Separating grazing and rainfall effects at regional scale using remote sensing imagery: A dynamic reference-cover method

Remote Sensing of Environment 121 (2012) 443–457

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Separating grazing and rainfall effects at regional scale using remote sensing imagery: A dynamic reference-cover method G. Bastin a,⁎, P. Scarth b, V. Chewings a, A. Sparrow a, R. Denham b, M. Schmidt b, P. O'Reagain c, R. Shepherd c, B. Abbott d a

CSIRO Ecosystem Sciences, PO Box 2111, Alice Springs, NT 0871, Australia Queensland Department of Environment and Resource Management, GPO Box 2454, Brisbane, QLD 4001, Australia Queensland Department of Employment, Economic Development and Innovation, PO Box 976, Charters Towers, QLD 4820, Australia d CSIRO Ecosystem Sciences, PMB Aitkenvale, QLD 4814, Australia b c

a r t i c l e

i n f o

Article history: Received 22 September 2011 Received in revised form 20 February 2012 Accepted 23 February 2012 Available online 22 March 2012 Keywords: Benchmark Climate variability Foliage projective cover Grazing trial Ground cover Land condition Landsat Monitoring

a b s t r a c t Remote detection of management-related trend in the presence of inter-annual climatic variability in the rangelands is difficult. Minimally disturbed reference areas provide a useful guide, but suitable benchmarks are usually difficult to identify. We describe a method that uses a unique conceptual framework to identify reference areas from multitemporal sequences of ground cover derived from Landsat TM and ETM+ imagery. The method does not require ground-based reference sites nor GIS layers about management. We calculate a minimum ground cover image across all years to identify locations of most persistent ground cover in years of lowest rainfall. We then use a moving window approach to calculate the difference between the window's central pixel and its surrounding reference pixels. This difference estimates ground-cover change between successive below-average rainfall years, which provides a seasonally interpreted measure of management effects. We examine the approach's sensitivity to window size and to cover-index percentiles used to define persistence. The method successfully detected managementrelated change in ground cover in Queensland tropical savanna woodlands in two case studies: (1) a grazing trial where heavy stocking resulted in substantial decline in ground cover in small paddocks, and (2) commercial paddocks where wet-season spelling (destocking) resulted in increased ground cover. At a larger scale, there was broad agreement between our analysis of ground-cover change and ground-based land condition change for commercial beef properties with different a priori ratings of initial condition, but there was also some disagreement where changing condition reflected pasture composition rather than ground cover. We conclude that the method is suitably robust to analyse grazing effects on ground cover across the 1.3 × 10 6 km 2 of Queensland's rangelands. Crown Copyright © 2012 Published by Elsevier Inc. All rights reserved.

1. Introduction Rangelands comprise the dry subhumid to hyper-arid regions and occupy ~50% of the global land area (Friedel et al., 2000). They provide important ecosystem services by way of provisioning (forage for livestock, firewood, fresh water), support (soil formation and conservation, nutrient cycling), regulation of water and climate, and culture (e.g., cultural identity and diversity, aesthetics, tourism) (MEA, 2005). This means that rangelands have multiple, and sometimes conflicting, values for stakeholders (Diaz et al., 2007; Zendehdel et al., 2008). Associated spatial complexity of landscapes and temporal variability of production mean that monitoring and adaptive responses are critical to appropriate management (Brown & Havstad, 2004; Reed et al., 2006). At the

⁎ Corresponding author. Tel.: + 61 8 89507137; fax: + 61 8 89507187. E-mail address: [email protected] (G. Bastin).

property scale, pastoralists must manipulate grazing pressure within paddocks to optimise livestock production while ensuring longerterm supply of palatable forage species (Ash et al., 1997; Holechek et al., 1995). At regional to national scales, jurisdictional administrators require land management practices that are compatible with sustainable pastoralism, maintain biodiversity, and support viable rural communities (Novelly et al., 2008). Therefore, rangeland monitoring must provide information useful for managers operating at different scales and take account of multiple ecological processes (Havstad & Herrick, 2003). Within Australia, change in some biophysical and socio-economic properties of the rangelands is reported with varying rigour and precision through the Australian Collaborative Rangelands Information System (ACRIS, http://www.environment.gov.au/land/ rangelands/acris/index.html; Bastin and ACRIS Management Committee, 2008). However, holistic data based on multiple-use monitoring to underpin such reporting remain a major challenge (Bastin et al., 2009; Watson et al., 1996, 2007).

0034-4257/$ – see front matter. Crown Copyright © 2012 Published by Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2012.02.021

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The large spatial extent of rangelands means that monitoring ground cover using remote sensing technologies has become common; such monitoring provides repeatable, spatially distributed information about important ecosystem processes at relatively low cost (e.g., Booth & Tueller, 2003; Danaher et al., 2010; DeSoyza et al., 2000). At the most basic level, herbaceous ground cover protects the soil surface against wind and water erosion (Li et al., 2007; Ludwig et al., 1997). With increasingly sophisticated analysis, the spatial arrangement of persistent ground cover indicates ecohydrological status (Urgeghe et al., 2010; Wilcox et al., 2003) and landscape function (Bastin et al., 2007; Ludwig et al., 1997, 2007). Remotelysensed ground cover can also yield required inputs for modelling changes in soil organic carbon at multiple scales (Allen et al., 2010; Schimel et al., 1997). In Queensland Australia, the region of this research, estimates of ground cover are derived on an annual or biennial basis from Landsat TM and ETM+ data for the entire state (~ 1.7 × 10 6 km 2, 80% of which is rangelands) (see Section 2 for information on image processing). The ground cover index (GCI) is calculated from a multiple regression model between Landsat bands 3, 5 and 7, and ground cover measured at sites covering much of the variation in climate, soils and vegetation throughout Queensland (Karfs et al., 2009; Scarth et al., 2006). GCI integrates total organic soil surface cover, including green and senescent grasses and forbs, grass and tree litter and cryptogams. Application and interpretation of GCI is limited by the relatively high root mean square (RMS) error in the calibration regression (approximately ±13%; Scarth et al., 2006). This RMS error is particularly important when surface cover levels are low and when wooded

