Mapping inundation in the heterogeneous floodplain wetlands of the Macquarie Marshes, using Landsat Thematic Mapper

Journal of Hydrology 524 (2015) 194–213

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Mapping inundation in the heterogeneous floodplain wetlands of the Macquarie Marshes, using Landsat Thematic Mapper Rachael F. Thomas a,b,⇑, Richard T. Kingsford b, Yi Lu c, Stephen J. Cox a, Neil C. Sims d, Simon J. Hunter a a

Office of Environment and Heritage, PO Box A290, Sydney South, NSW 1232, Australia Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of NSW, Sydney, NSW 2052, Australia c NSW Office of Water, Department of Primary Industries, PO Box 3720, Parramatta, NSW 2124, Australia d CSIRO Land and Water, Private Bag 10, Clayton Sth, Victoria, Australia b

a r t i c l e

i n f o

Article history: Received 20 March 2014 Received in revised form 25 January 2015 Accepted 16 February 2015 Available online 21 February 2015 This manuscript was handled by Konstantine P. Georgakakos, Editor-in-Chief, with the assistance of Matthew McCabe, Associate Editor Keywords: Water index Vegetation index Semi-arid Environmental flows

s u m m a r y Flood dependent aquatic ecosystems worldwide are in rapid decline with competing demands for water. In Australia, this is particularly evident in the floodplain wetlands of semi-arid regions (e.g. the Macquarie Marshes), which rely on highly variable flooding from river flows. Environmental flows mitigate the impacts of river regulation, inundating floodplains, thereby rehabilitating degraded habitats. Mapping flooding patterns is critical for environmental flow management but challenging in large heterogeneous floodplains with variable patterns of flooding and complex vegetation mosaics. We mapped inundation in the Macquarie Marshes, using Landsat 5 TM and Landsat 7 ETM+ images (1989–2010). We classified three inundation classes: water, mixed pixels (water, vegetation, soil) and vegetation (emergent macrophytes obscuring inundation), merged to map inundated areas from notinundated areas (dry land). We used the Normalised Difference Water Index (NDWIB2/B5), masked by the sum of bands 4, 5, and 7 (sum457), to detect water and mixed pixels. Vegetation was classified using an unsupervised classification of a composite image comprising two dates representing vegetation senescence and green growth, transformed into two contrasting vegetation indices, NDVI and NDIB7/B4. We assessed accuracy using geo-referenced oblique aerial photography, coincident with Landsat imagery for a small and large flood, producing respective overall accuracies of inundated area of 93% and 95%. Producer’s and user’s accuracies were also high (94–99%). Confusion among inundation classes existed but classes were spectrally distinct from one another and from dry land. Inundation class areas varied with flood size, demonstrating the variability. Inundation extent was highly variable (683–206,611 ha). Floods up to 50,000 ha were confined to the north and south wetland regions. Connectivity to the east region only occurred when flooding was greater than 51,000 ha. Understanding the spatiotemporal dynamics of inundation is critical for quantifying the environmental flow requirements across the suite of biota in the Ramsar-listed Macquarie Marshes. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction Around the world the biodiversity of freshwater ecosystems is in rapid decline (Dudgeon et al., 2006; Kingsford, 2011), with competing demands for water for environmental and human requirements (Poff et al., 2003). In Australia, floodplain wetlands in semi-arid regions rely on variable flooding from river flows (Puckridge et al., 1998) for characteristic ‘‘boom and bust’’ ecological productivity (Bunn et al., 2006). Many of these rivers are regulated by dams, altering their flow regimes and precipitating ⇑ Corresponding author at: Office of Environment and Heritage, PO Box A290, Sydney South, NSW 1232, Australia. Tel.: +61 299955660; fax: +61 299955924. E-mail address: [email protected] (R.F. Thomas). http://dx.doi.org/10.1016/j.jhydrol.2015.02.029 0022-1694/Ó 2015 Elsevier B.V. All rights reserved.

ecological decline (Bunn and Arthington, 2002; Kingsford et al., 2006). This is particularly evident in Australia’s most regulated river basin, the Murray-Darling Basin (Fig. 1, Kingsford, 2000), but also globally (Poff and Zimmerman, 2010). Rehabilitation requires environmental water (Acreman and Dunbar, 2004; Arthington and Pusey, 2003; Richter, 2009) with a key target of inundating major wetland systems. Tracking the effects of environmental flows on inundation for large floodplains is critical because these drive ecological processes and responses of a wide range of organisms from vegetation, invertebrates, fish to waterbirds. Effective quantification of the spatial and temporal patterns of flooding is a critical measure for environmental flow management (Overton, 2005; Thomas et al., 2011; Young et al., 2006).

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Fig. 1. (a) Location of the Macquarie Marshes (MM) in the Macquarie-Bogan River Catchment (dark grey) of the Murray-Darling Basin (light grey) within mean annual rainfall areas (Xu and Hutchinson, 2011) and (b) the Macquarie Marshes floodplain (grey) with its main rivers (1-Macquarie River, 2-Bulgeraga Creek, 3- Gum-Cowal Terrigal Creek), and main wetland regions (dashed line): south (S), north (N) and east (E), north of river flow gauges at Marebone Weir (MB) including conservation reserves (green) and Ramsar (hatched). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Floodplain wetlands are large, complex systems with diverse ecological communities (Ward et al., 2002) and dynamic water regimes (Lunetta and Balogh, 1999), creating highly variable surface conditions. Complex mosaics of wetland vegetation vary spatially and temporally with flooding. Further complicating investigation, many semi-arid wetland ecosystems are often remote and inaccessible with poorly gauged flow data (Kennard et al., 2010). Remote sensing provides a useful method for quantifying the variability of inundation and understanding resulting ecosystem responses (Alsdorf and Lettenmaier, 2003). Landsat Thematic Mapper (TM) imagery is routinely captured (every 16 days), has a unique archive and covers entire floodplains (Wulder et al., 2008), making it suitable for monitoring inundation of large floodplain wetlands (Ozesmi and Bauer, 2002). The mid-infrared spectral band 5 (1.55–1.75 lm) and band 7 (2.08–2.35 lm) of Landsat-5 TM and Landsat-7 Enhanced TM Plus (ETM+) are sensitive to water, strongly absorbing electromagnetic radiation in these wavelength ranges and effectively separating water from dry land in open water body landscape features (Frazier and Page, 2000; Hudson and Colditz, 2003; Pietroniro and Prowse, 2002; Smith, 1997; Xu, 2006). Several different water indices have been developed to detect water in landscape features, namely water bodies. Water indices combine visible and infrared bands to enahnce water and suppress non-water by establishing a threshold (NDWIB4/B5, Gao, 1996; NDWIB2/B4, McFeeters, 1996; NDWIB2/B5, Xu, 2006). Indices that utilise the mid-infrared band (Gao, 1996; Xu, 2006) are the most effective at detecting water bodies (Hui et al., 2008; Soti et al., 2009; Sun et al., 2012) or different water availabilities (Campos et al., 2012) in arid regions. Knight et al. (2009) found that the NDWIB2/B4 index was almost comparable to a rigorously developed method that classified multi-temporal

images based on a set of decision rules to map the maximum extent of wetland inundation. The premise of these studies is the detection of water in the landscape based on a homogeneous water signature. Alternatively, other studies have used vegetation indices (NDVI, Zhao et al., 2011) while other studies have combined indices to improve discrimination in variable landscapes (Beeri and Phillips, 2007; Ouma and Tateishi, 2006). In floodplain wetlands, the complexity of the water and vegetation mosaic remains a significant challenge for mapping inundation, because spectral signatures form zones of mixed pixels, including water, vegetation and soil (Ozesmi and Bauer, 2002; Thomas et al., 2011; Zhao et al., 2011). Further, spectra vary with the spatial and temporal interaction between flooding, vegetation composition, density and vigour (Johnston and Barson, 1993; Silva et al., 2008), hence the difficulty in capturing all inundation water characteristics using a single spectral band or single index in heterogeneous landscapes (Bhagat and Sonawane, 2011; Knight et al., 2009; Sun et al., 2012). To overcome this problem, previous studies have combined multiple bands to map water in wetlands by classifying the multi-spectral bands (Johnston and Barson, 1993; Kingsford et al., 2004; Lunetta and Balogh, 1999; MacAlister and Mahaxay, 2009; Thomas et al., 2011). We investigated variability of inundation in the heterogeneous floodplain wetland in semi-arid Australia, the Macquarie Marshes (Thomas et al., 2011), using a combination of water and vegetation indices derived from Landsat TM, 1989–2010, to map inundated area during flood events for environmental flow monitoring. Our specific objectives were to examine the efficacy of using a combination of water and vegetation indices to detect inundation as classes that represent water with increasing amounts of vegetation (or sediment): water, mixed, and vegetation, in contrast to dry

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Table 1 Inundation status class descriptions and their spectral characteristics in the Landsat Thematic Mapper (TM) bands: Band 1 (visible blue: 0.45–0.52 lm), Band 2 (visible green: 0.52–0.60 lm), Band 3 (visible red: 0.63–0.69), Band 4 (near-infrared (NIR): 0.76–0.90), Band 5 (mid-infrared: 1.55–1.75 lm) and Band 7 (mid-infrared: 2.08–2.35 lm). Inundation status

Class

Description

Spectral characteristics

Inundated

Water

Homogenous expanse of water, varying in depth and water quality, with little or no vegetation within frequently flooded lagoons, river channels, marshes, river red gum swamps; and off-river water storages, and; infrequently flooded gilgaied floodplain covered with woodlands, grasslands, shrubs, or bare ground Flooded vegetation and soil forming a transition of mixed pixels between water, flooded wetland vegetation, dry land vegetation and bare ground soil), occurring on the edges of open water lagoons, flooded marshes, river red gum swamps, within narrow creeks and floodplains Mostly dense emergent macrophytes in flood, including monotypic stands of the tall (1–3 m) perennial common reed, (Phragmites australis), but also water couch (Paspalum distichum), spikerush (Eleocharis spp.) and cumbungi (Typha spp.). Phenological phases of vigorous green growth, flowering and senescence (Fig. 2) influence spectral reflectance

Low percentage reflectance (q) in all Landsat TM spectral bands. q close to zero(strongly absorbed by water) in the mid-infrared ranges of Bands 5 and 7

