GEO 6370 - Lecture 3 Mapping and Monitoring Vegetation Using

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GEO 6370 - Lecture 3 Mapping and Monitoring Vegetation Using Powered By Docstoc

        •    Monitoring Drylands - Problems
        •    The Vegetation Problem
        •    Vegetation and Soil Signatures
        •    Extracting Information
        •    Vegetation Indices


Land Degradation Monitoring in Drylands
 • Land degradation is a complex ensemble of surface processes (e.g.
   wind erosion, water erosion, soil compaction, salinisation, and soil
 • These can ultimately lead to "desertification".
 • As the increasing world population places more demands on land for
   food production etc., many marginal arid and semiarid lands will be
   at risk of degradation.
 • The need to maintain sustainable use of these lands requires that
   they be monitored for the onset of land degradation so that the
   problem may be addressed in its early stages.
 • Monitoring will also be required to assess the effectiveness of
   measures to control land degradation

EARTH OBSERVATION CENTRE, UKM                                             2

Problems with Monitoring Dryland Vegetation

 • Remote sensing of arid regions is difficult and necessitates
   innovative techniques.
 • Desert plants typically manifest long periods of dormancy
   interspersed with brief "greenings" associated with storms
   or seasonal rainfall.
 • During these relatively short productive periods, the
   characteristic spectral features of desert plants change, as
   does total vegetation cover
 • Current long repeat times of Landsat and other present
   satellite sensors provide insufficient temporal resolution to
   reliably capture the short, but critical, greening.

EARTH OBSERVATION CENTRE, UKM                                 3

    Specific Challenges for Land Degradation Monitoring

•   Arid region vegetation is intrinsically difficult to study remotely
(1) vegetative cover usually is sparse compared to soil background,
(2) soil and plant spectral signatures tend to mix non-linearly, and
(3) arid plants tend to lack the strong red edge found in plants of
    humid regions due to ecological adaptations to harsh desert
• A very important result of these studies is that conventional
    vegetative red indices can be unreliable measures of arid region
    plant cover with potential for over- or underestimation of the
    actual vegetative cover.

EARTH OBSERVATION CENTRE, UKM                                       4

Satellite Remote Sensing as a Monitoring Tool – Pro’s
 • The operational costs of satellite systems are significantly lower than for
   other platform types (e.g. aircraft).
 • Satellite systems provide automatically repeating coverage along
   predictable flight paths with little variance compared to aircraft flight
   lines. This provides the ability to track seasonal changes and, over a
   longer time scale, changes related to climatic variability.
 • This capability may also enable differentiation between anthropogenic
   land degradation and natural variations.
 • A satellite system also provides automatic coverage of much of the entire
   globe, and therefore, potentially, may enable some degree of global
 • Lastly, a satellite system monitoring drylands on a global scale has a
   greater potential for producing data useful for currently unanticipated
   needs than does dedicated airborne data collection.

 EARTH OBSERVATION CENTRE, UKM                                             5

             Airborne Remote Sensing ?
  • Airborne remote sensing is not an efficient tool suitable for such
    monitoring for many reasons.
  • First, airborne sensors can only provide a relatively local view.
  • Each acquisition of data using an airborne system requires an
    active decision to fly the instrument over the target area.
  • It is extremely difficult to accurately reproduce flight lines,
    which dramatically increases the difficulty of analysing and
    interpreting the monitoring data.
  • Airborne instruments suffer through flight stresses each time that
    the instrument is flown, which can compound the difficulty of
    comparing data acquired at different times.
  • The operating expenses for an airborne instrument are very high

EARTH OBSERVATION CENTRE, UKM                                            6

                   Vegetation Signatures
• The most vital single parameter for dryland monitoring is the
  signature of vegetation cover.
• Vegetation provides protection against degradation processes
  such as wind erosion, and subtle changes in vegetation are
  likely to be a precursor of wind erosion.
• Decreasing vegetation cover, and changes in the population
  of the vegetation cover, (e.g., from creosote bush to
  bursage), are sensitive indicators of land degradation.
• Vegetation reflects the hydrological aspects of arid regions,
  and provides an indicator of current and recent hydrological

EARTH OBSERVATION CENTRE, UKM                                8

                  Signature Specifics

  • The 0.4-1.0 µm part of the electromagnetic
    spectrum contains the red edge feature of the
    green vegetation reflectance spectrum which
    is exploited by standard vegetation indices.
  • Laboratory and field spectra of some desert
    plants indicates that there are also interesting
    features in the 2.0--2.5 µm range related to
    leaf coatings, but the visible wavelength
    pigment features are more easy to sense.

