An image-based diversity index for assessing land degradation in an arid environment in South Australia

ARTICLE IN PRESS Journal of Arid Environments Journal of Arid Environments 72 (2008) 1282–1293 www.elsevier.com/locate/jaridenv

An image-based diversity index for assessing land degradation in an arid environment in South Australia R. Jafaria,b,, M.M. Lewisa, B. Ostendorfa a

School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia b Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, Iran Received 24 January 2007; received in revised form 17 November 2007; accepted 14 February 2008 Available online 10 April 2008

Abstract One of the major indirect effects of artificial watering points in a grazed landscape is the development around them of a zone of extreme degradation called a piosphere. We investigated the use of the moving standard deviation index (MSDI) applied to Landsat TM band 3 data for detection and assessment of these zones in the arid grazing lands of South Australia. We found that watering points had significantly higher MSDI values (po0.001) than non-degraded reference areas in four different land systems. Comparison of two vegetation indices, the normalised difference vegetation index (NDVI) and perpendicular distance vegetation index (PD54), used as reference indices, showed that the PD54 was more appropriate than NDVI in this perennial-dominated arid environment. Piospheres were found to be more heterogeneous in vegetation cover, with higher MSDI values, compared with non-degraded areas, and spatial heterogeneity in cover decreased with increasing distance from water points. This study indicates that MSDI can be used as an appropriate method for land degradation assessment in naturally heterogeneous arid lands of South Australia. r 2008 Elsevier Ltd. All rights reserved. Keywords: Arid land degradation; Landsat TM; MSDI; NDVI; PD54; Piosphere

1. Introduction Arid lands occupy nearly one-third of the world’s total land surface and support about 13% of the world’s population (Matlock, 1981). Drought, low and variable rainfall and high temperature and evaporation are the main characteristics of these dry lands, defined as areas falling within the annual rainfall range of 0–300 mm (FAO, 1987). Because of variability in rainfall and the short growing period of less than 74 days (FAO, 1987), these areas are not suitable for intensive agriculture, and the main land use is grazing based mostly on native vegetation cover. Research by FAO in 36 dry countries showed that without changes in areas under grazing, the numbers of stock increased from 400 million head in 1961 to 600 million in 1995 (FAO, 1996). As a result, increase in stock numbers has been one of the main causes of land degradation in these low productive lands. Corresponding author. Current address: Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, Iran. Tel.: +98 311 3913580; fax: +98 311 3912840. E-mail address: [email protected] (R. Jafari).

0140-1963/$ - see front matter r 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jaridenv.2008.02.011

