Monitoring Drylands - Problems The Vegetation Problem

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

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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
  water-logging).
• 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

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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.

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Specific Challenges for Land Degradation
Monitoring

• Arid region vegetation is intrinsically difficult
  to study remotely because
• vegetative cover usually is sparse compared to
  soil background,
• soil and plant spectral signatures tend to mix
  non-linearly, and
• arid plants tend to lack the strong red edge
  found in plants of humid regions due to
  ecological adaptations to harsh desert
  environment
• 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.

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Satellite Remote Sensing as a Monitoring Tool
Pros

• 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 generalization.
• 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.

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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

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VEGETATION SIGNATURES
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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 fluxes.

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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.

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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.

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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 sensing.

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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 plants.
• 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.

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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.

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VEGETATION INDICES
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• 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.

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Points to Note Soils

• Soil components that affect spectral reflectance
  can be grouped into three components
• Colour
• Roughness
• Water content
• Roughness also has the effect of decreasing
  reflectance because of an increase in multiple
  scattering and shadowing.
• Analysis has shown that for a given type of soil
  characteristic, variability in one wavelength is
  often functionally related to the reflectance in
  another wavelength.

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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 variation.
• However, Jasinki and Eagleson (1989) showed that
  when experimentally varying three soil parameters
  together, the composite line is generally linear,
  but can exhibit scatter.

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Points to Note Vegetation Indices

• There are three types of vegetation Index
  available
• Simple, Intrinsic Indices
• Indices which use a soil line
• Atmospherically Corrected Indices

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Points to Note Vegetation Indices

• Within these, there have been four general
  approaches taken, based on the characteristics of
  the tasselled-cap.
• 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).
• The second approach is to assume that the
  isovegetation lines all intersect at a single
  point.
• The third approach is to recognise that lines do
  not intersect at a single point.
• The final possibility is to assume that the
  isovegetation lines are non-linear.

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Simple Vegetation Indices

• As the first approximation, Jordan (1969)
  developed the ratio vegetation index
• RVI NIR
• RED
• 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
• NIRRED RVI - 1

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• 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.

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NDVI

• 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 (NDVI1)
• 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.

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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.

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Indices Using the Soil Line
NIR
Soil line
a
Red

• 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.

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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 (1L)
• NIRREDL
• 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.

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TSAVI

• For high vegetation cover, the value of L is 0.0,
  and L is 1.0 for low vegetation cover.
• For intermediate vegetation cover L0.5, 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)/Ra(NIR-b) 0.08(1a2)
• 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

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MSAVI

• 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(NIR1)) - (((2NIR)1)2 -
  8(NIR-red))1/2).

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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) / (NIRRB)
• Where RB is a combination of the reflectances in
  the Blue (B) and Red (R) channels
• 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.

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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.

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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.

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Overall

• 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 noise.
• 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.