Polarimetric synthetic aperture radar study of the Tsaoling landslide generated by the 1999 Chi-Chi earthquake, Taiwan

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. F1, 6006, doi: /2003jf000037, 2003 Polarimetric synthetic aperture radar study of the Tsaoling landslide generated by the 1999 Chi-Chi earthquake, Taiwan Kristina R. Czuchlewski Lamont-Doherty Earth Observatory and Department of Earth and Environmental Sciences, Columbia University, New York, USA Jeffrey K. Weissel Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York, USA Yunjin Kim Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA Received 22 March 2003; revised 1 July 2003; accepted 29 July 2003; published 11 October [1] We employ L-band airborne synthetic aperture radar (SAR) polarimetry to detect surface changes produced by the Tsaoling landslide, the largest slope failure triggered by the September 1999 Chi-Chi earthquake in central Taiwan. Imaging polarimeters provide a complete description of the scattering properties of radar target materials. Resurfacing of forested hillslopes by landslides alters scattering mechanisms from those dominated by backscatter from trees to mechanisms associated with scatter from rough, bare surfaces. Scattering mechanism information on a per-pixel basis is extracted by decomposing matrices formed as the outer product of the complex scattering vector measured for each resolution cell. We classify surface cover type and thereby identify the extent of the landslide, using such polarimetric parameters as scattering entropy, anisotropy, and pedestal height, derived from the eigenvalues of the decomposition, as well as the weighted average scattering mechanism derived from the eigenvectors. We address the utility of full polarimetry versus dual polarimetry for landslide mapping purposes and show that fully polarimetric SAR is necessary for distinguishing water surfaces of varying roughness from the bare surfaces created by landsliding. We show that scattering entropy and average scattering mechanism, for example, can be used to identify the Tsaoling landslide source, run out area and impounded lakes as proficiently as maps obtained using satellite optical sensors, such as Landsat and the Indian Research Satellite. However, the operational advantages of radar over optical sensing techniques (namely, its day-night, allweather data acquisition capability) suggest that SAR polarimetry could play a leading role in the rapid assessment of landslide disasters. INDEX TERMS: 1640 Global Change: Remote sensing; 1824 Hydrology: Geomorphology (1625); 1821 Hydrology: Floods; 1894 Hydrology: Instruments and techniques; 3299 Mathematical Geophysics: General or miscellaneous; KEYWORDS: radar, hazard, scattering, Chi-Chi, Taiwan, earthquake Citation: Czuchlewski, K. R., J. K. Weissel, and Y. Kim, Polarimetric synthetic aperture radar study of the Tsaoling landslide generated by the 1999 Chi-Chi earthquake, Taiwan, J. Geophys. Res., 108(F1), 6006, doi: /2003jf000037, Introduction [2] Taiwan experienced its largest earthquake of the 20th century when a magnitude (M w ) 7.6 event struck the central part of the island at 0147 LTon 20 September 1999 (Figure 1) [Shin et al., 2000]. Many severe aftershocks occurred in the first few days following the earthquake, several with magnitudes over 6.5. The main shock and aftershocks together caused approximately 10,000 landslides of various types and sizes [Hung, 2000], and cost the lives of over 2400 people. Copyright 2003 by the American Geophysical Union /03/2003JF Landslides and rockfalls damaged highway transportation systems and isolated communities in the rugged Central Range. The largest landslide, Tsaoling (Figure 1), slid into the valley of the Chingshuichi from its northern side [Sitar et al., 2001], killing 34 people and requiring rapid construction of a new road for the rescue effort. The source area for the Tsaoling slide is estimated at 1.6 km 2 [Chigira et al., 2003]. Debris covered about 3.4 km 2 of the valley floor, damming the river and forming an artificial lake that had to be drained to avoid the possibility of dam failure during the monsoon rains [Chen, 2000; Chigira et al., 2003]. [3] Rapid disaster assessment is critical to authorities, given the possibility of strong aftershocks and imminent or 7-1

2 7-2 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES Figure 1. Tsaoling landslide area. (a) Air photograph mosaic of the Tsaoling landslide (K. Okunishi, A preliminary report on the landslides and other ground surface movement induced by the 1999 Chichi earthquake, Taiwan, 2000, available at html) which constitutes the focus of this study, obtained shortly after the 20 September 1999 M w 7.6 Chi-Chi earthquake. (b) Shaded topographic relief map of Taiwan, showing earthquake epicenter (star), the Chelungpu fault surface rupture [Shin et al., 2001], and the location of the kilometer-scale Tsaoling landslide (triangle). (c) Sketch map of the Tsaoling landslide, adapted from Chigira et al. [2003] (with permission from Elsevier). See color version of this figure at back of this issue. concurrent inclement weather. To assess disaster extent under such circumstances, synthetic aperture radar (SAR) has distinct operational advantages over passive/optical remote sensing technology, such as aerial photography (e.g., Figure 1a), Landsat, SPOT, and other satellite sensors, which have been widely used for disaster assessment. The use of optical remote sensing techniques for landslide detection and mapping is well constrained, in that landslides in humid mountain regions typically remove standing vegetation from hillslopes. Assessment strategies depend on the availability of preevent land cover information. Cover change is generally expressed as an albedo increase in panchromatic data (e.g., black and white air photographs), a strong color contrast with vegetated areas in color air photographs (Figure 1a), or as suppression of the high reflectance from healthy vegetation in the nearinfrared channels of multispectral imagery obtained over landslide-affected slopes. The landslide source and run-out areas essentially remain bare surfaces until regrowth processes take place. [4] Slopes denuded of vegetation by landslides differ markedly in their microwave scattering properties from adjacent unaffected hillslopes: Backscatter characteristic of rough, bare surfaces will occur from landslide areas, whereas volume or diffuse scattering will occur over forested hillslopes. Polarimetric SAR provides a complete description of terrain scattering properties on a per pixel basis, as shown below. SAR polarimeters measure amplitudes and phases for all possible combinations of polarized backscatter: hh, hv, vh, and vv, which can be received by a radar antenna capable of sending and receiving horizontally h and vertically v polarized waves. In this paper we show that scattering mechanism signatures obtained from fully polarimetric SAR readily discriminate between slopes disturbed by landsliding and those where vegetation cover has remained intact. We develop a surface classification scheme, based on