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According to the World Resource Institute (WRI, 1992), overgrazing has been the main cause of land degradation in Africa and Australia, affecting approximately 49% and 80% of arid lands, respectively. Arid lands cover more than 70% of Australia and are an important economic, ecological and cultural resource for both indigenous and non-indigenous populations. Approximately 85% of the state of South Australia (SA) is considered arid, with most of its southern rangelands grazed by sheep for more than 100 years (James et al., 1999). The carrying capacity of the area is considered to be 5.5–11.6 sheep/km2 (Kingoonya Soil Conservation Board, 1991; Tynan, 1995). To assist management, these rangelands have been fenced into large paddocks ranging from few to hundreds of square kilometres. Depending on the paddock size, one or more artificial watering points provide a source of drinking water for domestic stock, although they are also used by feral and native animals. As a result the watering points provide a focus for animals that can lead to localised land degradation. Land degradation in arid lands, also called desertification, has been defined as ‘‘land degradation in arid, semi-arid, dry subhumid areas resulting from various factors including climate variations and human activities’’ (Kassas, 1995). Overgrazing, as a human activity, has been recognised as one of the most important causes of land degradation (Hahn et al., 2005; Ruthven III, 2007; Zhao et al., 2005, 2007). The effect of grazing or overgrazing can be seen readily around the watering points used for stock drinking in arid and semi-arid regions, both Australia and internationally (Andrew and Lange, 1986a, b; James et al., 1999; Nangula and Oba, 2004). In this zone, also called the piosphere or sacrifice area (Lange, 1969), grazing changes the composition and density of vegetation, and the compaction and exposure of surface soil. Previous studies (e.g. Holm et al., 2003; Ludwig and Eager, 1999) have shown that degraded landscapes or regions in Australian arid lands, especially around watering points, are more spatially homogeneous in vegetation composition, cover and density than non-degraded areas. The reduction in heterogeneity is thought to occur as grazing reduces vegetative patches and causes changes in the soil surface, leading to more homogeneous landscapes. However, satellite image-based comparisons of heterogeneity in degraded environments have shown equivocal results. Tanser and Palmer (1999) used a spatial diversity index, the moving standard deviation index (MSDI) to assess land degradation in South Africa. In contrast to the Australian field studies, they found that degraded areas were more heterogeneous in reflectance than nondegraded areas. Similarly, Guo et al. (2004) found that grazed prairie grasslands in North America had higher spectral variations than conserved regions. In contrast to these studies, Fabricius et al. (2002) showed that degraded areas in the rangelands of South Africa were more homogeneous than non-degraded areas, which is similar to the findings of Australian field studies. They found that the coefficients of variation (CV) of image pixels in disturbed areas were smaller than in comparable undisturbed areas. Remote sensing has developed as an important tool for assessment and monitoring of vegetation, erosion and desertification (e.g. Ostir et al., 2003). It can provide calibrated, quantitative, repeatable and cost effective information for large areas and can be related empirically to field data (Graetz, 1987; Pickup, 1989; Tueller, 1987). Remote sensing has been used successfully in land degradation assessment and monitoring over a range of spatial and temporal scales (Bastin et al., 1993a; Geerken and Ilaiwi, 2004; Pickup and Nelson, 1984; Symeonakis and Drake, 2004; Wessels et al., 2004, 2007). In most instances the evaluation of land condition has been based on the spectral reflectance of vegetation cover using multispectral vegetation indices. However, most of the widely used vegetation indices such as the normalised difference vegetation index (NDVI) are less effective in arid and semi-arid environments where perennial vegetation dominates (O’Neill, 1996). In particular, the chenopod shrubs of southern Australian rangelands often lack the red/infrared reflectance contrast that forms the basis of the common vegetation spectral indices, making these plants difficult to distinguish from red-coloured soils (Graetz and Gentle, 1982). As an alternative, the perpendicular distance vegetation index (PD54), based on visible green and red reflectance, is less sensitive to differences in ephemeral plant greenness, and is a better indicator of perennial plant cover (Pickup et al., 1993). The PD54 has been widely demonstrated in rangeland monitoring and assessment in Australian rangelands (Bastin et al., 1993a, b, 1998; McGregor and Lewis, 1996; Pickup et al., 1994). Although the PD54 has proved to be a useful vegetation index in this context, its application in broad-scale repeated monitoring programs is limited by the need to subjectively define a soil line and vegetation dominated pixels in bi-spectral space. This process requires considerable expertise in image analysis, is subjective and may lead to inconsistencies in application of the index.

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As an alternative to vegetation indices, heterogeneity in land surface reflectance as captured by satellite images has potential for assessment of landscape condition and land degradation. In contrast to PD54, the MSDI suggested by Tanser and Palmer (1999) did not require image calibrations and it was simply calculated from the variance of digital values in a moving window across the imagery. Because of this simplicity, it may prove useful for the assessment of broad areas. However, since previous remote sensing studies report contrasting trends in image heterogeneity with land degradation, we aimed at investigating this in southern Australian chenopod shrublands where field research points to decreasing heterogeneity with degradation. We analysed how land degradation relates to heterogeneity in medium-resolution satellite imagery, comparing MSDI with PD54 and NDVI. We compared heterogeneity in piospheres around stock watering points with nearby reference areas with minimal grazing impact. To assess the relative performance of the three indices, we examined their variations along grazing gradients. As grazing pressure decreases with distance from watering points, we expected a consistent change of MSDI, PD54 and NDVI away from watering points. 2. Methods 2.1. Study area The study area was located in the southern arid lands of SA, covering 123 600 km2 across the Kingoonya and Gawler Soil Conservation Districts (KSCD and GSCD) (Fig. 1). The region lies between 281500 –331000 S and 1331000 –1381000 E. The topography is flat to undulating with an elevation of about 300 m above sea level. The climate is characterised by hot dry summers and cool mild winters (Kingoonya Soil Conservation Board, 1991; Tynan, 1995). The mean daily maximum temperature ranges from 35 1C in summer to approximately 17 1C in winter and mean daily minimum from 15 1C in summer to about 5 1C in winter. Rainfall is variable and irregular, with annual means varying across the districts from less than 150 mm in the north (KSCD) to approximately 300 mm in the south (GSCD). The mean annual potential evaporation rate is near 2500 mm. This region includes a diversity of land systems and some large salt lakes. Land system is defined as ‘‘an area or group of areas throughout which there is a recurring pattern of topography, soils, and vegetation’’ (Mabbutt, 1973). A land system can comprise a single land unit or a combination of land units with specific patterns, while a land unit is an area that includes only similar vegetation, soils and landform. The main land units of the study area are sand plains with open woodland, calcareous plains with pearl bluebush

Fig. 1. Location of study area within the Kingoonya and Gawler Soil Conservation Districts. Coondambo, Yudnapinna, Kolendo and Arcoona land systems and locations of the 10 water points and associated reference site used for analysis in each land system are also shown.

