REFORESTATION OF BURNED AREAS MONITORED BY SAR DATA AND A SCATTERING MODEL

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1 REFORESTATION OF BURNED AREAS MONITORED BY SAR DATA AND A SCATTERING MODEL Fabio DEL FRATE, Andrea MINCHELLA 2, Domenico SOLIMINI 2 GEO-K s.r.l., Via del Politecnico, I-33 Rome, Ital 2- Tor Vergata Uniersit, Dipartimento di Informatica Sistemi e Produzione, Via del Politecnico, I-33 Rome, Ital, delfrate@disp.uniroma2.it ABSTRACT The rate of biomass re-growth in burned areas is a particularl releant quantit in assessing the forest recoer from a fire eent. The reforestation process ma be fast and the burned area repopulated after a few ears, or the process can take een decades, with a much heaier enironmental and economic impact. Satellite obserations offer the means of sstematicall collecting forest data. Microwae backscattering is modified b a fire eent, which abruptl alters the bio-geo-phsical parameters and correspondingl changes the wae-canop interaction. The re-forestation of a burnt area induces opposite changes of the canop parameters, hence of the resultant microwae interaction, which progressiel go in the reerse direction, according to the re-growth of the egetation. This means that multi-temporal radar obserations can proide data on the interening post-fire forest recoer. We consider a dramatic fire eent which occurred in the Castel Fusano coastal pinewood, located few kilometres from the Rome, Ital, in Jul 2. A set of 34 ERS SAR images proided the backscattering time series oer the period from one ear before the fire till three ears after it. The objectie is to assess the potential of multitemporal SAR data for monitoring the forest recoer process. A re-growing egetation rate (biomass per unit area per ear has been calculated through the Water Cloud model ad hoc calibrated b using Tor Vergata scattering model for coniferous egetation. The retrieed alue is in agreement with the ground truth measured b the Plant Biolog research group of La Sapienza Uniersit, Rome, Ital. Kewords: fire, biomass retrieal, radar, satellite INTRODUCTION Nowadas, there is a good deal of conjecture on the utilit of SAR data for burnt area mapping and fire damage assessment in forested areas. The research shows that SAR has unique capabilities that ma compliment and enhance the more traditional multispectral techniques, oertaking the main limitation of this latter technolog that is the inabilit to penetrate cloud and allowing a continuous monitoring. In fact man tests hae proed Spaceborne radar effectie in monitoring and assessing the extent and seerit of wild fires in the boreal forests of Alaska and Canada (Bourgeau-Chaez et al. 997, in the tropical forests of Indonesia (Kuntz et al. 999 and in Mediterranean forest enironments (Gimeno et al. 24, Catalucci et al. 24. All of the studies hae found differences in the backscatter signal between predominantl undisturbed and fire-disturbed areas, caused b the tree canop remoal and the increased ground surface exposure. Indeed microwae backscattering from a forest is modified b a fire eent, which abruptl alters the bio-geo-phsical parameters of the egetation and correspondingl changes the wae-canop interaction (Menges et al. 24. Depending on the fire seerit, the egetation ariet and the soil tpe, ground soil moisture is the dominant factor causing the different backscatter from burned forest. On its side, the re-forestation of a burnt area induces opposite changes of the canop parameters, hence of the resultant microwae interaction, which progressiel modifies in the reerse direction, following to the re-growth of the egetation. This means that multi-temporal radar obserations could proide data on the interening post-fire forest recoer (Couturier et al Beside to experimental results demonstrating that radar sensitiit to biomass extends oer wide dnamic ranges of the backscatter coefficient σ (Dobson et al. 992, electromagnetic models hae been deeloped to inestigate separatel the σ dependence on the arious egetation parameters. Models gie a phsical basis to experimental correlations between radar response and medium parameters, allow identification of the different scattering sources within the canop and make it possible to carr out parametric inestigations. In this work we consider a dramatic fire eent which occurred in the Castel Fusano coastal pinewood, located few kilometres from the Rome, Ital, main