Mixed

Vegetation

Not inundated

Dry land

Not flooded grasslands, shrub lands, woodlands, bare ground (e.g. scalded land, fallow paddocks) and artificial surfaces (e.g. quarries, urban areas)

land, as the basis for mapping inundated area. We tested this by comparing our classification to independently collected and classified data using coincident aerial photography. 1.1. Study area The Macquarie Marshes (30°500 S, 147°300 E) (the ‘‘Marshes’’) are in the low reaches of the Macquarie-Bogan River catchment (74,700 km2) of the Murray-Darling Basin in semi-arid Australia (Fig. 1a). They are one of the large (>270,000 ha) freshwater wetlands in the Murray-Darling Basin (Fig. 1a). Renowned for their ecological importance, sections are protected as a Nature Reserve and listed as a wetland of international importance under Ramsar (Kingsford and Thomas, 1995) (Fig. 1b). Their flooding is primarily dependent on Macquarie River flows, generated from variable rainfall and runoff in the upper catchment in the south east (Herron et al., 2002) with a mean annual rainfall of >700 mm, compared to <500 mm within the Marshes (Fig. 1a, Xu and Hutchinson, 2011). Average daily temperatures range from 3–6 °C during winter to 30–36 °C during summer. The Macquarie River is highly regulated by large dams (Kingsford, 2000) (Fig. 1a), with most agricultural diversions occurring upstream of Marebone Weir (Fig. 1b). River regulation has altered river flows to the Marshes (Ren and Kingsford, 2011) and reduced flooding patterns (Thomas et al., 2011; Wen et al., 2013) with detrimental ecological impacts (Kingsford and Thomas, 1995; Kingsford and Johnson, 1998; Rayner et al., 2009; Thomas et al., 2010). The Marshes have access to a volume of environmental water each year (>300 GL) to mitigate the effects of river regulation, targeting the core wetland area. The Marshes are on a complex dryland alluvial floodplain with anastomosing and distributary channels (Ralph and Hesse, 2010). Wetlands are formed along the three main watercourses: the Macquarie River, Bulgeraga Creek and the Gum Cowal-Terrigal Creek, with flows managed to three main regions: south, north and east (Thomas et al., 2011, Fig. 1b). In the Marshes different volumes of flow produce spatially variable floods forming a mosaic of different inundation states. At any one point in time flood waters inundate areas of temporary lagoons or floodplains as a homogenous expanse of water (water class), they mix with vegetation and soil (mixed class) and they infiltrate emergent wetland vegetation (vegetation class) in contrast to areas not flooded (dry land class)

Variability in q due to mixing of vegetation and water signals, hence a dampening (decrease) of the vegetation or soil radiometric signal especially in the near to mid infrared regions where water absorption is stronger Vigorous green vegetation growth has high q in the nearinfrared range of Band 4 (reflected by cell structure), correspondingly low q in the visible red range of Band 3 (strongly absorbed by leaf pigments). Senescent or flowering vegetation has relatively low q in Band 4 and relatively high q in Band 3. Also relatively high in band 7 due to lignin content hence radiometric signal similar to dry soil Relatively high q in all bands, radiometric signal governed by cover type.

(Table 1, Fig. 2). This in turn influences the variability of the spectral characteristics creating uncertain boundaries between homogenous cover types. Also the vegetation mosaic in the Marshes varies in growth forms (herbaceous, grass, shrubland, woodland or forest), and in cover ranging from dense to sparse, which also influences spectral characteristics. Densely vegetated marshes, dominated by tall emergent macrophytes such as common reed (Phragmites australis), obscure water detection from optical satellite sensors (Beeri and Phillips, 2007; Thomas et al., 2011) (Fig. 2). 2. Methods We produced inundation maps using five steps (Fig. 3): (1) image selection and pre-processing; (2) water and vegetation index calculations; (3) classification of three inundation classes (water, mixed, and flooded emergent vegetation) and a not inundated class (dry land) for each image; (4) production of inundated area maps; and (5) accuracy assessment for two image dates. We also examined how inundation classes varied with total inundation extent and how this varied across different wetland regions (north, south, east) in the Marshes, with different flood sizes. All our image analyses were performed in ERDAS Imagine Professional 9.2 (Leica Geosystems Geospatial Imaging, 1991–2008). 2.1. Image selection and pre-processing We selected Landsat TM images over a range of flood magnitudes (1989–2011), based on the river flow peaks of 30 day cumulative Macquarie River discharge (30Q) at the gauge downstream of Marebone Weir (Station No. 421090) (DNR, 2006), because of its significant relationship with inundated area (Thomas et al., 2011) (Fig. 4, Table S1). An inundation threshold of 30Q = 30 GL defined ‘small’ and ‘large’ floods (Fig. 4), based on a daily flow threshold (>1 GL day1) known to trigger flooding in the southern Macquarie Marshes (Driver and Knight, 2007). We selected 80 images from the rising limb, peak or falling limb of 17 flood events, producing 35 images with a large flood and 45 images with a small flood (Fig. 4, Table S1). All images were in row 81 and 82 of path 92 of the World Reference System (WRS-2) (NASA, 2010) and acquired as a variable window, map oriented product geo-rectified to the GDA94 datum in MGA55 grid coordinates, with a minimum spatial

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(a)

V(G) M W D (b)

V(S) W

D (c)

V(G) W

197

the simplest and most widely used technique (Song et al., 2001), which subtracts the minimum digital number in the histogram from the entire scene in each band. We then applied sensor gains and offsets, converting the digital numbers to radiance (Furby and Campbell, 2001). Potential pseudo-invariant features (PIFs) (95 in total) were visually identified across the reference image displayed as a false colour composite (B457; RGB), in three pixel brightness groups: bright (16), medium (18) and dark (61) (c.f. Furby and Campbell, 2001). However we retained a final set of 57 PIF values (bright (12), medium (9) and dark (36)) discarding those where coefficients of variation (CV) for pixel brightness were above the upper 15% of the CV of each PIF group (Sims, 2007). For each uncalibrated image PIF values were extracted for each spectral band. The least variant PIFs were retained within their brightness group (bright, medium, dark) if the value conformed in four or more bands to a percentile threshold (calculated across a time series of six images) for each group: 0.5 (bright); 0.25–0.75 (medium) and; 0.2–0.3 (dark). We maintained an equal number of PIFs within each group and also ensured 10–20 points were included in the model. We calculated calibration coefficients for each spectral band from the linear relationship between retained PIF pixel values sourced from the reference and uncalibrated images, establishing a good fit across all images (R2 Mean (min–max) – Band 1: 0.951 (0.897– 0.999); Band 2: 0.972 (0.952–0.999); Band 3: 0.975, (0.962–1.0); Band 4: 0.988 (0.970–0.999); Band 5 0.991 (0.967–1.0); Band 7 0.991 (0.981–1.0)). Each image was then normalised using the regression model. We evaluated the effectiveness of the normalisation by comparing PIF multi-spectral curves from the reference image (February 16, 2000) and two contrasting calibrated images: large flood (October 16, 1998) and small flood (November 01, 2001). We selected an independent set of PIFs from the three brightness groups, dark (10), medium (5), bright (10) that were invariant across the three images. Within each brightness group we tested the sensitivity of each index to the variations in the normalisation. Using a one-way analysis of variance (ANOVA) we compared for differences among image dates within brightness groups and within spectral bands or indices, ensuring ANOVA assumptions were met. 2.2. Image index calculation

Fig. 2. Three inundation classes: water (W), mixed (M), vegetation (V) (growing (G) or senescent (S)) and dry land (D) in the Macquarie Marshes, under different flooding magnitudes: (a) 30Q = 22 GL (photo January 2008, R.F. Thomas) (b) 30Q = 37 GL (photo August 1993, W. Johnson) and (c) 30Q = 107 GL (photo December 2010, R.F. Thomas).

resolution of 25 m. The positional accuracy (root mean square error 60.5 pixel) of each image was well aligned for temporal analysis (Congalton and Green, 2009). To assess classification accuracy, we selected two images dates: a large (2010) and a small (2008) flood (Fig. 4). We radiometrically normalised our Landsat imagery, using a band by band linear regression model method (Sims, 2007) that described the relationship between pixel values of one reference image and the uncalibrated images (Furby and Campbell, 2001). The reference image (February 16, 2001) was selected on the basis of its high dynamic range and it was corrected to surface reflectance to remove sensor, illumination and atmospheric effects using FLASH ENVI 4.3 (ITT Industries, 2006) (Sims, 2007). In the absence of a working atmospheric correction model we corrected for most atmospheric differences in each uncalibrated image using the image based technique of dark object subtraction (Chavez, 1989),

For each inundation image, we calculated two image indices, successfully used to detect water: the NDWIB2/B5 water index (Xu, 2006) (Eq. (1)) and sum457 (Beeri and Phillips, 2007) (Eq. (2)).

qBand2  qBand5 qBand2 þ qBand5 sum457 ¼ qBand4 þ qBand5 þ qBand7

NDWIB2=B5 ¼

ð1Þ ð2Þ

We also calculated two contrasting vegetation indices: NDVI (Tucker, 1979) (Eq. (3)) enhancing vigorous growth and NDIB7/B4(Eq. (4)) enhancing senescent vegetation from the surrounding cover types. Senescent vegetation causes high reflectance in Band 7 and a relatively low reflectance in Band 4 (Miller and Thode, 2007) hence their utility in the Normalised Burn Ratio (Miller and Thode, 2007) and the Normalised Difference Infrared Index (Hunt and Rock, 1989).

qBand4  qBand3 qBand4 þ qBand3 q  qBand4 NDIB7=B4 ¼ Band7 qBand7 þ qBand4

NDVI ¼

where q = % reflectance in each spectral band.

ð3Þ ð4Þ

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(1)

(2)

(3)

W

30Q Flows

(5) Cloud mask

(a)

(a) Class thresholds

NDWIB2/B5

Landsat images

(4)

Inundation image

M

D+V

(b)

(b) Density slice

sum457

Water mask

Image calibration

(c) NDVI

Growth Image (G) NDIB7/B4

(c) Composite image

NDVI

Senescent image (S) NDIB7/B4

ISODATA

V

Inundated W M V Not inundated D

Fig. 3. Five methodological steps used to process Landsat imagery: (1) image selection and pre-processing; (2) water and vegetation index calculation; (3) classification using (a) class thresholds for water (W), mixed (M), and dry land plus vegetation (D + V), (b) application of water mask and (c) ISODATA clustering for vegetation (V); (4) map production utilising cloud mask if applicable; and (5) accuracy assessment (a) oblique photography coincident with Landsat inundation image, (b) geo-rectified oblique photography, and (c) stratified random sampling of reference locations.

Fig. 4. Cumulative flows of previous 30 days (30Q, Marebone Weir, Fig. 1b) to the Macquarie Marshes, 1989–2011, showing timing of 80 (filled circles) Landsat images (February 1989–May 2003: Landsat-5 TM and Landsat-7 ETM+; May 2003–December 2010, Landsat-5 TM due to errors in Landsat-7 ETM+, Wulder et al., 2008) used to map inundation. Threshold 30Q (30 GL in previous 30 days signified by dashed line) defined small and large floods. Open red circles identified images for small and large floods, selected to assess classification accuracy.

