147 CHAPTER-7 INTERFEROMETRIC ANALYSIS OF SPACEBORNE ENVISAT-ASAR DATA FOR VEGETATION CLASSIFICATION 7.1 INTRODUCTION: Interferometric synthetic aperture radar (InSAR) is a rapidly evolving SAR remote sensing technology that directly measures the phase change between two phase measurements of the Earth s surface (Zhou et al., 2009). Two coherent synthetic aperture radar (SAR) phase images of the same portion of the Earth s surface are required to form a phase difference image, an interferogram in which a fringe pattern appears. These two coherent SAR images can be acquired either from single pass SAR interferometry in which two antennas on the same space platform are separated perpendicularly to the flight direction (azimuth direction), or from repeat-pass interferometry in which two images are acquired in different passes of the same SAR antenna at different times. Applications of InSAR data include surface displacements, land topography, land changes, land subsidence/uplift, water levels, soil moisture, snow accumulation, stem volume of forest, etc. (Fieigl et al., 2002; Arnadottir, 2005) SAR interferometry is based on a coherent combination of images acquired from the same orbit and the complex correlation between above said images is called interferometric coherence (Engdahl, 2003).
148 Interferometric data has contributed in improving the land cover classification (Askne, 1997). The coherence band helps in classification between forest and non-forest. Several studies have shown the use of multi-temporal SAR data for land cover changes in boreal forest (Wegmuller et al., 1995 and Floury et al., 1996) and tropical forest (Stussi et al., 1996). It was also shown that the coherence component derived from an interferometric pair gives additional information for land cover classification (Wegmuller et al., 2000). The coherence is generally low over forested areas and high over open fields which make difference between forested and non-forested areas (Wegmuller, 1997 and Hagberg et al., 1995). Further, it was also demonstrated that combined use of coherence band with SAR backscatter improved the sensitivity of SAR system for forestry applications (Srivastava et al., 2006). Using coherence image, multitemporal images and seasonal backscatter change images of appropriate seasons discriminated various classes within the forest with satisfactory accuracy (Martinez et al., 1998; Askne et al., 2003). Environmental conditions at the acquisition time may also lead to the different coherence levels (Santoro et al., 2007, Pullianen et al., 2003, Wagner et al., 2003 and Askne et al., 2005). In the present study, ENVISAT-ASAR repeat pass interferometric data was analysed for vegetation classification. This chapter explains
149 interferometric principle, selection of interferometric pair, generation of coherence image and use of coherence image in vegetation classification. 7.1.1 Interferometry principle - Geometry of insar pair: The geometry of repeat-pass interferometric SAR is shown in figure 7.1. A1 and A2 are the two radar antennas (fig 7.1) that view the same surface separated by a baseline vector with length B and angle, θ with respect to horizontal. B 90+θ-α α A1 90-θ+α θ ρ A2 ρ+ ρ x (azimuth) h P z(y) y (Range) Fig 7.1: Principle of interferometry The distance between A1 and the point, P on the ground that is being imaged is the range vector, ρ while the distance between A2 and the same point is given by the range vector, ρ+ ρ. The topography z(y) is given by z( y) = h ρ * cosθ --------- (7.1)
150 The phase difference between two images is given in fringe pattern and is called interferogram. It is proportional to the difference in path delays from two antennas and is given by, Radar phase, 4π φ = * δρ -------- (7.2) where, λ is the wavelength λ Since, λ φ φ is measured, δρ is obtained as δρ = -------- (7.3) 2π Using law of cosines, the topography z(y) is calculated as 2 2 B ( λ φ) z ( y) = h *cosθ -------------- (7.4) 2( λ φ Bsin( α θ )) The main factor that affects an interferometric analysis is land cover or vegetation type which includes height of trees, forest biomass or stem volume, type of trees. In forested areas, Wegmuller and Werner, (1995) showed that the coherence between SAR images is very low. In higher biomass regions, the coherence tends to be the lower. 7.2 METHODOLOGY FOR GENERATION OF COHERENCE IMAGE USING INSAR DATA: In general, SAR interferometry processing includes an image co-registration, baseline estimation, interferogram formation, calculation of the interferometric coherence, adaptive filtering and coherence image generation. The generation of coherence image from ASAR SLC data pair is given in fig 7.2 and explained as follows:
151 SAR SLC data pair Baseline estimation Coregistration of InSAR data pair Synthetic phase generation Reference DEM Interferogram generation Interferogram flattening Adaptive filtering and Coherence generation Fig 7.2: Methodology flow chart Generation of coherence image 7.2.1 Selection of interferometric pair: To generate an interferogram or DEM, there should be a significant correlation and good coherence between two sets of signals recorded during the two repeat passes. The selection of an interferometric pair is made on the basis of baseline length and the time period between two temporal image acquisitions. 7.2.1.1 Baseline estimation: The generation of an interferogram is possible when the ground reflectivity was acquired with at least two antenna