2 urban area, in Jul 2. More than 25 ha of forest burned, substantiall modifing the forest local enironment. The aim is to inestigate the potentialities of C band data and a scattering model to derie some significant index for the monitoring of the recoer process inside the burned area. In particular b means of multitemporal C band ERS- SAR data oer the period from one ear before the fire eent till three ears after it, we focus on a methodolog to determine a qualitatie index of the reforestation process and a quantitatie retrieal of biomass growing rate (ton/ha per ear emploing Water Cloud model calibrated b means of Tor Vergata scattering model and measured σ alues. 2 THE METHODOLOGY A significant set of ERS-SAR images has been collected before and after the fire eent consisting in an oerall number of 34 satellite passes. Reminding that the fire eent occurred on 4 Jul 2, the considered time window goes from Februar 999 to October 23 so it includes more than one ear before the fire eent and three ears after. For each pass, inestigation on meteorological condition has been carried out; this latter is indicated in the rightmost two columns of Table where the complete list of the considered SAR images is reported. Table List of the SAR acquisitions emploed. The UP column shows the number of das occurring between the last rain da and the da of the measurement ( means rain in the date of acquisition; - means that such number was greater than 7. Date Das from eent fire Satellite Weather cond. UP 4 Feb ERS-2 Clear 2 2 March ERS-2 Clear - 25 April ERS-2 Scattered Clouds 3 Ma ERS-2 Clear - 4 Jul ERS-2 Clear - 8 Aug ERS-2 Clear - 2 Sep ERS-2 Clear 5 7 Oct ERS-2 Partl Cloud 2 No ERS- Thunderstorm 29 Jan ERS- Mostl Cloud 4 Ma 2-5 ERS-2 Clear 5 8 Jun. 2-6 ERS-2 Partl Cloud 2 7 Jul ERS-2 Clear - Oct ERS-2 Partl Cloud 5 No ERS-2 Scattered Clouds 2 Dec ERS-2 Partl Cloud - 4 Jan ERS-2 Scattered Clouds 5 8 Feb ERS-2 Partl Cloud - 3 June ERS-2 Scattered Clouds - 8 Jul ERS-2 Scattered Clouds - 2 Aug ERS-2 Clear - 6 Sep ERS-2 Mostl Cloud 2 Oct ERS-2 Clear - 25 No ERS-2 Clear 6 4 Apr ERS-2 Light Rain 23 Jun ERS-2 Clear - 28 Jul ERS-2 Clear - Sep ERS-2 Mostl Cloud No ERS-2 Partl Cloud 2 9 Jan ERS-2 Clear 3 Mar ERS-2 Clear - 8 Jun ERS-2 Scattered Clouds - 7 Aug ERS-2 Clear - 26 Oct ERS-2 Clear 3 Gien the high temporal decorrelation effect, the analsis has been limited to the radar amplitude. A ground range backscatter intensit time series of ERS-SAR images has been carried out b deeloping and optimising a procedure that performs an automatic calibration and a fine coregistration of data. Beside to ERS data, two airborne ortophotos, m resolution, acquired before and immediatel after the fire eent, hae been emploed to reconstruct the scenario, and all the data hae been georeferenced and oerlapped in a G.I.S.. B means of Hi-Re data, historical fire archies and the actual time plan of interention on fire-affected region established b Rome Municipalit, 4 different regions of interest (ROI hae been chosen and grouped into the following 3 greater thematic classes:. a class, labeled as Burned area (Ba with a surface of 27 ha, made up of regions destroed b the fire where the re-growth process started or b artificial interentions of reclaiming and plantation or completel spontaneous with no man-made actions; 2. a class, labeled as Not Burned area (NBa with a surface of 8 ha, made up of oung and old pinewood and Mediterranean scrub egetation not damaged b the fire eent; 3. a class, labeled as Bare Soil Like area (BSLa with a surface of 2 ha, basicall consisting of soils characterized b absence of egetation or rare grass egetation. In Fig the time trend of backscattering coefficient for each class is plotted. It is eident the different behaior of the three macro-regions. The NBa cure is enough stable and comprised in a little range of ariation, about db, without to be much affected from seasonal changes. On the other side, the LBSa cure has oscillating alues, strongl connected to

