IntroductionEvery year in Indonesia, fires starting inJuly are extinguished in September withthe beginning of the wet season [O.Arino et al., 1998]. During the autumnof 1997, a particularly intense drynessassociated with the absence of themonsoon engendered an exceptionalfire event in Indonesia. This phenome-non, probably related to ‘El Niño’, sawthe destruction of a large part of theIndonesian tropical forest and releaseda significant amount of trace gas andaerosols into the atmosphere. The main

fires occurred in the eastern region ofSumatra, in the Indonesian part ofBorneo (Kalimantan), in Sulawesi(Celebes) and in the Java mountains. At the beginning of October 1997, theWorld Wide Fund for Nature (WWN)estimated the burned areas in Indonesiaas about 750 000 ha [Le Monde,08.10.1997].

Because they embark several comple-mentary instruments (optical to micro-wave spectral bands, low- and high-resolution), the ERS satellites were of

particular interest in studying thisexceptional fire event. The low-resolu-tion ATSR-2 instrument allowed thelocation of active fires during nightacquisition while the high-resolutionSAR instrument allowed a more preciseestimation of the extent of the burnedareas. Furthermore, recent studiesshow that interferometric ERS tandemdata, in particular the phase coherence,are a valuable source of land cover typeinformation [Wegmuller et al., 1997].The main objective of the studies hasbeen to assess the burned areas pro-duced by the fire event during the period August-November 1997, by correlation of the information providedby ATSR data, together with intensityand coherence information derived fromthe SAR data.

Data processing and locationThe ERS-2 Along-Track ScanningRadiometer (ATSR-2) data acquiredduring the autumn of 1997 have beencalibrated and geolocated. The activefires (i.e. hot spots) correspond to thesaturated pixels in the 3.7 µm channel(values higher than 312 K) of ATSRacquired at night-time, i.e. ascendingpass [Buongiorno et al., 1997]. Hotspots centre coordinates have beenrecorded in latitude and longitude(Fig. 1).

The first phase of the study was limitedto the area with the highest ATSR hot

sar/atsr synergy 34

Deforestation evaluation by synergetic use of ERSSAR coherence and ATSR hot spots: The Indonesian

fire event of 1997E. Antikidis, O. Arino, H. Laur & A. Arnaud

ESA Directorate of Applications, Remote Sensing Exploitation Department, ESRIN, Frascati, Italy

The synergetic use of the ERS SAR and ATSR data allows the evaluation of the Kalimantan deforestation during the Indonesianfire event of 1997. Changes in forest coherence signature given by the interferometric SAR data before and after the fire eventsare highly correlated to hot spots detected with the ATSR during the fire events themselves. These specific coherence temporal signatures are then used to perform a classification of the deforested regions. The validation with in-situ data is nowongoing in coordination with the TREES project. The method strongly suggests that regional maps of burned forest can bederived from ATSR and tandem SAR data.

Monthly maps of active fires detected by ATSR, August to November 1997, Borneo& Sumatra, Indonesia. Active fires corresponding to saturated pixels acquired atnight-time in the 3.7 µm channel of ATSR have been plotted to display the fire eventdistribution.

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spots located close to the TelukSebangan Bay in Kalimantan (Fig. 2).ERS SAR data covering the area beforeand after the fire events were thenselected (Table 1).

Since several studies indicated the ability of SAR intensity data to assessburned areas [Rignot et al, 1994;Bourgeau-Chavez et al., 1997), a multi-temporal SAR PRI image was gene-rated first. Potential burned areas wereindeed identified (Fig. 3), but supervisedand unsupervised classification did notproduce convincing results. However,because the fire event occurred duringthe ERS tandem mission (1-day repeatinterval) dedicated to SAR interfero-metry (InSAR), it was then decided touse InSAR coherence data as an addi-tional source of information. Two tan-dem couples, one acquired before theexceptional fire event (April 1996) andone afterwards (October 1997) wereused in order to study the temporalbehaviour of the tandem coherence.

The coherence images were generatedwith the Interferometric Quick Look(IQL) processor. This processor, essen-tially used to assess the quality and thepotential of the ERS InSAR data, is ableto generate intermediate images withvariable pixel spacing ranging from 35to 200 m [Laur et al., 1998]. Together

with the coherence image, the IQL generates three other ground range co-registered images: the inter-ferometric phase image, the intensityimage of the first pass and the intensityimage of the second pass. For thisstudy, a combination of 50-m pixelspacing images, i.e. co-registeredimages of coherence (red channel),average of intensities (green channel)and intensities difference (blue channel)(Figs. 4 & 5) were used. The areas withhigh coherence appear in red. Thegreen/blue areas correspond to lowcoherence and medium intensity, i.e.probably to the tropical forest. The areaswith low coherence and low intensity(i.e. water) appear in black. Finally, theareas with high coherence and highintensity appear in yellow and corres-pond most likely to villages or cities.