vegetation is dense. Danaher et al. (2010) recommend use of GCI only in areas having wooded foliage projective cover (FPC) ≤20% where wooded FPC is the vertically projected percentage cover of photosynthetic foliage from tree and shrub life forms >2 m in height. The generally sparse foliage and irregular crown shapes of most Australian plant communities means that FPC is a more reliable measure of wooded cover than canopy cover (Specht & Specht, 1999). Spatio-temporal analysis of rangeland ground cover to separate management effects from landscape and climate variability presents particular challenges (Pickup, 1989; Washington-Allen et al., 2006; Wessels et al., 2007). While landscape stratification can reduce the effect of spatial variability, temporal changes in ground cover are largely driven by inter-annual rainfall variability (Fig. 1). It is only cover change counter to seasonal expectations that is reliably indicative of management effects, suggested for image analysis by McVicar & Jupp (1998) and demonstrated in site-based monitoring data in parts of the Australian rangelands (Bastin and ACRIS Management Committee, 2008; Novelly et al., 2008). For high-frequency timeseries of remotely sensed data, methods exist for detrending seasonal effects (e.g., Donohue et al., 2009; Kennedy et al., 2007; Lu et al., 2003; Verbesselt et al., 2010), but in Queensland the annual to biennial frequency of GCI imagery precludes this form of analysis for separating management effects from those due to seasonal variability. Relatively undisturbed areas, such as stock-proof exclosures and areas remote from watering points, can provide ecological benchmarks (references) for separating seasonal and management effects (Lange, 1969; Pickup et al., 1994; Washington-Allen et al., 2006). Much of applied rangeland ecology is founded on the notion of 10

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Fig. 1. Mean ground cover (%GCI) and wet-season decile rainfall for the 10,070 km2 Cape-Campaspe Plains subIBRA in north east Queensland (IBRA = Interim Biogeographic Regionalisation for Australia; IBRA, 2008). Rainfall decile was calculated by spatially averaging accumulated monthly rainfalls (between November and April) compared to the long-term (1890–2008) record. Rainfall data were obtained from the Australian Bureau of Meteorology (http://www.bom.gov.au/watl/, accessed 21 January 2011).

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benchmarks and comparisons of managed areas with benchmarks (e.g., Liang et al., 2009; Miller, 2008). Reference levels of ground cover have previously been applied to remote sensing-based monitoring of cattle grazing impact in the arid rangelands of central Australia (Pickup & Chewings, 1994; Pickup et al., 1994, 1998). GIS-defined areas remote from water points (e.g. >8 km) in large paddocks (typically >100 km 2) provide a seasonal signal of cover against which to compare the signal of areas closer to water that are more intensively grazed, and potentially impacted, by livestock (Bastin et al., 1993). However, there are limitations to applying this ‘grazing gradient’ method to Queensland GCI data. Paddocks in much of Queensland's rangelands are smaller than in central Australia (tens compared with hundreds of square kilometres) and reference areas sufficiently remote from water generally don't exist. Furthermore, the grazing gradient method requires an operator to evaluate the suitability of water-remote areas as a reference, which is obviously impractical for multi-year analysis of GCI imagery for Queensland's 1.3 × 10 6 km 2 of rangelands. Therefore, an alternative approach to benchmark identification is required for effective monitoring using the Queensland GCI data. In this paper we propose a largely automated remote-sensing method for estimating seasonally adjusted trend in GCI, referenced against benchmarks identified solely from a sequence of annual or biennial GCI images. For any pixel in any image (a focal pixel), the benchmark is defined as the set of pixels, within a surrounding window of fixed size, that have the most persistent non-wooded ground cover across all images in the sequence (reference pixels). Persistent ground cover during drier years indicates a benchmark defining a resilient and productive rangeland landscape, including low erosion potential and high landscape functionality (Ludwig et al., 1997). Differences over time between the ground cover of the focal pixel and the average cover of the corresponding set of reference pixels provides the seasonally interpreted measure of management effects, except where wildfires have caused cover changes. Seasonally adjusted ground cover can be examined at pixel level or, more usefully, pixel values within an area of interest (e.g. paddock) can be spatially averaged to determine management effects. We first describe preparatory processing required for GCI and then provide the conceptual basis for the dynamic reference-cover method. Next, we describe a series of tests (with results) to best parameterise the method and then test its performance at increasing spatial scale and with decreasing certainty of grazing effects on cover change. We conclude with a discussion of the method's suitability and steps leading to its implementation. This progressive development and testing is mapped (Fig. 2) to help illustrate processing steps and the structure of the paper. 2. Image processing Eighty nine Landsat TM and ETM+ images are acquired on an annual basis in the mid to late dry season (June–October) to cover all of Queensland. These dates are selected to enhance spectral contrast between evergreen tree and shrub canopies and the predominantly senescent ground cover. All images are corrected to minimise the confounding effects of geometric distortion, radiometric variability and illumination geometry using the procedure described in Danaher et al. (2010). Areas of cloud, associated shadow and water contamination are masked. The pre-processed images are used to produce two state-wide products for change detection, wooded foliage projective cover (FPC) and the index of ground cover (GCI) (Fig. 2, Danaher et al., 2010). Both indices are calculated using models linked to an intensive field sampling program whereby 537 sites covering a wide variety of vegetation, soil and climate types were sampled to measure overstorey and ground cover, following the procedure outlined in Muir et al. (2011). Parametric techniques such as multiple linear regression

