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Tyler A - UEI Day 1 - Kochi Jan18

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Tyler A - UEI Day 1 - Kochi Jan18

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Tyler A - UEI Day 1 - Kochi Jan18

  1. 1. Exploiting EO capability to monitor the water quality status and change in freshwater environments Andrew Tyler & Vagelis Spyrakos Expert Toolkit Improving freshwater monitoring frameworks and data for research and management
  2. 2. Overview • The Development of the Science • Overview of the State of the Art: GloboLakes • Example data from Lake Vembanad • Outcomes from the IUKWC workshop Stirling: Enhancing Freshwater Monitoring through Earth Observation • Opportunities and Considerations IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  3. 3. Inspired by opportunity and need… Lake Balaton, Hungary Landsat 7 Need to monitor for management, protection and resilience Recognition of the spatial and temporal heterogeneity Tendency for reactive monitoring Scale of the problem Algorithm stability Challenges of optically complex waters Growing capacity and capability of satellite platforms Tyler et al., 2006, IJRS
  4. 4. Airborne Hyperspectral: PC retrieval Hunter, et al. (2008). RSE Hunter, et al. (2008). Limnol. Ocean Hunter, et al. (2009). Envi. Sci. Tech. NERC ARSF: AISA Eagle and Hawk IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  5. 5. MERIS in inland waters Envisat: MERIS IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  6. 6. Elterwater Lat: 54.4273 / Long: -3.0230 World View-2: Water optical type classification Turbid NIR-red ratio R754/R659 Clear green-blue ratio R546/R478 In-water algorithm Mapped Level-2 Chla Hunter and Tyler (2012) EA Report Inner basin 48.4 mg Chla m-3 Middle basin 13.1 mg Chla m-3 Outer basin 1.00 Chla mg m-3 Loughrigg Tarn STIR: 27.8 mg m-3 EA: 27.2 mg m-3
  7. 7. Inner basin 48.4 mg Chla m-3 Middle basin 13.1 mg Chla m-3 Outer basin 1.00 Chla mg m-3 Loughrigg Tarn STIR: 27.8 mg m-3 EA: 27.2 mg m-3 IS R(608) > 0.04 YES: Chla ~ R(754)/R(659) NO: Chla ~ R(546)/R(478) Hunter and Tyler et al. (2012) EA Report Report With Citizen Science Based Validation
  8. 8. EO Challenges – Global Scale EO key challenges:  Diversity in retrieval algorithms & validation approaches  Inland water remote sensing community appears fragmented Search Keys: remote sensing, water quality, lakes (2015) Filter: use of in-situ data for development/validation Number of lakes per publication: IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  9. 9. Our approach Lake ecology & modelling Global lakes observatory for 1000 study lakes Environmental statistics EO lake water quality EO + modelled catchment drivers EO lake water temperature Change over time (within / between lakes) Attributing drivers of environmental change Time-series data & web visualization IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  10. 10. A Global Partnership: LIMNADES www.limnades.org o data from almost 1500 inland systems o radiometric data ~4000 stations >250 lakes o at least 40 peer-reviewed papers IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management Parameter Units Range Median Lakes Chla mg m-3 0.03-13296.70 12.34 208 PC mg m-3 0 – 24677 28.79 60 TSM mg L-1 0.09-2533.30 10.54 81
  11. 11. OWT classification • ~4,000 Rrs spectra • K-means clustering • Optimum number of clusters determined statistically Spyrakos et al.,2017, Limnol. Ocean. IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  12. 12. OWT classification Lake Balaton, HungaryTONLÉ SAP Lake, Cambodia Spyrakos et al., submitted …. IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  13. 13. Algorithm validation Step 1: Validation of original algorithms Step 2: OWT cluster-wise algorithm tuning Step 3: Algorithm selection per cluster or cluster family LIMNADES in situ MERIS matchups Optical Water Types IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  14. 14. In situ algorithm validation Y=0.8313x + 0.2197 R2=0.7969 MAPE=92.96 Dynamic algorithm selection per cluster Neil et al., in review, RSE IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  15. 15. POLYMER C2R Lakes GloboLakes OWT optical water types [LIMNADES 2016] Reflectance algorithms algorithm mapping algorithm blending chlorophyll-a, suspended matter, phycocyanin passes database S-3 [SAFE] MERIS [FSG/SAFE] ESA catalogue Discover Download Ingest Subset Idepix MPH Masking RGB Quality flags, surface properties, pixel statistics 1-d aggregate 1-w aggregate 1-m aggregate GloboLakes, v1.04 ‘level 1B’ data ‘level 2’ processing ‘level 3’ mapping & aggregation Architecture and performance IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  16. 16. Operationalisation IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  17. 17. Operationalisation IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management https://globolakes.eofrom.space
  18. 18. Operationalisation IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management https://globolakes.eofrom.space
  19. 19. Operationalisation IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management TUNGABHADRA RESERVOIR https://globolakes.eofrom.space
  20. 20. Operationalisation: ESA Ocean Colour CCI Portal IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management Monthly time series over box in northern Tanganyika Data at 4-km but 1-km daily also demonstrated Monthly time series over box in northern Tanganyika https://www.oceancolour.org/portal/
  21. 21. Vembanad Lake: Kerala IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management Setinel 2 B Christmas Day 2017 C2RCC neural network for Sentinel 2 and 3 Brockmann, C & Doerffer, R. (2016).. Proc. Living Planet Symposium, ESA SP-470.
  22. 22. Vembanad Lake: Kerala IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management Setinel 3 B Christmas Day 2017 Brockmann, C & Doerffer, R. (2016).. Proc. Living Planet Symposium, ESA SP-470. C2RCC neural network for Sentinel 2 and 3 Brockmann, C & Doerffer, R. (2016).. Proc. Living Planet Symposium, ESA SP-470.
