Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany
"> Figure 1
<p>Study area at River Saale in Saxony-Anhalt, Germany, and visualization of test classes (polygons) in TerraSAR-X data (17 January 2011, © DLR 2014) and aerial photographs (17 January 2011 © LHW 2011).</p> "> Figure 2
<p>(<b>a</b>) Visualization of the water levels at gauging station Rischmuehle at River Saale for the period 1 July 2009 to 31 July 2013; (<b>b</b>) SAR acquisition dates and water levels at gauging station Rischmuehle for the period 1 December 2009 to 31 May 2013 and (<b>c</b>) 1 May 2013 to 31 July 2013.</p> "> Figure 3
<p>Workflow for time-series analysis.</p> "> Figure 4
<p>Time course of σ<sub>0</sub> (dB): Mean and standard deviation for test class 1 (Permanent water).</p> "> Figure 5
<p>Time course of σ<sub>0</sub> (dB): Mean and standard deviation for test class 2 (Deciduous forest, dense, flooded).</p> "> Figure 6
<p>Time course of σ<sub>0</sub> (dB): Mean and standard deviation for test class 3 (Deciduous forest, dense, non-flooded).</p> "> Figure 7
<p>Histograms of class 2 and 3: TerraSAR-X SM (17 January 2011), ALOS PALSAR (21 January 2011), and RADARSAT-2 (16 January 2011).</p> "> Figure 8
<p>Time course of σ<sub>0</sub> (dB): Mean and standard deviation for test class 4 (Deciduous forest, sparse, flooded).</p> "> Figure 9
<p>Time course of σ<sub>0</sub> (dB): Mean and standard deviation for test class 5 (Cropland, non-flooded).</p> "> Figure 10
<p>Time course of σ<sub>0</sub> (dB): Mean and standard deviation for test class 6 (flooded cornfield).</p> "> Figure 11
<p>Histograms of class 2 and 3: TerraSAR-X SM (17 January 2011), ALOS PALSAR (21 January 2011), and RADARSAT-2 (16 January 2011).</p> ">
Abstract
:1. Introduction
Reference | Vegetation Type | Study Area | SAR System | Main Results |
---|---|---|---|---|
Engheta & Elachi [14] | Swamp with deciduous forest | Arkansas, USA | Seasat (L-band) | σ0 increase: Δ3–6 dB |
Ormsby et al. [15] | Pine, ash, other deciduous | Maryland, USA | Seasat (L-band) | σ0 increase: Δ2.5–6.3 dB |
Richards et al. [1] | Mono-specific eucalyptus forest | Murray River, Australia | SIR-B (L-band) | σ0 increase: Δ9.7 dB |
Wang & Imhoff [29] | Mangrove forests: Sundri and Gewa trees | Ganges delta, Bangladesh | SIR-B (L-band) | σ0 increase in dependence of inc. angle [°]: Gewa: Δ3.4 (26°), Δ3.4 (48°) Gewa with Sundri: Δ1.6 (26°), Δ3.3 (48°) Sundri with Gewa: Δ3.8 (26°), Δ1.6 (48°) |
Ramsey [18] | Coastal wetlands/black needlerush marsh | Florida, USA | ERS-1 (C-band) | Fluctuations in water level of 25 cm above to 7 cm below the marsh surface results in variations of SAR signal of −15.1 to −6.8 dB. |
Rignot et al. [31] | Tropical moist rain forest | Rondonia, Brazil | JERS-1 (L-band), SIR-C (C-/L-band) | σ0 increase: Δ3.0 dB at LHH and Δ2.9 dB at CHH beetween primary forest and flooded dead forest (moist trunks and absence of live canopy) |
Townsend [2] | Forested wetland | Roanoke River, North Carolina, USA | RADARSAT-1 (C-band) | Threshold of flooded leaf-off forest: −4.2 dB Threshold of flooded leaf-on forest: −5.1 dB and lower (at least 1 dB lower than over leaf-off forests) Threshold for flooded forest data with steep incidence angle: −4.21 dB Threshold for flooded forest data with shallow incidence angle: −8.21 dB → Difference of Δ4 dB between flooded forests imaged in S2 and S6 modes |
