USING REMOTE SENSING TO MAP THE INVASIVE WEED PRICKLY ACACIA (Acacia nilotica ssp. indica) IN THE

MITCHELL GRASS DOWNS BIOREGION

Jasmine Muir

Remote Sensing Centre Department of Environment and Resource Management

80 Meiers Road, Indooroopilly, Queensland Phone: +61738969343 Fax: +61738969843

Jasmine.Muir@derm.qld.gov.au

Abstract

Prickly acacia (Acacia nilotica ssp. indica) is regarded as one of Australia’s worst weeds due to its invasion of large areas, resulting in serious environmental and economic impacts (CRC Weed Management 2003). It forms dense woody thickets in areas that are predominately grassland, converting these areas to thorny woodland. This results in reduced primary productivity, difficulties in stock mustering and restriction of stock access to water.

Remote sensing has been used to map numerous weed species, and provides a viable method for detecting weeds, where manual survey is impractical due to time, access and cost constraints. A variety of remote sensing techniques and sensors were evaluated to determine the most spatially accurate approach for mapping the core infestation area of prickly acacia, in the Mitchell Grass Downs (MGD) biogeographic region of Central Western Queensland. Analysis showed supervised maximum likelihood classification had the highest classification accuracy across three trial study sites in the region, and this method was used to map prickly acacia, across the MGD bioregion, using Landsat 5 TM and Landsat 7 ETM+ imagery.

The classification was refined using an object-oriented approach, to further increase output mapping accuracy, based on occurrence of prickly acacia along bore drains, near dams and in open grassland paddocks, within the region. Overall accuracy achieved was 71%, with a user's accuracy of 94% and producer's accuracy of 44%. The high user's accuracy indicates that areas mapped as prickly acacia have a high accuracy, however the low producer's accuracy indicates that a number of areas of prickly acacia were missed in the classification.

The developed methodology was used to map prickly acacia across the region, for the years 1987, 1999 and 2008, and was used to show the change in extent and cover over time. These outputs can be used by managers to target infestations, determine areas where control has been successful, as well as

providing detailed information on the nature of prickly acacia infestation within the region.

The results from this project can be applied to detection of other weed species, using remote sensing techniques. With continued advancement in sensor technology, successful results from remote sensing projects, and increased uptake by weed management authorities, it is anticipated that in the future, remote sensing will play an increasing role in weed management within Australia.

Introduction

Invasive weeds are non-native species persisting in an environment where they were not previously found (Williamson 1996). They pose a significant threat to native species and have the potential to invade large areas, resulting in loss of native biodiversity, land degradation and reduced agricultural and pastoral productivity (Burrows et al. 1990; Mack et al. 2000; Hejda et al. 2009).

Prickly acacia (Acacia nilotica ssp. indica) is regarded as one of Australia’s worst weeds due to its invasion of large areas, resulting in serious environmental and economic impacts (CRC Weed Management 2003). In the core infestation area, of the Mitchell Grass Downs (MGD) biogeographic region (bioregion) of Central Western Queensland, prickly acacia forms dense woody thickets in native grassland, converting these areas to thorny woodland. The result is reduced primary productivity of grasslands, difficulties in stock mustering and restriction of stock access to water.

One of the first steps in controlling an outbreak of an invasive species is to have accurate and timely information on the species distribution and spatial extent (Byers et al. 2002; Rew et al. 2005). Remote sensing offers a viable means of identifying early encroachment of invasive species and monitoring infestations over large areas, on a repeat basis (Underwood et al. 2003; Lass et al. 2005). The spatial information gained on invasive species distribution, change in cover, and infestation extent can enable critical areas to be targeted for weed control, improving the likelihood of achieving management goals (Underwood et al. 2003; Shaw 2005).

Previous remote sensing of prickly acacia has been confined to small target infestations, where prickly acacia was the only woody vegetation species in a grassland environment (Lawes & Wallace 2008; Brown & Carter 1998). Additionally, two surveys of landholders have been used to map prickly acacia distribution across the core infestation area in the Mitchell Grass Downs (MGD) at the lot and plan property scale (Carter et al. 1991; Reynolds & Carter 1993; Bolton & James 1985). While this mapping provides some level of detail on prickly acacia infestation occurrence, it was identified that further research was necessary for determining success of management outcomes, and for providing information on the extent of prickly acacia infestations, at the landscape scale.