152 overlap. When the perpendicular component of the baseline (B n ) increases beyond a limit known as the critical baseline, no phase information is preserved, coherence is lost, and interferometry is not possible. Therefore, to generate an interferogramme, the baseline of the acquired datasets should be well below the critical baseline. The critical baseline B ncr is calculated from the equation as follows: B ncr λr tan(θ ) = ----------- (7.5) 2R r where R is the range distance of the target from the sensor; R r is the pixel spacing in range, and θ is the incidence angle. For ENVISAT ASAR, the critical baseline is approximately 1.1 km. No suitable interferometric pair with low baseline was available over the two study areas - Dandeli (Karnataka) and Rajpipla (Gujarat). However, interferometric pair with suitable baseline was acquired for Achanakmar- Amarkantak Biosphere Reserve in Bilaspur study area and coherence image was generated from the interferometric pair of the ENVISAT-ASAR data and was used for the vegetation classification. The datasets used for the interferometric analysis of ASAR data were acquired for Achanakmar-Amarkantak Biosphere Reserve in Chattisgarh on 24 th Sep 2006 and 29 th Oct 2006. The baseline length for the data pair is 203 m, which is well below the critical baseline. The minimum temporal gap
153 between the two acquisitions in the same mode and angle is 35 days for the interferometric data pair acquisition. 7.2.2 Co registration of the images: Co-registration of two images of the same scene acquired in same mode with same incidence angle at two different time periods makes it possible to use them in the same geometry. One of the images is considered as master and the other, slave in the slant range geometry that have the same orbit and acquisition mode. Co registration of the images was carried out with the orbital parameters. Co registration is carried out to correct relative translational shift, rotational and scale differences. For co-registrtion, pixels in different images should correspond to pixel-by-pixel comparisons. 7.2.3 Synthetic phase generation: Phase expected for a flat earth or known topography are extracted in the process of synthetic phase generation. This provides an unwrapped phase and better interferogram for SAR data pairs with large baselines. Phase value is estimated from the formula as follows: 4π φ( P) = [ r m r s ] ------------- (7.6) λ where, φ (P) is the phase computed for each target, P; r m and r s are the master and slave satellite distances of the object, P.
154 7.2.4 Interferogram generation: After image co-registration, an interferometric phase φ is generated by multiplying one image by the complex conjugate of the second one. A complex interferogram is formed. Spectral shift filtering is performed on the image pair, and the Hermitian product is calculated. φ = I m I --------- (7.7) * * s where, I m is the master image, I s * is the complex conjugate of second image The intensity of the interferogram is a measure of cross correlation of the images. Closer the fringes more are the topographical changes or height variations. 7.2.5 Interferogram Flattening: Interferogram is the process in which the fringe and phase effects due to the shape of the earth ellipsoid have been removed. A flattened interferogram contains only the fringes contributed by topographic terrain. Flattening involves the removal of the low frequency phase difference from the interferogram and it also removes the systematic phase difference from the interferogram and to the difference in position of antennas. The phase expected for a flat Earth is separated from the residual phase by estimating the average fringe frequency using Fast Fourier Transform algorithm. 7.2.6 Interferogram filtering and coherence image generation: A special filter or spectral shift is applied on the input SLC images to provide better results even in SAR pairs with large baseline. Filtering of
155 flattened interferogram smoothes the interferometric phase and hence reduces noise. Spectral shift filter used in the processing of interferogram provides capacity to generate a better quality interferogram. Phase noise of an interferogram can be estimated from the complex correlation coefficient of the SAR image pairs. The complex correlation coefficient of the SAR images is known as coherence and it can be used as a measure for the accuracy of the interferometric phase. Coherence for two co-registered SAR images is computed as follows: 1 * s1( x). s2 ( x) γ = ---------------------- (7.8) 2 2 s ( x). s ( x) 2 where s1 and s2 are coregistered complex SAR images and γ is the coherence image. 7.2.7 Use of coherence based for vegetation classification: The acquired ASAR SLC datasets were preprocessed as discussed in chapter-4 and mean backscatter intensity and backscatter difference images were generated. The interferometric coherence generated was used in conjunction with mean backscatter and backscatter difference for vegetation classification. The methodology flow chart is given in fig 7.3.