Hess & Melack [16] | Woody and herbaceous vegetation | East Alligator River, North Australia | SIR-C (C-/L-band, HH and HV) | Backscatter in October (dry season) compared to April (wet season) : Maximum decrease from April to October in median σ0 (HH) for an individual stand: Δ5.8 dB at CHH and Δ6.4 dB at LHH Cross-polarized differences are smaller: Δ2.3 and Δ2.8 dB decreases at CHV and LHV. |
Hess et al. [28] | Herbaceous, shrub, woodland, forest, palms | Central Amazon | JERS-1 (L-band) | Class median of non-flooded forest: −7.4 dB σ0 increase due to flooding: Δ2.1 dB Median σ0 for aquatic macrophytes (herbaceous-flooded): −8.3 dB Woodland-flooded (median σ0: −6.8 dB): largest dynamic range (~7 dB) separating the 5% (−11.5 dB) and 95% quantile (−4.6 dB) Shrub-non-flooded (median σ0: −8.8dB) with herbaceous-flooded Shrub-flooded (median σ0: −4.5 dB) with forest-/woodland-/palm-flooded A threshold of −6.5 dB separates flooded from non-flooded forest |
Horritt et al. [21] | Emergent salt marsh | East coast UK | Airborne SAR (C-/L-band) | σ0 increase of ~Δ1.2 dB at C-band, and 180° HH-VV phase differences at L-band. |
Martinez & Le Toan [17] | Alluvial forest | Amazon River, Brazil | JERS-1 (L-band) | σ0 increase of ca. Δ 2.0–Δ 3.0 dB during seasonal flooding |
Lang et al. [13] | Forest (tupelo-cypress, bottomland hardwood) | Roanoke River, North Carolina, USA | RADARSAT-1 (C-band) | σ0 decrease with increasing incidence angle during floods (leaf-on period/incidence angles: 23.5°–43.5°: Δ0.62 dB; leaf-off period/incidence angles: 23.5°–47.0°: Δ2.45 dB |
Pulvirenti et al. [22] | Olive groves, deciduous forest | Tuscany, Italy | COSMO-SkyMed (X-band) | σ0 increase of Δ7.0–8.6 dB (biomass: 50 t/ha) and Δ10 dB (biomass: 25 t/ha) Thresholds: Flooded areas: Δ > 7.0 dB Non-flooded areas: Δ < 3.0 dB |
Voormansik et al. [20] | Coniferous, deciduous leaf-off forest | Estonia | TerraSAR-X (X-band), Envisat ASAR (C-Band) | Mixed forest: TerraSAR-X σ0 increase: Δ3.2 dB; Envisat ASAR: Δ6.9 dB Deciduous forest: TerraSAR-X σ0 increase: Δ6.2 dB; Envisat ASAR: Δ4.6 dB Coniferous forest: TerraSAR-X σ0 increase: Δ4.0 dB; Envisat ASAR: Δ6.4 dB |
Martinis et al. [24] | Grassland and foliated shrubs | Caprivi, Namibia | TerraSAR-X (X-band) | σ0 over flooded vegetation −4 to −5 dB. σ0 difference to pre-flood conditions (Δ6.5–7.5 dB) |
2. Methodology
2.1. Study Area
2.2. Data Set
Sensor Type | Mode | Pol. | Inc. Angle Range | Product Type | Acquisition Date |
---|---|---|---|---|---|
TerraSAR-X | SM | HH | 31.7–33.6 | EEC | 2009-12-17, 2009-12-28, 2010-01-08, |
2010-01-19, 2010-01-30, 2010-02-10, | |||||
2010-03-15, 2010-03-26, 2010-04-06, | |||||
2010-05-20, 2010-06-11, 2010-06-22, | |||||
2010-07-03, 2010-07-14, 2010-07-25, | |||||
2010-08-05, 2010-08-16, 2010-08-27, | |||||
2010-09-07, 2010-09-18, 2010-09-29, | |||||
2010-10-10, 2010-11-01, 2010-11-12, | |||||
2010-12-15, 2010-12-26, 2011-01-17, | |||||
2011-02-08, 2011-02-19, 2011-03-02, | |||||
2011-03-24, 2011-04-04, 2011-04-15, | |||||
2011-04-26, 2011-05-07, 2011-05-18, 2011-05-29 | |||||
TerraSAR-X | SC | HH | 31.7–40.4, | EEC | 2013-06-04, 2013-06-09 |
43.3–50.5 | |||||
ALOS PALSAR | FBS | HH | 31.6–36.8 | Level 1.5 | 2010-03-22, 2010-12-23, 2011-01-21 |