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(Maireana sedifolia F.Muell) and bladder saltbush (Atriplex vesicaria Benth.), sand dunes with native pine (Callitris glaucophylla Joy Thoms and L.A.S.Johnson) or mulga (Acacia aneura F.Muell. ex Benth), tableland with bladder saltbush and samphire (Halosarcia pergranulata J.M.Black), and granitic hills (White and Gould, 2002). The study focussed on four extensive land systems that included ungrazed reference sites: Coondambo, Yudnapinna, Kolendo and Arcoona. A summary description of these land systems in the study area is given in Table 1. The major land use within the study area is sheep grazing for the production of wool and meat (White and Gould, 2002). Grazing-induced piospheres are common in the region, with pronounced degradation within 500 m and extending up to 1500 m away from stock watering points (Department of Water, Land Biodiversity and Conservation, 2002; James et al., 1999; Kinloch et al., 2000). To assist assessment of land condition, reference sites had been established by the SA pastoral land management authority as benchmarks for comparison with grazed areas of the same land unit. The reference sites are located far from watering points (approximately 5 km in sheep grazing country) to ensure that grazing domestic stock cannot reach them (Fleming et al., 2002). 2.2. Satellite imagery A full scene of Landsat Thematic Mapper (TM) (path 99, row 82) was acquired during a dry season (31 January 1991), and geometrically rectified to Map Grid of Australia coordinates using image-to-image registration and a first order polynomial (root mean square error ¼ 0.49 pixels). The MSDI was calculated by passing a 3  3 filter across the Landsat TM band 3 and calculating the standard deviation for any nine pixel window (Tanser and Palmer, 1999). As in Tanser and Palmer (1999), we used TM band 3 (red wavelengths) because compared with other TM bands it displayed a greater range of standard deviation values around watering points (degraded areas) and reference areas (non-degraded areas). Furthermore, other studies have shown that the red band provides more information about spectral contrasts in soil and vegetation cover than other Landsat spectral bands in arid and semi-arid environments (Chavez and Mackinnon, 1994; Pilon et al., 1988). For comparison with MSDI we used the NDVI (Rouse et al., 1974) and PD54 (Pickup et al., 1993) vegetation indices. The PD54 was calculated using Landsat TM band 2 (green) and band 3 (red) (Bastin et al.,

Table 1 A brief description of the study land systems (Pastoral Board, 1991) Land system

Land form

Coondambo

Undulating calcareous plains

Landform-vegetation association

 Calcareous plains of myall open woodland over pearl bluebush and cassia  Sandspreads of mulga and myall woodland over pearl bluebush, cassia and woollybutt; flats of mulga and dead finish over saltbush and blackbush

 Low shrubland sandy plains of bladder saltbush and low bluebush with blackbush Yudnapinna

Dissected, undulating plains

 Gilgai plain with low shrubland of bladder saltbush, slender glasswort, bluebushes and bush minuria, or bladder saltbush, low bluebush and blackbush

 Low lying areas with low shrubland of blackbush, low bluebush, bladder saltbush, bush minuria and cottonbush Kolendo

Undulating alluvial plains

 Broad plains with low shrubland of blackbush and bladder saltbush  Plains of bladder saltbush, low bluebush and satin bluebush low shrubland  Gentle stony slopes of bladder saltbush, three-wing bluebush, silvertails and isolated mulga

 Blackbush watercourses with myall, dead finish or spiny goosefoot Arcoona

Undulating tablelands

 Undulating tableland with bladder saltbush, neverfail, plover daisy and glasswort  Escarpment of bladder saltbush, glasswort and woolly bluebush  Swamps of blackbush, nitre goosefoot, cottonbush or canegrass; watercourses of dead finish

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1999). A mean 3  3 filter was applied to the PD54 and NDVI to transform data into the same filter size as the MSDI. 2.3. Analysis Watering points and reference sites were used to compare degraded and non-degraded areas, respectively. A reference site and its nearest 10 water points in each land system were located on the MSDI, PD54 and NDVI images. Reference sites were chosen at distance from salt lakes and roads to exclude the potential edge effect of these features in the calculation of the MSDI. We extracted the mean MSDI, NDVI and PD54 values within a 500 m radius around each watering point and the centre of associated reference sites. Fence lines were used to limit the extraction of data within the paddocks. One sample t-tests (comparing a single observation with a sample) were used to test for significant differences between the mean index values for 10 piospheres and the corresponding reference site in the four-land system. We used the 10 watering points nearest to the reference sites. Livestock grazing is more intensive near the water points and decreases with distance. Hence, distance can be used as a surrogate for grazing pressure and land degradation. This means that image indices of degradation should show a monotonous change away from watering points. A detailed examination of how image indices change with distance to the watering point may therefore allow us to judge the performance of the indices. To investigate how the spectral indices change with different levels of disturbance, we selected the nearest water point to the reference site for all land systems used in the previous section. Using the nearest water point had the advantage that soil, vegetation and land form were most similar to the undisturbed areas for comparison. MSDI, PD54 and NDVI values were calculated for increasing distances from water points using a series of buffers at 50 m intervals ranging from 50 to 1500 m (Fig. 2). We used linear regression to calculate the slopes of the image index/distance relationships for 500 m wide moving intervals. We expected these slopes to be positive for the vegetation indices and negative for MSDI along grazing gradients. In the absence of any natural or grazing gradients, the expected slope for any index is zero. 3. Results 3.1. Comparison of degraded and non-degraded areas The mean values of MSDI, PD54 and NDVI within 500 m radius samples of the reference sites and their 10 nearest water points in four different land systems are given in Fig. 3. All degraded sites had higher mean Mid-lines of buffer rings

