Flood Mapping With TerraSAR-X in Forested Regions in Estonia

562 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 7, NO. 2, FEBRUARY 2014

Flood Mapping With TerraSAR-X in ForestedRegions in Estonia

Kaupo Voormansik, Jaan Praks, Member, IEEE, Oleg Antropov, Student Member, IEEE, Jüri Jagomägi, andKarlis Zalite

Abstract—In this study, an extensive flood in Estonia duringspring 2010 was mapped with TerraSAR-X data acquired overboth open and forested areas. This was the first time when alarge scale flooding area was mapped in Estonia by means ofspaceborne remote sensing. This was also the first time whenX-band SAR images were successfully used for flood mappingunder the forest canopy in the temperate forest zone. The treeheight in the study region was 15–25 meters on average, and maintree species were birch (leaf-off condition), pine and spruce. Theresults were compared with ALOS PALSAR and Envisat ASARimages of the same flooding event. In the study area, TerraSAR-Xprovided on average 3.2 dB higher backscatter over mixed forestflooded areas compared to non-flooded areas. In deciduous andconiferous forests the difference in average backscatter betweenflooded and non-flooded forests was even greater, 6.2 dB and4.0 dB, respectively. A supervised classification algorithm wasdeveloped to produce high resolution maps of the flooded areafrom the TerraSAR-X images to demonstrate the flood mappingcapability at X-band. Our results show, that spaceborne X-bandSAR data, which currently has the highest resolution among theSAR instruments in space, can be used to map floods under forestcanopy in temperate zone despite its short wavelength and highattenuation.

Index Terms—Flood mapping, synthetic aperture radar (SAR),temperate forest, X-band.

I. INTRODUCTION

S YNTHETIC aperture radar (SAR) is an established remotesensing tool for flood mapping. Though, the use of optical

remote sensing is also a common for flood mapping, e.g., [1],[2], SAR imaging is superior in several aspects. Particular ad-vantages of SAR include independence of sunlight and cloudcover, as well as the ability to penetrate to considerable extentthrough forest canopy depending on the wavelength.

Manuscript received April 10, 2013; revised July 07, 2013; accepted August22, 2013. Date of publication October 16, 2013; date of current version February03, 2014.K. Voormansik is with the Institute of Physics, University of Tartu, 51010

Tartu, Estonia (e-mail: [email protected]).J. Praks is with the Department of Radio Science and Engineering,

Aalto University, FI-00076 Aalto, Finland (corresponding author, e-mail:[email protected]).O. Antropov is with the VTT Technical Research Centre of Finland, FI-02044

VTT, Finland (e-mail: [email protected]).J. Jagomägi is with the Regio Ltd., 51010 Tartu, Estonia (e-mail: juri.jago-

[email protected]).K. Zalite is with the Institute of Physics, University of Tartu, 51010 Tartu, Es-

tonia, and also with the Tartu Observatory, 61602 Tõravere, Tartumaa, Estonia(e-mail: [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/JSTARS.2013.2283340

Already during the early days of SAR remote sensing it wasnoted that floods under a forest canopy can be detected due tosignificant increase in the backscattering level caused by water-trunk reflection [3]. Place [4], Ormsby et al. [5], and Imhoffet al. [6] demonstrated that the SAR sensors are more appro-priate than optical instruments in detecting floods under forestcanopy. Hess et al. [7] showed that L-band SAR is able to de-tect floods under almost any type of forest and thus should bea preferred wavelength. However, also the C-band SAR datawas shown to provide a good capability for flood mapping overlarge forested areas [8]. The main drawback of mapping floodsin forests with C-band SAR is the decreased canopy penetra-tion since the backscatter is affected considerably by interactionwith leaves and needles [9]. Other properties that must be con-sidered for flood mapping are incidence angle and polarization.Many studies have shown that steep incidence angles are bettersuited for flood mapping in forests than shallow ones [10]–[12].In terms of polarization, HH polarization provides better resultsin flood mapping than VV [13]–[15]. The choice of correct po-larization is critical, e.g., for open areas optical data may providebetter results than C-band VV-polarized SAR [16].Besides the basic backscatter analysis, others advanced SAR

techniques, such as SAR interferometry, have been used forflood information retrieval. Even though low coherence pre-vents elevation calculation of open water bodies, water levelmeasurements with SAR interferometry are possible in floodedvegetation [17]. Smooth ground can be easily confused with stillwater when using only backscatter information, however theinterferometric coherence is much higher over smooth groundthan over still water which makes the distinction easier [18],[19]. Coherence information could also be used for flood ex-tent estimation when it is not possible to acquire data at peakwater level. After water levels have subsided, the areas previ-ously covered have a generally lower coherence level than thosenot affected by flooding, and thus predicting the area of floodmaximum is possible [20], [21].While the physical basis for SAR-based flood mapping is rel-

atively mature, more recent research efforts have concentratedon the development of optimal unsupervised flood mapping al-gorithms [22]–[25] that exploit previous knowledge regardingscattering mechanisms. In particular object based classificationmethods [26] and exploitation of a priori land cover informa-tion [27] have proven necessary to achieve most accurate floodmaps. Well-known solutions for flood mapping at L-band andC-band suffer from poor ground resolution, drawing attentiontowards the possibility of using higher resolution and shorterwavelength SAR instruments. The new generation of space-borne SAR instruments, like TerraSAR-X, provide enhanced