  23. 23. Existing Projects EU HORIZON 2020- SC5 2016-2017 (2018-2022) Multiscale Observation Networks for Optical monitoring of Coastal waters Lakes and Estuaries IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management Existing Projects: Bridging the gap
  24. 24. Hyperspectral Drones • Shallow waters, indented coastlines, narrow channels make access difficult • Develop a hyperspectral sensor mounted on a quad-copter, to study intermediate scales between satellites and in situ observations • Components are available in the market. IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management Other Platforms In-Situ – WISP-3
  25. 25. Near-real time monitoring of reservoir storage using satellite observations in India Vimal Mishra and Amardeep Tiwari Indian Institute of Technology (IIT) Gandhinar Near real time reservoir storage IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  26. 26. Near real time reservoir storage IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management Figure 1.Changes in groundwater storage from observation well and GRACE data during 2002-2013. Monthly trends in groundwater anomaly are from GRACE (in cm/year) and in-situ well observations from the CGWB for 2002-2013. Stippling in A-D indicates statistically significant changes at 5% level. (E-H) Wells that experienced significant declines and increases in groundwater levels (cm/year) during 2002–2013.. Asoka et al. 2017, Nature Geoscience
  27. 27. Reservoir storage area IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management Reservoirs  Dantiw ada Hirakund Rengali Supa Tungabhadra Date 15-Oct- 15 23-Oct-15 30-Sep-15 24-Sep-15 19-Oct-15 LANDSAT Area (km2) 38.5029 710.1774 301.959 84.4560 298.4139 MODIS Area (km2) 32.6875 625.3750 262.9375 94.9375 290.6250 Percentage Error (%) 15.10 11.94 12.92 12.41 2.61
  28. 28. Reservoir storage height IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management • Reservoir surface area were estimated using K-means classification of EVI images followed by image enhancement. • Reservoir water surface elevation estimated from NASA ICESat/GLAS datasets. • Water surface area and water surface elevation are combined to calculate area elevation relationship for a reservoir • Using depth-area relationship, reservoir storage was estimated in near-real time
  29. 29. Reservoir storage with rainfall IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  30. 30. Drought Monitoring System IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management Linking reservoir information to drought monitoring system https://sites.google.com/a/iitgn.ac.in/high_resolution_south_asia_drought_monitor/home South Asia Drought Monitoring Ending on 18/01/2018
  31. 31. Terrestrial Water Storage (TWS) Helen Bonsor, Alan MacDonald, Murray Lark, Kay Smith, Laurent Longuevergne Application of GRACE data to understand groundwater resources – examples from Africa and SE Asia IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  32. 32. Total water storage from Grace (CSR release 04) Rainfall data from NASA TRMM Soil Moisture from NOAH land model of GLDAS Statistical approach – empirical temporal variograms, linear mixed models Data Sets Used IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  33. 33. Global/Continental TWS Models IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management
  34. 34. SummaryIUKWC EO Workshop Summary I IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management List of variables that EO could monitor in relation to SDG6
  35. 35. SummaryIUKWC EO Workshop Summary II IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management • Problems of Water Quality and Quantity are multidimensional • Need for data on water quality and quantity from EO • EO could make a real difference by delivering freely available data to promote data democracy. • Need to grow capacity and capability to exploit EO capability • two way exchange of knowledge and skills, PhDs • promoting the development of validation sites • EO used to understand water use and: • promote water use efficiency • resolve tensions and conflicts in water use • improve surface water quality to reduce pressures on groundwater • Input to models to derive conventional parameters of water quality such as nutrients and pathogens. • Citizen science based observatories to promote data validation and environmental stewardship
  36. 36. SummaryIUKWC EO Workshop Summary III IUKWC-Kochi January 2018: Improving freshwater monitoring frameworks and data for research and management • Integrate across systems to: • Cross-calibrate across platforms to provide consistent product for freshwater monitoring (Satellite, UAV- Drone, In-situ and Citizen – smartphone) • Couple satellites with in-situ capability to deliver higher frequency data to fill data gaps and characterise the changing phenology • Link terrestrial/catchment processes to water, groundwater to surface water, headwaters to coastal waters to understand the water continuum • Deliver a complete understanding of the pressures on water resources and the consequences of policy and management decisions • Promote interdisciplinarity • Promote interaction between Universities, Research Institutions, Business and Industry, NGOs, Governance and Society for develop sustainable futures
  37. 37. Thank you! Andrew Tyler Professor of Environmental Monitoring Associate Dean for Research Faculty of Natural Sciences Biological and Environmental Sciences University of Stirling t +44 1786 467838 e a.n.tyler@stir.ac.uk w www.stir.ac.uk w www.globolakes.ac.uk w www.limnades.org follow @globolakes Acknowledgements Natural Environment Research Council UK ESA Diversity II (Brockmann Consult) Over 30 data contributors around the world H2020 funding: EOMORES, CoastObs, Monocle, DANUBIUS-RI

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