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Journal of Arid Environments 113 (2015) 59e68

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Journal of Arid Environments

journal homepage: www.elsevier .com/locate/ jar idenv

Riparian vegetation NDVI dynamics and its relationship with climate,surface water and groundwater

Baihua Fu*, Isabela BurgherFenner School of Environment and Society, Australian National University, Canberra, ACT 0200, Australia

a r t i c l e i n f o

Article history:Received 9 July 2013Received in revised form2 June 2014Accepted 24 September 2014Available online

Keywords:Remote sensingEnvironmental flowRegression tree analysisGroundwaterNDVI

* Corresponding author.E-mail address: baihua.fu@anu.edu.au (B. Fu).

http://dx.doi.org/10.1016/j.jaridenv.2014.09.0100140-1963/© 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

Maintaining the integrity of riparian ecosystems whilst continuing to reserve and extract water for otherpurposes necessitates a greater understanding of relationships between riparian vegetation and wateravailability. The Normalised Difference Vegetation Index (NDVI) is a good indicator for identifying long-term changes in vegetated areas and their condition. In this study, we use regression tree analysis toinvestigate long term NDVI data (23 years) at semi-arid riparian areas in the Namoi catchment, Australia.Climatic factors (temperature and rainfall), surface water (flow and flooding) and groundwater levels areanalysed collectively. We find that in general maximum temperature is the variable that primarily splitsNDVI values, followed by antecedent 28-day rainfall and then inter-flood dry period and groundwaterlevels. More rain is required in the warmer months compared to cooler months to achieve similar meanNDVI values in tree patches or areas of high NDVI in riparian zones, presumably because of higherevaporation. Inter-flood dry period is shown to be important for maintenance of NDVI levels, particularlywhen rainfall is limited. Shallower groundwater levels sustain the NDVI and hence vegetation greennesswhen conditions are cooler and wetter.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Dams, surface water extraction and groundwater pumping forhuman uses have contributed to serious changes in the functioningof riparian ecosystems e areas which directly adjoin and influenceinland water bodies (Allan and Castillo, 2007; Nilsson andBerggren, 2000; Poff et al., 1997). This is especially so for riparianecosystems in arid and semi-arid regions wherewater is scarcer yetin high demand for human use, resulting in greater extraction ofsurface water and groundwater resources (Stromberg et al., 1996).Maintaining the integrity of riparian ecosystems whilst continuingto reserve and extract water for other purposes necessitates agreater understanding of relationships between riparian vegetationandwater availability (Cunningham et al., 2011). As water resourcesthat we have some level of control over, this is particularly true ofriver flow and groundwater, an understanding of which can helpinformwhat surface water and groundwater regimes are needed tomaintain the integrity of riparian ecosystems.

Vegetation dynamics, especially over large scales, can bemonitored using remote sensing (Barbosa et al., 2006; Gaughanet al., 2012; McGrath et al., 2012; Wang et al., 2003). Of the

spectral indices derived from remote sensing which identifyvegetated areas and their condition, the Normalised DifferenceVegetation Index (NDVI) is still the most well-known andfrequently used (Bulcock and Jewitt, 2010; Sims and Colloff, 2012).NDVI is based on the differential reflectance that plants exhibit fordifferent parts of the solar radiation spectrum. Healthy green leavesstrongly absorb photosynthetically active radiation for energy inphotosynthesis, whereas internal mesophyll structures in the leafscatter radiation in the near-infrared region to prevent overheatingof the plant (Bulcock and Jewitt, 2010; Wang et al., 2003). Calcu-lated by obtaining the difference between the remotely sensedvisible (red) and near-infrared bands and normalising it over thesum of the two, NDVI is a good indicator of the ability of vegetationto absorb photosynthetically active radiation and therefore of land-cover which comprises unstressed vegetation (Otto et al., 2011;Wang et al., 2003). NDVI values have been correlated with anumber of vegetation structures and functions such as biomass(Cho et al., 2007; Hansen and Schjoerring, 2003), primary pro-ductivity (Goward and Dye,1987), and Leaf Area Index (Bulcock andJewitt, 2010).

The importance of water availability to vegetation vigour hasseen NDVI commonly used to investigate relationships betweenterrestrial vegetation and climate (e.g. Barbosa et al., 2006;Gaughan et al., 2012; Ji and Peters, 2003; McGrath et al., 2012).