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VOORMANSIK et al.: FLOOD MAPPING WITH TERRASAR-X IN FORESTED REGIONS IN ESTONIA 563

Fig. 1. Overview map of the study area in Estonia. Light grey regions depict the flooded area, permanent water bodies are denoted in dark grey and dotted regionsrepresent urban areas.

imaging possibilities for various mapping applications, in par-ticular very high spatial resolution. When using these instru-ments, floods can be detected in high detail in open areas [28],[29], and, to some extent, even in urban environment [30]. [31],[32], which is an actively emerging area of research. However, itis well-known that X-band forest canopy penetration is compar-atively limited [33]. Therefore the performance of X-band radarfor forested area flood mapping requires more rigorous evalua-tion in different scenarios.In Estonia, floods have not been mapped by means of remote

sensing before, although significant areas are affected by floodsseveral times every decade. One of the largest regular floodedareas in Estonia is the Alam-Pedja Nature Reserve. No accurateflood maps have existed previously for this area so it was se-lected to be the study site. Also, the landscape of Alam-Pedjaprovided an opportunity to test X-band mapping capabilities inforested areas.The goal of this study was to produce a detailed map of ex-

tensive floods in Estonia over open and forested areas, usingspaceborne SAR imagery as a primary input data. In order toachieve higher spatial resolution, TerraSAR-X was selected asthe main imaging instrument. In order to assess the relative per-formance of X-band SAR data in forested areas, TerraSAR-Ximagery [34] was compared with longer wavelength AdvancedLand Observing Satellite (ALOS) Phased Array type L-bandSynthetic Aperture Radar (PALSAR) and Envisat AdvancedSynthetic Aperture Radar (ASAR) images of the same floodevent.The paper is organized as follows. In Section II, the mate-

rial and test site of the study are presented along with hydro-logical characteristics of the flood during the study period. InSection III, the methodology for flood map generation and theperformance of X-band comparison with L-band and C-banddata are described, Section IV presents the performance assess-ment and the flood mapping results together with a discussion.

Section V summarises the most important results and gives rec-ommendations for future work.

II. MATERIAL

A. Study Area

The study was carried out within the drainage basin of theEmajõgi river in Estonia, mostly in the vicinity of the Alam-Pedja Nature Reserve. The corner coordinates of the test area areas follows: 58.62 N, 25.87 E (top-left) and 58.23 N, 27.05E (bottom-right). The total area of the Alam-Pedja Nature Re-serve is 342 km [35]. The actual study region was selected to besomewhat wider in order to cover the floods that occurred alongother rivers as well, but did not fit into the current borders of thereserve. An overviewmap of the study region is shown in Fig. 1,where a rough estimate (a manually digitized and smoothed En-visat ASAR image from April 14, 2010) of the flooded area isshown in light grey.The terrain of the study area is relatively flat. The elevation

near the rivers ranges from 30 to 50 m above the local sea levelaccording to the digital elevation model from Shuttle Radar To-pography Mission [36]. Unregulated rivers flow through theregion, most notably Emajõgi and its tributaries. These riversare surrounded by glades, deciduous, coniferous, and mixedtemperate forests, as well as large bogs. The deciduous forestsconsist of birches, whereas coniferous forests are mainly com-posed by pine groves. Mixed forests in this region typically ex-hibit a multi-layer wooden vegetation with birch as the mainspecies (50–90%) and spruce as the most numerous coniferoustree species (10–50%). The average tree height in the region is15–25 m [37]. There are no man-made controlled canals anddams.The drainage basin of the Emajõgi river is regularly flooded

since the terrain is very flat and the river is unregulated. This


Fig. 2. Yearly water level maximums in cm for the Emajõgi river measured at Tartu over a base level of 0 cm. The record level observed in 1956, as well as the2010 level, are shown in a darker tone [35].

Fig. 3. TerraSAR-X, ALOS PALSAR and Envisat ASAR acquisition dates and corresponding water levels of the Alam-Pedja Nature Reserve rivers (measurementstation location given in brackets) [46].

happens regularly every spring just before foliage starts to de-velop. Regular floods have shaped the biotope which is nowunder protection. Thanks to the low population density in Alam-Pedja, flooding is not a serious threat to the local inhabitants noreconomy.The flood in the spring of 2010 was one of the largest over the

past 70 years. The water level maximum of Emajõgi in 2010was330 cm (over a local long term 0) and this was the third highestin measurement history in Tartu since 1942 [38], see Fig. 2 fordetails. In 2010 the water level on the Alam-Pedja rivers startedto rise rapidly on March 28, achieved a maximum around April14, and after that receded slowly until mid-June 2010. The waterlevel chart is depicted in Fig. 3.