2.3. Classification Using NDWIB2/B5 as the primary determinant of inundation we developed a method to establish the threshold for the water and the mixed class. To accommodate the variations in water spectral reflectance, we selected 50 training locations from a range of landscape features: lagoons (15), swamps (15), floodplains (15) and irrigation dams (5) where we had existing historic anecdotal landholder observations of inundated or not inundated (Thomas et al., 2011). At each of the 50 locations we visually delineated homogeneous pixel clusters (Areas of Interest, AOI, n pixels = 16–414)

using eight false-colour composite (RGB, 472) images of differing sized floods. For each image we allocated an inundation status (inundated or not inundated, Table 1) to the 50 locations based on the historic landholder observations from 11 flood events and visual interpretation of the multispectral imagery, noting if vegetation was vigorous or senescent (Fig. 5). From the 50 AOIs we collected the NDWIB2/B5 signature statistics (minimum, maximum, mean and standard deviation). We discarded the AOI signatures that displayed high spectral variability (SD > mean and range >0.60) or if the AOI became visually heterogeneous, straddling more than

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199

(a)

NDWIB2/B5

(b)

(c)

(d)

Training locaons (AOI) Fig. 5. Mean (±SD) NDWIB2/B5 for each training AOI located in landscape features of lagoons (L), swamps (S), floodplain (F) and dams (D) (bottom x axis) and observed as inundated (I) or not-inundated (N) (⁄includes both I and N pixels in AOI), senescent (S) or vigorous (V) (top x axis) from images with a small flood (a) January 12, 1996 and (b) October 13, 1997 and a large flood (c) August 29, 1998 and (d) August 23, 1990. AOIs (red open circle) discarded due to high NDWIB2/B5 variability, heterogeneous cover type or senescence Thresholds for water class NDWIB2/B5 > 0 (blue line) and for mixed class 0 < NDWIB2/B5 < overall inundated AOI NDWIB2/B5 minimum (TM, orange line). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 6. Classification process using (a) an inundation Landsat image (RGB:472) in a large flood (3 November 1993) and derived water indices: (b) NDWIB2/B5 (i) grouped into value ranges to determine (ii) thresholds for the three classes of water (W) (blue), mixed (M) (orange) and dry land with vegetation (D + V) (brown) with TM the determined threshold between M and D + V, and (c) sum457 (i) density slice of histogram based threshold (t) to determine the (ii) water mask which was then applied to (b) to form (d) the masked NDWIB2/B5.

one cover type. AOIs were classed as water (W) if the AOI mean was greater than zero (mean NDWIB2/B5 > 0, Fig. 5, blue line) and the observed inundation status was used to confirm this. However inundated AOIs with means less than zero remained (Fig. 5c and d), indicating a transition zone of mixed cover types. From the retained group of inundated AOI’s minimum statistic, we calculated the minimum pixel value to determine the mixed class threshold (TM = minimum pixel value of all retained inundated AOIs) (Fig. 5, orange line). We assessed the data distribution of the mixed threshold (TM) values using all image dates (n = 80), large flood dates (n = 35) and small flood dates (n = 45). We displayed the NDWIB2/B5 in ArcGIS 9.2 (ESRI, 1999–2006) grouping values into classes based on the natural breaks in the data (Fig. 6). Class thresholds for water (W) pixels (NDWIB2/B5 > 0, Xu,

2006, Fig. 6), and for mixed (M) pixels (TM < NDWIB2/B5 < 0, Fig. 6) were inserted to display the inundation extents transitioning from water to dry land. These were compared to historical oblique photography where available (1990, 1992, 1993, 2005, 2006, and 2008). To further eliminate potential errors of commission caused by very bright pixels we combined the NDWIB2/B5 with a broad binary water mask based on a density slice of the sum457 index (Fig. 6). To create the mask, we established an empirical threshold based on each histogram by overlaying the NDWIB2/B5 and dividing the sum457 into 0.25 intervals of the standard deviation below the mean. The 0.25 intervals were iteratively set to transparent to reveal the NDWIB2/B5 masking potential commission errors and effectively slicing the sum457 histogram into two classes (Overton, 2005). Pixels above the threshold (bright in all three

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Fig. 7. Classification of emergent vegetation using (a) two Landsat images (RGB:472) showing (i) senescence (S) (15 August 1993) and (ii) green growth (G) (3 November 1993) and the vegetation indices (b) NDIB7/B4 and (c) NDVI used for ISODATA clustering to produce (d) vegetation (V)(green), and not emergent vegetation (NV) (grey) (subset from the north region, see Fig. 1b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 8. Inundation map construction from (a) masked NDWIB2/B5 classes water (W), mixed (M), and dry land with vegetation (D + V) (b) ISODATA classes emergent vegetation (V) and not emergent vegetation (NV) merged to form (c) final inundation map (subset from the north region, see Fig. 1b).

bands) were coded to zero and included dry land and emergent wetland vegetation (Beeri and Phillips, 2007). Pixels below the threshold were coded to one, establishing the mask. Pixels below the TM threshold were dry land but also included emergent wetland vegetation (D + V). Neither of the two water indices enabled the distinction of inundated wetland vegetation from dry land. To separate emergent wetland vegetation from dry land, we used two images dates selected within each flood event, representing contrasting growth phases of emergent wetland vegetation during flooding: a senescent image (S) (brightest in band 7 prior to flood) and a green growth image (G) (brightest in band 4 post flood) (Fig. 7). For each image, we calculated the two contrasting vegetation indices, NDVI and NDIB7/B4, and combined them into a four band composite image: NDIB7/B4(S) NDIB7/B4(G): NDVI(S): NDVI(G). We then applied an unsupervised clustering technique (ISODATA) to classify vigorous emergent vegetation (V) as a surrogate of its inundation. This provided an automated process for grouping the spectral response of flooded vegetation into one or two spectrally similar clusters confined mainly to the targeted regions of common reed and water couch vegetation communities. All other ISODATA clusters were merged to a ‘‘not vigorous emergent vegetation’’ class (NV).

For each image date we automated inundation map production by incorporating established class thresholds into an Imagine model (Leica Geosystems Geospatial Imaging, 1991–2008) effectively merging the masked NDWIB2/B5 classes (W, M, and D + V) with the ISODATA classes (V and NV). Decision rules for final map classes were: water (NDWIB2/B5 W), mixed (NDWIB2/B5 M), vegetation (overlap of NDWIB2/B5 D + V and ISODATA V) and dry land (overlap of NDWIB2/B5 D + V and ISODATA NV) (Fig. 8). A few images (8) were affected by up to 10% cloud cover and corresponding shadow which were manually digitised and allocated to a separate class (Overton, 2005; Beeri and Phillips, 2007). We also excluded from inundated area estimates the surface water in irrigation infrastructure (off-river storages and irrigated crops) because they were not connected to a flood pathway. We calculated the mean NDWIB2/B5 and mean sum457 as spectral signatures for the classes of water (W), mixed (M) and dry land with emergent vegetation (D + V). Similarly, we calculated the mean NDIB7/B4(S), NDIB7/B4(G), NDVI(S), NDVI(G) as spectral signatures for the final mapped classes of dry land (D) and vegetation (V) classes. We visually assessed our spectral signature residual plots to verify assumptions of normality and variance homogeneity (Zar, 1999). We used an analysis of variance to examine variation

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in water index means among the water, mixed and dry land with vegetation classes and vegetation index means between the vegetation and dry land classes for all images. We calculated the Transformed Divergence (TD) values (0–2000) to evaluate spectral separability by measuring the distance between spectral classes based on their means and covariances of the spectral indices (Jensen, 2004). This was done for all class pair combinations between the two water indices and for the class pair using all vegetation index combinations: four, three and two indices at a time. We calculated the TD mean and standard deviation across all images to assess overall performance of spectral indices in separating mapped classes by comparing our results to three established numerical TD value boundaries:>1900 classes completely separable; 1700–1900 separation is good; <1700 separation is poor (Jensen, 2004). 2.4. Accuracy assessment We assessed classification accuracy from two image dates January 29, 2008 (small flood) and December 20, 2010 (large flood) (Fig. 4) using standard error matrices (Stehman, 2009) between stratified randomly generated sample points of mapped classes and visually interpreted aerial photography obtained at the same time as the Landsat overpass. To obtain the aerial photography we conducted an aerial survey in a light aircraft on the same day of Landsat overpass and took oblique aerial photographs focusing on areas of complex flood boundaries with mixed pixels. Due to the oblique nature of the aerial photography we geo-rectified each photograph, using a spline transformation and cubic convolution re-sampling method in ArcGIS 9.2 (ESRI, 1999–2006) based on ground control points from a November 2004 SPOT 5 image

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(pan-sharpened multispectral 2.5 m pixel size) (Fig. 9). Cubic convolution uses more data points to resample the input image compared to other methods which was necessary due to the oblique nature of the input photography. It provided a sharp high contrast image necessary for visual inspection and was suited to the low surface gradient of the floodplain. Edges were clipped where georectification was poor. The final coverage of the geo-rectified photography was 5691 ha (2008) and 10,025 ha (2010) (Fig. 9). Within the photographic coverage of each assessment year (2008, 2010) we used the ArcGIS 9.2 extension Hawth’s Analysis Tools (Beyer, 2002–2006) to generate our sample of stratified random points: in 2008 a total of 250 points (water = 50, mixed = 50, vegetation = 50 and dry land = 100 and in 2010 a total of 400 points (water = 100, mixed = 100, vegetation = 100 and dry land = 100) (Fig. 9). We constructed error matrices for each date using all mapped classes and using merged inundation classes, to compare inundated and not-inundated accuracies. From the matrices, we calculated overall accuracy (overall percentage correct), and the class-specific measures of producer’s (mapped class omitted), user’s accuracy (mapped class incorrectly committed) (Stehman, 2009) and the corresponding 95% confidence intervals. We used the Z statistic (Lunetta and Balogh, 1999) to test for difference between the overall accuracy of the small and large sized floods. 2.5. Inundation extents We quantified inundation extent for each image date, as the sum of the inundation class areas: water, mixed and vegetation. Additionally, for each image date, we calculated inundated area within three wetland regions: north, south and east (Fig. 1b). We separately examined differences among classes and regions for

Fig. 9. Distribution of randomly stratified point locations for accuracy assessment in the Macquarie Marshes, comparing three inundation classes: water (blue), mixed (orange) and vegetation (green); and dry land (grey), corresponding to geo-rectified photographs (detail inset in north region) from images with (a) small flood (29 January 2008) and (b) large flood (20 December 2010). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 10. Evaluation of radiometric normalisation using an independent set of psuedo-invariant features (PIF) in three brightness groups (bright (orange circle), medium (green diamond) and dark (blue triangle) comparing (a) the mean percentage reflectance from the reference image (solid line) and two calibrated images, a large flood (short dash line) and a small flood (long dash line) and (b) the effect of normalisation on the mean water indices NDWIB2/B5 and sum457 and vegetation indices (NDVI and NDI). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

large and small floods using analysis of variance, after log transformation to improve normality which was visually assessed prior to analysis (Zar, 1999). Where there was significance (a = 0.05), a Tukey HSD was used for multiple comparisons of probabilities between class pairs and region pairs for all flood sizes, and for large and small floods. All statistical analyses were performed in SYSTAT 10 (SPSS, 2000).