ALOS PALSAR | FBD | HH/VV | 31.6–36.8 | Level 1.5 | 2009-12-03, 2010-05-07, 2010-06-05, 2010-07-21 |
RADARSAT-2 | Fine | HH | 30.2–33.6 | SGF | 2011-01-16 |
2.3. Preprocessing: SAR-Calibration, Speckle-Filtering and Image Registration
2.4. Selection and Statistical Analysis of Test Areas
3. Results and Discussion
4. Conclusions and Outlook
- Due to the influence of waves and seasonal freezing the variability of the backscatter over permanent open water areas (class 1) is very high (in X-band: ~Δ10 dB, in L-band: ~Δ4.5 dB). Analyses have shown that the optimum threshold value for separating open water surfaces and non-water areas has a high variability. Therefore, misclassifications can be reduced if the threshold value for separating water and non-water areas is set individually for each scene.
- Absolute values of σ0 show nearly identical values for both flooded dense (class 2) and flooded sparse forests (class 4) during the leaf-off phase in X-, C-, and L-band. Over a partially flooded cornfield (class 6), the backscatter shows similarly strongly enhanced values in X- (1.17 dB) and C-band (0.92 dB). In L-band, however, the backscatter appears to be much lower (−4.70 dB).
- Also, the backscatter differences in X- and L-band between pre-flood and flood conditions are nearly identical for both flooded dense (class 2; X-band: Δ4.42 dB, L-band: Δ4.23 dB) and sparse forests (class 4; X-band: Δ1.81 dB, L-band: Δ1.62 dB) during the leaf-off phase. Over a partially flooded cornfield (class 6), the backscatter difference between the flooding in January 2011 and the mean backscatter of the time course under non-flooded conditions is also nearly identical (X-band: Δ9.86 dB, L-band: Δ8.58 dB).
- Under certain circumstances, X-band data could be used for the detection of standing water beneath sparse vegetation such as cornfields or beneath forests during the leaf-off phase. However, in this study, inundated dense deciduous forests during the leaf-on phase only show a minor increase in backscatter of ~ Δ0.7 dB in comparison to the time series mean of the TerraSAR-X SM data and, therefore, can hardly be detected.
- In L-band, the results offer a wide range of backscatter increases between Δ4.23 dB for inundated leaf-off forest and Δ8.58 dB for a flooded cornfield. This very heterogeneous development for L-band can also be found in the literature, with backscatter increases in L-band varying between Δ1.6 dB and Δ9.7 dB for different vegetation types according to the studies listed in Table 1.
- The standard deviation does not give auxiliary information in detecting flooded dense (class 2) deciduous forests during the leaf-off phase. However, in X- and L-band a significant increase of this parameter could be determined over sparse flooded forests (test class 4). This class is affected by strong backscatter variation due to double bounce effects on the one hand and specularly reflecting water surfaces and radar shadowing effects within vegetation gaps on the other hand. Further, significant changes in the standard deviation can be found over a cornfield in X-band between flooded and non-flooded conditions. Further investigations are necessary if this parameter can be used in addition to backscatter information for improving the detection of flooded vegetation, e.g., in object based classification algorithms.
