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1 Cartography and GIS Research Group-Department of Geography 2 Department of Hydrology and Hydraulic Engineering

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  1. 32nd EARSEL Symposium 2012 “Advances in Geosciences” 21-24 May 2012 - Mykonos, Greece Session on “Imaging Spectroscopy” Use of Land-Cover Fractions Obtained from Multiple Endmember Unmixing of Chris/Proba Imagery for Distributed Runoff Estimation Luca Demarchi1, Eva M.Ampe2, Frank Canters1, Jonathan Cheung-WaiChan1,3, Juliette Dujardin2, ImtiazBashir2, OkkeBatelaan2 1Cartography and GIS Research Group-Department of Geography 2Department of Hydrology and Hydraulic Engineering 3Department of Electronics and Informatics VrijeUniversiteitBrussel, Belgium

  2. Introduction • Use of remote sensing and GIS technology in hydrological modeling has strongly increased in the last decades: • Mapping spatial variability of several parameters for deriving runoff estimation Land-cover types • Hyperspectral have opened up new possibilities for land-cover mapping • Recent work has focused on the potential of hyperspectral CHRIS/Proba data for estimating sub-pixel land-cover fractions in urban areas[1]. Multiple Endmember Spectral Mixture Analysis (MESMA) [1] Demarchi L., Canters F., Chan J.C.-W and Van de Voorde, T. (2012). Multiple endmember unmixing of CHRIS/Proba imagery for mapping of impervious surfaces in urban and suburban environments. IEEE Transactions on Geosciences and Remote Sensing, DOI: 10.1109/TGRS.2011.2181853.

  3. Objectives • Integrate the results of MESMA in the Wetspass model: • Spatially distributed hydrological model for estimating the main water balance components: evapotranspiration, surface runoff and groundwater recharge • Compare the effects of different land-cover input scenarios on the spatial distribution of runoff. • Study area: • Woluwe catchment • Brussels Capital Region, east of city center • High heterogeneity and dense urban morphology • Hyperspectral CHRIS/Proba: • Spectral range: 410 – 1050 nm • MODE3: 18 spectral bands, 18m spatial resolution • (August 2009)

  4. Overview • Introduction • Methodology: • Unmixing CHRIS/Proba data with MESMA • Wetspass hydological modeling • Improving Wetspass with MESMA results: scenarios definition • Results and discussion • Conclusions

  5. Mapping land-cover types with multiple endmember unmixing (MESMA) • Sub-pixel land-cover mapping with medium-resolution multispectral imagery (Landsat, SPOT,...) in urban areas: • Spectral similarity of impervious surfaces and other non-artificial land-cover types (bare soil, dark vegetated areas,...) • Spectral heterogeneity of impervious surfaces  difficult to define representative endmembers for unmixing • MESMA: effective method on increasing land-over mapping accuracy when heterogeneity of land-over classes is very high. • Brightness normalization: technique proposed by Wu [2], reduces within-class spectral heterogeneity and emphasizes the shape information [2] Wu C. (2003). Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment, vol. 93, no. 4, pp. 480-492

  6. Brightness normalization Brightness normalization • r’b is the normalized reflectance for band b, • rb is the original reflectance for band b, • μ is the average reflectance for the pixel, • M is the total number of bands. • Before • After

  7. Brightness normalization Brightness normalization • r’b is the normalized reflectance for band b, • rb is the original reflectance for band b, • μ is the average reflectance for the pixel, • M is the total number of bands. • Before • After

  8. Mapping land-cover types with multiple endmember unmixing (MESMA) • Linear spectral unmixing • Unique endmembers are used for the entire scene: • Differences in land-cover composition and spectral variations within the same land-cover type are not taken into account • rib is the reflectance of endmember i for a specific band b, • fi is the fraction of endmember i, • N is the total number of endmembers, • ebis the residual for band b. • Multiple endmember unmixing • Per-pixel basis approach • Each pixel is unmixed with multiple models: • all combinations of endmembers, using 2, 3 or 4 EMs: only one retained! • Different spatial distribution of models using 2-, 3- or 4- land-cover classes for unmixing  more efficient fraction estimation

  9. Spatial distribution of models selected by MESMA

  10. Sub-pixel validation • Stratified validation set: • Fractions derived for each land-cover class from 0.25m ortho-photos • 30 pixels for all combinations of 2-, 3- and 4- land-cover classes • 30 pure pixels for each of the four land-cover classes (grey sealed surfaces, red sealed surfaces, vegetation and bare soil) • 12 combinations=360 ground truth pixels • Amplitude • Overall • Systematic

  11. Land-cover fractions Impervious surfaces

  12. Land-cover fractions Vegetation

  13. Land-cover fractions Bare soil

  14. Overview • Introduction • Methodology: • Unmixing CHRIS/Proba data with MESMA • Wetspass hydological modeling • Improving Wetspass with MESMA results: scenarios definition • Results and discussion • Conclusions