3 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES 7-3 scattering properties and other criteria, that allows landslide identification to be performed efficiently using a single pass of polarimetric SAR. We assess the utility for landslide mapping of fully polarimetric versus dualpolarization and single (fixed) polarization SAR systems. This issue is of practical importance given the high data rate demands of SAR polarimeters compared to simpler SAR systems, and the immense cost of mounting new satellite SAR missions. The present generation of satelliteborne imaging SARs are either single-polarization (ERS-1, ERS-2, and Radarsat-1) or dual-polarization (Envisat ASAR) instruments operating in C band (0.06 m wavelength). Our study is timely because fully polarimetric space-based SARs, Radarsat-2 (C band) and ALOS PAL- SAR (L band, 0.24 m wavelength), are currently scheduled for launch in [5] In September 2000, as part of the PacRim II mission, NASA Jet Propulsion Laboratory s (JPL) Airborne SAR (AIRSAR) flew three flight lines in central Taiwan over terrain that sustained kilometer-scale landslides generated by the September 1999 Chi-Chi earthquake (Figure 1). Full SAR polarimetry was collected at L and P bands (0.25 m and 0.68 m wavelength). Two C-band (0.06 m) antennas were used for cross-track interferometry to produce a digital elevation model of the terrain. Consequently, C-band backscatter is restricted to a single vertical polarization. In this study we use scattering mechanism signatures extracted from L-band polarimetry collected over the Tsaoling landslide (Figure 1) to demonstrate the effectiveness of SAR polarimetry for distinguishing slopes affected by landsliding from slopes that are spared. P-band polarimetry was degraded by radio frequency noise and is therefore not used in the present work. The operational advantages of SAR over optical remote sensing techniques (namely, its daynight, all-weather data acquisition capability) provide the motivation for conducting this research. The central issue addressed in this work is the confidence with which landslides can be identified and inventoried in a time critical manner following a disaster, i.e., using a single-pass SAR operational strategy. 2. Methodology 2.1. Polarimetric SAR Backscatter [6] We can express the backscattered electric field E s received at the radar antenna as a linear transformation of the electric field E i incident upon target material: E s ¼ eikr r S Ei ; or, explicitly in terms of horizontal h and vertical v components of the electric fields, as [e.g., Nghiem et al., 1990] 2 4 E s h E s v S hh S hv 5 ¼ eikr 4 4 r S vh S vv 5 Ei h E i v ð1þ 3 5: ð2þ In equations (1) and (2), k denotes the wave number of the illuminating wave and r is the distance of the target from the radar. The complex 2 2 scattering matrix S describes how target properties transform an incident electric field E i into the backscattered field E s received by the antenna. The elements S pq of S depend on the geometry, roughness and electrical properties of the target material. For monostatic backscatter measurements (one antenna for both transmission and reception), we may take S hv = S vh in equation (2) because of reciprocity [Huynen, 1965]; thus only three elements of S are required to describe scattering. [7] An imaging polarimeter works by transmitting a linearly polarized wave (e.g., the h component of E i in equations (1) and (2) only) and receiving two orthogonally polarized waves (h and v) [Zebker et al., 1987]. Then, for the next pulse, the orthogonally polarized wave (v) is transmitted and two orthogonally polarized waves (h and v) are received. These transmit/receive protocols are repeated a sufficient number of times to synthesize the antenna aperture needed to achieve the desired data resolution. For example, the 5 m resolution cell size of AIRSAR requires a synthetic aperture length of 250 m using an L-band antenna with a physical length <2 m. In forming the synthetic aperture by coherent integration of the radar returns, signals corresponding to the different polarizations are isolated and integrated separately [Zebker et al., 1987]. In this way we populate the matrix S of scattering coefficients S pq (equation (2)) for every resolution cell of the polarimetric SAR scene. We point out that the resolution cell size in SAR image data (order 10 1 m) is much larger than the radar wavelength (order 10 2 m). Therefore a SAR resolution cell will contain many distributed scattering centers, and each center is represented by its own scattering matrix S. The measured scattering matrix for one cell therefore consists of the superposition of the individual matrices of all scattering centers resolved within the cell. From now on we will implicitly regard the elements of the scattering matrix as representing averaged quantities hs pq i. [8] There is an advantage in many applications to expressing target scattering properties in terms of a complex vector t i, comprising three elements in the monostatic backscatter case. This vector is related to the scattering matrix S by t i ¼ ½A B C Š T ¼ 1 2 trace ð S C Þ; ð3þ where A, B, and C stand for the vector elements, and C denotes a complete set of 2 2 complex basis matrices [Cloude and Pottier, 1996]. It is sufficient for our purposes to consider two choices for C: (1) one that provides a straightforward linear ordering of the elements of S, in which case the scattering vector becomes h pffiffi i T t L ¼ S hh 2 Shv S vv ; ð4þ and (2) the Pauli basis, which is formed from the Pauli spin matrices. The scattering vector in the Pauli basis is [Cloude and Pottier, 1996] t P ¼ p 1 ffiffi ½S hh þ S vv S hh S vv 2S hv Š T : ð5þ 2 An advantage of the Pauli basis equation (5) is that the scattering matrix S in equation (2) is projected on to