B. Fu, I. Burgher / Journal of Arid Environments 113 (2015) 59e6860

The linear relationship between vegetation NDVI and antecedentrainfall in arid to semi-arid regions has been well documented(Groenvald and Baugh, 2007; Ji and Peters, 2003; Peng et al., 2012;Wang et al., 2003). Less documented are NDVI responses to hy-drological factors. Marked NDVI responses to flooding events inlarge areas of naturally vegetated floodplain have been observed ineastern inland Australia (Parsons and Thoms, 2013; Sims andColloff, 2012). Relationships have also been found for NDVI andchanges in groundwater levels (Aguilar et al., 2012) and ground-water flow discharge (Petus et al., 2012). However, there is littleliterature that investigates the impact of climatic, surface water andgroundwater factors collectively on NDVI for riparian vegetation.

In this paper, we used 23 years of NDVI to examine semi-aridriparian vegetation responses to rainfall, flow, flood and ground-water across nine sections of the Namoi River Catchment in easternAustralia. We specifically looked at NDVI values of tree patches(dominated by river red gums) and riparian vegetation zones withdenser and sparser trees.

2. Study area

The Namoi River Catchment forms part of the Murray-DarlingBasin and drains an area of approximately 42,000 km2 in north-ern New SouthWales, Australia (Fig.1). From east towest across thecatchment, meanmaximum temperatures in January range from 27to 35 �C, while mean maximum temperatures in July range from 12to 17 �C (Australian Bureau of Meteorology, 2013b). Rainfallgenerally decreases across the catchment from east to west, withannual averages of 945 mm rainfall at Niangala near the headwa-ters, 620 mm at Gunnedah in the midsection of the catchment and

Fig. 1. Namoi River Catchment, showing the nine river sections we examined (i.e. assets), aHere the NDVI values across the catchment area are shown for two years e 1991 a wet ye

480 mm at Walgett in the low lying plains of the west (AustralianBureau of Meteorology, 2013b). Potential evaporation increasesacross the catchment from east to west, with very high potentialevaporation in the summer months compared to winter months.Mean daily evaporation rate during 1981e2010 at Gunnedah is8.2 mm in January, compared to 2.0 mm in June (Australian Bureauof Meteorology, 2013b).

This study focuses on the mid to lower sections of the NamoiRiver Catchment, downstream of Gunnedah. Annual flows gener-ally increase with catchment area but catchment flows in theNamoi decrease downstream of Gunnedah due to increased evap-oration, transmission losses andwater use. Themid to lower Namoihas a long history of river regulation and flow regime has beensignificantly altered (Sheldon et al., 2000). Namoi also has thehighest groundwater use in the Murray-Darling Basin. In2004e2005, groundwater extraction in the Namoi was estimatedto be 255 GL, accounting for 15.2% of the total groundwater use inthe Murray-Darling Basin (CSIRO, 2007). About 35% of thegroundwater extraction in the Namoi River Catchment was fromthe Lower Namoi Alluvium Groundwater Management Unit (CSIRO,2007).

The major streams and rivers of the catchment are dominatedby river oak (Casuarina cunninghamiana) and river red gum(Eucalyptus camaldulensis). Native floodplain vegetation commu-nities include open grassy woodlands dominated by poplar box(Eucalyptus populnea), black box (Eucalyptus largiflorens) andcoolibah (Eucalyptus coolabah), and native grasslands dominated byplains grass (Austrostipa aristiglumis). Large areas of riverine land inthe Namoi River Catchment have been converted to cropping andpastoral uses, thus except for habitat corridors and patches of

nd the broader catchment area for which Landsat derived NDVI values were obtained.ar and 2002 a dry year in the Namoi.

B. Fu, I. Burgher / Journal of Arid Environments 113 (2015) 59e68 61

riverine vegetation most of the native vegetation has been cleared(Eco Logical, 2009).

Within the study area, we specifically looked at dynamics be-tween NDVI and water availability along nine sections of river(Fig. 1). Thesewere ecological assets (specific locations of ecologicalinterest) previously selected by another study researching wateringneeds for the development of broader environmental flow guide-lines (BarmaWater Resources et al., 2012). All assets except MaulesCreek - which is considered to be in relatively natural condition -have a history of river regulation. Additionally, large areas of theseassets are classified as groundwater-dependent ecosystems by theAustralian Groundwater Dependent Ecosystems Atlas (AustralianBureau of Meteorology, 2013a).

