Table 1 PRISM/OB1 data summary in this study. PI400-2 Table 2 PRISM/OB1 data summary in this study. 2'#7-#/ :> 8>?= B-/"# 899:;98;<= :> 8A89

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1 PI400-1 EVALUATION ANALYSIS FOR DIGITAL TERRAIN MODELS GENERATED FROM ALOS/PRISM -OB2 DATA PI No. 400 Tomohito Asaka (PI) 1, Yoshiyuki Yamamoto (CI) 2 1 Nihon University, Izumi 1-2-1, Narashino Chiba, Japan, Tel: , asaka.tomohito@nihon-u.ac.jp 2 Aichi Institute of Technology, 1247 Yachigusa, Yakusa, Toyota, Aichi, Japan, y_yamamoto@aitech.ac.jp 1. INTRODUCTION In this study, we are aiming at evaluating the height accuracy of Digital Surface Model (DSM) generated from ALOS/PRISM data, and reviewing digital terrain models derived from the DSM. In addition to the DSM toward the practical use, we evaluate the digital terrain models (such as Drainage Direction Matrix, Slope Gradient Model) available to spatial information for a distributed rainfallrunoff model. Fig.1 illustrates work flow in this study. DSMs generated from ALOS/PRISM-OB1 data or -OB2 data are evaluated through comparison between the DSMs and existed 50m mesh DEMs (the Geographical Survey Institute DEM: GSI-DEM). Also, the purpose of theme1 depicted in Fig.1 is to review the DSM from result of the virtual reference station (VRS) method for the network based RTK-GPS survey. And, the purpose of theme2 depicted in Fig.1 is to evaluate for rainfall-runoff analysis using digital terrain models derived from the DSM and land classification data generated from AVNIR-2 data. 2. DSM GENERETED FORM PRISM-OB1 OR -OB2 In 2.1.-section and 2.2.-section, results and discussions of DSM generated from OB1 data are described. In section and 2.4.-section, results and discussions of DSM generated from OB2 data are also described. We manually selected GCPs on PRISM images, and then tie points collected by automatically image matching method for pair of images. The Triangulated Irregular Network (TIN) derived from image pairs is computed by the robust photogrammetric techniques using the suitable elements of exterior orientation. The DSM is generated from spatially interpolated of the TIN to grid data as a 50m resolution DSM by the liner rubber sheeting method. The reason for determining mesh size of the DSM, quantitative geometrical analysis assessed by comparison of the DSM with GSI-DEM. In this study, DSMs was computed using the Leica Photogrammetry Suite (LPS). Also, DSM s accuracy is discussed by NIMA (National Imagery and Mapping Agency) LE90. The NIMA LE90 statistic is based on the assumption that a normal distribution exists with the set of observations [1]. In this case, the set of observations is the DSM errors computed using DEM. The following equation is used to calculate NIMA LE90. The following equation is used to calculate NIMA LE90.!"#$%&'&()*+'$,(+-$./0"#1234$-+5&$ 5)6)$+($./0"#1237$-+5&$5)6) 899:()9;$)<<&<<-&'6$+,$!"#$=;$>"0?!@# 899:()9;$)<<&<<-&'6$+,$!"#$=;$A/"$/BC? >."$<:(D&;$ /)E',)FF?(:'+G$)')F;<E<$:<E'%$!"#$)'5$ F)'5$9F)<<EH9)*+'$5)6)$5&(ED&5$,(+-$ 8AI0/?7$5)6) Fig. 1 Work flows of the theme1 and the theme2. LE90 = ± #( e i " e ) Where, ei is absolute error of reference point i, e is mean absolute error for the entire set of reference points, n is total number of DEM reference pixels. The value of represents a 90% confidence interval derived from statistical tables Mountainous area in the east part of the Kouchi prefecture Mountainous area in the east part of Kouchi prefecture as a study area, the DSM was generated from PRISM- OB1 Level 1B1 data observed o February 15th, Table1 shows summary of the PRISM-OB1 data. Fig.2 shows gray scale image of the DSM. And, Fig.3 DEM. Moreover, the NIMA LE90 is calculated to be ± m. It seems that the error occurred by natural terrain features such as trees and topographical shadows. n (1)

