CHAPTER 11: 11
Remote Sensing of Vegetation g REFERENCE: Remote Sensing of the Environment John R. Jensen (2007) Second Edition Pearson Prentice Hall
THE EARTH'S SURFACE
The earth's surface. This is a composite of numerous satellite images, each selected to be cloud-free. It is unrealistic because, at any moment, half of the Earth is in nighttime darkness and much of it is cloud-covered. But this beautiful image lets us view the entire surface at once. It shows densely vegetated regions in green, dry deserts in yellow or brown, and ice-covered regions in white.
1
VEGETATION INDEX
2
Remote Sensing of Vegetation
Spectral Characteristics
Dominant Factors Controlling Leaf Reflectance
Water absorption bands: 0.97 m 1.19 m 1.45 m 1.94 m 2.70 m
3
Cross-section Through A Hypothetical and Real Leaff Revealing the Major Structural Components that Determine the Spectral Reflectance of Vegetation
Interaction of leaf structure with visible and NIR radiation
4
Chlorophyll b
Absorption Spectra of Chlorophyll a and b
Absoorption Efficiency
Chlorophyll a
lack of absorption
0.25
0.3
0.35
a.
0.4
0.45
0.5
0.55
Phycoerythrin
0.65
0.7
Phycocyanin
Chlorophyll a peak absorption is at 0.43 and 0.66 m.
Absorption Efficien ncy
Chlorophyll b peak absorption is at 0 45 and 0 0.45 0.65 65 m. m -carotene
0.25
b.
0.6
violet blue green yellow red Wavelength, m
Absorption Spectra of Chlorophyll a and b, -carotene, Pycoerythrin, and Phycocyanin Pigments
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
violet blue green yellow red Wavelength, m
0.7
Optimum chlorophyll absorption windows are: 0.45 - 0.52 m and 0.63 - 0.69 m
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Litton Emerge Spatial, Inc., CIR image (RGB = NIR,R,G) of Dunkirk, NY, at 1 x 1 m obtained on December 12, 1998.
Natural color image (RGB = RGB) of a N.Y. Power Authority lake at 1 x 1 ft obtained on October 13, 1997.
Spectral Reflectance Characteristics of Sweetgum Leaves (Liquidambar styraciflua L.)
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Spectral Reflectance Characteristics of Selected Areas of Blackjack Oak Leaves
3
1
2 a
a.
b. 4
c. 45 40 35
1
Green leaf Yellow
Percent Reflectance
Red/orange 30
2 3 4
Brown
25 20 15 10 5
d. 0 Blue (0.45 - 0.52m)
Green (0.52 - 0.60m)
Red (0.63 - 0.69m)
Near-Infrared (0.70 - 0.92m)
Hemispherical Reflectance, transmittance, and Absorption Characteristics of Big Bluestem Grass
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Hypothetical Example of Additive Reflectance from A Canopy with Two Leaf Layers
Distribution of Pixels in a Scene in Red and Near-infrared Multispectral Feature Space
8
Reflectance Response of a Single Magnolia Leaf (Magnolia grandiflora) to Decreased Relative Water Content
Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Datacube of Sullivan’s Island Obtained on October 26, 1998
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Imaging Spectrometer Data of Healthy Green Vegetation in the San Luis Valley of Colorado Obtained on September 3, 1993 Using AVIRIS
224 channels each 10 nm wide with 20 x 20 m pixels
Hyperspectral Analysis of AVIRIS Data Obtained on September 3, 1993 of San Luis Valley, Colorado
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Goniometer in Operation at North Inlet, SC
Remote Sensing of Vegetation
Temporal (Phenological Phenological)) Characteristics
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Predicted Percent Cloud Cover in Four Areas in the United States
Phenological Cycle of Hard Red Winter Wheat in the Great Plains
Winter Wheat Phenology
snow cover
SEP
OCT
NOV
DEC
JAN
FEB
APR
MAR
crop establishment
50 10 14
JUN
JUL
greening up heading mature
108 days 26
Sow Tillering Emergence
MAY
28 14
Dormancy
14
34 21
29 13
25
AUG
Harvest
21
Dead 47 9 5 ripe
Growth Jointing Heading resumes Boot Soft dough Maximum Coverage
Hard dough
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Phenological Cycles of San Joaquin and Imperial Valley, Valle California Crops and Landsat Multispectral Scanner Images of One Field During A Growing Season
Band 1 (blue; 0.45 0 45 – 0.52 0 52m) m)
Band 2 (green; 0.52 0 52 – 0.60 0 60m) m)
Band 3 (red; 0.63 0 63 – 0.69 0 69m) m)
Band 4 (near-infrared; 0.76 – 0.90m)
Band 5 (mid-infrared; 1.55 – 1.75m)
Band 7 (mid-infrared; 2.08 – 2.35m) Landsat Thematic Mapper Imagery of Imperial Valley, California, December 10, 1982
feed lot
Sugarbeets
Cotton
Alfalfa
Fallow
Landsat Thematic Mapper Imagery of the Imperial Valley, California Obtained on December 10, 1982
fl
Band 6 (thermal infrared; 10.4 – 12.5m)
Ground Reference
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Landsat Thematic Mapper Color Composites and Classification Map of a Portion of the Imperial Valley, California
125
a.