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(MLR) are commonly employed to estimate fractional vegetation cover (Lucas et al., 2006). MLR is similar to linear spectral unmixing when the calibration data are representative of the a priori distribution of the cover fraction (Settle & Campbell, 1998). FPC was then calculated using the model-type developed by Armston et al. (2009) where the predictors consist of transformed Landsat bands 2–7, cross products of these bands and a measure of vapour pressure deficit (VPD). GCI was similarly calculated but using only Landsat bands 3, 5 and 7 (Scarth et al., 2006). The final model form for GCI had an R 2 value of 0.93 when regressed against the ground data. As noted in the Introduction, application and interpretation of GCI is limited by the relatively high root mean square (RMS) error in the calibration regression (~±13%; Scarth et al., 2006). This RMS error is particularly important when surface cover levels are low and when wooded vegetation is dense. 3. Conceptual development We developed and tested the dynamic reference-cover method for a spatially heterogeneous area of north-east Queensland centred on Charters Towers (20.00 ° S, 146.14 ° E) and then applied the method to several adjacent landscapes, which are spatially less complex. The Charters Towers region has a semi-arid tropical climate with 80% of the rainfall occurring between November and April (the wet season). Median wet-season rainfall (1890–2008) for Charters Towers is 477 mm. The average length of pasture growth during the wet season is approximately 100 days (Ash et al., 2011) but this is highly variable in response to large inter-annual rainfall variability (e.g. wet-season decile 1 rainfall for Charters Towers is 260 mm). 3.1. Study area The method has been developed to cater for the heterogeneous nature of ground-cover distribution on hillslopes and their associated colluvial footslopes draining to watercourses and larger creeks. Grazing by cattle radially away from reliable water superimposes additional spatial complexity on ground-cover dynamics within each landscape element. The 4210 km 2 of ‘goldfields’ country surrounding Charters Towers is characterised by gentle hillslopes of highly erodible red, texturecontrast soils overlaying granodiorite (Bartley et al., 2006; Rogers et al., 1999). These slopes drain to tributary streams and creeks of the Burdekin River. Hillslopes have an open woodland of narrow-leafed ironbark (Eucalyptus creba) and red bloodwood (Corymbia erythrophloia) and scattered shrubs (mainly conkerberry, Carissa ovata). Wooded cover is thicker on the footslopes and is generally >20% in larger drainage lines and creeks (i.e., ground cover not calculated and thus excluded from analysis). In its undisturbed state, hillslope groundcover is dominated by native perennial grasses structured into patches of 50 to >200 m2 that regulate the downslope flow of water and sediments to drainage lines (Ludwig et al., 2005). Grazing reduces ground cover in the short term and in the longer term the hillslope patterning of vegetation can change where grazing is excessive. At finer scales (b1 ha), cattle preferentially graze the regenerating leaf material of previously grazed palatable, perennial grass tussocks (Mott, 1987). Repeated ‘patch grazing’ may kill individual plants resulting in patch disintegration (Ash et al., 1997). The expanding area between patches is colonised by shorter-lived species, particularly the introduced stoloniferous grass Indian couch (Bothriochloa pertusa). With drought and repeated heavy grazing these inter-patch areas may progress to a scalded B horizon where degradation is most severe (Bartley et al., 2010a). The net effect at larger scales is that loss of ground cover on upslope areas increases runoff and sediment transfer to footslopes and drainage lines (Bartley et al., 2010b). Heavy grazing, and an associated reduction in fire frequency, can also lead to woody thickening (Burrows et al.,

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Annual Landsat Imagery

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Compute ΔGC dry2 = GCI’dry2 – GCI ref2 Section 3.4

Compute ΔΔGC = ΔGC dry2 – ΔGC dry1

Testing and Validation Interpret statistics Validate with ground data, expert knowledge Section 5, Figure 10

Spatial unit analysis and statistics Sections 3.4, 3.5

Pixel level analysis and statistics Sections 3.4, 3.5

Fig. 2. Flow chart showing procedure for preparing data, parameterising and using the dynamic reference cover method to calculate reference cover and subsequent analysis to determine grazing effects.

1990; Scholes & Archer, 1997), further increasing grazing pressure on remaining grassy patches. Given the complexity of ground-cover dynamics at multiple spatial scales in response to both natural landscape variation and grazing, there is no simple, spatially explicit position in the landscape for determining reference cover. Even in undisturbed landscapes, the highest ground cover is likely to be in areas of maximum run-on (typically footslopes), and these run-on locations do not provide a representative or benchmark ground cover for upslope areas. 3.2. Derived measures of ground cover 3.2.1. Persistent ground cover The most persistent ground cover is present in the driest of years and thus the archive of GCI images since 1986 was examined to establish a measure of cover persistence in each pixel (Fig. 3a). Rather than use the absolute minimum GCI observed, we fit a beta distribution to the set of annual dry-season GCI values for each pixel through time and then calculate the distribution's fifth percentile as the measure of persistence (Fig. 3b; output = minimum ground cover image, GCImin).

The use of a fitted beta distribution to predict a minimum GCI for each pixel is preferred over using the simple minimum value recorded in each pixel as it provides a standardised fifth-percentile minimum that is not dependant on the number of images in the time series and reduces the sensitivity of the model to extreme low or high cover levels. The beta distribution has been used for statistical modelling of vegetation cover for many years, since it is bounded between 0 and 100% and the shape of the frequency distribution is easily controlled by two shape parameters (Chen et al., 2008). By fitting the beta distribution to the time series of GCI data for each pixel, the Queensland Government operationally produces 5th, 50th and 95th percentile images for all of Queensland (Danaher et al., 2010) and the first of these standard products was used in this analysis to define GCImin (Figs. 2 and 3). 3.2.2. Wooded vegetation as a component of persistent ground cover Currently, the Queensland Government uses remote sensing methods to monitor ground-cover at the pixel level in areas where the wooded vegetation cover is less than 20% (Danaher et al., 2010; Kitchen et al., 2010). Ground cover is only estimated for pixels below this threshold, but can still contain a degree of wooded cover,

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Fig. 3. Sequence of temporal and spatial procedures to calculate a reference GCI value for the focal pixel. Italicised section numbers refer to numbered headings in the text. The focal pixel is at the centre of the neighbourhood search area, which we recommend as >1000 pixels and lines, but illustrated here as an enlarged 100 pixel and line subset. The multitemporal image sequence (a) is used to calculate minimum pixel GCI (b), in this case bypassing on pixels with >20% wooded FPC. Wooded FPC is next subtracted from the temporal minimum image and dry-year images to be analysed so as to adjust for pixel-level wooded cover (c). The 90–95 percentile of the adjusted temporal minimum GCI image (d) is then used to identify reference-pixel locations in those two dry years (t1 & t2) selected for analysis (e). For this example, the mean GCI′ of these two pixel sets forms the reference GCI value of the focal pixel for each time period (table at (f)). The difference between reference and actual GCI (ΔGC) is also shown for each time period, as is the overall seasonally adjusted change in ground cover (ΔΔGC). Note that the representation of GCI changes between the upper (all dates) and lower (dry-time) sets of images, as shown by legends 1 and 2.

