1. Preliminary Results on High Accuracy Estimation of Shoreline Change Rate Based on Coastal Elevation Models Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability http://www.ual.es/GruposInv/ProyectoCostas/index.htm F.J. Aguilar a, I. Fernández a, J.L. Pérez b, A. López a, M.A. Aguilar a A. Mozas b, J. Cardenal b a Dept. of Agricultural Engineering, Almería University, Spain b Dept. of Cartographic Engineering, Geodesy and Photogrammetry, Jaén University, Spain Kyoto, Japan. 10 August 2010 Corresponding Author: F.J. Aguilar (faguilar@ual.es)

2. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 1 Detailed coastal topographic information will be the key variable in understanding the likely impacts of global anthropogenic and natural hazards (including SLR due to Climate Change). The shoreline is one of the most important and critical indicators of coastal evolution and vulnerability

3. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS Urban development on the coastal area and resource use conflicts spawn environmental degradation and increasing hazard vulnerability. In Spain, more than 44% population are now living in coastal areas (7% of territory) Anthropic Pressure  Increase of urban areas (soil sealing and change of ISA)  Shortage of planning  Sand extraction to attend the demand from greenhouse crops 2

4. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 3 Yes but, How to extract the shoreline? Some of the methods are based on the intersection between the Coastal Elevation Model (CEM) and the plane corresponding to the chosen tidal datum An open coast tide station very close to our working coastal area is needed to accurately estimate its MHW Sometimes it is recommendable to use as a more reliable vertical reference the Mean Sea Level (MSL) tidal datum

5. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 4 1. A classic approach: the Cross-Shore Profile method (CSP) (e.g. Stockdon et al., 2002*) R 2 = 0.98 * Stockdon, H.F., Sallenger, A.H., List, J.H., Holman, R.A., 2002. Estimation of shoreline position and change using airborne topographic Lidar data. Journal of Coastal Research, 18(3), pp. 502-513.

6. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 5 But most of times the results are not as we wish R 2 = 0.33 1. A classic approach: the Cross-Shore Profile method (CSP)

7. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 6 2. Our approach: the Elevation Gradient Trend Propagation method (EGTP)

8. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 7 2. Our approach: the Elevation Gradient Trend Propagation method (EGTP)

9. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 8

10. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 9 Main wave directionEast-West Average wave height1 m Maximum wave height5 m Maximum tide range (microtidal coast) 0.5 m ALMANZORA RIVER MOUTH

11. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 10 Data corresponding to 2001 come from an analogic RGB photogrammetric flight at an approximated scale of 1:5000 taken on 9 April 2001. 1 m grid-spacing CEM was carried out by means of stereo matching techniques ranking over previously digitized images and subsequent exhaustive and careful edition by one operator (SOCET SET® environment). The estimated vertical accuracy of the photogrammetrically- derived CEM was around 30 cm

12. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 11 Data corresponding to 2009 come from a combined flight General information Height above ground: 1000 m Number of strips: 4 Number of photographs: 86 Digital camera DMC (Digital Mapping Camera) Intergraph GSD (cm) 10 RGB+Nir (12 bits) Forward overlap (%) 65 Side overlap (%) 60 LiDAR sensor ALS60 LEICA FOV (º) 35 Max. laser pulse Rate (Hz) 96100 Max. point spacing across track(m) 1,33 Max. point spacing along track (m) 1,46 Average point density (points/m2) 1,61 Average point space (m) 0,79 Average point area (m2) 0,62 Estimated height accuracy (m) 0,08

13. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 12 Methodology to compute the corresponding shoreline change rate between 2001 and 2009 End Point Rate (EPR) Computation (m/year) erosion (-) or accretion (+) 95% confidence level

14. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 13 Performance of the Two Tested Shoreline Mapping Methods YearTransects lostUncertainty (σ xs ) CSPEGTPCSPEGTP 200112.84%3.19%2.954.10 m 200912.18%1.54%1.05 m1.48 m CSP method seems to be quite sensitive to noise due to an incorrect separation between water and land, unexpected artifacts along foreshore profile or actually non-straight profiles. EGTP can be deemed as much more robust than CSP, maintaining a greater number of useful transects

