence data over 2test sites were analysed in conjunction with backscatter intensity data.

Utilization of the Principle Component Analysis (PCA) for forest fire monitoring

has been reported by numerous researchers (Siegert and Hoffmann 1998,Gimeno et al. 2004). It is revealed that PCA products generated from the multi-temporal SAR images could be applied for change detection, particularly forbushfire mapping. The First Component PC1 or sometime even the SecondComponent PC 2 often represent non-changed features. Other componentscontain variations due to changes in ground surfaces. However, results aredepended on each dataset. In this study the PCA techniques was alsoevaluated.


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Visual interpretation is an important step for detecting and collecting relevantinformation about fire affected areas and other land cover features. Earlierstudies showed that the use of multi-temporal series SAR data improved featurediscrimination in comparison to uses of single-scene data (Gimeno et al. 2004).It is also revealed that more features can be distinguished in the RGB

composites than in a single image.In this study, burnt areas and other land cover classes are identified by visualinterpretation of RGB composites, in which Red, Green and Blue colourchannels were assigned to different dated SAR images or their derived products(TBchange , PC components or average of multidates SAR images). Then,properties of burnt areas as well as other features are collected for analysis andclassification process.3.1. Classification

Three classification techniques, namely Maximum Likelihood (ML), ArtificialNeural Network (ANN) and Support Vector Machine (SVM) were applied forburnt scars extractions. The classification results are compared with the burntareas derived from the Landsat TM data.

The ML algorithm is one of the classical classifiers and most commonly used forsatellite image classification. Its strengths include high speed computation andrather acceptable acuracies. The main constraint of the ML algorithm is itsassumption of normal distribution of input data which is not always adequate tomodel remotely sensed data.

ANN and SVM are recently developed techniques for image classification.Unlike the ML algorithm, these classifiers does not constrain to the normaldistribution assumption. Therefore, they are considered more appropriate forhandling satellite images, particularly for complex datasets. In this study, the

SVM classification using Radical Basic Function (RBF) was applied based on itsrobustness (Kavzoglu and Colkesen 2009) . For the ANN classifiers, theMultilayer Feed Forward Network which uses Back-propagation algorithm withthe sigmoid logistic activated function was implemented.3.2. Validation

Due to lacking of ground truths data the Landsat TM images acquired beforeand after fire event was used for validation purpose for both study sites. It isvery fortunate that burnt areas appear very clearly in these optical images.Firstly, the Normalized Difference Vegetation Index (NDVI) were computed foreach Landsat images. After that, before and after fire NDVIs were subtracted toeach other to create NDVI difference images. Finally, the NDVI difference

image will be segmented to extract burnt areas.4. RESULTS AND DISCUSSIONIn both test sites uses of the RGB colour composite generated from originalSAR backscatter images did not provide good separation between firesdamaged areas with other features. In these colour composition burnt areasappeared in light purple or pink colour but the difference was relatively smalland it was easy to be confused with other areas with similar colour.


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Fig 1: A) and C) showed fire affected areas are in purple to pink colour in multi-datecolour composites in 1 st and 2 nd study sites. B) and D) showed burnt areas (dark

colour) in corresponding false colour Landsat TM images.

The temporal backscatter change images TB change over 2 study areas weregenerated from multi-dated SAR images and analysed. These images showedparts of burnt areas more clearly in very bright tones and appear significantlydifferent from surrounding features. However, other changed features whichwere not subjected to fires also appear bright and it is difficult to differentiatethese changed features with burnt areas.

All interferometric coherence images had been evaluated for burnt areadetection. It is revealed that, for both study sites the coherence imagesgenerated from pairs of images acquired just before and after the bushfire wasuseful. Burnt areas appear very dark due to low coherence between pre- andpost-fire events and rather well separated from surrounding areas.Nevertheless, some other features such as un-burnt forest may also represent


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low coherence due to natural de-correlation factors (winds, changing of leaveposition .etc.) and consequently look similar in the coherence data.

Thus, the combination of backscatter intensity and inteferometric coherencedata was applied and gave much better results comparing to uses of any singledata sources.