2 Table 1 PRISM/OB1 data summary in this study. PI400-2 Table 2 PRISM/OB1 data summary in this study. 2'#7-#/ :> 8>?= <@9A B-/"# 899:;98;<= :> 8A89 <@89 C-%D7-#/ :> 8A:=. <@9A 2'#7-#/?:. 8<@9. =A9B C-/"# 899:;9<;=>?: 8<<> =A89 D-%E7-#/?:. 8BF9. =A9B *+! *+ Fig. 2 The gray scale image of DSM generated from PRISM/OB1 data. Fig. 4 The gray scale image of DSM generated from PRISM/OB1 data. Fig. 3 The scatter plot between DSM and DEM in the area of Fig.2. Fig. 5 The scatter plot between DSM and DEM in the area of Fig.4.

3 PI400-3 Table 3 PRISM-OB2 data summary in this study. Table 4 PRISM-OB2 data summary in this study. 7-/"# =9 8:=<. 9>99 899:;9<;88?-%@A-#/ =9 8BC9. D>9B 7-/"# => 8<>9. 9?99 => 8>CD. E:?9> *+! *+! Fig. 6 The gray scale image of DSM generated from PRISM/OB2 data. Fig. 8 The gray scale image of DSM generated from PRISM/OB2 data. Fig. 7 The scatter plot between DSM and DEM in the area of Fig.6. Fig. 9 The scatter plot between DSM and DEM in the area of Fig.8.

4 PI Plane area in the east part of the Chiba prefecture Plane area in the east part of Chiba prefecture as a study area, the DSM was generated from PRISM-OB1 Level 1B1 data observed o August 15th, Table2 shows summary of the PRISM-OB1 data. Fig.4 shows gray scale image of the DSM. And, Fig.5 DEM. Moreover, the NIMA LE90 is calculated to be ±40.251m. It seems that the error occurred by artificial structures such as buildings and roads. Wakaba Ward, Chiba city No.7 No.6 No.5 No.4 No.3 No.1 No.2 Yokoshiba Town No.8 The Pacific Ocean 2.3. Mountainous area in the west part of the Tokyo Mountainous area in the west part of Tokyo Metropolitan Government as a study area, the DSM was generated from PRISM-OB2 Level 1B1 data observed o May 22th, Table3 shows summary of the PRISM- OB2 data. Fig.6 shows gray scale image of the DSM. And, Fig.7 DEM. Moreover, the NIMA LE90 is calculated to be ± m. It seems that the error occurred by natural terrain features such as trees and undulating landscape. Mutsuzawa Town!" 10.0 km Fig. 10 Location map of field survey points (No.1 - No.8 ). These locations are n the east part of the Chiba prefecture. z 2.4. Mountainous area in the west part of the Kanagawa prefecture Mountainous area in the west part of the Kanagawa prefecture as a study area, the DSM was generated from PRISM-OB2 Level 1B1 data observed o February 8th, Table4 shows summary of the PRISM-OB2 data. Fig.8 shows gray scale image of the DSM. And, Fig.9 DEM. Moreover, the NIMA LE90 is calculated to be ±79.287m. It seems that the error occurred by natural terrain features such as undulating landscape and topographical shadows. 3. REVIEW OF DSM We observed a geographical coordinate by the VRS RTK-GPS survey on August 24th The field survey points were selected in the corner of intersection where can be interpreted from the triplet view images of PRISM. Fig.10 illustrates the location of the field survey points (No.1-No.8) on the map. The quadrilateral boundary labeled 1N on the map indicates the location of the PRISM-OB1 nadir view image of Table2. In this study, the PG-A1 (GPS antenna) and the GB-500GD (GPS receiver) from TOPCON co., ltd. are used as VRS RTK- GPS survey devices depicted in Fig.11. Fig.12 demonstrates scatter plot between the DSM of Fig.4 and the height of ground elevation observed with VRS RTK-GPS survey tools. As shown in illustration, Fig. 11 VRS RTK-GPS survey devices. there was good agreement between field survey points observed with VRS RTK-GPS survey and the DSM data. The linear regression presented a high determination coefficient (R 2 =0.952). It was attributed mainly to the location of the survey points are an open space where that area has the satellites cut-off elevation angles are 15 degree. Hence, we considered that the NIMA LE90 of Fig.5 represents little margin for error.