3. Methods

3.1. Calculating NDVI

Existing riverine vegetation in the Namoi River Catchment oftenoccurs in a fairly narrow fashion along water courses (see Fig. 2).We thus used remote sensing data acquired by the Landsat 5 TMand Landsat 7 ETM satellites as their 30 m resolution can targetsuch a fine spatial configuration. This avoids the inclusion ofpotentially irrigated crops which would have been almost impos-sible with commonly used larger resolution imagery such asMODISor AVHRR (with resolutions of 250 m or greater). Another impor-tant advantage of using Landsat imagery is the early start time ofLandsat 5 which has been operating since 1984, allowing foranalysis of a relatively long time-series of data. In contrast, 250 mresolution data from MODIS are only available from the year 2000.

We acquired Landsat images (path 91/row 81) from GeoscienceAustralia (1987e1998) and the United States Geological Survey(1999e2010) comprising a data time-series of 23 years. Ideally,Landsat offers images on a periodic 16-day basis. However, imageavailability from the data distribution agencies and obscuring ofassets by periodic cloud cover means that the number of usabledata points for the years examined is inconsistent. We extracted atotal of 228 usable images, with most years having between 8 and15 images, although the years 1987, 1995, 1998, 1999, 2005 and2008 have 5 images or fewer.

Using ENVI 5.0 (Exelis Visual Information Solutions, 2012), im-ages were corrected for sensor defects and sensor differences byconverting to top of atmosphere reflectance using published post-launch gains and offsets (Chander et al., 2009). Dark object sub-traction was then performed using the band minimum from eachimage. Dark object subtraction was chosen because this method isone of the simplest yet most widely used methods of atmosphericcorrection for land-use classification and change-detection

Fig. 2. Section of Wee Waa to Bugilbone as viewed by Google Earth showing selected treeriver buffer with crops clipped out (right). NDVI values tend to increase near the river chan

purposes (Song et al., 2001). Following this correction, NDVI wascalculated for all images as per Eq. (1) using bands 3 and 4 inLandsat which have been calibrated to sense radiation in the visible(Red) and near-infrared (NIR) regions of the spectrum respectively.

NDVI ¼ ðNIR� RedÞ=ðNIRþ RedÞ (1)

NDVI values range between �1.0 and 1.0 with values nearingzero and below indicating features which are not vegetated such aswater, snow, ice, clouds and barren surfaces.

We estimated NDVI values for two different types of riparianareas: general riparian vegetation zones which consist of ripariantrees and grasses, and selected tree patches within the riparianvegetation zones which are predominantly comprised of river redgums. For the tree patches, polygons were manually drawn aroundselected areas of forest adjacent to river channels within assetsusing Google Earth (Fig. 2). Mean tree patch NDVI across each assetwas calculated for each date. For riparian vegetation zones, the riverchannels within assets were buffered by 200 m on each side andareas corresponding with crops clipped out (Fig. 2). Then, in eachasset the area of each NDVI class: [e1, 0), [0, 0.2), [0.2, 0.4), [0.4, 0.6),[0.6, 0.8), [0.8, 1] was calculated. This area was then standardisedfor each asset by calculating the proportional area of each class ineach asset. We used NDVI classes rather than mean NDVI values forriparian vegetation zones because the variations of NDVI are toohigh due to large areas and different riparian vegetation types.Mean NDVI values cannot fully reflect the NDVI values in the ri-parian vegetation zones in each asset. In contrast, variations ofNDVI in tree patches are very small and mean NDVI values are goodindicators of NDVI values in each asset.

3.2. Rainfall and hydrological data

Daily rainfall records were obtained from the Australian Bureauof Meteorology website, using the most proximate station to eachasset which had complete or near complete data records(Australian Bureau of Meteorology, 2013b). In the vicinity of Dun-can's Junction, Wee Waa to Bugilbone and Bugilbone to Walgett,only one rainfall station had sufficient data, and this station is usedfor all three assets. Daily surface flow data before 2008 wereextracted from PINNEENA 9.2 (NSW Department of Water andEnergy, 2008), with more recent data taken from the NSW gov-ernment water information website (waterinfo.nsw.gov.au). Rivergauges were selected based on their proximity to the assets. His-torical groundwater bore data were obtained from GroundwaterPINNEENA 3.2 (NSW Office of Water, 2011). The groundwater boredata were interpolated into daily time series using a linearregression (Blakers, 2011). Bores were selected based on theirproximity to the asset, and themean value of the daily groundwater

patch polygons (left) and LandSat derived NDVI values, showing tree patch and 200 mnel due to presence of riparian vegetation.