EARTH OBSERVATION CENTRE, UKM                          9

          Extracting a vegetation signal
  • Current techniques of remotely measuring vegetation cover are
    based on the characteristics of humid vegetation with large leaf
    area, fairly continuous canopies, high chlorophyll content, and
    thin, translucent leaves.
  • Arid vegetation has special adaptations to the water and thermal
    stresses which occur in these regions.
  • The inability of arid region vegetation to regulate temperature
    through transpiration leads to small leaves and open canopies to
    improve the efficiency of cooling the leaves by moving air.
  • The small leaves reduce the amount of leaf area in arid
    vegetation, and the open canopies mean that a great deal of soil
    is visible through most arid vegetation canopies.

EARTH OBSERVATION CENTRE, UKM                                          11

       Extracting a vegetation signal (cont.)
• Further compounding the problem is the fact that arid
  plants tend to have vertically oriented leaves to avoid
  direct sunlight during midday, which is when remote
  sensing observations are generally made in order to have
  the brightest lighting and the fewest shadows.
• The edge-on view of these leaves means that little of the
  small amount of leaf area present in arid plants can be seen
  with remote sensing.
• Other plants change the orientation of their leaves by
  rolling and unrolling or steering the leaves which has the
  same effect of reducing the leaf area visible to remote

EARTH OBSERVATION CENTRE, UKM                               12

   Extracting a vegetation signal (cont.)
• Many arid region plants have leaf hairs and coatings
  which alter the spectral properties of the leaves, and they
  often have less chlorophyll concentration than humid
• On a larger scale, desert shrubs, which are the dominant
  plant type in the vast majority of deserts around the
  world, are sparsely distributed.
• This sparse distribution of shrubs, coupled with the open
  canopies of the shrubs means that variability of the soil
  background will be very significant in the reflected
  spectrum in arid regions.

EARTH OBSERVATION CENTRE, UKM                                   13

Extracting a vegetation signal (cont.)
• The nature of the soil ``noise,'' which is partially due to
  non-linear spectral mixing, will be different than that
  observed in humid regions because very little light
  physically passes through the leaves in arid plants, while
  significant amounts pass through humid plant leaves
  (Roberts et al., 1994).
• There is high variability in the nature, appearance, and
  behaviour of arid vegetation with respect to recent rainfall.
• There are also significant variations in the appearance of
  plants due to seasonal effects.
• Lastly, spectral characteristics differ significantly between
  shrub types.

EARTH OBSERVATION CENTRE, UKM                                14


•   The lower right boundary of this sort of plot is taken to be formed by pixels
    containing only bare soil, and this boundary is referred to as the soil line.
•   The “tip” opposite the soil line, which has high NIR reflectance and low red
    reflectance, is taken to be where pixels completely covered with
    vegetation plot on this diagram.
•   All pixels covered by a mixture of bare soil and vegetation will plot
    between these two extremes. This sort of figure is sometimes called a
    tasselled-cap, because of its shape.

             Points to Note: Soils
•    Soil components that affect spectral reflectance can be
     grouped into three components:
    1. Colour
    2. Roughness
    3. Water content
•    Roughness also has the effect of decreasing reflectance
     because of an increase in multiple scattering and
•    Analysis has shown that for a given type of soil
     characteristic, variability in one wavelength is often
     functionally related to the reflectance in another
EARTH OBSERVATION CENTRE, UKM                                  17

              Points to Note: Soils
• Thus, variation in any one soil parameter can give rise to a
  line on a 2D scattergram.
• For RED-NIR scattergrams, this is termed the “soil line”,
  and is used as a reference point in most vegetation studies.
• The problem is that real soil surfaces are not
  homogeneous, and contain a composite of several types of
• However, Jasinki and Eagleson (1989) showed that when
  experimentally varying three soil parameters together, the
  composite line is generally linear, but can exhibit scatter.