500 m buffer

Reference site

Paddock boundary

Water point

Fig. 2. Extraction of MSDI values at a water point and reference site within the Coondambo land system.

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Water points (degraded areas) Reference sites (non-degraded areas)

4.5 4 3.5

MSDI

3 2.5 2 1.5 1 0.5 0 Coondambo

Yudnapinna

Kolendo

Arcoona

Land system 235

PD54

230 225 220 215 210 205 200 195 190 185 Coondambo

Yudnapinna

Kolendo

Arcoona

Land system 0.56 0.555

NDVI

0.55 0.545 0.54 0.535 0.53 Coondambo

Yudnapinna

Kolendo

Arcoona

Land system Fig. 3. Means and standard errors of MSDI, PD54 and NDVI values for 10 water points compared with the associated reference site in different land systems.

MSDI and generally lower vegetation index values (PD54 and NDVI) than non-degraded areas in the fourland systems (Table 2). Although all the watering points had the highest and reference sites the lowest MSDI values, these values differed amongst land systems. Coondambo had the highest MSDI for both watering points and the reference site, followed by Arcoona and Yudnapinna. Watering points and the reference site in Kolendo land system had the lowest MSDI values. In addition, the magnitude of difference between MSDI for degraded and non-degraded areas varied among the land systems. Arcoona showed the highest difference

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Table 2 Comparison of spectral indices between watering points and reference sites in different land systems Land system

Image indices

Land condition Degraded Mean

Non-degraded SD

t-Statistic

p-Value

Mean

Coondambo

MSDI PD54 NDVI

3.9 204 0.55

0.5 9.8 0.008

3.2 227 0.554

4.8 7.19 3.2

o0.001 o0.001 0.011

Yudnapinna

MSDI PD54 NDVI

3.4 210 0.547

0.37 4.5 0.005

2.7 222 0.549

5.9 8.6 0.65

o0.001 o0.001 0.532

Kolendo

MSDI PD54 NDVI

2.9 215 0.545

0.6 6.0 0.004

2.0 223 0.556

5.3 4.4 7.0

o0.001 o0.001 o0.001

Arcoona

MSDI PD54 NDVI

3.8 215 0.541

0.9 10.0 0.006

2.6 231 0.548

4.4 5.00 2.9

o0.001 o0.001 o0.018

The mean and standard deviation of the degraded conditions is based on the ten nearest watering points around the reference area.

(1.2), followed by Kolendo land system (0.9), while differences in Coondambo and Yudnapinna were similar (0.7). These differences in MSDI values within and between land systems may result from different vegetation and land surface characteristics (Table 1). For example, high MSDI for the 10 watering points and the reference site in Coondambo may result from the high contrast between the dominant vegetation types (mulga and myall trees with dark canopies) and the bright sandy and calcareous soils in this land system. The NDVI results differed in the various land systems. There were significant differences (po0.001) between the watering points and reference site in Kolendo land system but smaller differences (po0.05) in Arcoona and Coondambo land systems. There was no significant difference in the Yudnapinna land system (p ¼ 0.53). Different vegetation types appear to be the reason for these varying results in land systems (Table 1). The NDVI performs well in land systems with more green plant cover (e.g. woodlands) compared with shrublands (e.g. saltbushes and bluebushes) with grey colour and low near-infrared reflectance (Graetz and Gentle, 1982). In addition, the reference site in Yudnapinna land system was closer to water points than those in other land systems and it appeared to be grazed more than other reference sites. As a result, the NDVI was not able to capture the lower contrast in plant cover between water points and the reference site. The relative performance of the three indices across all land systems is presented in Table 2. The t-statistic is estimated based on the difference between mean values for the 10 degraded sites and the single reference site (one-sample t-test). Assuming that the main systematic difference between piospheres and the adjacent reference areas is grazing-induced degradation, the t-values are indicators of how well the image indices depict degradation. As the same number of sites (n ¼ 10) was used in each land system, the t-values are directly comparable. With the exception of Kolendo, PD54 had the highest t-values. The NDVI did not provide a consistent performance, having very variable and low t-values. The MSDI was very consistent, with t-values ranging from 4.4 to 5.9, but the PD54 generally outperformed the MSDI as a condition indicator. 3.2. Gradients of image indices with distance to water point Means of MSDI, PD54 and NDVI with distance from water points are shown in Fig. 4(a)–(c). All indices showed the effect of disturbance around watering points. However, the examples from different land systems showed marked differences and the performance of the different indices corroborates the previous results (presented in Section 3.1). MSDI and PD54 showed clear and consistent gradients with distance from water but NDVI showed no clear trend along grazing gradients.

