Lect_6_Veg

Lect_6_Veg - Remote Sensing of Vegetation Remote Sensing of...

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Unformatted text preview: Remote Sensing of Vegetation Remote Sensing of Vegetation Remote Spectral Spectral Characteristics Characteristics ERS-185 Lecture #6 ERS February 4 & 9, 2009 Litton Emerge Spatial, Inc., Litton Emerge Spatial, Inc., CIR image (RGB = CIR image (RGB = NIR,R,G) of Dunkirk, NY, at NIR,R,G) of Dunkirk, NY, at 11xx11m obtained on m obtained on December 12, 1998. December 12, 1998. Spectral Reflectance Curves of Selected Materials Spectral Reflectance Curves of Selected Materials Jensen, 2000 Jensen, 2000 ERS-185 Lecture #6 ERS February 4 & 9, 2009 Natural color image (RGB = Natural color image (RGB = RGB) of aaN.Y. Power RGB) of N.Y. Power Authority lake at 11xx11ft Authority lake at ft obtained on October 13, obtained on October 13, 1997. 1997. Spectral Reflectance Spectral Reflectance Spectral Characteristics of Characteristics of Sweetgum Leaves Sweetgum Leaves ((Liquidambar Liquidambar styraciflua L.) styraciflua L.) Jensen, 2000 Jensen, 2000 ERS-185 Lecture #6 ERS February 4 & 9, 2009 Chlorophyll b Chlorophyll a AbsorptionEfficiency Absorption Efficiency Absorption Spectra of Chlorophyll a and b lack of lack of absorption absorption Absorption Spectra of Absorption Spectra of Absorption Chlorophyll a and b,, Chlorophyll a and b β--carotene,Pycoerythrin, carotene, Pycoerythrin, β and Phycocyanin Pigments and Phycocyanin Pigments Chlorophyll a peak absorption is Chlorophyll a peak absorption is Chlorophyll at 0.43 and 0.66 μm. at 0.43 and 0.66 μm. Chlorophyll b peak absorption is Chlorophyll b peak absorption is at 0.45 and 0.65 μm. at 0.45 and 0.65 μm. 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 a. violet blue green yellow red Wavelength, μm Phycoerythrin Phycocyanin Absorption Efficiency Absorption Efficiency β-carotene Optimum chlorophyll absorption Optimum chlorophyll absorption windows are: windows are: 0.45 --0.52 μm and 0.63 --0.69 μm 0.45 0.52 μm and 0.63 0.69 μm 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.25 0.3 0.35 b. violet blue green yellow red Wavelength, μm Jensen, 2000 Jensen, 2000 Transm itted (0-40% of P AR) Ab sorbed (48-94% of P AR) PAR – Photosynthetically Active Radiation Reflected (6-12% of P AR) Heat (75-97% of Abs) Photochem istry (0-20% of Abs) Zarco-Tejada t al 2004 Zarco-Tejada eetal.,.,2004 Fluorescence (3-5% of Abs excitation energy) ERS-185 Lecture #6 ERS February 4 & 9, 2009 Three Paths of Leaf Light: Reflectance, Transmittance, and Absorption Three Paths of Leaf Light: Reflectance, Transmittance, andAbsorption Absorption Jensen, 2000 Jensen, 2000 Three Paths of Leaf Light: Reflectance, Transmittance, and Absorption Three Paths of Leaf Light: Reflectance, Transmittance, andAbsorption Absorption Jensen, 2000 Jensen, 2000 ERS-185 Lecture #6 ERS February 4 & 9, 2009 Three Paths of Leaf Light: Reflectance, Transmittance, and Absorption Three Paths of Leaf Light: Reflectance, Transmittance, andAbsorption Absorption Jensen, 2000 Jensen, 2000 0.5 0.4 Reflectance 0.3 Chl a +b =20 μg/cm 2 0.2 0.1 Chl a +b =80 μg/cm 0 400 Wavelength (nm) 2 800 Leaf-level spectral Leaf-level spectral measurements measurements ERS-185 Lecture #6 ERS February 4 & 9, 2009 Light absorption by leaf varies directly with leaf pigment concentrations Light absorption by leaf varies directly with leaf pigment concentrations Transmittance Light absorption decreases as chlorophyll concentration decreases Very low chlorophyll concentration, very low light absorption Very high light absorption, very high Very high light absorption, very high chlorophyll concentration chlorophyll concentration ERS-185 Lecture #6 ERS February 4 & 9, 2009 Cross-section Through A CrossCross-section Through A Hypothetical and Real