A semi-automated approach for quantitative mapping of woody cover from historical time series aerial photography and satellite imagery

Journal Pre-proof A semi-automated approach for quantitative mapping of woody cover from historical time series aerial photography and satellite imagery

Timothy G. Whiteside, Andrew J. Esparon, Renée E. Bartolo PII:

S1574-9541(19)30323-1

DOI:

https://doi.org/10.1016/j.ecoinf.2019.101012

Reference:

ECOINF 101012

To appear in:

Ecological Informatics

Received date:

21 February 2019

Revised date:

19 August 2019

Accepted date:

4 October 2019

Please cite this article as: T.G. Whiteside, A.J. Esparon and R.E. Bartolo, A semiautomated approach for quantitative mapping of woody cover from historical time series aerial photography and satellite imagery, Ecological Informatics(2019), https://doi.org/ 10.1016/j.ecoinf.2019.101012

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© 2019 Published by Elsevier.

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A semi-automated approach for quantitative mapping of woody cover from historical time series aerial photography and satellite imagery 1,*

1

Timothy G. Whiteside , Andrew J. Esparon , and Renée E. Bartolo 1

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Environmental Research Scientist of the Supervising Scientist, Darwin, Australia

Abstract:

Key words:

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Savanna landscapes are characterised by a canopy of discontinuous tree cover overlying an understorey of shrubs and continuous grass cover. The distribution of trees (woody cover) is variable both spatially and temporally. Analysis of woody cover dynamics can provide a spatial and temporal envelope encompassing variability is useful for informing mine closure criteria. With the impending closure of Ranger uranium mine, ecologically appropriate closure criteria for ecosystem restoration are being developed through a framework of rehabilitation standards. One such closure criteria is canopy cover and historical woody cover is being used to derive the range in woody cover that can be expected over time once the mine site has been revegetated. This study reports on the development and testing of a technique for extracted woody cover from remotely sensed data (in the form of historical aerial photography and satellite imagery) in the areas adjacent to Ranger uranium mine in the World Heritage listed Kakadu National Park, northern Australia. An object-based image analysis technique was applied to four data sets from four different dates: greyscale, true colour and colour infrared aerial photo mosaics (from 1964, 1976 and 1981 respectively) and a high spatial resolution satellite image (from 2010). Overall accuracies of woody cover from each of the data sets exceeded 94%. In addition, proportional cover derived from this method displays linear relationships to cover derived from visual estimates. Due to the success of the technique, it will be applied to more data sets from different dates over the study area to assess the variability of woody cover over time to inform ecosystem restoration criteria for the mine closure.

object-based image analysis, savanna woody cover dynamics, Kakadu National Park, timeseries 1. Introduction

1.1 Variability in savannas and dynamics

Savannas are one of the largest terrestrial biomes covering over one eighth of the Earth’s land area (e.g.Scholes & Archer 1997, Sankaran et al 2004). Savanna landscapes typically consist of an upper stratum of discontinuous tree (referred to here as woody) cover and a mostly continuous grass understorey (Hutley & Setterfield 2008). The distribution and proportions of the tree-grass mix in savanna are variable over time and space (Stevens et al 2017). The key drivers behind these dynamics are fire and herbivory, with other disturbances including extreme weather events (such as cyclones) and human activity. In regions where the mean annual precipitation is above 650mm, savannas are considered ‘unstable’ systems annual precipitation is sufficient for woody canopy closure (forest), however disturbances such as fire and herbivory maintain the coexistence of trees and grass (Sankaran et al 2005).

*

Corresponding author – [email protected]

Journal Pre-proof A spatial hierarchy has been described for the drivers of savanna dynamics (Coughenour & Ellis 1993) At the continental scale, climatic patterns determine the savanna extent; at the regional to landscape level, the drivers for variability are rainfall, topography and hydrology; and at the local scale, drivers are disturbances and water availability (Coughenour & Ellis 1993). At the landscape level, the importance of rainfall in determining savanna structure has been identified in northern Australia, with above ground woody biomass, stem density, overstorey leaf area index (LAI) and canopy height all declining as mean annual rainfall decreases along a 1000 km southbound gradient (Hutley et al 2011). At the local scale, the drivers for spatial dynamics are assumed to be fire and herbivory (Lehmann et al 2009b, Werner & Prior 2013)

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Meta-analysis of research into woody cover change in savanna has indicated a global trend of woody encroachment into savanna (Stevens et al 2017). While it is a trend, changes in woody cover are varied, as are the causes of change. For example, in South Africa results vary between studies. A multi-temporal study (1944-1996) of forested areas in KwaZulu Natal (Lawes et al 2004) used digitised air photos, the analysis was a manual process and the patch size for analysis was limited to forest patches. The areal extent of forest showed a reduction, however no analysis was undertaken to understand changes at canopy density level. Levick and Rogers (2011) found an increase in woody cover on hill tops but a reduction in woody cover of riparian and lowland areas possibly as a result of increased herbivory In their study of two sites in South Africa, Buitenwerf et al (2012) show an increase in woody cover over a 30 plus year period which has been attributed to rising CO2 levels.