B. SAR Data

For this study, TerraSAR-X, ALOS PALSAR and EnvisatASAR images were used. Four TerraSAR-X HH polarizationStripmap images were taken on the following days: on April 5,

6, 27, and 28, 2010. To cover the entire area, two Stripmap im-ages were needed since the width of the test region was greaterthan the 30 km TerraSAR-X Stripmap swath. A 22-day intervalwas used to obtain the same acquisition geometry to allow for afair comparison. Additionally, one TerraSAR-X ScanSAR imagefrom April 15 was used. For L-band and C-band comparison, anALOS PALSAR and an Envisat ASAR image were exploited.The Envisat ASAR image was acquired on April 14 in the WideSwath Mode (WSM) in HH polarization with 150 m spatial res-olution and mean incidence angle of 24 over the study region.The ALOS PALSAR image was acquired on April 16 in theFine Beam Single (FBS) polarization mode with HH polariza-tion, 12.5 m spatial resolution and mean incidence agle of 34over the study region. Fig. 3 indicates the SAR data acquisitiondates and the corresponding water levels in the test-site rivers.Details about SAR images used in the study are given in Table I.All TerraSAR-X images used in this study were Multi Look

Ground Range Detected (MGD) products. Stripmap data was


TABLE ISAR IMAGERY PARAMETERS

Fig. 4. Aerial photo of the flooded area near Emajõgi taken on April 13, 2010 [47].

spatially enhanced, whereas ScanSAR data was radiometricallyenhanced. Exact TerraSAR-X imagery parameters used in thecurrent study are given in [34, Tables 4.1 and 4.7]. TerraSAR-XMGD products have approximately square-shaped resolutioncells projected onto the WGS84 ellipsoid with no terrain distor-tion corrections. It should be noted that terrain distortion correc-tions were not applied in the processing steps of the study sincethe landscape in the study region is very flat (elevation rangesfrom 30–50 m) and almost no foreshortening, nor overlay, norshadow effects were present. The Envisat ASAR image was de-livered in 1-bit processing level (approximately square sizedpixels in azimuth, ground range coordinates with a coarse lat.long. coordinate grid attached, no terrain correction), ALOSPALSAR product was delivered in 1.5 level (Universal Trans-verse Mercator map projection, no terrain correction).

C. Reference Data

A ground observation campaign was carried out at the studysite. On the same day as TerraSAR-X acquisition, several geo-tagged photographs were taken at the test sites to document theconditions (see Fig. 4). GPS track logs regarding the provisional“shoreline” were also recorded in several locations. The groundobservation activities are described in detail in Table II.Daily in situwater level measurements from the EstonianMe-

teorological and Hydrological Institute (EMHI) on the Emajõgi,Pedja, and Põltsamaa rivers were used for the exact planning ofthe TerraSAR-X acquisitions and for verifying the conclusionsabout the flood extent. Thewater levels (relative to the local longterm zero at the measurement station) of the Alam-Pedja riverscompared to the selected SAR data acquisition dates are shown


TABLE IIGROUND OBSERVATION DATA AT THE ALAM-PEDJA NATURE RESERVE DURING THE FLOOD

in Fig. 3. As can be seen, the single TerraSAR-X ScanSARimage together with the ALOS PALSAR and Envisat ASAR ac-quisitions have captured the flood maximum on Emajõgi quiteprecisely.Archive data from various sources was used for ancillary

purposes and later statistical analysis. CORINE 2006 [39] landcover data was used for assessing which land cover types andecosystems were affected by the flood. Very high resolution(25 cm) ortho-photos from the Estonian Land Board were usedto identify the scenery objects on the SAR images. EMHI rain-fall data from in situ meteorological stations was used to verifythat the enhanced radar scattering in the area was caused by asnow melt produced flood and not by recent rainfall. Accordingto the Tõravere meteorological station, there was no precipi-tation 24 hours prior to any of the satellite image acquisitionslisted in Table I [40]. The Tõravere meteorological station is theclosest station and is located about 20 km south from the centerof the test site.

III. METHODOLOGY

A. Comparison of L-Band, C-Band and X-Band Performancefor Forest Flood Detection

In order to evaluate the ability of X-band SAR to detect waterunder forest canopy, X-band images were compared with avail-able L-band and C-band images of the same flood event.For the comparison, an Envisat ASAR WSM image from

April 14, a TerraSAR-X ScanSAR image from April 15 and anALOS PALSAR FBS image from April 16 were used. Since thewater level of the Emajõgi river was nearly constant from the14th through the 16th (see Fig. 3), temporal effects could effec-tively be ignored. The backscatter behavior of the scene duringthis time period was expected to be nearly the same.It should be noted that the spatial resolution of TerraSAR-X

and ALOS images in these particular imagery products are sim-ilar (18 m and 12.5 m, respectively), whereas the Envisat ASARWSM spatial resolution is much coarser (150m). Unfortunately,no ASAR imagery of higher resolution was available for theflood event. Although moderate resolution X-band images wereused, the results should apply also to high resolution X-band im-ages, enabling potentially much higher spatial accuracies thanwith other available spaceborne microwave instruments.

The data processing steps necessary for the comparison aredescribed further.

B. Calibration, Co-Registration and Speckle Reduction

A subset representing the same area around the Emajõgiriver from each of the images was cut and calculated tobackscatter. All the images were co-registered using the ArrayNEST (Next ESA SAR Toolbox) 3C software package [41].The ALOS PALSAR image was used as a master image asit had the highest resolution (12.5 m, 6.25 m pixel spacing)whereas TerraSAR-X (18 m resolution) and Envisat ASAR(150 m resolution) were used as slaves. The procedure includedre-sampling to the master image pixel size using bi-linear inter-polation, plus stretching and shifting the slave images in orderto achieve the best correlation with the master image. Thisprocedure should ensure better than one pixel accuracy betweenthe images [41]. The resulting co-registered TerraSAR-X andEnvisat ASAR images were resampled to 6.25 m pixel spacing.After co-registration, the Frost speckle filter was applied

using 5 5 pixel sliding window and a damping factor of 1.Frost filter appeared to preserve the SAR image details bestamong all the filters implemented in NEST 3C. The observationis in line with a review [42], where Frost was found to be best indetail preserving among simple speckle suppression filters. Theresulting images after co-registration and speckle suppressionare shown in Fig. 8(a)–(d).

