STUDY FOR LAND SURFACE PROPERTIES WITH ALOS PALSAR PI No. 017 PI: Guo huadong, CI: Table II Center for Earth Observation & Digital Earth, Chinese Acad

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1 STUDY FOR LAND SURFACE PROPERTIES WITH ALOS PALSAR PI No. 017 PI: Guo huadong, CI: Table II Center for Earth Observation & Digital Earth, Chinese Acadey of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing, P. R. China Tel: , Fax: , Eail: 1. ABSTRACT China is a country with vast land and various kinds of land surface. Study for land surface properties with the advanced ALOS/PALSAR syste will contribute to solving scientific probles addressing any environental issues in a broad range of Earth science disciplines. Owing to the rapid econoic developent and population expansion in the past two decades, any environental probles are increasingly serious, such as farland decreasing, soil erosion increasing, deforestation, and desertification. With the advanced ALOS/PALSAR and other sensors as suppleental data acquisition sources, it is possible to conduct research works addressing any environental issues related to land surface properties. This report will give results we obtained in different areas, such as glacier oveent, earthquake, Great wall study and etc.., all these study show powerful potential of ALOS sensors in environent change study.. 2. INTRODUCTION Global environental change has brought widespread attention all over the world. Global Environental Change is giving big ipact to huan society, and it has attracted greatest attentions of various countries and becoing one ajor issue for researchers. The report is focus on the environent change onitoring using ALOS PALSAR, several aspects are studied. An pixel offset track ethod was ipleented and the Muztagh glacier was studied and the velocity of the glacier is obtained. An iproved HIS fusion ethod and PC fusion ethod which are pixel-level fusion were ipleented for iage fusion processing. The skeleton extraction ethod that can extract spatial location of the linear object autoatically was copleted, and was used to autoatically extract accurate spatial location of the Ming Great Wall based on fused iage of PC ethod. The new roll invariant target scattering decoposition-touzi target scattering decoposition has been copleted. An Genetic Algorith for the inversion of the surface paraeters retrieve was copleted, use this ethod the inverted paraeters such as RMSH Correlation Length and the Soil oisture content (in volue) can be obtained. InSAR process flow for PALSAR was finished and Chengdu and Yushu earthquake data were analyzed. We obtained soe good result. 3. GLACIER MOVEMENT MONITORING Muztagh glacier located in Xinjiang Province, the geographical position is N38 00 ' ~38 30 ', E74 50 ' ~75 40 '. The area of this glacier reduced 1.11% during 1965 and The peak of the ountain is 7546 eters, the largest glacier is located in east part of the area, called Kekeshayi glacier, the area is about 86.5 k^2. It is the ost iportant water supply of Tari basin. The velocity of the glacier is an iportant paraeter of glacier, in this study we design an offset track ethod and obtain the velocity of the glacier. The work flow is as Fig.1. Fig.1 work flow of offset track ethod We obtain two PALSAR iage of Jan and March 2009, using offset track ethod we obtained the 2D velocity ap of kekeshayi glacier, the results show that the largest velocity is about eters per onth. Fig.2 2D velocity ap of KeKeshayi glacier For intuitionistic know the velocity and the topography, the 2D velocity ap is overlaid on a DEM, the result shows in Fig.3. Fro Fig.3 we could see strong relationship between topography and velocity of the glacier.

