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Evaluation of Landsat TM vegetation indices for estimating vegetation cover on semi-arid rangelands

– A case study from Australia

Y. CHEN*† and D. GILLIESON‡ †CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia

‡School of Earth & Environmental Sciences, James Cook University, PO Box 6811, Cairns, QLD 4870, Australia

The accurate monitoring of vegetation cover of globally extensive arid and semi-arid environments is important and challenging. This study examined the capacity of five Landsat TM spectral bands and 17 VIs in estimating saltbush and total vegetation cover in semi-arid rangeland environments. It investigated the relationships between ground-surveyed vegetation cover and VIs derived from Landsat TM images and coincident ground reflectance measurements at Lake Mungo and Fowlers Gap of New South Wales, Australia, both vegetated by perennial chenopod shrublands, principally saltbush. Vegetation cover percentage and ground reflectance were measured along 147 sub-transects and at 147 corresponding quadrats, respectively. Linear regression was calculated to relate the mean reflectance values of TM single spectral bands and VIs to vegetation cover data at each site. Mid-infrared wavelengths and VIs derived from them were found being better at characterising vegetation cover than spectral measures that relied only on visible and near infrared wavebands. The VIs most suitable for large-scale surveys in semi-arid rangelands were also identified, where a cost effective evaluation of vegetation cover is required. Keywords: Landsat TM; Vegetation Indices; semi-arid rangelands; saltbush;

total cover 1. Introduction Arid and semi-arid rangelands cover 70% of the Australian continent, make up almost 25% of the earth’s landscapes, and support the livelihoods of more than one billion people. Vegetation cover, and its changes over time on these vast rangelands, is a critical factor in studying the properties and dynamics of terrestrial ecosystems, and other environmental issues relevant to global changes, such as land degradation, carbon cycle, climate variability and global warming (Liu and Kafatos, 2005; Fensholt et al., 2006).

Remote sensing technology has the ability to estimate vegetation cover and changes in cover over large areas more quickly and cheaply than on-ground surveys. Quantifying vegetation cover using remote sensing is usually performed through the _______________________________________________________________

*Corresponding author. Email: yun.chen@csiro.au

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development of an empirical relationship between vegetation cover and the value of pixels from a satellite image. Prior to such quantification, the imagery is usually transformed into various so-called vegetation indices (VIs) to improve discrimination between objects. Since the 1970s, VIs derived from Landsat MSS and TM, and Advanced Very High Resolution Radiometer (AVHRR) have been widely used in correlation with the amount of vegetation cover present. Much effort has also been made in recent years to develop more advanced VIs utilising a new generation of improved spectral, spatial and temporal resolution satellite sensors specially designed for vegetation monitoring. Such sensors include the Moderate Resolution Imaging Spectrometer (MODIS), the Medium Resolution Imaging Spectrometer (MERIS), and the VEGETATION on SPOT, and some other hyperspectral sensors (Huete and Jackson, 1987; Eastwood et al., 1997; Lawrence and Ripple, 1998; Purevdor et al., 1998; Broge and Leblanc, 2000; Thenkabail et al., 2000; Nagler et al., 2001; Price et al., 2002; Metternicht, 2002; Huete et al., 2002; Zha et al., 2003; Liu et al., 2004; Liu et al., 2005; Fensholt et al., 2006).

However, the validity of all these approaches is constrained by the accuracy of the derived VIs in representing the actual amount of vegetation on the ground. Consequently, remotely sensed data can be validly used only if it is calibrated against ground-based data. Evaluation of VIs is therefore an important and necessary ongoing process when developing new Earth Observation data products providing global coverage.