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Richards, J.A.; Woodgate, P.W.; Skidmore, A.K. An explanation of enhanced radar backscattering from flooded forests. Int. J. Remote Sens. 1987, 8, 1093–1100. [Google Scholar] [CrossRef]
- Townsend, P.A. Mapping seasonal flooding in forested wetlands using multi-temporal SAR. Photogramm. Eng. Remote Sens. 2001, 67, 857–864. [Google Scholar]
- Martinis, S.; Twele, A.; Voigt, S. Towards operational near-real time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Nat. Hazards Earth Syst. Sci. 2009, 9, 303–314. [Google Scholar] [CrossRef]
- Martinis, S.; Kersten, J.; Twele, A. A fully automated TerraSAR-X based flood service. ISPRS Int. J. Photogramm. Remote Sens. 2015, 104, 203–212. [Google Scholar] [CrossRef]
- Pulvirenti, L.; Pierdicca, N.; Chini, M.; Guerriero, L. An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic. Nat. Hazards Earth Syst. Sci. 2011, 11, 529–540. [Google Scholar] [CrossRef]
- Matgen, P.; Hostache, R.; Schumann, G.; Pfister, L.; Hoffman, L.; Svanije, H.H.G. Towards and automated SAR based flood monitoring system: Lessons learned from two case studies. Phys. Chem. Earth 2011, 36, 241–252. [Google Scholar] [CrossRef]
- Schumann, G.; di Baldassarre, G.; Alsdorf, D.; Bates, P.D. Near real-time flood wave approximation on large rivers from space: Application to the River Po, Italy. Water Resour. Res. 2008, 46. [Google Scholar] [CrossRef]
- Schlaffer, S.; Hollaus, M.; Wagner, W.; Matgen, P. Flood delineation from synthetic aperture radar data with the help of a priori knowledge from historical acquisitions and digital elevation models in support of near-real-time flood mapping. Proc. SPIE 2012, 8548. [Google Scholar] [CrossRef]
- Westerhoff, R.S.; Kleuskens, M.P.H.; Winsemius, H.C.; Huizinga, H.J.; Brakenridge, G.R.; Bishop, C. Automated global water mapping based on wide-swath orbital synthetic-aperture radar. Hydrol. Earth Syst. Sci. 2013, 17, 651–663. [Google Scholar] [CrossRef]
- Bourgeau-Chavez, L.L.; Kasischke, E.S.; Brunzell, S.M.; Mudd, J.P.; Smith, K.B.; Frick, A.L. Analysis of space-borne SAR data for wetland mapping in Virginia riparian ecosystems. Int. J. Remote Sens. 2001, 22, 3665–3687. [Google Scholar] [CrossRef]
- Wu, S.T.; Sader, S.A. Multipolarization SAR data for surface feature delineation and forest vegetation characterization. IEEE Trans. Geosci. Remote Sens. 1987, 25, 67–76. [Google Scholar] [CrossRef]
- Townsend, P.A. Relationships between forest structure and the detection of flood inundation in forest wetlands using C-band SAR. Int. J. Remote Sens. 2002, 23, 332–460. [Google Scholar] [CrossRef]
- Lang, M.W.; Townsend, P.A.; Kasischke, E.S. Influence of incidence angle on detecting flooded forests using C-HH synthetic aperture radar data. Remote Sens. Environ. 2008, 112, 3898–3907. [Google Scholar] [CrossRef]
- Engheta, N.; Elachi, C. Radar scattering from a diffuse vegetation layer over a smooth surface. IEEE Trans. Geosci. Remote Sens. 1982, 20, 212–216. [Google Scholar] [CrossRef]
- Ormsby, J.P.; Blanchard, B.J.; Blanchard, A.J. Detection of lowland flooding using active microwave systems. Photogramm. Eng. Remote Sens. 1985, 51, 317–328. [Google Scholar]
- Hess, L.L.; Melack, J.M. Remote sensing of vegetation and flooding on Magela Creek floodplain (Northern Territory, Australia) with the SIR-C synthetic aperture radar. Hydrobiologia 2003, 500, 65–82. [Google Scholar] [CrossRef]
- Martinez, J.; le Toan, T. Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote Sens. Environ. 2007, 108, 209–223. [Google Scholar] [CrossRef]
- Ramsey, E.W. Monitoring flooding in coastal wetlands by using radar imagery and ground-based measurements. Int. J. Remote Sens. 1995, 16, 2495–2502. [Google Scholar] [CrossRef]