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While there are a number of long term studies of vegetation dynamics in Australian tropical savanna, most studies are limited in either spatial context using non-contiguous data or subsamples of an area (Lehmann et al 2008b), or temporal scale, such as using only two dates (Bowman et al 2001, Sharp & Bowman 2004). None of these studies are spatially contiguous instead focussing on a selection of sites across a landscape. The reasons for this may include data availability, limited computer processing resources, or the technical or experiential background of the researchers. Lehmann et al (2008b) found tree cover varied greatly for savanna in the Kapalga region, located within Kakadu National Park (KNP), in northern Australia between 1964 and 2004. They determined that the dynamism within savanna was driven by frequency and intensity of fire, which in turn can be influenced by grazing of feral animals, specifically water buffalo. The buffalo have an impact on the floristics and structure of lowland woodland communities, including the suppression of perennial species of grasses and recruitment of trees to the canopy (Werner et al 2006). Extreme weather events such as cyclones may also be potential agents of change (Lehmann et al 2009a). A number of studies (pre 2010) in northern Australia have reported thickening of woody cover within savanna (Chen et al 2003, Sharp & Bowman 2004, Bowman et al 2008), although each is limited in either spatial or temporal extent. Banfai and Bowman (2006) identified an overall expansion of rainforest patches adjoining savanna in KNP during the period 1964-2004 using aerial photography analysis. In assessing the drivers for change, they found that there was a synergistic impact involving mainly fire and the presence of feral animals. Conversely, recent research shows that thickening of woody cover in northern Australian tropical savanna may not be occurring and that fire frequency and intensity plays important role in preventing woody cover expansion by suppressing woody growth in savanna (Murphy et al 2014). 1.2 Woody cover dynamics and mine site rehabilitation

Closure criteria for the ecological restoration of mine sites typically require that the rehabilitated mine site resembles the environmental conditions present either in the pre mine environment or in adjacent undisturbed areas (reference ecosystems). For savanna landscapes, 2

Journal Pre-proof the ecological restoration closure criteria, in particular for revegetation, should encompass the spatial and temporal variability of these landscapes because single or a small number of measurement points in either time or space may not represent the dynamics of the landscape. Spatio-temporal analysis of the woody cover of the landscape within which a mine is located can provide valuable information on the environmental conditions and the spatial and temporal variability of savanna components. 1.3 Mapping savanna woody cover

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Methods for mapping contiguous woody cover at the canopy scale in Australian tropical savanna are not well documented in the literature and tend to be limited in spatial extent or to a particular type of data such as high spatial resolution satellite data (Whiteside & Boggs 2009). The development of automated and semi-automated approaches to mapping woody cover in savanna would enable effective analysis of spatially and temporally extensive datasets thereby providing a better indication of landscape scale trends in woody cover. Development of such methods is not without challenges. Woody canopies in Australia tend to be quite open primarily due to erectophile leaves (King 1997), clumpiness of the foliage and branch shedding (Jacobs 1955). For example, savanna Eucalypts in particular show leaf area indices of 0.6 and 1 for dry and wet seasons respectively (O'Grady et al 2000). Due to this openness, sub-canopy background noise is included within canopy boundaries in remote sensing data. Therefore, a method for delineating tree cover will require a means of highlighting contrast between tree crowns and background land cover. The contrast is maximised when understorey grasses are senescent.

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Numerous studies have utilised manual approaches for mapping woody cover from imagery (Fensham & Fairfax 2003, Banfai & Bowman 2006, Lehmann et al 2009c). These approaches can be time consuming and therefore are typically limited in spatial extent or detail. In addition, due to reliance on visual interpretation, results can be subjective and can vary between interpreters. While visual interpretation of aerial photography has proven to be an accurate way to estimate rates of crown thickening (Fensham et al 2002), the interpretation needs to be calibrated against ground sites. This is because the relationship between photo and on-ground cover is not one to one, nor linear and other factors such as scale and land type must be accounted for (Fensham et al 2002). For example, the scale of the aerial photography has been shown to influence the amount of cover that is detected (Fensham & Fairfax 2007). Automated or semi-automated remote sensing approaches for mapping cover, while not without their own challenges, can be efficient, can be applied to entire areas, and tend to be more objective and readily repeatable when compared to manual methods (Browning et al 2011). The application of automated and semi-automated methods for the extraction of woody cover and tree crowns is well documented in the literature. The data that has been used includes aerial photography (Erikson 2004, Leckie et al 2005), high resolution satellite imagery (Whiteside et al 2011b) and hyperspectral airborne image data (Bunting & Lucas 2006). Algorithms for tree crown detection assume that tree crowns are typically radiometrically brighter than the background and that the apex of the crown is the brightest. Approaches used for detection follow either region growing or thresholding procedures or a combination of both. Most of the approaches work well when there is clear delineation of crown boundaries and encounter problems when tree density causes overlap of canopy (Culvenor 2002, Bunting & Lucas 2006). Object-based image analysis approaches have been shown to successfully extract woody cover from savanna type landscapes (Laliberte et al 2004, Levick & Rogers 2008, Levick & Rogers 2011). Furthermore, object-based approaches have outperformed pixel-based approaches for classifying savanna cover types in northern Australia (Whiteside et al 2011a). The objective of this study was to develop and validate a semi-automated object-based technique for extracting contiguous woody cover from multiple types of remote sensing data to 3

Journal Pre-proof facilitate the creation of a time series woody cover data set. Due to limitations with the data (predominantly historical aerial photography at varying scales) and analysis methods, our approach will not attempt to delineate individual tree crowns but extract the woody cover extent (tree crowns overlapping or otherwise) from the imagery. The primary purpose for this dataset was to enable the assessment of long-term variability of woody cover in the savanna landscape surrounding Ranger uranium mine. The technique was tested on four types of remotely sensed data: greyscale, true colour and false colour infrared aerial photography and high spatial resolution satellite data. Results of the technique accuracy are presented along with some analysis. In depth analysis of landscape object fate and statistical inference of the drivers for change are beyond the scope of this paper and will be investigated in future publications. 2. Methods 2.1 Study area

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The study area covers approximately 400 km2 in area of Kakadu National Park (KNP) surrounding Ranger uranium mine in the monsoonal tropics of Australia’s Northern Territory (Figure 1). Ranger uranium mine is scheduled for closure. Mining operations at Ranger are to cease in 2021 with all rehabilitation activities to be completed by 2026. The Supervising Scientist Branch of the Australian Department of the Environment and Energy have provided a rehabilitation standard for ecosystem restoration (Supervising Scientist 2018) outlining criteria for closure to be complete. One key criterion of the standard for assessing community structure is canopy cover.