Semi-arid regions generally have sparse vegetation coverage that varies strongly in both space and time, in association with large variations in precipitation (Peel et al., 2005). Attempts to correlate vegetation characteristics either with the original spectral bands collected remotely by sensors such as Landsat MSS and TM, or with most commonly applied VIs derived from them such as the Ratio Vegetation Index (RVI), the Normalized Difference Vegetation Index (NDVI), have met with limited success in Australian semi-arid rangelands (Graetz and Gentle, 1982; Foran and Pickup, 1984; Pickup and Nelson, 1984; Pech et al., 1986; Foran, 1987; Graetz et al., 1988; Williamson, 1989; Arthur and James, 1992; Anderson et al., 1993; Pickup et al., 1993; Billington and Lewis, 1996; Everitt et al., 1996; O’Neill, 1996; Price et al., 2002). However, Foran and Pickup (1984) showed that a subtraction ratio of MSS Bands 4, 5 and 7 gave a suitable index for monitoring rangelands with moderate vegetation cover and that in dry years, or in bare landscapes, Band 5 alone could be used. Graetz et al. (1988) believed that the ‘cover’ measure, which is a composite variable comprising all the percentage cover of living and non-living plant material and contrasts with the bright red soils, could be extracted from MSS Band 5 alone under dry conditions in sparsely vegetated rangelands of South Australia. Billington and Lewis (1996) found a strong correlation between comparative yield and Landsat TM Band 5 within the dune-field vegetation community when using Landsat TM to predicting vegetation biomass in semi-arid northwest Victoria. O’Neill (1996) criticised the most commonly used VIs calculated from MSS data and showed that they are inappropriate in the Australian semi-arid environments. Her study in semi-arid shrubland of New South Wales concluded that the Stress Related Vegetation Index (SRVI) most strongly correlated to total vegetation cover in both winter and summer, and that there was a significant relationship between NDVI and the green winter total vegetation cover, but no significant correlation between NDVI and summer perennial vegetation cover. Although numerous previous researchers have investigated various VIs, there remains a need to

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systematically evaluate the capacity of the most widely used VIs developed in the past, using medium-resolution, low-cost multi-temporal TM images to provide reliable, quantitative information on vegetation cover change in semi-arid environments.

The objectives of this paper are to examine how well extensive ground measurements of vegetation cover percentage in two semi-arid regions of Australia relate to TM spectral bands and their derived VIs, and then to identify the optimal specific TM bands and their VI derivatives for estimating vegetation cover in this environment. Our goal is to contribute to the development of reliable, timely and cost-effective techniques for representing spatio-temporal changes in vegetation coverage on semi-arid rangelands. 2. Study Areas The two study areas, Lake Mungo and Fowlers Gap, are located in western New South Wales, Australia (Figure 1). They are classified as rangelands and situated within the semi-arid climate zone (generally defined by the 500 mm rainfall isohyet).

The vegetation cover of the two study areas can be characterised as low, woody and open shrubland. Perennial chenopod shrublands dominated by saltbush are the most extensive forms of vegetation cover in both areas. The key biophysical characteristics of these regions are summarised in Table 1.

Figure 1. Location of study areas. The 500 mm isohyet is based on the mean annual rainfall (1980–1999)

of Australia. The images are colour composites of Landsat 7 (2002) bands 7, 4 and 2 (RGB).

Table 1. Key biophysical charactreristics of the two study areas.

STUDY SITE FOWLERS GAP LAKE MUNGO

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LOCATION

31o05’S, 141o45’E Area = 389 km2

33o45’S, 143o05’E Area = 155 km2

CLIMATE Mean Annual Rainfall = 249 mm Mean Annual Temperature = 19.2 oC

Mean Annual Rainfall = 250 mm Mean Annual Temperature = 18.1 oC

LANDFORMS Undulating lowland with low ridges (10-30 m relief) and rounded hills, ranges and foothills (up to 100 m relief) and alluvial plains

Dry lake floor with scalds, clay-rich lunette, sand plain with dunes, dune plains and dune-fields with different type of dunes

DOMINANT VEGETATION

Chenopod low shrublands (dominated by bladder saltbush Atriplex vesicaria, less than 80 cm in height), acacia tall shrubland and tussock grassland

Chenopod low shrublands (dominated by bladder saltbush Atriplex vesicaria, less than 80 cm in height), belah-rosewood open woodland, and mallee shrublands

MAIN SOILS Loamy and sandy soils (with or without gilgai), calcareous earths and duplex soils, as well as selfmulching clays

Grey clays, selfmulching clays, scald clays, calcareous clays and earths, duplex soils, as well as siliceous sands and red earths

PRESENT LANDUSE

Research Station since 1973 with some sheep grazing (also grazed by macropods, avian fauna and insects, e.g. kangaroos, goats, and locusts)

National Park since 1978 (formerly grazed by sheep, now disturbed by other grazing animals, e.g. kangaroos, rabbits, and locusts)

3. Methodology 3.1. Ground (in situ) measurements From 1996 to 1998, six field trips to both study areas were conducted in the southern hemisphere summers (between December and April) when there was little influence of herbaceous growth following the winter-dominated precipitation patterns of the area. This period was chosen as representative of the average land cover condition for perennial vegetation because at this time of year most of the spring ephemerals have dried off. In consequence, the reflectance signals received from the vegetation are primarily from perennial vegetation. In addition, the timing of the fieldwork nearly coincided with the acquisition dates of TM imagery.