Soybeans 100
Soybeans
75
100% ground cover
50 snow cover
50%
25 cm height
JAN
FEB
MAR
APR
Dormant or multicropped
MAY
JUN
JUL
AUG
Initial growth Development
SEP
OCT
NOV
DEC
Harvest
Maturity
300
b.
Phenological Cycles of Soybeans and Corn in S th South Carolina
250
Corn
Corn 200 100%
150 125 100 50%
75 50 snow cover 25 cm height JAN
FEB
MAR
APR
Dormant or multicropped
MAY 8-leaf
JUN
JUL
AUG
SEP
OCT
NOV
DEC
12-14 Tassle Blister Dent/Harvest Dormant or multicropped leaf
10 - 12leaf
14
100
a.
Winter Wheat 75 50
100% snow cover ground cover
25 cm 50% JAN
FEB
Tillering
MAR
Jointing
APR Booting
150
MAY
JUN
JUL
AUG
Harvest
Head
Winter Wheat Phenology
125
SEP
OCT
NOV
DEC Seed
Dormant or multicropped
b. Cotton
100 75 50
50%
snow cover
100% ground cover
25 cm height
JAN
FEB
MAR
APR
MAY
JUN
JUL
Seeding
Dormant or multicropped
AUG Fruiting
SEP Boll
OCT
NOV
DEC
Maturity/harvest
Phenological Cycles of Winter Wheat, Cotton, and Tobacco in South Carolina
Pre-bloom
125
c.
100
Tobacco
75
100%
50 snow cover
50%
25 cm height
JAN
FEB
MAR
Dormant or multicropped
APR
MAY
JUN
Transplanting Development
JUL
AUG
Maturity/harvest
SEP
OCT
NOV
DEC
Dormant or multicropped
Topping
Phenological Cycle of Cattails and Waterlilies in Par Pond, S.C.
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Phenological Cycle of Smooth Cordgrass (Spartina alterniflora) Biomass in South Carolina Smooth Cordgrass (
1500
Spartina alterniflora
)
Dry Weight Biomass, g/ m2
Live Biomass 1250
Dead Biomass
1000 750 500 250 0
J
F
M
A
M
J
J
A
Band 1 (blue; 0.45 – 0.52 m)
Band 2 (green; 0.52 – 0.60 m)
Band 3 (red; 0.60 – 0.63 m)
Band 4 (red; 0.63 – 0.69 m)
Band 5 (near-infrared; 0.69 – 0.76 m)
Band 6 (near-infrared; 0.76 – 0.90 m)
Band 7 (mid-infrared; 1.55 – 1.75 m)
Band 8 (mid-infrared; 2.08 – 2.35 m)
Band 9 (thermal-infrared; 10.4 – 12.5 m)
S
O
N
D
Nine Bands of 3x3m Calibrated Airborne M ltispect al Multispectral Scanner (CAMS) Data of Murrells Inlet, SC Obtained on August 2, 1997
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Calibrated Airborne Multispectral Scanner Data of Murrells Inlet, S.C. Obtained on August 2, 1997
Natural Color Composite (Bands 3,2,1 = RGB)
Masked and Contrast Stretched Color Composite
Calibrated Airborne Multispectral Scanner Data of Murrells Inlet, S.C. Obtained on August 2, 1997
Color Infrared Composite (Bands 3,2,1 = RGB)
Masked and Contrast Stretched Color Composite
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18
Remote Sensing of Vegetation
Instruments to study the V Vegetation i
Spectral Reflectance Measurement using a Spectroradiometer
radiometer in backpack
personal computer detector
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In Situ Ceptometer Leaf-Area-Index Measurement • LAI may be computed using a Decagon Accupar Ceptometer™ Cepto ete tthat at consists co s sts of o a linear ea array a ay of o 80 adjacent 1 cm2 photosynthetically active radiation (PAR) sensors along a bar.
• Incident I id t sunlight li ht above b the th canopy, Qa, and d the th amount of direct solar energy incident to the ceptometer, Qb, when it was laid at the bottom of the canopy directly on the mud is used to compute LAI.
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In Situ Ceptometer Leaf-Area-Index Measurement
Temporal Resolution
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Relationship Between Calibrated Airborne Multispectral Scanner (CAMS) Band 6 Brightness g Values and in situ Measurements of Spartina alterniflora Total Dry Biomass (g/m2) at 27 Locations in Murrells Inlet, SC Obtained on A August g st 2 and 3, 1997
CAMSBands 1,2,3 (RGB)
CAMSBands 6,4,2 (RGB)
Biomass in a Portion of Murrells Inlet, SC Derived from 3 x 3 m Calibrated Airborne Multispectral Scanner (CAMS) Data Obtained on August 2, 1997 Total Biomass (grams/m 2 ) TM Bands 5,3,2 (RGB)
NASA Calibrated Airborne Multispectral Scanner Imagery (3 x 3 m) and Derived Biomass Map of a Portion of Murrells Inlet, South Carolina on August 2, 1997
500 - 749 750 - 999 1000 - 1499 1500 - 1999 2000 - 2499 2500 - 2999
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Total Above-ground Biomass in Murrells Inlet, S. C. Extracted from Calibrated Airborne Multispectral Scanner Data on August 2, 1997
Total Biomass (grams/m2) 500 - 749 750 - 999 1000 - 1499 1500 - 1999 2000 - 2499 2500 - 2999
Remote Sensing of Vegetation
Indices of Vegetation based on the R fl Reflectance
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Simple Ratio Vegetation Index SR The near-infrared (NIR) to red simple ratio (SR) is the first true vegetation index. It takes advantage of the inverse relationship between chlorophyll absorption of red radiant energy and increased reflectance of near-infrared energy for healthy plant canopies (Cohen, 1991).