The objective of this project was to apply remote sensing to the mapping of prickly acacia extent and cover, and to determine the change in infestation extent over time, in the core infestation area of the MGD. Mapping was

completed for the years 1987, 1999 and 2008, showing the extent and foliage projective cover of current and historical infestation. Output extent mapping was achieved with acceptable accuracy levels, using maximum likelihood supervised classification of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery. Landsat imagery has previously been used for numerous landscape scale remote sensing projects due to its scale, affordability, and repeat coverage. Outputs from the completed mapping will allow land managers to determine change in cover and infestation extent, as a possible result from management initiatives or prickly acacia encroachment into areas where it was previously not present.

Study Area

The MGD region extends in a band across central western Queensland, covering approximately 23 million hectares or 14% of the state. Prickly acacia is mainly found in the Northern and Central Downs. The majority of the area is leasehold land, with some freehold occurring in the south-east. Principal settlements include Longreach, Winton and Hughenden (Figure 1).

The landscape consists primarily of gently undulating grassland, with woody vegetation along creeks and rivers, and along the edges of the downs. Native tussock grasses are the primary land cover species, with perennial Mitchell Grass (Astrebella spp.) predominating. Annual species and forbs intersperse the perennials. Grass species include Curly Mitchell (Astrebla lappacea), Barley Mitchell (Astrebla pectinata) and Flinders grasses (Iseilema spp.). Woody vegetation species include gidgee (Acacia cambagei), boree (Acacia tephrina), Georgina gidgee (Acacia georginae), and cassias (Senna spp.) (Sattler & Williams, 1999).

Dominant soils are either grey or brown cracking clays with a self-mulching or rocky surface in places. Relief is generally flat with slopes gently undulating at less than 3 degrees within the grassland areas. Mean elevation is 250 m, rising to 350 m in the north and falling to 150 m in the south.

Figure 1: Location of fractional cover transect sites, and classification training points for prickly acacia mapping, in the MGD Biogeographic region of Queensland.

Satellite Imagery

Eight Landsat images were required to cover the study area. Landsat 5 TM images were acquired for the years 1987 and 2008, and Landsat 7 ETM+ images were acquired for 1999 (Table 1) from the Australian National Earth Observation Group. All images were corrected to a standardised geometric baseline of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, using at least fifty ground control points to obtain a Root Mean Squared Error (RMSE) of less than 0.5 pixels (Armston et al. 2002). As part of the geometric correction process, imagery was re-sampled to a 25 m ground resolution element (GRE). A radiometric correction procedure, developed by the Remote Sensing Centre of the Queensland Department of Environment and Resource Management (DERM), was applied to all images. The procedure uses the post-launch gains and offsets supplied by NASA to convert the digital numbers to top-of-atmosphere reflectance based on a bi-directional reflectance distribution function (BRDF) (de Vries et al. 2007; Danaher et al. 2001; Collett et al. 1998). All images were cloud free, with no noise in the data observable. Images were acquired during the dry season period from July through to November when ground cover is senescent, and prickly acacia maintains its leaves, making it easier to distinguish from the surrounding cover.

Table 1: Dates of Landsat 5 TM and 7 ETM+ image acquisition.

Field Survey

Transect sampling was completed at 12 field sites (Figure 1) in June and July 2008. A point observation transect method (Figure 2) was used, for recording over-storey, mid-storey, and ground cover vegetation (Scarth et al. 2006, Armston et al. 2009). This method was chosen as it is appropriate to the scale of imagery to be used, allowing the characterisation of a 3 * 3 pixel area in a Landsat scene, and is an objective method of assessing site vegetation cover (Scarth et al. 2006).

The transect method uses three 100 m measuring tapes laid out in a star formation, with the first tape running north-south and the second and third tapes being placed at 60° and 120° respectively (Figure 2 ), and sampling conducted at each metre interval. The central geographic position of each transect was measured using a sub-meter Fugro Omnistar Omnilite 132 differential global positioning system (DGPS). Overstorey vegetation was classified as woody vegetation greater than or equal to 2 m in height, and point sampled using a densitometer to determine presence of canopy gap, branch, green leaf or dry leaf. Mid-storey vegetation was classified as woody or herbaceous vegetation less than 2 m in height, and sampled by intersection with the densitometer pole, as green leaf, dry leaf or branch. Ground cover was determined using a laser pointer to pinpoint the position directly below the metre interval marker on the measuring tape, and classified as dry leaf, green leaf, rock, bare soil, litter or cryptogam. Photos were taken, from the centre point of the star transect, along each transect tape, to provide a visual record of vegetation at the site.

