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OTHER SATELLITE DATASETS 1.Ocean Biology 2.Vegetation Cover 3.Fire Counts.

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Presentation on theme: "OTHER SATELLITE DATASETS 1.Ocean Biology 2.Vegetation Cover 3.Fire Counts."— Presentation transcript:

1 OTHER SATELLITE DATASETS 1.Ocean Biology 2.Vegetation Cover 3.Fire Counts

2 OCEAN BIOLOGY FROM SPACE Chlorophyll is a marker of ocean productivity because phytoplankton need chlorophyll (and sunlight) to convert nutrients into plant material Plankton includes microscopic organisms as well as large organisms such as jellyfish Links to Atmospheric Composition: 1.Source of organics 2.Related to nutrient deposition?

3 OCEAN COLOUR Pure Water: deep-blue/black Productive Water: blue-green  chlorophyll absorbs red light, but reflects blue & yellow light = GREEN True Colour = light intensity at visible wavelengths

4 OCEAN COLOUR SENSORS MODIS: Moderate Resolution Imaging Spectrometer Terra (1999-) Aqua (2002-) SeaWifs: Sea-viewing Wide Field-of- view Sensor SeaCare (1997- CZCS: Coastal Zone Color Scanner Nimbus 7( )

5 RETRIEVAL OF CHLOROPHYLL Water-leaving reflectance 443 nm to 550 nm 1.“Atmospheric Correction”: remove atmospheric component to backscattered radiance (including Rayleigh and multiple-scattering from aerosol) 2.Convert water-leaving radiance to chlorophyll concentrations. * note correction for white caps can be complicated [Gordon et al., 1997] “Universal” relationship SeaWiFS uses 490nm & 555 nm CZSC wavelengths

6 SEAWIFS CHLOROPHYLL-A Annual mean ( ) The plankton populations are dependent on a variety of factors, including ocean currents, temperature, availability of nutrients, amount of sunlight, and ocean depth

7 SPRINGTIME CHLOROPHYLL (MODIS & SEAWIFS) Bloom in the Northern oceans is the result of cold winter waters which encourage mixing from the deep (and hence the presence of nutrients at the surface) + the springtime sunlight.

8 VEGETATION COVER Links to Atmospheric Composition: 1.Natural emission source 2.Biomass burning fuel load 3.Surface roughness (deposition) Instrument requirements include: spectral coverage (visible) and small footprint AVHRR MODISSPOT Examples:

9 NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) Plants absorb in the visible (PAR) and scatter in the NIR (where energy isn’t useful for organic synthesis) NDVI is a QUALITATIVE measure of vegetation density HEALTHYSPARSE

10 NDVI FROM DIFFERENT SENSORS 628 nm vs. 850 nm (broader bands)653 nm vs 865 nm

11 MAPS OF NDVI… AVHRR NDVI can also be used as an indicator for drought AUG 93

12 LEAF AREA INDEX (LAI) LAI=2.25 LAI=4.75 Soybean LAI is a QUANTITATIVE measure of vegetation density The relationship between LAI & NDVI is not unique (depends on veg/land type) LAI is related to primary production (PP)

13 LAI RETRIEVALS Retrieval (like ocean biology) involves removing an atmospheric component and then looking at the spectrally resolved light radiance using a LUT based on a canopy radiation model. In this case, biomes are assumed to constrain architecture of individual trees (transmission and reflectance of light through the canopy depends on this) [Myeni et al., 2002]

14 LAI FROM AVHRR ( ) ANIMATION: March 1991

15 CANADIAN VEGETATION COVER FROM SPOT-4

16 FIRE COUNTS Links to Atmospheric Composition: 1.LARGE emission source (often in remote regions) Fires smoulder (500K) or flame (1200K) with very strong IR emissions

17 CONTEXTUAL ALGORITHMS FOR FIRE DETECTION Compare pixel to its nearest neighbours (based on brightness temperatures in Channels 3 and 4 – 3.7  m and 10.8  m): Advantage: adaptive algorithm that doesn’t depend on the environment. Earlier threshold techniques would require (for example) different threshold of response over a cool forest vs. a dry savannah. As developed for AVHRR: 1.Identify pixels that MIGHT be a fire: T 3 > 311 K, T 3 -T 4 > 8 K 2.Remove pixels with high reflectance:  2 <20% 3.Compare pixel in question to its neighbours: Neighbours define a background (b) Fire identified when: T 3 -[T 3b +2  T3b ] > 3 K T 34 > T 34b +2  T34b [Flasse and Ceccato, 1997]

18 AVHRR FIRE DETECTION [Stroppiana et al., 2000; Dwyer et al., 2000]

19 MODIS FIRE DETECTION Based on similar principles used with AVHRR and uses similar wavelengths (4  m and 11  m).  There are two 4  m channels with different saturation T (331K and 500K).  Multiple additional channels are used for cloud masking, rejection of false alarms  Absolute thresholds are also applied T 4 >360K (320 K at night) [Giglio et al., 2003] September 4, 2001 [Justice et al., 2002] ANIMATION:

20 MODIS BURNED AREA PRODUCT Burned area is not necessarily directly proportional to fire counts. We would need to account for where the fire is burning and with what intensity.  Need to link the change in vegetation with fire activity  =effective fire area per fire pixel [Giglio et al., 2006] High uncertainties on these products, tough to validate.

21 FIRE RADIATIVE POWER FRP=The instantaneous rate of energy released from combustion (megawatts) Estimated from empirical relationship [Kaufman et al., 1998]: T 4 =4  m brightness T A pix =total area (km 2 ) of the pixel Integral over lifetime of fire (Fire Radiative Energy) should be proportional to total mass of fuel burned An example from geostationary SEVERI Feb 7, 2004 Over Angola in 2003

22 MODIS RAPID RESPONSE PROVIDES GLOBAL NRT FIRE PRODUCTS Rapid Response Imagery is 10 day composite of MODIS Terra & Aqua detected fire locations (daily data is available). 04/01/10 – 04/10/10


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