THE PYLA 2001 EXPERIMENT : EVALUATION OF POLARIMETRIC RADAR CAPABILITIES OVER A FORESTED AREA M. Dechambre 1, S. Le Hégarat 1, S. Cavelier 1, P. Dreuillet 2, I. Champion 3 1 CETP IPSL (CNRS / Université de Versailles St-Quentin en Yvelines) 10 12 Avenue de l Europe 78 140 Vélizy France, e-mail : monique.dechambre@cetp.ipsl.fr 2 ONERA, 91761 Palaiseau cedex, France 3 INRA, BP 81, 33883 Villenave d Ornon, France 1. INTRODUCTION Full polarimetric capabilities associated with low frequency bands (P or L bands) of SAR systems represent a promising tool for Earth Observation (Ulaby et Elachi, 1990), more specifically at P- band, because of the great capability of ground or vegetation wave penetration. Unfortunatly, spaceborne P-band SAR are not yet operating, essentially because of three major reasons : The antenna size The disturbing ionospheric effects (Faraday effect and phase and amplitude scintillation) For the moment, the unauthorized frequency It is hoped that in a next future, these drawbacks will be solved, and it is essential for the moment to prepare this future. In that context, airborne P-band existing radars - real-aperture or synthetic aperture radar (SAR) systems - are used in order to begin the analyze the polarimetric radar signal acquired in natural conditions over several continental surfaces as well as to help at designing future space borne missions. There is also a strong need for polarimetric scattering model from bare soils as well as for vegetation (forest) surfaces and a corresponding need of good data for validation purposes. In order to test the potential of the P-band, a multi-purpose radar experiment named PYLA 2001 was conducted during April / May 2001 over a specific test site, with the high resolution, multi-frequency, full polarimetric airborne SAR facility, RAMSES developed by ONERA and operating at 435 MHz. The Pyla area located near Bordeaux (France) was chosen as a suitable multi-thematic test site and in addition, a dedicated calibration site was set up into this area. This experiment was performed within the low frequency radar working group set up by the CNES and supported by the CNES and the PNTS (Programme national de télédétection spatiale). The main objectives are to explore the potential of low frequency as well as polarimetry for sub-surface moisture detection (Pyla sand dune), biomass evaluation and polarimetric investigations (Nezer Forest), mapping of the ocean bathymetry and salinity (basin of Arcachon, estuary of the Gironde) and archeology (St Germain d Esteuil, Dignac, Moulin du Fâ). We present here the first results obtained over the Nezer forest located inside the Pyla 2001 test site, in terms of some polarimetric characteristics. 2. THE RAMSES AIRBORNE SAR AND THE PYLA 2001 EXPERIMENT 2.1 RAMSES RAMSES is an airborne multi-frequency full polarimetric SAR developed by ONERA (Table 1), installed onboard a Transall C160 aircraft fitted out with a GPS and an inertia central for trajectory and attitude monitoring (Figure 1). The full polarimetric P-band operates at 435 MHz (wavelength = 70 cm) using a 1.3 m by 0.8 m patch antenna with an incidence angle ranging from 40 to 80. The large bandwidth available leads to a relatively high range resolution of 3.5 m. The emitted power is greater than 500W, several operating mode are available (direct chirp, deramping chirp, step frequency), internal calibration is performed.
