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VEGETATION • Monitoring Drylands - Problems • The Vegetation Problem • Vegetation and Soil Signatures • Extracting Information • Vegetation Indices EARTH OBSERVATION CENTRE, UKM 1 VEGETATION 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 EARTH OBSERVATION CENTRE, UKM 2 VEGETATION 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 VEGETATION Specific Challenges for Land Degradation Monitoring • Arid region vegetation is intrinsically difficult to study remotely because: (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 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. EARTH OBSERVATION CENTRE, UKM 4 VEGETATION 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 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. EARTH OBSERVATION CENTRE, UKM 5 VEGETATION 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 VEGETATION 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. EARTH OBSERVATION CENTRE, UKM 8 VEGETATION 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 VEGETATION 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 VEGETATION 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. EARTH OBSERVATION CENTRE, UKM 12 VEGETATION 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. EARTH OBSERVATION CENTRE, UKM 13 VEGETATION 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 VEGETATION VEGETATION INDICES EARTH OBSERVATION CENTRE, UKM 15 • 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. VEGETATION 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 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. EARTH OBSERVATION CENTRE, UKM 17 VEGETATION 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. EARTH OBSERVATION CENTRE, UKM 18 VEGETATION 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 VEGETATION 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 = 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 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. NIR Red 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 (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 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. 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) 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 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. NIR Red 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 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*(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) 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. 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. 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.
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