Additionally, a description was recorded at each site including: soil and rock colour, slope, aspect, disturbance, erosion and dominant plant species. Tree basal area was also recorded at seven points around the transect site (Figure 2), using an optical wedge with a calibration factor of 1.1. The basal area measurements were used to calculate the average stand basal area (SBA) for each site.

Figure 2: Fractional cover transect. The first tape is placed in a north – south direction and the second and third tapes are placed at 60 and 120 degrees respectively. Photos

were taken at the centre point, along each of the transect arms and basal area measurements of trees were taken at the centre point and 25 m from the centre point

around the transect.

Due to the large area of the MGD, transects completed on foot were impractical to collect sufficient training data for classification algorithms. To overcome this problem, data was collected using observations from a moving vehicle, in April 2009. Observations were made on the presence and absence of prickly acacia and other dominant species. Location was noted using a Garmin© Geographical Positioning System (GPS) with an accuracy range of 5-10 m, connected to a laptop. Points were collected along designated roads and within properties where landholder permission was granted for entry (Figure 1). Photos of land cover were taken in representative areas, for reference during development of classification algorithms.

Foliage Projective Cover

Foliage projective cover (FPC) is defined as the horizontally projected percentage cover of photosynthetic foliage of all strata, or the fraction of the vertical view that is occluded by foliage (Armston et al. 2009). In plant communities dominated by sparse vegetation cover, such as Australia, it is a more suitable indicator of the amount of vegetation intercepting radiation, than plant canopy cover (Specht & Specht 1999 in: Armston et al. 2009). The density of prickly acacia infestation was investigated using a correlation to (FPC).

Transect measurements were used to determine overstorey FPC at each site (FPCT) using equation 1:

25m

North

South

0 m

100 m

200 m 200 m

300 m

120°

Basal Area

Measurement

60°

180°

240°

300°

100 m

0°

FPCT = 100 POGL / 1 – POB Equation 1

where POGL is equivalent to the proportion of transect overstorey intercepts classified as green leaf and POB is the proportion of overstorey intercepts classified as branches; essentially the fraction of green vegetation adjusted to remove branch intercepts, likely to occlude observer vision of foliage (Armston et al. 2009).

Based on extensive field data collection, Armston et al. (2009) have developed and validated an equation for mapping FPC from Landsat 5 TM and Landsat 7 ETM+ imagery (FPCL). Development of this equation is described in detail in Armston et al. (2009) and will not be further discussed here. This equation has been applied to each of the Landsat scenes acquired for mapping prickly acacia. Field measurements of FPCT collected at each site have been related to FPCL using regression analysis, to determine if FPCL can be used as a potential surrogate for prickly acacia cover across the MGD.

Mapping Prickly Acacia Extent

Supervised maximum likelihood classification is a statistical method whereby each pixel is assigned to the class to which it most likely belongs (Jensen et al. 2007). This method of classification was chosen for mapping prickly acacia in the MGD, based on its higher classification accuracies at three of the field transect sites (Bowen Downs, Olga Downs and the Grove). Other trialled classification methods included random forests, support vector machines, generalised linear modelling, canonical variates analysis, density slicing, and the normalised difference vegetation index (NDVI) (Muir 2009).

A supervised maximum likelihood classifier program was written in the Python programming language. It allowed the user to input a set of ROI for use in classification, and quick replication of classifications with differing input images. Algorithms used in the classifier were as presented in a standard remote sensing text (Jensen 1996).

Field observations were used to define regions of interest (ROI) for prickly acacia presence and absence. In order to map prickly acacia across the MGD, initially ROI were created for four classes (prickly acacia, other woody vegetation, water and ground cover) across the entire region. Using a mosaiced Landsat 5 TM image for 2008 of the entire MGD, prickly acacia was classified in one run of the python classifier. However visual inspection of results showed that there were large areas of wrongly classified prickly acacia using the mosaiced image.