2 The profile (Fig.6) runs north-south across the eastern extension of the fault and shows a great difference of up to ore than 40c in the deforation, with the northern part of fault oving away in LOS direction and oppositely in the southern part of the fault. The profile nn shows a clear uplift trend in the northern part of fault and subsidence trend in the southern part of the fault. The profiles of oo and hh also show the sae trend as the profiles of and nn. But hh has a sall change rate copared to that of the other three profiles with sudden change. The signal on the deforation center area is lost because of low coherence due to heavily change of the surface. Fig.3 3D velocity ap of KeKeshayi glacier 4. YUSHU EARTHQUAKE CO-SEISMIC DEFORMATION FIELD Two PALSAR iages (see TABLE I) taken on January and April 17, 2010, both with the FBS (fine bea single) ode, are used to generate the co-seisic D- InSAR iage. The perpendicular baseline is around the iddle of the iage. These iages are acquired fro the ascending orbit, thus the line-of-sight (LOS) is as shown in the Fig 1. The incidence angle is 34.3 fro the vertical. TABLE I. PALSAR PARAMETERS FOR SAR INTERFEROMETRY Date Track Frae Perpendicular Baseline () JAN.15, Teporal baseline (d) APR.17, As can be seen in Fig.4, interferoetric signal shows that there is a linear feature trending southeast-northwest, deonstrating active deforation in this area. This southeast-northwest trending deforation zone is along the Yushu-Ganzi fault and about 40k wide. There are three color cycle fringes could be counted in both side of the earthquake fault. One color cycle corresponding half of the wavelength is about 11.8c along the LOS direction. Fig.5. Co-seisic unwrapped interferogra ap.four profiles cross the heavy change part of fault. Fig.4. Co-seisic D-InSAR interferogra ap.a and B are the epicenters corresponding to the instruentalepicenter and acro-epicenter. Inset C is the siulation result fro China seisological Bureau. To illustrate the deforation trend, three profiles cross the fault are extracted fro the interferogra (Fig.5 red lines). Fig.6 Profiles across the earthquake fault. The ain goal of using the D-InSAR is to investigate the surface deforation of the region of the earthquake and to analysis the relation between the observed deforation field and the discrete fault. It is discovered fro D-InSAR

3 results that there are two deforation centers, corresponding to two epicentres, along the Yushu-Ganzi fault which occurred the earthquake (Fig.4). The Yushu city, which suffering the heavy daage due to the earthquake, is prone to seisicity corresponding to one epicenter of earthquake. The reduction of coherence is generally due to the change in surface properties, the low coherence part is due to the change of surface cover between aster and slave iages with differently acquired tie. The area of low coherence near the fault region, where is a lake, is likely due to destruction by the strong otion. The seisic hazard for producing disasters is highly related to geological, physiographical, and tectonics factors such as population density, regional deforation, construction code in the affected area and soil and rock types aong others; and is closely correlated with various intensities of earthquakes and shocks Uncertainties of LOS displaceents inferred fro the interferogra are hard to assess. The error and its spatial correlation in InSAR data are controlled by various factors, including the baseline length of satellite orbit pair and atospheric disturbance, which the shorter of perpendicular baseline the ore accuracy of the deforation in LOS direction, but how they contribute to the error is partly still enigatic. We neglect the atospheric contribution in this work to siplify the calculation and because the deforation signal is so large that the atospheric contribution does not play a significantly role in the finally data analysis. Fro the final interferogra, the overall trend of the earthquake deforation is 120º fro the north. The nearer is fro the Yushu-Ganzi surface fault zone, the greater the ground deforation is deduced in the interferogra. Fro the fringe density of the both sides, the change gradient of the north part is uch greater than that of the south part of the Yushu-Ganzi fault. The largest deforation detected by the D-InSAR approach is around the 30.0ºN, 96.8ºE along the fault zone. The largest deforation along the radar look sight is up to 57.8c. The result derived fro the interferogra agrees with the axiu deforation center of field investigation. The surface uplift and sinistral strike-slip oveent of the Yushu-Ganzi fault presents opposite contributions to the deforation along the LOS direction. The vertical otion and sinistral strikeslip otion could not be inverted fro the only deforation along the LOS direction directly. 