156 ASAR SLC 1 (24 Sep 2006) ASAR SLC 2 (29 Oct 2006) Co-registration of data pair Interferometric Analysis Co-registered multi-look images Coherence image generation Statistical Analysis Mean backscatter, backscatter difference and Coherence bands Feature extraction Supervised Classification Vegetation classes Fig 7.3: Methodology flowchart Vegetation classification using interferometric coherence A composite image (fig 7.4) was generated using the derived mean backscatter (red), backscatter difference (green) and coherence image (blue). This image was subjected to supervised classification using maximum likelihood classifier, by giving training areas based on groundbased information, and the accuracy assessment of the classified outputs has been carried out by generating confusion matrices and kappa statistics.
157 7.3 RESULTS AND DISCUSSIONS: Coherence image was generated from the ASAR InSAR pair and it was used in conjunction with mean backscatter and backscatter difference of the two time period images to delineate different vegetation classes in the study area. The coherence values ranges between 0 and 1 and is a function of systemic spatial decorrelation, the additive noise, and the scene decorrelation that takes place between the two acquisitions. High coherence values i.e., corresponding to values approaching 1 appear bright in the image, while dark values (approaching zero) are those areas where changes (or no radar return, radar facing slope, etc.) occurred during the time interval. The thematic information content decreases with increasing acquisition interval, mainly due to phenological or man-made changes of the object or weather conditions. For dry areas, high coherence information is observed even over long timescales. Figure 7.4 shows a composite image of mean backscatter, backscatter difference and coherence of the ASAR data of the study area. Visual analysis of the composite image showed clear discrimination of barren areas (in blue color), because of high coherence values. Agriculture areas, which were characterized by crop-growth patterns during the study period, were in green color due to differences in backscatter while other areas under agricultural land use were in yellow. Water appeared as black in the composite image (fig 7.3) due to its low
158 coherence and low mean backscatter values. Forested regions of the study area were interspersed with agriculture land use. 22 45'0"N 81 15'0"E 81 20'0"E 81 25'0"E 81 30'0"E 81 35'0"E 81 40'0"E ± 22 10'0"N 22 10'0"N 22 15'0"N 22 15'0"N 22 20'0"N 22 20'0"N 22 25'0"N 22 25'0"N 22 30'0"N 22 30'0"N 22 45'0"N 22 35'0"N 22 35'0"N 22 40'0"N 22 40'0"N 81 15'0"E 81 20'0"E 0 3.75 7.5 15 Kms 81 25'0"E 81 30'0"E 81 35'0"E 81 40'0"E Fig 7.4: Composite image of mean intensity (Red), intensity difference (green) and coherence (blue) generated from ASAR images in parts of Achanakmar- Amarkantak Biosphere Reserve
159 The discrimination of forest and agriculture classes is attributed to the large coherence values of agricultural areas compared to forests and marked backscatter difference between the two land cover types. Coherence is a function of systemic spatial decorrelation, additive noise, and the scene decorrelation that takes place between the two acquisitions (Wegmuller et al., 1997). The average value of coherence in the study area was very small (<0.5) as the majority area is covered by vegetation. This can be attributed to the wind patterns in the study area that might alter the orientation of scattering objects (leaves, secondary branches) in the vegetation layer. The largest coherence values were obtained in barren areas followed by agricultural areas, which are in accordance with the results reported in earlier studies (Wegmuller and Werner, 1995), which suggested urban areas, agricultural areas, bushes and forests have different correlation characteristics, with urban areas showing the largest correlation and forest the smallest. Agricultural areas in the study area showed medium coherence and large backscatter difference values. As the temporal gap between the two images acquired is 35 days, agriculture areas showed some difference during the two acquisitions. Both the coherence and backscatter difference values of the forested areas were small compared to other vegetation types. This may be attributed to the temporal decorrelation effect that occurs on longer time scales: the growth of the vegetation, human made changes, etc (Srtozzi et al., 2000).