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Climate is wet-dry monsoonal with annual rainfall for the area is approximately 1500 mm with almost all rain occurring in the wet season between December and March. The landscape surface is a lateritic erosional lowland known as the Koolpinyah surface (Wells 1979). The area under study is centred on Ranger uranium mine located within the World Heritage Listed KNP. For proximity purposes, the extent of the study was limited to a 10 km buffer around the mine site. Further from the site, the landscape changes noticeably. To the east and south of the site, the landscape is predominantly sandstone escarpment and plateau. To the west and north are alluvial floodplains. This study is further confined to the land unit classes described as ‘Undulating upland terrain’ as detailed by Wells (1979). These classes were considered representative of the predominant land units of the mine site in its pre-mining state. The units have slopes not greater than 3% soil and soils are mostly red and yellow massive earths to varying depths. The overlying vegetation is Eucalyptus spp. dominant (mostly E. tetrodonta and E. miniata) open woodland to open forest typically with a seasonally dense grass cover and occasional shrub understorey (Wells 1979). The region has been inhabited for over 40K years and for much of that time fire has been a management tool for the savanna areas. Prior to becoming a National Park in 1979, land use was mainly foraging, pastoralism and buffalo hunting (Levitus 1995). In the early 1970s, a number of uranium deposits were discovered in the region, with mining in a number of regions including Ranger commencing soon after (Press & Lawrence 1995). Fire plays a large role in the ecology of the region with large extents of the woodlands in the lowland area of KNP experiencing early dry season fires (Russell-Smith et al 2017).

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2.2 Data description 2.2.1 Image data

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Figure 1. Location of the study area in the Alligator Rivers Region of the Australia’s Northern Territory.

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To test the utility of the woody cover extraction method, four data sets representative of the full archive spanning 46 years from 1964 to 2010 were assessed. The data sets included greyscale, true colour (RGB) and false colour infrared (CIR) aerial photo mosaics and high spatial resolution satellite imagery (WorldView-2). The data for each year was captured in the early dry season between May and July (Table 1). Imagery captured at that time of year provides optimal spectral contrast between the green canopy of the trees and the senescent grasses in the background as well as a reduced likelihood of cloud cover, fire scars and smoke haze. Table 1. Details of imagery for each date. No. images

Scale

Original mosaic GSD

GSD after resampling

28 June & 6 July Greyscale aerial photos 1964

74

1:16,000

30 cm*

1m

29 June, 6 July & Colour Infrared (CIR) aerial 16 July1976 photos

126

1:15,000

30 cm*

1m

5 & 7 June 1981

50

1:25,000

40 cm*

1m

WorldView-2 pan-sharpened 1 N/A 60 cm* multispectral satellite data *ground sample distance = image pixel size. Aerial photo mosaics were provided by the photogrammetry company with these pre-determined GSDs. 5

1m

Dates of capture

11 May 2010

Data set

Colour (RGB) aerial photos

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2.2.2 Vector data

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Two vector layers were included in the analysis: (a) the GIS layer of Wells (1979) land units was used to confine analysis to the regions of interest (upland savanna land units), and (b) a grid composed of square 1 ha polygons to provide a standard arbitrary unit to enable analysis of proportional cover change (per hectare) between dates. The land units GIS layer consists of the 15 land units from Wells (1979) that are described as occurring upon an undulating upland terrain surface. Details of the units are included in the Supplementary material (Figure F1 and Table T1). The land units chosen are similar to the landscape of the majority of mined area premining. This layer was clipped to a buffer of 10 km around the mine site to meet adjacency requirements. The boundaries of the polygons within the layer were also reverse buffered by 100 m to mitigate any influence of mis-registration between the GIS layer and the image data. The second vector layer included is a graticule of 100 x 100 m (one ha) squares used to create a consistent arbitrary unit for the calculation of proportional cover within the land unit objects for the data set for each date. 2.3 Data pre-processing 2.3.1 Aerial photography

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The historical aerial photography runs were scanned by a commercial aerial survey company at 12 µm using a high resolution photogrammetric scanner. Combining individual photographs into a mosaic can affect image classification due to errors introduced by different sun angles and view directions, as well as inconsistencies in film processing and scanning. To address this, where possible the scanned photos were corrected for vignetting and brightness using functions within the image manipulation software RawTherapee v4.2 (rawtherapee.com). In addition, colour differences between photos for the same year were balanced. There were no observed variations in brightness associated with topographic effects in the area under study so corrections for this were not necessary. Images for each year were then mosaicked and georeferenced to WGS84 zone 53 south datum by the survey company (Figure 2a, b and c). This produced mosaics of varying ground sample distances (GSDs) (Table 1). An artefact removal process was applied to mitigate the appearance of scratches, specks of dirt and pen marks on each aerial photo mosaic (Esparon et al. in prep). 2.3.2 Satellite imagery

The radiometric and geometric pre-processing of the WV-2 2010 imagery has previously been described in detail in Whiteside and Bartolo (2015). To summarise, radiometric correction of the imagery used the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) atmospheric correction model in ENVI (www.harrisgeospatial.com) for converting at-sensor digital numbers to surface reflectance (Matthew et al 2003). The imagery was geometrically calibrated using differential GNSS referenced ground control points. Although WV-2 provides 8 bands of multi-spectral data, just three bands were selected for this project: NIR1 (770-895 nm), Red (630-690 nm), Green (510-580 nm), consistent with the 1976 CIR orthophoto mosaic (Figure 2d). The three bands with 2.4 m ground sample distance (GSD) were then pan-sharpened to 0.6 m GSD using the HSV (Hue, Saturation, Value) method, whereby the bands were transformed to hue, saturation and intensity (value) colour space and intensity was replaced by the panchromatic band (Tu et al 2004). The modified HSV colour space was then converted back to the original colour space (NIR1, Red and Green bands). The spatial extent of the 2010 WV-2 is less than for the three aerial photo mosaics and all data available for that date.