B. Fu, I. Burgher / Journal of Arid Environments 113 (2015) 59e6862

levels at Pipe 1 (the pipe with the shallowest opening) of thesebores was used to represent the groundwater level for that asset.

3.3. Exploratory analyses

Three types of analyses were undertaken to investigate NDVIdynamics of tree patches and riparian vegetation zones: 1) longterm trend of NDVI in the past 23 years; 2) seasonal behaviour ofNDVI; and 3) relationships between NDVI and climate and hydro-logical variables using classification. Annual average NDVI treepatch and class area percentage values for each asset were used toidentify long-term trends and variability over the 23 year period.Average monthly NDVI values over the 23 year period were used toassess seasonality over the year. Regression tree analysis was usedfor classification.

Regression tree analysis is a commonly used statistical methodfor non-parametric regression and classification that has beenwidely applied for ecological data (De'Ath and Fabricius, 2000). Itgenerates a regression tree that classifies a response variable basedon explanatory variables; in doing so thresholds can be identified tobest separate values of the response variable. We used the ctree()function in the party package in R (Hothorn et al., 2006) forregression tree analysis. This method tests the global null hypoth-esis of independence between any of the explanatory variables andthe response variable. It selects the explanatory variable with thestrongest association to the response variable. This association ismeasured by a p-value corresponding to the hypothesis test (weused KruskaleWallis test). Then the algorithm implements anoptimal binary split in the selected explanatory variable using apermutation test. The classification stops when the p-value isgreater than a threshold (we used 0.01). The algorithms can befound in Hothorn et al. (2006).

The explanatory variables used for regression tree analysisinclude daily maximum temperature (Temp_max), antecedentrainfall (Rainfall_total), antecedent flow (Flow_total), groundwaterlevel (Groundwater), inter-flood dry period (indicated by days sincelast flood, Days_lastflood), the proportion of flow relative to theoverbank flood threshold (Perc_low), and the mean Perc_low overthe past days (Perclow_mean). We used Perc_low to account forvariation in channel capacity across assets, thus a Perc_low value of1 indicates that the flow is at the overbank flood thresholdregardless of the channel size at different assets. We used ante-cedent rainfall, antecedent flow and Perclow_mean to account forpotential lag response of NDVI to rainfall and flow. The lag wasidentified by calculating 1e60 days (at a 1 day interval) of totalrainfall, total flow and mean percentage low, then estimating cor-relations between NDVI and these antecedent values. The lags that

0.3

0.4

0.5

0.6

0.7

0.8

1985 1990 1995 2000

Annu

al M

ean

Tree

patc

h N

DVI

Fig. 3. Annual mean NDVI of selected tree p

correspond to the highest correlation were used to calculateRainfall_total, Flow_total and Perclow_mean.

These explanatory variables were related to three sets ofresponse variables for regression tree analysis: 1) mean NDVIvalues of the tree patches at all assets; 2) proportion of area that hashigh NDVI (i.e. NDVI > 0.6) in each of the three riparian vegetationzones that have dense trees (i.e. assets 5e7: Duncan's Junction,WeeWaa to Bugilbone, Bugilbone to Walgett); and 3) proportion of areathat has high NDVI (i.e. NDVI > 0.6) in each of the two riparianzones that have sparse trees (i.e. assets 3 and 4: Upstream Molleeand Mollee to Gunidgera). Calculation of these NDVI was describedin Section 3.1. We separated NDVI data for riparian zones withdense and sparse tree vegetation in order to reduce the impact ofland clearing on areas of NDVI values.

4. Results

4.1. Long-term trends

Annual rainfall averaged across assets shows high variabilitybetween years. The years 1994 and 2002 were comparatively verydry, whereas relatively large amounts of rain fell in the years 1991,1998, 2004 and 2010 (FigS1, electronic version only). Annual flowsare mostly less than 300 GL/year in most years across the assets.High annual flows are found for periods during 1989e91, and theyears 1998, 2000 and 2010. Mean groundwater levels are fairlyconstant across the 23 years, but have slightly deepened since2003.

Annually averaged NDVI values for tree patches are fairly stablefor all assets over the 23 year period examined (Fig. 3). There is aclear difference in the NDVI ranges between assets, with Pian Ckshowing a lower range (0.41e0.51) quite apart from the rest of thegroup. Gunnedah on the other hand shows the highest range(0.52e0.74). Eastern assets including Gunnedah, Maules Ck, andBarbers Lagoon have higher ranges of annual NDVI values andhigher variability over the time period than do western assets (PianCk, Duncan's Junction and Wee Waa to Bugilbone).