C. Selection of the Test Area

It is known from earlier works, e.g., [7], and supported byour ground reference measurements, that L-band SAR imageryis a suitable tool for flood mapping in almost all types of forest.Therefore, the ALOS PALSAR data was used as the cross-val-idation source for TerraSAR-X performance assessment. Theflooded area borders on L-band images were validated withground measurement and flood extent maps based on L-bandimages and ground measurements were used as a basis for fur-ther work with X-band.Deciduous, coniferous, and mixed forest regions in the

flooded area were selected using CORINE 2006 land-use data.A binary flooded vs. non-flooded forest bitmap was calcu-lated over the selected forest regions using ground truth dataalong with ALOS PALSAR imagery. For the first step, ground


Fig. 5. Raster operations with SAR data.

truth validated flooded and non-flooded forest regions fromthe ALOS PALSAR image of April 16 were selected for thebackscatter histogram calculation. Using the ground truth basedbackscatter histograms for flooded and non-flooded forests, athreshold of 4 dB was established in between the two peaks,so that the sum of the overlapping histogram parts would beminimized. Using the obtained threshold, forest pixels on theALOS PALSAR image were binary classified as flooded andnon-flooded areas. After this, six types of test regions withthe following areas were established: i) non-flooded decid-uous forest (375 ha); ii) flooded deciduous forest (832 ha);iii) non-flooded coniferous forest (119 ha); iv) flooded conif-erous forest (336 ha); v) non-flooded mixed forest (158 ha);and vi) flooded mixed forest (178 ha).The statistics including histograms, mean values, medians

and standard deviation for each band and forest type were cal-culated. For TerraSAR-X imagery, the statistics were calculatedtwice: using the original resolution (18m oversampled to 6.25mpixel spacing), and with the Envisat ASAR WSM resolution(150 m) in order to make a more direct comparison betweenthe C- and X-band data. A 24 24 pixel (150 m 150 m) slidingaveraging boxcar filter was used for resampling purposes.

D. Raster Operations for Flood Maps Production

It should be noted that working out a universal methodologyfor floodmappingwas not the goal of this study; rather, a customapproach was applied for every single image in order to obtainthe most accurate results in the flooded area mapping. The soft-ware used for the image processing and statistics calculationwas the Array NEST 3C [41], ERDAS Imagine 8.7 [43], andMapInfo Professional 9.5 [44].The processing was performed in two phases: raster opera-

tions were followed by vector operations, which produced thefinal vector map of the flooded area. The raster phase processingsteps are shown in Fig. 5.For the coordinate grid correction, new geo-coding was ap-

plied using ground control points (GCPs). The GCPs were se-lected among easily identifiable small objects such as road junc-tions and bridges over narrow streams. Reference coordinatesof the objects were taken from the orthophotos provided by the

Estonian Land Board server [45]. Speckle was removed usinga Frost filter with a window size of 7 7 (3 3 for the ScanSARimage) and a damping factor of 1.Further steps involved carrying out supervised classification.

For each of the TerraSAR-X acquisitions (April 5, 6, 15, 27,and 28) ground observation validated training regions of floodedopen areas, flooded forests, and non-flooded regions were drawnon the SAR images. Some of the April 6 training regions nearKärevere are shown in Fig. 6. The training areas ranged fromclass-by-class (flooded open areas, flooded forests, and non-flooded regions) and acquisition-by-acquisition 9,112–439,148pixels (see Appendix 1 for details). The training regions werethen used to calculate the probability distribution of the floodedand non-flooded areas. Exact calculation results are given inAppendix 1. Using the histogram parameters, threshold valuesfor SAR image amplitudes were established in order to calcu-late the bitmaps of flooded forests and open areas. In case dif-ferent class histograms overlapped slightly, a threshold was es-tablished in the middle, between the two closest overlappinglimit values.In order to smooth the bitmaps and create contiguous flooded

areas, morphological opening followed by morphologicalclosing using a 5 5 round kernel was performed. Morpho-logical opening removed small isolated “lakes” while closingexcluded small isolated “islands”. Finally, the L-EST97 projec-tion was applied and the bitmaps were vectorised thus keepingthe flooded forests and flooded open areas in separate vectorlayers.

E. Vector Operations for Flood Map Calculation

Common SAR data interpretation pitfalls were avoided byusing archived geospatial data and by performing vector areasubtractions. For example, shadows of buildings as well as air-fields and wide tarmac roads could have been interpreted asflooded open areas. Also, double bounce backscattering frombuildings could have been interpreted as being those from a wetforest. In order to eliminate these effects, building areas with abuffer size of 10 m, main tarmac roads, and airfields were sub-tracted from the flooded area vector map. The sequence of pro-cessing steps is shown in Fig. 7.Closer inspection of the SAR data also revealed that some

backscatter from wet fields was very similar in nature to theones of flooded forests. In order to distinguish between thesetwo classes, fields were separated from the flooded forest areasusing the Estonian forest cover vector data layer from the RegioLtd. database.