4 7-4 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES orthogonal basis matrices that represent simple physical scattering mechanisms [Cloude, 1992]. The first component S hh + S vv in equation (5) dominates in single-bounce surface scatter, the second component S hh S vv dominates in double-bounce scatter from dihedral corner reflectors, and the S hv element will be strong for backscatter from depolarizing media. The numerical factors in the expressions for the scattering vectors (4) and (5) arise because of conservation of the total power scattered by the target [Boerner and El-Arini, 1981; Cloude and Pottier, 1996]. [9] The strength of imaging SAR polarimetry lies in its potential for discriminating among different types of scattering mechanisms. Recall that the elements of the scattering matrix S (or the scattering vector t i ) measured for each resolution cell are average quantities that represent the contribution from all scattering centers within the illuminated cell. Methods have been developed for attempting to separate multiple scattering mechanisms occurring within each cell, with the aim of extending structure in scattering mechanism patterns spatially over a region of neighboring cells [Freeman and Durden, 1998; Cloude and Pottier, 1996]. First, correlation among the scattering coefficients for a cell is determined by forming a matrix Z i defined as the expected value of the outer product of the generic scattering vector t i (equation (3)): 2 3 haa*i hab*i hac*i Z i ¼ht i t þ i i¼4hba*i hbb*i hbc*i 5; ð6þ hca*i hcb*i hcc*i where angle brackets denote spatial (ensemble) averaging, asterisk denotes the complex conjugate, and plus denotes the transpose of the conjugate. In the SAR literature, this correlation matrix Z i (equation (6)) is called the covariance matrix C when using the scattering vector t L expressed in the linear basis (equation (4)), and the coherency matrix T for the scattering vector t P in the Pauli basis (equation (5)) [Boerner and El-Arini, 1981; Cloude, 1992]. [10] Next, an eigenvalue/eigenvector decomposition is performed on either the covariance matrix C or the coherency matrix T forms of equation (6): Z i ¼ l 1 k 1 k þ 1 þ l 2 k 2 k þ 2 þ l 3 k 3 k þ 3 : ð7þ This allows us to partition a resolution cell s scattering properties into as many as three orthogonal (or independent) scattering mechanisms given by the eigenvectors k i, whose relative importance is determined by the eigenvalues l 1 l 2 l 3. The challenge lies in relating the scattering mechanisms represented by the eigenvectors to polarimetric backscatter from common surface cover types like rough surfaces, water bodies, vegetation, etc. [11] We note that although the eigenvalues will be the same for decomposition of the covariance matrix C and the coherency matrix T forms of equation (6), the resulting eigenvectors will differ. This arises through the different bases used to express the scattering vectors t L (equation (4)) and t P (equation (5)), which may be obtained from one another by orthogonal rotations. [12] Two parameters obtained from the eigenvalues in equation (7) characterize the relative importance of the scattering mechanisms found from the decomposition of equation (6): The first, scattering entropy H H ¼ X3 n¼1 P n log 3 ðp n Þ; 0 H 1; ð8þ where P n ¼ l n = P3 l m, is a measure of the randomness m¼1 ordisorder of scattering. For entropy H = 0, there is only one eigenvector associated with a nonzero eigenvalue, and thus the resolution cell of the data is characterized by a single, discrete scattering mechanism. At H = 1, the three eigenvalues are equal, indicating no dominant scattering mechanism for that cell, and that the scattering process can be considered random. At intermediate values of entropy, the second parameter, polarimetric anisotropy A, given by A ¼ ðl 2 l 3 Þ= ðl 2 þ l 3 Þ; 0 A 1; ð9þ addresses situations in which the minor eigenvalues of the decomposition (equation (7)) are not equal [Cloude and Pottier, 1997; Pottier, 1998]. In such cases, anisotropy contains additional information on scattering mechanisms occurring within the resolution cell. High anisotropy, A! 1 indicates that the second scattering mechanism is relatively important, whereas the third one is not. [13] In decompositions of equation (6) where the Pauli basis (equation (5)) is used to represent the scattering vector t i, the eigenvectors k i (equation (7)) can be written in the form [Cloude and Pottier, 1997] k i ¼ cos a i sin a i cos b i e idi sin a i sin b i e ig T i : ð10þ All parameters in the eigenvectors equation (10) represent angular rotations. The angle b is the orientation of the scattering object about the radar line of sight. The angles d and g are phase angles characteristic of the target material. Angle a yields direct information about the scattering mechanism represented by each eigenvector. From equations (10) and (8), we calculate the weighted average scattering mechanism a for the image resolution cell a ¼ X3 i¼1 P i a i ; 0 a 90 : ð11þ The dominant scattering mechanism for a data resolution cell is given by the value of a, which varies from a ¼ 0 for single bounce from an isotropic flat surface, through a ¼ 45 for dipole scattering, to a ¼ 90 for double (dihedral) bounce [Cloude and Pottier, 1997]. Note that the full 90 range in a is only accessible at zero entropy (equation (8)), where a single, dominant scattering mechanism is present (i.e., a ¼ a 1 ). As H increases, the range of a decreases from the extremes because of averaging across the eigenvectors, such that at H = 1, we have a single value a ¼ 60, indicating an indeterminate scattering mechanism Application to Mapping the Tsaoling Landslide [14] We now employ the above methodology to distinguish the Tsaoling landslide (Figure 1) from surrounding

