WaMaPro a user friendly tool for water surface derivation from SAR data and further products derived from optical data

Transcription

1 WaMaPro a user friendly tool for water surface derivation from SAR data and further products derived from optical data J. Huth a, M. Ahrens a, I. Klein a, U. Gessner a, J. Hoffmann b, C. Kuenzer a a German Aerospace Center (DLR), Earth Observation Center (EOC), German Remote Sensing Data Center (DFD), Land Surface Department, Oberpfaffenhofen, Wessling, Germany. b German Aerospace Center (DLR), Space Agency, Bonn, Germany.

2 Overview WaMaPro introduction Implementation as a tool WaMaPro application in environmental research projects Further products related to water body mapping activities

3 Introduction -Slide 3

4 WaMaPro Aims and Goals WaMaPro = Water Mask Processor Original Ideas: Data analyses over time with a focus on thematic analyses Create a simple image processing tool possible to hand over to project partners and people working in developing countries (access to software) Target user group: environmental scientists, remote sensing beginners, etc. Data we process with WaMaPro: TerraSAR-X Envisat-ASAR Sentinel-1A other SAR data possible

5 WaMaPro Principles and Techniques B1 (water) Image Dilation (sesize) Input data (DN) Medianfilter (3x3) Threshold Definition (water and land threshold) Convert to binary images B2 (land) Comparison (B3 && B2) B1 Watermask Remove islands and lakes (island size, lake size) B5 Morphological Closing (fix value) B4 DN values 3x3 Median Filter (speckle effect) Threshold definition (water and land) B1 confident water: image dilation (growing with sesize) = B3 B2 confident land Comparison of B2 and B3 (buffer area of unconfident land/water e.g. more soils) = B4 Morphological Closing (grow+shrink) for edge smoothing with fixed value (as if sesize is1) Removing of lakes and islands with user given size B3

6 WaMaPro Principles and Techniques B1 - confident water B2 - confident land B3 - dilate B1 B4 water after buffer comparison

7 WaMaPro test cases Mekong Delta, Vietnam TSX WaMaPro - watermask Overlay

8 WaMaPro test cases Yellow River Delta, China TSX WaMaPro - watermask Overlay

9 WaMaPro test cases Mali, Africa TSX WaMaPro - watermask Overlay

10 MARTINIS, S., KUENZER, C., WENDLEDER, A., HUTH, J., TWELE, A., ROTH, A., DECH, S.: Comparing four different approaches for operational SAR-based water and flood detection. Submitted to International Journal of Remote Sensing WaMaPro test cases North sea coast - Watt, Netherlands Challenges: Wind artefacts on open ocean water; inland water shows good result TSX WaMaPro - watermask Overlay

11 Accuracy Assessment (e.g. Vietnam flood vs. dry season) Flood season Dry season Validation data of Jan Blue water 20 km

12 Accuracy Assessment percentage of val.points [%] Watermask Validation for m 1-2m 2-5m 5-8m >8m distance between measured and calculated land-water boundary [m] GPS points at the land-water boundary with simultaneuosly acquired TerraSAR-X data 25% of points located at the land-water boundary 55% within 1-8m distance of watermask to land-water boundary GPS accuracy 2-10m (DGPS not possible) Geometric accuracy of TSX data approx. 1 pixels 20% outliers (> 8m) source: WISDOM project Huth et al. (2009): Automated inundation monitoring using TerraSAR-X multi-temporal imagery. European Geosciences Union, EGU, General Assembly 2009, Apr. 2009, Vienna, Austria.

13 Implementation as a tool -Slide 13

14 WaMaPro open-source based tool implementation

15 WaMaPro open-source based tool implementation GUI

16 WaMaPro open-source based tool implementation result

17 -Slide 17 Application in environmental research projects

18 Slide 18

19 -Slide 19

20 KUENZER, C., GUO, H., LEINENKUGEL, L, HUTH, J., LI, X., and S. DECH, 2013: Flood mapping and flood dynamics of the Mekong Delta: An ENVISAT-ASAR-WSM based Time Series Analyses, Remote Sensing 5 (doi: /rs ), Slide 20

21 KUENZER, C., GUO, H., LEINENKUGEL, L, HUTH, J., LI, X., and S. DECH, 2013: Flood mapping and flood dynamics of the Mekong Delta: An ENVISAT-ASAR-WSM based Time Series Analyses, Remote Sensing 5 (doi: /rs ), Slide : 51 observations 10 per year in rainy season, ASAR, 150m

22 Mekong Delta - Rainy Season 2007 b a c b Gulf of c Thailand South China Sea N 45 km 0 15 Largest common coverage Coastline

23 Inundation mapping with TerraSAR-X vs. ASAR Kuenzer et al., (2013): Varying Scale and Capability of Envisat ASAR-WSM, TerraSAR-X Scansar and TerraSAR-X Stripmap Data to Assess Urban Flood Situations: A Case Study of the Mekong Delta in Can Tho Province. In: Remote Sensing