3 the seasonal periods of the ear, with excursions of about 5-6 db. A Reforestation Index (RI per ear has been defined as the difference between the Ba and BSLa annual peak to peak backscattering transition: RI MAX "! MIN " MAX "! MIN Ba BSLAa = ( ear =,.., N.( This index is directl connected with the biomass content hence, as showed in Fig 2, it allows to follow the egetation re-growing after the fire eent. The estimated RI trend is in agreement with that one measured in situ b some researchers of the Facult of Plant Biolog of La Sapienza Uniersit. Figure. σ time trend corresponding to the Ba, NBa and BSLa classes In this case, the soil moisture, driing the dielectric constant alue of the soil, influences significantl the σ alue. Inside the Ba σ cure, three different phases can be separated: before the fire eent the behaior is close to that of NBa, right after the eent it becomes closer to that of BSLa while in the period corresponding to the last two ears the similarit to bare soil seems to decrease with the time. In fact, as the reforestation goes on in the burned area, the corresponding backscattering tends to be less affected b the surface scattering effects tpical of the soil, becoming more similar to the olume scattering tpical of the parts of the pinewood not affected b the fire. This result suggests the possibilit for monitoring the pinewood in the burned region, tracking how much its trend is similar to the backscattering of the LBSa. In particular, a measure of this similarit could be represented b the ealuation of annual Ba backscattering excursion from the positie peak to the negatie peak and comparing it to the corresponding of LBSa. In Table 2, the annual peakto-peak backscattering transitions occurring in the period from Februar 999 to October 23 are reported. THE SCATTERING MODEL Besides proposing a RI using SAR data time series, we present a method for a quantitatie retrieal of biomass re-growing rate (ton/ha per ear b means of a scattering model. To retriee the rate, the σ measured in Ba has been put into relation with the plant biomass per unit area through the Water Cloud model calibrated emploing Tor Vergata microwae scattering model b considering a parametric ariation of biomass, finall estimated b minimizing the distance between simulated results and measured data. Table 2 The annual peak-to-peak backscattering transitions from Februar 999 to October 23 relatiel to the Ba, BSLa and NBa. Annual peak-to-peak backscattering transition (db Ba BSLa NBa 999,84 4,36,64 From Januar 2 to June 2 before the fire eent From Jul 2 to December 2 after the fire eent,5 3,97,56 3,85 3,58,72 2 3,66 4,52, ,69 4,6, ,58 4,42,72 Figure 2 Reforestation Index: Ba and BSLa annual peak to peak transition difference before (upper diagram and after the fire eent (below diagram. Assuming that the soil backscattering term σ s adds incoherentl to the egetation one σ, the total backscattering coefficient from the canop ma be expressed in the following form:

4 L " " s = (m 2 m -2 (2 L " can + 2 exp( k hsec! =, where k e (m - is the olume e extinction coefficient of the egetation medium and h is the egetation height, accounts for the attenuation loss experienced b the wae due to propagation through the loss egetation laer. In the Water cloud model (Attema et al. 978, the olume extinction coefficient is proportional to the egetation water content m.(kg/m 3, that is = A, and (2 is expressed as: ke m # ( sec + (" = B cos"! exp(! 2A mh " + " ( 2A m hsec! can (3 exp s # Doing the following positions, w = m h (kg/m 2, (4 which is the canop water content per m 2, d = 2A sec!, (m 2 /kg (5 c = B cos!, (6 (3 can be compacted in the following expression ( $ = c( " exp( " d! w + # exp( " d w # (7 can s! In the Water Cloud model, the coefficients c and d are calculated using regression analsis fitting the measured data in conjunction with each radar obseration. In the present work, the coefficients c and d hae been calculated using the Tor Vergata discrete element scattering model for coniferous egetation (Ferrazzoli et al The hpotheses assumed to run the model are related to the egetation deelopment, to the egetation water content and to the dimension of branches. With reference to first one, canop randoml filled with clinders has been considered and it has been supposed that the clinder radius grows up from cm to.5 cm, with a linear trend relatie to the time, starting the process soon after the fire till October 23. This hpothesis is deried considering the egetation ariet present in Castel Fusano area and some collections of ground