5. DETECTING THE GREAT WALL IN THE NINGXIA AND SHANXI PROVINCE How to trace the Great Wall which had disappeared fro the earth s surface, and how to further research on the environent around it based on the spatial location of the Great Wall which has been detected, first of all, we ust detect the Great Wall. So we ust choose an optiu way to locate exactly the spatial location of the Great Wall. In our study, we chose the ALOS PALSAR (L-HH) as our eans of detection, assisting with optical iage Landsat ETM+ by ulti-sensors reote sensing iage fusion. a.binary iage based on fused iage b. the extracting inforation by skeleton ethod c. the accurate location of the Ming Great Wall Fig.7 the procedure chart of extracting inforation of the Ming Great Wall using skeleton ethod It is different fro conventional inforation extraction of linear object based on visual interpretation; the skeleton extraction ethod could extract spatial location of the linear object autoatically. So the skeleton ethod was used to autoatically extract accurate spatial location of the Ming Great Wall based on fused iage of PC ethod. But road and the Ming Great Wall which are two linear objects in extraction iage are easy to confuse, so spectru threshold of road and the Ming Great Wall which was extracted fro fused iage by respectively choosing 10 sapling points was used to distinguish the Ming Great Wall fro other linear objects. The process of extracting inforation of the Ming Great Wall using skeleton ethod is showed in Fig 7. In our study area, there are two SAR iage, they are SIR- C and ALOS Palsar respectively. In the iaging region, Guo Huadong found soe detection results about the Ming Great Wall based on SIR-C (R:L-HH G:L-HV B : C-HV). For exaple, there are three Great Wall Nearly parallel in this region, two of the built in Ming dynasty were respectively called as the first wall and the second wall, and one of the built in Sui dynasty was called as the Sui Great Wall. The position of the first wall and the Sui Great Wall is very closely in space, and the second wall is uch far away fro the first wall s north. Their spatial locations are showed in Fig 8.

4 a SIR-C Fig.8 the detection result of the Great Wall in our study area based on SIR-C (L-HH) But the detection result of the Great Wall displayed in ALOS PALSAR is different fro the SIR-C s in sae study area with sae band (L band) and sae polarization ode (HH). The detection result of the Great Wall displayed in ALOS PALSAR is showed in Fig 9-a and Fig 9-b. a b Fig.9 the detection result of the Great Wall in our study area based on ALOS Palsar (L-HH) For explaining this phenoenon, firstly we should unify their resolution to 15 eter because they have different spatial resolutions that respectively are 25 eter and 10 eter; secondly we should adjust their gray value into the sae distribution scope. Then we got Fig 10. The second wall still couldn t be detected in ALOS PALSAR. Firstly, we excluded the possibility of which the second wall was copletely disappeared as a result of either natural or an-ade destruction, because any effect of corner reflection that fored by wall and earth surface should be detected by SAR with super powerful penetration capability even the second wall was destroyed and copletely disappeared fro the earth surface. So the reason that the second wall is disappeared in ALOS PALSAR iage is ostly caused by the iaging paraeters of ALOS PALSAR different fro SIR-C s. b ALOS Palsar Fig.10 the iage of SIR-C and ALOS Palsar after unifying the sae resolution and the sae distribution scope of gray value Because the flying orientation of two SAR sensors is very close, we exclude the reason caused by their different direction of view. There are four prie iaging paraeters affect radar return; they are band, polarization, incidence, and direction of view. But according to above paraeters introduction about SIR-C and ALOS PALSAR, only different incidence and direction of view between SIR-C and ALOS PALSAR could result in above phenoena. And we all know that the incidence is saller, radar return is stronger. At the sae tie, we found the axial incidence of SIR-C is still uch saller than the center incidence of ALOS PALSAR. So their different incidence ay be the ain reason which result in the second wall was disappeared in ALOS PALSAR. Of course, it ay be caused by soe other syste paraeters of SIR-C and ALOS sensor, so we need ore ALOS PALSAR data and