Trends of NDVI change for tree patches through the 23 years aresimilar across assets, with significant falls in NDVI in the years 1994,2002 and 2007 for all assets (except for Gunnedah in 2007). Theselow NDVI periods broadly correspond to low rainfall periods in theregion. Notable rises in NDVI occur in the 2003e04 period and2008 across assets. Other periods exhibit mixed change in treepatch NDVI. For example, in 2010 western assets decreased in NDVIin while eastern assets increased for this year.

Annual mean NDVI class areas within assets showed patternslargely reflecting those of annual mean tree patch NDVI, with 1994

2005 2010

Gunnedah

Barbers LagoonMollee to GunidgeraDuncans JunctionWee Waa to BugilboneBugilbone to WalgettPian Creek

Maules Creek

Upstream Mollee

atches across assets from 1987 to 2010.

B. Fu, I. Burgher / Journal of Arid Environments 113 (2015) 59e68 63

and 2002 showing markedly large areas of low NDVI (<0.4) andtherefore reduced areas of higher NDVI across all assets (Fig. 4).Years where there were markedly large areas of high NDVI wereinconsistent across assets, although the years 1990, 1996, 2003 and2008 showed relatively large areas of high NDVI (>0.6) for manyassets.

4.2. Seasonality

We found strong seasonality over the years with very low NDVI(i.e. 1994, 2002 and 2007 e presumably dry periods) (FigS2, elec-tronic version only). In these cases NDVI values tend to be highestin the middle months of the year (corresponding with the southernhemisphere winter) and lowest in summer. Seasonality over theyears which have average or high levels of NDVI is less obvious. TheNDVI values are slightly lower in summer months particularlyNovember for most assets, but are slightly higher in March, Apriland May in the western assets, and August, September and Octoberfor the eastern assets. Similar seasonality patterns were found forNDVI class areas in riparian vegetation zones.

4.3. Regression tree analysis

4.3.1. Mean NDVI for tree patchesCorrelation analysis between NDVI and 1e60 days of antecedent

rainfall and flow values suggested that 28-days (e.g. total rainfall ofthe current day and the previous 27 days) is the best lag time. Formean NDVI for tree patches, the correlations are 0.30, 0.25 and 0.25between NDVI and 28 days of total rainfall, total flow and meanPerc_low (i.e. proportion of flow relative to the overbank floodthreshold) respectively. The highest correlations were also foundbetween proportion of high NDVI areas (i.e. NDVI > 0.6) and 28 daysof total rainfall, total flowandmean Perc_low (correlations are 0.30,

Fig. 4. Annual mean area of NDVI class value as a percentage of entire asset: a) BarbersLagoon and b) Wee Waa to Bugilbone.

0.32 and 0.32 respectively). Therefore, 28 days of total rainfall(Rainfall_total), total flow (Flow_total) and mean Perc_low (Per-clow_mean) were used as explanatory variables in classificationanalysis. Datasets with Perc_low values greater than 1 (i.e. datesduring flooding) were excluded in the classification analysisbecause the NDVI values for these datasets are underestimated dueto of the reflectance of flood water.

Regression tree analysis of mean NDVI for tree patches at allassets showed that the major variable to separate NDVI values wasgroundwater levels. In areas where the groundwater is deeper than18.94 m below ground, the mean NDVI values for tree patches are0.48 (sd ¼ 0.08); while in areas where the groundwater is equal toor shallower than 18.94 m below ground, the mean NDVI values are0.57 (sd ¼ 0.08). The former category (Groundwater > 18.94 m) ismostly comprised of the asset Pian Ck where groundwater levelsrange from 19 to 27 m in the 23 years period.

When groundwater levels exceed 18.94 m, dates with shorterinter-flood dry period (Days_lastflood � 96 days) have higher NDVIvalues (Node 2 in Fig. 5, mean NDVI ¼ 0.54, sd ¼ 0.07) than dateswith longer inter-flood dry period (mean NDVI ¼ 0.47, sd ¼ 0.07).After this, maximum temperatures are seen to relate to NDVI valueswith temperatures below 27.2� more related to higher NDVI (meanNDVI ¼ 0.49, sd ¼ 0.07) (Node 4 in Fig. 5) than temperatures abovethis threshold (mean NDVI¼ 0.44, sd¼ 0.06) (Node 5 in Fig. 5). Thisimplies that stressed riparian trees in deeper groundwater zonesare associated with longer dry periods (more than 3 months) andhotter weather (more than 27� maximum temperature).