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(b) 1976 CIR mosaic

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(a) 1964 Greyscale mosaic

(d) 2010 False colour WV-2

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(c) 1981 True colour mosaic

Figure 2. Images from each date with the ‘Undulating upland terrain’ land units from Wells (1979) overlaid in yellow for reference. CIR is colour infrared and WV-2 is WorldView-2. 2.3.3 Standardising the data sets

All image data sets were co-registered to a 2004 orthomosaic of the entire KNP supplied by Geoscience Australia with 25 cm GSD resolution using the Georeference function in ArcGIS Pro (www.esri.com). All registrations were undertaken using a 1st order polynomial transformation and nearest neighbour resampling using control points located at a number of readily identifiable features in the datasets. Accuracy of each of the registrations was conducted using independent sets of reference points (Table 2). A lack of infrastructure in the 1964 mosaic meant natural features were used increasing the potential for higher error. Due to the variety of spatial resolutions within the aerial photography mosaics and satellite imagery, all datasets were resampled to 1 m GSD using the nearest neighbour algorithm for consistency during analysis. Table 2. Root mean square error (RMSE) of the registration the data sets to the 2004 image based on independent reference points. Date No. control points No. reference points RMSE (m) 1964 55 20 12.1 1976 25 20 7.8 1981 29 20 7.5 2010 25 20 3.0 7

Journal Pre-proof 2.4 Ruleset development for analysis

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The object-based image analysis for each date was conducted using eCognition software v9.2 (www.trimble.com) and followed the flow chart below (Figure 3).

Figure 3. Flow chart showing the analysis technique for extracting woody cover from the data sets. The numbers in the graphical examples correspond to numbers in flowchart nodes. 2.4.1 Selecting the image enhancement derivative.

For three of the dates (1976, 1981 and 2010), the image layers are three bands, either RGB or CIR. For the 1964 dataset, the orthomosaic is a single band greyscale image. In an attempt to extract further information from the imagery, a derivative data set was created for each date. To highlight the evergreen woody cover against the seasonal grasses, spectral indices were applied to the multiband datasets. In the case of the RGB imagery, the Triangular Greenness Index (TGI; equation 1) was utilised due to it being strongly correlated to leaf chlorophyll (Hunt et al 2013): (1) where (λr - λb) and (λr - λg) is the estimated distance between the central wavelength for Red and Blue, and Red and Green bands respectively. For this paper, (λr - λb) = 190 and (λr - λg)= 100. For the CIR photo mosaic and the satellite imagery – a normalised difference vegetation index NDVI (Rouse et al 1973) was used as the derivative (equation 2): 8

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(2) For both indices, in instances where the denominator was zero, the index for that pixel was assigned as -1.

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It was not possible to create a band ratio or spectral index from the 1964 greyscale image. Therefore, second-order co-occurrence texture metrics (Haralick et al 1973) were calculated for the 1964 greyscale image. Four texture metrics were tested as to their suitability for woody cover detection: mean, variance, dissimilarity and homogeneity. After testing, the homogeneity metric was determined to be the most useful for woody cover delineation as the metric increased contrast between the tree canopies, shadow and the senescent grasses. Homogeneity (inverse difference moment) is:

(3)

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where i,j are coordinates of the pixel in the co-occurrence matrix space, P(i,j) is the relative frequency with which two neighbouring cells occur on the image, one with grey tone i and one with grey tone j. Ng is the dimension of the co-occurrence matrix. The matrix dimension was set at 9 x 9 pixels with a displacement of one pixel (Warner 2011).

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Prior to the woody cover analysis, a Gaussian low pass filter was applied to the image derivatives for each date to mitigate any excessive within the field variation (spectral noise) that existed (Figure 4). A 5 x 5 pixel window was determined to best minimise the noise while retaining the texture of the canopy. A smaller window retained the noise, while larger windows smoothed the canopy into the background.

Figure 4. A 1 ha cell from the 1981 RGB aerial photo mosaic (a), the TGI image derived from the RGB mosaic, and the TGI derivative after a 5x5 Gaussian low pass filter has been applied. 2.4.2 Object-based analysis of data sets to extract woody cover

For each date, a geographic object-based image analysis approach (Blaschke 2010) was used to extract the woody cover within the land unit polygons and then assign proportional cover to cells within a 1 ha grid within the land unit polygons. The technique used in this paper created a two-level hierarchy of objects. The top level of objects (L2) was created using the clipped layer of undulating upland land units from Wells (1979) to confine the analysis to within these land units. The L2 objects were duplicated below to create a level of sub-objects (L1). The L1 sub-objects were then segmented using a threshold segmentation process based upon the image enhancement derivative (column (a) in Table 3). The segmentation created woody cover 9

Journal Pre-proof candidate objects from pixels with values above the set threshold. Due to the different derivatives and base data for each year, the threshold for each data set is specific to that data set (initial threshold value (b) in Table 3). Table 3. Derivatives (a) and threshold values used for the initial segmentation for woody cover (b), lower threshold values used for the local maxima finding (c), and higher values for resegmentation of chunks (d) for each date.