5 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES 7-5 Figure 2. Gray scale images of polarimetric parameters (a) entropy H, ( b) weighted average scattering mechanism a, and (c) anisotropy A, obtained through decomposition of the coherency matrix, Z i = T. Pale yellow indicates areas of no data. H and a are high over the forest and low over the landslide surface. A, which is highly variable, is a poor discriminator between forested and bare slopes. The Tsaoling landslide source area slopes at approximately 16 toward the radar, i.e., to the south. The debris apron in the valley is generally flat but undulating. The incident angle for the slide area ranges from approximately 33 to 39, south to north. See color version of this figure at back of this issue. vegetated hillslopes, using L-band polarimetry from the 27 September 2000 AIRSAR deployment over Taiwan. Our purpose is to demonstrate that scattering mechanism signatures derived from the radar target decomposition techniques described above (equations (1) (11)) can be applied successfully to landslide identification. [15] In Figure 2, gray scale images of entropy H, weighted average scattering mechanism a, and anisotropy A are shown for the landslide and surrounding area. The gray scales are linear between the theoretical lower (black) and upper (white) bounds of these parameters (equations (8), (9), and (11)). Entropy is higher over the forested area surrounding the Tsaoling landslide than over the slide source area on the northern valley side and the debris apron over the valley floor (Figure 2a). This occurs because depolarizing effects of volume scatter from structural elements of trees produce more disorder in scattering compared to the backscatter from mainly bare surfaces of the landslide source and run out areas. [16] Figure 2b shows that weighted average scattering mechanism a (equation (11)) takes low values over the landslide-affected areas and distinctly higher values over the surrounding forested hillslopes. The partitioning of the a image into high and low values, which correlate with the entropy H variations (Figure 2a), indicates differences in scattering properties of forested hillslopes compared to the landslide source area and debris apron surfaces. These differences can be used as the basis for landslide mapping. [17] Figure 2c shows that scattering anisotropy A is a poor discriminator between forest and landslide areas. Anisotropy is, however, slightly elevated over the rougher parts of the surfaces of the lakes impounded by the landslide debris, as discussed below. Similar analysis of SIR-C C- and L-band polarimetry for ocean surface and sea ice studies [Scheuchl et al., 2001] has shown that the sea surface is marked by a high degree of scattering anisotropy, particularly at C band. [18] Scattering properties obtained through decomposition equation (10) of the coherency matrix T are readily revealed by plotting entropy H against weighted average scattering mechanism a for each resolution cell of the data [Cloude and Pottier, 1997; Cloude et al., 2002]. The H a plane can be partitioned into a number of zones, each of which can be identified roughly with a particular type of scattering mechanism. We use the nine zone segmentation scheme (Figure 3a and Table 1) proposed by Cloude and Pottier [1997] to assist in the interpretation of scattering mechanisms over the area of the Tsaoling landslide. [19] Color-coded two-dimensional (2-D) histogram plots of the L-band H a values from three identically sized regions of forest, landslide scar, and debris run-out material (Figure 3b) are shown in Figures 3d 3f. The scattering properties of the landslide source and debris areas are different from those of the forested area. Values for cells from the undisturbed forested region (Figure 3d) are centered in the moderate entropy part of H a space (Table 1, zone 5) that corresponds to scattering from dielectric dipoles representing structural elements of trees (branches, twigs, trunks, etc.). The cells from the landslide scar (Figure 3e) fall into one cluster centered at low entropy and small a (Table 1, zone 9). These H a values are consistent with single bounce scattering from a rough, bare surface. The cells from the debris apron (Figure 3f) have a similar scattering mechanism signature to the landslide scar region with scattering from rough, bare surfaces being prominent. However, the signature extends toward moderate entropy (Table 1, zones 5 and 6) which we take as evidence for multiple bounces from vegetation (compare Figure 3d). An implication of the last result is that regrowth of the tropical forest has commenced on the debris apron in the valley floor during the 1-year interval between the Tsaoling landslide event and the AIRSAR acquisition. [20] We note that the scatterplots in Figure 3 appear noisy. This can be attributed to the effect of speckle, which is multiplicative noise in SAR data that arises because of the coherent superposition of backscatter from numerous scattering centers within the resolution cell.

6 7-6 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES Figure 3. (a) Partitioning of the entropy H average alpha a plane into scattering mechanism domains (Table 1) [Cloude and Pottier, 1997]. The physically feasible region of H a space, which ranges from 0 < a <90 and 0 < H < 1, is delineated by the two bounding curves. ( b) Equivalent-area regions of interest, where green is a sample of forest, red a sample of landslide scar, and blue is a sample from the debris apron. (c) Color bar scale for Figures 3d 3f: Two-dimensional histograms of H and a for (d) forest, (e) landslide scar, and (f ) debris apron. See text for analysis of the three H a scatterplots. See color version of this figure at back of this issue. Speckle is expected to be high at 5 m resolution of the AIRSAR data, which is achieved by combining nine independent looks during processing. Most workers apply polarization-preserving speckle reduction filtering [e.g., Lee et al., 1999] before decomposing the coherency matrix equation (6). We have not done this because we want to show that the scattering mechanism extraction method provides usable results from data at the maximum spatial resolution provided by normal AIRSAR data processing (5 m pixels, in this case). Speckle suppression is a compromise: it increases the within-class uniformity while effectively decreasing the resolution of the analysis. The trade-off between speckle noise reduction and landslide mapping resolution is an active area of research. 3. Discussion 3.1. Development of a Classification Scheme for Recognizing Landslides [21] Our interest is in developing SAR-based tools for rapid response to natural disasters. In order to do this, we develop an algorithm that readily identifies newly generated landslides in humid/tropical environments using SAR polarimetry. Maps produced using this algorithm need only a single data acquisition campaign, provided that preevent land cover information is available. The method is based on decomposition of a simplified form of the covariance matrix Z i = C in equation (6) to find entropy of scattering H (equation (8)), and application of empirically based criteria for recognizing scattering from bare surfaces and forests. The covariance matrix is formed using the linear order basis for the scattering vector t L (equation (4)). We will use elements of a simplified covariance matrix C to allow the use of empirical criteria for discriminating among surface cover types. As with the above decomposition of the coherency matrix Z i = T in equation (6), we will use L-band polarimetry acquired across the Tsaoling landslide in September [22] First, we make the simplifying assumption of reflection symmetry, so that the copolarized and cross-polarized Table 1. The H - a Plane Partitioned Into Nine Zones a Zone a Entropy H Scattering Type High Entropy high entropy multiple high entropy vegetation high entropy surface (outside of the feasible range) Medium Entropy medium entropy multiple medium entropy vegetation medium entropy surface Low Entropy low entropy multiple low entropy dipole low entropy surface a From Cloude and Pottier [1997].