Further investigation showed that by running the supervised classifier on each of the eight Landsat scenes individually, a much better visual classification result could be achieved. Additionally by limiting the classification to areas of the scene within the MGD, and using ROI which captured the variability found within each of the classes, classification accuracy improved. The output classification for each Landsat scene was mosaiced together to form the classification across the MGD.

Initial classification was completed on 2008 imagery, as training data for the algorithms was available from field work completed at this time. This method was then applied to historical imagery. In order to classify historical imagery, the point location of the ROI for 2008, were used to extract new ROI values for the other years to be classified, minimizing the effect of spectral variability between images from different years.

For this approach to work, areas chosen as ROI needed to be the same class in all years (i.e. for the prickly acacia class the ROI area must have been covered by prickly acacia in 1987, 1999 and 2008). In some areas this was difficult to achieve, due to only a small proportion of the current extent being infested in 1987 and 1999.

Refinement Using Object-Oriented Classification

Field work and published literature suggested that the majority of prickly acacia infestations in the MGD are confined to open grassland paddocks (i.e. are not mixed in with native vegetation) and are often found along open bore drains (CRC Weed Management 2003). To further increase mapping accuracies, an object-oriented approach was used to include this location information in output classifications.

Regional Ecosystem (EPA 2008) mapping was used to determine areas of native woody vegetation within the MGD bioregion, and these areas were removed from the classification of prickly acacia. Additionally bore drains infested with prickly acacia were not readily detected in the supervised classification method, due to their linear nature and small feature width (one to two pixels in Landsat imagery). In order to include prickly acacia found along bore drains in the overall classification, the Geoscience Australia (GA) Topographic 1:250,000 bore drains from the hydrology feature class (GA, 2006) were used, as well as additional digitisation of bore drains from the Landsat imagery, to create a MGD bore drain layer. To include prickly acacia found in proximity to these features, 100m buffers were applied to all bore drains, and any woody vegetation found within the buffer, was added to the final classification as prickly acacia. Woody vegetation was determined from the DERM Landsat Foliage Projective Cover (FPCL) mapping for individual years (Armston et al. 2002). Areas where FPC was greater than 5% were assigned to the prickly acacia class.

Mapping Prickly Acacia Density

Density of prickly acacia was mapped by intersecting the FPC Landsat images with the extent of prickly acacia mapped for each individual year. Classes with low FPC cover were mapped as those with an FPC <=10%; medium as >10% and <=15%; and high as >15%. These classes are arbitrary and intended for use in detection of change between years. There will also be some error introduced by climate fluctuations in the FPC product causing change between classes where there is no real on ground change in tree canopy cover.

Accuracy and Error Assessment

Due to the lack of available historical validation data, the 2008 classification was the only mapping output that could be formally validated using a standard accuracy and error assessment procedure. Validation of prickly acacia extent mapping was completed using Google Earth© to check for the presence of prickly acacia, based on visual assessment, and combined with a knowledge of infestation locations from field work. Google Earth© imagery in the MGD is typically Quickbird II or Spot 5 imagery, acquired in the time period from 2004 to 2009. This approach of using Google Earth© for presence and absence validation of features has been previously used in remote sensing studies (Helmer et. al. 2009). It does however assume that the images in Google Earth are accurately positioned, and final accuracy figures may be influenced by spatial registration errors. Because the Google Earth© imagery was not current for 2008, the DERM State-wide Landcover and Tree Study (SLATS) annual clearing data (Scarth et. al. 2008) was used to check that clearing had not occurred between the period of capture of the imagery in Google Earth, and the mapping of prickly acacia for 2008.

In order to complete the accuracy assessment 200 points were allocated to each of the three main classes used in mapping (prickly acacia, other woody vegetation and ground cover), and placed randomly within the class. Each point was then visually assessed against the Google Earth© imagery, for presence or absence of the feature class in question. From this, formal analysis of statistical accuracy was completed.

Results

Output classifications of prickly acacia extent were obtained for the years 1987, 1999 and 2008 across the core infestation area in the MGD. An example of classification results for the “The Grove” site is shown in Figure 3. This shows classification results compared to high resolution Quickbird II imagery (0.6 m pan-sharpened imagery). Visually, the results compared well to what was observed in Google Earth © and field observations.