15. Source of variationDegrees of freedom Sum of squaresFSignific. (p<0.05) Method (A)10.6590.8530.355 Tidal datum (B)10.0370.0480.826 Transect spacing (C) 21.0730.6940.499 Zone (D)2833809.291562.34<0.001* A*B10.1340.1740.676 A*C20.8340.5390.582 B*C20.0850.0550.946 A*B*C20.02920.0370.962 A*D2566.7563.455<0.001* B*D2849.0992.268<0.001* A*B*D259.9910.5170.977 C*D5641.9450.9690.540 A*C*D475.7440.1581 B*C*D561.8020.0411 A*B*C*D471.0030.0.0271 Error124129592.72 Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 14 Quantitative Analysis (ANOVA where EPR is the explained variable) A (method) = CSP and EGTP B (tidal datum) = MSL and MHW C (transect spacing) = 5 m, 10 m and 20 m D (Zone) = 29 homogeneous areas Kyoto, Japan. 10 August 2010

16. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 15 Kyoto, Japan. 10 August 2010 ZoneEPR (m/year)Useful transectsEPR (m/year)Useful transects Method CSPMethod EGTP 1 03-0.9516 2----------0.039 3 3.7792.9110 4 01-0.199 5 -0.4017-0.3415 6 014015 7 0.2880.098 8 ---- 0.273 9 -0.1015-0.2821 10 2.5082.3914 11 ---- 05 12 ---- -3.974 13 3.4643.8524 14 0.30130.1218 15 1.7221.467 16 -0.0916-0.1329 17 -1.0720-1.0126 18 3.76943.64122 19 2.84522.9256 20 -2.1682-2.1282 21 0230 22 2.02961.9084 23 0.27240.3020 24 -0.58102-0.30102 25 -0.0758-0.1660 26 -1.3843-1.1042 27 2.05251.9829 28 -2.1775-2.1759 29 047-0.0447 Average 0.398510.55959

17. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 16 Kyoto, Japan. 10 August 2010 ORTHOPHOTO 2009 PUNTA DE LOS HORNICOS 1989 2001 2009

18. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 17 Kyoto, Japan. 10 August 2010 PALOMARES BEACH VERA BEACH 1989 2009 2001

19. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 18 Kyoto, Japan. 10 August 2010 VERA BEACH 1989 2009 2001 ACCRETION EROSION

20. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 19 Kyoto, Japan. 10 August 2010 QUITAPELLEJOS BEACH

21. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 20 Kyoto, Japan. 10 August 2010 The new grid-based approach can be strongly recommended because its precision, local slope acquisition, robustness regarding the presence of noise and outliers, and capability to deal with very curved and even closed coastal features. The preliminary results also indicate that, though the global rate- of-change for the whole coastline between 2001 and 2009 may be catalogued as relatively low (0.55 ± 0.50 m/year of net accretion), the local results for every one of the 29 homogeneous units considered have been extremely variable and statistically significant. Many local phenomena, registered in a short-term period and mainly due to human activities such as the presence of new engineered structures and artificial beach regeneration, may strongly affect the shoreline evolution in certain and localized areas

22. Research Project RNM 3575: Multisource Geospatial Data Integration and Mining for the Monitoring and Modelling of Coastal Areas Evolution and Vulnerability INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 21 Kyoto, Japan. 10 August 2010 Thank you very much for your kind attention

23.  2009 LiDAR data. Vertical Accuracy Average dz +0.029 Minimum dz -0.284 Maximum dz +0.180 Std deviation 0.089 N = 62

24.  2009 LiDAR data. Vertical Accuracy

25. tgα ezez Ground truth MDE e lc MSL HIGH WATER LINE

26. Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS LEAST SQUARES ESTIMATION Theoretical framework to estimate the point shoreline error (xs) propagated from CSP method FUNCTIONAL MODEL 1. A classic approach: the Cross-Shore Profile method (CSP)

27. Kyoto, Japan. 10 August 2010 INTRODUCTION SHORELINE EXTRACTION METHODS STUDY SITE, DATASETS & METHODOLOGY RESULTS & DISCUSSION CONCLUSIONS 9 2. Our approach: the Elevation Gradient Trend Propagation method (EGTP) Computed uncertainty for the shoreline determination