1964 1976 1981 2010

(b) Initial threshold value 0.85 0.13 0.05 0.40

(c) Lower threshold value 0.75 0.12 0.04 0.35

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(d) Higher threshold value 0.90 0.15 0.06 0.50

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Because the technique to this point is based on a single threshold (different for each date), woody cover may be under represented in imagery in areas of high reflectance (bare ground) or low reflectance and limited contrast (recently burnt areas) (Figure 5). Therefore, to identify potential candidate objects within the areas where woody cover could be under represented, a local maxima finding algorithm was used on the derivatives for each date. The algorithm identified pixels (seeds) with the maximum value within a search range of 6 pixels. These local maxima seeds were then ‘grown’ into potential woody cover objects by enveloping neighbouring pixels above a threshold value slightly lower than the value used for previous segmentation step (lower threshold value (c) in Table 3). If the local maxima seeds did not grow into neighbouring pixels, they were assumed to not be woody cover candidates and were removed from further analysis. Those seeds that did grow were assigned as woody cover candidate objects.

(a)

(b)

Figure 5. Examples of lack of contrast between tree canopy and background, (a) fire scar on 1976 CIR imagery and (b) green grass and a fire scar on 1981 RGB imagery. Once the woody cover candidate objects were created at L1, objects over a certain size were determined to be an over representation of woody cover. This over representation may have been either from over-lapping canopy (occasional), green understorey (probable), or colour bleeding in the hardcopy photos (also probable). Woody canopy of Australian vegetation can be difficult to distinguish if there is a green herbaceous layer as a background (McCloy & Hall 1991). Visual assessment confirmed that if the objects contained over 5000 pixels, there was potential for confusion of woody cover with the background. Therefore, further segmentation of these objects was undertaken based upon a slightly higher threshold (column (d) in Table 3). 10

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2.4.3 Calculating proportional cover

The super-object layer L2 was segmented into a 1 ha grid using the 100 x 100 m cell vector layer. The proportion of L1 woody cover per L2 cell was calculated for each date. The 1 ha cells at L2 for each date were then assigned to one of the following 10 classes: 0-10 %, 1120%, 21-30 %, 31-40%, 41-50%, 51-60%, 61-70%, 71-80%, 81-90%, or 91-100%. 2.5 Validation

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Due to the historical nature of the image data, standard methods for testing the accuracy of the woody cover classification against validation data collected in the field at the same time as image capture was not possible. Therefore, to assess the effectiveness of the object-based approach, 499 random points were generated for the polygons of the Wells Land unit layer for each of the dates. For each year, points were either classed as wood or non-woody based on visual interpretation of the original imagery for each date. Where each of the 499 points intersected with the tree cover derived from the object-based approach, the point was also attributed with object-based woody cover. The visually derived cover for each point could then assessed against the object-based cover using error matrices (Congalton & Green 2009) to test the accuracy of the object based classification for each date. Overall accuracy of the classifications was calculated as were the User and Producer accuracies (Story & Congalton 1986). For the validation of the 2010 woody cover classification only 403 points were available for use because the image extent was less than for the other dates and 96 of the random points lay outside this extent. 2.6 Comparison of methods for measuring woody cover

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The proportional cover at 1 ha from the object-based technique was compared to a visual analysis based on a proven manual method for deriving quantitative woody cover from aerial photographs (Fensham et al 2002). A variation of the method has also been used by other studies of woody cover in savanna (Bowman et al 2001, Banfai & Bowman 2006, Lehmann et al 2009a). The visual process involves overlaying a lattice of points on the imagery and assigning either the class ‘tree’ or ‘no tree’ depending what underlies the point. One hundred of the 1 ha cells were randomly selected across the scene within the Wells (1979) upland terrain polygons. The same 100 cells were used for each year. Within each selected cell, a lattice of 10 x 10 points spaced 10 m apart overlaid the photomosaic. For each date, an independent observer recorded whether the original image mosaic underlying each point was tree or not tree. The number of points identified as tree within a cell provided percentage of observed woody cover.. The point-based percentage observed and percentage of area from the objectbased woody cover for each cell were then compared for each year using regression analysis. 3. Results 3.1 Accuracy assessment

The validation of woody cover derived from the object-based analysis was undertaken against visually assessed woody cover for each date. A summary of the error matrices for each year (Table 4) shows the overall accuracies for each date were high (over 93%). The highest overall accuracy (95.3 %) was attained for the woody cover extracted from the pan-sharpened WorldView-2 satellite imagery. The woody cover from the 1964 panchromatic aerial photo mosaic had the lowest overall accuracy (93.2%). The lower accuracy could be attributed to the lack of colour available as a method for distinguishing woody canopy from shadow. User and Producer accuracies for all data sets were also high. The lowest user accuracy was for the 2010 woody cover extraction (90.6 %), while the highest was for the 1976 woody cover (95.7%). 11

Journal Pre-proof The lowest producer accuracy was with the 1964 extraction (89.3 %), while the highest was for 2010 (93 %). The high accuracies for each data type indicate that the technique for extracting woody cover from the imagery is sound and that the woody cover layers produced are suitable for assessing woody cover dynamics over the region between dates. Full error matrices for each date are provided in the supplementary material (Table T2). Table 4. Accuracy results for the object-based classification method for each date. Overall Accuracy (%)

1964 1976 1981 2010

93.2 94.6 95.2 95.3

Woody User Accuracy (%) 94.6 95.7 94.4 90.6

Woody Producer Accuracy (%) 89.3 90.0 91.1 93.0

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3.2 Woody cover

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Woody cover maps for each date show the variability of cover, both spatially and between dates (Figure 6). Human activities including clearing and infrastructure development including the township and mine site have reduced woody cover. Between 1964 and 1976, some clearing of land occurred in the north-west region of the study site (within the red ellipse in Figure 6b) which has changed the cover in the area noticeably. Also in the region where activities for developing the mine site have taken place there is a gradual reduction in woody cover between 1976 and 2010 (the blue circle in Figure 6b, c and d). Fire scars can reduce the woody cover signal and this can be seen in a number of the polygons (for example green circle in Figure 6b). The regions of no cover recorded in the south and east of the 2010 map lie beyond the extent of the imagery.