- Kundus, P.; Karszenbaum, H.; Pultz, T.; Parmuchi, G.; Bava, J. Influence of flood conditions and vegetation status on the radar backscatter of wetland ecosystems. Can. J. Remote Sens. 2001, 27, 651–662. [Google Scholar] [CrossRef]
- Voormansik, K.; Praks, J.; Antropov, O.; Jagomägi, J.; Zalite, K. Flood mapping with TerraSAR-X in forested regions in Estonia. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 562–577. [Google Scholar] [CrossRef]
- Horritt, M.S.; Mason, D.C.; Cobby, D.M.; Davenport, I.J.; Bates, P.D. Waterline mapping in flooded vegetation from airborne SAR imagery. Remote Sens. Environ. 2003, 85, 271–281. [Google Scholar] [CrossRef]
- Pulvirenti, L.; Pierdicca, N.; Chini, M.; Guerriero, L. Monitoring flood evolution in vegetated areas using COSMO-SkyMed data: The Tuscany 2009 case study. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 99, 1–10. [Google Scholar] [CrossRef]
- Martinis, S.; Twele, A. A hierarchical spatio-temporal Markov model for improved flood mapping using multi-temporal X-band SAR data. Remote Sens. 2010, 2, 2240–2258. [Google Scholar] [CrossRef]
- Martinis, S.; Kuenzer, C.; Twele, A. Flood Studies Using Synthetic Aperture Radar Data. Remote Sensing of Water Resources, Disasters and Urban Studies; Taylor and Francis: London, UK, 2015. [Google Scholar]
- Refice, A.; Capolongo, D.; Pasquariello, G.; D’Addabbo, A.; Bovenga, F.; Nutricato, R.; Lovergine, F.; Pietranera, L. SAR and InSAR for flood monitoring: Examples with COSMO-SkyMed data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 2711–2722. [Google Scholar] [CrossRef]
- Mosleh, M.K.; Hassan, Q.K.; Chowdhury, E.H. Application of remote sensors in mapping rice area and forecasting its production: A review. Sensors 2015, 15, 769–791. [Google Scholar] [CrossRef] [PubMed]
- Arnesen, A.; Silva, T.; Hess, L.L.; Novo, E.; Rudorff, C.; Chapman, B.; McDonald, K. Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images. Remote Sens. Environ. 2013, 130, 51–61. [Google Scholar] [CrossRef]
- Hess, L.L.; Melack, J.M.; Novo, E.M.L.M.; Barbosa, C.C.F.; Gastil, M. Dual-season mapping of wetland inundation and vegetation for the central Amazon basin. Remote Sens. Environ. 2003, 87, 404–428. [Google Scholar] [CrossRef]
- Wang, Y.; Imhoff, M.L. Simulated and observed LHH radar backscatter from tropical mangrove forests. Int. J. Remote Sens. 1993, 14, 2819–2828. [Google Scholar] [CrossRef]
- Wang, Y.; Hess, L.L.; Filoso, S.; Melack, J.M. Understanding the radar backscattering from flooded and non-flooded Amazonian forests: Results from canopy backscatter modeling. Remote Sens. Environ. 1995, 54, 324–332. [Google Scholar] [CrossRef]
- Rignot, E.; Salas, W.A.; Skole, D.L. Mapping deforestation and secondary growth in Rondonia, Brazil, using imaging radar and thematic mapper data. Remote Sens. Environ. 1997, 59, 167–179. [Google Scholar] [CrossRef]
- Evans, D.C.; Farr, J.P.; Forf, J.P.; Thompson, T.W.; Werner, C.L. Multipolarization radar images for geologic mapping and vegetation discrimination. IEEE Trans. Geosci. Remote Sens. 1986, 24, 246–257. [Google Scholar] [CrossRef]
- Leckie, D.G. Forestry applications using imaging radar. In Manual of Remote Sensing: Principles and Applications of Imaging Radar, 3rd ed.; Henderson, F.M., Lewis, A.J., Eds.; John Wiley and Sons: New York, NY, USA, 1998; pp. 435–509. [Google Scholar]
- LAU—State office Saxony-Anhalt for Environment Protection. Management Plan Fort the “FFH-Area Saale-, Elster-, Luppe-Aue between Merseburg and Halle” 2011. Available online: http://www.lau.sachsen-anhalt.de/fileadmin/Bibliothek/Politik_und_Verwaltung/MLU/LAU/Naturschutz/Natura2000/Managementplanung/Dateien/Saale-Elster-Luppe-Aue-zw-Mers-u-Hal_ges.pdf (accessed on 19 January 2015).