SR
NIR Red
Notice that this equation is wrong in the book!
Normalized Difference Vegetation Index NDVI The generic normalized difference vegetation index (NDVI) has provided a method of estimating net primary production over varying biome types (e.g. Lenney et al., 1996), identifying ecoregions (Ramsey et al., 1995), monitoring phenological patterns of the earth’s vegetative surface, and of assessing the length of the growing season and dry-down periods (Huete and Li 1994). Liu, 1994)
NDVI
NIR red NIR red
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GLOBAL NDVI
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GLOBAL NDVI IN 2000
Advanced Very High Resolution Radiometer
AVHRR Band 1 2 3 4 5
Wavelength (mm) 0.58--0.68 0.58 0.72 0.72--1.10 3.55--3.93 3.55 10.5--11.5 10.5 11.5--12.5 11.5
Normalized Difference Vegetation Index (NDVI)
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27
NDVI OF WESTERN PR
Infrared Index II An Infrared Index (II) that incorporates both near and middlei f infrared d bands b d is i sensitive ii to changes h i plant in l bi biomass and d water stress in smooth cordgrass studies (Hardisky et al., 1983; 1986):Healthy, mono-specific stands of tidal wetland such as Spartina often exhibit much lower reflectance in the visible (blue, green, and red) wavelengths than typical terrestrial vegetation due to the saturated tidal flat understory. In effect, the moist soil absorbs almost all energy incident to it. This is why wetland often appear surprisingly dark on traditional infrared color composites.
II
NIR TM NIR TM
4 4
MIR TM MIR TM
5 5
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Moisture Stress Index MSI Rock et al. (1990) utilized a Moisture Stress Index (MSI): based on the Landsat Thematic Mapper nearinfrared and middle-infrared bands
MSI
MidIR TM 5 NIR TM 4
Soil Adjusted Vegetation Index SAVI Recent emphasis has been given to the development of improved vegetation indices that may take advantage of calibrated sensor systems such as the moderate resolution imaging spectrometer MODIS (Running et al., 1994). The improved indices incorporate a soil adjustment factor and/or a blue band for atmospheric normalization. The soil adjusted vegetation index (SAVI) introduces a soil calibration factor, L, to the NDVI equation to minimize soil background influences resulting from first order soil-plant spectral interactions (Huete et al., 1994): An L value of 0.5 minimizes soil b i ht brightness variations i ti and d eliminates li i t th the need d for f additional dditi l calibration for different soils (Huete and Liu, 1994).
SAVI
(1 L )( NIR red ) NIR red L
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Soil and Atmospherically Resistant Vegetation Index SARVI Huete and Liu (1994) integrated the L function from SAVI and a blueblue band normalization to derive a soil and atmospherically resistant vegetation index (SARVI) that corrects for both soil and atmospheric noise. The technique requires prior correction for molecular scattering and ozone absorption of the blue, red, and near-infrared remote sensor data, hence the term p*.
SARVI
p * nir p * rb p * nir p * rb
Where,
p * rb p * red ( p * blue p * red )
Enhanced Vegetation Index EVI The MODIS Land Discipline Group proposed the Enhanced Vegetation Index (EVI) for use with MODIS Data. Data The EVI is a modified NDVI with a soil adjustment factor, L, and two coefficients, C1 and C2 which describe the use of the blue band in correction of the red band for atmsoperhic 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 improved vegetation monitoring thorugh a de-coupling of the canopy background signal and a reduction in atmospheric influences (Huete and a d Justice, u , 1999). 999)
EVI
p * nir p * red p * nir C 1 p * red C 2 p * blue L
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Enhanced Vegetation Index EVI The Enhanced Vegetation Index (EVI) improves on the venerable NDVI. Derived f from state-of-the-art t t f th t satellite data provided by the MODIS instrument, EVI improves on NDVI's spatial resolution, is more sensitive to differences in heavily vegetated areas (as seen here in the Yucatan Peninsula), and better corrects for atmospheric haze as well as the land surface beneath the vegetation. Early data from MODIS shows the differences between EVI and NDVI.
Landscape Ecology Metrics
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THE CARBON CYCLE
Geological Applications
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Heavy Metals Affecting Vegetation
Developing a protocol to use remote sensing as a cost effective tool to monitor contamination of mangrove wetlands Johannes H. Schellekens Fernando GilbesGilbes-Santaella
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