Remote Sensing for Water Resource Management

Transcription

1 Remote Sensing for Water Resource Management Natascha Oppelt Kiel University Department of Geography Kiel University Ludewig-Meyn-Str 14 Department for Geography Kiel

2 Why Collecting Data? One goal of resource management is to protect the environment and improve human quality of life. Gain knowledge about previous/current status and underlying processes. Observations and measurements: the physical world (e.g. atmosphere, water, soil, rock), its living inhabitants (e.g. humans, flora, fauna), the processes at work (e.g. cycles of matter, erosion, deforestation, flooding, urban sprawl).

3 What is Remote Sensing?

4 Electromagnetic Radiation c v c = speed of light, 3 * 10 8 [m s -1 ] = wavelength [µm] = frequency [s -1 ] (Campbell 2006) Note that frequency is inversely proportional to wavelength. The longer the wavelength, the lower the frequency, and vice-versa

5 Sources of Electromagnetic Radiation Stefan-Boltzmann law M λ = σt 4 M = Total emitted radiation [W m -2 ] = Stefan-Boltzmann constant, * 10-8 [W m -2 K -4 ] T = Temperature [K] Jensen. Remote Sensing of the Environment. Prentice Hall, 2009

6 Sources of Electromagnetic Radiation Wien s displacement law max = a T max = Wavelength with max emitted energy [µm] a = constant, [ µm K] T = temperature [K] Jensen 2009

7 Sensors in Space (image source: NASA)

8 In Situ or Remote Sensing Data? Summer (May-Sept) chlorophyll a concentrations in European seas from in situ data Summer (May-Sept) chlorophyll a concentrations in European seas from SeaWIFS data (Data from Coppini et al The use of ocean colour data to estimate chl-a trends in European Seas. Int J Geosci 4: )

9 In Situ or Remote Sensing Data? In situ measurements punctual, no defined extent representative? accurate? Remote sensing integral measurement of defined area accurate? R²=0.53 Bias = 1.10 [mg m -3 ] RMSE = 4.46 [mg m -3 ] In situ data = evidence? Keep in mind that this reference is inaccurate! (Data from Coppini et al The use of ocean colour data to estimate chl-a trends in European Seas. Int J Geosci 4: )

10 RS at Different Geographic Scales local regional SR = 0.5 m SR = 30 m regional/national SR = 30 m SR = 1500 m continental SR = 5000 m hemispherical

11 RS at Different Geographic Scales global Global coverage requires mosaicking of images acquired in one orbital cycle (Campbell. Introduction to Remote

12 Why is Remote Sensing Important? (Jensen )

13 Different Sensors for Different Scales (Hurrican Fran 1996, image source: NASA) (Glaser, A Satellite Imagery. Princeton )

14 Why is Remote Sensing Important?

15 Land Use and its Change Deep water Shallow water Wetland Vegetation Agriculture Sand dunes Deep water Shallow water Wetland Vegetation Agriculture Sand dunes

16 Change Detection & Hazard Management

17 Why is Remote Sensing Important?

18 Radiation Beyond our Visual Perception Natural color ( nm) Near-infrared ( nm)

19 Why Differ Satellite Images from Photographs? Landsat TM 5 images of central China: true colour image using TM bands 3,2 and 1 Landsat TM 5 image of central China: false colour image using TM bands 4,3 and 2 RS sensors can collect electromagnetic radiation which humans cannot see

20 The Electromagnetic Spectrum Q h hc Q = Radiation intensity [J] h = Planck constant * [Js] v = frequency [s -1 ] = wavelength [µm] (Modified from Albertz. (Modified from Albertz. Remote sensing. Einführung Springer) in die Fernerkundung. 2000)

21 Textbook Spectral Reflectances Reflectance = The part of incoming radiation reflected by the Earth s surface [%] = MIR Reflectance depends on wavelength!!!! (RSACL 2000)

22 Reflectance [%] Spectral Bands dry soil vegetation water Wavelength [µm] The above sensor provides six bands in the solar domain All bands obtained at same time and at exact same location All pixel have same spatial resolution (at least with most sensors) Spectral resolution (how many bands covering which wavelengths)

23 Spectral Bands If a single band is displayed on the monitor it appears in grey values Bright pixel represent areas where a lot of radiation is being reflected in that particular band Band 1 Band 7 Band 2 Band 3 Band 4 Band 5

24 Colour Composites

25 Field Instrument AVIS CHRIS Landsat TM Spectral vs. Spatial Resolution m 17 m 2m ASD 0.2 m

26 Applications (modified from Oppelt et al Fundamentals of remote sensing. NASA handbook of remote sensing. Taylor & Francis)

27 Remote Sensing for Water Resource Management

28 RS Products: Precipitation (FAO Sahel weather and crop situation report. GIEWIS Sahel Report 4.)

29 RS Products: Land Use / Land Cover (Oppelt et al Întegration of land use data into the SWAT model. ESA SP 707) (Murawski Sustainable development in the peri-urban regions of Chennai. Study project.)

30 Precipitation April [mm] RS Products: Soil Moisture Bremen Soltau April 30 Lüchow May 1st Difference 3 Diepholz Celle Gardelegen Hannover Osnabrück Braunschweig Hildesheim Salzgitter 0 Bad Salzuflen Brocken < >50 Soil Moisture from ERS [Vol %] dry wet > >5 Difference [Vol %] (Oppelt et al. 1998; Schneider & Oppelt 1998)

31 RS Products: Soil Moisture SMOS soil moisture map covering the period 8-15 June 2010 (resolution 50km)

32 RS Products: Vegetation Indices (Atzberger et al Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series. Remote Sensing 6(1): )

33 RS Products: Water Quality (Zhang et al A spectral decomposiiton algorithm for estimating chl-a concentrations in Lake Taih, China. Remote Sensing 6(6): )

34 RS Products: Water Quality (Photographs: M. Liekefett)

35 RS Products: Water Quality (Liekefett Verwendung von Landsat 8 OLI Daten zur Modellierung von Wasserinhaltsstoffen im Kummerower See. Master thesis) (Doernhoefer et al Mapping fresh water macrophytes and shallow water bathymetry. ESA Water Mapping Workshop.)

36 Some Global RS Services Parameter Soil moisture Evapotranspiration Land surface temperature Precipitation Snow cover Product ASCAT soil moisture product ET MODIS ET product MODIS land surface temperature (LST) MSG-LST Accumulated precipitation at ground MSG snow cover (SC) MODIS snow cover Spatial resolution 1/12,5/25/50 km 5.6 km 1,0 /5,0 km ,0/5,0 km km 30 km 5.6 km 0,5/ 1 / 5 km 0.05 Spatial coverage 25 N - 75 N, 25 W - 45 E -40 N - 40 N, 26 E - 78 E global Temporal resolution 36 hours Accuracy not yet available 30 min 20 % 1 day not yet available Distributer EUMETSAT H-SAF EUMETSAT H-SAF NASA global 1 day 1 K NASA -81 N 81 N, 79 W 79 E 25 N - 75 N, 25 W - 45 E -40 N -40 N, 26 E - 78 E global 15 min 2 K 3 hours 40 % 1 day 1 day falsche Zuordnung < 3 % 93 % for the 5.5 km Product EUMETSAT H-SAF EUMETSAT H-SAF EUMETSAT H-SAF NASA

37 Conclusion