Pian Ck was then removed from regression tree analysis so as toexamine the relationships between NDVI and climatic and hydro-logical variables at areas where groundwater levels did not alwaysexceed 19 m below ground. In this case, maximum temperature isthe primary variable classifying the NDVI data, with coolermaximum temperatures (<33.9 �C) tending to relate to slightlyhigher NDVI for tree patches thanwarmer maximum temperatures(p < 0.001) (Fig. 6). In either case, after temperature, antecedentrainfall (i.e. 28 days total rainfall) becomes the next variable whichsplits the data. When maximum temperatures are cooler(�33.9 �C), higher antecedent rainfall (>23.8 mm) is related to

Fig. 5. Regression tree showing classification of mean NDVI for the tree patches fordates when groundwater levels exceed 18.94 m.

Fig. 6. Regression tree showing classification of mean NDVI for the tree patches in all assets except Pian Ck.

B. Fu, I. Burgher / Journal of Arid Environments 113 (2015) 59e6864

observationswith the highest NDVI ranges (mean¼ 0.60, sd¼ 0.07)in the tree patches (Node 6 in Fig. 6). When antecedent rainfall islower (<23.8 mm) inter-flood dry period becomes an importantsplit for the data (p < 0.001), with shorter dry periods associatedwith higher NDVI.

When the weather is hotter (Temp_max > 33.9 �C) and whenthe antecedent rainfall in the past 28 days exceeds 70 mm, highNDVI values (mean ¼ 0.59, sd ¼ 0.07) were recorded for the treepatches (Node 11 in Fig. 6). This is similar to cooler weather thoughwith a lower rainfall threshold (23.8 mm) to achieve similarly highNDVI values (Node 6 in Fig. 6). However, if the weather was hotter(Temp_max > 33.9 �C) and dryer (Rainfall_total� 70mm), andwithlonger dry periods (Days_lastflood > 271 days), the lowest range ofNDVI values was recorded for the tree patches in the assets with amean of 0.46 (sd ¼ 0.07) (Node 10 in Fig. 6).

4.3.2. NDVI class areas for dense riparian vegetation zonesCompared to tree patches (Fig. 6), a similar regression tree was

generated when relating proportion of NDVI class areas with cli-matic and hydrological variables (Fig. 7). It depicts maximum

Fig. 7. Regression tree showing classification of proportion of area with high

temperature above or below 25.5 �C as the primary determinant ofthe proportion of areas of high NDVI (i.e. NDVI > 0.6). Lowermaximum temperatures are generally related to larger proportionsof high NDVI area.

When maximum temperatures are lower than 25.5 �C, largerproportions of high NDVI area were found in situations when theantecedent rainfall is low (Rainfall_total � 42 mm) but cooler(Temp_max � 14.4 �C) (Node 4 in Fig. 7), or when antecedentrainfall is high (Rainfall_total > 42 mm) and groundwater is shal-lower (Groundwater � 16.04 m) (Node 7 in Fig. 7). In contrast,smaller proportions of areas of high NDVI were found related tosituations when it is warm and dry (Node 5 in Fig. 7), or wet butwith deeper groundwater (Node 8 in Fig. 7). When maximumtemperatures are cooler (Temp_max � 25.5 �C) and antecedentrainfall is higher (Rainfall_total > 42 mm), area of high NDVI tendsto comprise more than 40% of the assets if groundwater is shal-lower than 16.04m (Node 7 in Fig. 7), but less than 40% of asset areaif groundwater is deeper (Node 8 in Fig. 7).

When the weather is warmer (Temp_max > 25.5 �C), the nextdeterminant of high NDVI area is the inter-flood dry period.

NDVI (>0.6) for the assets with dense riparian vegetation (assets 5e7).

B. Fu, I. Burgher / Journal of Arid Environments 113 (2015) 59e68 65

Situations when the last flood occurred more than 52 days ago arerelated to very low areas of high NDVI (in average 7%, Nodes 13 and14 in Fig. 7), unless antecedent rainfall has been very high(Rainfall_total > 95.5 mm) (Node 15 in Fig. 7). In comparison, theNDVI value is higher when the weather is warmer and the inter-flood dry period is less than 52 days. Under such conditions onaverage 20% of the asset areas has high NDVI (Node 10 in Fig. 7).