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MSDI

10 8 6 4 2

Regression slope (MSDI)

0.005 Coondambo Yudnapinna Kolendo Arcoona

12

0

-0.005 -0.01 -0.015 -0.02 -0.025 500-950 50-500 1050-1500 Interval distance from water points (m)

500 1000 1500 Distance from waterpoint (m)

0.04 0.035 0.03 0.025 0.02 0.015 0.01 0.005 0 -0.005 -0.01 50-500 500-950 1050-1500 Interval distance from water point (m)

230 225 220 215 210 205 200 195 190 185 180 0

0

500 1000 1500 Distance from waterpoint (m)

Regression slope (PD54)

PD54

0

0.000025

0.56

0.00002

Regression slope (NDVI)

0.565

0.555 0.55 NDVI

1289

0.545 0.54 0.535 0.53 0.525 0

500 1000 1500 Distance from water point (m)

0.000015 0.00001 0.000005 0 -0.000005 -0.00001 -0.000015 50-500 500-950 1050-1500 Interval distance from water point (m)

Fig. 4. Mean and regression slopes of MSDI, PD54 and NDVI with distance from water points in different land systems. The slopes were computed using a linear regression of image indices over distance for a 500 m moving interval. Positive slopes for PD54 and NDVI and negative slopes for MSDI indicate that the image indices are able to correctly detect grazing gradients (shaded grey).

In addition, to quantify the change of the indices, we computed regression slopes of MSDI, PD54 and NDVI for moving intervals of 500 m (Fig. 4(d)–(f)). Each point in these figures represents the change of the indices for 10 distance bands (500 m). Grazing gradients are expected to show consistent positive slopes for PD54 and NDVI. Based on our initial hypothesis that degradation increases heterogeneity, we expect MSDI to decrease along grazing gradients and accordingly show a consistent negative slope. Most obvious is the clear depiction of grazing gradients in all land systems for the PD54. Slope remained positive for distances up to the interval of 950–1400 m for all but the Yudnapinna land system, where no increase in PD54 was

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detectable above the interval 250–700 m. NDVI, in contrast, only showed increase along the grazing gradient for Coondambo and Kolendo. In Arcoona and Yudnapinna land systems, NDVI decreased away from watering points, hence showing very limited detection of grazing gradients. MSDI consistently decreased up to 1050–1500 m for Arcoona, 650–1100 m for Kolendo, 550–1000 m for Yudnapinna and 350–800 m for Coondambo. 3.3. Relationships between MSDI diversity index and PD54 The scatterplot of MSDI and PD54 shows clear trajectories away from watering points (Fig. 5). This further demonstrates a high level of consistency between the two indices. For all but the Coondambo land system the trajectory terminates in a cluster, indicating that grazing gradients are limited to the 1500 m radius. For Yudnapinna, MDSI appears to detect a gradual difference away from the watering point, which PD54 does not reveal. The phase diagram representation also suggests the combination of MDSI and PD54 can clearly separate the different land systems. Reference sites form distinct clusters, as do the tails of the trajectories. Also evident from this representation of the data is that final clusters of the trajectory generally do not end in the reference site cluster. This indicates a high spatial variability of image values in the region. However, both indices show a marked similarity of their response along a grazing gradient, independent of large background variability. 4. Discussion Our results show that grazing-induced degradation increases heterogeneity in surface cover as measured by the MSDI: piospheres had higher MDSI than areas distant from water. This may result from a number of factors. Because of the high concentration of animals in these regions, soil condition changes dramatically and this directly influences image digital values. Soil compaction, sheep tracks and dung deposition are higher in areas closer to water compared with more distant or reference areas (Andrew and Lange, 1986a). As a result these regions have higher variation in soil surface reflectance. In addition to changes in the soil surface, alteration in vegetation cover and composition near watering points is another factor that may add to the spectral reflectance variation. Vegetation in piospheres is often a combination of overgrazed, less-grazed and dead trees, shrubs or litter, and consequently has different reflectance from less-severely grazed or healthy vegetation far from water.