7 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES 7-7 scattering coefficients of the covariance matrix C are uncorrelated, i.e., hs hh S* hv i = hs vv S* hv i =0[Nghiem et al., 1992]. The remaining elements of the covariance matrix are related to normalized radar cross sections and correlation coefficients s pqrs : 4p s pqrs ¼ lim hs pq S rs *i; A c!1 A c ð12þ where indices p, q, r, s stand for either horizontal h or vertical v polarizations, and A c is radar illuminated area [Nghiem et al., 1992]. In particular, when p = r and q = s, we obtain the copolarization and cross-polarization radar cross sections from the elements on the leading diagonal of the covariance matrix C. [23] For the landslide identification problem, our primary objective is to classify three types of terrain: (1) bare surfaces (landslide scar, debris apron, and lake surface), (2) forest, and (3) ambiguous. Previous work has shown that scattering from slightly rough, bare surfaces can be described by the following two relations among the cross sections s pqrs : < ðs hhvv Þ > s hvhv ; s vvvv > s hhhh ; ð13þ where < means the real part of the complex variable [van Zyl, 1989; Freeman and Durden, 1992, 1998; Kim and van Zyl, 2002]. Three scattering properties are implied by the conditions in equation (13). First, the copolarized returns are in phase, indicating single-bounce scattering. Second, the copolarization backscatter cross section is larger than the cross-polarization counterpart. Third, for bare surfaces the vertical backscatter cross section is larger than the horizontal cross section. [24] Forest-covered cells are identified where threshold values for entropy H (equation (8)), and two new parameters, radar vegetation index V and pedestal height, are exceeded. Radar vegetation index is given by V ¼ 8 s hvhv s t ; ð14þ where s t = s hhhh + s vvvv +2s hvhv is the total backscatter power [Kim and van Zyl, 2002]. Radar vegetation index weighs the contribution of the cross-polarized returns to total power. It will be relatively high where there is diffuse, volume scattering from vegetation branches and leaves. Pedestal height is derived from the eigenvalues (equation (7)) of the decomposition of the simplified covariance matrix: ¼ min ðl 1 ; l 2 ; l 3 Þ= ðl 1 þ l 2 þ l 3 Þ; ð15þ where 0 1/3. Pedestal height increases with the amount of depolarized energy in the return signal [Zebker et al., 1987]. Forest, like most types of vegetation cover, is a depolarizing medium. Last, entropy H is high for forest cover, as we have already shown (Figure 2a). [25] Forest-covered areas are identified where V > V min, > min, and H > H min. Values for V min, min, and H min are thresholds obtained empirically from areas known to be forest-covered from other data [Kim and van Zyl, 2002]. Using optical remote sensing data from the Tsaoling landslide and surrounding undisturbed forest (Figures 4c and 4d), we found that V > 0.6, > 0.15, and H >0.8 (compare Figure 3) give good results for identifying forest-covered cells using L-band polarimetry. We note that the range of entropy over the forest region for the coherency matrix decomposition technique is 0.6 < H < 0.8 (Figure 3d). The reflection symmetry assumption in the simplified covariance matrix reallocates the cell s entropy budget among the other elements, thereby increasing the overall entropy in the diagonal terms. It is this manipulation of the covariance matrix that results in the increased entropy level for forest surface cover in the classification scheme Comparison With Optical/Passive Remote Sensing Data [26] Figure 4a shows the surface cover classification of the Tsaoling landslide area based on decomposition of the simplified L-band covariance matrix, and the empirical threshold values for forest cover. Areas that have experienced landsliding show as predominantly single-bounce scattering from bare surfaces, as do water surfaces from the impounded lakes. For comparison, L-band horizontally copolarized backscatter cross section s hhhh (or simply, L hh ) is shown in Figure 4b. Except for subtle textural differences, the large landslide area is difficult to recognize in the L hh backscatter data. We infer that by itself, single or fixed polarization SAR data are of limited use for identifying and mapping landslides. Chorowicz et al., [1998] similarly concluded that landslides are generally difficult to observe in single-polarization ERS-1 (C band) and JERS-1 (L band) SAR backscatter data because intensity variations due to surface roughness or soil moisture contrasts are not large enough for landslide recognition. Singhroy et al. [1998] employ an alternate strategy by combining single-pass, single-polarization SAR data with optical data to map landslides. As we have shown above, fully polarimetric SAR can identify landslide areas (e.g., Figure 4a) because we can extract microwave scattering mechanisms caused by landslide resurfacing of the terrain. [27] The L-band classification map is compared to optical satellite data over the Tsaoling landslide in Figures 4c and 4d. The landslide is very well depicted as the highalbedo area in the IRS panchromatic data obtained on 31 October 1999, 6 weeks after the Chi-Chi earthquake (Figure 4d). The area of high albedo in the IRS data is 5.9 km 2, which is very similar to the 5.8 km 2 area of low scattering entropy shown in Figure 2a and low a in Figure 2b. The small difference might be attributable to vegetation growth during the eleven months between data acquisitions. Note that although the 5 m pixel size of the IRS data is the same as the resolution cell size of the AIRSAR polarimetry, the speckle present in all SAR data makes the radar results appear lower in resolution. [28] Landsat 7 data shown in Figure 4c were acquired in February 2001, 5 months after the AIRSAR data and 17 months after the Chi-Chi earthquake. Although the Landsat data are coarser resolution (28.5 m pixels) than both the AIRSAR and IRS data (5 m resolution), the

8 7-8 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES Figure 4. Remote sensing data obtained over the Tsaoling landslide which occurred 20 September (a) Cover type classification map from decomposition of the covariance matrix Z i = C and empirical criteria for forest cover, using AIRSAR L-band polarimetry (acquired 27 September 2000). Purple, bare surface; green, forest; black, other; pale yellow, no data. (b) L hh backscatter intensity. (c) Landsat TM (acquired February 2001), short-wavelength infrared band 7 ( mm), nearinfrared band 4 ( mm), and visible red band 3 ( mm) in a RGB false color composite, (d) IRS visible band panchromatic data (acquired 31 October 1999) obtained within six weeks of the landslide. The IRS and AIRSAR data both have 5 m pixels. The Landsat TM has 28.5 m pixels. See color version of this figure at back of this issue. Tsaoling landslide area and the impounded lakes are readily identified. From these data, we estimate the landslide area to be 5.9 km 2. We note that the area estimates derived from the remote sensing data presented here are between 16 and 18% greater than that of Chigira et al. [2003]. Notice vegetation regrowth on the landslide toe area that occurred between the time of the landslide (best depicted by the IRS data (Figure 4d)) and the Landsat data take, as indicated by the green areas (Figure 4c) between the two lakes. Revegetation of the landslide debris apron is also apparent from the L-band polarimetry (Figures 2a, 2b, 3f, and 4a). In the classification map (Figure 4a), we observe a higher percentage of green pixels over the debris apron than over the landslide source area. Comparison with the Landsat and IRS data (Figures 4c and 4d) eliminates the possibility that the green pixels in the L-band classification map are due to vegetation debris in the slide run out material Is Fully Polarimetric SAR Necessary for Landslide Identification? [29] We showed above that single polarization (amplitude) SAR data are probably insufficient for distinguishing and mapping landslides (Figure 4b). To determine if dual-polarization data are sufficient for recognizing land-