IV. RESULTS OF THE SAR DATA ANALYSIS

In this section, the forest flood detection abilities with L-band,C-band, and X-band are discussed. The main results of the 2010Alam-Pedja flood maps, which were based on TerraSAR-X im-ageries, are presented.It is known that single-image analysis cannot fully separate

the backscatter effects caused by flooding and forest canopybackscatter spatial variation. In order to achieve more compre-hensive comparison, a time series with images before and afterthe flood would be required. However, the forest in the testregion is believed to be sufficiently homogeneous for reliable


Fig. 6. Ground observation validated regions on the TerraSAR-X Stripmap image from April 6. Validated flooded open areas are marked in green, flooded forestsin purple, and non-flooded regions in red.

Fig. 7. Vector operations for final refinement.

single image analysis. The average tree height is 15–25 m forboth flooded and non-flooded regions [37] and is relativelyuniform with the forest density based on ortho-photo inspection[45]. It is assumed that multi-temporal analysis would notchange the results significantly and the conclusions wouldremain the same. Based on these arguments, a single-imageanalysis is presented further.

A. Visual Interpretation

ALOS PALSAR, TerraSAR-X, and Envisat ASAR imagesacquired at the time of the flood maximum near Kärevere areshown in Fig. 8(a), (b), and (c), respectively. The RGB com-posite with ALOS image as the R-channel, TerraSAR-X as theG-channel, and ASAR image as the B-channel is presented inFig. 8(d). The 8-bit pixel values on the RGB composite corre-spond to 95% of each image histogram span. Fig. 8(e) showedthe forest types of the area according to CORINE 2006 data.Dark grey areas are coniferous forests, light grey areas are de-ciduous forests, striped areas correspond to mixed forests, whilewhite areas are non-forested open areas. An orthophoto cov-ering the same region is given in Fig. 8(f).Visual analysis reveals the black region in the center of

all the SAR images (Fig. 8(a)–(c)) that corresponds to theflooded open areas on the shores of the Emajõgi river. Theareas of white and light-grey can be seen in all the SAR im-ages and these correspond to flooded forests, which is due tothe increased radar backscatter attributed to water tree-trunkdouble reflections. There are many red and pink areas on the

RGB-composite forest type map (Fig. 8(d)), which mainlycorrespond to coniferous and mixed forests. Thus, the ALOSPALSAR backscatter is higher in flooded coniferous and mixedforest than TerraSAR-X backscatter. This suggests that thecanopy penetration (to produce the strong water tree-trunkdouble reflection, since at least a portion of the SAR energyneeds to reach the ground) in temperate coniferous and mixedforest at L-band is remarkably better than at X-band. Signif-icantly stronger backscatter at L-band in flooded coniferousand mixed forests can also be easily seen when comparing theindividual images (Fig. 8(a) and (b)). Whereas for temperatedeciduous forests under leaf-off conditions, the difference inthe forest canopy penetration between L-band, C-band, andX-band seems to be not so significant, since large parts of theseareas are practically white in the RGB-composite.There are also some greenish and bluish areas in the middle

of the floodplain which reveal higher C-band and X-bandbackscatter than the backscatter from L-band. This could bedue to the sparse wooden vegetation (shrubland) in the region,which was almost “invisible” for L-band, but produced volumeor double bounce scattering at X-band and C-band. The smallbluish regions on the RGB-composite show higher C-bandbackscatter at open higher ground areas. This is assumed tobe a mainly temporal effect, as the ASAR image was acquiredon April 14 (one day before TerraSAR-X and two days beforeALOS acquisition). During the same time, the conditions in thelower floodplain remained almost stable in this 3-day period.Higher grounds dried quickly as the weather was clear and


Fig. 8. (a) ALOS PALSAR, (b) TerraSAR-X, and (c) Envisat ASAR images regarding the floodplain near Kärevere. The RGB-composite from ALOS, Ter-raSAR-X, and (d) Envisat images, (e) forest type map, and (f) the orthophoto [42] covering the same area.

sunny with almost no precipitation registered between April 14and 16 [40]. Hence, the fields were assumed to be wet enoughto yield strong backscatter on April 14, but were already drierthe next day during the TerraSAR-X acquisition.

B. Quantitative Performance Assessment

In order to assess the flood detection capability quantitatively,the SAR backscatter probability distributions were calculated.The results for deciduous, mixed, and coniferous forests forboth TerraSAR-X and Envisat ASAR are shown in Fig. 9. Ter-raSAR-X histograms were calculated twice: i) with the original18 m resolution (5 5 pixel window, Frost speckle filtered), andii) with under-sampled to ASAR WSM resolution (150 m).In general, all the histograms exhibit a good separation be-

tween flooded and non-flooded forests. After under-sampling byspatial averaging, the TerraSAR-X performance is very similarto ASAR, since averaging reduces the standard deviation andthe overlapping region on the histograms is also reduced. Wheninterpreting the results it is important to account for the differ-ence in the incidence angle. The ASAR data was acquired with arelatively steep incidence angle (24 ), whereas the TerraSAR-Ximage was acquired with a shallower incidence angle (42 ).According to the previous research, steeper incidence anglesare favourable for flood mapping in forests. Wang and Imhoff