3. Results 3.1. Radiometric normalisation The normalisation performed best in the least variant feature type: the bright group, in all bands and the spectral curves from the small flood were most similar to the reference image

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Fig. 11. NDWIB2/B5 threshold distribution for fractions of the mixed class, classified from 80 Landsat TM images, for all floods (black circles), small floods (open triangles) and large floods (open squares).

(Figs. 10a and S1). For bright and medium features the normalisation was more variable in Bands 5 and 7 (Fig. S1) and for the medium PIFs the difference in reflectance among the image dates in the short-wave infrared Band 7 was significant (Adjusted R2 = 0.641, F = 10.73, p = 0.002) (Fig. 10a). Differences in reflectance among images within the dark features were significant in the visible red Band 3 (Adjusted R2 = 0.641, F = 10.73, p = 0.002). These differences were evident in the large flood (Fig. 10a) however the indices used to map inundation were not sensitive to these differences (a = 0.05) (Fig. 10b).

3.2. Classification The mixed class thresholds (TM) varied between 0.663 and 0.001, with a median of 0.248 (n = 80, Fig. 11). Mixed NDWIB2/B5 thresholds derived from images with small floods were more variable (range = 0.663, median = 0.265) than their equivalent from large floods (range = 0.406, median = 0.224) (Fig. 11). There were significant differences in NDWIB2/B5 means among the water (W), mixed (M), and dry land with vegetation (D + V) classes and between each combination of class pairs (Table 2, Fig. 12) despite the large standard deviations of the NDWIB2/B5 class means from each image (Fig. S2). Similarly, the overall sum457 means were significantly different among classes and between each combination of pairs (Table 2, Fig. 12). The NDWIB2/B5 significance among and between class pairs was also evident for small and large floods (Table S2, Fig. S3). Therefore when combined, the NDWIB2/B5 and sum457 indices consistently separated the water (W) class from both the mixed

(M) and dry land with vegetation (D + V) classes (Table 3, Fig. S4). For the class pairs of water and dry land with vegetation (W:D + V) and water and mixed (W:M), complete spectral separability (TD = 1901–2000) was achieved in 92% and 87% of images, producing respective mean TDs of 1974 ± 73 and 1951 ± 147. Separability of M:D + V class pairs was variable: 43% of images achieved complete separability, 36% achieved separability (TD = 1701–1900) and 20% were poor (TD = 0–1700). The overall mean TD for the M:D + V class pairs indicated separability, 1838 ± 196 due to poor separability in the M:D + V class pairs for large flood size images (37%), resulting in a low mean TD of 1773 ± 126 (Table S3). The overall NDIB7/B4(G) and NDVI(G) means were significantly different between the vegetation and dry land classes (Table 2, Fig. 13). There was a significant difference in NDVI(S) between vegetation and dry land classes but there was little variation explained. NDIB7/B4 (S) was not significantly different between the vegetation and dry land classes, except in large floods (Table S2, Fig. S5a). When vegetation indices were combined, there was spectral separability between dry land and vegetation. The four multi-temporal vegetation index combination, NDIB7/B4(S): NDIB7/B4(G): NDVI(S): NDVI(G), completely separated the dry land and vegetation classes in 83% of image dates (overall mean TD 1933 ± 21, Table 3). Another 6% had good separability while 11% had poor (TD = 0–1700). For this combination of indices, separability was best in the large floods (94% complete, 3% good, 3% poor) (Table S3). The best overall separability, using the three index combinations, was evident in NDIB7/B4(S):NDIB7/B4(G):NDVI(S) (1985 ± 84) and in NDIB7/B4(S):NDVI(S):NDVI(G) (1984 ± 87) both allowing complete separability of 98% of images (Table 3). The same pattern was observed for small and large flooded images with 100% of the latter achieving complete separability (Table S3). The paired combinations of vegetation indices that produced the best separability were when a multi-temporal set of indices were utilised in either the same index or in contrasting indices (Table 3). Complete separability was achieved in 100% of all images using the index combinations NDIB7/B4(S):NDIB7/B4(G), NDIB7/B4(S):NDVI(G) and NDIB7/B4(G):NDVI(S) and in 98% of images using the NDVI(S):NDVI(G) combination (Table 3). The poorest performing combination of indices included senescent images for both indices, NDIB7/B4(S):NDVI(S) with the vegetation class inseparable from the dry land class in 97% of all images. The combination of the green growing image dates for both indices, NDIB7/B4(G):NDVI(G), produced varying separability between the vegetation and dry land classes (78% complete, 12% good, 9% poor). Separability was consistently improved in the images with large

Table 2 Descriptive statistics and class comparisons (ANOVA F test, R2, significance) of water indices: NDWIB2/B5 and sum457 for water (W), mixed (M), and combined dry land plus vegetation (D + V) classes, and of vegetation indices: NDIB7/B4(S), NDIB7/B4(G), NDVI (S) and NDVI (G) for vegetation (V) and dry land (D) classes for all flood sizes. Index Water

NDWIB2/B5

sum457

Vegetation

NDIB7/B4(S) NDIB7/B4(G) NDVI(S) NDVI(G)

a

Class

n

min

max

Mean ± s.d.

95% CI

Fdf

R2

pa

W M D+V W M D+V

74 72 62 69 67 59

0.179 0.474 0.528 0.039 0.000 0.200

0.686 0.036 0.066 0.268 0.268 0.413

0.363 ± 0.125 0.175 ± 0.092 0.370 ± 0.099 0.090 ± 0.031 0.172 ± 0.036 0.307 ± 0.049

(0.334, (0.197, (0.395, (0.083, (0.163, (0.294,

0.392) 0.154) 0.345) 0.098) 0.181) 0.319)

8802205

0.896

⁄⁄⁄

4952192

0.838

⁄⁄⁄

D V D V D V D V

65 65 65 64 65 65 65 65

0.693 0.692 0.320 0.713 0.113 0.017 0.138 0.470

0.012 0.05 0.055 0.475 0.844 0.765 0.42 0.902

0.360 ± 0.215 0.299 ± 0.172 0.080 ± 0.100 0.614 ± 0.063 0.459 ± 0.202 0.361 ± 0.200 0.262 ± 0.079 0.688 ± 0.105

(0.413, 0.306) (0.341, 0.256) (0.105, 0.055) (0.63, 0.598) (0.409, 0.508) (0.312, 0.411) (0.243, 0.282) (0.662, 0.714)

3.181128

0.024

ns

13031127

0.911

⁄⁄⁄

7.621128

0.056

⁄⁄

6801127

0.842

⁄⁄⁄

Tukey HSD adjustment for multiple comparison (⁄⁄p < 0.01,

⁄⁄⁄

p < 0.0001).

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(92%) but relatively low for the small flood (users: 74%; producers: 84%). There was consistent confusion between the water and the mixed classes, increasing for the small flood (Table S4). Classification accuracies for the mixed class were the lowest among all classes, ranging from 60% to 78% (Table 4). Mixed class confusion in the large flood occurred when mixed pixels were incorrectly classified as water or vegetation. In the small flood, mixed pixels were mostly incorrectly classed as water (Table S4b). The user’s (86–92%) and producer’s (72–89%) accuracies for vegetation were consistently high (Table 4). Accuracies for the dry land class were high. In the large flood, the dry land class was mostly confused with the mixed class whereas, the confusion lay with the vegetation class in the small flood, but at a relatively small error rate (Table S4b). 3.4. Inundation extents Fig. 12. Relationship between overall mean NDWIB2/B5 and normalised sum457 (crosses) and 95% confidence ellipses for all mapped water (W) (blue circles), mixed (M) (orange triangles) and dry land with vegetation (D + V) (brown stars). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

floods for all index combinations, except for NDIB7/B4(S) and NDVI(S) (Table S3). 3.3. Accuracy assessment Overall accuracy of inundated area (merged water, mixed and vegetation) was 95% + 2% for the large flood and 93% ± 3% for the small flood (Table 4). For the large flood, the user’s and producer’s accuracies of the inundated class were respectively 95% and 99% and 87% and 96% for the not inundated class. Similarly for the small flood, producer’s and user’s accuracy were respectively 94% and 96% for the inundated class and 91.7% and 88% for the not inundated class (see Table S4 for error matrices). The overall accuracy based on the individual inundation classes was comparatively low for the large and small floods because classes where errors were most likely to occur (e.g. in the mixed class) were specifically included in the classification procedure. The overall accuracy for the large flood was 86.5% + 3.4, significantly higher (Z = 2.36, p = 0.05) than for the small flood (79% + 5) (Table 4). User’s and producer’s class accuracies were also consistently higher for the large flood, compared to the small flood, with the exception of the producer’s accuracy of the dry land class (Table 4a). The water class accuracies were high for the large flood

Inundated areas of the Macquarie Marshes were highly variable ranging from 683 ha to a maximum of 206,611 ha and a median area of 10,643 ha (Table 5a, Figs. 14 and 15). Area varied significantly among the three inundation classes, across all flood sizes despite the large standard deviations in class mean area (F2,224 = 10.094, R2 = 0.083, p < 0.0001) (Table 5a, Fig. 14). While little variation was explained, there was a significant difference between water and mixed classes (p < 0.0001) and between the mixed and vegetation classes (p = 0.015). The water class area was the most widely dispersed of all classes, ranging from 67 to 195,886 ha (Fig. 14a), with a median of more than a thousand hectares (Table 5a). The mixed class area was also variable, reaching a maximum of about a third of the water class maximum (Fig. 14a), but with a median larger than that of the water class (Table 5a). The vegetation class was comparatively small with a narrow range (0–8719 ha) (Table 5a, Fig. 14a). The 30Q threshold (30 GL) equated to a total inundated area of 14,691 ha (log(area) = log(30Q)  0.996–0.599, R2 = 0.883, p < 0.0001), with a 0.43–0.44 exceedance (Fig. 14a). Area was considerably more extensive for large floods (n = 35), compared to small floods (n = 45) (F1,78 = 197, R2 = 0.717, p < 0.0001) (Table 5a). Exceedance reflected these differences for small and large floods, respectively 0.73 and 0.21 (Fig. 14a). Areas of inundation classes varied with flood size (Figs. 14a and 15a). Water class area was significantly more extensive in large floods (F1,78 = 100.728, R2 = 0.564, p < 0.0001), wide ranging and larger than all other class extents in 43% of the 35 maps (Table 5a, Fig. 14a). When total inundated area exceeded