9 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES 7-9 Figure 5. Gray scale images of radar vegetation indices (a) V, using equation (14) and (b) V d, using equation (16) for the Tsaoling landslide area. Pale yellow indicates areas of no data. V is obtained from the full L-band polarimetry, whereas V d emulates a vegetation index obtained with a dual-polarization SAR instrument, as described in the text. See color version of this figure at back of this issue. slides, we assume a SAR system capable of sending only h-polarized waves, but able to receive both copolarized hh and cross-polarized hv returns. To assess such a dual polarimeter for landslide mapping, we compute a simplified vegetation index V d by modifying equation (14) under the assumption that s hhhh = s vvvv : 4 s hvhv V d ¼ : s hhhh þ s hvhv ð16þ In Figure 5 we compare the full-polarization vegetation index V (equation (14)) with the dual-polarization index V d (equation (16)) over the Tsaoling landslide area. The grey scale images are virtually identical (Figure 5), and we conclude that an L-band system with dual copolarization and cross-polarization channels is sufficient for identifying landslides which solely de-vegetate the landscape. This result is important because the currently operational Envisat ASAR system has dual polarization modes, and can therefore be used to obtain landslide inventories. A drawback with Envisat is that its site revisit time is 35 days, an interval that is too long for a rapid response to landslide disasters. Envisat also operates in C band (0.06 m) rather than L band (0.24 m), and it has yet to be established whether C band is as useful as L band for identifying landslides. At C band, the vegetation scattering mechanism dominates at much lower biomass than L band because the higher-frequency backscatter is affected mainly by leaves. There are a number of minor disturbances resulting in the loss of leaves which, at C band, could be misinterpreted as a landslide. In addition, if the revisit time is not sufficient, C-band data may not detect a landslide due to regrowth of vegetation. This is particularly true in a densely vegetated, tropical area. Therefore we expect L band to be better at detecting significant vegetation removal. A more detailed comparison of the effectiveness of fully polarimetric, dualpolarization and single-polarization SAR systems for classification of agricultural and forest cover types is given by Lee et al. [2001] Distinguishing Water From Bare Land Surfaces With SAR Polarimetry [30] In the L-band classification map of the Tsaoling landslide area (Figure 4a) both the landslide and rough water areas of the impounded lakes are assigned to the same bare surface cover type. While this is technically correct, it limits the usefulness of the method for identifying and mapping landslides. A closer look at the L-band polarimetric backscatter response of water surfaces (Figure 6) provides a way to solve this problem. Backscatter from water surfaces is dominated by Bragg scattering, which is a resonance effect between the Fourier components of the surface relief and the wavelength and incidence angle properties of the incident wave [Mouchot and Garello, 1998]. For smooth water surfaces, with little or no relief at the scale of the radar wavelength, there will be little return energy as the incident wave undergoes specular reflection or forward bounce. The low backscatter intensity over most of the impounded lake areas (Figures 4b and 6a) is indicative of generally smooth water surfaces. These areas are associated with high a and high entropy H (Figures 2a and 2b), the latter indicating that very low amplitude backscatter is mainly random noise. Some parts of the impounded lakes, however, have higher surface roughness owing to winds or high current speeds (e.g., area enclosed by the red line, Figure 6a). Such areas are marked by a small but significant amount of dominantly vertically copolarized vv backscatter (Figure 6a), commensurate with Bragg scattering from a slightly rough surface [Zebker et al., 1987]. Note that cross-polarized hv returns are expected to be very small for Bragg scattering; thus, the vegetation indices (14) and (16) will be close to zero for rough water surfaces (Figure 5). It is these areas of the lakes

10 7-10 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES using SAR polarimetry to determine roughness and moisture content of unvegetated surfaces [Allain et al., 2002; Schuler et al., 2002], our results indicate that the water surface is smoother than the landslide surface at the scale of the radar wavelength (0.25 m). We can now use these results to provide a correct classification of the two areas of surface scatter, water and landslide. Figure 6. (a) RGB false color composite of the L hh, L hv, L vv radar cross sections from the AIRSAR L-band polarimetry. The downstream debris-dammed lake is outlined in white and the rough water surface is outlined in red. (b) Two-dimensional histogram of entropy H and weighted average scattering mechanism a for the rough water area, i.e., the area outlined in red. See text for analysis of the scatterplot. See color version of this figure at back of this issue. that are classified as bare surfaces in the L-band classification map (Figure 4a). [31] The magenta color in the RGB composite of the L-band radar cross sections (Figure 6a) suggests that the copolar returns (hh and vv) from the landslide source and run-out surfaces have approximately the same signal strength. This stands in contrast to the vv-dominated Bragg scattering from the rough water surface (Figure 6a, blue). Two points emerge from this observation: (1) scattering from bare land surfaces can be distinguished from water surfaces and (2) the second of the two criteria for scattering from bare surfaces given in equation (13) might not always hold for land surfaces which are very rough. We quantify the difference in scattering mechanisms between the water and landslide surfaces by computing the H a signature for the area of rough water outlined in red (Figure 6a). A comparison with the H a signature from the bare landslide surface (Figure 3e) shows that although both clusters occupy the surface scatter region of H a space (Table 1, zone 9), the center of the water pixels cluster (Figure 6b) is located at a tightly constrained H a region and with a slightly lower H than the landslide cluster. In accordance with recent work 4. Conclusions [32] Extraction of scattering mechanism signatures from SAR polarimetry is a new and efficient way to identify landslides. The surficial changes wrought by the Tsaoling landslide in Taiwan, where vegetated slopes were replaced with rough, bare surfaces, are associated with changes in scattering properties over the affected areas. Parameters such as scattering entropy H, and average scattering mechanism a, which are obtained by eigenvector/value decomposition of the coherency matrix formed from L-band airborne polarimetry, can be used to distinguish terrain affected by landsliding from adjacent slopes that have remained vegetated. In addition, surface cover classification maps, which discriminate between bare surfaces and forest cover, can be made using the entropy H and pedestal height obtained from the decomposition, the radar cross sections of the resolution cells, and a radar vegetation index V derived from the cross sections. Empirical thresholds for entropy, pedestal height and vegetation index, however, need to be established for forest cover. Areas affected by landsliding found using the polarimetric classification method agree well with landslide areas derived from available passive/optical remote sensing imagery. We demonstrated that landslides can be identified using dualpolarization SAR systems, like the space-borne Envisat ASAR instrument currently in operation. Rough water and landslides can be differentiated by their polarimetric entropy H a signatures, even though both fall in the general category of surface scatter. Distinguishing between the two surface types is important because landslide debrisdammed lakes might subsequently become flooding hazards. [33] Rapid response is key to saving lives and assessing property damage when natural disasters strike. Darkness, clouds or smoke over devastated areas can delay urgent relief efforts. Radar is not hampered by these conditions and can provide the needed information in a timely manner. Our work indicates that SAR polarimetry could play a leading role in the rapid detection and assessment of disasters that change the scattering properties of land surfaces. Future research will therefore address (1) the extension of SARbased identification of landslides to the mapping of source and deposition areas, (2) the trade-off between speckle reduction filtering and landslide mapping resolution, and (3) the utility of SAR technology for rapid response to disasters caused by other natural hazards such as floods, volcanic eruptions, and wildfires. [34] Acknowledgments. This research was supported by National Aeronautics and Space Administration (NASA) grant NAG (J.K.W.), and Earth System Science Fellowship award NGT (K.R.C.). The manuscript was improved by careful reviews from Jonathan Barbour, Fausto Guzzetti, Andrea Taramelli, and anonymous reviewers. LDEO contribution 6492.