[10] found that the difference in backscatter between floodedand non-flooded forest decreases up to 2.2 dB (from 3.8 dB to1.6 dB) when changing the incidence angle from 26 to 46 ,respectively, at L-band. This allows one to assume that the av-erage values of X-band backscatter for flooded and non-floodedforests would lie even further apart when using steeper inci-dence angles. In the current comparison, Envisat ASAR datashould have an advantage as its data was acquired with a steeperincidence angle.The differences between the average backscatter of flooded

and non-flooded forests are shown in Table III. For deciduousforests the backscatter differences are very similar (6.2 dB and6.9 dB) for TerraSAR-X and ASAR, allowing for a very gooddiscrimination of flooded against non-flooded forests. In mixedand coniferous forests, C-band seems to allow a better separa-tion of flooded forests from non-flooded, but even for X-band,the backscatter differences between flooded and non-floodedforests are observable (3.2 dB for mixed forests and 4.0 dB forconiferous forests).In order to separate flooded forests from non-flooded

forests, a simple thresholding approach was used, since it is arobust and proven method previously successfully used withTerraSAR-X data [28]. The threshold was set between the twohistogram peaks. Even when setting the threshold in such a


Fig. 9. Comparison of TerraSAR-X and Envisat ASAR flood detection ability in deciduous, mixed, and coniferous forests.

TABLE IIIAVERAGE BACKSCATTER DIFFERENCES BETWEEN FLOODED ANDNON-FLOODED FORESTS FOR TERRASAR-X AND ENVISAT ASAR

way that the sum of the percentages (left-out flooded forestsplus non-flooded forests inside the flooded backscatter range)is minimized, a certain percentage of pixels is still classifiedincorrectly when the histograms of the two classes overlap.Table IV provides the numerical results of the performance ofthis procedure regarding the Alam-Pedja test site.A closer inspection of Table IV reveals a large difference be-

tween the original 18 m resolution TerraSAR-X results (per-centage in brackets) and the percentages after the strong 150

m resolution spatial averaging operation. It is possible to in-crease the general reliability (reduce noise) by averaging, but thedrawback is then a reduced spatial resolution. Even without spa-tial averaging, TerraSAR-X performance for deciduous forestsduring the no-foliage season is very good. Only 3.1% of non-flooded and 5.2% of flooded forests would be misclassified.Somewhat surprisingly the flooded forest distinction ability forX-band in coniferous forests is better than for mixed forests.One reason might be due to the multilayer vegetation present inthe Alam-Pedja mixed forests, whereas the coniferous forestsat the same site were mainly pine groves with very little under-growth [37]. Therefore, the canopy attenuation in mixed forestswas probably stronger than in coniferous forests at Alam-Pedja.For C-band the performance in all the forest types was verygood.

C. Ground Observation Verification

During the ground observation campaign numerous geo-ref-erenced photographs were taken and some GPS tracks were


TABLE IVPERFORMANCE OF THE HISTOGRAM-BASED CLASSIFICATION OF FLOODED FORESTS AND THE PROBABILITY OF MISCLASSIFIED PIXELS

Fig. 10. (a) GPS track (magenta) along the flood “shoreline” depicted on a Stripmap image from April 27. (b) An orthophoto [42] of the same area.

recorded along the provisional shoreline. A GPS log regardingthe border between flooded and non-flooded areas depicted on aTerraSAR-X Stripmap image, is shown in Fig. 10(a). The GPSlog was recorded on April 26 while the Stripmap image wasacquired one day later on April 27. A remarkable backscatterdifference between flooded and non-flooded forests can easilybe seen from the SAR image. Tree heights near the GPS trackranged from 18 to 25 m and main species were birch and pine[37].The relatively good canopy penetration observed for X-band

is in line with work done by Hong et al. [46], where floodedforest coherence was found to be stable even for long tem-poral baselines (up to 33 days). The stable coherence was ex-plained by water tree-trunk double reflections as the significantbackscattering mechanism within a flooded forest. This meansthat at least a portion of the X-band radiation must penetrate thecanopy and reach ground. The same effect was also confirmedby a recent study by Praks et al. [47], where X-band interferom-etry was employed for height retrieval of boreal forests.

D. Flood Maps

A total of three flood maps based on TerraSAR-X imagerywere created. Two flood maps were formed using the Stripmapimages, combining April 5 and 6, and April 27 and 28 acqui-sitions, respectively. A third flood map based on the ScanSARimage from April 15 represents the situation during the flood

maximum; the result is shown in Fig. 11. In this figure, perma-nent water bodies are marked with a black tone while the solidblack line represents the Emajõgi river that starts from LakeVõrtsjärv and flows through a meandering stream bed to thetown of Tartu. There are tens of square kilometers of inundatedareas, mostly near river banks, with some fragments existing fa-ther away from the main flood plain. As can be seen, most of theflooded areas exist in forests.Fig. 12 represents an aerial image of the Emajõgi river taken

near Tartu two days before the ScanSAR acquisition (whichwas the source of the previous flood map). Although no aerialimagery within the Alam-Pedja Nature Reserve was available,Fig. 12 depicts the vegetation of the area very well, and showsthe rivers surrounded by open glades and deciduous forests,where birch is the main tree species.When compared to the ScanSARmode, the Stripmap acquisi-

tions offer a much better spatial resolution: 18 m vs. 3 m. A frag-ment of the TerraSAR-X Stripmap based flood map laid over anorthophoto is shown in Fig. 13.Even if the X-band canopy penetration for Estonian forests is

sufficient enough for detectingwater under trees, another furtherchallenge also exists. Wet fields produced very high backscatterwhich appears similar to flooded forests even though the fieldsmight not be flooded according to conventional understanding.An illustration of this phenomenon is shown in Fig. 14, wherethe SAR image (a) taken at the beginning of April is comparedagainst an orthophoto (b) from July 2010. It can easily be seen