Based on this strategy the most useful RGB colour composite was found,where: Red was assigned to Average SAR images (or the First PC component),Green was assigned to Temporal Backscatter Change images (TB change ) andBlue was assigned to interferometric coherence data generated from an acrossfire image pair.

This RGB composite showed burnt areas in Yellow to Little Red colour due tostrong backscatter (Red), big changes (Green) and low coherence (Blue).

Fig 2: A) and C) are RGB colour composites of Average SAR image (R), TemporalBackscatter Change (G) and across fire coherence data (B) in 1 st and 2 nd study sites.

Burnt areas appeared Yellow to Reddish colour in A) and C) RGB composites.


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PCA images were generated from multi-dates SAR backscatter images. ThePC1 components were very similar with the average of all SAR images and thePC3 and PC2 components contained changed information for 1 st and 2 nd sitesrespectively. However, information on changed features can be readily obtainedfrom the temporal backscatter change image without concerns about noises as

a case of the PC2, PC3 components.Based on results of visual interpretation several land cover classes weredeveloped for each test site.

Site 1: Highly_burnt forest, Shadow_burnt, Un-burnt forest and Grass.

Site 2: Highly_burnt forest, Medium_burnt forest, Un-burnt forest, Urbanresidence, Grass and Bare ground.

Details of their backscatters and coherence characteristics were described inthe diagrams below.

Fig 3: Backscatter values for burnt and unburnt classes over the 1 st test site


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Fig 4: Coherence values for burnt and unburnt classes over the 1 st test site

Fig 5: Backscatter values for burnt and unburnt classes over the 2 nd test site


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Fig 6: Coherence values for burnt and unburnt classes over the 2 nd test site

It is interesting that the backscatters increased after the bushfires over bothstudy sites. The reason is in the 2009 Victorian bushfire for many regions onlyleaves or parts of leaves burnt out while tree trunks remained. Consequently,there are strong corner reflectors effects on the SAR images resulting inincrease in backscatter. For the 2 nd test site, as Highly-burned forest andMedium-burnt forests were identified based on level of their severe burning andcan be detected. The most challenging task was to identify the burnt areaswhich were in shadow areas. The clues to map this class were analysedterrains and its relative position to other burnt areas.

It is very clear that coherence values of fire affected areas were very low inacross fire image pairs (30/12/2008 & 14/02/2009 in 1 st test site and 12/01/2009& 08/03/2009 in 2 nd test site) making them very distinguishable from otherfeatures.

The classification process was performed over 2 test sites with classesmentioned previously. At the later steps, classification results were merged intoonly 2 classes, namely burnt and un-burnt classes. This final result was thencompared with the burnt areas extracted from the Landsat TM images forvalidation. Since it is rather cloudy in a lower part of the Landsat TM imageacquired on 25/02/2009 over the 1 st site, the Landsat TM data for this site onlyused for general comparison. Details quantitative assessments were carried outover the 2 nd study site.

Following datasets were selected for classification:

Case 1: All of PALSAR backscatter data


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Case 2: Combination of Average SAR image + Backscatter Change Image +across fire coherence images

Case 3: All of PALSAR backscatter data + selected coherence images (before +across + after fire data)

Results of burnt areas classification was given in the table 5 below:

Table.5: Overall classification accuracy assessment for various combined datasets andclassifiers over the 2 nd test site

Classifiers/ Datasets

MaximumLikelihood

Artificial NeuralNetwork (ANN)

Support VectorMachine (SVM)

Case 1 81.11 % 80.49 % 81.26 %

Case 2 84.50 % 81.25 % 84.90 %

Case 3 85.42 % 80.47 % 84.35 %

It is clear that in general the burnt unburnt mapping using multi-temporal SARimages was agreed well with the results derived from Landsat TM images (over80% agree). The uses of combined SAR backscatter and coherence dataimproved the classification accuracy significantly by more than 4% for the MLand SVM classifiers. Although it is expected that the ANN and SVM classifierswill gave higher accuracy than the traditional ML classifier, but it is not a casefor this study. The classification accuracy for ML and SVM classifiers was verysimilar, but it was noticeably decreased for the ANN classifier. The reason couldlied upon validation data generated from the Landsat TM images, which maynot represent burnt areas correctly. In a case that real ground truth data areavailable that allows better accuracy assessment, the performance of ANN and

SVM classifiers may probably improve.