5 PI400-5 Fig. 12 Regression plots of DSM data versus height of ground elevation observed at the field survey points. Fig. 13 Location map of the Iwaharabashi upper river basin, the Iwaharabashi gauging station and the Ashigara precipitation station. The balck solid line represents the boundary of the Iwaharabashi upper river basin. These locations are in the west part of the Kanagawa prefecture. Fig. 14 Drainage direction matrix (DDM) and the Iwaharabashi upper river basin simulated from DSM, respectively. The blue dots line represents the DDM, the red solid line area represents the Iwaharabashi upper river basin.

6 PI RAINFALL-RUNOFF ANALYSIS We analyzed rainfall-runoff analysis using the Distributed Tank Rainfall-runoff Model (DTRM) to validate the utility of digital terrain models derived from the DSM. The DTRM is a pixel based distributed hydrology model for flood forecasting, can be used spatial information such as a drainage direction matrix, a slope gradient model and a land cover classification generated from satellite data [2]. For rainfall-runoff analysis, we selected the Iwaharabashi upper river basin as study area where is located in the part of the Kanagawa prefecture. Fig.13 illustrates the location of the Iwaharabashi upper river basin, the Iwaharabashi gauging station and the Ashigara precipitation station. We used as measured discharge data observed at the Iwaharabashi gauging station and measured rainfall intensity data observed at the Ashigara precipitation station for rainfall-runoff analysis. In addition, Fig.14 demonstrates the digital terrain models derived from the DSM of Fig.8 and the land cover classification derived from AVNIR-2 data of Fig.15 are also used for spatial information of the DTRM. The area of simulated Iwaharabashi upper river basin depicted in Fig.14 is calculated to be 48.72km 2. Meanwhile, the nominal area of the Iwaharabashi upper river basin is 46.70km 2. The simulated Iwaharabashi upper river basin is similar in basin area and watershed boundary to the actual basin. Hence, the DSM generated from ALOS/PRISM is useful for simulating digital surface models. Table5 shows rain duration for rainfall-runoff analysis. Fig.16 demonstrates hydro- and hyeto-graph of result of the rainfall-runoff analysis computed from the DTRM. As a result, simulated discharge is within acceptable error in practical advantage. 5. CONCLUSION In this work, it is shown that DSMs generated from PRISM-OB1 or -OB2 data is available as substitution of existed DEM. In conclusion, we can use DSMs derived from ALOS/PRISM in place of DEMs. Therefor, a pixel based distributed hydrology model is formed using spatial information reflected latest situation under ALOS/PRISM is able to observe the earth. In other words, flood forecasting based on the latest spatial information is available for an underdeveloped area where we are seldom used existing maps as the spatial information. In future study, we are currently at work on availability of ALOS/PALSAR analysis data such as InSAR DEM for spatial information of rainfall-runoff analysis [3]. 6. REFERENCES [1] Department of Defense, Mapping, Charting and Geodesy Accuracy, MIL-STD , [2] Tomohito Asaka, Study of Distributed Tank Rainfall- Runoff Model for Mountainous Watersheds, doctor thesis, Nihon University, [3] Tomohito Asaka, et al., "Evaluating the quality of the hydrological features derived from ALOS/PALSAR InSAR DEM", The 49th Conference of the RSSJ, pp , 2009.!"#$% &#'(!"#$%&'&($ #)*+,-#.$ #)*+/-".$ #)*+0-!. Fig. 13 The AVNIR-2 image orthorectified using the DSM of Fig.8. Table 5 Rain duration for rainfall-runoff analysis in this study. )*%+,&-.+//$ )00)1*010*,0200,3,)00)1*0104,)5200 Fig.16 Result of rainfall-runoff analysis. The red solid line represents the observation data derived from the Iwaharabashi gauging station, the green dotted line represents the simulation data derived from the distributed tank rainfall-runoff model, and the blue bar chart represents rainfall intensity data derived from the Ashigara precipitation station.