Table 3 Descriptive statistics of Transformed Divergence (TD) for all images using (a) water indices: NDWIB2/B5 and sum457 for class pairs between water (W), mixed (M), and dry land with vegetation (D + V) (n = 69) and (b) vegetation indices: NDIB7/B4(S), NDIB7/B4(G), NDVI (S) and NDVI (G) for the class pair between vegetation (V) and dry land (D) (n = 65). Percentage of images with TD values in the separability classes included: poor (P) (TD 6 1700), separable(S) (1700 < TD 6 1900) and complete (C) (1900 < TD 6 2000). Index type

Index combination

Class pair

Transformed divergence

% Of images

min

max

Mean ± sd

P

S

C

Water

NDWIB2/B5:sum457

W:M W:D + V M:D + V

901 1504 695

2000 2000 2000

1951 ± 147 1974 ± 73 1828 ± 196

4 1 20

9 7 36

87 92 43

Vegetation

NDIB7/B4(S):NDIB7/B4(G):NDVI (S):NDVI (G) NDIB7/B4(S):NDIB7/B4(G):NDVI (S) NDIB7/B4(S):NDIB7/B4(G):NDVI (G) NDIB7/B4(S):NDVI (S):NDVI (G) NDIB7/B4(S):NDVI (S):NDVI (G) NDIB7/B4(S):NDIB7/B4(G) NDIB7/B4(S) NDVI (S) NDIB7/B4(S):NDVI (G) NDIB7/B4(G):NDVI (S) NDIB7/B4(G):NDVI (G) NDVI(S):NDVI (G)

V:D V:D V:D V:D V:D V:D V:D V:D V:D V:D V:D

1486 1321 1320 1302 1744 1984 132 1972 1985 921 1878

2000 2000 2000 2000 2000 2000 1788 2000 2000 2000 2000

1933 ± 121 1985 ± 84 1957 ± 108 1984 ± 87 1969 ± 51 1998 ± 4 1006 ± 390 1998 ± 6 1999 ± 3 1875 ± 280 1993 ± 19

11 2 3 2 0 0 97 0 0 9 0

6 0 6 0 8 0 3 0 0 12 2

83 98 91 98 92 100 0 100 100 78 98

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Fig. 13. Relationship between overall mean NDIB7/B4(S), NDIB7/B4(G), NDVI(S) and NDVI(G) (crosses) and 95% confidence ellipses for all mapped vegetation (green squares) and dry land (grey stars). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 4 Percentage overall, producer’s and user’s accuracies (±95% confidence interval) of merged inundation classes, inundated and not inundated, and of mapped classes resulting from classification of water and vegetation indices derived from Landsat TM 5, acquired during a small flood (29 January 2008) and a large flood (20 December 2010). n = number of stratified random sample points. Class

Small flood

Large flood

n

User’s

Producer’s

Merged

Inundated (W + M + V) Not inundated (D)

150 100

96 ± 2.7 88 ± 6.4

94 ± 3.2 92 ± 5.5

Mapped

Water (W) Mixed (M) Vegetation (V) Dry land (D)

50 50 50 100

74 ± 12.2 60 ± 13.6 86 ± 9.6 88 ± 6.4

84 ± 1.2 60 ± 1.4 72 ± 1.1 92 ± 5.5

80,000 ha (0.20 exceedance) water class areas were larger than all other classes in 75% of maps (Fig. 14a). In small floods the water class areas ranged from 67 to 4694 ha and were consistently (91%, n = 45) lower than the corresponding areas of other inundation classes (Fig. 14a). Mixed class area was also significantly more extensive in large floods (F1,76 = 154.954, R2 = 0.671, p < 0.0001) and comparatively similar to the water class area (Table 5a, Fig. 14a). But, when total inundated area was small, the corresponding mixed class area was greater than the water class area in 91% of the maps (Fig. 14a). In just under half (44%) of the small flood maps, the mixed class area was greater than corresponding water and vegetation classes. Vegetation class area did not differ

Overall 93 ± 2.8 79 + 5.0

n

User’s

Producer’s

Overall

300 100

95 ± 2.5 96 ± 3.8

99 ± 1.4 87 ± 6.4

95 ± 2.1

100 100 100 100

92 ± 5.3 66 ± 9.3 92 ± 5.3 96 ± 3.8

92 ± 5.3 78 ± 8.9 89 ± 6.1 87 ± 6.4

87 + 3.4

between flood size (F1,67 = 0.164, R2 = 0.002, p = 0.687); hence the vegetation class contributed more extent to the total inundated area in small floods than in large floods (Table 5a, Figs. 14a and 15a). Inundated area also varied significantly among the three wetland regions for all flood sizes (F = 8.0092,237, R2 = 0.063, p < 0.0001) but little variation was explained (Table 5b, Fig. 14b). The area of the east region was the most variable, with the largest range compared to north and south regions which had similar variability (Table 5b, Fig. 14b). Inundated area in the east region was significantly different to the areas of the north (p < 0.0001) and south (p = 0.012). The regional pattern of inundated area varied

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Table 5 Descriptive statistics of total inundated area (hectares) for the Macquarie Marshes: (a) inundation classes and (b) wetland regions across images of all floods (n = 80, 1989–2010), small floods (n = 45) and large (n = 35) floods sizes. Flood size Panel (a) All

Small

Large

Flood size Panel (b) All

Small

Large

a

Class

Total inundated Water Mixed Vegetation Total inundated Water Mixed Vegetation Total inundated Water Mixed Vegetation Regiona

East North South East North South East North South

Min

Max

Median

Mean ± S.D

683

206,611

10,643

36,830 ± 48,467

67 0 0 683

195,886 67,283 8719 14,248

1121 6376 2947 6826

19,669 ± 38,351 14,215 ± 17,389 2946 ± 1923 6613 ± 3635

67 0 0 15,906

4694 10,150 5458 206,611

409 2845 2715 63,544

831 ± 1076 3219 ± 2586 2563 ± 1882 75,680 ± 51,758

207 0 701

195,886 67,283 8719

29,684 21,470 3457

43,890 ± 48,398 28,352 ± 18,115 3438 ± 1889

Max

Median

Mean ± S.D

104,866 54,305 47,440 3848 6501 5582 104,866 54,305 47,440

1715 5411 4298 503 3405 2703 23,149 25,986 14,901

14,619 ± 24,401 12,879 ± 14,268 9333 ± 10,680 815 ± 863 3261 ± 1824 2537 ± 1396 32,366 ± 28,392 25,244 ± 13,743 18,070 ± 11,081

Min

48 222 316 48 222 316 1881 7533 4288

Regional land areas (ha): East = 140,238, North = 68,487 and South = 63,757.

with flood size (Table 5b, Fig. 14b). When total inundated area was small, there was a significant difference in inundated area among the three wetland regions (F = 43.7842,132, R2 = 0.339, p < 0.0001). In small floods, mean inundated area in the north (3261 ± 1824 ha) and south (2537 ± 1396 ha) regions were comparable but, in the east region, mean inundated area (815 ± 863 ha) was significantly lower than in the north (p < 0.0001) and in the south (p < 0.0001) regions (Table 5b, Figs. 14b and 15a). Contrastingly, when total inundated area was large, there was no significant difference in inundated area among the three wetland regions (F = 1.5972,102, R2 = 0.030, p = 0.207) (Table 5b, Figs. 14b and 15a). In very large floods (>100,000 ha, 0.15 exceedance, Fig. 14b), inundation in the east region expanded the most, to a maximum almost double the maxima of the north and south regions (Table 5b, Figs. 14b and 15a). The east, north and south regions connected when the total inundated areas were greater than 51,000 ha (0.25 exceedance) (Figs. 14b and 15b). 4. Discussion We effectively characterised the variability of flooding in a large heterogeneous floodplain wetland, the Macquarie Marshes, by combining water and vegetation indices derived from Landsat TM satellite imagery (1989–2010). We spectrally classified three inundation classes: water, mixed and vegetation, which we merged to map inundated areas with an overall accuracy of up to 95% (Table 4, Fig. 15). The complexity of spectral reflectance due to variable flooding regimes and vegetation responses contributed to the challenges. Our study is a comprehensive multi-temporal approach for monitoring inundation extent comparable over a flood event (months) and flooding regimes (decades), enabled by the Landsat image archive. This information is critical for different ecological and management analyses: providing landscape scale

Fig. 14. Exceedance (proportion of sample exceeded) for total inundated area (ha) (filled circles) in the Macquarie Marshes and the contributing areas of (a) inundation classes water (blue triangles), mixed (orange diamonds) and vegetation (green squares) and (b) the Macquarie Marshes regions north (blue), south (red) and east (green) from 80 satellite images. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

information on the primary driver for many ecological indicators (biota and processes) and allowing derivation of flow and flooding relationships, critical for environmental flow management.