  15. Wetspass: a spatially distributed hydrological model for runoff estimation • Stands for Water and Energy Transfer between Soil, Plants and Atmosphere under quasi-Steady State [3]. • Physically based model able to simulate long-term average spatial patterns of groundwater recharge, surface runoff and evapotranspiration • It is able to handle the spatial distribution of several inputs such as soil types, land-use types, slope, groundwater depth and long-term average climatic data • Fully integrated in a geographical information system as a raster model [3] Batelaan, O. and De Smedt, F. (2001). WetSpass: a flexible, GIS based, distributed recharge methodology for regional groundwater modeling. Impact of Human Activity on Groundwater Dynamics, (IAHS Publ. No. 269). pp. 11-17.

  16. Wetspass: a spatially distributed hydrological model for runoff estimation • Water balance computation at cell level : • For each raster cell, the balance is split into independent water balances • By summing up each water balances, weighed by the corresponding fraction component, the total water balance at raster level can be obtained

  17. Overview • Introduction • Methodology: • Unmixing CHRIS/Proba data with MESMA • Wetspass hydological modeling • Improving Wetspass with MESMA results: scenarios definition • Results and discussion • Conclusions

  18. Improving Wetspass estimations using land-cover fractions from MESMA • Wetspass defines default fractions for each land-use type.

  19. Improving Wetspass estimations using land-cover fractions from MESMA • Wetspass defines default fractions for each land-use type. • Sub-pixel estimates obtained from CHRIS/Proba imagery are used in this study to improve runoff mapping within Wetspass. • Scenario 1: Semi-distributed (based on default Wetspass parameters) • av, ab, aiand aw are fixed a priori for each land-use class • Scenario 2: Semi-distributed (based on MESMA derived parameters) • av, ab, aiand aw are fixed a priori for each land-use class • Mean land-cover fractions are calculated from MESMA results for each land-use type • Scenario 3: Fully-distributed (pixel-based derived from MESMA) • av, ab, aiand aw are obtained at pixel-level from the MESMA results • In each scenario, average estimation and standard deviation of runoff are calculated for each land-use type

  20. Overview • Introduction • Methodology: • Unmixing CHRIS/Proba data with MESMA • Wetspass hydological modeling • Improving Wetspass with MESMA results: scenarios definition • Results and discussion • Conclusions

  21. Improving Wetspass estimations using land-cover fractions from MESMA • In scenario 2 new average land-cover fractions were calculated based on MESMA results - 10%

  22. Improving Wetspass estimations using land-cover fractions from MESMA • In scenario 2 new average land-cover fractions were calculated based on MESMA results + 10%

  23. Results of Runoff estimates • For each scenario, average runoff and standard deviation values have been calculated for each land-use type Urban land-use classes - 10%

  24. Results of Runoff estimates Urban land-use classes + 10%

  25. Results of Runoff estimates High standard deviations

  26. Scenario 1 and 2: similar values of runoff, spatial pattern very similar and clearly linked to the pattern of land-use

  27. Scenario 1 and 2: similar values of runoff, spatial pattern very similar and clearly linked to the pattern of land-use

  28. Scenario 1 and 2: similar values of runoff, spatial pattern very similar and clearly linked to the pattern of land-use • Variations within each land-use type are limited, confirming the small standard deviation obtained

  29. Scenario 3: strong local variation • In each pixel the Runoff is derived from its land-cover class composition and not from the land-use type • High local variability= high standard deviation. and therefore more realistic hydrological parameters estimates

  30. Runoff scenario 1

  31. Land-use map

  32. Runoff scenario 3

  33. Impervious surfaces from MESMA

  34. Overview • Introduction • Methodology: • Unmixing CHRIS/Proba data with MESMA • Wetspass hydological modeling • Improving Wetspass with MESMA results: scenarios definition • Results and discussion • Conclusions

  35. Conclusions • For most urban land-use classes, land-cover fraction values derived from RS are different from Wetspass default parameters: • Impervious surfaces level is systematically overestimated in Wetspass • Smaller runoff values are produced when RS data are used • Strong link between runoff and imperviou-sness level within each land-use type • Sub-pixel estimates derived from MESMA directly used at cell level: • Local variation of land-cover composition fully taken into account: high local variation of runoff within each land-use type • Benefits of using RS for obtaining more detailed information on the spatial pattern of runoff. • Combining MESMA-per-pixel basis unmixing approach-with Wetspass-a spatially distributed modeling-allows to generate fully distributed and more realistic hydrological estimates. • Limitations of CHRIS/Proba in urban areas are pointed out: • Spectral similarity of some land-cover types may negatively affect the quality of runoff in some locations. • Hyperspectral data with higher spectral resolution and wider spectral range may enhance this distinction and therefore runoff estimation

  36. Conclusions Thank you