160 Figure 7.5 shows a scatter plot of interferometric coherence with SAR backscatter for the four vegetation classes. From the figure, we can infer that the vegetation classes which had poor separability on the SAR backscatter image were clearly separable on the coherence image, which is in accordance to the observations of similar studies (Srivastava et al., 2006). 0.5 Interferometric coherence 0.4 0.3 0.2 0.1 0-25 -24-23 -22-21 -20-19 -18 Backscatter difference (db) Sal MixedForest Mixe d moi st De ciduous Agriculture Barren Fig 7.5: Scatter plot showing variation of interferometric coherence with SAR backscatter for different vegetation classes Barren Coherence Water Agriculture Sal gregarious forests Mixed Moist Deciduous forests Backscatter difference Fig 7.6: Feature space image of different vegetation
161 22 45'0"N 81 15'0"E 81 20'0"E 81 25'0"E 81 30'0"E 81 35'0"E 81 40'0"E ± 22 10'0"N 22 10'0"N 22 15'0"N 22 15'0"N 22 20'0"N 22 20'0"N 22 25'0"N 22 25'0"N 22 45'0"N 22 30'0"N 22 30'0"N 22 35'0"N 22 35'0"N 22 40'0"N 22 40'0"N 81 15'0"E 81 20'0"E 81 25'0"E 81 30'0"E 81 35'0"E 81 40'0"E 0 3.75 7.5 15 Kms Water Agriculture Barren Mixed moist deciduous Sal mixed Fig 7.7: Vegetation classified map of parts of Achankmar-Amarkantak Biosphere Reserve generated from interferometric coherence
162 Feature space images between the backscatter difference and the coherence band (fig 7.6) clearly distinguishes the different vegetation classes. Forest areas have both the coherence and backscatter difference as low. There is an overlap between the two types of forests i.e., mixed moist deciduous and Sal gregarious forests. Agriculture areas have medium coherence and high backscatter difference. Barren areas have high coherence and low backscatter difference values. Classification into different classes viz.,., water, barren, agriculture, mixed moist deciduous forest and Sal mixed forest was carried out using the bands of mean backscatter, backscatter difference and coherence of the ASAR datasets (fig 7.7). Table 7.1: Confusion matrix (in percentage) of different classes in parts of Bilaspur study area Class name Agriculture Mixed moist deciduous forest Sal gregarious forest Agriculture 83.3 6.7 3.6 6.5 Mixed moist deciduous forest 7.5 87.5 4.6 0.6 Sal gregarious forest 1.4 5.2 90.6 2.8 Barren Barren 7.9 0.7 1.3 90.1 Confusion matrix for different vegetation classes was derived and is given in Table 7.1. The overall classification accuracy and kappa statistics were 82.5% and 0.76 respectively. Statistical analysis based on Kappa statistics was carried out to test the precision and significance of the results. Accuracy table and kappa statistics are given in table 7.2. The results are in
163 correspondence with earlier studies which showed that the mean backscatter, backscatter difference and coherence band increased the classification accuracy (Araujo et al., 1999). Table 7.2: Accuracy table and kappa statistics of different classes in parts of Bilaspur study area Class name Producer's accuracy Users accuracy Kappa statistics Agriculture 71.43% 92.12% 0.89 Barren 100% 69.23% 0.603 Mixed moist deciduous forest 83.33% 90.91% 0.8701 Sal mixed forest 80.00% 66.67% 0.619 Though there is marked difference between forest and non-forest, there was difficulty in differentiating mixed moist deciduous and Sal gregarious forests of the study area. In terms of backscatter and coherence, the effect of small changes in species composition within forest is expected to be negligible. The classification accuracy was significant within the forest in plain areas; however, there was some misclassification in hilly regions. This may be due to slopes, which is a major cause for misclassification (Arne, 1995). Forests in the sloping terrain areas were misclassified as the barren areas in sloping regions facing the sensors. The use of coherence for vegetation classification is reasonable since low coherence is observed in forested areas, in comparison with bare soil and agriculture, which have high coherence. Interferometric coherence carries more land-cover related information than the backscattered intensity (Engdahl et al., 2003).
164 7.4 CONCLUSIONS: In the present study, an attempt was made to delineate four vegetation classes viz., agriculture, mixed moist deciduous forest, sal dominated forest and barren using ASAR interferometric coherence data over tropical forested regions of central India. It was inferred from the study that coherence from ASAR data can be used to differentiate major vegetation classes with a marked difference between forest and non-forest types. More ground knowledge on sample points of forest types aided with value additions to the information given by ENVISAT ASAR data can be very useful in discriminating different vegetation classes in the Indian region.