THE PYLA 2001 EXPERIMENT : EVALUATION OF POLARIMETRIC RADARCAPABILITIES OVER A FORESTED AREA

M. Dechambre1, S. Le Hégarat1, S. Cavelier1, P. Dreuillet2, I. Champion3

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, France3 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 apromising tool for Earth Observation (Ulaby et Elachi, 1990), more specifically at P- band, because of the greatcapability of ground or vegetation wave penetration. Unfortunatly, spaceborne P-band SAR are not yetoperating, 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 thisfuture. 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 severalcontinental surfaces as well as to help at designing future space borne missions. There is also a strong need forpolarimetric scattering model from bare soils as well as for vegetation (forest) surfaces and a corresponding needof good data for validation purposes.

In order to test the potential of the P-band, a multi-purpose radar experiment named PYLA 2001 was conductedduring April / May 2001 over a specific test site, with the high resolution, multi-frequency, full polarimetricairborne SAR facility, RAMSES developed by ONERA and operating at 435 MHz. The “Pyla area” located nearBordeaux (France) was chosen as a suitable multi-thematic test site and in addition, a dedicated calibration sitewas set up into this area. This experiment was performed within the “low frequency radar working group” set upby the CNES and supported by the CNES and the PNTS (Programme national de télédétection spatiale). Themain objectives are to explore the potential of low frequency as well as polarimetry for sub-surface moisturedetection (Pyla sand dune), biomass evaluation and polarimetric investigations (Nezer Forest), mapping of theocean bathymetry and salinity (basin of Arcachon, estuary of the Gironde) and archeology (St Germaind’Esteuil, Dignac, Moulin du Fâ).

We present here the first results obtained over the Nezer forest located inside the “Pyla 2001” test site, in termsof 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), installedonboard a Transall C160 aircraft fitted out with a GPS and an inertia central for trajectory and attitudemonitoring (Figure 1).

The full polarimetric P-band operates at 435 MHz (wavelength = 70 cm) using a 1.3 m by 0.8 m patch antennawith an incidence angle ranging from 40° to 80°. The large bandwidth available leads to a relatively high rangeresolution 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 aresolution degradation and this combination has not been used during the “Pyla 2001” experiment.

Band P L S C X Ku Ka WFrequencyGHz

0.435 1.6 3.2 5.3 9.6 14.3 35 95

BandwidthMHz

70 200 300 300 600 300 800 500

Transmittedpolarization

V/H V/H V/H V/H V/H V/H V L/R

Emittedpolarization

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 meteorologicalconditions corresponding to more than 50 acquisition paths. The specific paths over the Nezer forest aresummarized in the table 2

Flight number Path number Cape off North (°) Altitude (ft) Incidence (°)3 5 187 11700 403 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-controlledtest site managed for several years by INRA. Many parcels of maritime pine trees of different ages are presentand the geometry is accurately measured and described for several test fields. Simultaneous gravimetric soilmoisture measurements were also conducted over the test fields during the experiment. The Nezer forest map isshown on figure 2, the test fields are located with red symbols (left), as well as a colored quick look band PRAMSES 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 bandcolored 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 samearea, but with two orthogonal viewing angles.

Two classification schemes have been carried out, the “Van Zyl classification” (VZ) and the “Cloude and Pottierclassification” (CP). Both are based on a specific analysis of the polarimetric information. They uses anidentification 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 identified1 Backscattering corresponding to an odd number of reflection2 Backscattering corresponding to an even number of reflection3 Diffuse backscattering

These scattering properties can be simply related to polarimetric properties, i.e. to the relative phases of theelements of the scattering matrix S:

1 φ HH - φ VV = 0° i.e. surface scattering2 φ HH - φ VV = 180° i.e. dihedral reflection3 any φ HH - φ VV, S HH and SVV are decorrelated i.e. volume scattering

The classification is automatic and supervised (the classes are defined a priori) and the result is more or lessnoisy, depending on the number of pixels taken to compute the phase difference .This approach leads to 3 classesand 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, theidentification 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 statisticalmodel 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, rangingfrom 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 toassociate observables with physical properties of the investigated medium.

A H-α unsupervised classification scheme can be achieved and all random scattering mechanisms can berepresented in this space. In the H-α space, 9 specific zones corresponding to 9 physical scattering characteristicsare 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 scatter8 low entropy dipole scattering 5 medium entropy vegetation scattering 2 high entropy vegetation scatter7 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) theimage of the CP classification (9 classes). The color codes corresponding to the different classes are plottedunder 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 andurban 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 tograss fields and forested parcels. As expected, the P-band has penetrating capacities, soils covered with grass areseen as bare soils, and forested areas correspond to volume scattering mechanisms. The noisy aspect of theimage depends on the size of the averaging window (speckle filtering) used to derive the phase differences. Weverified 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 theclassification are not sensitive to the direction of the viewing angle, which is not surprising for the surfacemechanism.

• CP classification

This classification is largely guided by the values of the entropy H. High values of H are interpreted as highdegree of complexity, all the scattering mechanisms are randomly present and have the same probability ofoccurrence. 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 notnoisy.

Comparing 502 and 503 through the referenced parcels designed on the images, it is seen that the multiplescattering 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 highresolution, 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 polarimetricinformation delivered by RAMSES: the “Van Zyl classification” (4 classes) and the “Cloude and Pottierclassification” (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 ofthe VZ results is well correlated to the more or less high values of the entropy, describing the degree ofcomplexity of the wave – medium interactions. These first results also show that the classifications are almostnot sensitive to the direction of the radar viewing angle as well as to the incidence angle. In addition, the soilcontribution seems of the same order for bare soils and soils covered with vegetation (crops, grass). This result isrelated 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 getmore 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 polarimetricSAR, 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. Ongeoscience and remote sensing, vol 27, no.1, pp36-45.