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typically scattered trees and shrubs. It is possible that pixels in higher percentile ranges of GCImin include a greater wooded component than pixels in lower percentiles. If this occurs, then pixel locations for the increasingly higher percentile ranges provide increasingly less suitable reference cover for focal pixels with no or minimal wooded cover. We compared mean GCI and wooded FPC for five-percentile ranges of GCImin across the Charters Towers Landsat scene (WRS-2 path 95, row 74; Fig. 1). Mean wooded FPC was higher and positively correlated with mean GCImin between its 80th and 100th percentiles (Fig. 4, dashed line) while the difference between pixel-level minimum ground cover and wooded FPC reduced this correlation (Fig. 4, solid line). In the current absence of a suitably robust and operational mixture model to segregate the fractional components of wooded and non-wooded cover (see Discussion), we propose that GCI be adjusted for probable wooded cover based on available pixel-level wooded FPC data. This adjustment (Figs. 2, 3c) is applied as: ′

GCI min ¼ GCImin –FPC ′

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3.3. Reference pixel determination As well as having the most persistent ground cover in the driest of years, reference pixels must be located within a neighbourhood of the focal pixel that transcends local management (grazing) effects, and has essentially the same climate, particularly rainfall (Fig. 2). For our dynamic reference-cover method, we implemented this requirement as follows. Using a moving-window neighbourhood of fixed size around each pixel, we search the GCI′min image for each focal pixel's reference pixels with high persistent ground cover. The most persistent ground cover is usually located exclusively on footslopes and may not be a suitable benchmark for upslope locations because of the former's run-on geomorphological characteristics and higher mean wooded cover. Thus we define reference pixel populations below the 100th percentile GCI′min (e.g., either the 85–90 or 90–95 percentile ranges) (Fig. 3d). The mean GCI′ value at the reference-pixel locations for the monitoring year of interest (GCIref) provides the reference ground cover for the focal pixel in the same monitoring year (Fig. 3e) with derived statistics (Section 3.4) shown in Fig. 3f. Where the focal pixel is woody (FPC > 20%), or is a water body, cloud or cloud shadow, the flag value of the input ground-cover image is echoed to the output reference-cover image. A separate flag value is written where >85% of pixels in the search neighbourhood have

Spatially averaged FPC (%)

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wooded FPC> 20% and insufficient candidate pixels are deemed present to calculate a realistic reference level of ground cover. 3.4. Derived statistics Two sets of statistics are derived from reference ground cover (Fig. 3f). For each suitably dry monitoring year t, the pixel-level difference between focal (actual) and reference ground cover is calculated as: ΔGC ðt Þ ¼ GCI’ðt Þactual –GCI ðt Þref :

ð3Þ

Suitably dry years for analysis are selected on the basis of wetseason rainfall ranked against the long-term record; e.g., accumulated November-April rainfall from the closest recording station that is ≤decile 3. Change in the ΔGC for a pixel between successive dry years t1 and t2 is called ΔΔGC and calculated as a simple difference: ΔΔGCðt 1 ; t 2 Þ ¼ ΔGCðt 2 Þ–ΔGCðt 1 Þ:

ð4Þ

Pixel-level ΔGC and ΔΔGC can be spatially averaged for defined GIS map polygons (typically paddocks or properties) and reported as a simple spatial statistic. ΔGC is the seasonally interpreted level of ground cover for the polygon in a particular dry year and ΔΔGC is the polygon-level change between dry years. 3.5. Interpretation framework The dynamic reference-cover method provides a climaticallyadjusted level of ground cover change for each focal pixel or a management unit, such as a paddock, where the spatial mean of aggregated pixels is calculated. Since dry years selected for monitoring are rarely identical in terms of their effect on ground cover, antecedent rainfall and its temporal distribution can affect individual and spatially averaged GCIref independently of any effects of management. ΔΔGC thus provides a measure of trend in management effects. The classification of expected types of change (shown schematically in Fig. 5) usefully provides an interpretative framework for judging the effects of grazing management separate to climate variability. 4. Sensitivity analysis Identification of reference pixels and calculation of pixel-level GCIref is affected by the size of the search window around each focal pixel and by the percentile range applied to the GCI′min image (Fig. 2). Here, we demonstrate the sensitivity of these two variables and, based on recommended settings, characterise the locations of reference pixels and their frequency of use in calculating reference ground cover for an analysis area near Charters Towers.

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Percentile of temporal minimum ground cover Fig. 4. Spatially averaged levels of wooded FPC for five-percentile ranges of minimum ground cover (GCImin) prior to, and following, pixel-level adjustment for wooded FPC. Both images were composited for the period 1986–2010. Analysis area is the Charters Towers Landsat scene (WRS-2 path 95, row 74) where FPC ≤ 20%.

Locating reference pixels within a smaller search window around each focal pixel is efficient in terms of computation. However, a small window also means that reference-pixel locations may be more influenced by local management effects (e.g., the search window is entirely within a heavily grazed paddock). Enlarging the search window reduces this risk, but increases the probability that the neighbourhood transcends natural environmental gradients, such as changes in longterm annual rainfall or spatially varying rainfall in the year of monitoring. The optimal neighbourhood search window would transcend local variability due to management, and minimise computing time. We tested the effect of varying the search window on GCIref for hillslope country in the Charters Towers area using the 2004 image (see Fig. 1 for indicative preceding rainfall). We evaluated five window sizes between 500 pixels and lines (156 km 2, Landsat TM

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Fig. 5. A schematic framework for interpreting change in ground cover after allowing for seasonal effects. Change in actual ground cover between two dry periods is shown by the solid line in each plot. Change in reference cover is depicted with the dashed line. Columns represent wetter, similar and drier seasonal conditions at the second time period relative to the first. Rows represent where, after accounting for each set of seasonal conditions, ground cover improved, remained unchanged or declined.

resampled to 25-m pixel size) and 2000 pixels and lines (2500 km 2) around 24 randomly selected focal pixels such that none of the smallest windows (500 pixels and lines) overlapped. This test used the 90–95 percentile range of GCI′min. We first calculated the spatial coefficient of variation (CV) of GCIref values for each window size at the 24 locations and then calculated the mean and standard error of CV for each window size. The mean CV of GCIref and its associated standard error decreased as the size of the search window around each focal pixel increased

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(Fig. 6). This decrease was consistent across the range of window sizes indicating that the effects of local variability due to management were minimised with larger window sizes. Based on these results, we applied a window size of 1500 pixels and lines for all subsequent testing and analyses. 4.2. Percentile range of minimum ground cover image It is necessary to confirm that the higher percentile ranges of GCI′min have the most stable and therefore persistent ground cover across a wide range of seasonal conditions. This is confirmed if declining percentile ranges of GCI′min produce declining spatially averaged levels of reference cover in a dry year. For an arbitrary focal pixel located on hillslope country near Charters Towers using a 1500 pixel and line search window, reference cover was higher and relatively more stable in dry years based on reference-pixel locations specified by higher percentile ranges of the GCI′min image (Fig. 7). Conversely, reference cover was very low and highly variable when calculated from locations specified by the lowest percentile ranges of GCI′min in the driest years. Similar results were found for other test areas (results not shown). This confirms that higher percentile ranges of the GCI′min image are appropriate for deriving the reference cover for focal pixels. As there was little difference in GCIref between the upper three percentiles (Fig. 7), but a much higher tree and shrub cover in the 95–100 percentile range (Fig. 4), we used the 90–95 percentile range for calculating reference levels of ground cover.