- MLU—Ministry for Regional Development, Agriculture and Environment of the Federal State Saxony-Anhalt & State Office Saxony-Anhalt for Environment Protection. Landscape Structure of Saxony-Anhalt. A Contribution to the Perpetuation of the Landscape Program of the Federal State Saxnoy-Anhalt 2011. Available online: http://www.lau.sachsen-anhalt.de/fileadmin/Bibliothek/Politik_und_Verwaltung/MLU/LAU/Naturschutz/Landschaftsprogramm/Dateien/Landschaftsgliederung_Fachtext.pdf (accessed on 19 January 2015).
- LHW—State Office for Flood Control and Water Management Saxony-Anhalt. January 2011 Flood Report. 2011. Available online: http://www.hochwasservorhersage.sachsen-anhalt.de/dokumente/hochwasserberichte/jan_2011/abschlussbericht_2011.pdf (accessed on 19 January 2015).
- LHW—State Office for Flood Control and Water Management Saxony-Anhalt. June 2013 Flood Report in Saxony-Anhalt. Emergence, Course, Management and Statistical Placement 2013. Available online: http://www.lhw.sachsen-anhalt.de/fileadmin/Bibliothek/Politik_und_Verwaltung/Landesbetriebe/LHW/neu_PDF/4.0/SB_Hochwasserschutz/Hochwasserbericht_2013.pdf (accessed on 19 January 2015).
- Infoterra. Radiometric Calibration of TerraSAR-X data, 2014. Available online: http://www2.geo-airbusds.com/files/pmedia/public/r465_9_tsx-x-itd-tn-0049-radiometric_calculations_i3.00.pdf (accessed on 5 May 2015).
- O’Grady, D.; Leblanc, M.; Bass, A. The use of radar satellite data from multiple incidence angles improves surface water mapping. Remote Sens. Environ. 2014, 140, 652–664. [Google Scholar] [CrossRef]
- Baatz, M.; Schäpe, A. Object-oriented and multi-scale image analysis in semantic networks. In Proceedings of the 2nd International Symposium on Operationalization of Remote Sensing, Enschede, The Netherlands, 16–20 August 1999.
- Auquière, E.; Defourny, P.; Baltazart, V.; Guissart, A. ERS SAR time series analysis for maize monitoring using experimental and modeling approaches. In Proceedings of the ESA ERS Conference, Florence, Italy, 5 March 1997; pp. 147–152.
- Engman, E.T. Applications of microwave remote sensing of soil moisture for water resources and agriculture. Remote Sens. Environ. 2001, 35, 213–226. [Google Scholar] [CrossRef]
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Martinis, S.; Rieke, C. Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany. Remote Sens. 2015, 7, 7732-7752. https://doi.org/10.3390/rs70607732
Martinis S, Rieke C. Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany. Remote Sensing. 2015; 7(6):7732-7752. https://doi.org/10.3390/rs70607732
Chicago/Turabian StyleMartinis, Sandro, and Christoph Rieke. 2015. "Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany" Remote Sensing 7, no. 6: 7732-7752. https://doi.org/10.3390/rs70607732