The P-band operating mode can be combined to another frequency band such as S, C or X, but at a price of a resolution degradation and this combination has not been used during the Pyla 2001 experiment. Band P L S C X Ku Ka W Frequency 0.435 1.6 3.2 5.3 9.6 14.3 35 95 GHz Bandwidth 70 200 300 300 600 300 800 500 MHz Transmitted V/H V/H V/H V/H V/H V/H V L/R polarization Emitted polarization V/H V/H V/H V/H V/H V/H V L/R Table 1 : The available frequency band within the RAMSES system Figure 1 : The RAMSES SAR installed onboard the The Transall C160 2.2 The PYLA 2001 experiment and the Nezer forest test site Four RAMSES P-band flights were performed during April and May 2001, under good meteorological conditions corresponding to more than 50 acquisition paths. The specific paths over the Nezer forest are summarized in the table 2 Flight number Path number Cape off North ( ) Altitude (ft) Incidence ( ) 3 5 187 11700 40 3 7 277 3 8 5 2 187 5 3 277 6 8 187 50 Table 2 : flight characteristics over the Nezer forest test site The Nezer forest is a part of les Landes forest, located in the South of the Arcachon basin. It is a well-controlled test site managed for several years by INRA. Many parcels of maritime pine trees of different ages are present and the geometry is accurately measured and described for several test fields. Simultaneous gravimetric soil moisture measurements were also conducted over the test fields during the experiment. The Nezer forest map is shown on figure 2, the test fields are located with red symbols (left), as well as a colored quick look band P RAMSES image of a part of this area (right).
Figure 2 : Map of the Nezer forest (maritime pines) and the location of the test fields in red ( left) and a P band colored RAMSES image of a part of this area with the location of the corner reflectors (right) 3. A FIRST POLARIMETRIC ANALYSIS OF TWO RAMSES P-BAND IMAGES This first analysis is based on a classification of polarimetric parameters derived from two images 502 (flight 5, path 2) (fig. 4(a)) and 503 (flight 5, path 3) (fig.5(a)) acquired with a same incidence (40 ) over quite the same area, but with two orthogonal viewing angles. Two classification schemes have been carried out, the Van Zyl classification (VZ) and the Cloude and Pottier classification (CP). Both are based on a specific analysis of the polarimetric information. They uses an identification method of backscattering mechanisms and a classification method. 3.1 Three major scattering mechanisms classification scheme (van Zyl, 1989) In the VZ classification scheme, three major mechanisms are identified 1 Backscattering corresponding to an odd number of reflection 2 Backscattering corresponding to an even number of reflection 3 Diffuse backscattering These scattering properties can be simply related to polarimetric properties, i.e. to the relative phases of the elements of the scattering matrix S: 1 φ HH - φ VV = 0 i.e. surface scattering 2 φ HH - φ VV = 180 i.e. dihedral reflection 3 any φ HH - φ VV, S HH and S VV are decorrelated i.e. volume scattering The classification is automatic and supervised (the classes are defined a priori) and the result is more or less noisy, depending on the number of pixels taken to compute the phase difference.this approach leads to 3 classes and an extra one corresponding to a non azimutal symmetry hypothesis (urban zones). 3.2 Entropy based classification scheme (Cloude and Pottier, 1997) This method is based on an estimation of the degree of complexity of the backscattering, and, if possible, the identification of a dominant backscatter mechanism. It relies on an eigen value analysis of the coherency matrix, for extracting average parameters from experimental data, and is free of physical constraints. The statistical model sets that there is always a dominant average scattering mechanism in the resolution cell.