Fig. 11. A general map concerning the flood maximum derived from April 15 ScanSAR image: flooded areas are in grey, forests are marked as dotted regions,permanent water bodies are black, and non-flooded open areas are white. The box represents the area of interest which is used in statistical calculations.

that some of the fields appear very bright on the SAR image andmimic the flooded forest near the river at the top of the image.According to ground observation data no temporary lakes

formed at the bright areas, however, immediately after thesnowmelt the surface was very wet. The mechanism of creatingsuch a high backscatter from wet fields was probably causedby the fact that the field surface was rough enough to directthe incident radiation back to the sensor, whereas the wetnessof the surface enhanced the backscattering efficiency. It mustbe noted that the phenomenon was extensive only in the earlyApril imagery, but later when the fields had dried up, the effectdecreased and high backscatter was mostly caused by theflooded forests existing at river floodplains.In order to avoid this type of a misclassification, high

backscatter areas on the SAR images were separated into twoclasses (forest and non-forest) using the base map of Estonia[48].

E. Comparison With CORINE 2006 Land Cover Data

For calculating the flooded area distribution by land covertype, an area was first defined that included most of the flood-plains in the Alam-Pedja region. The corner coordinates of thearea were the following: 58.5837 N, 26.0108 E, (top-left) and58.2860 N, 26.6990 E, (bottom-right) (see the black box inFig. 11 for an illustration). The total area of the region of in-terest (ROI) was 1,216 km .According to type, the flooded areas inside the ROI for all

of the TerraSAR-X derived flood maps are shown in Fig. 15. Itis easy to see that the open landscape and forest flooded areasfollow the river water level diagram (Fig. 3) quite accurately.The flooded area reaches its peak near the water level maximumaround April 15, but is significantly lower 10 days earlier and

only slightly lower 12 days after the floodmaximum. This asym-metry is partially due to the water levels on these rivers recedingmore slowly than rising.However, a different behavior was noted to exist for wet field

areas. Immediately after the snowmelt on April 5 and 6, therewere 138 km of wet fields in the region. Just 10 days later, thearea of the wet fields had shrank roughly three times, being only48.3 km in mid-April and only 36.5 km at the end of April.The relatively dry and sunny weather in Estonia during April2010 probably helped to speed up the drying process. Fourteendays out of 24 were sunny between April 5 and 28 (EMHI 2011).In order to understand which types of land cover were most

affected by the flood, a comparison against CORINE 2006 datawas performed. The April 15 ScanSAR based flood map as wellas the previously defined ROI were used to produce the resultsshown in Fig. 16.Fig. 16 indicates that the largest part of the flooded area

(121 km ) resided in deciduous forests with only 14 km and13 km existing in mixed and coniferous forests, respectively.Still, it must be noted that the classifier probably underesti-mated the coniferous and mixed forest flooded areas slightlydue to the inferior canopy penetration in these forest types forX-band when compared to deciduous forest.

V. CONCLUSION

In this work, an extensive flooding event was studied with thehelp of spaceborne SAR images of three different wavelengthsincluding L-band, C-band and X-band. The X-band data of Ter-raSAR-X were used to create three flood maps that captured thesituation in Alam-Pedja during April 5, 6, 15, 27, and 28, 2010.The obtained results confirmed that it is possible to distinguishflooded from non-flooded forests at least during no-foliage sea-sons and thus produce flood maps over forested regions usingX-band SAR imagery, in addition to established C-band and


Fig. 12. Aerial image of the flooded area [47].

Fig. 13. The flood map for April 27 demonstrating the details of TerraSAR-X Stripmap data, where flooded forest areas are shown with a transparent light bluecolor and flooded open areas near the rivers in a darker transparent blue. The orthophoto [42] in the background was taken in the spring of 2007.

L-band techniques. In flooded deciduous, mixed, and coniferousforests, the average backscatter at X-band was 6.2 dB, 3.2 dB,and 4.0 dB higher than in non-flooded forests, respectively.When comparing TerraSAR-X, Envisat ASAR and ALOS

PALSAR flood detection capabilities ALOS PALSAR isconfirmed to be superior for detecting floods even underdense canopy. However, detecting floods of open areas ALOSPALSAR might perform not so well, because open water andflat terrain open areas might provide equally low backscatterlevels causing confusion between flooded and non-floodedopen areas. Both properties are thanks to its long 23 cm wave-

length electromagnetic waves which penetrate easily canopy,but do not discriminate surfaces which roughness dimensionis less than 23 cm. Envisat ASAR seems to be a good com-promise between Envisat ASAR and TerraSAR-X with its5.6 cm wavelength with better canopy penetration propertiesthan TerraSAR-X and better open area flood detection abilitiesthan ALOS PALSAR, but its spatial resolution is only 30 m (inimage mode) compared to 12.5 m of ALOS PALSAR and 3 mof TerraSAR-X. TerraSAR-X has superior spatial resolution(3 m in Stripmap mode) and its flood detection ability undertemperate no-foliage deciduous forest and coniferous forest


Fig. 14. (a) A subset of the TerraSAR-X image near the Emajõgi river from April 6 compared to (b) an orthophoto from July 2010 [42].