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Fig. 7. (a) Mean percentage GCI′ from 1986 to 2008 of pixels located at the highest and lowest 5-percentile ranges of GCI′min for a 1500 pixel and line (1406 km2) test area near Charters Towers. Mean annual levels of percentage GCI′ for intervening percentile ranges (15–20 to 80–85) plot between the 10–15 and 85–90 percentile ranges. (b) Wet season (November–April) rainfall preceding each year of image acquisition and the long-term (1890–2008) median rainfall for Charters Towers.

4.3. Locations of reference pixels and frequency of selection Reference pixels based on the 90–95 percentile range of GCI′min are located in more densely vegetated parts of the landscape. For an arbitrary area of hillslope country near Charters Towers (Fig. 8), reference landscape components include hillslopes and run-on areas adjacent to watercourses, both of which retain higher vegetation cover. Intensive field surveys over a number of years have shown that native tussock grasses persist, even in drier years, for areas of generally higher vegetation cover (Bartley et al., 2006, 2010a). The more densely timbered watercourses were excluded from analysis because they have FPC > 20%. If all pixels in any 1500 pixel and line analysis area have wooded FPC ≤20%, then each pixel can potentially be selected 2.25 × 10 6 (1500 2) times when calculating reference ground cover for the 1406 km 2 area of a window (i.e., 2.25 × 10 6 * 25-m pixels). We calculated the frequency of pixel selection for an enlarged area to that shown in Fig. 8 using the 90–95 percentile range of GCI′min. We found a large range in frequency of selection (once to ~ 500,000) when calculating reference ground covers for all focal pixels in the test area (part area shown in Fig. 9a). For this small 25 km 2 demonstration area, creeks and watercourses (linear dark brown features) were excluded as candidate reference areas because they had wooded FPC >20%. Many reference pixels occur adjacent to watercourses but

are also concentrated on well-vegetated upland areas, particularly in a NW–SE direction near the middle of the image area, but also in the southern part. The north east portion of the image is a separate heavily grazed paddock with low ground cover, and the few reference pixels in this area occurred near watercourses. There was no statistical relationship between the frequency in selecting reference pixels and the position of reference pixels in the landscape as determined by catchment size (r = −0.010, n = 27,801, Fig. 9b). For this test we derived catchment size from a pit-filled 30-m Shuttle Radar Topography Mission digital elevation model (resampled to 25-m pixel size) for a 400 km 2 test area surrounding Fig. 9b. These examples, and evaluations for other test areas, provide confidence that reference-pixels are located where ground cover is more persistent, and that these areas are not exclusively associated with lower run-on parts of the landscape. These findings indicate that our reference-cover method can dynamically locate suitable reference pixels across different landscape elements according to the image characteristics of the search window. There does not appear to be undue bias towards downslope areas where maximum run-on occurs. 5. Testing and validation Using a 1500 pixel-line window size about each focal pixel and the 90–95 percentile of GCI′min, we tested the accuracy and robustness of

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treatments and in the ungrazed exclosure (Fig. 10). Both treatments are continuously grazed, with MSR stocked at the calculated long term carrying capacity of the land types involved (8 ha/animal equivalent, AE = 450 kg steer) and the HSR stocked at 4 ha/AE, twice the long term carrying capacity. Reference cover for each focal pixel within each paddock was calculated for the dry years of 1995 (before the start of the trial) and for 2004 using the 1500 pixel-line window and 90–95 percentile range of GCI′min. Resultant pixel-level GCI′, GCIref and ΔGC values for each time period were then spatially averaged to give GCI′ , GCI ref and ΔGC for each paddock. Similarly, pixel-level ΔΔGC was also averaged by paddock. We found that spatially averaged levels of actual and reference ground cover were lower in 2004 than 1995 (Fig. 11a). The decline in ground cover was much greater in the HSR paddocks. Because of the small area of the grazing trial relative to the 1500 pixel-line window, all paddocks had very similar levels of spatially averaged reference cover at each time interval and only one pair of dry-period values is shown here. Seasonally

a 1 km Fig. 8. Locations of reference pixels (shown as red at 25-m Landsat TM pixel resolution) based on the 90–95 percentile range of GCI′min. Reference pixels are shown on a 16 km2 area of true-colour Quickbird imagery acquired near Charters Towers in May 2004 (pixel size 2.4-m). Bright areas represent bare soil, brown depicts exposed redbrown soil with very low cover, and dark areas are dense wooded vegetation in creeks.

>450,000 f requency of selection

5.1. Smallest scale: Wambiana grazing trial; exclosed, heavy and moderate stocking treatments

1

1 km

b

>625 62.5 6.3 0.6

The grazing trial is located on low-fertility tertiary sediments on the Wambiana pastoral lease (20.54 ° S, 146.13 ° E) approximately 60 km south of Charters Towers (Fig. 10). The trial's objectives are to compare the performance of different grazing strategies in a savanna woodland and to generate empirical evidence on which to base commercial grazing-management strategies (O'Reagain et al., 2009). Prior to 1997, the trial area was grazed as part of the larger surrounding commercial paddock. Then in 1997 five grazing treatments were replicated across three soil types within small paddocks of approximately 100 ha (O'Reagain et al., 2008). A small area was exclosed and protected from grazing. Our interest here is in actual and seasonally adjusted change in ground cover in the heavy (HSR) and moderate (MSR) stocking-rate

0.1

catchment size (ha)

the dynamic reference cover method for discriminating meaningful change in ground cover within an area of 160,000 km 2 in the Charters Towers region. These analyses were conducted at three scales, ranging from small (~100 ha) research paddocks to large commercial cattle stations (most >10,000 ha) (Figs. 2, 10). Our primary interest was in larger-scale analysis such that the results for bioregional groups of commercially grazed beef properties are, in time, reported through ACRIS. The smaller-scale case studies were included to provide adequately controlled and well understood examples to initially test the suitability of the method. The main land types of the general area include the previously described ‘goldfields’ country surrounding Charters Towers, tall woodlands of narrow-leaved ironbark and red bloodwood above native perennial grasses on level to gently undulating clay loams and a mixed woodland of eucalypt and Acacia species over perennial grasses on undulating coarse textured, generally infertile, soils (Rogers et al., 1999).

