WETLAND-RADAR Wetland vegetation mapping using X- and C- band SAR time series data

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1 Funded by DLR within the joint C/X Band Initiative of CSA & DLR (FKZ 50EE1511, FKZ 50EE1512) WETLAND-RADAR Wetland vegetation mapping using X- and C- band SAR time series data Symposium Neue Perspektiven der Erdbeobachtung, Köln Tanja Riedel, Christiane Schmullius Friedrich-Schiller-Universität Jena Achim Roth, Christian Wohlfart DLR-DFD Kathrin Weise, Rene Höfer Jena Optronik GmbH

2 Overview Background SWOS Products, software, SDGs Wetland Radar Test sites and EO data Main objectives Algorithm development for Water extent mapping LULC mapping Conclusion TS-X VV / VH - span Peace Athabasca Delta, Canada / /

3 Wetland Ecosystem services Wetlands are the most fragile and threatened ecosystems 64% of wetlands lost since % of freshwater plants and animals disappeared in the last 40 years (WWF Living Planet report) Wetlands give us ecosystem services for free! Water supply & purification Erosion control Flood and drought risk reduction Food supply Recreation areas Climate change mitigation / Carbon sequestration 3

4 Short-term / long-term changes (Landsat-8) (Sentinel-2) (Sentinel-2) 4 The temporal scale: Monitoring of short term changes / wetland characteristics (Azraq Oasis, Jordan) 1972 (Landsat MSS) 1988 (Landsat TM) 2005 (Landsat TM) The temporal scale: Monitoring of long term changes / decreasing water table (Dead Sea, Jordan/Israel) 4

5 Long-term changes 5

6 Short-term changes 6

7 Peatland Fires in Russia, Tver After extensive peat and forest fires in 2010, affecting especially Moscow region, Russia has rewetted 65,000 ha of drained peatlands in the Moscow region (involving 100million Euro) to prevent further fires. 7

8 Short-term changes Russia, Tundra, Nenets Autonomous Okrug The Nenets Autonomous Okrug (NAO) and the Komi Republic contains the main part of frozen or permafrost peatlands in north-east Russia, these areas are considered to be the key carbon pool of the globe Melting Process, R: , G: , B: , VV polarization, descending orbit ( Contains modified Copernicus Sentinel - data 2017) 8

9 SWOS is a service Maps and indicators production Software delivery Training/Capacity Building Portal (Middleware/Data broker/clients) Service components Service demonstration via Multilevel Service cases This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No

10 SWOS service lines Map and indicator production Maps: Land Use Land Cover Land Use Land Cover Change Surface temperature Surface water dynamics Inventory and delineation Surface Moisture Water quality GEOclassifier Indicators, derived from maps: 1. Total wetlands extent 2. Change in wetland area 3. Change to Agriculture & Urbanization 4. Wetlands artificialization and degradation 5. Status of Wetland Threats 6. Extent of Open Water 7. Status and Trend of Water Quality 8. Ecosystem Fragmentation 9. Wetland Ecosystem Services Sub-indicators for Indicator 1: 1.0 Total Wetlands extent 1.1 Natural wetland extent 1.2 Artificial wetland extent 1.3 Vegetated Wetlands Extent (SDG 6.6.1) 1.4 Open Water Bodies (SDG 6.6.1) 1.5 River Water Bodies (SDG 6.6.1) 1.6 selected classes area 10 10

11 SWOS Toolbox - GEOclassifier Software delivery 11

12 SWOS Portal - Products Portal (Middleware/Data broker/clients) 12

13 SWOS Portal - Products Portal (Middleware/Data broker/clients) 13

14 SWOS Portal - Products Portal (Middleware/Data broker/clients) 14

15 SWOS and SDGs

16 Wetland Radar - Test Sites Wetland Radar test sites Lake Starnberg, Germany RS-2 span / / Lake Neusiedl, Austria RS-2 span / / Peace Athabasca Delta, Canada TSX VV/VH span / / Azraq Oasis, Jordan RS-2 VV / / Mer Bleue, Canada TSX HH/VV span / / Lake Burullus, Egypt RS-2 span / /

17 EO data processing EO-data: TSX, RS-2 & S1 timeseries preprocessing by DLR-DFD Ground Segment User Kennaugh Decomposition k 0, k 1, TerraSAR-X RADARSAT-1/2 Sentinel-1 DEM-DB w42 SAR Ortho-rectification β 0 (radar brightness) Calibration σ 0 (incidence angle corrected) Image enhancement (multi-scale multilooking) Classification Pickup Point ALOS PALSAR 1/2 ERS-1/2, ASAR Other decompositions eg. Freeman Durden 17