Cloude and Pottier proposed a parametrization of the coherency matrix that leads to define an entropy H, ranging from 0 to 1, derived from its eigen values and related to the degree of randomness, and a angle αranging from 0 to 90. These parameters relate directly the underlying physical scattering mechanisms and may be used to associate observables with physical properties of the investigated medium. A H-α unsupervised classification scheme can be achieved and all random scattering mechanisms can be represented in this space. In the H-α space, 9 specific zones corresponding to 9 physical scattering characteristics are defined and outlined in fig. 3. Figure 3: 9 zones in the H-α plane for random media scattering problems (from Cloude and Pottier, 1997) The related 9 zones are. For more details see Cloude and Pottier, 1997. 9 low entropy surface scatter 6 medium entropy surface scatter 3 high entropy surface scatter 8 low entropy dipole scattering 5 medium entropy vegetation scattering 2 high entropy vegetation scatter 7 low entropy multiple scattering 4 medium entropy multiple scattering 1 high entropy multiple scattering 3.3 Results The results are plotted in figures 4 (flight 502, North South path) and 5 (flight 503 West East path). (a) is the image of the HH backscattered power, (b) is the image of the VZ classification (4 classes) and (c) the image of the CP classification (9 classes). The color codes corresponding to the different classes are plotted under are also plotted. The red, dark or white lines, splitting the images into two parts in fig. 4 give the limits between the forested area, composed of forest and deforested (grass) fields (on the left side of on each image) and the agricultural and urban area (on the right side on each image). VZ classification Two classes are dominant, the surface (red) class and the volume (green) one corresponding respectively to grass fields and forested parcels. As expected, the P-band has penetrating capacities, soils covered with grass are seen as bare soils, and forested areas correspond to volume scattering mechanisms. The noisy aspect of the image depends on the size of the averaging window (speckle filtering) used to derive the phase differences. We verified that the noisy aspect (mixing of green and red) decreases as the window size increases. The results are almost the same for the images 502 and 503 as can be seen. This means that the results of the classification are not sensitive to the direction of the viewing angle, which is not surprising for the surface mechanism. CP classification This classification is largely guided by the values of the entropy H. High values of H are interpreted as high degree of complexity, all the scattering mechanisms are randomly present and have the same probability of occurrence. Low values of H correspond to one dominant determinist scattering mechanism.
(a) (b) (c) Figure 3 : image 502 3(a) image of HH amplitude (db) 3(b) image of van Zyl classification 3(c) image of Cloude Pottier classification van Zyl (b) surface dihedral volume azimutal asymetry Cloude and Pottier (c) surface H small 9 surface H medium 6 multiple H small 7 multiple H medium 4 multiple H large 1 dipole H small 8 Dipole H medium 5 dipole H large 2 (a) (b) (c) Figure4 : image 503 4(a) image of HH amplitude 4(b) image of an Zyl classification 4(c) image of Cloude Pottier classifiaction These results were verified by analyzing and comparing the 2 CP classifications through several test fields, plotted on the CP images. On 502, H increases as the VZ classification is more noisy, which is a coherent result. On 503, high values of H were observed and VZ is very noisy. H is small for fields 611 612 and VZ is not noisy.
Comparing 502 and 503 through the referenced parcels designed on the images, it is seen that the multiple scattering mechanisms are not sensitive to the viewing angle and almost not to the incidence angle, as expected (590 632 642). The same is observed for the surface mechanisms (611-612 - 1-5). 4. CONCLUSION A multi-thematic radar experiment was conducted during April / May 2001 over a specific test site, with the high resolution, multi-frequency, full polarimetric airborne SAR system, RAMSES operating at P-band (435 MHz). Two classification schemes have been carried out, in order to roughly analyse, in a first step, the polarimetric information delivered by RAMSES: the Van Zyl classification (4 classes) and the Cloude and Pottier classification (9 classes), both based on a specific analysis of the polarimetric information. The results presented are colored images representing all classes related to the different scattering mechanisms. They exhibit a good coherency between the VZ and CP classifications, i.e. the more or less noisy character of the VZ results is well correlated to the more or less high values of the entropy, describing the degree of complexity of the wave medium interactions. These first results also show that the classifications are almost not sensitive to the direction of the radar viewing angle as well as to the incidence angle. In addition, the soil contribution seems of the same order for bare soils and soils covered with vegetation (crops, grass). This result is related to the P band penetration properties. In a next step of investigation, all images acquired over the Nezer forest will be analysed, and an effort to get more insight into the polarimetric parametrization will be conducted. References Ulaby F.T. and Elachi C., 1990, Radar Polarimetry for geoscience applications, Artech House, Boston, London. Cloude S.R., and Pottier, E., 1997, An entropy based classification scheme for land applications of polarimetric SAR, IEEE Trans. On Geoscience and remote sensing, vol. 35, no.1, pp68-78. Van Zyl, J.J., 1989, Unsupervised classification of scattering behavior using polarimetry data, IEEE trans. On geoscience and remote sensing, vol 27, no.1, pp36-45.