7

MSDI diversity index

6 5 4 3 2 1 180

Coondambo (1) Yudnapinna (2) Kolendo (3) Arcoona (4)

190

200 210 220 PD54 vegetation index

230

Fig. 5. Scatter plot of MSDI versus PD54 at different distances from water points and reference sites (50 m interval up to 1500 m) in different land systems. Numbers indicate values for pixels within reference sites in each land system.

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According to previous studies (e.g. Holm et al., 2003) vegetation patchiness in piospheres decreases due to high grazing pressure and potentially this makes degraded regions more homogeneous in appearance than non-degraded areas. However, because of the high concentration of animals in piospheres and along preferred paths, soil and vegetation diversity are more spectrally and spatially variable than non-degraded or reference areas. This variability decreases gradually with distance from high impact areas. One of the limitations of MSDI, as mentioned by Tanser and Palmer (1999), is that this index can not identify degradation in an area less than 8100 m2 due to the filter size (3  3) applied to the Landsat TM imagery with 30 m ground resolution. Detection of variability around watering points strongly depends on the spatial resolution of the imagery and the size of the analysis window. A high variability is evident if the piosphere is denuded and if the radius of the MSDI window is large enough to include relatively undisturbed, vegetated pixels with different reflectance characteristics. This index is also sensitive to the edges of natural features as well as any disturbance in the landscape: high MSDI is expected at the edge of rivers, salt lakes and any man-made features such as roads. The PD54 and NDVI that were used as reference indices for MSDI showed differing results. PD54 performed better for all the land systems in the study area and this confirms previous studies that this index is more appropriate than NDVI for assessing and monitoring land condition in this arid environment (Jafari et al., 2007). PD54 had the most consistent differences between degraded and non-degraded areas across the land systems and this confirmed that it can be used as a good indicator of land degradation. This study supports the findings of O’Neill (1996) that the usefulness of NDVI may be reduced in perennial-dominated arid environments. Whereas the index showed significant differences between degraded and non-degraded areas in most land systems, its performance was less consistent than PD54. In Yudnapinna, the land system dominated by chenopod shrublands in gilgai plains, the low near-infrared reflectance of the vegetation limited the usefulness of NDVI for detecting degradation (Graetz and Gentle, 1982). As expected, the degradation around the stock watering points was apparent with all the indices studied, though each showed this impact differently along gradients away from water. The MSDI appeared to distinguish grazing gradients around the water points. It showed decreasing values with increasing distances from water points, though these trends varied in different paddocks and land systems due to varying vegetation and soil characteristics and grazing intensities. This index had high values in areas of low vegetation cover and decreased with increasing vegetation cover or PD54 and NDVI values. The PD54 appeared to perform better and consistently showed an increase with distance from watering points in all land systems. However, in Yudnapinna, the expected increase was weak and limited to a narrow distance band. The NDVI showed no clear grazing gradient for Yudnapinna and the Arcoona land system. In general all the spectral indices showed the highest and lowest variations in Coondambo and Yudnapinna land systems, respectively. This suggests that these indices are more applicable in open woodland vegetation than chenopod shrublands. This might be related to the greenness and density of tree cover that dominates on sandy soils and the consequent contrast between vegetation and soil background in these areas. Vegetation indices have been widely used to determine land condition. However, most of the commonly used vegetation indices are less appropriate in Australian arid and semi-arid regions due to the low cover and the dominance of the chenopod shrublands in these regions. Chenopod shrublands usually have low spectral reflectance in the near-infrared region of the electromagnetic spectrum; as a result this makes the detection of this type of vegetation cover from highly variable soil in brightness and colour difficult. More suitable vegetation indices such as PD54 have the disadvantage of subjectivity in their calculation (Jafari et al., 2007). Hence the relatively good performance of MSDI indicates its potential usefulness as a simple indicator of land condition. It requires less radiometric image calibration than vegetation indices and this is a benefit in remote sensing of land condition over broad areas on a repetitive basis. This index does not depend on the multispectral response of vegetation cover but utilises spatial pattern in land cover. 5. Conclusions Our results showed that degradation in piospheres is detectable using spatial heterogeneity in satellite image digital values. MSDI showed significantly higher values around degraded water points compared with ungrazed reference sites. In other words, piospheres were found to be more heterogeneous in digital values

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than comparable non-degraded reference areas. MSDI also successfully detected grazing gradients around stock watering points. This study showed that spatial heterogeneity in digital values may be used as an indicator of land degradation in naturally heterogeneous arid lands of southern Australia. Acknowledgements We would like to thank the SA Pastoral Board, and Department of Water, Land and Biodiversity Conservation, in particular Amanda Brook, for providing GIS data and also James Cameron, Department of Environment and Heritage for his help in selecting an appropriate Landsat image.