18 EO data processing Kennaugh elements for TS-X HH/VV data, Lake Neusiedl, Austria, k0 span k3 difference double bounce and surface scattering k4 difference horizontal and vertical reflection k7 phase difference double bounce and surface scattering 18

19 Water extent mapping SAR parameters Co-pol HV High contrast to other land surfaces But: affected by wind, rain etc. (X-band>C-band, VV>HH) Advantage over co-pol: not affected by wind, rain etc. Seasonal surface water is characterized by high mean annual variability Not applicable for smooth surfaces, e.g. sand Methodology: consideration of both co- and cross-polarization 19

20 Water extent mapping Co-pol: common problem: increase in radar backscatter due to wind / rain effects Kennaugh element k7 - phase difference double bounce and surface scattering For all test sites/acquisitions: no impact of rain / wind But: not applicable for small water areas Test site Lake Neusiedl Test site Peace Athabasca Delta TSX span HH/VV TSX k7 HH/VV TSX span HH/VV TSX k7 HH/VV

21 Water extent mapping Common problem in arid / semi-arid regions: misclassification of water and smooth bare surfaces test sites Lake Burullus and Azraq Additional usage of coherence information Test site Lake Burullus, Egypt S1 multitemp. Mean VV S1 coherence median

22 Water extent mapping Potential of coherence information to map water extent changes in arid regions S1 VV S1 coherence / S2A S1 HV MNDWI S1 VV S1 coherence / LS8 OLI MNDWI

23 Water extent mapping Methodology Processed SAR data - Intensities & Kennaugh elements - Isodata-Clustering MMIN co-pol, cross Segmentation GeoClassifier Toolbox Intensities MMIN, MMEAN, MAX cross Class assignment and object statistics: class majority & class fraction Regions covered by bare soil/rocks: SAR coherence - Thresholding - Thresholding MMAX cross, co-pol Permanent water Maximum water extent map (count gt 50%) Time series analysis Cross, arid regions: co-pol + coherence Water map for each acquisition Neighbourhood analysis Reclassification of island polygons Availability of co-pol only Check for changes in k7 (not affected by wind / rain Final water map for each acquisition date 23

24 Water extent mapping Results Azraq, S1-data Note: S1 and optical data different acquisition days 24

25 Water extent mapping Results Lake Neusiedl, RS-2 Daten RS-2 water maps for different acquisition days change April - July change July - September MNDWI of optical data 25

26 LULC mapping - methodology SAR parameters class-specific, characteristic, stable Processed SAR data Intensities + Kennaugh elements Segmentation GeoClassifier Toolbox Intensities multitemp. mean Isodata-Clustering Class-specific SAR parameters Class assignment Cluster statistics Class 1 n Object statistics - Class majority - Majority count Initial classification result Neighbourhood analysis Reclassification of island polygons Final LULC map Optional: Potential training samples 26

27 LULC mapping Definition of stable and transferable SAR parameters example: urban areas Parameter: SAR coherence Texture 5x5 window: mean difference of central pixel and all neighbouring pixels with distance of 2 best results for k4, alternative: HH or VV (depending on data availability) RS-2 K4 mean difference for Lake Burullus (left), Lake Neusiedl (middle) and Azraq oasis (right) 27

28 LULC mapping Definition of stabe and transferable SAR parameters Example: reed mapping Timing: early spring / growing season (snow and ice-free) Parameter: k0 (exclusion of small open water stands within reed belt), k4, k7, coherence (misclassification with urban areas) Mer Bleue, TSX, k4 Lake Neusiedl, RS k7 Lake Burullus, RS k7 28

29 Reed monitoring / stand characterization Potential for detection of young, emerging plants - SAR vs. optical data LS8 OLI NIR / R / G TSX k3 mean summer TSX k

30 LULC mapping Example: reed mapping RS-2 data Left: Lake Neusiedl, right: Lake Burullus 30

31 LULC mapping Example LULC map and potential training samples - Lake Neusiedl RS-2 31

32 Conclusions / Summary Processing chain for SAR-based monitoring of water extent and LULC Applicable for S1, RS-2 and TSX data Implementation in GeoClassifier Toolbox (in progess) Integrated in the SWOS Project Kennaugh elements - important information for wetland monitoring, especially Reed mapping Water mapping - k7 not affected by wind / rain SAR data - advantage over optical data: detection of young, emerging reed stands with low density 32

33 Thanks!! For more information, contact Friedrich Schiller Universität Jena Jena-Optronik GmbH phone +49 (0)

34 Reed monitoring / stand characterization TSX 2016 MMEAN summer k0 / k3 / k7 Open water Dense, vital green reed (pink) Vital reed, slight less density (purple) Sparse reed (yellow) Heavily sparse reed (green) 34