24 Zeitreihenuntersuchung Überflutung Coastal analyses Inundation frequency ASAR WSM data 150 m resolution -Slide 24

25 canal oil field Yellow River aquaculture wetland (reed) brine ponds TSX stripmap data 3 m resolution KUENZER, C., HUTH, J., MARTINIS, S., LU, L., DECH, S., 2015: SAR Time Series for the Analysis of Inundation Patterns in the Yellow River Delta, China. In: Kuenzer, C., Dech, S., Wagner, W. (eds.), 2015: Remote Sensing Time Series Analyses revealing Land Surface Dynamics. In print, will be published in April, Springer, The Netherlands

26 Inundation Frequency at East Dongting Lake Datasets: TSX stripmap 3m resolution scenes (Jan Dec) scenes (Jan Mar) - Yellow to orange flood prone - Blue permanent water bodies

27 Flood Mapping of Northern Namibia with Sentinel-1A

28 Publications related to WaMaPro - GSTAIGER, V., GEBHARDT, S., HUTH, J., WEHRMANN; T. and C. KUENZER, 2012: Multi-sensoral derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data. International Journal of Remote Sensing, Vol. 33, 22, , DOI: / KUENZER, C., GUO, H., LEINENKUGEL, L, HUTH, J., LI, X., and S. DECH, 2013: Flood mapping and flood dynamics of the Mekong Delta: An ENVISAT-ASAR-WSM based Time Series Analyses, Remote Sensing 5 (doi: /rs ), KUENZER, C., GUO, H., SCHLEGEL, I., VO, Q.T., LI, X., DECH, S., 2013: Scale and the Capability of Envisat ASAR-WSM, TerraSAR-X Scansar, and TerraSAR-X Stripmap Data to assess urban Flood Situations: A Case Study in Can Tho Province of the Mekong Delta, Remote Sensing, 5, ; doi: /rs KUENZER, C., HUTH, J., MARTINIS, S., LU, L., DECH, S., 2015: SAR Time Series for the Analysis of Inundation Patterns in the Yellow River Delta, China. In: Kuenzer, C., Dech, S., Wagner, W. (eds.), 2015: Remote Sensing Time Series Analyses revealing Land Surface Dynamics. In print, will be published in April, Springer, The Netherlands - MARTINIS, S., KUENZER, C., WENDLEDER, A., HUTH, J., TWELE, A., ROTH, A., DECH, S., 2015: Comparing four different approaches for operational SAR-based water and flood detection. Submitted to International Journal of Remote Sensing - HUTH, et al., 2015: Deriving Water Surfaces with WaMaPro Observations of Water Surface Dynamics of the Yellow River Delta, China. Accepted oral presentation at ISRSE36, Berlin

29 Ausbreitung von Aquakulturflächen

30 Global WaterPack -Slide 30

31 Processed Data for 2013

32 Used Data for Classification Ancillary Data Filter to remove misclassification Input MODIS daily data (09GQ) Terra + Aqua MODIS daily data (10A1) Terra + Aqua MODIS Water (MOD44W) DTM Workflow: water detection 1. Preprocessing 2. Water detection Intermediate result 3. Temporal scan and remove of clouds and no data NIR Thematic info: cloud, lake ice, ocean Static Water Mask (training areas) Calculates the NIR mean for individual tile within training areas (excluding NIR > 20%, cloud covered or no data pixel) Dynamic upper threshold for individual tile: mean + 2std DT classification Thematic product for each tile of Terra and Aqua: water, land, cloud*, lake ice* -----> combination of both for each day (*from 10A1 product) 1. Temporal filter to remove cloud shadows (logical scan of pixel within the temporal stack) 2. Temporal filter to remove clouds/no data and replace by values before/after the cloud Mosaiking, resampling and tiling to MODIS extent and 250-m resolution -> Slope calculation Slope to remove misclassification due to relief End result Cloud free product (water, land) for each day Water Cover Duration Confidence layer with amount of cloud per pixel Min & Max extent

33 Global WaterPack 2013

34 High intra-annual variability Lake Poyang (China) Lake Dongting (China)

35 Hydropower dams and water reservoirs Koksaray Reservoir American Falls Reservoir Shardara Reservoir DOY

36 Publications related to Global WaterPack - Klein, I., Dietz, A.J., Gessner, U., Galayeva, A., Myrzakhmetov, A., Kuenzer, C., Evaluation of seasonal water body extents in Central Asia over the past 27 years derived from mediumresolution remote sensing data. Int. J. Appl. Earth Obs. Geoinf. 26, Klein, I., Dietz, A., Gessner, U., Dech, S., Kuenzer, C Results of the Global WaterPack: a novel product to assess inland water body dynamics on a daily basis. In: Remote Sensing Letters (6), 78-87

37 Software to share: WaMaPro Tool contact:

38 Thank you very much for your attention!