Fieldwork was carried out systematically along six main transects, each 2-3km long, out of which a further 49 sites were selected, including 147 subtransects of 200m lengths and 147 associated 3×3m quadrats (Figure 2). The positions of all sampling locations were determined and recorded using a theodolite combined with an Electronic Distance Meter, and a differential GPS with a final estimated positional accuracy of 1-1.5m.

The field survey measured cover percentage and recorded spectral reflectance. Vegetation cover was defined as the vertical projection of the crown or shoot area of both perennial and ephemeral plants, including green vegetation and brown (dead standing) vegetation with green, to the ground surface. Cover percentage data were recorded by means of a Wheel Point Apparatus (Tidmarsh and Havenga, 1955) that consists of a wheel (2m circumference) with ten 50cm spokes, of which one is marked. The number of point measurements per site was designed to enable relatively rare components and vegetation clumping to be measured with suitable precision. Therefore, using at least three fixed 200m sub-transects at each site as replicates, a minimum three hundred wheel point hits were measured linearly up and then down each line. Each hit was counted by a hand-held computer attached to the wheel point. In this study, 14 700 sample points were measured at 49 sites along the 147 subtransects. Ground cover points were then placed into one of 11 categories: bare ground, gravel, litter (forb, grass, or shrub), grass, herbage, copperburr, nitre bush, black bluebush, bladder saltbush, other bluebush and other saltbush. Litter is defined as unattached yellowed plant material, and

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grass and herbage are attached green or yellow plant material. For each site two summary statistics, mean perennial saltbush (bladder saltbush and other saltbush) percentage and mean total cover (same as vegetation cover) percentage (Figures 3 and 4), were calculated for use in the correlation with the VIs and TM waveband reflectance.

The spectral characteristics of the ground surface were also defined at 147 corresponding quadrats/plots in association with the vegetation cover percentage measurements by using a Cropscan Radiometer (Cropscan Inc., 2007). The MSR model used in this study is a five-band Landsat TM compatible model which records relative radiance (percent reflectance) in the following wavelengths: TM1: 485 nm; TM2: 560 nm; TM3: 660 nm; TM4: 830 nm; and TM5: 1650 nm, excluding TM7. Three scans were recorded at each plot, meaning that a total of 2 205 records were taken at 49 sites; the three scans were then averaged to obtain a mean for each band for each plot. All readings were taken 3 meters above ground at zenith under clear skies.

Figure 2. An example of field survey design at Fowlers Gap.

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

SIT E

% Saltbush Cover

% Total Cover

Figure 3. Percentage of saltbush and total cover at Lake Mungo, summer, 1997.

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

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% Saltbush Cover

% Total Cover

Figure 4. Percentage of saltbush and total cover at Fowlers Gap, summer, 1997. 3.2. Remote sensing data processing Four TM images were collected, coinciding with the fieldwork from 1996 to 1997 (Table 2). They are cloud free with relatively high sun angles. All images were processed using ER Mapper software. First, they were all registered and rectified as accurately as possible to the Australian Map Grid zone 54 at a 25 m resolution, using about 30 ground control points (GCPs) distributed around each study area. The overall accuracy of geometric correction was managed down to an RMS error of less than half a pixel, or 12.5m. Second, haze correction was applied to three TM visible bands (TM1, TM2, TM3) using the regression method of dark object subtraction technique (Crippen, 1987). A scattergram or regression method was used to estimate the haze component. Digital number values (DNs) of the visible band (blue/green/red) were plotted against the near-IR band. The line of best fit intercepted the short-wavelength axis at a DN approximating the haze component. This value was then subtracted from all the pixels in visible bands to remove the haze component. Finally, the images were calibrated using the method described by Price (1987). DNs in each spectral band were eventually converted to calibrated reflectance values for direct comparison with field measured reflectance data. 3.3. Derivation of VIs Ground radiometric reflectance of TM bands and seventeen various VIs (Table 3) were derived for the 147 field quadrats. The image-based spectral radiance was extracted from 147 sub-transects as the mean values of corresponding TM wavebands. Some VIs, such as DVI, PVI, WDVI, SAVI-2 and MSAVI, must be referenced to a soil line. The soil line is a hypothetical line in spectral space that describes the variation in the spectrum of bare soil in the image (Pech et al., 1986). It is suggested that the feature space bounded by MSS Bands 5 and 7 (approximately TM3 and TM4 in spectral extent, respectively) is the most useful for monitoring vegetation of semi-arid shrublands (Graetz and Gentle, 1982; Pickup et al., 1993). Thus, the simplest way of determining a soil line is to make a scatter plot of the red against NIR values for the pixels in the image, and then define the line by a regression least-squares fit.