Fig. 9. (a) Locations of reference pixels and their frequency of selection (legend) in calculating reference ground cover for part of a 1500 pixel and line (1406 km2) area. The image area is 25 km2 showing Landsat TM bands 2 (blue), 3 (green) and 4 (red). (b) Locations and frequency of selection of reference pixels with regard to catchment size for the same example area. Catchment size has log10 scaling.

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Soil type

Smallest scale: Wambiana grazing trial HSR SOI

Grazing treatment rep = replicate VSR Seasonally variable stocking rate SOI Stocking rate set according to Southern Oscillation Index HSR Heavy stocking rate MSR Moderate stocking rate R/Spell Rotation/spell across 3 paddocks Exc Exclosure

MSR

rep 1

VSR

duplex clay

rep 1 rep 1

rep 1

black cracking clay red/yellow earths

VSR rep 2

MSR rep 2

Exc HSR rep 2

SOI rep 2

Largest scale: Commercially grazed beef properties

Intermediate scale: Wet-season spelled paddocks

Charters Towers

Wambiana

Stations in: consistently good condition consistently poor condition

study area

Qld

improving condition declining condition

Fig. 10. Three scales of spatial analysis to evaluate the performance of the dynamic reference-cover method for analysing seasonally adjusted change in ground cover. The smallest scale is a set of paddocks (~ 100 ha) in a grazing trial. The intermediate scale is a group of wet-season spelled paddocks on a commercially grazed property, and the largest scale is a set of beef properties with a priori determined trends in land condition.

adjusted, spatially averaged ground cover (ΔGC) increased between 1995 and 2004 in the exclosure, remained relatively constant under moderate stocking and decreased substantially in the HSR treatment (Fig. 11b). Based on our interpretation framework (Fig. 5); there was a grazing-related loss of ground cover in HSR paddocks during the experimental period, while the decline in MSR paddocks was largely attributable to seasonal differences. This trend is confirmed by the ΔΔGC statistic, which was strongly negative for HSR paddocks (rep 1 = −23.2, rep 2 = −14.3), close to zero for MSR paddocks (rep 1 = 1.3, rep 2 = −1.6) and positive for the exclosure (11.8). This test provides confidence that the dynamic reference-cover method can discriminate the effects of grazing treatment on ground cover where such effects are dominant to those caused by interannual seasonal variability in rainfall (illustrated in Fig. 1). Although this analysis is restricted in terms of regional application of the

method, the trial paddocks are sufficiently large that Landsat TMbased monitoring has demonstrated real changes in ground cover due to stocking rate differences. 5.2. Intermediate scale: wet-season spelled paddocks Spelling or destocking paddocks during the wet season is a recommended strategy for increasing ground cover and the abundance of native palatable perennial grasses (Ash et al., 1997, 2002, 2011; Chilcott et al., 2003). This practice was implemented across three paddocks of hillslope country on granodiorite soils by the managers of a pastoral lease north east of Charters Towers (Fig. 10). Wetseason spelling was applied in the three years prior to and including 2004 (Bartley et al., 2010a,b). Prior to this spelling, the three paddocks were continuously stocked. Spatial statistics of ground cover were calculated for the three paddocks as a whole (combined

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80

a

5.3. Largest scale: commercially grazed beef properties

75 70

GCI' (%)

65 60 55 50 HSR (rep 1) HSR (rep 2) MSR (rep 1) MSR (rep 2) Exclosure reference GCI

45 40 35

1995

10

2004

b

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0

ΔGC

-5 -10 -15 -20 -25 -30

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HSR (rep 1) HSR (rep 2) MSR (rep 1) MSR (rep 2) Exclosure

-35

1995

2004

Fig. 11. Change in spatially averaged actual and reference levels of (a) ground cover, and (b) seasonally adjusted cover ( ΔGC) between 1995 and 2004 in the moderate (MSR) and heavy (HSR) stocking treatments of the Wambiana grazing trial and an adjacent ungrazed exclosure. Paddock locations are shown in Fig. 10.

area = 21.3 km 2) and compared with that on an adjacent control property with conventional grazing management (Bartley et al., 2010b). The spatially averaged level of ground cover increased considerably with wet-season spelling and produced a large increase in seasonally adjusted cover ( ΔΔGC = 25.6) (Fig. 12). Spatially averaged ground cover was similar in 1995 and 2004 on the control property while seasonally adjusted cover decreased (ΔΔGC = −6.4). The increase in ground cover with wet-season spelling was confirmed on the ground by Bartley et al. (2010b). We conclude that wet-season spelling promoted a relatively large increase in ground cover compared with the lesser improvement or loss occurring under conventional grazing management on surrounding properties. The ability to discriminate positive management-related differences in ground cover at the scale of commercially grazed paddocks provides further confidence in the robust performance of the dynamic referencecover method.

