Classification and Analysis of Multifrequent and Multipolarimetric Airborne E-SAR Data for Hydrological Applications

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1 Classification and Analysis of Multifrequent and Multipolarimetric Airborne E-SAR Data for Hydrological Applications Herold, M., Hochschild, V., Klenke, M. & Müller, A. Introduction Hydrological applications, i.e. physically-based, distributed hydrological modelling, require areal analysis of water cycle components. Active microwave remote sensing research shows the eligibility of multifrequent and multipolarimetric SAR data to retrieve land use (Hall et al. 1994, Ulaby et al. 1996), soil moisture (Oh et al. 1992, Dubois et al. 1995, Ulaby et al. 1996) and biomass (Ranson et. al. 1995, Le Toan et. al. 1992, Ferrazzoli et. al. 1997). Particularly longer wavelengths, like L- Band (23 cm wavelength) penetrating through vegetation into the soil surface, are suitable to quantify hydrologically relevant parameters. In the next years new spaceborne systems with a multipolarimetric L-Band SAR (i.e. LightSAR) will require the development and evaluation of new algorithms and techniques to analyse and describe hydrological parameters as shown in the following examples. Fig. 1: Location of the Bröl basin within the river Sieg catchment in Western Germany.

2 SAR data and test site The test area is located in the catchment of the river Bröl (216,5 km²) in the Rheinische Schiefergebirge. The river Bröl is a tributary of the river Sieg (2853 km²) flowing into the river Rhine north of Bonn. Research activities, i.e.distributed hydrological modelling was carried out (Flügel 1995, Bende 1994) in these catchments. The SAR data were taken in June 1996 using the E-SAR-sensor of the German Space Agency (DLR). The E-SAR is a multifrequent and multipolarimetric airborne Experimental Synthetic Aperture Radar with a spatial resultion of 2,5 m. The backscatter intensities were acquired in X-, C- and L-Band; X-HH, C-HH and C-VV with 8 look processing and L- Band in HH, VV and HV polarizations with 4 looks. Groundthruth measurements and mapping were done during the SAR data acquisition. Preprocessing The preprocessing of the SAR data comprised geometric and radiometric corrections. For geocoding the FLIGHT and STG moduls of PCI were used to correct the C-HH intensity image, including track-parameters and a DEM with 10 m horizontal. All other images were coregistered to the warped C-HH image. The local incidence angle, needed for the calculation of the calibrated backscatter coefficient σ and for radiometric topography corrections, was computed as described in Su et al. (1997): θ = cos 1 ( cosα cosγ + sin α sin γ cos ( τ β )) θ - local incidence angle α - slope γ - local zenith angle β - aspect (north=0, increasing clockwise) τ - flight track angle (north=0, increasing clockwise) The input variables could be derived from flight track parameters and the DEM. The local incidence angle and the backscatter intensity were used to compute the calibrated backscatter coefficient σ and to correct topography-based radiometric distortions: θ - local incidence angle β - backscatter coefficient σ - calibrated backscatter coefficient β 2 [ db] = 10 log ( DN ) 60 0 σ [ db] = β sin θ 0 A Frost 3x3 adaptive filter was used for speckle reduction. 0

3 Fig. 2: Airborne E-SAR data of the , X-HH, L-VV and L-HV composite of the Bröl testsite (3 x 3 Frost filtering, geocoded to UTM, Zone 32 N). The size of the subset is 10 km² (2.7 km wide, 3.7 km length). Forest areas, settlements, grassland and agricultural areas are easily to detect. Landuse classification The multispectral and multipolarimetric landuse classification was done using the X-HH-, C-HH-, L- HH-, L-HV- and L-VV-bands. To investigate the separability of the main landuse categories water, agriculture, grassland, urban and forest, visual interpretations, unsupervised classifications and spectral statistics of the categories (mean, standard deviation) and the histograms of the different frequencies and polarizations were used. The results show the best spectral separability in the three L-Band polarizations with significant differences between the co- and the cross-polarized images. An unambiguous unsupervised extraction of four thematic classes in an image subset (shadow/water, agriculture/grassland, urban/forest and irradiation areas) was possible. These areas were fixed with binary masks for further, more detailed classifications. The shadow/water and irradiation areas were excluded from the following classifications. In the agriculture/grassland areas clear differences of five hydrologic relevant landuse categories, three grassland classes, mowed, unmowed and grassland with shrubs and two agricultural classes, were visible. The supervised classification was done by