Table 2. Details of TM data acquisition.

STUDY AREA DATE (DD/MM/YY) PASS/ROW SCENESIZE (KM) LINE/PIXEL SUN ANGLE (O)

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LAKE MUNGO 05/03/96 95/83 26 x 26 1040 x 1040 38.64

09/04/97 95/83 26 x 26 1040 x 1040 34.53

FOWLERS GAP 09/12/96 96/82 28 x 28 1120 x 1120 55.14

27/02/97 96/82 28 x 28 1120 x 1120 44.55

Table 3. Vegetation indices examined in this study.

NAME ACRONYM EXPRESSION * AUTHOR Simple VI or Ratio VI SVI / RVI NIR / R Jordan (1969) Band5 Band7 Ratio RAT57 R / NIR Foran (1987) Normalised Difference VI NDVI (NIR – R) / (NIR + R) or

(SVI - 1) / (SVI + 1) Kriegler et al. (1969)

Transformed VI TVI SQRT (NDVI + 0.5) Rouse et al. (1973) Difference VI DVI DVI = sNIR - R Richardson & Wiegand (1977) Perpendicular VI PVI sin (a) NIR - cos (a) R or

[sNIR - R - b] / SQRT (1 + s x s) Richardson & Wiegand (1977)

Weighted Difference VI WDVI WDVI = NIR - sR Clevers (1988) Soil Adjusted VI SAVI-1 (1+ L) (NIR - R) / (NIR + R + L) Huete (1988) Infrared Percentage VI IPVI (NDVI + 1) / 2 = NIR / (NIR + R) Crippen (1990) Soil Adjusted Ratio VI SAVI-2 NIR / (R + b / s) Major et al. (1990) Modified Soil Adjusted VI MSAVI (1+ k) (NIR - R) / (NIR + R + k) Qi et al. (1994) Stress Related-1 SRVI-1 (R x MIR) / NIR Thenkabail et al. (1994) Stress Related-2 SRVI-2 NIR / (R x MIR) Thenkabail et al. (1994) Stress Related-3 SRVI-3 NIR / (R + MIR) Thenkabail et al. (1994) Mid-infrared-1 MVI-1 NIR / MIR Thenkabail et al. (1994) Mid-infrared-2 MVI-2 NIR / SWIR Thenkabail et al. (1994) Mid-infrared-3 MVI-3 NIR (MIR + SWIR) Thenkabail et al. (1994)

* In the formulas: R=Red; NIR=Near-infrared; MIR=Mid-infrared; SWIR=Short wave infrared; a=the angle between the soil line and the NIR axis; b=the soil line intercept; 0 (for high vegetation cover) <L<1 (for low vegetation cover); k=1-2s (NDVI) (WDVI); s=slope of the soil line.

3.4. Statistical Analysis (Regression) The procedures outlined above provided two corresponding sets of data: the field-based data including measured values of cover percentage, ground reflectance and relevant VIs, and the image-based data involving extracted information of TM spectral bands and VIs. The most common and practical ways to explore the relationship between spectral measures and surface cover are by the use of regression models. Such relationships are expressed in mathematical forms that are characterized with different coefficients reflecting variability in vegetation cover. This will provide a statistical basis for estimating vegetation cover for the whole study area directly from the image data.

The regression analyses conducted in this study used ground-measured and image-derived mean reflectance of individual TM bands and the VIs (listed in Table 3), as independent variables and the mean percentage of saltbush and total cover as dependent variables. Linear regression models were adopted for all analyses after tests with other models showed no significant improvement in the results. 4. Results and Discussion 4.1. Correlation between ground reflectance, VIs, and ground cover measurements The correlation coefficients (r2) for each combination of TM band/VI and ground cover measures are reported in Table 4, which shows:

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(1) All correlation coefficients (r12) for saltbush cover and ground reflectance in both

areas ranged from 0 to 0.54. Most correlations were statistically significant. (2) All coefficients (r2

2) obtained by the linear regression of total cover and ground reflectance in both areas ranged from 0 to 0.36. Most correlations were statistically insignificant at the P = 0.05 level.