4.1. Classification Our combination of different water and vegetation indices maximised the detection of inundation in this large vegetated floodplain wetland, an approach increasingly recommended for highly variable flooding in wetlands (Beeri and Phillips, 2007; Campos et al., 2012; Davranche et al., 2010; Johnston and Barson, 1993; Sun et al., 2012). The NDWIB2/B5 water index was the main driver of water detection, effectively separating inundation from dry land (Campos et al., 2012; Sun et al., 2012; Xu, 2006) and further improved by removing bright pixels using the sum457 index (Fig. 6, Table 4). Despite high variability within the water class of each image analysed (Fig. S1), water (W) NDWIB2/B5 and sum457 means were significantly different from mixed (M) and dry land with vegetation classes (D + V) (Table 2). Therefore, the utility of both the sum457 and NDWIB2/B5 indices consistently separated water from the mixed and dry land with vegetation classes in most (>95%) images (Table 3). We deliberately classified mixed pixels in

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209

Fig. 15. Distribution of inundation classes (water (blue); mixed (orange); vegetation (green), cloud (tan)) across (a) three wetland regions S-south, N-north and E-east of the Marshes, for a small flood (i) 7554 ha (29 January 2008), and three large floods: (ii) 55,066 ha (3 November 1993), (iii) 112,872 ha (03 May 1990), and (iv) 173,939, ha (20 December 2010), and (b) insets showing connectivity of inundation extents at locations (arrows) at the junction of the three wetland regions.

recognition that transition zones between water and dry land, and complex gradients of water and vegetation are ubiquitous in intermittently flooded landscapes, such as the Macquarie Marshes (Ozesmi and Bauer, 2002; Thomas et al., 2011; Zhao et al., 2011). This was reinforced by spectral distinctiveness between the mixed (M) class and the water (W) and dry land classes (Table 2, Fig. 10), regardless of variation in the NDWIB2/B5 mixed class threshold (Fig. 6). Such variation is expected in a landscape where water signatures vary spatially and temporally, and we found the mixed class threshold varied with flood size (Fig. 11). We initially tested the NDWIB2/B4 (McFeeters, 1996) and there was high variability in known homogenous locations, compared to NDWIB2/B5, introducing large commission and omission errors into the classification, identified elsewhere (Campos et al., 2012; Soti et al., 2009; Xu, 2006). We also focused on the dense vegetation cover of emergent macrophyte vegetation where Landsat does not penetrate to underlying water (Hess and Melack, 2003). To solve this problem, we classified emergent macrophyte vegetation separately using vegetation indices because during flooding the wide range of reflectance values of emergent aquatic vegetation can lead to misclassification with dry land (Silva et al., 2008) (Figs. 5 and 8, Table 3). We capitalised on the phenological growth phases of dense emergent macrophyte vegetation (Davranche et al., 2010; Lunetta and Balogh, 1999; Ozesmi and Bauer, 2002), detectable using Landsat imagery. This needed a combined multi-date (contrasting growth phases) and multi-spectral (contrasting vegetation

indices) approach to effectively discriminate flooded emergent macrophyte vegetation from dry land (Fig. 7). The combination of multi-date indices representing different growth phases completely separated vegetation from dry land, using either the NDVI or NDIB7/B4 indices (Table 3, Fig. 11). The lag interval between selected dates was commensurate with response due to flooding. One of the reasons the vegetation indices so effectively detected emergent aquatic vegetation is that NDVI saturates at high biomass (Pettorelli et al., 2005) providing high contrast to other less dense or vigorous vegetation types. 4.2. Accuracy The effectiveness of our classification was revealed in the accuracy assessment. Ultimately, the best assessment of accuracy depends on accessing coincident higher resolution imagery or ground flood data. Ground reference data were too difficult to collect over such a large, remote and inaccessible flooded landscape. Instead, we captured oblique aerial photography, coincident with Landsat imagery which was cost effective high resolution data for a remote location, with flexibility to target the timing of the Landsat pass. The high resolution of the digital photography allowed for visual interpretation of classes at specific locations, including homogenous and heterogeneous landscapes with mixed flooding and no flooding (Fig. 9). To achieve this, our strategy of selecting training locations (AOIs) from within a range of wetland landscape features that flood (e.g. lagoons, swamps and

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floodplains) was necessary to effectively represent the wide variations in water spectral reflectance, compared to training in water bodies with homogeneous water signatures (Frazier and Page, 2000; Hui et al., 2008; Soti et al., 2009; Tulbure and Broich, 2013; Xu, 2006). Most meaningful for managers, we captured the full extent of the inundation across this area with high accuracy. Our overall accuracies of 95% (large flood) and 93% (small flood) for total inundated area derived from merging inundation classes were higher than the overall accuracies derived from the inundation classes, 87% for large floods and 78% for small floods (Table 4). This was expected because our accuracies were based on mapped inundation classes that included areas where errors were most likely to occur (i.e. mixed class) but as a whole, accuracy of inundated area was high (Tables 4 and S4). This provided insight into classification confusion, mostly among inundation classes, which was most pronounced in the small flood (water user’s accuracy = 74%, Table 4) when water was confined to small, narrow landscape features (e.g. creeks), where there is high mixing between cover types (Frazier and Page, 2000; MacAlister and Mahaxay, 2009; Tulbure and Broich, 2013; Xu, 2006). Despite this confusion, our class accuracies for water detection were comparable to other accuracies in homogeneous open water bodies (Frazier and Page, 2006; Tulbure and Broich, 2013; Xu, 2006). Our approach in combining multi-temporal vegetation indices separated emergent macrophytes from dry land with high accuracy (Table 4) comparable to classification accuracies for mapping similar vegetation types, using SPOT-5 (Davranche et al., 2010). The vegetation class was an accurate proxy for inundated areas obscured by emergent wetland vegetation because we have measured the response of this vegetation at an appropriate lag interval commensurate with response to flooding. A recent study of an Australian floodplain wetland successfully used NDVI response to indicate the effective inundation extent (Powell et al., 2014). There remain intractable sources of uncertainty from contributions within each class, availability of imagery, seasonal effects and impacts of cloud. Validation of flooding in remote and cloudy regions is difficult (Hess et al., 2003). Although cloud cover is rarely a problem in semi-arid regions because local rainfall is infrequent, difficulties with cloud cover and local rainfall were encountered with the 2008 image classification (Fig. 15a) and accuracy assessment (Table 4). To minimise misclassification we manually removed cloud and cloud shadow through interactive image interpretation because it has been shown to be the best procedure (Beeri and Phillips, 2007). The availability of a truly senescent image was sometimes limited, potentially confounding classification, as well as the presence of fire scars and local rainfall responses during very dry periods. We found that variation remained across image dates after normalisation within Band 3 for dark pseudo-invariant features (PIFs) and Band 7 for medium PIFs. Dark PIFs, located in irrigation dams or in lagoons, were inherently highly variable in this landscape (Figs. 10 and S1). This explains the differences found in the visible red band (Band 3) which occurred in irrigation dams, variable due to increased sediment (Fig. S1). Also in dark features, differences in infrared band 4, although not significant, were evident in lagoons, variable due to vegetation (Figs. S1 and 10). For medium range PIFs differences in Band 7, and to a lesser extent Band 5, evident in the large flood image, are most likely explained by the subtle variations in moisture causing large differences in reflectance detectable in the short-wave infrared range bands (Furby and Campbell, 2001). We also found it difficult to locate medium range PIFs, a similar finding in another study (Furby and Campbell, 2001). Our approach to mapping inundation in a heterogeneous floodplain wetland is computationally simple yet an effective process that is semi-automated for processing large numbers of images.

We separated inundated area into three distinct classes, representing the total wetland inundation dynamic: water, mixed and vegetation. The method requires interpretation by a trained analyst to delineate homogenous pixel clusters (areas of interest, AOI) in different landscape features representing the full range of variability of inundation from which signature statistics were used to determine the mixed class threshold. This was an initial essential labour intensive step which realistically identifies cover type boundaries and uncertainties inherent in the heterogeneous landscape of the Macquarie Marshes. Reducing such inherent uncertainty, by estimating the class proportions within the mixed class whilst incorporating the intra-class variability, remains a challenge for further study. 4.3. Inundation extents We mapped inundation extent for the Macquarie Marshes over a period of 22 years, demonstrating its high flooding variability (Table 5, Figs. 14 and 15). Small floods (<15,000 ha, Table 5a) occurred frequently (>0.44 exceedance), mostly confined to the north and south regions (Fig. 14b). Large floods were wide ranging in extent, 15,000 to over 200,000 ha (Table 5b, Figs. 14 and 15). However, inundation extents needed to be greater than 50,000 ha before north and south region connectivity occurred with the east region (Fig. 15). Understanding connectivity between rivers and floodplains and across landscape features is fundamental to ecosystem responses and processes (Ward et al., 2002). Our inundation mapping will contribute to understanding the spatial arrangement of floods and flow paths, potentially improving connectivity to wetlands previously disconnected from their river channel due to floodplain development (Steinfeld and Kingsford, 2011). The role of the flooding regime, extent, frequency and duration, is also critical for dependent organisms, including vegetation (Alexander et al., 2008; Casanova and Brock, 2000; Roberts et al., 2000), micro-invertebrate density (Jenkins and Boulton, 2003), fish movement and spawning (King et al., 2009), and waterbird abundances (Kingsford et al., 2010) at the landscape scale. However defining the flooding thresholds which trigger ecological responses of interest is difficult in complex systems, especially regulated systems where there are competing demands for water (Acreman and Dunbar, 2004). Our flooding maps and relationships to flow provide a data set for examining these ecosystem links for development of management scenarios (Kingsford, 2011). Linking river flows to inundated areas at landscape scales is a critical first step (Mertes, 2002; Smith, 1997) for heterogeneous wetlands (Frazier et al., 2003; Overton, 2005; Powell et al., 2008), including the Macquarie Marshes (Ren et al., 2010). This allows retrospective analysis of changes to flooding regimes and ecological impacts, based on flows (Frazier and Page, 2006; Ren and Kingsford, 2011; Wen et al., 2013) as well as opportunities for rehabilitation. Inundation mapping from our study provided an opportunity to examine changes in inundation over time in the Macquarie Marshes (Ren et al., 2010) as well as simulate hydrological characteristics, based on input from the river hydrological model (IQQM), used for water resource sharing and management in the Macquarie River (Wen et al., 2013). 4.4. Environmental flow management There is >300 gigalitres (GL) of environmental water available when storage dams are full, mostly for the Macquarie Marshes. This volume needs to be released from storage to produce environmental outcomes (Arthington and Pusey, 2003), demanding a strong management focus. The actual amount of environmental water that reaches the Macquarie Marshes varies with availability,

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including water not diverted for human uses. So environmental flows and consequently amount of flooding varies each year and needs to be driven by specific ecological objectives met by particular floods, measured as a volume of environmental flow. Further, environmental flows usually only represent a fraction of the entire flow regime and so quantification requires understanding of the volumes of water required to drive ecological processes, and what component of this represents the environmental flow. Once relationships between ecological indicators and flooding are identified then this can be linked to our inundation mapping to provide confidence in the spatially explicit realisation of environmental outcomes. As a result, our measure of mapping floods is already implemented as a surrogate of ecosystem responses to environmental flows. Long-term monitoring of inundation extent, linked to lagged ecological responses (e.g. river red gum condition, George et al., 2005), can also help with meeting restoration objectives (Acreman and Dunbar, 2004). Relationships between ecological and hydrological variables in highly unpredictable systems such as the Macquarie Marshes, requires long-term data, such as that provided by the free USGS/EROS Landsat archive.