4 applying the Maximum Likelihood Classifier. The distinction of urban/forest areas using spectral properties was not possible. Different 5 x 5 texture filters (homogeneity, entropy and mean) were used to extract the textural information. The filtered synthetic bands were than included in a new Maximum Likelihood Classification, that separates urban areas, coniferous and deciduous forest. Following the classification results of agriculture/grassland-, urban forest- and shadow/water areas were fitted to the final landuse map. The classification accuracy was assessed using groundtruth mapping and a landuse classification from Landsat TM Data acquired in 1992 (Klenke 1999). The results showed a recognition of the agriculture/grassland areas between %. The classification of the urban/forest areas were more inaccurate, especially the separation of coniferous and deciduous forest were unsatisfying (55 %). If the two forest classes were fit together the classification accuracy changes to 85 %. The total classification accuracy was 89 %. X-HH, L-HV, L-VV Farm- and Grasland Settlement/Forest X-HH, C-HH, L-VV Shadow/Water Irradiation Unsupervised Neglection- Classification- Classification Masks Masks Fig. 3: Schematic view of the mask generation for the classification. Soil moisture estimation Techniques and algorithms for retrieving near surface soil moisture from bare soil using active microwave remote sensing were developed during the last years (Dobson & Ulaby 1986, Oh et al. 1992, Fung et al. 1992, Dubois et al. 1995, Su et al. 1997). The empirical approaches of Oh et al.

5 1992 and Dubois et al are suitable for applications using polarimetric L-band data with limited groundtruth measurements of radar-backscatter biasing parameters. Fig. 4: Result of the supervised multispectral classification of the farmland (left) and result of the texture based distinction between settlements and forest (right). Fig. 5: Final result of the multifrequent and multipolarized classification of the E-SAR data from

6 Both models use the backscatter coefficient σ from bare soil in different polarizations to extract the dielectric constant ε for every pixel. The extraction of the dielectric constant and the volumetric soil moisture could be carried out after Hallikainen et al. (1985). Before the soil moisture calculation, the influence of the vegetation on the backscatter has to be reduced. After Ulaby et al. (1996) grassland areas with little biomass, like mowed grassland, have a negligible influence on the backscatter in L- Band. To correct the backscatter coefficients in the copolarized L Bands, a correction factor was computed using the mean of the landuse class Grassland/mowed in the copolarized and the crosspolarized L-Band and the backscatter coefficient for every pixel: (ms cop+ ms crp) s vc-cop = s cop (s cop + s crp ) ms cop ms crp s cop s crp s vc-cop - mean of backscatter coefficient in mowed grassland areas of the co-polarized band - mean of backscatter coefficient in mowed grassland areas of the cross-polarized band - bachscatter coefficient of the co - polarized band - backscatter coefficient of the cross polarized band - vegetation corrected backscatter coefficient of the co polarized band The results show a good visual correction. The new images contain mainly soil moisture information. First visual checks of the soil moisture distribution in the test area with the DEM confirm the expected facts of pretty dry slopes and higher areas and moist or wet lowland and valley areas. The next step is to use the empirical models to compute the volumetric soil moisture. After that a validation with ground measurements is needed. Fig. 6: Correction mask of local incidence angle (left, bright large incidence angle, dark small incidence angle) and distribution of soil moisture within the testsite around Lindscheid (right, yellow dry, blue wet). The straight blue lines are metallic fences.