(3) TM single bands and VIs were correlated more strongly with saltbush cover rather than total cover.

(4) The results of regression at Lake Mungo were similar to those at Fowlers Gap in that DVI, PVI and the VIs based on TM mid-infrared wavelengths were found to be well-related to saltbush cover.

Table 4. Results of regressing TM2, 3, 4, 5 and VIs against vegetation cover % and total cover %.

LAKE MUNGO (n=19) FOWLERS GAP (n=28) Vegetation Indices (VIs)

Saltbush Cover (r12) Total Cover (r2

2) Saltbush Cover (r12) Total Cover (r2

2) SVI / RVI 0.25* 0.15 0.17* 0.03

RAT57 0.27* 0.15 0.21* 0.00 NDVI 0.26* 0.15 0.22** 0.01 IPVI 0.26* 0.15 0.22** 0.01 TVI 0.26* 0.15 0.22** 0.01 DVI 0.42** 0.27* 0.34*** 0.07 PVI 0.42** 0.27* 0.34*** 0.07 WDVI 0.03 0.06 0.24** 0.05 SAVI-1(L=0.5) 0.26* 0.15 0.22** 0.01 SAVI-1'(L=1.0) 0.25* 0.14 0.22** 0.01 SAVI-2 0.03 0.05 0.14* 0.02 MSAVI 0.00 0.01 0.04 0.10 SRVI-1 0.49*** 0.25* 0.32** 0.00 SRVI-2 0.47*** 0.25* 0.29** 0.03 SRVI-3 0.29* 0.24* 0.41*** 0.06 MVI-1 0.31** 0.22* 0.41*** 0.05 MVI-2 N/A N/A N/A N/A MVI-3 N/A N/A N/A N/A TM2 0.37** 0.20* 0.02 0.17* TM3 0.40** 0.21* 0.04 0.01 TM4 0.41** 0.32** 0.36*** 0.08 TM5 0.54*** 0.27* 0.26** 0.01 TM7**** N/A N/A N/A N/A

* Significant at 0.05 probability level; ** Significant at 0.01 probability level; *** Significant at 0.001 probability level; **** TM7 was not recorded by using the MSR5 model of Cropscan radiometer in the field.

(5) The most significant relationships in Lake Mungo were mainly derived from the

regression of TM5, SRVI-1 (Figure 5, (a) and (b)) and SRVI-2 with saltbush cover. The highest correlation coefficients in the Fowlers Gap area came from SRVI-3 and MVI-1 (Figure 5, (c) and (d)).

(6) The correlation between percentage saltbush cover and TM4 and TM5 consistently outperformed the other single spectral bands in both study areas.

The most significant relationships derived from the regressions between ground reflectance measures and saltbush cover percentage are presented in Table 5. These results indicated that although no correlations are very strong, some TM bands, mid-infrared in particular, and VIs derived from them have potential for relating ground cover to satellite reflectance on rangelands. This is a similar result to those found in semi-arid areas (Huete and Jackson, 1987; Arthur and James, 1992; O’Neill, 1996).

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y = -1.2683x + 55.184

R2 = 0.5351

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ush (n = 19)

y = -1.1787x + 43.302

R2 = 0.4878

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SRVI-1

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(n = 19)

(a) (b)

y = -17.653x + 21.98

R2 = 0.4089

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(n = 28)

y = -8.8912x + 19.861

R2 = 0.4064

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(c) (d)

Figure 5. The most significant regression relationships between in situ measurements of ground reflectance (or VIs derived from them) and saltbush cover percentage in the two study areas. (a) and (b):

Lake Mungo; (c) and (d): Fowlers Gap.

Table 5. The most significant (P >= 0.01 level) relationships (r12) developed from linear regressions of

ground-based spectral data with saltbush cover % for two study areas. VIs TM2 TM3 TM4 TM5 DVI PVI SRVI-1 SRVI-2 SRVI-3 WDVI MVI-1 L.M.a 0.37* 0.40* 0.41* 0.54** 0.42* 0.42* 0.49** 0.47** - - 0.31* F.G.b - - 0.36** 0.26* 0.34** 0.34** 0.32* 0.29* 0.41** 0.24* 0.41** a: Lake Mungo; b: Fowlers Gap; * Significant at 0.01 probability level; **Significant at 0.001 probability level. 4.2. Correlation between ground-measured and image-derived reflectance Before correlating satellite data with vegetation cover percentage, it is necessary to examine the relationships between ground-based and image-based spectral reflectance measurements in study areas, although theoretically there should be a high, positive correlation between the two datasets. The Pearson Correlation Coefficient (r) showed that for Lake Mungo data, they were correlated with each other (r ranging from 0.55 to

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0.73 for TM2 to TM5); for the Fowlers Gap site, there is good correlation in some bands, especially in TM4 (r = 0.68).

