remote sensing for water resource management
TRANSCRIPT
Natascha Oppelt
Kiel University
Department for Geography
Remote Sensing for Water Resource Management
Natascha Oppelt
Kiel University
Department of Geography
Ludewig-Meyn-Str 14
24098 Kiel
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).
What is Remote Sensing?
Electromagnetic Radiation
vc
c = speed of light, 3 * 108 [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
Sources of Electromagnetic Radiation
Stefan-Boltzmann law
𝑀𝜆 = 𝜎𝑇4
M = Total emitted radiation [W m-2] = Stefan-Boltzmann constant, 5.6697 * 10-8 [W m-2 K-4]
T = Temperature [K]
Jensen. Remote Sensing of the Environment. Prentice Hall, 2009
Sources of Electromagnetic Radiation
Jensen 2009
Wien´s displacement law
𝑚𝑎𝑥 =𝑎𝑇
max = Wavelength with max emitted energy [µm]
a = constant, 2897.8 [ µm K] T = temperature [K]
Sensors in Space
(image source: NASA)
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. 2013. The use of ocean colour data to estimate chl-a trends in European Seas. Int J Geosci 4:927-949)
In Situ or Remote Sensing Data?
In situ measurements
• punctual, no defined extent
• representative?
• accurate?
Remote sensing
• integral measurement of defined area
• accurate?
In situ data = evidence?
Keep in mind that this reference is
inaccurate!
(Data from Coppini et al. 2013. The use of ocean colour data to estimate chl-a trends in European
Seas. Int J Geosci 4:927-949)
R²=0.53 Bias = 1.10 [mg m-3] RMSE = 4.46 [mg m-3]
RS at Different Geographic Scales
local regional continental regional/national
SR = 0.5 m SR = 30 m SR = 1500 m SR = 5000 m SR = 30 m
hemispherical
RS at Different Geographic Scales
Global coverage requires mosaicking of
images acquired in one orbital cycle
global
(Campbell. Introduction to Remote Sensing. Taylor & Francis 1996)
Why is Remote Sensing Important?
(Jensen. 2009.)
Different Sensors for Different Scales
(Hurrican Fran 1996, image source: NASA)
(Glaser, A. 2007. Satellite Imagery. Princeton )
Why is Remote Sensing Important?
Land Use and its Change
Deep water Shallow water Wetland Vegetation Agriculture Sand dunes
Deep water Shallow water Wetland Vegetation Agriculture Sand dunes
1992 2000
Change Detection & Hazard Management
Why is Remote Sensing Important?
Radiation Beyond our Visual Perception
Near-infrared (700 – 1100 nm) Natural color (400 – 700 nm)
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
The Electromagnetic Spectrum
(Modified from Albertz. 2007. Remote sensing. Springer)
(Modified from Albertz. Einführung in die Fernerkundung. 2000)
hchQ
Q = Radiation intensity [J]
h = Planck constant 6.626 * 10-34 [Js] v = frequency [s-1] = wavelength [µm]
Textbook Spectral Reflectances
(RSACL 2000)
Reflectance = The part of incoming radiation reflected by the Earth‘s surface [%]
= MIR
Reflectance
depends on wavelength!!!!
1 2 3 4 5 7
Wavelength [µm]
Re
fle
cta
nce [
%]
Spectral Bands
dry soil
vegetation
water
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)
Spectral Bands
Band 1
Band 2 Band 3
Band 7
Band 4 Band 5
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
Colour Composites
Spectral vs. Spatial Resolution
0
10
20
30
40
50
400 450 500 550 600 650 700 750 800 850 900 950 1000 1050 1100 Landsa
t TM
CH
RIS
AVIS
Fie
ld
Inst
rum
ent
ASD 0.2 m
30 m
2m
17 m
Applications
(modified from Oppelt et al. 2015. Fundamentals of remote sensing. NASA handbook of remote sensing. Taylor & Francis)
Remote Sensing for Water Resource Management
RS Products: Precipitation
(FAO. 2000. Sahel weather and crop situation report. GIEWIS Sahel Report 4.)
RS Products: Land Use / Land Cover
(Murawski. 2014. Sustainable development in the peri-urban regions of Chennai. Study project.)
(Oppelt et al. 2012. Întegration of land use data into the SWAT model. ESA SP 707)
RS Products: Soil Moisture
Soil Moisture from ERS [Vol %]
<10 12.5 17.5 22.5 27.5 32.5 42.5 37.5 47.5 >50
Pre
cipitat
ion
Apri
l 2
9-3
0 [
mm
] Bremen Soltau Lüchow
Diepholz Gardelegen
Osnabrück
Celle
Hannover
Braunschweig
Hildesheim Salzgitter
Bad Salzuflen
Brocken
3
0
(Oppelt et al. 1998; Schneider & Oppelt 1998)
Difference
0 2 8 6 4 >9 2 4 >5 dry wet
Difference [Vol %]
April 30 May 1st
RS Products: Soil Moisture
SMOS soil moisture map covering the period 8-15 June 2010 (resolution 50km)
RS Products: Vegetation Indices
(Atzberger et al. 2014. Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series. Remote Sensing 6(1):257-284)
RS Products: Water Quality
(Zhang et al. 2014. A spectral decomposiiton algorithm for estimating chl-a concentrations in Lake Taih, China. Remote Sensing 6(6):5090-5106)
RS Products: Water Quality
(Photographs: M. Liekefett)
RS Products: Water Quality
(Liekefett. 2015. Verwendung von Landsat 8 OLI Daten zur Modellierung von Wasserinhaltsstoffen im Kummerower See. Master thesis)
(Doernhoefer et al. 2015. Mapping fresh water macrophytes and shallow water bathymetry. ESA Water Mapping Workshop.)
Some Global RS Services
Parameter Product Spatial
resolution
Spatial
coverage
Temporal
resolution Accuracy Distributer
Soil moisture ASCAT soil
moisture product 1/12,5/25/50 km
25°N - 75° N,
25° W - 45° E 36 hours
not yet
available
EUMETSAT
H-SAF
Evapotranspiration
ET 5.6 km -40° N - 40° N,
26° E - 78° E 30 min 20 %
EUMETSAT
H-SAF
MODIS ET
product
1,0 /5,0 km
0.05° global 1 day
not yet
available NASA
Land surface
temperature
MODIS land
surface
temperature
(LST)
1,0/5,0 km
0.05° global 1 day 1 K NASA
MSG-LST 5.6 km -81° N – 81°N,
79° W – 79°E 15 min 2 K
EUMETSAT
H-SAF
Precipitation
Accumulated
precipitation at
ground
30 km 25°N - 75° N,
25° W - 45° E 3 hours 40 %
EUMETSAT
H-SAF
Snow cover
MSG snow cover
(SC) 5.6 km
-40° N -40° N,
26° E - 78° E 1 day
falsche
Zuordnung <
3 %
EUMETSAT
H-SAF
MODIS snow
cover
0,5/ 1 / 5 km
0.05° global 1 day
93 % for the
5.5 km
Product
NASA
Conclusion