In order to use the possible synergybetween both instruments, ATSR hotspots were co-registered with the twoSAR tandem images. The hot spot representation is arbitrary as more thanone hot spot of variable size and temperature may saturate the ATSRpixel [Dozier et al., 1981].

Coherence temporal behaviourThe coherence, for repeat-pass inter-ferometry, describes the ‘similarity’ oftwo complex SAR signals. Basically,when two signals are the same, thecorresponding coherence values areclose to one. If the signals are com-pletely different, the coherence valuesare close to zero.

indonesian fire35

Accumulation of ATSR hot spotsbetween 1 August and 7 October1997 over Borneo. ERS SAR tandemdata used for the study are indicated.

Satellite Date Orbit Ordered SAR Perpendicular Product Baseline

ERS-1 29/04/96 25041 RAWERS-2 30/04/96 05368 RAW 100ERS-2 22/10/96 07873 PRIERS-1 06/10/97 32556 RAWERS-2 07/10/97 12883 RAW & PRI 400

Table 1. ERS SAR RAW Tandem & PRI Data (Frames : 3636-3654-3672)

ERS SAR multi-temporal intensity image; red channel: 22 October 1996; green &blue channels: 7 October 1997.

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The value of the coherence can bedecomposed into two contributions[Dammert et al, 1995]:

- the temporal coherence, inherent to temporal changes of the elementary scatterers within the resolution cell. If their geometric absolute locations and/or their dielectric properties change (e.g. due to humidity), the temporal coherence is reduced;

- the geometric coherence, related to the satellites inter-distance at the time of acquisition. When the inter-distance increases, the location of the elementary scatterers relative to the satellite varies and, by con-sequence, the geometric coherence decreases. In the case of volume scattering, this effect is amplified by the scatterers dispersion and the multiple reflections.

Forest is seen by radar sensor as layersof scatterers (volume scattering), whichreduces the geometric coherence com-ponent. Additionally, the leave motioncontributes to the temporal coherenceloss. Consequently, the tropical forestappears incoherent. However, a burnedforest should have a higher coherentresponse due to the strong reduction ofvolume scattering. Therefore, a multi-temporal image of the coherence

sar/atsr synergy 36

Interferometric quick-look image fromERS SAR tandem data of 29/30 April1996.

Same as 4, on 6/7 October 1997. Red channel: tandem coherence;green: intensities average; blue: inten-sities difference. Areas with highcoherence appear in red.

Geolocated ERS SAR tandem data of6/7 October 1997 co-registered toATSR hot spots (white).

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should allow the discrimination offorests from deforested areas.

When comparing the tandem imagesacquired before (Fig. 4) and after (Fig. 5)the fire event, clear changes appear inthe coherence channels (red channels):several areas of the image show anincrease of the coherence (i.e. more redareas). This indicates a strong landcover change for those areas, mostlikely related to the fires.

The ATSR hot spots image is then co-registered with the SAR tandemimages. Figure 6 shows the co-registra-tion of the October 1997 tandem image(Fig. 5) with the ATSR hot spots. Mostof the hot spots correspond to the landcover change areas previously identi-fied, which confirms our previousassumptions. Because the burned forest should appear as areas wherecoherence has increased and activefires are detected, we deduce thatthese areas have probably burnedbetween August and October 1997(ATSR hot spots image).

Burned forest classificationThe ATSR hot spots overlaid on theSAR images are used to select areas ofinterest (AOI) in order to computecoherence statistics on forest andburned forest classes. Figure 7 is thescatterogram of coherence for the twoselected AOIs. Two different coherenceclusters are completely discriminated.The coherence threshold methodretains the coherence histogram partcentred on the mode plus or minus thestandard deviation. Table 2 gives thesecoherence thresholds for the twocoherence images. The coherence forthe same forest sample has decreased(Table 2) due to the baseline increasebetween the two tandem acquisitions(Table 1). As we want to emphasise

areas where coherence has increased,it is not necessary to take a baselineeffect into consideration. Moreover, theselected classes allow discrimination ofdeforestation areas (low coherence inApril 1996 versus high coherence inOctober 1997). The resulting classific-ation is shown in Figure 8. Red areas are burned forests, green areas represent forest unchanged between1996 and 1997, and black areas arenot classified. Figure 9 shows the ATSRhot spots (yellow) overlaying the classification.