(a) 1964

(b) 1976

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Figure 6. Woody cover extent for each date. The red ellipse in (b) highlights some land clearing since (a). The blue circle that grows from (b) to (d) is changes associated with the development of Ranger uranium mine. On the 2010 map (d), the blank polygons to the right and bottom are areas of no data.

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3.3 Proportional woody cover

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The proportion as a percentage of woody cover per 1 ha cell for each date is shown in Figure 7. The number of 1 ha cells available for analysis for 1964, 1976 and 1981 were 4240, however due to the limited spatial extent of the imagery, only 3274 cells were available for 2010. Therefore, any change detected between 1981 and 2010 must be considered in context of the different image extents .The four maps highlight the within land unit polygon variability that exists, both spatially and temporally. Most of the areas displaying great reductions in cover have been subject to land clearing or building of infrastructure (red circles show examples in Figure 7b and d).

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Figure 7. Proportional woody cover per hectare cell for each date: (a) 1964, (b) 1976, (c) 1981 and (d) 2004.

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Figure 8 shows the distribution of 1 ha cells across the proportional cover classes. For each date, most (over 70 %) of the 1 ha cells belong to classes with between 11 % and 50 % proportional woody cover. For the first three dates (1964, 1976, and 1981), most cells belong to the 31-40 % cover class (24 %, 23 % and 33 % respectively). For 2010, the largest proportion of cells (27 %) sit within the 21-30 % cover class. The 1964 and 1976 woody cover data sets have almost double the proportion of cells in the 1-10 % class than the 1981 and 2010 data sets. The data sets for each year have less than 5 % of cells within the 61-70 % class and fewer with greater than 70% cover.

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Figure 8. Percentage of cells per proportional cover class for each year. Note: Classes with cover above 70% are merged into one due to low values.

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3.4 Comparing object-based approach with manual interpretation

The object-based approach was compared to manual interpretation of woody cover for 100 randomly selected one ha cells from each year. For each date, the comparison showed a linear relationship (Figure 9). The best match between the object-based approach and manual interpretation was for the 2010 WV-2 image with an R2 = 0.842 with a standard error (se) of 7.1. The weakest relationship based on regression was for the woody cover derived from the 1981 RGB mosaic with R2 = 0.38 (se = 10.5). For 1964 and 1976, R2 values were 0.482 (se = 7.1) and 0.495 (se = 8.3) respectively. RMSE are 9.82%, 8.32%, 15.88% and 7.74% for 1964, 1976, 1981 and 2010 respectively (Table 5). Bias values are for 1964 and 1981 are 5.16% and 8.36% respectively, suggest an underestimation of cover for these dates compared to the reference data (Table 5). . Biases of 1.29%, and -2.11% for 1976 and 2010 respectively, show the cover data from these dates is closer to the reference data. The linear models for the results from both data sets containing near infrared information are highly correlated. For the both the 1964 greyscale and 1981 RGB mosaics, the object-based approach provides lower values for woody cover when compared to manual interpretation particularly in cells with higher densities of cover.

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Figure 9. Linear comparison of manually derived visual cover estimates to the proportional cover extracted using object based approach for each date: (a) 1964, (b) 1976, (c) 1981 and (d) 2010. Linear models for all dates have a p-value < 0.0001. Dotted line in each plot shows 1:1 relationship. Table 5. RMSE and Bias values for comparison between visually derived cover and cover from the object-based approach. Year 1964 1976 1981 2010

RMSE (%) 9.82 8.32 15.88 7.74

Bias (%) 5.16 1.29 8.36 -2.11

4. Discussion The study does show that the technique described here was successful in providing contiguous woody cover estimates from a range of data sets. By using threshold values specific for each 16

Journal Pre-proof data set, it was possible to extract woody cover from greyscale, true colour and colour infrared aerial photo mosaics as well as from high spatial resolution satellite imagery. The technique used iterative techniques to adjust for areas where there was over estimation and underestimation due to a lack of contrast between woody cover and background. Proportional cover estimates derived from the technique were compared with cover estimated using visual interpretation techniques and showed strong linear relationships particularly for cover derived from datasets with near infrared information. The best results were for the two dates that used NDVI as the derivative data set, 1976 and 2010. The results for the other two dates show that texture measures (homogeneity) and a greenness index (TGI) can be used for greyscale and RGB data respectively to extract woody cover, in cases where there is no near infrared information.

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Although not the main aim of this study, the basic analysis undertaken here of woody cover from the four dates, shows that no real trends in change between dates were detected. By grouping the 1 ha cells of proportional cover into classes, the distribution of cover was able to be readily observed, and although there was spatial variability in cover between dates, the proportional distribution of cover over the site was consistent. This aligns with recent research in the region (Murphy et al 2014). The dynamism of woody cover shown is consistent with other findings (Lehmann et al 2008b). A number of studies in the region report thickening of woody cover in savanna which preliminary results from this study do not support. However, these studies are either of a short time frame (Chen et al 2003), were very localised (Beringer et al 2007), or of low temporal resolution using few dates (Sharp & Bowman 2004).