Spatially averaged change in actual and reference ground cover was examined for 24 commercially grazed beef properties in the Charters Towers region (Fig. 10) where land condition was assigned a priori as consistently good, consistently poor, improving or declining during the period 1995–2004. Condition rating was based on the expert judgement of experienced rangeland ecologists who made ground-based visual inspections covering parts of each property. Land in good condition has relatively dense palatable perennial grasses, a moderate to high ground cover relative to that of trees and shrubs, and minimal evidence of erosion or weeds (Chilcott et al., 2003; Karfs et al., 2009). Almost half of the commercial beef properties were rated as being in consistently good condition. In 2004, their actual ground covers (GCI′ actual ) were close to the seasonally adjusted reference ground cover (i.e., ΔGC 2004 slightlyb 0) (Fig. 13). They had no real change to a considerable increase in seasonally adjusted cover between the dry years of 1995 and 2004 (ΔΔGC ≥ 0). Correspondingly, nearly all beef properties ranked as being in consistently poor condition had more negative values for ΔGC 2004 and little change to a large decrease in seasonally adjusted cover between the two dry years ( ΔΔGC ≤ 0). Properties considered to have improved in condition had either no change or a small increase in ΔΔGC between 1995 and 2004 after allowing for seasonal effects, as did the two properties that were considered to have declined in condition. At this largest scale of analysis there was broad correspondence between a priori determination of land condition by expert judgement and our analysis of change in seasonally adjusted ground cover based on remote sensing. However, there were also some notable exceptions. Two cattle properties judged to have declined in condition did not lose ground cover after allowing for seasonal variation. In these cases, it is plausible that ground cover was maintained while the composition of palatable perennial grasses continued to decline, or there was an increased abundance of weedy species. Also, several properties rated in consistently good condition gained cover after seasonal adjustment. This could occur where palatable perennial grasses are a stable component of the pasture but their cover fluctuates with seasonal quality. The four ‘improving’ properties had little or no real improvement in ground cover, and may instead reflect recruitment of desirable perennial grasses or reduced weediness. It could also be due to less active local erosion when visual assessments were made, but when averaged across the whole of each station there were no seasonally adjusted changes in ground cover. Countering these possibilities is the local experience of two of the authors that, at this spatio-temporal scale of visual assessment, it is simply more difficult to objectively recognise trend than to consistently rate land condition as either good or poor. 6. Discussion Our dynamic reference-cover method presented here provides a solution to the challenge of objectively separating management effects from those due to inter-annual climate variability (principally rainfall). We demonstrated that this method applies to the analysis of remotely sensed monitoring data for an extensive area of Queensland's savanna rangeland. The method achieves this result by comparing trends between relatively dry years for all pixels vs. reference pixels in a way that requires neither prior expert knowledge nor other ground-based data to identify reference areas. It does not require a high frequency of imagery such as is necessary to implement the temporal decomposition methods of Donohue et al. (2009), Verbesselt et al. (2010) and others. In applying the reference-cover method to rangelands, we have excluded (or at least minimised) the potentially confounding effects of wooded plant cover by following the recommendations of

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-15

65 wet-season spell GCI'

60

conventional management GCI' wet-season spell Δ GC

-20

conventional management Δ GC

55 -25

-30

45

ΔGC

GCI' (%)

50

40 -35 35 -40 30

-45

25 1995

2004

Fig. 12. Change in spatially averaged actual (GCI′) and seasonally adjusted ground cover (ΔGC) between 1995 and 2004 for wet-season spelled paddocks on a commercial beef property near Charters Towers and a nearby control property with conventional grazing management. The location of wet-season spelled paddocks is shown in Fig. 10.

Danaher et al. (2010) to exclude areas with wooded FPC > 20%. In addition, we recommend that GCI in less wooded pixels be adjusted for any residual wooded cover (GCI′ = GCI − FPC) until a prototype mixture model of multitemporal fractional ground cover (Scarth et al., 2010) is further developed and made operational to improve the discrimination between ground and wooded cover. Measured rangeland trend can be summarised for any paddock, property, district or region of interest as a single number (the value of ΔΔGC) whose sign and magnitude clearly aligns with traditional assessments of trend in range condition (i.e., ΔΔGC > 0 is “improving condition”; ΔΔGC b 0 is “declining condition”) and whose magnitude can be evaluated against the no-trend null model ( ΔΔGC = 0). We demonstrated that the reference-cover method works well at smaller local scales, and could also be automated for use in assessing change over very large areas (e.g., all 1.3 × 10 6 km 2 of rangelands in Queensland). The reference-cover methodology is conceptually and philosophically grounded in the well-founded ecological concept that persistent ground cover of native species generally indicates high landscape 30 20

ΔΔGC

10 0

-10 -20 consistently good condition consistently poor condition improve in condition decline in condition

-30 -40 -50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

ΔGC2004 Fig. 13. Scatterplot of commercial beef properties ranked in different condition states according to their difference between spatially averaged levels of actual and reference ground cover (ΔGC) in 2004 and change in seasonally adjusted cover (ΔΔGC) between the dry years of 1995 and 2004. The location of properties is shown in Fig. 10.

functionality and good rangeland condition. Further, there is evidence that rangeland ground cover that persists during times of environmental stress (e.g., drought) usefully indicates protection against erosion and efficiency of biogeochemical cycling (Bartley et al., 2006, 2010a,b; Ludwig et al., 1997, 2007), with flow-on benefits to ecosystem productivity and biodiversity (Alcaraz-Segura et al., 2009; Duro et al., 2007; Hill & Hanan, 2010). Monitoring ground cover dynamics alone obviously does not provide stakeholders with complete information about the status of rangelands' broader suite of ecosystem services or multifunctional values but it does provide a good indicator of the core functioning and health of rangelands and is consistent with a long history of development of theory and testing of cover change in previous literature. Within our study area, there are exceptions to these functional relationships. For example, the introduced stoloniferous Indian couch can generate relatively high ground cover in wetter years but it has reduced landscape functionality compared with the same cover of native perennial grasses (Bartley et al., 2010a). Our method, being remote sensing-based, cannot detect pasture compositional changes caused by grazing that are not reflected in similar changes in persistence of ground cover. Mostly, however, higher levels of persistent residual ground cover within the same environmental setting in dry periods provide a useful benchmark against which to judge the effects of grazing management. Implementing the reference-cover method over large areas requires automatic detection of suitable reference pixels prior to calculating an appropriate level of reference or benchmark cover. To this end, the high percentile ranges of the GCImin image provided a novel and robust criterion for such a reference pixel set. This was confirmed by the relatively invariant nature of ground cover through time for pixels in high percentile cover ranges. The method's focus on estimating change between successive droughts rather than wetter years also arises from the ecological and management significance of persistence, since it is during drier periods that the largest and thus most reliable measures of differences between each focal pixel and its corresponding set of reference pixels occurs. Equally important to discriminating management effects from those due to climate variability is an interpretation framework that caters for the degree of temporal variability experienced. We used an interpretive framework in the form of a meta-analysis related to the method applied by the ACRIS for inferring management-related effects on site-based vegetation monitoring data (Bastin and ACRIS