Differences between ground and satellite measurements were caused by the comparison of point readings from the 1.5m diameter of the field of view on the ground against image reflectance from 25m pixels, and the different solar and view angles. The difference in performance between the two areas might also be accounted for by the fact that the pixels in Lake Mungo imagery consist of a flat and relatively uniform surface, whereas the pixels from the Fowlers Gap imagery integrate a more complicated undulating surface with a greater variety of soils, gravels, and grazing (of both domestic and non-domestic fauna). 4.3. Correlation between image reflectance, VIs, and percentage ground cover Table 6 shows the r2 coefficients of the regressions of site percentage means of perennial saltbush and total cover against TM spectral bands and VIs derived from each sub-transect extracted from Lake Mungo and Fowlers Gap images. These values were for linear relations only, but there were no significant improvements when curvilinear relationships were imposed by transforming the data, or by using higher order regression equations.

The results presented in Table 6 for the two study regions differed because of the inherent capacity of many semi-arid plants to respond to rainfall at any time of the year. Besides total annual rainfall, the seasonal rainfall incidence controls vegetation cover and is reflected in the perennial cover percentage (Graetz, 1987; Foran, 1987). Details of these differences are provided below. 4.3.1 Lake Mungo. The results of regression using image-based data were consistent with those of using ground-based data. Moreover, the results derived from the two years for the two cover datasets were quite similar. Table 6. Results of regressing TM bands and VIs against perennial saltbush % (r1

2) and total cover % (r22)

for the two study areas.

Lake Mungo (n = 63) Fowlers Gap (n = 84)

Year 1996 Year 1997 Year 1996 Year 1997

Vegetation Indices (VIs)

r12 r2

2 r12 r2

2 r12 r2

2 r12 r2

2

TM2 0.32** 0.17 0.52*** 0.47*** 0.03 0.06 0.05 0.01

TM3 0.39** 0.26* 0.54*** 0.53*** 0.19* 0.03 0.04 0.04

TM4 0.31** 0.15 0.55*** 0.52*** 0.14* 0.05 0.04 0.00

TM5 0.38** 0.25* 0.54*** 0.51*** 0.26* 0.01 0.01 0.03

TM7 0.45*** 0.34** 0.52*** 0.53*** 0.23** 0.04 0.01 0.03

SVI / RVI 0.43*** 0.59*** 0.50*** 0.46*** 0.33** 0.01 0.03 0.39***

RAT57 0.44*** 0.60*** 0.50*** 0.48*** 0.33** 0.01 0.03 0.39***

NDVI 0.44*** 0.60*** 0.50*** 0.47*** 0.33** 0.01 0.03 0.39***

IPVI 0.44*** 0.60*** 0.50*** 0.47*** 0.33** 0.01 0.03 0.39***

TVI 0.44*** 0.60*** 0.50*** 0.48*** 0.33** 0.01 0.03 0.39***

DVI 0.47*** 0.53*** 0.50*** 0.53*** 0.26** 0.01 0.03 0.20*

PVI 0.47*** 0.53*** 0.50*** 0.53*** 0.26** 0.01 0.03 0.20*

WDVI 0.09 0.39** 0.01 0.00 0.12 0.10 0.00 0.44***

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SAVI-1 (L=0.5) 0.44*** 0.60*** 0.50*** 0.47*** 0.33** 0.01 0.03 0.39***

SAVI-1'(L=1.0) 0.44*** 0.60*** 0.50*** 0.47*** 0.33** 0.01 0.03 0.39***

SAVI-2 0.06 0.32** 0.01 0.00 0.13 0.10 0.00 0.47***

MSAVI 0.44*** 0.63*** 0.34** 0.52*** 0.32** 0.00 0.02 0.38***

SRVI-1 0.45*** 0.38** 0.54*** 0.53*** 0.29** 0.01 0.01 0.03

SRVI-2 0.43*** 0.32** 0.58*** 0.50*** 0.31** 0.01 0.01 0.07

SRVI-3 0.31** 0.57*** 0.42*** 0.41*** 0.37*** 0.09 0.00 0.08

MVI-1 0.02 0.01 0.40*** 0.34** 0.17* 0.29** 0.00 0.06

MVI-2 0.01 0.07 0.12 0.06 0.14* 0.35*** 0.00 0.08

MVI-3 0.00 0.03 0.38** 0.29* 0.16* 0.32** 0.00 0.07

* Significant at 0.05 probability level; ** Significant at 0.01 probability level; *** Significant at 0.001 probability level.