Fig 7: A) Burnt scars extraction from Landsat TM images, B) Burnt scars extractionbased on SVM classification (case 3). The Red areas represent burnt scars.


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5. CONCLUSIONSMulti-temporal ALOS/PALSAR images have been applied successfully toidentify and map the 2009 Victorian bushfire. Results indicated that using SARdata, particularly ALOS/PALSAR is a feasible and effective approach for firemonitoring.

Uses of only backscatter intensity or interferometric coherence data did not givegood separation between burnt and unburnt areas. However, the synergisticuse of these data allows significant improvement on detections and extractionsof fire damage areas. It is found that, the RGB colour composite consisted ofaverage multi-temporal SAR images, temporal backscatter images and acrossfire interferometric coherence was most efficient dataset.

Three classification techniques were implemented to classify burnt scars. Burntareas extracted from Landsat TM images acquired before and after fire wereused as validation data. The ANN classifier produced lowest classificationaccuracy compared to other two techniques. The ML and SVM algorithm gavesimilar accuracy. It is anticipated that using better ground-truth data willenhance classification accuracy of ANN and SVM classifiers.

ACKNOWLEDGEMENTALOS PALSAR Level 1.1 product was processed by ERSDAC, Japan.Copyright of raw data belongs to METI and JAXA.

ReferencesAlmeida-Filho, R., Rosenqvist, A., Shimabukuro, Y.E., and Silva-Gomez, R.,2007, Detecting deforestation with multitemporal L-band SAR imagery: a casestudy in western Brazilian Amazonia. International Journal of Remote Sensing ,28:1383-1390.

Almeida-Filho, R., Rosenqvist, A., Shimabukuro, Y.E., and Sanchez, G. A.,2007, Using dual-polarized ALOS PALSAR data for detecting new fronts ofdeforestation in the Brazilian Amazonia. International Journal of Remote Sensing , 30:3735-3743.

Antikidis, E., Arino, O., Laur, H., and Arnaud, A. (1998), ERS SAR coherenceand ATSR hot spots: a Synergy for Mapping Deforested Areas. The SpecialCase of the 1997 Fire Event in Indonesia. In Proceedings of the Retrieval of Bio and Geo-Physical Parameters from SAR Data for Land Applications Workshop ESTEC , 2123 October, Netherlands, ESA SP 441.

Chu, H.T., & Ge, L., 2010. Synergistic use of multi-temporal ALOS/PALSAR

and SPOT multi-spectral satellite imagery for land cover mapping in the Ho ChiMinh city area, Vietnam. IEEE International Geoscience and Remote SensingSymposyum. (IGARSS), Honolulu, Hawaii, USA, 25-30 July.

Gimeno, M., San-Miguel-Ayanz, J., and Schmuck, G., 2004, Identification ofburnt areas in Mediterranean forest environments from ERS-2 SAR time series.International Journal of Remote Sensing , 25:4873-4888.


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Kavzoglu, T., and Colkesen, I., 2009, A kernel functions analysis for supportvector machines for land cover classification, International journal of Applied Erath Observation and Geoinformation, 11: 352 359.

Siegert, F., and Hoffman, A. A., 2000, The 1998 Forest Fires in East Kalimantan(Indonesia): A Quantitative Evaluation Using High Resolution, MultitemporalERS-2 SAR images and NOAA-AVHRR Hotspot Data.

Siegert, F., and Ruecker, G., 2000, Use of multitemporal ERS-2 SAR imagesfor identi. cation of burned scars in south-east Asian tropical rainforest.International Journal of Remote Sensing , 21: 831-837.

Takeuchi, S., and Yamada, S., 2002, Monitoring of Forest Fire Damage byUsing JERS-1 InSAR. IEEE International Geosciences and Remote Sensing Symposium, IGARSS 02, 24-28 June, Torongto, Canada .

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