Transcript Applications: Remote Sensing of Vegetation and Ecosystems

Remote Sensing of Vegetation
Vegetation and Photosynthesis
• About 70% of the Earth’s land surface is covered by
vegetation with perennial or seasonal photosynthetic
activity
Significance of Vegetation Mapping
• Species and community distribution
– land cover mapping
– estimating biodiversity
• Phenological (growth) cycles
• Vegetation health
• Temporal variations (change detection)
– land cover change
– slow vs. fast changes
Physical Basis for Remote
Sensing of Vegetation
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Photosynthesis
Pigmentation
Leaf structure
Plant water content
Canopy structure
Phenological cycles
Photosynthesis and Spectral
Characteristics
• Energy-storage in plants, powered by light
absorption by leaves
• Leaf structures have adapted to perform
photosynthesis, hence their interaction with
electromagnetic energy has a direct impact
on their spectral characteristics
Visible, NearIR and Middle IR Interactions
Cross-section through a
hypothetical and real leaf
revealing the major
structural components
that determine the
spectral reflectance
of vegetation
Near IR Interactions within the Spongy
Mesophyll
• High leaf reflectance in the NIR results from
scattering/reflectance from the spongy
mesophyll
• This layer is composed of cells and air
spaces (lots of scattering interfaces)
Reflectance, Transmittance, and Absorption Characteristics of Big Bluestem Grass
Multiple Scattering in the Plant Canopy
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
Vegetation Indices
• A vegetation index is a simple mathematical
formula
• Used to estimate the likelihood that
vegetation was actively growing at the time of
data acquisition
• Widely used over several decades
• New, more sensitive vegetation indices have
been developed
Vegetation Indices
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Make use of the red vs. NIR
reflectance differences for green
vegetation
Veg indices are associated with
canopy characteristics such as
biomass, leaf area index and
percentage of vegetation cover
Normalized Difference Vegetation Index (NDVI)
r NIR  r red
NDVI 
r NIR  r red
rred = Reflectance in red channel
rNIR = Reflectance in NIR channel
Healthy, dense vegetation has high NDVI
Stressed, or sparse vegetation produces lower NDVI
Bare rock, soil have NDVI near zero
Snow produces negative values of NDVI
Clouds produce low to negative values of NDVI
Global NDVI from the
Advanced Very High
Resolution Radiometer
NDVI as an indicator of drought:
Cautions about NDVI
• Saturates over dense vegetation
• Less information than original data
• Any factor that unevenly influences the red and NIR
reflectance will influence the NDVI
– such as atmospheric path radiance, soil wetness
• Pixel-scale values may not represent plant-scale
processes
• Derivatives of NDVI (FAPAR, LAI) are not physical
quantities and should be used with caution
Other vegetation indices:
• Soil-adjusted Vegetation Index (SAVI)
• Soil and Atmospherically-Resistant
Vegetation Index (SARVI)
• Moisture Stress Index (MSI)
• Global Monitoring Environmental Index
(GEMI)
• Enhanced Vegetation Index (EVI)
Enhanced Vegetation Index (EVI)
Compensates for atmospheric and soil effects
EVI  G *
rNIR
rNIR  rred
 C1  rred  C2  rblue  L
rred = Reflectance in red channel
rNIR = Reflectance in NIR channel
rblue = Reflectance in blue channel
C1 = Atmospheric resistance red correction coefficient (C1 = 6)
C2 = Atmospheric resistance red correction coefficient (C2 = 7.5)
L = Canopy background brightness correction factor (L = 1)
G = Gain factor (G = 2.5)
EVI vs NDVI
The EVI is a modified NDVI with a soil adjustment factor, L, and
two coefficients, C1 and C2 which are used to correct for
atmospheric scattering
The coefficients, C1 , C2 , and L, are empirically determined (from
observations using MODIS data)
The EVI has improved sensitivity to high biomass regions and
improved vegetation monitoring through a de-coupling of the
canopy background signal and a reduction in atmospheric
influences (Huete and Justice, 1999).
Middle IR Interactions with Water
in the Spongy Mesophyll
• Plant water content absorbs middle IR
radiation
• Middle IR plant reflectance increases as leaf
moisture content decreases
• Middle IR reflectance can be used to monitor
plant water stress
Reflectance response of a single Magnolia leaf
(Magnolia grandiflora) to decreased relative water content
Thermal Emission and Plant Water Stress
• Measures of thermal emission can be used to derive surface
temperature for a crop
• As water transpires from a plant, it’s leaves are cooled
• If a plant is stressed, transpiration is reduced and leaf
temperature increases
red=warmer
blue=cooler
Thermal IR image showing plots of irrigated cotton
Aquatic Plants
• Immersed aquatic plants absorb solar energy
and emit thermal radiation (warmer than
surrounding water)
• This can be detected in thermal imagery
water hyacinth plumes in
Lake Victoria
Angular Reflectance Properties of
Vegetation
• Vegetation reflects light unevenly, in different
directions (“anisotropic reflectance”)
• Depends on:
– leaf shape
– canopy height
– vegetation density
• Described by “Bidirectional Reflectance
Distribution Function” (BRDF)
Vegetation Structure from Lidar Waveform
Phenological Cycles
• Temporal characteristics of vegetation growth
• Depends on:
– plant available water: rainfall/irrigation
– land surface temperature
– vegetation type (evergreen vs. deciduous)
• Crop cycles (depends on planting/harvesting cycle)
• Deciduous cycles (depends on seasonality of rainfall
and temperature)
Phenological cycles
of San Joaquin and
Imperial Valley,
California crops and
Landsat Multispectral
Scanner images of
one field during a
growing season