Remote%20Sensing%20of%20Vegetation

1
Remote Sensing of Vegetation
2
Vegetation and Photosynthesis

• About 70 of the Earths land surface is covered
  by vegetation with perennial or seasonal
  photosynthetic activity

3
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

4
Physical Basis for Remote Sensing of Vegetation

• Photosynthesis
• Pigmentation
• Leaf structure
• Plant water content
• Canopy structure
• Phenological cycles

5
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

6
Visible, NearIR and Middle IR Interactions
7
Cross-section through a hypothetical and real
leaf revealing the major structural components
that determine the spectral reflectance of
vegetation
8
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)

9
Reflectance, Transmittance, and Absorption
Characteristics of Big Bluestem Grass
10
Multiple Scattering in the Plant Canopy
11
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
12
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

13
Vegetation Indices

• 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

14
Normalized Difference Vegetation Index (NDVI)

• 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

15
Global NDVI from the Advanced Very High
Resolution Radiometer
16
NDVI as an indicator of drought
17
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

18
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)

19
Enhanced Vegetation Index (EVI)
Compensates for atmospheric and soil
effects 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)
20
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).
21
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

22
Reflectance response of a single Magnolia leaf
(Magnolia grandiflora) to decreased relative
water content
23
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, its leaves are
  cooled
• If a plant is stressed, transpiration is reduced
  and leaf temperature increases

redwarmer bluecooler
Thermal IR image showing plots of irrigated cotton
24
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
25
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)

26
Vegetation Structure from Lidar Waveform
27
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)

28
Phenological cycles of San Joaquin and Imperial
Valley, California crops and Landsat
Multispectral Scanner images of one field during
a growing season