11 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES 7-11 References Allain, S., L. Ferro-Famil, E. Pottier, and I. Hajnsek, Extraction of surface parameters from multi-frequency and fully-polarimetric SAR data, in IEEE 2001 International Geoscience Remote Sensing Symposium, pp , Inst. of Electr. and Electron. Eng., New York, Boerner, W.-M., and M. B. El-Arini, Polarization dependence in electromagnetic inverse problems, IEEE Trans. Antennas Propag., 29, , Chen, S.-C., The types of natural dams caused by the Chichi earthquakes, in Proceedings of the International Workshop on Annual Commemoration of Chi-Chi Earthquake, vol. III, Geotechnical Aspect, edited by C.-H. Loh and W.-I. Liao, pp , Natl. Cent. for Res. on Earthquake Eng., Taipei, Taiwan, Chigira, M., W.-N. Wang, T. Furuya, and T. Kamai, Geological causes and geomorphological precursors of the Tsaoling landslide triggered by the 1999 Chi-Chi earthquake, Taiwan, Eng. Geol., 68, , Chorowicz, J., J.-Y. Scanvic, O. Rouzeau, and G. Vargas Cuervo, Observations of recent and active landslides from SAR ERS-1 and JERS-1 imagery using a stereo-simulation approach: Example from the Chicamocha Valley in Colombia, Int. J. Remote Sens., 19, , Cloude, S. R., Uniqueness of target decomposition theorems in radar polarimetry, in Direct and Inverse Methods in Radar Polarimetry, Part 1, NATO-ARW, edited by W.-M. Boerner et al., pp , Kluwer Acad., Norwell, Mass., Cloude, S. R., and E. Pottier, A review of target decomposition theorems in radar polarimetry, IEEE Trans. Geosci. Remote Sens., 34, , Cloude, S. R., and E. Pottier, An entropy based classification scheme for land applications of polarimetric SAR, IEEE Trans. Geosci. Remote Sens., 35, 68 78, Cloude, S. R., E. Pottier. and W.-M. Boerner, Unsupervised image classification using the entropy/alpha/anisotropy method in radar polarimetry, in Proceedings of the 2002 AIRSAR Earth Sciences and Applications Workshop [CD-ROM], 20 pp., Jet Propul. Lab., Pasadena, Calif., 2002 (Available at T2.pdf). Freeman, A., and S. Durden, A three-component scattering model to describe polarimetric SAR data, Proc. SPIE, 1748, , Freeman, A., and S. L. Durden, A three-component scattering model for polarimetric SAR data, IEEE Trans. Geosci. Remote Sens., 36, , Hung, J.-J., Chi-Chi earthquake induced landslides, in Proceedings of the International Workshop on Annual Commemoration of Chi-Chi Earthquake, vol. III, Geotechnical Aspect, edited by C.-H. Loh and W.-I. Liao, pp , Natl. Cent. for Res. on Earthquake Eng., Taipei, Taiwan, Huynen, J. R., Measurement of the target scattering matrix, Proc. IEEE, 53, , Kim, Y., and J. van Zyl, Comparison of forest estimation techniques using SAR data, in IEEE 2001 International Geoscience Remote Sensing Symposium, vol. 3, pp , Inst. of Electr. Electron. Eng., New York, Lee, J.-S., M. R. Grunes, and G. De Grandi, Polarimetric SAR speckle filtering and its implication for classification, IEEE Trans. Geosci. Remote Sens., 37, , Lee, J.-S., M. R. Grunes, and E. Pottier, Quantitative comparison of classification capability: Fully polarimetric versus dual and single-polarization SAR, IEEE Trans. Geosci. Remote Sens., 39, , Mouchot, M.-C., and R. Garello, SAR for Oceanography, in Manual of Remote Sensing, vol. 2, 3rd ed., edited by F. M. Henderson and A. J. Lewis, pp , John Wiley, Hoboken, N. J., Nghiem, S. V., M. Bourgeau, J. A. Kong, and R. T. Shin, Polarimetric remote sensing of geophysical media with layer random medium model, in Progress in Electromagnetic Research, vol. 3, Polarimetric Remote Sensing, edited by J. A. Kong, pp. 1 73, Elsevier Sci., New York, Nghiem, S. V., S. H. Yueh, R. Kwok, and F. K. Li, Symmetry properties in polarimetric remote sensing, Radio Sci., 27, , Pottier, E., Unsupervised classification scheme and topography derivation of PolSAR data based on the H/A/a polarimetric decomposition theorem, paper presented at 4th International Workshop on Radar Polarimetry, Nantes, France, Scheuchl, B., R. Caves, I. G. Cumming, and G. Staples, H/A/a-based classification of sea ice using SAR polarimetry, paper presented at 23rd Canadian Symposium on Remote Sensing, Can. Remote Sens. Soc., Quebec, Aug Schuler, D. L., J.-S. Lee, D. Kasilingam, and G. Nesti, Surface roughness and slope measurement using polarimetric SAR data, IEEE Trans. Geosci. Remote Sens., 40, , Shin, T. C., K. W. Kuo, W. H. K. Lee, T. L. Teng, and Y. B. Tsai, A Preliminary Report on the 1999 Chi-Chi (Taiwan) Earthquake, Seismol. Res. Lett., 71, 24 30, Shin, T. C., F. T. Wu, J. K. Chung, R. Y. Chen, Y. M. Wu, C. S. Chang, and T. L. Teng, Ground displacements around the fault of the September 20th 1999, Chi-Chi Taiwan earthquake, Geophys. Res. Lett., 28, , Singhroy, V., K. E. Mattar, and A. L. Gray, Landslide characterisation in Canada using interferometric SAR and combined SAR and TM images, Adv. Space Res., 21, , Sitar, N., J. P. Bardet, M.-L. Lin, J. Hu, J.-J. Hung, B. Khazai, S. L. Kramer, W. J. Perkins, and R. H. Wright, Landslides, in 1999 Chi-Chi, Taiwan, Earthquake Reconnaissance Report, Earthquake Spectra, 17, Suppl. A, 61 76, van Zyl, J. J., Unsupervised classification of scattering behavior using radar polarimetry data, IEEE Trans. Geosci. Remote Sens., 27, 36 45, Zebker, H. A., J. J. van Zyl, and D. A. Held, Imaging radar polarimetry from wave synthesis, J. Geophys. Res., 92, , K. R. Czuchlewski and J. K. Weissel, Lamont-Doherty Earth Observatory of Columbia University, 61 Route 9W, Palisades, NY 10964, USA. (krodrig@ldeo.columbia.edu; jeffw@ldeo.columbia.edu) Y. Kim, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive., Pasadena, CA , USA. (Yunjin.Kim@ jpl.nasa.gov)