5. Conclusions Global water scarcity and dependencies of large wetland ecosystems on flooding mean that mapping of wetlands and floodplains will be increasingly important for determining ecological resilience, impacts of development and potential rehabilitation using environmental flows. We developed semi-automated analytical techniques which were effective for a large variable wetland in semi-arid Australia. Given many similarities in the distribution of water and vegetation across different regions of the world, these techniques are likely generally applicable if appropriate training sites are selected to capture the full spectral variability of inundation. Importantly, the high variability of flooding regimes for our region allowed us to understand effectiveness of mapping, critical for management, over a long time period, particularly for wetlands of high conservation value. The combination of water and vegetation spectral indices provided considerable confidence that inundation response was measured well. Choosing appropriate spatial and temporal resolutions, and analytical methods remains challenging for monitoring wetlands, especially over large areas and long time frames. Inevitably there will be trade-offs between efficiency and effectiveness for the specific management purpose. Future management challenges remain to ensure the critical ecological structure and functions of the Marshes are maintained and degraded systems are restored. Management decisions are underpinned by robust techniques, such as ours, for measuring flooding regimes.

Acknowledgments This study was funded by: (i) the NSW State Government and the Australian Government’s Water for the Future under the New South Wales (NSW) Wetland Recovery Program (WRP), (ii) the NSW Office of Environment and Heritage and (iii) the Centre for Ecosystem Science, UNSW, Australia. We thank Bill Johnson and Debbie Love for sharing their extensive knowledge of environmental flow management in the Macquarie Marshes. Bill Johnson provided historical oblique aerial photographs for validation. Shannon Simpson collated river flow data and historic photographs. Ray Jones provided logistic support within the Macquarie Marshes. Richard Byrne piloted the aerial survey work. Landholders of the Macquarie Marshes provided their anecdotal observations of past

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flood events. We thank three anonymous reviewers for their constructive comments which have improved this manuscript. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jhydrol.2015.02. 029. References Acreman, M., Dunbar, M.J., 2004. Defining environmental river flow requirements – a review. Hydrol. Earth Syst. Sci. 8, 861–876. http://dx.doi.org/10.5194/hess-8861-2004. Alexander, P., Nielsen, D.L., Nias, D., 2008. Response of wetland plant communities to inundation within floodplain landscapes. Ecol. Manage. Restor. 9, 187–195. http://dx.doi.org/10.1111/j.1442-8903.2008.00417.x. Alsdorf, D., Lettenmaier, D.P., 2003. Tracking fresh water from space. Science 301, 1491–1494. http://dx.doi.org/10.1126/science.1089802. Arthington, A.H., Pusey, B.J., 2003. Flow restoration and protection in Australian rivers. River Res. Appl. 19, 377–395. http://dx.doi.org/10.1002/rra.745. Beeri, O., Phillips, R.L., 2007. Tracking palustrine water seasonal and annual variability in agricultural wetland landscapes using Landsat from 1997 to 2005. Glob. Change Biol. 13, 897–912. http://dx.doi.org/10.1111/j.13652486.2006.01306.x. Beyer, H.L., 2002–2006. Hawth’s Analysis Tools Version 3.27. (accessed 30.09.14). Bhagat, V.S., Sonawane, K.R., 2011. Use of Landsat ETM plus data for delineation of water bodies in hilly zones. J. Hydroinform. 13, 661–671. http://dx.doi.org/ 10.2166/hydro.2010.018. Bunn, S.E., Arthington, A.H., 2002. Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity. Environ. Manage. 30, 492–507. http://dx.doi.org/10.1007/s00267-002-2737-0. Bunn, S.E., Thoms, M.C., Hamilton, S.K., Capon, S.J., 2006. Flow variability in dryland rivers: boom, bust and the bits in between. River Res. Appl. 22, 179–186. http:// dx.doi.org/10.1002/rra.904. Campos, J.C., Sillero, N., Brito, J.C., 2012. Normalized difference water indexes have dissimilar performances in detecting seasonal and permanent water in the Sahara–Sahel transition zone. J. Hydrol. 464–465, 438–446. http://dx.doi.org/ 10.1016/j.jhydrol.2012.07.042. Casanova, M.T., Brock, M.A., 2000. How do depth, duration and frequency of flooding influence the establishment of wetland plant communities? Plant Ecol. 147, 237–250. http://dx.doi.org/10.1023/A:1009875226637. Chavez Jr., P.S., 1989. Radiometric calibration of Landsat Thematic Mapper multispectral images. Photogramm. Eng. Remote Sens. 55, 1285–1294. Congalton, R.G., Green, K., 2009. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, second ed. Taylor & Francis Group, Boca Raton, FL. Davranche, A., Lefebvre, G., Poulin, B., 2010. Wetland monitoring using classification trees and SPOT-5 seasonal time series. Remote Sens. Environ. 114, 552–562. http://dx.doi.org/10.1016/j.rse.2009.10.009. DNR, 2006. Pineena Version 9. NSW Department of Natural Resources. Driver, P.D., Knight, C., 2007. Macquarie Marshes 2005/06 Environmental Flows. Response of groundcover plants to environmental flows. Report to the Environmental Flows Reference Group. Department of Natural Resources. Dudgeon, D., Arthington, A.H., Gessner, M.O., Kawabata, Z.-I., Knowler, D.J., Le´vêque, C., Naiman, R.J., Prieur-Richard, A., Soto, D., Stiassny, M.L.J., Sullivan, C.A., 2006. Freshwater biodiversity: importance, threats, status and conservation challenges. Bio. Rev. 81, 163–182. http://dx.doi.org/10.1017/ S1464793105006950. ESRI, 1999–2006. ArcGIS Version 9.2. Environmental Research Institute, California. Frazier, P.S., Page, K.J., 2000. Water body detection and delineation with Landsat TM data. Photogramm. Eng. Remote Sens. 66, 1461–1467. Frazier, P.S., Page, K., 2006. The effect of river regulation on floodplain wetland inundation, Murrumbidgee River, Australia. Mar. Freshw. Res. 57, 133–141. http://dx.doi.org/10.1071/MF05089. Frazier, P.S., Page, K.J., Louis, J., Briggs, S., Robertson, A.I., 2003. Relating wetland inundation to river flow using Landsat TM data. Int. J. Remote Sens. 24, 3755– 3770. http://dx.doi.org/10.1080/0143116021000023916. Furby, S., Campbell, N., 2001. Calibrating images from different dates to ‘like-value’ digital counts. Remote Sens. Environ. 77, 186–196. http://dx.doi.org/10.1016/ S0034-4257(01)00205-X. Gao, B.C., 1996. NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257–266. http:// dx.doi.org/10.1016/s0034-4257(96)00067-3. George, A.K., Walker, K.F., Lewis, M.M., 2005. Population status of eucalypt trees on the River Murray floodplain, South Australia. River Res. Appl. 21, 271–282. http://dx.doi.org/10.1002/rra.846. Herron, N., Davis, R., Jones, R., 2002. The effects of large-scale afforestation and climate change on water allocation in the Macquarie River catchment, NSW, Australia. J. Environ. Manage. 65, 369–381. http://dx.doi.org/ 10.1006/jema.2002.0562. Hess, L., Melack, J., 2003. Remote sensing of vegetation and flooding on Magela Creek Floodplain (Northern Territory, Australia) with the SIR-C synthetic

212

R.F. Thomas et al. / Journal of Hydrology 524 (2015) 194–213

aperture radar. Hydrobiologia 500, 65–82. http://dx.doi.org/10.1007/978-94007-1084-9_4. Hess, L., Melack, J., Novo, E., Barbosa, C., Gastil, M., 2003. Dual-season mapping of wetland inundation and vegetation for the central Amazon basin. Remote Sens. Environ. 87, 404–428. http://dx.doi.org/10.1016/j.rse.2003.04.001. Hudson, P.F., Colditz, R.R., 2003. Flood delineation in a large and complex alluvial valley, lower Panuco basin, Mexico. J. Hydrol. 280, 229–245. http://dx.doi.org/ 10.1016/S0022-1694(03)00227-0. Hui, F.M., Xu, B., Huang, H.B., Yu, Q., Gong, P., 2008. Modelling spatial-temporal change of Poyang Lake using multitemporal Landsat imagery. Int. J. Remote Sens. 29, 5767–5784. http://dx.doi.org/10.1080/01431160802060912. Hunt, E.R., Rock, B.N., 1989. Detection of changes in leaf water content using nearand middle-infrared reflectances. Remote Sens. Environ. 30, 43–54. ITT Industries, 2006. ENVI 4.3. ITT Industries. Jenkins, K.M., Boulton, A.J., 2003. Connectivity in a dryland river: short-term aquatic microinvertebrate recruitment following floodplain inundation. Ecology 84, 2708–2723. http://dx.doi.org/10.1890/02-0326. Jensen, J., 2004. Introductory Digital Image Processing: A Remote Sensing Perspective, third ed. Prentice-Hall, New Jersey. Johnston, R.M., Barson, M.M., 1993. Remote sensing of Australian wetlands: an evaluation of Landsat TM data for inventory and classification. Aust. J. Mar. Freshw. Res. 44, 235–252. http://dx.doi.org/10.1071/MF9930235. Kennard, M.J., Pusey, B.J., Olden, J.D., MacKay, S.J., Stein, J.L., Marsh, N., 2010. Classification of natural flow regimes in Australia to support environmental flow management. Freshw. Biol. 55, 171–193. http://dx.doi.org/10.1111/j.13652427.2009.02307.x. King, A.J., Ward, K.A., O’Connor, P., Green, D., Tonkin, Z., Mahoney, J., 2009. Adaptive management of an environmental watering event to enhance native fish spawning and recruitment. Freshw. Biol. 55, 17–31. http://dx.doi.org/10.1111/ j.1365-2427.2009.02178.x. Kingsford, R., 2000. Ecological impacts of dams, water diversions and river management on floodplain wetlands in Australia. Austral Ecol. 25, 109–127. http://dx.doi.org/10.1046/j.1442-9993.2000.01036.x. Kingsford, R.T., 2011. Conservation management of rivers and wetlands under climate change – a synthesis. Mar. Freshw. Res. 62, 217–222. http://dx.doi.org/ 10.1071/MF11029. Kingsford, R.T., Johnson, W.J., 1998. The impact of water diversions on colonially nesting waterbirds in the Macquarie Marshes in arid Australia. Colon. Waterbirds 21, 159–170. Kingsford, R., Thomas, R., 1995. The Macquarie Marshes in Arid Australia and Their Waterbirds: a 50-Year History of Decline. Environ. Manage. 19, 867–878. http:// dx.doi.org/10.1007/BF02471938. Kingsford, R., Brandis, K., Thomas, R., Crighton, P., Knowles, E., Gale, E., 2004. Classifying landform at broad spatial scales: the distribution and conservation of wetlands in New South Wales, Australia. Mar. Freshw. Res. 55, 17–31. http:// dx.doi.org/10.1071/MF03075. Kingsford, R.T., Lemley, A.D., Thompson, J.R., 2006. Impacts of dams, river management and diversions on desert rivers. In: Kingsford, R.T. (Ed.), Ecology of Desert Rivers. Cambridge University Press, Cambridge, pp. 203–247. Kingsford, R.T., Roshier, D.A., Porter, J.L., 2010. Australian waterbirds – time and space travellers in dynamic desert landscapes. Mar. Freshw. Res. 61, 875–884. http://dx.doi.org/10.1071/mf09088. Knight, A.W., Tindall, D.R., Wilson, B.A., 2009. A multitemporal multiple density slice method for wetland mapping across the state of Queensland, Australia. Int. J. Remote Sens. 30, 3365–3392. http://dx.doi.org/10.1080/ 01431160802562180. Leica Geosystems Geospatial Imaging, 1991–2008. ERDAS IMAGINE 9.2 Professional. Lunetta, R.S., Balogh, M.E., 1999. Application of multi-temporal Landsat 5 TM imagery for wetland identification. Photogramm. Eng. Remote Sens. 65, 1303– 1310. MacAlister, C., Mahaxay, M., 2009. Mapping wetlands in the Lower Mekong Basin for wetland resource and conservation management using Landsat ETM images and field survey data. J. Environ. Manage. 90, 2130–2137. http://dx.doi.org/ 10.1016/j.jenvman.2007.06.031. McFeeters, S.K., 1996. The use of normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17, 1425–1432. http:// dx.doi.org/10.1080/01431169608948714. Mertes, L.A.K., 2002. Remote sensing of riverine landscapes. Freshw. Biol. 47, 799– 816. http://dx.doi.org/10.1046/j.1365-2427.2002.00909.x. Miller, J.D., Thode, A.E., 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens. Environ. 109, 66–80. NASA, 2010. The Worldwide Reference System. (accessed 16.03.14). Ouma, Y., Tateishi, R., 2006. A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: an empirical analysis using Landsat TM and ETM+ data. Int. J. Remote Sens. 27, 3153–3181. http://dx.doi.org/10.1080/ 01431160500309934. Overton, I.C., 2005. Modelling floodplain inundation on a regulated river: integrating GIS, remote sensing and hydrological models. River Res. Appl. 21, 991–1001. http://dx.doi.org/10.1002/rra.867. Ozesmi, S.L., Bauer, M.E., 2002. Satellite remote sensing of wetlands. Wetlands Ecol. Manage. 10, 381–402. http://dx.doi.org/10.1023/A:1020908432489. Pettorelli, N., Olav Vik, J., Mysterud, A., Gaillard, J., Tucker, C., Stenseth, N.C., 2005. Using the satellite-derived NDVI to assess ecological responses to