Figure 3: Supervised maximum likelihood classification of “The Grove”, using Landsat 5 TM imagery, compared to Quickbird II imagery (displayed as bands 4,3,2).

Statistics were generated on the total area in the MGD, detected as being infested with prickly acacia in 1987, 1999 and 2008 (Figure 4). For the year 2008, the area of detected infestation was approximately 140,000 ha, a reduction from approximately 200,000 ha and 185,000 ha in 1999 and 1987, respectively.

Figure 4: Area of prickly acacia mapped from supervised classification of landsat

imagery for the years 1987, 1999 and 2008 for the Mitchell Grass Downs Bioregion.

Classifications for each year were used to determine the change in prickly acacia extent over time (Figure 5). For example, during field work at the “Harrogate” site, it was noted that a large area of prickly acacia had been cleared before 2008, as shown in the change detection from classification outputs.

Figure 5: Change in prickly acacia extent from 1987 to 2008 at the “Harrogate” field

site. Landsat 5 TM and Landsat 7 ETM+ imagery is shown for comparison. Note that a large area of prickly acacia is no longer present in 2008.

Normalised linear regression was used to relate FPC derived from prickly acacia transect sites, with the Landsat FPC product for 2008 (Figure 6). An R2 value of 0.4724 indicates a weak positive relationship between the two variables. Although there is some correlation between prickly acacia cover and remotely sensed FPC from Landsat 5 TM imagery, where FPCT was low, the values in the Landsat product tended to vary. Also at the Olga Downs site a high level of FPCT had a relatively moderate level of FPCL. Further field measurements are required to better investigate the relationship.

Figure 6: Linear regression for the 12 sites where transects were completed in 2008, showing field derived foliage projective cover (FPCT) and foliage projective cover from

Landsat 5 TM imagery (FPCL) for 2008. FPCT was determined using the method developed by Armston et al. (2009). The trend line is shown in black. R2 value is

0.4724.

Accuracy statistics showed that overall accuracy was 71%, while the producer’s accuracy was 44% and the user’s accuracy 94% (Table 2). This means overall that 71% of features are mapped correctly (i.e. as prickly acacia and non-prickly acacia). The producer’s accuracy represents the number of times a feature is included in the map when it is actually present on the ground. A low producer’s accuracy of 44% for the prickly acacia class suggests that more than half of the prickly acacia infestations have been omitted from the mapping. This is likely to be due to low density infestations not detected in the supervised classification from Landsat imagery (25 m pixels), but which are visible in the high resolution Quickbird imagery used in Google Earth (0.6 m pixels pan-sharpened). The user’s accuracy represents the number of times a feature was mapped correctly or the number of instances which are shown on the map and are actually present as the mapped class on the ground. A very high user’s accuracy of 94% suggests that the majority of areas mapped as prickly acacia are actually prickly acacia on the ground.

The Kappa Value represents the overall accuracy, but takes into account the contribution of all classes shown in the confusion matrix (i.e. those points mapped correctly as well as those mapped incorrectly). The low Kappa value of

41 suggests that there is some confusion between classes. This is due to the low producer’s accuracy for the prickly acacia class.

Ideally the overall accuracy of output mapping should be as high as possible and have a high producer’s accuracy to ensure that mapping will detect new and/or sparse infestations. Although the producer’s accuracy and Kappa Value are quite low for the 2008 supervised maximum likelihood classification of prickly acacia, the high user’s accuracy of the product will allow for the output mapping to be used to accurately identify existing areas of prickly acacia.

Table 2: Accuracy results for mapping of prickly acacia across the MGD in 2008, using Landsat 5 TM imagery and a supervised classification approach. Reference data used was Quickbird imagery in Google Earth over the region. 600 points were used in the

validation process, 200 for each of the final classes.

Discussion

The output mapping from this project can provide land managers with details of prickly acacia extent, whereas previously mapping could only specify the presence of prickly acacia in a particular region. Output mapping accuracies, were very high for the user’s classification accuracy at 94%, however the producer’s accuracy was quite low at 44%. This may have been due to a number of reasons, including the use of moderate resolution imagery which was much larger than individual prickly acacia trees, and variation between sites in the spectral reflectance of prickly acacia and surrounding land cover.