Fig. 15. The development of the flood in Alam-Pedja.

Fig. 16. The distribution of flooded areas during the flood maximum on April 15 according to land cover type.


TABLE VTERRASAR-X STRIPMAP APRIL 5, 2010

assuming a normal distributionTABLE VI

TERRASAR-X STRIPMAP APRIL 6, 2010

assuming a normal distributionTABLE VII

TERRASAR-X SCANSAR APRIL 15, 2010

assuming a normal distributionTABLE VIII


assuming a normal distributionTABLE IX


assuming a normal distribution

canopy is proven sufficient according to this study. It mightonly underestimate the flooded area under very dense canopies.

In order to further understand X-band SAR potential forforest flood detection in different conditions, additional tests


should be carried out that exploit detailed information regardingtree species, stand age, height, and density. Comparing SARimagery during leaf-on and leaf-off seasons should also beinvestigated. Taking advantage of the X-band SAR system’sbetter spatial resolution has been encouraging even whenhaving to pay a limited penalty due to its reduced canopypenetration.

APPENDIX ISTATISTICS ABOUT GROUND OBSERVATION REGIONS

Presented in Tables V–IX.

ACKNOWLEDGMENT

The author would like to thank the German Aerospace Centre(DLR) for the TerraSAR-X imagery used in the current study,and ESA ESRIN and JAXA for the Envisat ASAR and ALOSPALSAR images. Thanks also go to Regio Ltd. for the variousgeospatial data used as reference material and to Dr. Toomas Al-tosaar for a critical language review of themanuscript. The workwas carried out during Kaupo Voormansik’s doctoral studies atthe University of Tartu under the Foundation Archimedes DoRascholarship program.

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Kaupo Voormansik is a doctoral student at theUniversity of Tartu, Estonia. His research focus ison synthetic aperture radar (SAR) applications. Hereceived the M.Sc. degree in space studies fromthe International Space University, France, in 2009and the M.Sc. degree in computer science from theUniversity of Tartu, Estonia, in 2009.Since 2010, he has studied SAR including a re-

search visit to the German Aerospace Center (DLR)Information Retrieval working group in 2011–2012.He has been mainly working on flood mapping and

grassland parameters retrieval with polarimetric SAR.

Jaan Praks (S’98–M’02) was born in Estonia onNovember 16, 1971. He received the M.Sc. degreein physics from the University of Tartu, Estonia,in 1996, and the D.Sc. (Tech) degree from AaltoUniversity, Finland, in 2012.Currently, he is working as an University Teacher

of space technology at Aalto University. He startedwith research in Tartu Observatory vegetationremote sensing group in 1995. In 1997 he continuedresearch and studies in Helsinki University of Tech-nology, with the Laboratory of Space Technology.

The laboratory is now incorporated into the Department of Radio Scienceand Engineering of Aalto University, where he is currently teaching spacetechnology and coordinates student nanosatellite projects. During his career,he has participated in many international remote sensing projects on opticaland synthetic aperture radar (SAR) topics, managed airborne measurementcampaigns and coordinated projects. His main research interests are SARpolarimetry, SAR interferometry, SAR polarimetric interferometry, spacetechnology and nanosatellites.

Oleg Antropov (S’11) was born in Almaty, Kaza-khstan, in 1984. He received the M.S. degree incomputer science in 2006 and the Candidate ofSciences degree in radio physics in 2010 fromthe Dnepropetrovsk National University (DNU),Ukraine. He is working toward the Doctor of Tech-nology degree at Aalto University, Espoo, Finland,in the framework of the VTT Graduate School.During 2006–2009 he was a Senior Teacher and

Researcher with DNU, where he was developingsignal and image processing algorithms for mi-

crowave nondestructive testing applications. Since 2010, he has been with theVTT Technical Research Centre of Finland, in the Remote Sensing group. Hiscurrent research is concentrated on application of polarimetric and interfero-metric SAR imagery for global environmental and forest resource mapping.

Jüri Jagomägi received the M.Sc. degree in physicalgeography and landscape ecology at the University ofTartu, Estonia, in 2000, the M.Sc. degree in forestryfrom the Estonian Academy of Agriculture, EstonianUniversity of Life Sciences, in 1966.In 1967–2004, he worked as an engineer, re-

searcher, and senior researcher at the Chair ofPhysical Geography, University of Tartu, Estonia. In1993–2004, he was a Chief Cartographer at RegioLtd, and since 2004 an advisor in geoinformatics andgeomatics at Regio Ltd. His research has focused

on landscape ecology (landscape mosaics and dynamics, spatial heterogeneity,methods of landscape assessment) and recreational aspects of forest. Hehas developed methodologies for spatial planning, assessments of naturalresources, designating ecological networks (networks of compensative areas,green network). He has participated in several applied research projects (GISapplications in spatial planning, planning social and technical infrastructure,remote sensing).

Karlis Zalite received the M.Sc. degree in computersciences from the Ventspils University College,Ventspils, Latvia, in 2007. He is pursuing the Ph.D.degree in physics from the University of Tartu,Estonia.Since 2011, he has been with the Tartu Observatory

in the Department of Space Technology. His currentresearch is concentrated on application of SAR im-agery for environmental monitoring.

Documents

Flood Mapping With TerraSAR-X in Forested Regions in Estonia