(1) Correlation coefficients from regressing saltbush percentage against TM bands and VIs (r1

2) varied from 0 to 0.58. Most correlations were statistically significant at different probability levels. The most significant relationship was with SRVI-2. TM single bands performed best in 1997 (Figure 6 (a) & (b)).

Both SRVI-1 and SRVI-2 (Figure 6 (c) & (d)) were found to be not only reasonably but also constantly related to saltbush cover data. DVI and PVI also performed well in the two years. These results confirmed those of O’Neill (1996) that SRVIs are related to vegetation cover in this area, and agreed with a similar finding of Billington and Lewis (1996) that there is a good correlation between TM5 and vegetation in semi-arid environments of Australia.

(2) Correlation coefficients for regressing total cover against TM bands and VIs (r22)

ranged from 0 to 0.63. NDVI and MSAVI (Figure 6 (e) & (f)) were significantly correlated to total cover percentage. This suggested that NVDI is not only related to total vegetation cover in the winter time, as indicated by O'Neill (1996), but also in summer at Lake Mungo.

The high correlation between percentage saltbush cover and TM red, near-infrared and mid-infrared bands can be explained by the leaf spectral properties. In general, within this range of wavelengths (about 0.70-2.30m), the longer the wavelength is, the higher the reflectance and the lower the absorbance. This wavelength dependency is crucial to remote sensing of vegetation (Goel, 1988) in this portion of the electro-magnetic spectrum. In addition, the horizontal leaf angle distribution of saltbush (rather than the vertical distribution of leaves found for most grasses) may contribute to its higher spectral reflectance in the above wavebands, especially in TM5 and TM7. 4.3.2 Fowlers Gap. The results of regression using image-based data were consistent with those of using ground-based data in 1996, but different in 1997.

During the relatively dry year of 1996: (1) Correlation coefficients for percent saltbush against TM bands and VIs (r1

2) ranged from 0 to 0.37. The relatively stronger relationship came from the correlation of SRVI-3 with percentage perennial saltbush. RVI, RAT57, NDVI, IPVI, TVI, and SAVI-1 (L = 0.5, or 1.0) continually fared as well among the other single spectral bands and VIs. MSAVI and SRVI-2 were also relatively significant.

(2) In contrast to r12, all r2

2 coefficients associated with regressing total cover against TM bands and VIs were low, varying from 0 to 0.35. MVI-1, MVI-2 and MVI-3 were the only three outperforming VIs. The others produced insignificant results.

12

During the relatively wet year of 1997: (1) All results for regressing saltbush cover against TM bands and VIs were poor

with values of r12 ≤ 0.05, and all were statistically insignificant.

(2) In comparison with r12, all r2

2 coefficients for regressing total cover against TM bands and VIs ranged from 0 to 0.47. Most relationships were statistically significant at different probability levels. The highest values were produced from the relatively high correlation of WDVI and SAVI-2 with percent total cover (Figure 6 (g) & (h)).

In summary, vegetation behaviour in moisture-limited semi-arid rangelands is largely rainfall-driven. Rainfall is highly episodic and growth occurs in flushes. After a flush, vegetation dries off and the resultant standing dry matter is either grazed by a variety of species, converted into surface litter, or burnt, mainly by wildfire. It is quite common to find changes in cover of more than 50% in periods of less than two years (Foran, 1987). This variability makes it difficult to achieve consistent assessment of the performance of VIs in estimating vegetation cover. Fowlers Gap is a typical example, where rainfall is more unpredictable because there is greater annual, seasonal and spatial variation than Lake Mungo.