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4.1 Benefits and limitations of using a semi-automated technique

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There are a number of advantages to using a semi-automated technique (Table 6). The biggest advantage is that the data provided is continuous across the study area. Too undertake analysis of the same area using visual interpretation would be totally impractical. A further key advantage of the technique described in this paper is that much of the analysis is based upon a single derivative layer reducing processing and analysis effort including eliminating the need for multi-parameter analysis. Another advantage of the technique is that is repeatable between datasets and operators. Once the ruleset has been created and tested it is quick to implement. The technique may more correctly represent woody cover (as sampling is independent of scale) than manual estimates using point-based method. The same approach can be used for other land units from Wells (1979) in the region. Table 6. Advantages versus limitations of using the semi-automated approach to extract woody cover from remote sensing data Advantages  Provides a continuous data set across the entire site.  Based upon single derivative layer reducing need for multivariate analysis.  Repeatable between data sets and operators

Limitations  Underestimation of woody cover in areas of low contrast

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Scale independent

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Overestimation of cover in areas with green understory

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Correlation in point based visual methods is not high in most cases

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Requires a remote sensing expert

In addition to the advantages, there are a number of limitations when using a semi-automated approach. For example, there is a tendency for the technique to underestimate woody cover in fire affected areas. Conversely, there can also be some overestimation of woody cover in 17

Journal Pre-proof regions with green grass understorey. Therefore, the optimal time for data capture to maximise the effectiveness of this approach would be after the senescence of the grass understorey but prior any burning. Another limitation is that the cover derived from this method varies from visual methods of obtaining canopy cover, particularly when using imagery lacking near infrared information. The proportions of woody cover derived from the object-based analysis are areal based whereas visual estimates are calculated using point based assessments. Although, the comparisons show good correlation, this difference in methodology makes a comparison between the two methods problematic. Finally, the technique does require remote sensing expertise. 4.2 Sources of uncertainty in the process

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In a study such as this, there are multiple sources for potential uncertainty that can compromise the precision of the results and as such need to be considered. Sampling uncertainty may occur as the temporal data set is incomplete. Historical image archives do not cover all dates and have not been systematically planned by researchers. Temporal analysis is limited to what is available and data are not evenly distributed across the period or coincident with environmental events. Further uncertainty is introduced through the timing of data capture. Imagery from different times can produce differences in plant phenology, reflectance and shadow. The timing can also influence the amount of fire scars, canopy gaps and sparseness seen in the imagery. Ideally, to help minimise the impact between dates, it is beneficial that all imagery are captured at a similar time of day at a similar time of the year. The senescent grasses enhance contrast with canopy greenness to enable more ready discrimination of woody cover. While steps were taken to minimise these issues they may still affect analysis.

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Uncertainty can also be introduced through error. Sources of error exist in all steps of the process including image acquisition, pre-processing, analysis and validation. For example in aerial photo data capture, lens distortion creates vignetting resulting in reduced radiometric quality toward the edges of aerial photos. Vignetting, combined with minimal sidelap between flight lines, can cause spectral inconsistencies across a mosaic. Other issues that may influence analysis include differences in shadow orientation between two flight lines in a mosaic. Errors introduced in pre-processing of the data include warping and geometric misalignment. These are mitigated by geo-registering all data sets to a one geometrically accurate image. Potential errors can further occur in analysis and validation as a result of image spectral resolution. For example, the 1964 data is black and white (greyscale) aerial photography and the lack of spectral detail presented a challenge for analysis. Although there was contrast between crown and background, there was limited contrast primarily between crown and crown shadow making it difficult to delineate crown boundaries correctly in both the automated approach and the visual interpretation. The use of the texture based derivative layer helped reduce this error. True colour (RGB) and false colour infrared (CIR) imagery also presented a number of issues during analysis including the presence of fire scars that reduce the intensity of crowns against background. These crowns were not detected in the initial threshold segmentation and required further analysis using a local maxima approach. Instances of green understorey (such as grass) can also provide a lack of contrast to the canopy which confuses analysis providing an over estimation of woody cover. To overcome this issue, further analysis was needed to attempt to extract the woody cover from the background using further segmentation based upon a higher threshold for the derivatives. The condition of data was quite varied providing a source of potential error. The quality of historical aerial photography particularly if the images are scanned hardcopies may not be optimal. Changes in colour associated with ageing, along with damage such as scratches can affect image quality and influence analysis. While much of the pre-processing mitigated the effects there will still be some impact. 18

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Classification error can result in both over and under estimation of woody cover. As mentioned above, the underestimation of canopy occurred in regions where canopy had comparatively low reflectance to background (such as bare ground and fire scars) and overestimated where ground cover had similar reflectance to canopy. Where there was potential for these errors to occur, the method has steps that were used to mitigate the classification error. For example, the local maxima approach was able to identify potential tree crowns from areas of low contrast that were missed during the first segmentation. In addition, the re-segmentation of large areas also was able to reduce the over classification of woody cover.

4.3 Scale as an issue

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The validation procedure can also introduce errors into the analysis. Due to the use of historical imagery, it is highly unlikely there will be historical field data available for the region to verify woody cover. Therefore, validation of the woody cover classification was conducted against visual interpretation of the imagery. The analysts undertaking the visual interpretation of the mosaics found the task difficult primarily due to the lack of spectral detail in the mosaics, particularly in the data sets with no near infrared information. For example in the 1964 greyscale mosaic, although there was contrast between tree crowns and background, there was limited contrast primarily between crowns and shadow making it difficult for the interpreter to fully differentiate the crowns. This could be one reason for the higher values of woody cover for the manual interpretation compared to the semi-automated approach for that date and subsequent larger error. A similar result can be seen for the 1981 RGB data set where contrast is less than for the data sets with near infrared bands. As such the semi-automated object-based approach may have actually been better at detecting woody cover than the method used for collecting the reference data.