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Management Committee, 2008; Novelly et al., 2008). ACRIS uses cross tabulation to report the proportion of sites showing improvement, no change or decline in different indicators of rangeland functionality against terciles of seasonal quality based on rainfall contributing to the monitored response. Our interpretive framework applied here provides additional explanatory power because it is focused on ground cover change between successive dry years (i.e., the ‘belowaverage’ seasonal quality component of the ACRIS matrix), and it recognises that seasonal differences among such dry years can still affect remotely-sensed ground cover separate to management. Our dynamic reference-cover method successfully detected management-related change in ground cover between successive dry years in Queensland tropical savanna woodlands. It detected changes in ground cover caused by extended periods of changed management (e.g., increased stocking rates in the Wambiana grazing trial and decreased stocking rates during the growing season at a wetseason spelled property); findings verified by field measurements (Bartley et al., 2010a,b; O'Reagain et al., 2008). However, at the scale of the set of commercial beef properties, where rangeland condition was subjectively assessed by rangeland field ecologists, findings were less clear. Although there was broad agreement between remote sensing-based analysis of ground-cover change and a priori specified property-level land condition for properties rated in consistently good or poor condition, we found little (or no) correspondence for properties considered to have improved or declined in condition over the same period. The limited number of a priori field-specialist assessments of land condition used here may not provide a suitably precise classification of management-related grazing effects against which to judge the performance of our method. There are several reasons why differences would occur: (1) different criteria were used to judge whether and how change had occurred, (2) field-based assessment (of land condition) was largely qualitative and personal experience shows it is difficult to consistently recognise long-term trend with human observation, and (3) field-based assessment is likely to have covered only part of the total area of each station whereas ground-cover analysis was across that area of each property where wooded FPC ≤ 20%. The case-study examples demonstrate that the method has utility for monitoring grazing effects at paddock scale and results of this type (Figs. 11–13) may have extension value for government advisory officers and their pastoralist clients. Delivering on this possibility requires further consideration of suitable image products (e.g. paddock maps of pixel-level ΔGC and ΔΔGC) and their appropriate interpretation when combined with available ground data. We offer no explicit advice here as our primary interest is in regional-scale analysis and reporting. The smaller-scale studies were chosen to be places where clear change was well understood and supported by adequate ground data to encompass associated landscape heterogeneity. In reality, grazing-related change within most commerciallygrazed properties will be more complex than for our case studies and local expertise will be required to suitably tailor results from applying this method to the requirements of individual land managers. Given the consistent performance of the dynamic reference-cover method for clearly separating grazing effects from those due to seasonal variability at paddock scale, we are confident that broaderscale analysis of management effects on ground cover can now be conducted. We intend to implement a largely automated analysis of approximately decadal change in pixel-level ground cover (currently adjusted for wooded FPC) for most of Queensland's rangelands. Our aim will be to report seasonally adjusted change (ΔΔGC ) for mapped components of rangeland bioregions (IBRA, 2008) and report findings through ACRIS. Until now, ACRIS has relied on ground data collected at sparsely distributed monitoring sites to report change in components of ecosystem function in Australia's rangelands (Bastin and ACRIS Management Committee, 2008). Results from the robust analysis of Queensland's ground cover index will increase the spatial

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comprehensiveness of ACRIS reporting and reduce one of its current limitations (Bastin et al., 2009). We are confident that where a suitable long-term index of ground cover exists, the dynamic reference cover method can be adapted to monitor rangeland health elsewhere in Australia and more globally. Application elsewhere should first involve suitable testing of parameter settings, i.e., appropriate window size and percentile range of the GCImin image. Measures of state and trend derived from seasonally adjusted levels of ground cover in dry years for known examples of management-related change should then be validated against available ground data. When validated results are obtained, it should then be possible to apply the method more systematically, although suitably validating results at the scale of properties to regions may remain challenging. Our flow chart showing critical steps (Fig. 2) should provide a useful guide to adapting and implementing the method in other rangeland environments. Of course, there will be regional differences and possibly some shortcuts: in grasslands or where a suitable mixture model exists, it should not be necessary to adjust ground cover for residual wooded FPC. In arid environments with aseasonal and highly variable rainfall, it may first be necessary to analyse regional rainfall data to identify suitable dry periods within the driest years. Additional image dates may then have to be retrospectively acquired from the image archive and processed to produce a suitably comprehensive record of dry-period cover dynamics. 7. Conclusion We have developed a largely automated method that identifies reference areas from an annual to biennial sequence of ground cover derived from Landsat TM and ETM+ imagery. Reference ground cover allows management-related effects to be separated from effects due to inter-annual rainfall variability. The method first calculates a minimum ground cover image across all years to identify locations of most persistent ground cover in years of lowest rainfall. We then use a moving window approach to calculate the difference in ground cover between the central focal pixel and surrounding reference pixels in each window. This difference estimates groundcover change between successive below-average rainfall years, which provides a seasonally interpreted measure of management effects. Based on sensitivity analysis, we suggest that reference-pixel locations will be suitably identified in savanna woodlands using an approximate 1400 km 2 neighbourhood search window and values from the 90–95 percentile range of the minimum ground-cover image. However, we recommend that before applying the method in markedly different rangeland environments, suitable testing should occur to determine the most appropriate values for these two variables. The method successfully detected management-related change in ground cover in Queensland tropical savanna woodlands at three scales: (i) a grazing trial where heavy stocking resulted in substantial decline in ground cover in small paddocks, (ii) intermediate scale where wet-season spelling of commercial paddocks resulted in increased ground cover and (iii) large scale that produced broad agreement between our analysis of ground-cover change and a priori ranked condition change for commercial beef properties using ground-based assessment. The method should also apply to other grazed rangelands in Australia and elsewhere in the world where a suitable sequence of ground cover derived from annual Landsat imagery exists. Acknowledgements Australian Government funding through its Caring for our Country program to the Australian Collaborative Rangelands Information System (ACRIS) supported Gary Bastin and Vanessa Chewings in the conduct of this work. Seed funding to the Queensland Department of

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Environment and Resource Management under the same program initiated this work and strengthened that agency's collaboration with ACRIS. Funding was managed by Ninti One Ltd. The Wambiana grazing trial is co-funded by Meat and Livestock Australia and the Department of Employment, Economic Development and Innovation. We thank Bob Karfs and Neil Flood for advice and encouraging our work reported here. We also thank John Ludwig, Tim McVicar, Jasmine Muir, Rebecca Trevithick and three anonymous reviewers whose comments improved earlier versions of this manuscript. References Alcaraz-Segura, D., Cabello, J., Paruelo, J., & Delibes, M. (2009). Use of descriptors of ecosystem functioning for monitoring a national park network: A remote sensing approach. Environmental Management, 43, 38–48. Allen, D. E., Pringle, M. J., Page, K. L., & Dalal, R. C. (2010). A review of sampling designs for the measurement of soil organic carbon in Australian grazing lands. Rangeland Journal, 32, 227–246. 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