4.3.3. NDVI class areas for sparse riparian vegetation zonesSimilar to tree patches and dense riparian vegetation zones, the

primary split in high NDVI area for riparian vegetation communitywith sparse trees is determined by maximum temperature, with21.2 �C as the pivotal temperature (Fig. 8). Below or equal to 21.2 �C,area of high NDVI has a large spread but has an average of about40% of the assets. Above amaximum temperature of 21.2 �C, rainfallbecomes the next most important variable. When antecedentrainfall is less than 54.8 mm and days since last flood exceed 36days, areas of high NDVI are very low (Node 6 in Fig. 8). Warmertemperature, lower rainfall but recently flooded dates (Node 5 inFig. 8) have higher NDVI areas which are within a similar range tocooler temperature (Node 2 in Fig. 8). If antecedent rainfall isgreater than 54.8 mm and maximum temperature is higher than21.2 �C (Node 7 in Fig. 8), this can produce a slightly lower range ofhigh NDVI area as compared to when the weather is cooler (Node 2in Fig. 8).

5. Discussion

This paper presents an exploratory analysis of how wateravailability affects vegetation vigour in the riparian zone by relatinga range of climatic and hydrological variables to NDVI. Of thesevariables, the regression trees presented here consistently indicatethat maximum temperature is the variable that primarily splitsNDVI values, followed by antecedent 28-day rainfall and then the

Fig. 8. Regression tree showing classification of proportion of area with high N

hydrological variables of inter-flood dry period and groundwaterlevels. We believe this is because while rainfall, flood andgroundwater are water supplies to riparian vegetation, tempera-ture moderates the availability of this water and hence NDVI.

5.1. Climatic effects on NDVI

The relative impacts of climatic factors (including rainfall andtemperature) on NDVI differ between climates and bioregions.Research on arid and semi-arid regions where water is the limitingfactor on productivity tends to focus on rainfall alone (Barbosaet al., 2006; Gaughan et al., 2012). On the other hand, tempera-ture is a strong driver of NDVI in higher latitudes (Ichii et al., 2002),at high altitudes (Hu et al., 2011; Liang et al., 2012) and for bio-regions where deciduous vegetation exists (see Wang et al., 2008).

Our exploratory regression tree analyses consistently showmaximum temperature to be the dominating variable influencingriparian NDVI values in our study area (excluding Pian Ck). This isdifferent with some other studies at the mid-latitudes, in whichtemperature is often reported to be secondary to rainfall in terms ofinfluencing NDVI (Peng et al., 2012; Wang et al., 2003). However,these studies are often focussed on biomes that are not semi-aridand look at NDVI only within the distinct growing season. Argu-ably there is no distinct growing season for the eucalypt dominantvegetation of our study region. And if we were to characteriseseasons by temperature, looking at individual seasons in our studyregion would show antecedent rainfall to be the dominant influ-ence on NDVI. Wen et al. (2012) found that at Macquarie Marshes(located in a study region biogeographically relatively similar toNamoi Catchment), monthly total rainfall is more influential tomonthly mean NDVI than the mean minimum daily temperature ina month. However, maximum temperature was not included in themodelling. In contrast, our study investigated daily NDVI and itsrelationships with daily maximum temperature.

DVI (>0.6) for the assets with sparse riparian vegetation (assets 3 and 4).

B. Fu, I. Burgher / Journal of Arid Environments 113 (2015) 59e6866

In our study, maximum temperature is negatively related toNDVI. Higher maximum temperature is found associated withlower mean NDVI for the tree patches and smaller areas of highNDVI in riparian zones. This is consistent with other studies whichsuggested temperature to be negatively correlated with NDVIduring spring (Yang et al., 1997) and summer (Wang et al., 2003).This can be due to lower soil moisture caused by higher tempera-ture, especially in semi-arid regions when rainfall is depleted. Soilmoisture is widely seen to be the link between rainfall, temperatureand NDVI (Wang et al., 2003). Though temperature does notdirectly influence howmuch water enters the soil, it moderates soilmoisture levels through its ability to drive potential evaporation.The importance of evaporation and its negative impact on NDVIhave been reported in other studies (Yang et al., 1998, 1997). In ourstudy, more rain falls in the warmer months of the year in theNamoi catchment, but these months are also strongly coupled withhigh potential evaporation, driven in great part by the highertemperatures. Our results suggest that the moderating effect oftemperature on soil moisture can be significant so that much of thewater entering the soil at this time soon becomes unavailable toplants, resulting in lower NDVI. Conversely, the smaller amount ofrain that enters the soil in the cooler months is likely to be moreavailable to vegetation due to low potential evaporation, resultingin generally higher NDVI.

