Territoires, Environnement, Télédétection et Information Spatiale MONTPELLIER, FRANCE

Transcription

1 Territoires, Environnement, Télédétection et Information Spatiale MONTPELLIER, FRANCE Morphologic segmentation of shallow rivers from satellite very high spatial resolution images: case study of the Durance river J.S. Bailly, C. Puech, Y. Le-Coarer, S. Reyes-Castillo, J. Damis, C. Delenne GT5, Hydrology Context Conflictual management of surface waters between power supply, hydro-eco systems protection, water supply especially in mediterranean areas Need to better know the dynamic 3D geometry of shallow river systems under anthropogenic pressures and its consequences: river geomorphology Major topics : Hydraulics (water and solid transport) Hydro-Ecology (fish habitats, ) Usual ground surveys are limited in spatial extension Ability of remote sensing imagery to help upscaling? 1

2 Preliminary studies [Chaponnière, 2003] Analysis of the best resolution to explain river depth through UAV images Depth-reflectances regressions Statistical Results R²_RIFFLE_t30 Spatial resolutions: from 0.02 m to 3 m R² 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 R²_POOLS_t PIXEL SIZE (cm) Higher R² values when PIXEL SIZE > 1m (smoothed local effects: waves, gravel shadows...)[carbonneau et al., 2005] Compatible with Very High Spatial Resolution VHSR satellite images ORFEO Study objectives Mapping shallow rivers (< 2m depth) geomorphology by use of VHSR multi-spetctral satellite imagery Variables of interest: -Water depths (Z) BUT ALSO: -Horizontal geometry of riverbed (XY): delineation -Functionning segments : s, pools Criteria: -Lower accuracy than ground survey (of course) BUT: -reproductility, spatial homogeneity, -faster mapping over greater extent -help for more accurate in situ measurement? 2

3 Study area 45 km of the Durance gravel bed river Constant discharge: 7 m3/s, Hydropower stations Morphogenic flood ~once 2-3 years: high riverbed changes Ground survey data Data Hydromorphologic survey along the river 45 km long, 2005 ~3000 points of bathymetry, velocity, july 2006 on ~2 km long Satellite data 3 Quickbird Images RGB+NIR, 2.4 m, june 2006 MS: 2.4 m, P+XS: 0.6 m Secchi~0.6 m 3

4 Methodological scheme -Level 1 (X,Y): River bed delineation, River bed width mapping, -Level 2 (Z): Depth mapping -Level 3 : Hydromorphologic segmentation on X,Y,Z into s, pools, pool pool pool [Wasson, 2005] Level 1: River bed delineation [Reyes Castillo, 2007] 1. Image segmentation (shapes and radiometries criteria) 2. Segment classification on radiometries (NIR, shapes...) ~97% of accuracy (area criteria) Non significant differences between MS 2.4 m and P+XS, 0.6 m 4

5 Level 1: River width mapping Derivative information from riverbanks delineation: - Based on skeletonisation of riversbanks - And distances computations River width m Level 2-Depth mapping from radiometries Physics: Exponential decrease [Beer Lambert Law] ρ = ρ f - (ρ f - ρ ). (1 -.e-2kz ) réfraction Bottom attenuation z More stable when working with ratio Reflectance ρ ρ ρ spectra1 spectra2 Best explanation ( R² ~0.6) by log (green/red) LGR ratio [Leigleiter and Roberts, 2005] - (classical MLR R²: 0.6 ) More turbid ratio depth 5

6 Level 2-Depth mapping: raw regression model Usual regression model: 17 cm for RMSE Sources of errors? DEPTH Y LGR X High absolute errors Shadows (riparian vegetation) Down to s (specular surface and bubles) Algaes «Ambiguous Areas» Level 2-Depth mapping: improved model Depth = f(lgr) 1. Ambiguous areas removing: Threshold in [LGV, NIR] plane : : -14 % of pixels NDVI 2. Robust regression [Huber, 1981]: smooth outliers effects 3. Separation into 2 «sub-models» : 1) LGR < 0.65 : simple regression 2) LGR> 0.65 depth > 0.74 m (1 m arbitrary) RESIDUALS LGR Before improvments X LGR

7 Level 2-Depth mapping: model control and application Statistical results (Cross validation) after improvments: 9 cm for RMSE Accuracy compatible with hydrological purposes Better statistical results with MS: 2.4 m [Damis, 2007] Depth prediction along 45 km river + mask of ambiguous areas Level 3: Clustering from widths-depths grids 1. Z information: Depth estimation maps Associated to Mask of ambiguous areas 2. X,Y information : Changes on widths : enlargments, Width upstream-downstream Gradient : flow convergence index Flow Convergence Index Clustering to s and pools? 7

8 Level 3: Hydro-morphology segmentation AHC Clustering in 7 classes Cluster 1 Mean Depth ++ Width gradient Type ambiguous areas [Clusters] [2005 reference] Same rythms of pools / s alternance to 2005 ground survey: Mean width : 42 m Mean distance between s : 490 m 480 = 42 * 11.7 Conclusion Goals of the study: Depth mapping: accuracy ~1 dm. Limited to very shallow and clea waters (~up to secchi depth) Combinaison of riverbed planimetric shape parameters and depth estimation method is more informative All obtained from VHRS multispectral satellite imagery MS: 2.4 m, good compromise Advantages : Radiometric homogeneity in satellite imagery permits to study whole river system Method using satellite imagery is a complementary solution to alternative technics (SoNAR,LiDAR) suitable for deeper waters but not for very shallow waters. Limits and future works: Combinaison to diachronic images to reduce calibration set of points Improvments in geometric approximations (width estimate...) Better results expected with end-winter image (depth prediction) 8

9 Publications Student reports: Reyes-Castillo S., 2007, Segmentation morphologique de rivière par imagerie satellite THRS, master of Ecology, Montpellier university Damis J., 2007, Bathymétrie à partir d images de télédétection sur rivière : Application à la Durance,Polytech Master STE, Montpellier university Seminars, Symposium: European Geophysical Union Morphologic segmentation of rivers from satellite high spatial resolution multi-spectral images, Bailly, J.S.; Puech, C. ; Le Coarer, Y. ; Reyes-Castillo S.,EGU, Rivers and Remote sensing session, Vienna, 2008 Performances comparison of bathymetry on rivers from various visible high resolution images, Damis, J.; Delenne, C.; Bailly, J.S.; Puech, C., EGU, Rivers and Remote sensing session, Vienna, 2008 Article: Morphologic segmentation of rivers from satellite high spatial resolution multi-spectral images, Bailly, J.S.; Puech, C. ; Le Coarer, Y. ; Reyes-Castillo S.,,Submitted to Earth, Processes and Landforms Thanks for attention! 9