7 Conlusion Multifrequent and multipolarimetric airborne E-SAR data were used for landuse mapping and soil moisture estimation to support the parameterization of hydrological catchment modelling. The major objective have therefore been investigations of signatures of different surface materials and their specific runoff components. On selected slopes in the river Bröl catchment in Siegerland (Germany) differences of the backscatter coefficients between single hydrological units should be worked out with the aim of field measurements (air- and soil temperature, soil moisture, wind direction and speed, radiation) and the simultaneous field mapping. The single steps of image processing have been: - geocoding and selection of subsets of the airstrips - filtering - histograms and statistics - calculation of backscatter coefficient under consideration of the local incidence angle - unsupervised classification - supervised classification of the agricultural areas - texture analyses for discrimination of settlement and forest classes The classification was done first using an unsupervised classification to distinguish between low and high frequency areas, which have than been classified in more detail (supervised classification to distinguish within the agricultural areas texture analysis to differentiate between settlements and forests). The result of the different classification methods is, that the discrimination between different grassland types is straight foreward. The distinction between mowed and not mowed grassland as well as between different agricultural areas have been possible as well. The texture analyses enabled the differentiation between inhomogeneous settlements and homogeneous forests. For the derivation of soil moisture first a local incidence correction has been applied to make the radiometry of the data independent of the relief. Afterwards a vegetation correction was necessary since the plant coverage induces volume scattering and is not comparable to bare soil surfaces. A visual inspection of the soil moisture distribution in the test site showed a good correlation with the dry hilltops and the moist floodplain areas. References BENDE, U. 1994: Abgrenzung von landnutzungsspezifischen Chemical Response Units (CHRU s) unter Verwendung eines GIS zur Regionalisierung der Stoffdynamik im Einzugsgebiet der Bröl. - Diplomarbeit Universität Bonn. DUBOIS, P. C., VAN ZYL, J. & ENGMAN, T. 1995: Measuring soil moisture with imaging radars, IEEE Transactions an Geosience Remote Sensing, 1995, 4, DOBSON, M. & ULABY, F. 1986: Active microwave soil moisture research. IEEE Transactions on Geosience and Remote Sensing, 1986, 24, 23 36

8 DOBSON, M. C., ULABY, F. T., PIERCE, L. E., SHARIK, T. L., BERGEN, K. M., KELNDORFER, J., KENDRA, J. R., LI, E., LIN, Y. C., NASHASHIBA, A., SARABANDI, K. & SIQUEIRA, P. 1995: Estimation of forestal biophysical characteristics in northern Michigan using SIR-C/X-SAR, IEEE Transactions on Geosience and Remote Sensing, 1995, 33, FERRAZOLI, P., PALOSCIA, S., PAMPALONI, P., SCHIAVON, G., SIGISMONDI, S. & SOLOMINI, D., 1997: The potential of multifrequency polarimetric SAR in assessing agricultural and arboreous biomass. IEEE Transactions an Geosience and Remote Sensing, 1997, 1, 5-17 FLÜGEL, W. A. 1995: Delineating hydrological Response units by Geographical Information Systems analysis for regional hydrological modelling using PRMS/MMS in the drainage basin of the river Broel, Germany, Hydrological Processes, 9, FUNG, A. K., LI, Z. & CHEN, K. S. 1992: Backscattering from a randomly rough dielectric surface, IEEE Transactions in Geosience and Remote Sensing, vol. 30. HALL, F., TOWNSHEND, J. & ENGMAN, T. 1995: Status of remote sensing algorithms for estimation of land surface state parameters, Remote Sensing of Environment, 1995, 51, HALLIKAINEN, M. T., ULABY, F. T., DOBSON, M. C., EL-RAYES, M. A. & WU, L. K. 1985: Microwave Dielectric Behavior of wet soil Part 1: Empirical models and experimental observations. IEEE Transactions an Geosience Remote Sensing, 1985, 1, KLENKE, M. 1999: GIS-gestützte Landnutzungsklassifikationen auf Grundlage von Daten passiver und aktiver Fernerkundungssensoren zur distributiven Flußeinzugsgebietsmodellierung. - Dissertation Universität Jena. LE TOAN, T., BEAUDOIN, A., RIOM, J. & GUYON, D. 1992: Relating forest biomass to SAR data. IEEE Transactions an Geosience and Remote Sensing, 1992, 30, OH, Y., SARABANDI, K. & ULABY, F. T. 1992: An empirical model and an inversion technique for radar scattering from bare soils, IEEE Transactions an Geosience and Remote Sensing, 1992, 30, RANSON, J., SAATCHI, S. & SUN, G. 1995: Boreal ecosystem characterization with SIR-C/X- SAR. IEEE Transactions an Geosience and Remote Sensing, 1995, 33, SU, Z., TROCH, P. A. & DE TROCH, F. P. 1997: Remote sensing of bare soil moisture using EMAC/ESAR data. International Journal of Remote Sensing,1997, vol. 18, no. 10, ULABY, F. T., DUBOIS, P. C. & VAN ZYL, J. 1996: Radar mapping of surface soil moisture. Journal of Hydrology, 1996, 184, 57-84