measurements. The plant water content it has been assumed equal to 7% b weight and the clinder length fixed to 5 times the radius dimension. Concerning the σ s term, the measured backscattering alues relatie to BSLa hae been used in the model. According to the preious hpotheses, the model has been run b considering a parametric ariation of biomass, finall estimated b minimizing the RMSe between simulated results and measured data: MIN RMSe N! i= = i i 2 ( ( (# can" simulated " # can" measured N (8 where i =,, N is the acquisition date. In Fig. 3 is showed the RMS error cure (db with the minimum corresponding to a alue of.85 ton/ha ear..85 ton/ha per ear Figure 3: RMSe considering the simulated and measured alues of σ can relatiel to the period soon after the fire till October 23. As it is possible to obsere, the cure sensitiit is not high and this could be explained considering the noise of terrain changes caused b fire, which in general are present until the following ear after the fire. Taking into account that this noise is much less present or ended after the first ear, we run the model excluding the first ear after the fire. The resulting biomass alue is.8 ton/ha per ear with a much better cure sensitiit (Fig.4 than in the preious one..8 ton/ha per ear Figure 4: RMSe calculated considering the simulated and measured alues of σ can, skipping the first ear after the fire The simulated σ can trend corresponding to the biomass alue of.8 ton/ha per ear is shown in Fig 5 together with the measured one. The retrieed rate of.8 ton/ha per ear has been confirmed as a reasonable re-growing rate b ground measurements

5 taken b some biologists of La Sapienza Uniersit. Figure 5: Simulated σ can trend relatiel to.8 ton/ha per ear biomass rate alue CONCLUSION A multitemporal analsis of the backscattering coefficient measured oer the Castel Fusano pinewood, partiall destroed in a fire eent of Jul 2, has been carried out using C band ERS SAR data. The measurement of the similarit between the backscattering of the burned area and the backscattering of a bare soil around or inside the burned area, proide a qualitatie index of the reforestation process in the burned area. A retrieal of biomass growing rate (ton/ha per ear after the fire has been calculated b means of the Water Cloud model calibrated using the Tor Vergata microwae scattering model: the retrieed alue of.8 ton/ha per ear is in agreement with in-situ measures done b Plant Biolog research group of La Sapienza (Rome Uniersit. ACKNOWLEDGMENTS This unnumbered section is used to identif those people who hae aided the authors in understanding or accomplishing the work presented and to acknowledge sources of funding. REFERENCES Bourgeau-Chaez, L. L., Harrell, P. A., Kasischke, E. S, French, N. H. F. 997 The Detection and Mapping of Alaskan Wildfires Using a Spaceborne Imaging Radar Sstem, Int. J. Remote Sens,Vol. 8, Kuntz, S., Siegert, F. Ruecker, G ERS SAR Images for Tropical Rainforest and Land Use Monitoring: Change Detection oer Fie Years and Comparison with Radarsat and JERS SAR Images. In Proceedings of IGARRS 999 Gimeno, M., San-Miguel, J., Barbosa, P., Schmuck, G. 22, Using ERS-SAR Images for Burnt Area Mapping in Mediterranean Landscapes, in Viegas (Ed., Forest Fire Research & Wildland Fire Safet, Mill press, Rotterdam, Catalucci, F., Del Frate, F., Minchella, A., Paganini, M. 24. Multitemporal ERS and ENVISAT Imager for the estimation of the reforestation process of burned areas. Enisat & ERS smposium, Salzburg (Austria, 6- September 24 Menges, C. H., Bartolo, R. E., Bell D., Hill G. J. E. 24. The effect of saanna fires on SAR backscatter in northern Australia. Int. J. Remote Sens, ol. 25, pp Couturier, S., Chin Liew, S., Nakaama M. Lim H Monitoring Vegetation Regeneration in Fire-Affected Tropical forest using ERS/JERS Snthetic Aperture Radar IEEE Trans. Geosci. Remote Sens., ol. 24, pp Dobson, M. C., Ulab, F. T., Le Toan T., Beaudoin A., Kasischke E. S., Christensen N Dependence of radar backscatter on coniferous forest biomass, IEEE Trans. Geosci. Remote Sens., ol. 3, pp , 992. Attema, E.P.W., Ulab, F.T Vegetation modeled as a water cloud, Radio Science, ol 3, number 2, pp Ferrazzoli, P., Guerriero. L Radar Sensitiit to Tree Geometr and Wood Volume: A Model Analsis, IEEE Trans. Geosci. Remote Sens., ol. 33, pp , 995