Hypothetical and Real Leaf Revealing the Major Leaf Revealing the Major Structural Components Structural Components that Determine the that Determine the Spectral Reflectance Spectral Reflectance of Vegetation of Vegetation Jensen, 2000 Jensen, 2000 0.5 0.4 Reflectance 0.3 Chl a +b =20 μg/cm 2 0.2 0.1 Chl a +b =80 μg/cm 0 400 Wavelength (nm) 2 800 Leaf-level spectral Leaf-level spectral measurements measurements ERS-185 Lecture #6 ERS February 4 & 9, 2009 Changes in Leaf optical properties due to pigment content variation Zarco-Tejada t al 2004 Zarco-Tejada eetal.,.,2004 Dominant Factors Controlling Leaf Reflectance Dominant Factors Controlling Leaf Reflectance Water Water absorption bands: absorption bands: 0.97 μm 0.97 μm 1.19 μμm 1.19 m 1.45 μμm 1.45 m 1.94 μμm 1.94 m 2.70 μm 2.70 μm Jensen, 2000 Jensen, 2000 ERS-185 Lecture #6 ERS February 4 & 9, 2009 Absorption Coefficients of leaf biochemical constituents Absorption Coefficients of leaf biochemical constituents Reflectance Response of a Single Magnolia Leaf Reflectance Response of a Single Magnolia Leaf Reflectance ((Magnolia grandiflora)) to Decreased Relative Water Content Magnolia grandiflora to Decreased Relative Water Content Jensen, 2000 Jensen, 2000 ERS-185 Lecture #6 ERS February 4 & 9, 2009 Remote Sensing of Vegetation Remote Sensing of Vegetation Remote Canopy Canopy Structural Structural Characteristics Characteristics In Situ Ceptometer Leaf-Area-Index Measurement In SituCeptometer Leaf- AreaCeptometer Leaf-Area-Index Measurement ERS-185 Lecture #6 ERS February 4 & 9, 2009 Hypothetical Hypothetical Example of Example of Additive Additive Reflectance from Reflectance from A Canopy with A Canopy with Two Leaf Layers Two Leaf Layers Jensen, 2000 Jensen, 2000 Phenological Cycle of Cattails and Waterlilies in Par Pond, S.C. Phenological Cycle of Cattails and Waterlilies in Par Pond, S.C. Jensen, 2000 Jensen, 2000 ERS-185 Lecture #6 ERS February 4 & 9, 2009 Phenological Cycle of Hard Red Winter Wheat in the Great Plains Phenological Cycle of Hard Red Winter Wheat in the Great Plains Winter Wheat Phenology snow cover SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG crop establishment greening up heading mature Harvest Dead ripe 50 10 14 26 108 days 14 28 14 21 34 13 29 25 21 47 9 5 Sow Tillering Emergence Dormancy Growth Jointing resumes Heading Boot Soft dough Maximum Coverage Hard dough Jensen, 2000 Jensen, 2000 Phenological Cycles of Phenological Cycles of San Joaquin and San Joaquin and Imperial Valley, Imperial Valley, California Crops and California Crops and Landsat Multispectral Landsat Multispectral Scanner Images of One Scanner Images of One Field During A Field During A Growing Season Growing Season Jensen, 2000 Jensen, 2000 ERS-185 Lecture #6 ERS February 4 & 9, 2009 125 Soybeans 100 75 50 snow cover 25 cm height 50% a. Soybeans Soybeans 100% ground cover JAN FEB MAR APR MAY JUN JUL AUG SEP Maturity OCT NOV Harvest DEC Dormant or multicropped 300 Initial growth Development Phenological Cycles Phenological Cycles of Soybeans and of Soybeans and Corn in South Corn in South Carolina Carolina b. 250 Corn 200 100% Corn Corn 150 125 100 75 50 snow cover 25 cm height JAN FEB APR MAY 8-leaf 50% MAR JUN JUL AUG SEP OCT NOV DEC Dormant or multicropped 12-14 Tassle Blister leaf Dent/Harvest Dormant or multicropped 10 - 12 leaf Jensen, 2000 Jensen, 2000 100 Winter Wheat 75 50 25 cm 50% JAN Tillering 150 125 100 75 50 snow cover 25 cm height FEB MAR 100% snow cover ground cover APR Booting MAY Head a. Winter Wheat Winter Wheat JUN Harvest JUL AUG SEP OCT NOV DEC Seed Jointing Dormant or multicropped Winter Wheat Phenology b. Phenological Cycles Phenological Cycles of Winter Wheat, of Winter Wheat, Cotton, and Tobacco Cotton, and Tobacco in South Carolina in South Carolina CottonCotton Cotton 100% ground cover 