Due to these limitations, output mapping should be used as an indicator of change between years, on a qualitative, rather than quantitative scale: particularly because historical mapping or high resolution satellite data was not available for validation of output mapping, except in 2008. While the mapping is able to provide details of prickly acacia extent within property boundaries, the low producer’s accuracy means that some infestations have been missed.

One of the factors influencing whether vegetation is classified as prickly acacia or other vegetation appears to be density. The main omission errors occur when foliage projective cover is low, while the main commission errors occur when dense prickly acacia is classified as other woody vegetation.

The classification success tended to rely on differentiating prickly acacia from ground cover. In areas where patches of prickly acacia occurred in grassland areas, it was generally reliably identified as prickly acacia, although areas where prickly acacia cover was low were under represented in the classification. However, where prickly acacia occurred amongst other woody vegetation species, it was often misclassified. In the MGD this was not a major limitation, as the majority of prickly acacia occurs in open grassland areas. However, this presents problems when adapting the method to areas where species composition is more complex, and suggests that there is limited spectral reflectance differentiation between prickly acacia and other woody vegetation.

The use of a mask, removing areas classified as other woody vegetation types in the Regional Ecosystems mapping, from the classification of prickly acacia, was useful in removing some of the commission errors associated with the misclassification of prickly acacia as other vegetation. However it also means that prickly acacia present amongst other vegetation types won’t be detected in the mapping. Again, while this is not such an important consideration in the MGD, when considering applicability of the mapping approach for detecting new infestations (which may occur amongst other vegetation) and also for detecting infestations where vegetation structure is more complex and tree species richness is higher, the methods used here to map prickly acacia may not be suitable.

The regression analysis for the Landsat FPC product and transect FPC for prickly acacia at the site “Olga Downs”, suggest that areas which have a relatively high stem density and canopy cover of prickly acacia may not be detected in the Landsat FPC as having a high FPC value. This could be attributed to the low leaf area index (LAI) of prickly acacia in comparison to other vegetation types with the same stand basal area (SBA). Also, at low FPCT, there was not a strong relationship with FPCL. This is most likely due to FPCL accuracy being lower at values less than 20% (Armston et. al. 2009). Additional field measurements are required to better test this relationship.

Future research

A number of areas are a priority for further research in mapping prickly acacia. Time series analysis of frequent Landsat imagery from the recently available United States Geological Survey archive, may allow for differentiation of prickly acacia from other vegetation based on its unique seasonal response. For example, prickly acacia tends to lose its leaves during the dry season, whereas this is less common in native vegetation. Initial investigative research in this area looks promising. A time series of 5 dates of imagery per year was used to complete initial mapping of prickly acacia using this technique. Output mapping

appeared accurate on visual inspection, and it is likely that the addition of extra dates for each year would further improve classification results.

Further research on object oriented classification of prickly acacia in order to refine mapping rules, is likely to improve mapping accuracy. Object oriented classification lends itself to mapping prickly acacia in the MGD, due to clumping of trees in patches within otherwise open grassland, and location based on proximity to landscape features such as dams and bores.

Conclusions

Prickly acacia extent and cover were mapped across the MGD in 1987, 1999 and 2008. To achieve this, a combination approach was adopted utilising supervised maximum likelihood classification and object-oriented techniques to refine the mapping process. Output accuracies showed that mapping could reliably be used to find prickly acacia infestations, although not all infestations were able to be mapped.

A number of limitations occurred with mapping, mainly due to resolution of Landsat imagery. Further research to improve mapping could be conducted in the areas of time-series remote sensing analysis and object based mapping.

Output maps of prickly acacia extent and tree cover are able to provide a valuable resource for natural resource management agencies and land managers, when planning investments and management initiatives to target areas of prickly acacia infestation.

Acknowledgements

The author would like to acknowledge the funding received to complete this research from the Queensland state government initiative “Blueprint for the Bush”, and would also like to thank Nathan March (the National Co-ordinator for the Prickle Bush Management Group) and Brett Carlson (formerly of Desert Channels NRM) who spent time assisting in site selection and field work. Further thanks goes to Neil Flood from the Department of Environment and Resource Management (DERM) who kindly wrote the python code for the supervised classifier, as well as Jeff Milne (formerly DERM) and Kerry Speller (DERM), who assisted in field work. Additionally many thanks to the numerous property owners, who spent time discussing their experience with prickle bushes in the MGD, and allowed access to their properties.

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