Download - Chapter6 Vegetation

7/28/2019 Chapter6 Vegetation

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EARTH OBSERVATION CENTRE, UKM

VEGETATION

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Monitoring Drylands - Problems

The Vegetation Problem

Vegetation and Soil Signatures

Extracting Information

Vegetation Indices


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VEGETATION

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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 soilwater-logging).

These can ultimately lead to "desertification".

As the increasing world population places more demands on land forfood production etc., many marginal arid and semiarid lands will beat risk of degradation.

The need to maintain sustainable use of these lands requires thatthey 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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VEGETATION

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Problems with Monitoring Dryland Vegetation

Remote sensing of arid regions is difficult and necessitatesinnovative techniques.

Desert plants typically manifest long periods of dormancy

interspersed with brief "greenings" associated with stormsor seasonal rainfall.

During these relatively short productive periods, thecharacteristic spectral features of desert plants change, asdoes total vegetation cover

Current long repeat times of Landsat and other presentsatellite sensors provide insufficient temporal resolution toreliably capture the short, but critical, greening.


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VEGETATION

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Specific Challenges for Land Degradation Monitoring

Arid region vegetation is intrinsically difficult to study remotelybecause:

(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 ofhumid 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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VEGETATION

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Satellite Remote Sensing as a Monitoring Tool Pros

The operational costs of satellite systems are significantly lower than forother 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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VEGETATION

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Airborne Remote Sensing ?

Airborne remote sensing is not an efficient tool suitable for suchmonitoring 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

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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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VEGETATION

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Signature Specifics

The 0.4-1.0 m 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 m range related toleaf coatings, but the visible wavelength

pigment features are more easy to sense.


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VEGETATION

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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 thermalstresses 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 aridvegetation, and the open canopies mean that a great deal of soil

is visible through most arid vegetation canopies.


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VEGETATION

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Extracting a vegetation signal (cont.)

Further compounding the problem is the fact that aridplants tend to have vertically oriented leaves to avoiddirect sunlight during midday, which is when remotesensing 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 seenwith remote sensing.

Other plants change the orientation of their leaves by

rolling and unrolling or steering the leaves which has thesame effect of reducing the leaf area visible to remotesensing.


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VEGETATION

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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 dominantplant 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 soilbackground will be very significant in the reflected

spectrum in arid regions.


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VEGETATION

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The nature of the soil ``noise,'' which is partially due tonon-linear spectral mixing, will be different than thatobserved 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, andbehaviour of arid vegetation with respect to recent rainfall.

There are also significant variations in the appearance ofplants due to seasonal effects.

Lastly, spectral characteristics differ significantly betweenshrub types.


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VEGETATION

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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 redreflectance, 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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VEGETATION

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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 andshadowing.

Analysis has shown that for a given type of soilcharacteristic, variability in one wavelength is oftenfunctionally related to the reflectance in another

wavelength.


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VEGETATION

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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 whenexperimentally varying three soil parameters together, the

composite line is generally linear, but can exhibit scatter.


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VEGETATION

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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


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VEGETATION

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Points to Note: Vegetation Indices

Within these, there have been four general approachestaken, based on the characteristics of the tasselled-cap.

1. The first approach is to measure the distance betweenwhere the pixel plots in the tasselled cap plot from the

soil line. (The soil line is used because it is generallyeasier to find than the 100% vegetation point).

2. The second approach is to assume that the isovegetationlines all intersect at a single point.

3. The third approach is to recognise that lines do notintersect at a single point.

4. The final possibility is to assume that the isovegetationlines 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

NIR+RED RVI - 1


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Both RVI and NDVI basically measure the slope of the linebetween the origin of red-NIR space and the red-NIR valueof the image pixel.

Red

NIR


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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 withthan the infinite range of the RVI.

NDVI can also be considered to be an improvement of DVI which eliminateseffects of broad-band red-NIR albedo through the normalization.

Crippen (1990) recognized that the red radiance subtraction in the numeratorof NDVI was irrelevant, and he formulated the infrared percentage vegetation

index (IPVI):

IPVI = NIR = (NDVI+1)

NIR + RED

IPVI is functionally equivalent to NDVI and RVI, but it only ranges in valuefrom 0.0-1.0.

It also eliminates one mathematical operation per image pixel which isimportant 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 willvary for different NIR and red band passes.

Table 9 gives the slope and intercept for the soil line calculated from AVIRISdata for different bandpasses.

The clear implication is that the only truly valid way of making use of avegetation 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 eachinstrument once might suffice.

Of course, even the assumption that all of the bare soil spectra in a single imageform a line may also be inaccurate.

Elvidge and Chen (1995) found that SAVI and PVI consistently provided betterestimates of LAI and percent green cover than did NDVI or RVI.

They also found that there was a steady improvement in all of these vegetationindices as narrower and narrower bands were used for the near-infrared and redreflectances, with SAVI being the best index at the very narrowest bandwidth.

The advantage of narrow bands for use with vegetation indices providesadditional arguments for the use of high spectral resolution remote sensing.


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Table 9: Red-NIR Soil Line Parameters for AVIRIS Data Sampled atDifferent Band-passes

Instrument

Simulated

Red Band-pass

(m)NIR Band-pass

(m) Slope Intercept

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


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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.

Red

NIR

a

Soil line

Indices Using the Soil Line


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Huete (1988) suggested a new vegetation index which was designed tominimize the effect of the soil background, which he called the soil-adjustedvegetation index (SAVI). This vegetation index takes the form:

SAVI = NIR-RED (1+L)

NIR+RED+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 specifiedvegetation 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.

Red

NIR

Soil Adjusted VI


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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 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


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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 vegetationwhich is called MSAVI2:

MSAVI2 = 1/2 * ((2*(NIR+1)) - (((2*NIR)+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 = (NIRRB) / (NIR+RB)

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 otherindices. 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 toaccount 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, andrepresents plant information at least as well as NDVIbutis 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 anoptimised 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 moreappropriate for all other applications.

However, in Niger, Leprieur et al (1996) found GEMI to

be less sensitive to the atmospherehowever, 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 changingsoil background is an increase in the sensitivity to variations in theatmosphere (Leprieur et al., 1994; Qi et al., 1994b).

There have been several approaches in the development of vegetationindices which are less sensitive to the atmosphere, such as theAtmospherically Resistant Vegetation Index (ARVI) of Kaufman and

Tanr (1992) and the Global Environmental Monitoring Index (GEMI)of Pinty and Verstraete (1991).

Chehbouni has data demonstrating that GEMI is highly sensitive to soilnoise.

Qi et al. (1994b) demonstrated that soil noise caused GEMI to violently

break down at low vegetation covers, and that all of the vegetationindices designed to minimize the effect of the atmosphere haveincreased sensitivity to the soil, which makes these indices completelyunsuitable for arid regions.

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