5 ore integrated experient that synchronize satellite experient with ground experient. 6. LAND COVER CHARACTERIZATION AND CLASSIFICATION USING POLARIMETRIC ALOS PALSAR Terrain and land cover classification is one of the ost iportant applications of polarietric synthetic aperture radar(polsar) sensing. The objective of the incoherent target decoposition theory is to express the average scattering echanis as the su of independent eleents in order to associate a physical echanis with eachcoponent. Polarietric SAR target decoposition odel perits the extraction of target characteristic inforation, for exaple, Cloude-Pottier polarietric target decoposition is presently the ost used ethod for decoposition of natural extended target scattering, fro the characteristic decoposition of Heritian target coherency atrix and α - β odel introduced by Cloude and Pottier, soe key paraeters, such as α -scattering type paraeter, H - coherency entropy, A - anistropy, target orientation angle β, target phase angle Φ ( i = 1,2, 3 ) etc have been obtained, the ost used i paraeters are H and α that leads to ost popular approach H/α for target scattering classification. Recently, soe new soe concerns have been raised with regard to the Cloude-Pottier decoposition. Cloude Alpha s scattering type abiguities ay occur for certain scatters; and certain Cloude-Pottier s paraeters like β and the target-phase angles Φ i ay not be roll-invariant for asyetric targets. A new roll invariant target scattering decoposition, the Touzi target scattering decoposition has been recently introduced. In contrast to the Cloude-Pottier decoposition, the scattering type was introduced as a coplex entity in ter of the syetric scattering type agnitude α and phase Φ. The new decoposition uses the target helicity to asses the syetric nature of targets. So in this study, fro the ALOS/PALSAR polarietric data, the land cover characteristics and classification potential are investigated using these two decoposition odels, At last, the conventional polarietric descriinators are also tested to assess target inforation that ay not be retrieved with the two target scattering decopositions. Study area locates at the Weinan region of Shan xi province, in China. The centre latitude /longitude of PALSAR scene is N and , this region is one of the ost iportant agriculture areas in Shan xi province. One ALOS/PALSAR polarietric data scenes has been acquired, the acquisition date are Septeber 4, To analyze and validate the result, one Landsat TM iage acquired at August, 9, 2006 is collected. Fig.11 is the HH backscattering iage of study area, Fig.12 is the TM iage of study area on 9, August, s τ α s The Wei river cross this area fro north to south in the left side of iage; in the iddle of iage, there have two large reservoir. During this period, the ain land covers of this area are autun corps (such as corn, potato, cotton and vegetables etc.), fruit trees (such as apple and pear trees etc), forest (ainly locates at ountain area in the right side of iage), shrubs, bare soil field, urban, river etc. Fig.13 is the entropy iage of test site derived fro α β or Touzi scattering vector odel, high entropy ainly corresponds to crops area and forest area located at the ountain area in the right side of iage. Fig.14 is the τ iage derived fro the roll-invariant scattering vector odel. This paraeter is used to assess the syetric nature of targets, fro Fig.14, it can be seen that theτ of the ost of test area is around the zero, so we can say that the ost of target of test site have reflect syetry property, but, in soe area, especially for rectangle area A, B and C, the absolute value of τ is larger than that of the ost of area, so, these natural targets presents the asyetric property ainly caused by terrain relief and heterogeneous ixed distribution of bare soil and vegetation etc. Fig.15 shows the Alpha iage derived fro α β odel, and Fig.16 shows the syetric scattering type agnitude, α s, iage obtained fro the Touzi rollinvariant scattering vector odel, the τ iage shown at Fig.14 will be cobined to explain the result difference between Fig.15 and Fig.16. Fro the Figure, it can be seen that the result are identical for area that τ is around zero, for exaple, in the city area, reservoir area and bare soil area. At the area that τ presents large value, the difference between α andα s is significant, andα is the function of α s and Φ α s. For exaple, for rectangle area A, B, C and the botto area of the iage, the ost of the corresponds to terrain relief or heterogeneous area of ixed vegetation and bare soil so that the reflect syetry property of the targets is daaged. Fro physical viewpoints, the Φα s is the phase difference between the vector coponents in the trihedral-dihedral basis, this paraeter is polarization basis invariant, and also invariant under a change of antenna wave polarization basis. When target is syetric scatter, this paraeter is identical to the phase difference φ2 φ1 derived fro the α β odel. Furtherore, for aziuthally syetric natural targets, this paraeter was interpreted as a easure of relative agnitude of HH and VV, which ay be used to separate the Bragg surface fro ultiple scattering echaniss. But, in the α β odel, this paraeter is not used for scattering type