The reason SRVI-1 and SRVI-2 appeared significantly related to saltbush and total cover at Lake Mungo is probably because of their plant-water sensitivity. The ability of VIs which incorporate mid-infrared wavebands to account for complex data is consistent with studies reported in the literature. The mid-infrared TM5 and TM7, as well as VIs developed from them, performed equal to or even better than those based on the widely used red and near-infrared TM3 and TM4. Particularly significant improvements in correlation were found with perennial saltbush cover at Lake Mungo. This was an encouraging result considering the lack of VIs applicable in this area.

y = -4.9833x + 82.531

R2 = 0.5396

0

5

10

15

20

25

12 13 14 15 16 17

TM5

% S

altb

ush

y = -13.115x + 71.821

R2 = 0.5171

0

5

10

15

20

25

3.5 4.0 4.5 5.0 5.5

TM7

% S

altb

ush

(a) Lake Mungo 1997 (b) Lake Mungo 1997

y = -3.724x + 61.399

R2 = 0.5418

0

5

10

15

20

25

10 11 12 13 14 15 16 17

SRVI-1

% S

altb

ush

y = 689.37x - 41.66

R2 = 0.5793

0

5

10

15

20

25

0.06 0.07 0.08 0.09 0.10

SRVI-2

% S

altb

ush

(c) Lake Mungo 1997 (d) Lake Mungo 1997

13

y = 344.1x + 17.278

R2 = 0.5958

15

20

25

30

35

0.00 0.01 0.02 0.03 0.04 0.05

NDVI

% T

otal

Cov

er

y = 211.75x + 17.524

R2 = 0.6255

15

20

25

30

35

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

MSAVI

% T

otal

Cov

er

(e) Lake Mungo 1996 (f) Lake Mungo 1996

y = 2.552x - 7.7324

R2 = 0.444

15

20

25

30

35

10 11 12 13 14 15 16 17

WDVI

% T

otal

Cov

er

y = 249.92x - 175.56

R2 = 0.4721

15

20

25

30

35

0.78 0.80 0.82 0.84

SAVI-2

% T

otal

Cov

er

(g) Fowlers Gap 1997 (h) Fowlers Gap 1997

Figure 6. The most significant regression relationships between image-derived reflectance (or VIs) and cover percentage at Lake Mungo (n= 63) and Fowlers Gap (n=84).

The difference in results derived from the two study areas were thought to be due to the difference in the ‘complexity’ of the landscape: a mixture of topographic features, soil surface condition, and vegetation cover (especially saltbush cover) between the two regions. Lake Mungo is relatively flat and has a more uniform vegetation cover, whereas Fowlers Gap has undulating landforms associated with spatially heterogeneous soils, the presence of gravels and rock, sparse vegetation and human-induced grazing activities. This finding agrees with those of previous researchers working in arid and semi-arid rangelands of Australia, US and China (Huete et al., 1984; Elvidge and Lyon, 1985; Huete et al., 1985; Heilman and Boyd, 1986; Huete and Jackson, 1987; Graetz et al., 1988; Liu and Kafatos, 2005). The only viable approach, then, is to select a VI that is as robust as possible and use it as a generalised measure of cover when needed.

Another significant finding of this study is that even with the extensive and comprehensive field survey conducted, only 50-60% of the total variation in measured vegetation cover could be explained by the best VIs or TM wavebands at the resolution of 25m in the two study areas. The remaining variation is mainly due to atmospheric effects, topographic impacts, sun-view geometry and vegetation/soil artefacts. This highlights a limitation in using TM data at the limits of its spectral/spatial resolution for large-scale surveys: it is too coarse to account for local variation. 5. Conclusions

14

The conclusions were that different indices for estimating vegetation cover on semi-arid rangelands should be applied to different conditions. In relatively flat areas covered with dry, low and uniform vegetation like Lake Mungo, SRVI-1, SRVI-2 and some TM single spectral bands, especially the mid-infrared wavebands (TM5 and TM7), seemed to have the most predictive power when estimating both percentage saltbush and total cover; NDVI and MSAVI provided the most promising estimators for total cover. In more ‘complex’ environments where a relatively undulating landscape is associated with sparse vegetation cover and variety of soil surface conditions, such as at Fowlers Gap, WDVI and SAVI-2 could be related to total cover in a relatively wet year, and SRVI-3 was a possible predictor for saltbush cover in a relatively dry year.

The results from this study showed that some TM bands with mid-infrared (MIR) wavelength and the VIs derived from them have potential to estimate vegetation cover on semi-arid rangelands. MIR channels are therefore needed when developing new sensors for monitoring vegetation in semi-arid environments around the world.

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