environmental change. Trends Ecol. Evol. 20, 503–510. http://dx.doi.org/ 10.1016/j.tree.2005.05.011. Pietroniro, A., Prowse, T., 2002. Applications of remote sensing in hydrology. Hydrol. Process., 1537–1541 http://dx.doi.org/10.1002/hyp.1018. Poff, N.L., Zimmerman, J.K.H., 2010. Ecological responses to altered flow regimes: a literature review to inform the science and management of environmental flows. Freshw. Biol. 55, 194–205. http://dx.doi.org/10.1111/j.13652427.2009.02272.x. Poff, N.L., Allan, J.D., Palmer, M.A., Hart, D.D., Richter, B.D., Arthington, A.H., Rogers, K.H., Meyer, J.L., Stanford, J.A., 2003. River flows and water wars: emerging science for environmental decision making. Front. Ecol. Environ. 1, 298–306. http://dx.doi.org/10.1890/1540-9295(2003)001[0298:RFAWW E]2.0.CO;2. Powell, S.J., Letcher, R.A., Croke, B.F.W., 2008. Modelling floodplain inundation for environmental flows: Gwydir wetlands, Australia. Ecol. Model. 211, 350–362. http://dx.doi.org/10.1016/j.ecolmodel.2007.09.013. Powell, S.J., Jakeman, A., Croke, B., 2014. Can NDVI response indicate the effective flood extent in macrophyte dominated floodplain wetlands? Ecol. Ind. 45, 486– 493. http://dx.doi.org/10.1016/j.ecolind.2014.05.009. Puckridge, J.T., Sheldon, F., Walker, K.F., Boulton, A.J., 1998. Flow variability and the ecology of large rivers. Mar. Freshw. Res. 49, 55–72. http://dx.doi.org/10.1071/ MF94161. Ralph, T.J., Hesse, P.P., 2010. Downstream hydrogeomorphic changes along the Macquarie River, southeastern Australia, leading to channel breakdown and floodplain wetlands. Geomorphology 118, 48–64. http://dx.doi.org/10.1016/ j.geomorph.2009.12.007. Rayner, T.S., Jenkins, K.M., Kingsford, R.T., 2009. Small environmental flows, drought and the role of refugia for freshwater fish in the Macquarie Marshes, arid Australia. Ecohydrology 2, 440–453. http://dx.doi.org/10.1002/eco.73. Ren, S.Q., Kingsford, R.T., 2011. Statistically integrated flow and flood modelling compared to hydrologically integrated quantity and quality model for annual flows in the regulated Macquarie River in Arid Australia. Environ. Manage. 48, 177–188. http://dx.doi.org/10.1007/s00267-011-9673-9. Ren, S., Kingsford, R.T., Thomas, R.F., 2010. Modelling flow to and inundation of the Macquarie Marshes in arid Australia. Environmetrics 21, 549–561. http:// dx.doi.org/10.1002/env.1002. Richter, B., 2009. Re-thinking environmental flows: from allocations and reserves to sustainability boundaries. River Res. Appl. 26, 1052–1063. http://dx.doi.org/ 10.1002/rra.1320. Roberts, J., Young, B., Marston, F., 2000. Estimating the water requirements for plant of floodplain wetlands: a guide. Occasional Paper 04/00, Land & Water Resources Research and Development Corporation, Canberra. Silva, T.S.F., Costa, M.P.F., Melack, J.M., Novo, E.M.L.M., 2008. Remote sensing of aquatic vegetation: theory and applications. Environ. Monit. Assess. 140, 131– 145. http://dx.doi.org/10.1007/s10661-007-9855-3. Sims, N.C., 2007. A method for spectrally calibrating Landsat TM images for change detection, Client Report No. 1796, Ensis, Clayton. Smith, L.C., 1997. Satellite remote sensing of river inundation area, stage, and discharge: a review. Hydrol. Process. 11, 1427–1439. Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., Macomber, S.A., 2001. Classification and Change Detection Using Landsat TM Data. When and How to Correct Atmospheric Effects? Remote Sens. Environ. 75, 230–244. Soti, V., Tran, A., Bailly, J.S., Puech, C., Lo Seen, D., Begue, A., 2009. Assessing optical earth observation systems for mapping and monitoring temporary ponds in arid areas. Int. J. Appl. Earth Obs. Geoinf. 11, 344–351. http://dx.doi.org/10.1016/ j.jag.2009.05.005. SPSS, 2000. SYSTAT Version 10. SPSS Inc. Stehman, S.V., 2009. Sampling designs for accuracy assessment of land cover. Int. J. Remote Sens. 30, 5243–5272. http://dx.doi.org/10.1080/01431160903131000. Steinfeld, C.M.M., Kingsford, R.T., 2011. Disconnecting the floodplain: earthworks and their ecological effect on a dryland floodplain in the Murray-Darling Basin, Australia. River Res. Appl. 29, 206–218. http://dx.doi.org/10.1002/rra.1583. Sun, F.D., Sun, W.X., Chen, J., Gong, P., 2012. Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. Int. J. Remote Sens. 33, 6854–6875. http://dx.doi.org/10.1080/01431161.2012.692829. Thomas, R.F., Bowen, S., Simpson, S.L., Cox, S.J., Sims, N.C., Hunter, S.J., Lu, Y., 2010. Inundation response of vegetation communities of the Macquarie Marshes in semi-arid Australia. In: Saintilan, N., Overton, I. (Eds.), Ecosystem Response Modelling in the Murray Darling Basin. CSIRO Publishing, Melbourne. Thomas, R.F., Kingsford, R.T., Lu, Y., Hunter, S.J., 2011. Landsat mapping of annual inundation (1979–2006) of the Macquarie Marshes in semi-arid Australia. Int. J. Remote Sens. 32, 4545–4569. http://dx.doi.org/10.1080/ 01431161.2010.489064. Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150. Tulbure, M.G., Broich, M., 2013. Spatiotemporal dynamic of surface water bodies used Landsat time-series data from 1999 to 2011. ISPRS J. Photogramm. Remote Sens. 79, 44–52. http://dx.doi.org/10.1016/j.isprsjprs.2013.01.010. Ward, J.V., Tockner, K., Arscott, D.B., Claret, C., 2002. Riverine landscape diversity. Freshw. Biol. 47, 517–539. http://dx.doi.org/10.1046/j.13652427.2002.00893.x. Wen, L., Macdonald, R., Morrison, T., Hameed, T., Saintilan, N., Ling, J., 2013. From hydrodynamic to hydrological modelling: investigating long-term hydrological regimes of key wetlands in the Macquarie Marshes, a semi-arid lowland floodplain in Australia. J. Hydrol. 500, 45–61. http://dx.doi.org/10.1016/ j.jhydrol.2013.07.015.

R.F. Thomas et al. / Journal of Hydrology 524 (2015) 194–213 Wulder, M.A., White, J.C., Goward, S.N., Masek, J.G., Irons, J.R., Herold, M., Cohen, W.B., Loveland, T.R., Woodcock, C.E., 2008. Landsat continuity: issues and opportunities for land cover monitoring. Remote Sens. Environ. 112, 955–969. http://dx.doi.org/10.1016/j.rse.2007.07.004. Xu, H., 2006. Modification of Normalised Difference Water Index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27, 3025–3033. http://dx.doi.org/10.1080/01431160600589179. Xu, T., Hutchinson, M., 2011. ANUCLIM Version 6.1 User Guide, The Australian National University, Fenner School of Environment and Society, Canberra.

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Young, W., Brandis, K., Kingsford, R.T., 2006. Modelling monthly streamflows in two Australian dryland rivers: Matching model complexity to spatial scale and data availability. J. Hydrol. 331, 242–256. http://dx.doi.org/10.1016/ j.jhydrol.2006.05.014. Zar, J.H., 1999. Biostatistical Analysis, fourth ed. Prentice-Hall, Upper Saddle River, New Jersey. Zhao, X., Stein, A., Chen, X.L., 2011. Monitoring the dynamics of wetland inundation by random sets on multi-temporal images. Remote Sens. Environ. 115, 2390– 2401. http://dx.doi.org/10.1016/j.rse.2011.05.002.