6 identification because it is not roll invariant. For syetric target, the inforation provided by this paraeter will significantly iprove scattering classification. Classification Potential Study of Roll-invariant target scattering decoposition Based on TM iage, the classification potential of Touzi scattering vector decoposition odel is investigated and validated. Fro the Fig.17 (a), it can be seen that the result of supervised classification ap fro entropy and alphas angle is better than that of entropy and alpha angle. For exaple, for city and farer residence area and crop area, the classification result of entropy and alphas is better. In the classification ap, the river and reservoir are not correctly identified; the reason is not clear at current tie because the interval of acquisition tie is ore than one onth between SAR iage and TM iage. Maybe, in Septeber, the water level of river and reservoir is low or dry. Fig.18 (a) is the supervised classification ap using entropy, alphas andτ, it is better than Fig.17 (a). The ain reason is that the ost of target has quasi syetry property, so the dynaic range of τ is sall, and this is not good for target identification. Fig.18 (b) is the supervised classification ap usingτ, alphas and Φ α s, it can be seen that the result is worst copared with above all result because the city area and fraer residence area is poorly classified. Fro above analysis, we can conclude that the paraeters derived fro the Touzi scattering vector odel can be effectively used for land cover classification, furtherore, the classification result for the cobination of entropy, alphas and Φα s is the best one. Fro the paraeters derived fro Touzi scattering vector odel, the land cover characteristics are copared and discussed, and the potential for these paraeters used for land cover classification is investigated. At last, using these paraeters and axiu likelihood supervised classification, the classification experiental are ipleented, the result indicates that the paraeters obtained fro Touzi decoposition odel can be effectively used for land cover classification, and the classification result for the cobination of entropy, alphas and is best one. Fig.11 HH backscattering iage of study area Fig.12 TM iage of study area on 9, August, 2006 Fig.13 the entropy iage of ALOS/PALSAR data Fig.14 The τ iage derived fro the roll-invariant odel

7 Fig.15 Alpha iage derived fro α β Fig.17 (b) The supervised classification ap using entropy, odel Fig.16 Alphas iage derived fro Touzi scattering vector odel Fig.17(a) The supervised classification ap using entropy and alphas angle alphas angle and Φαs angle Fig.18(a) The supervised classification ap using entropy, alphas angle and τ Fig.18 (b) The supervised classification ap alphas angle and Φαs using τ, angle. Surface roughness paraeters and soil oisture retrieve ethod design and test. The surface paraeters such as surface roughness paraeters and soil oisture content are iportant factors in RADAR science, especially in bare soils. In order to retrieve related paraeters entioned above, we apply the

8 Genetic Algorith in the inversion of surface paraeters. The study area, Ejin Banner, lies in the west of Nei Monggol altiplano, it ainly consists of desert and Gobi landscape, the faous Ejin oasis is located in the lower reaches of the Heihe River. When the satellite passed, we easured the field surface paraeters, because this area belongs to very arid areas, we only easured the roughness paraeters. The next section describes the field surface results easured, RADAR iages adopted and the results inverted. Fig.19 displays the gravels of this area. Fro Fig.19, we can see the different diension of the gravels. The surface type of this area is typical of desert and Gobi, and the terrain is relatively flat, so the surface root ean square of surface height (RMSH) is not big, nevertheless, in the different part of the study area, the gravels in scale are different. Fig. 20 displays the instruent for easuring roughness paraeters, and the Fig. 21 shows the plot of the saples. When we easured the roughness paraeters, we got three to five pictures for each saple point. We considered the averaged value as the final result when dealing with these pictures. Figure 22 deonstrates the ALOS PALSAR iages, which includes HH and HV polarization iage. By coparing the two iages, we find the PALSAR iage ay have errors in HV polarization, which will induce errors when retrieving surface paraeters. Fig. 21 the plot of saples in 2007 (a) ALOS PALSAR HH Fig. 19 the gravels of different diension Fig. 20 the instruent for easuring surface roughness paraeters (b) ALOS PALSAR HV Fig.22 the RADAR backscattering coefficients of ALOS PALSAR We apply the Genetic Algorith in the inversion of the surface paraeters, the inverted paraeters are RMSH