50% JAN FEB MAR APR MAY JUN Seeding JUL AUG Fruiting SEP Boll OCT NOV DEC Dormant or multicropped Maturity/harvest Pre-bloom 125 100 75 50 snow cover 25 cm height 50% c. Tobacco Tobacco Tobacco 100% JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Dormant or multicropped Transplanting Development Topping Maturity/harvest Dormant or multicropped Jensen, 2000 Jensen, 2000 ERS-185 Lecture #6 ERS February 4 & 9, 2009 Band 1 (blue; 0.45 – 0.52 μm) Band 2 (green; 0.52 – 0.60 μm) Band 3 (red; 0.63 – 0.69 μm) Landsat Thematic Landsat Thematic Mapper Imagery of Mapper Imagery of the Imperial the Imperial Valley, California Valley, California Obtained on Obtained on December 10, 1982 December 10, 1982 Band 4 (near-infrared; 0.76 – 0.90 μm) Band 5 (mid-infrared; 1.55 – 1.75 μm) Band 7 (mid-infrared; 2.08 – 2.35 μm) Landsat Thematic Mapper Imagery of Imperial Valley, California, December 10, 1982 feed lot Sugarbeets fl Cotton Fallow Alfalfa Band 6 (thermal infrared; 10.4 – 12.5 μm) Ground Reference Jensen, 2000 Jensen, 2000 Relationship Between NIR Relationship Between NIR and in situ Measurements n and iin situ Measurements of Spartina alterniflora of Spartina alterniflora Spartina Total Dry Biomass (g/m22) Total Dry Biomass (g/m ) Jensen, 2000 Jensen, 2000 ERS-185 Lecture #6 ERS February 4 & 9, 2009 Distribution of Pixels in a Scene in Distribution of Pixels in a Scene in Distribution Red and Near-infrared Multispectral Feature Space Red and Near-infrared Multispectral Feature Space Jensen, 2000 Jensen, 2000 Infrared/Red Ratio Vegetation Index Infrared/Red Ratio Vegetation Index The near-infrared (NIR) to red simple ratio (SR) is the first true vegetation index: Thenearnear-infrared (NIR) to red simple ratio (SR) is the first truevegetation index: vegetation SR = NIR red It takes advantage of the inverse relationship between chlorophyll absorption of It takes advantage of the inverse relationship betweenchlorophyll absorption of chlorophyll red radiant energy and increased reflectance of near-infrared energy for healthy red radiant energy and increased reflectance ofnearnear-infrared energy for healthy plant canopies (Cohen, 1991) . . plant canopies (Cohen, 1991) ERS-185 Lecture #6 ERS February 4 & 9, 2009 Normalized Difference Vegetation Index Normalized Difference Vegetation Index The generic normalized difference vegetation index (NDVI): The generic normalized difference vegetation index (NDVI): NDVI = NIR − red NIR + red has provided aamethod of estimating net primary production over varying has provided method of estimating net primary production over varying biome types (e.g. Lenney et al., 1996), identifying ecoregions (Ramsey et biome types (e.g. Lenney et al., 1996), identifying ecoregions (Ramsey et al., 1995), monitoring phenological patterns of the earth’s vegetative al., 1995), monitoring phenological patterns of theearth’ vegetative earth’s surface, and of assessing the length of the growing season and dry-down surface, and of assessing the length of the growing season anddrydry-down periods (Huete and Liu, 1994). Huete periods((Huete and Liu, 1994). Enhanced Vegetation Index (EVI) Enhanced Vegetation Index (EVI) The MODIS Land Discipline Group proposed the Enhanced Vegetation Index (EVI) for use The MODIS Land Discipline Group proposed theEnhanced Vegetation Index (EVI) for use The Enhanced with MODIS Data: with MODIS Data: EVI = p * nir − p * red p * nir + C1 p * red − C2 p * blue + L The EVI is aamodified NDVI with aasoil adjustment factor, L, ,and two coefficients, C1 and The EVI is modified NDVI with soil adjustment factor, L and two coefficients, C1 and ed C2 which describe the use of the blue band in correction of the red band for atmsoperhic C2 which describe the use of the blue band in correction of therred band for atmsoperhic aerosol scattering. The