References Andrew, M.H., Lange, R.T., 1986a. Development of a new piosphere in arid chenopod shrubland grazed by sheep. 1. Changes to the soil surface. Australian Journal of Ecology 11, 395–409. Andrew, M.H., Lange, R.T., 1986b. Development of a new piosphere in arid chenopod shrubland grazed by sheep. 2. Changes to the vegetation. Australian Journal of Ecology 11, 411–424. Bastin, G.N., Pickup, G., Chewings, V.H., Pearce, G., 1993a. Land degradation assessment in central Australia using a grazing gradient method. Rangeland Journal 15 (2), 190–216. Bastin, G.N., Sparrow, A.D., Pearce, G., 1993b. Grazing gradient in central Australian rangelands: ground verification of remote sensingbased approaches. Rangeland Journal 15 (2), 217–233. Bastin, G.N., Tynan, R.W., Chewings, V.H., 1998. Implementing satellite-based grazing gradient methods for rangeland assessment in South Australia. Rangeland Journal 20 (1), 61–76. Bastin, G.N., Chewings, V.H., Pearce, G., McGregor, F., 1999. Assessment of grazing impact on the Barkly tablelands: vegetation index and software (CD-ROM). CSIRO Report to Department of Land, Planning and Environment, Northern Territory, Australia. Chavez, P.S., Mackinnon, D.J., 1994. Automatic detection of vegetation changes in the southern United States using remotedly sensed images. Photogrammetric Engineering and Remote Sensing 60 (5), 571–583. Department of Water, Land Biodiversity and Conservation, 2002. Pastoral lease assessment, technical manual for assessing land condition on pastoral leases in South Australia, 1990–2000. Pastoral Program, Sustainable Resources, DWL&BC, Adelaide, South Australia. Fabricius, C., Palmer, A.R., Burger, M., 2002. Landscape diversity in a conservation area and commercial and communal rangeland in Xeric Succulent Thicket, South Africa. Landscape Ecology 17, 531–537. FAO, 1987. Committee on Agriculture, Improving Productivity of Dryland Areas, Rome. FAO, 1996. AGROSTAT, Statistics Division, FAO, Rome. Fleming, M., Della Torre, B., Kutsche, F., 2002. Establishing pastoral reference areas, report. Department of Land, Water and Biodiversity Conservation and Department for Environmen and Heritage, Adelaide. Geerken, R., Ilaiwi, M., 2004. Assessment of rangeland degradation and development of a strategy for rehabilitation. Remote Sensing of Environment 90, 490–504. Graetz, R.D., 1987. Satellite remote sensing of Australian rangelands. Remote Sensing of Environment 23, 313–331. Graetz, R.D., Gentle, M.R., 1982. Study of the relationships between reflectance in the Landsat wavebands and the composition of an Australian semi-arid shrub rangeland. Photogrammetric Engineering and Remote Sensing 48, 1721–1730. Guo, X., Wilmshurst, J., mcCanny, S., Fargey, P., Richard, P., 2004. Measuring spatial and vertical heterogeneity of grasslands using remote sensing techniques. Journal of Environmental Informatics 3 (1), 24–32. Hahn, B.D., et al., 2005. A simulation model of long-term climate, livestock and vegetation interactions on communal rangelands in the semi-arid Succulent Karoo, Namaqualand, South Africa. Ecological Modelling 183 (2&3), 211–230. Holm, A.M., Watson, I.W., Loneragan, W.A., Adams, M.A., 2003. Loss of patch-scale heterogeneity on primary productivity and rainfall-use efficiency in Western Australia. Basic and Applied Ecology 4, 569–578. Jafari, R., Lewis, M.M., Ostendorf, B., 2007. Evaluation of vegetation indices for assessing vegetation cover in southern arid lands in South Australia. Rangeland Journal 29 (1), 39–49. James, C.D., Landsberg, J., Morton, S.R., 1999. Provision of watering points in the Australian arid zone: a review of effects on biota. Journal of Arid Environments 41 (1), 87–121. Kassas, M., 1995. Desertification: a general review. Journal of Arid Environments 30, 115–128. Kingoonya Soil Conservation Board, 1991. Kingoonya Soil Conservation Board District, general summary of lease assessment findings. Rangeland Assessment Unit, Pastoral Management Branch, Department of Environment and Planning, Adelaide, South Australia. Kinloch, J.E., Bannari, A., Tongway, D.J., 2000. Measuring landscape function in chenopod shrublands using aerial videography. In: Proceeding of the 10th Australian Remote Sensing and Photogrammetry Conference. The Remote Sensing and Photogrammetry Association of Australia Ltd., Adelaide, South Australia, Australia, pp. 480–491. Lange, R.T., 1969. The piosphere: sheep track and dung patterns. Journal of Range Management 22, 396–400. Ludwig, J.A., Eager, R.W., 1999. Declines in vegetation patches, plant diversity, and grassshopper diversity near cattle watering points in the Victoria river district, northern Australia. Rangeland Journal 21, 135–149.