9 Correlation Length and the Soil oisture content (in volue). The cost function is designed as: F = (1 + σ _ real( i, j, k) σ _ theory( i, j, k) ) pq i = 1 2 n1, j = 1 2 n2, k = 1 2 n3 F denotes the cost function, p and q eans the polarization, real and theory eans the backscattering coefficient fro RADAR iages and odel, i j and k eans RADAR band the incidence angle and the type of polarization, n1 n2 and n3 eans the total nubers. Firstly, we attained the backscattering coefficients and incidence angle of each saple point by coparing between the geographical locations ordinates fro the RADAR iages and the GPS receiver, because the saples ainly lie in the areas covered by ALOS PALSAR iages, we ainly used the data fro PALSAR; secondly, we retrieved the surface paraeters using the genetic algorith. The retrieval ethod needs the backscattering coefficients fro backscattering odel, we adopted two odels to testify the precision, one is AIEM odel, and another one is the Oh odel. Fro the coparison between the field easured values and inverted values, we conclude that this inversion ethod is efficient. At the sae tie, the PALSAR iage in HV polarization ay have errors, which ay induce unknown errors when retrieving surface paraeters. So, we hope the quality of the HV polarization iage is iproved in the future. CONCLUSION The report give soe results and show the great potential of ALOS PALSAR data in land surface change onitoring such as glacier oveent, Surface roughness paraeters, earthquake and etc since its long bandwidth it could preserve high coherence in ountain area, this could be better for C band SAR sensor, and with the ulti-polarization, we could got ore inforation. And we believe that the next generation ALOS-2 could bring ore powerful energy for earth observation. ACKNOWLEDGEMENT Thanks JAXA for data providing, and thanks other personal that invent the algoriths that used in this paper. pq Transactions on Geoscience and Reote Sensing. 34(2): Curlander, J., and R. McDonough, Synthetic aperture radar systes and signal processing, New York: John Wiley & Sons Fielding, E. J., Wright, T. J., Muller, J., Parsons, B. E., & Walker, R. A seisic deforation of a fold-and-thrust belt iaged by synthetic aperture radar interferoetry near Shahdad, Southeast Iran. Geology, 7, Freean A and Durden S L. (1998). A three-coponent scattering odel for polarietric sar data. IEEE Transactions on Geoscience and Reote Sensing 36(3): Massonnet, D., Feigl, K.L., Radar interferoetry and its application to changes in the earth s surface. Reviews of Geophysics 36, Schuler D L, J S Lee, Kasilinga D and Nesti G. (2002). Surface Roughenss and Slope Measureents Using Polarietric SAR Data. IEEE Transactions on Geoscience and Reote Sensing, 40(3): Touzi R, Goze S, Toan T L and Mougin E. (1992). Polarietric Discriinators for Sar Iages. IEEE Transactions on Geoscience and Reote Sensing, 30(5): Touzi R.(2007). Target Scattering Decoposition in Ters of Roll-Invariant Target Paraeters. IEEE Transactions on Geoscience and Reote Sensing, 45(1): Yaaguchi Y, Moriyaa T, Ishido M and Yaada H. (2005). Four-coponent scattering odel for polarietric SAR iage decoposition. IEEE Transactions on Geoscience and Reote Sensing, 43(8): Table II. list of Co-Investigators Nae Wang ChangLin wcl_irsa@163.co Li XinWu xinwuli@irsa.ac.cn Liu Guang liug@tsinghua.org.cn Corr. Author Zhang Lu zhanglu0008@163.co Zhu LanWei zhulanwei@yahoo.co.cn Yang huaining huainingy@126.co Yan shiyong yanshiyong@gail.co Ruan zhixin zhixing_ruan@ail.bnu.edu.cn REFERENCE Burgann, R., Rosen, P. A., & Fielding, E. J.. Synthetic aperture radar interferoetry to easure Earth's surface topography and its deforation. Annual Review of Earth and Planetary Sciences, 28, ,2000 Chen, C. W., & Zebker, H. A. (2002). Phase unwrapping for large SAR interferogras: Statistical segentation and generalized network odels. IEEE Transactions on Geoscience and Reote Sensing, 40, Cloude S R and Pottier E. (1996). A review of target decoposition theores in radar polarietry. IEEE