EARTH OBSERVATION CENTRE, UKM                               18

Points to Note: Vegetation Indices

      •      There are three types of vegetation
             Index available:

             1. Simple, Intrinsic Indices
             2. Indices which use a soil line
             3. Atmospherically Corrected Indices

EARTH OBSERVATION CENTRE, UKM                       19

   Points to Note: Vegetation Indices
• Within these, there have been four general approaches
  taken, based on the characteristics of the tasselled-cap.
   1. The first approach is to measure the distance between
      where the pixel plots in the tasselled cap plot from the
      soil line. (The soil line is used because it is generally
      easier to find than the 100% vegetation point).
   2. The second approach is to assume that the isovegetation
      lines all intersect at a single point.
   3. The third approach is to recognise that lines do not
      intersect at a single point.
   4. The final possibility is to assume that the isovegetation
      lines are non-linear.
EARTH OBSERVATION CENTRE, UKM                               20
          Simple Vegetation Indices
• As the first approximation, Jordan (1969) developed the
  ratio vegetation index:


• RVI itself is no longer generally used in remote sensing.
  Instead a index known as the normalized difference
  vegetation index (NDVI) is used.

NIR-RED       RVI +1
NIR+RED       RVI - 1
   Both RVI and NDVI basically measure the slope of the line
    between the origin of red-NIR space and the red-NIR value
    of the image pixel.


• The only difference between RVI and NDVI is the range of values that the
  two indices take one. The range from -1.0-1.0 for NDVI is easier to deal with
  than the infinite range of the RVI.
• NDVI can also be considered to be an improvement of DVI which eliminates
  effects of broad-band red-NIR albedo through the normalization.
• Crippen (1990) recognized that the red radiance subtraction in the numerator
  of NDVI was irrelevant, and he formulated the infrared percentage vegetation
  index (IPVI):

   IPVI =        NIR               = ½ (NDVI+1)
                 NIR + RED

• IPVI is functionally equivalent to NDVI and RVI, but it only ranges in value
  from 0.0-1.0.
• It also eliminates one mathematical operation per image pixel which is
  important for the rapid processing of large amounts of data.
                               Soil Line ??
•   The soil line will be different for different areas (soil types) and the soil line will
    vary for different NIR and red band passes.
•   Table 9 gives the slope and intercept for the soil line calculated from AVIRIS
    data for different bandpasses.
•   The clear implication is that the only truly valid way of making use of a
    vegetation index which uses a soil line is to compute the soil line for each image.
•   If a good calibration is available, calculating the soil line for each target for each
    instrument once might suffice.
•   Of course, even the assumption that all of the bare soil spectra in a single image
    form a line may also be inaccurate.
•   Elvidge and Chen (1995) found that SAVI and PVI consistently provided better
    estimates of LAI and percent green cover than did NDVI or RVI.
•   They also found that there was a steady improvement in all of these vegetation
    indices as narrower and narrower bands were used for the near-infrared and red
    reflectances, with SAVI being the best index at the very narrowest bandwidth.
•   The advantage of narrow bands for use with vegetation indices provides
    additional arguments for the use of high spectral resolution remote sensing.
Table 9: Red-NIR Soil Line Parameters for AVIRIS Data Sampled at
Different Band-passes
Instrument   Red Band-pass   NIR Band-pass
                                             Slope     Intercept
Simulated    (m)            (m)

MSS          0.6-0.7         0.8-1.1         0.9034    52.95

TM           0.63-0.68       0.8-0.9         0.7939    71.39

AVIRIS       0.674           0.755           0.8863    55.00
               Indices Using the Soil Line

             NIR                                  Soil line

• The perpendicular vegetation index (PVI) of Richardson and Wiegand (1977)
  assumes that the perpendicular distance of the pixel from the soil line is linearly
  related to the vegetation cover. This index is calculated as follows:

 PVI NIR red = - sin a (NIR) cos a (red)

• where (NIR) is the near-infrared reflectance, (red) is the red reflectance and (a) is
  the angle between the soil line and the near-infrared axis. This means that the
  isovegetation lines (lines of equal vegetation) would all be parallel to the soil line.
                       Soil Adjusted VI
•   Huete (1988) suggested a new vegetation index which was designed to
    minimize the effect of the soil background, which he called the soil-adjusted
    vegetation index (SAVI). This vegetation index takes the form:

                            SAVI = NIR-RED (1+L)

•   Huete showed evidence that the isovegetation lines do not converge at a single
    point, and he selected the L-factor in SAVI based where lines of a specified
    vegetation density intersect the soil line.
•   The net result is an NDVI with an origin not at the point of zero red and near-
    infrared reflectances.