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Mabbutt, J.A., 1973. Edited, lands of fowlers gap station. Fowlers gap arid zone research station series No. 3. University of New South Wales, New South Wales, Australia. Matlock, W.G., 1981. Realistic Planning for Arid Lands: Advances in Desert and Arid Land Technology and Development, vol. 2. Harwood academic publishers, New York. McGregor, K.F., Lewis, M.M., 1996. Quantitative spectral change in chenopod shrublands. In: Proceedings of Ninth Biennial Conference, Focus on the Future-the Heat Is on! The Australian Rangeland Society, September 24–27, Port Augusta, South Australia, pp. 153–154. Nangula, S., Oba, G., 2004. Effects of artificial water points on the Oshana ecosystem in Namibia. Environmental Conservation 31 (1), 47–54. O’Neill, A.L., 1996. Satellite-derived vegetation indices applied to semi-arid shrublands in Australia. Australian Geographer 27 (2), 185–199. Ostir, K., Veljannovski, T., Podobnikar, T., Stancic, Z., 2003. Application of satellite remote sensing in natural hazard management: the Mount Mangart Landside case study. International Journal of Remote Sensing 24 (20), 3983–4002. Pastoral Board, 1991. (CD-ROM), Pastoral Board. Department of Water, Land Biodiversity and Conservation, Adelaide, South Australia. Pickup, G., 1989. New land degradation survey techniques for arid Australia: problems and prospects. Rangeland Journal 11 (2), 74–82. Pickup, G., Nelson, D.J., 1984. Use of Landsat radiance parameters to distinguish soil erosion, stability, and deposition in arid central Australia. Remote Sensing of Environment 16, 195–209. Pickup, G., Chewings, V.H., Nelson, D.J., 1993. Estimating changes in vegetation cover over time in arid rangelands using Landsat MSS data. Remote Sensing of Environment 43, 243–263. Pickup, G., Bastin, G.N., Chewings, V.H., 1994. Remote-sensing-based condition assessment for nonequilibrium rangeland under largescale commercial grazing. Journal of Ecological Applications 4 (3), 497–517. Pilon, P.G., Howarth, P.J., Adeniyi, P.O., 1988. An enhanced classification approach to change detection in arid and semi-arid environments. Photogrammetric Engineering and Remote Sensing 54, 1709–1716. Rouse, J.W., Haas, R.W., Schell, J.A., Deering, D.W., Harlan, J.C., 1974. Monitoring the vernal advancement and retrogradation (green ware effect) of natural vegetation. NASA/GSFCT, Type 3, Final Report. Greenbelt, MD, USA. Ruthven III., D.C., 2007. Grazing effects on forb diversity and abundance in a honey mesquite parkland. Journal of Arid Environments 68 (4), 668–677. Symeonakis, E., Drake, N., 2004. Monitoring desertification and Land degradation over Sub-Saharan Africa. International Journal of Remote Sensing 25 (3), 573–592. Tanser, F.C., Palmer, A.R., 1999. The application of remotely-sensed diversity index to monitor degradation patterns in a semi-arid, heterogeneous, South Africa landscape. Journal of Arid Environments 43, 477–484. Tueller, P.T., 1987. Remote sensing science applications in arid environment. Remote Sensing of Environment 23 (2), 143–154. Tynan, R., 1995. Lease assessment overview report, Gawler Ranges Soil Conservation District (GRSCD). Department of Environment and Natural Resources, Adelaide. Wessels, K.J., Prince, S.D., Frost, P.E., Zyl, D.V., 2004. Assessing the effects of human-induced land degradation in the former homelands of northern South Africa. Remote Sensing of Environment 91, 47–67. Wessels, K.J., et al., 2007. Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. Journal of Arid Environments 68 (2), 271–297. White, J., Gould, P., 2002. Gawler Bioregion Vegetation Monitoring, Pastoral Program, Sustainable Resources. Department of Conservation, Adelaide, South Australia. WRI, 1992. World Resources 1992/1993. World Resource Institute, Oxford University Press, New York. Zhao, H.-L., Zhao, X.-Y., Zhou, R.-L., Zhang, T.-H., Drake, S., 2005. Desertification processes due to heavy grazing in sandy rangeland, Inner Mongolia. Journal of Arid Environments 62 (2), 309–319. Zhao, W.Y., Li, J.L., Qi, J.G., 2007. Changes in vegetation diversity and structure in response to heavy grazing pressure in the northern Tianshan Mountains, China. Journal of Arid Environments 68 (3), 465–479.