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1 Cartography and GIS Research Group-Department of Geography 2 Department of Hydrology and Hydraulic Engineering 3 Department of Electronics and Informatics Vrije Universiteit Brussel, Belgium Session on “Imaging Spectroscopy” Luca Demarchi 1, Eva M.Ampe 2, Frank Canters 1, Jonathan Cheung-Wai Chan 1,3, Juliette Dujardin 2, Imtiaz Bashir 2, Okke Batelaan 2 Use of Land-Cover Fractions Obtained from Multiple Endmember Unmixing of Chris/Proba Imagery for Distributed Runoff Estimation 32 nd EARSEL Symposium 2012 “Advances in Geosciences” May Mykonos, Greece

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Page 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 Multiple Endmember Spectral Mixture Analysis (MESMA) 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]. [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: /TGRS

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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. Page 3 Objectives 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)

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

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Page 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

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

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

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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 Page 8 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 r ib is the reflectance of endmember i for a specific band b, f i is the fraction of endmember i, N is the total number of endmembers, e b is the residual for band b. Mapping land-cover types with multiple endmember unmixing (MESMA)

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Page 9 Spatial distribution of models selected by MESMA

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Page 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 OverallSystematic

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Page 11 Impervious surfaces Land-cover fractions

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Page 12 Vegetation Land-cover fractions

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Page 13 Bare soil Land-cover fractions

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

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Page 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]. [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 Physically based model able to simulate long-term average spatial patterns of groundwater recharge, surface runoff and evapotranspiration Fully integrated in a geographical information system as a raster model 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

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Page 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

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

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

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Page 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) a v, a b, a i and a w are fixed a priori for each land-use class Scenario 2: Semi-distributed (based on MESMA derived parameters) a v, a b, a i and a w 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) a v, a b, a i and a w 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

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

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Page 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%

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

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Page 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%

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

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Page 25 Results of Runoff estimates High standard deviations

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

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Page 28 Scenario 1 and 2: similar values of runoff, spatial pattern very similar and clearly linked to the pattern of land-use

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Page 29 Variations within each land-use type are limited, confirming the small standard deviation obtained Scenario 1 and 2: similar values of runoff, spatial pattern very similar and clearly linked to the pattern of land-use

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Page 30 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

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Page 31 Runoff scenario 1

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Page 32 Land-use map

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Page 33 Runoff scenario 3

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Impervious surfaces from MESMA Page 34

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Introduction Methodology: Unmixing CHRIS/Proba data with MESMA Wetspass hydological modeling Improving Wetspass with MESMA results: scenarios definition Results and discussion Conclusions Overview Page 35

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Page 36 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 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 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.

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Page 37 Conclusions

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