DiscussionA high degree of correlation betweenthe presence of ATSR hot spots detected during the fires events and theincrease of SAR coherence, particularlyin the north-east part of the image, canbe seen in Figure 9.

Some areas were classified as burnedforest even though no ATSR hot spots

were detected. This effect might haveseveral causes:

• deforestation which may be caused either by fire between April 1996 and August 1997 or forest clearing for infrastructure work (draining canals for the 1-million ha regional rice project: red straight lines in the image);

• non-forest surfaces displaying as deforested areas from a coherence point of view:– wetlands (visible meander in the

south-west of the image might be assimilated to wetland area due to its form and intensities which are different from deforesedareas);

– rice fields (red area located in the middle of the image in the centreof a black area is presumably rice fields location, close to a river, andits intensities behaviour).

Both types (wetland and paddies) wereseen as incoherent in April 1996because of flooding. However, duringdryness (the fire event of October 1997)these surfaces appear coherent sincethey are essentially bare soil.

This supervised classification requiresimprovements and a validation with in-situ data, but results are significant andthe synergy between SAR and ATSRprovide confidence in these earlyresults.

In order to validate these results, in-situdata are soon expected in coordinationwith the TREES Project [Achard et al.,1998]. Method refinement (includingdeeper intensity analysis) and auto-matisation are expected within the next study phase in order to derive thecomplete Kalimantan deforestationmosaic.

ReferencesAchard F, P Mayaux, H Eva & P Janvie:Production of tropical forest distributionmaps using remote sensing data atglobal scale, submitted to Jl. Physics &Chemistry of the Earth.

indonesian fire37

Scatterogram of burned and non-burned forest classes. The separationbetween both classes appears clearly.

Image layer Class Coherence threshold

Coherence 1996 Forest 0.16 - 0.27Coherence 1997 Forest 0.12 - 0.24

Burned Forest 0.27 - 0.56

Table 2. Coherence threshold

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Arino O, J-M Melinotte, J-M Rosaz, ABuongiorno & I Piccolini: World FireAtlas with AVHRR and ATSR, submittedto Jl. Physics & Chemistry of the Earth,1998.

Bourgeau-Chavez L.L., PA Harrells, ESKasischke & NHF French: The detectionand mapping of Alaskan wildfires usinga spaceborne imaging radar system,Int. J. of Remote Sensing, 18,2, 355-373, 1997.

Buongiorno A., O Arino, C Zehner , PColagrande & P Goryl: ERS-2 monitorsexceptional fire event in South-EastAsia, EOQ, 56-57, Dec. 1997.

Dammert PBG, LMH Ulander & JAskne: SAR interferometry for detectingforest stands and tree heights, Proc.European Symp. on Satellite Remote

Sensing II, Paris, France, Sept. 25-28,SPIE-2584, 384-390, 1995.

Dozier J: A method for satellite identifi-cation of surface temperature fields ofsubpixel resolution, Remote Sensing ofEnvironment, 11, 221-229, 1981.

Laur H, S Coulson, M Doherty & NWalker: ERS SAR processing and post-processing at ESA, Proc. of CEOS SARCalibration WS, Feb. 1998.

Rignot E & JB Way: Monitoring freeze-thaw cycles along North-South Alaskantransect using ERS-1 SAR, RemoteSensing of Environment, 49, 39-44,1994.

Wegmuller U & CL Werner: Retrieval ofvegetation parameters with SARinterferometry, IEEE Geosc. RemoteSensing, 35,1, 18-24, 1997.

WEB siteshttp://shark1.esrin.esa.ithttp://earthnet.esrin.esa.it

AcknowledgementsThe authors wish to thank Josep Closafor processing the InSAR data.

sar/atsr synergy 38

Deforestation classification using ERS1&2 SAR tandem coherences.

Deforestation classification & ATSR hotspots.

Red areas: deforested regions; green: forest; black: non-classified areas;yellow: ATSR hot spots.

Location of images presented onFigures 3, 6, 8 & 9 :longitude: 113.48 – 114.47 E latitude: 2.17 – 2.71 S.The river crossing the image isKahayan.

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