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It has been reported that scale can influence the amount of woody cover detected. Fensham et al (2002) found that analysis of aerial photography with decreasing (coarser) scale was associated with increased estimates of woody cover. Most of the uncertainty between dates should be eliminated through the rescaling of all imagery to a standard 1 m GSD. This GSD is coarser than the scanned scale of the original aerial photos and the pan-sharpened satellite imagery, but still finer enough to detect the woody cover objects. The semi-automated approach showed good correlation to the visually determined reference data even though one method was area based and the other was point based. Neither the original scale of the aerial photos nor the resolution of the satellite imagery appear to influence the results. There may be some of difference between the results for the two methods may be related to the scale of the reference data. The 10 m spacing of points used for visual interpretation that is described in the literature (Lehmann et al 2008a) might be insufficient or too many. While it is convenient for calculating percentage cover, 100 points per ha may not be suitable for accurately recording the amount of woody cover in savanna with discontinuous woody cover. This would require further investigation beyond the scope of this research.

5. Conclusion The above study developed and tested a semi-automated technique for extracting woody cover from remote sensing data in the context of historical change across a large area of savanna in northern Australia. The technique was applied to a number of data sets including greyscale, colour and infrared aerial photograph mosaics as well as high spatial resolution satellite imagery. The method included a means of addressing both over and under representation of woody cover during classification. The method can be run over the entire site in a timely 19

Journal Pre-proof manner making it less labour intensive and less prone to subjective interpretation. Results of validation indicate high accuracies of the method and its suitability for temporal and spatial analysis of savanna dynamics for the study region. The technique has since been applied to aerial photo mosaics and high spatial resolution satellite from other dates. Results from each date will be compared and used to create an envelope of woody cover distribution incorporating variability. This envelope will be used to develop ecosystem restoration targets for Ranger uranium mine closure criteria.

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Further analysis of the extended time series includes fate analysis of the woody cover objects and the application of landscape metrics to describe the dynamism within the region. Causes for the variability will also be investigated by linking spatial and temporal relationships to environmental variables and historical events. This will determine potential drivers for future change in the region.

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6. References

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Banfai DS & Bowman DMJS 2006. Forty years of lowland monsoon rainforest expansion in Kakadu National Park, Northern Australia. Biological Conservation 131 (4), 553-565. Beringer J, Hutley LB, Tapper NJ & Cernusak LA 2007. Savanna fires and their impact on net ecosystem productivity in North Australia. Global Change Biology 13 (5), 990-1004. Blaschke T 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65, 2-16. Bowman D, Riley J, Boggs G, Lehmann C & Prior L 2008. Do feral buffalo (Bubalus bubalis) explain the increase of woody cover in savannas of Kakadu National Park, Australia? Journal of Biogeography 35 (11), 1976-1988. Bowman DMJS, Walsh A & Milne DJ 2001. Forest expansion and grassland contraction within a Eucalyptus savanna matrix between 1941 and 1994 at Litchfield National Park in the Australian monsoon tropics. Global Ecology & Biogeography 10 (5), 535-548. Browning DM, Laliberte AS & Rango A 2011. Temporal dynamics of shrub proliferation: linking patches to landscapes. International Journal of Geographical Information Science 25 (6), 913-930. Buitenwerf R, Bond W, Stevens N & Trollope W 2012. Increased tree densities in South African savannas:> 50 years of data suggests CO2 as a driver. Global Change Biology 18 (2), 675-684. Bunting P & Lucas R 2006. The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data. Remote Sensing of Environment 101 (2), 230-248. Chen X, Hutley LB & Eamus D 2003. Carbon balance of a tropical savanna in northern Australia. Oecologia 137, 405-416. Congalton RG & Green K 2009. Assessing the accuracy of remotely sensed data: principles and practices. 2nd edn, CRC Press, Boca Raton, Fl. Coughenour MB & Ellis JE 1993. Landscape and climatic control of woody vegetation in a dry tropical ecosystem: Turkana District, Kenya. Journal of Biogeography, 383-398. Culvenor DS 2002. TIDA: An algorithm for delineation of tree crowns in high spatial resolution remotely sensed imagery. Computers and Geosciences 28 (1), 33-44. Erikson M 2004. Segmentation and classification of individual tree crowns in high spatial resolution aerial images. Ph D. thesis, Swedish University of Agriculture, Uppsala. Esparon, AJ, Bartolo, RE & Whiteside TG (in prepartion) A method for detection and removal of artefacts in historical aerial photography to improve object-based image analysis. 20

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Whiteside T & Boggs G A comparison of canopy cover derived from object-based crown extraction to pixel-based cover estimates. In SSC2009: Surveying and Spatial Sciences Institute Biennial International Conference. Adelaide 28 September - 2 October. Whiteside TG & Bartolo RE 2015. Mapping aquatic vegetation in a tropical wetland using high spatial resolution multispectral satellite imagery. Remote Sensing 7 (9), 11664-11694. Whiteside TG, Boggs GS & Maier SW 2011a. Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation 13 (6), 884-893. Whiteside TG, Boggs GS & Maier SW 2011b. Extraction of tree crowns from high resolution imagery over Eucalypt dominant tropical savanna. Photogrammetric Engineering and Remote Sensing 77 (8), 813-824.

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- Savanna tree cover is shows spatiotemporal variability - An object-based method extracted tree cover from aerial photos and satellite data - This semi-automated method is comparable to visually interpreted methods - Provides spatially continuous data sets for whole site analysis - Results can be used to describe variability over time

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