coefficients, C1 , ,C2 , ,and L, ,are empirically determined as 6.0, 7.5, aerosol scattering. The coefficients, C1 C2 and L are empirically determined as 6.0, 7.5, and 1.0, respectively. This algorithm has improved sensitivity to high biomass regions and and 1.0, respectively. This algorithm has improved sensitivityto high biomass regions and to improved vegetation monitoring thorugh aade-coupling of the canopy background signal and improved vegetation monitoring thorugh dede-coupling of the canopy background signal and aareduction in atmospheric influences (Huete and Justice, 1999). Huete reduction in atmospheric influences((Huete and Justice, 1999). ERS-185 Lecture #6 ERS February 4 & 9, 2009 Time Series of 1984 and 1988 NDVI Measurements Derived from AVHRR Global Area Time Series of 1984 and 1988 NDVI Measurements Derived fromAVHRR Global Area Time AVHRR Coverage (GAC) Data for the Region around El Obeid, Sudan, in Sub-Saharan Africa Coverage (GAC) Data for the Region around El Obeid, Sudan, inSub Sub-Saharan Africa Jensen, 2000 Jensen, 2000 Range in Airborne Data 0.6 Closed Canopy 0.5 Open Canopy 0.4 Reflectance 0.3 0.2 0.1 0 400 500 600 700 800 900 Wavelength (nm) Zarco-Tejada t al 2005 Zarco-Tejada eetal.,.,2005 ERS-185 Lecture #6 ERS February 4 & 9, 2009 Indices and Yield Relationships 0.8 Structural 0.6 NDVI RDVI MTVI1 MAX 0.8 Chlorophyll 0.6 MCARI TCARI OSAVI MAX 0.4 r r 0.4 0.2 0.2 0 0 -0.2 6/1 7/1 7/31 8/30 Date 9/29 10/29 -0.2 6/1 7/1 7/31 8/30 Date 9/29 10/29 0.8 Red Edge 0.6 λπ Rs λο MAX 0.8 Water Indices 0.6 mNDWI mSRWI PWI MAX 0.4 r r 0.4 0.2 0.2 0 0 -0.2 6/1 7/1 7/31 8/30 Date 9/29 10/29 -0.2 6/1 7/1 7/31 8/30 Date 9/29 10/29 Zarco-Tejada t al 2005 Zarco-Tejada eetal.,.,2005 ERS-185 Lecture #6 ERS February 4 & 9, 2009 Remote Sensing of Vegetation Remote Sensing of Vegetation Remote Illumination Illumination Characteristics Characteristics ADAR, 1m ADAR, 1m Mosaic of Midwest scenes Mosaic of Midwest scenes ERS-185 Lecture #6 ERS February 4 & 9, 2009 Bi-directional Reflectance Distribution Function (BRDF) Light reflecting off of surface is rarely isotropic. Most surfaces Light reflecting off of aasurface is rarely isotropic. Most surfaces exhibit anisotropic reflectance (reflectance amount varies with exhibit anisotropic reflectance (reflectance amount varies with direction). direction). BRDF Backscatter direction Forward scatter direction ERS-185 Lecture #6 ERS February 4 & 9, 2009 sensor sun Nadir view Back scatter view Forward scatter view Back scatter view Forward scatter view ERS-185 Lecture #6 ERS February 4 & 9, 2009 Back scatter view Forward scatter view ERS-185 Lecture #6 ERS February 4 & 9, 2009 Backscatter direction Forward scatter direction ERS-185 Lecture #6 ERS February 4 & 9, 2009 BI-directional Reflectance Distribution Function 1 2 Forward scatter view Goniometer in Operation at North Inlet, SC Goniometer in Operation at North Inlet, SC Goniometer Jensen, 2000 Jensen, 2000 ERS-185 Lecture #6 ERS February 4 & 9, 2009 “hot spot” Nadir Nadir Forward scatter Forward scatter Backscatter Backscatter B Baac ckks scca atttte err F Foorw rw ar ardd sscat cat te terr ERS-185 Lecture #6 ERS February 4 & 9, 2009 B •Illumination B •Illumination Geometry Geometry R R Spectral characteristic Spectral characteristic D •Sensor:Solar D •Sensor:Solar spectral sensitivity spectral sensitivity F F IFOV IFOV Bidirectional Reflectance Distribution Function Bidirectional Reflectance Distribution Function •Target •Target Structure Structure Chemical characteristics Chemical characteristics ERS 186 Environmental Remote Sensing Dr. Susan Ustin Dr. Tuesday & Thursdays Lecture: 0830AM-0950AM 0830AMLab: 1000AM-1150AM 1000AM1137 PES Bldg CRN’s CRN’ 186: 73548 73548 186L: 73549 73549 ERS-185 Lecture #6 ERS February 4 & 9, 2009 ...
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