•   For high vegetation cover, the value of L is 0.0, and L is 1.0 for low
    vegetation cover.
•   For intermediate vegetation cover L=0.5, and that is the values which is
    most widely used. The appearance of L in the multiplier causes SAVI to
    have a range identical to the of NDVI (-1.0 - 1.0).
•   Huete (1988) suggested that SAVI takes on both the aspects of NDVI
    and PVI.
•   A further development of this concept is the transformed SAVI (TSAVI)
    Baret and Guyot, 1991), defined as

    TSAVI = a(NIR-aR-b)/[R=a(NIR-b) + 0.08(1+a2)]

•   Where a and b are, respectively, the slope and intercept of the soil line
    (NIRsoil = aRsoil +b), and the coefficient value 0.08 has been adjusted to
    minimise soil effects
• Qi et al. (1994a) further developed a vegetation index which is
  basically a version of SAVI where the L-factor is dynamically adjusted
  using the image data.
• They referred to this index as the Modified Soil Adjusted Vegetation
  Index or MSAVI. The factor L is given by the following expression:

   L= 1 - (2 x slope x NDVI x WDVI)

• where WDVI is the Weighted Difference Vegetation of Clevers (1988)
  which is functionally equivalent to PVI and calculated as follows

   WDVI = NIR - (slope x RED)

• Qi et al. (1994a) also created an iterated version of this vegetation
  which is called MSAVI2:

   MSAVI2 = 1/2 * ((2*(NIR+1)) - (((2*NIR)+1)2 - 8(NIR-red))1/2).
                    Atmospherically Corrected Indices

• In order to reduce the dependence of the NDVI on the atmospheric
  properties, Kaufman and Tanere (1992) proposed a modification to the
  formulation of the index, introducing the atmospheric information contained
  in the BLUE channel, defining
  ARVI = (NIR – RB) / (NIR+RB)
• Where RB is a combination of the reflectances in the Blue (B) and Red (R)
  RB = R –  (B-R)
• And  depends on the aerosol type (a good value is  = 1 when the aerosol
  model is not available)
• The authors emphasise the fact that this concept can be applied to other
  indices. SAVI can be changed to SARVI by changing R to RB.
• However, Myneni and Asrar (1994) noted that although SAVI and ARVI
  correct for soil and atmospheric effects independently, they fail to do so when
  applied simultaneously.
     Atmospherically Corrected Indices

• Pinty and Verstraete, (1992) proposed a new index to
  account for soil and atmospheric effects simultaneously.
• This is a non-linear index called GEMI:

GEMI = n(1-0.25n) – (R-0.125)/(1-R)

Where n =
[2(NIR2-R2) + 1.5NIR + 0.5R] / (NIR + R + 0.5)

• This index is seemingly transparent to the atmosphere, and
  represents plant information at least as well as NDVI – but
  is complicated, and difficult to use and interpret.
                  Which One to Use ?

• In a simulation study, Rondaux et al., (1996) found that an
  optimised SAVI (OSAVI), where the value of X was tuned
  to 0.16 easily out-performed all other indices for
  application to agricultural surfaces.
• They found that a locally tuned SAVI (MSAVI) was more
  appropriate for all other applications.
• However, in Niger, Leprieur et al (1996) found GEMI to
  be less sensitive to the atmosphere – however, they found
  it incapable of dealing with variations in soil reflectance.
• They suggest that the use of MSAVI with an accurate
  atmospheric correction is essential or perhaps using a
  combination of GEMI and MSAVI.
• One important difficulty which has been encountered in using the
  vegetation indices which attempt to minimize the effect of a changing
  soil background is an increase in the sensitivity to variations in the
  atmosphere (Leprieur et al., 1994; Qi et al., 1994b).
• There have been several approaches in the development of vegetation
  indices which are less sensitive to the atmosphere, such as the
  Atmospherically Resistant Vegetation Index (ARVI) of Kaufman and
  Tanré (1992) and the Global Environmental Monitoring Index (GEMI)
  of Pinty and Verstraete (1991).
• Chehbouni has data demonstrating that GEMI is highly sensitive to soil
• Qi et al. (1994b) demonstrated that soil noise caused GEMI to violently
  break down at low vegetation covers, and that all of the vegetation
  indices designed to minimize the effect of the atmosphere have
  increased sensitivity to the soil, which makes these indices completely
  unsuitable for arid regions.

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