12 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES Figure 1. Tsaoling landslide area. (a) Air photograph mosaic of the Tsaoling landslide (K. Okunishi, A preliminary report on the landslides and other ground surface movement induced by the 1999 Chichi earthquake, Taiwan, 2000, available at which constitutes the focus of this study, obtained shortly after the 20 September 1999 M w 7.6 Chi-Chi earthquake. (b) Shaded topographic relief map of Taiwan, showing earthquake epicenter (star), the Chelungpu fault surface rupture [Shin et al., 2001], and the location of the kilometer-scale Tsaoling landslide (triangle). (c) Sketch map of the Tsaoling landslide, adapted from Chigira et al. [2003] (with permission from Elsevier). 7-2

13 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES Figure 2. Gray scale images of polarimetric parameters (a) entropy H, ( b) weighted average scattering mechanism a, and (c) anisotropy A, obtained through decomposition of the coherency matrix, Z i = T. Pale yellow indicates areas of no data. H and a are high over the forest and low over the landslide surface. A, which is highly variable, is a poor discriminator between forested and bare slopes. The Tsaoling landslide source area slopes at approximately 16 toward the radar, i.e., to the south. The debris apron in the valley is generally flat but undulating. The incident angle for the slide area ranges from approximately 33 to 39, south to north. Figure 3. (a) Partitioning of the entropy H average alpha a plane into scattering mechanism domains (Table 1) [Cloude and Pottier, 1997]. The physically feasible region of H a space, which ranges from 0 < a <90 and 0 < H < 1, is delineated by the two bounding curves. (b) Equivalent-area regions of interest, where green is a sample of forest, red a sample of landslide scar, and blue is a sample from the debris apron. (c) Color bar scale for Figures 3d 3f: Two-dimensional histograms of H and a for (d) forest, (e) landslide scar, and (f ) debris apron. See text for analysis of the three H a scatterplots. 7-5 and 7-6

14 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES Figure 4. Remote sensing data obtained over the Tsaoling landslide which occurred 20 September (a) Cover type classification map from decomposition of the covariance matrix Z i = C and empirical criteria for forest cover, using AIRSAR L-band polarimetry (acquired 27 September 2000). Purple, bare surface; green, forest; black, other; pale yellow, no data. (b) L hh backscatter intensity. (c) Landsat TM (acquired February 2001), short-wavelength infrared band 7 ( mm), nearinfrared band 4 ( mm), and visible red band 3 ( mm) in a RGB false color composite, (d) IRS visible band panchromatic data (acquired 31 October 1999) obtained within six weeks of the landslide. The IRS and AIRSAR data both have 5 m pixels. The Landsat TM has 28.5 m pixels. 7-8

15 CZUCHLEWSKI ET AL.: SAR POLARIMETRY 1999 TAIWAN LANDSLIDES Figure 5. Gray scale images of radar vegetation indices (a) V, using equation (14) and ( b) V d, using equation (16) for the Tsaoling landslide area. Pale yellow indicates areas of no data. V is obtained from the full L-band polarimetry, whereas V d emulates a vegetation index obtained with a dual-polarization SAR instrument, as described in the text. Figure 6. (a) RGB false color composite of the L hh, L hv, L vv radar cross sections from the AIRSAR L-band polarimetry. The downstream debris-dammed lake is outlined in white and the rough water surface is outlined in red. (b) Two-dimensional histogram of entropy H and weighted average scattering mechanism a for the rough water area, i.e., the area outlined in red. See text for analysis of the scatterplot. 7-9 and 7-10