Once temperature is controlled for, antecedent rainfall becomesthe dominant variable which splits NDVI values, with higher rain-fall related to higher NDVI values and larger areas of high NDVI. Theregression trees consistently show that more rain is required in thewarmer months compared to cooler months to achieve similarmean NDVI values in tree patches or areas of high NDVI in riparianzones, presumably because of higher evaporation. For example, toachieve similar mean NDVI values for tree patches, more than70 mm of antecedent rainfall is required when the maximumtemperature is greater than 33.9 �C, in comparison to 23.8 mm ofantecedent rainfall required when the maximum temperature islower than 33.9 �C (see Fig. 6).

5.2. Surface water influences on NDVI

Healthy riparian ecosystems depend on suitable flow regimes.The natural regime of stream flow e encompassing frequency,magnitude, duration and timing of water e is what many riparianorganisms have specifically adapted to (Poff et al., 1997). Changes tothe predictability and variability of stream flow due to waterimpoundment and extraction are likely to test an organism'sadaptability and thus ability to survive (Allan and Castillo, 2007).This situation has been observed in riparian ecosystems the worldover (Nilsson and Berggren, 2000; Poff et al., 1997) including alongAustralia's highly altered Murray-Darling River system wherechanges in the natural flow regime have led to reduced growthrates, accelerated mortality and reduced recruitment for eucalyptsin its river red gum (E. camaldulensis) forests (Bacon et al., 1993;Cunningham et al., 2009).

Positive NDVI responses to flooding events in large areas ofnaturally vegetated floodplain have been documented in easterninland Australia (Parsons and Thoms, 2013; Sims and Colloff, 2012).For example, Sims and Colloff (2012) reported a nearly 20% increasein NDVI for 13 months following flood recession as a result of aflood which inundated more than 50% of the Paroo River Wetlands.However, when combining hydrological and climatic factors, Wenet al. (2012) reported that rainfall rather than flow levels into awetland has a more significant relationship with mean NDVI. Sunet al. (2008) found a negative correlation between stream flowand NDVI during growing season in the Minjiang River region of

western China, and attributed this to water uptake by vegetation inthe growing season.

In our study, surface water variables were not indicated to be asinfluential as rainfall. Various surface water variables were inves-tigated, including antecedent flow (Flow_total), inter-flood dryperiod (Days_lastflood), the proportion of flow relative to theoverbank flood threshold (Perc_low), and the mean Perc_low overthe past days (Perclow_mean). Inter-flood dry period was identifiedto be the only significant surface water variable in regression trees.Generally this variable becomes important when limited water isavailable from rainfall. For tree patches and dense riparian zones,when antecedent 28-day rainfall is low, areas with less than 7e9months of inter-flood dry periods have relatively higher NDVIcompared to those haven't been flooded for more than 7e9months. For sparse riparian zones, much shorter dry period andhence more frequent flooding is associated with high NDVI. In thiscase, when antecedent 28-day rainfall is low (<54.8mm), areas thathave not been flooded in the past 36 days have significantly lowareas of high NDVI in the riparian zones (see Fig. 8). This is likelydue to the quicker browning off effect of non-tree vegetation,especially grasses, as opposed to trees which are more bufferedfrom changes in water availability due to deeper rooting systemsand greater carbohydrate reserves (Peng et al., 2012; Wang et al.,2003).

5.3. Groundwater influences on NDVI

Groundwater is an important water source for maintenance,abundance and composition of many riparian communities, espe-cially in arid and semi-arid regions. Depletion of groundwater hasbeen associated with stress, mortality and lack of recruitment ingroundwater-dependent vegetation in many parts of the world(Cunningham et al., 2009; Horton et al., 2001; Scott et al., 1999;Stromberg et al., 1996). Aguilar et al. (2012) reported strong linearrelationships betweenmean andmaximumNDVI and groundwaterlevels in dry years; however the relationships are much weaker inwet years.

In our study, the importance of groundwater level was identifiedin the classification of tree patches and of dense riparian zones. Forthe tree patches, groundwater level was the primary split, mostlydue to the effect of the asset Pian Ck which had significantly deepergroundwater levels than the rest of the assets. This is associatedwith markedly lower NDVI compared to rest of the assets (seeFig. 3). For dense riparian zones, groundwater is important insplitting NDVI at situations when the weather is cooler and wetter.In this case, situation with groundwater levels shallower than 16 mbelow ground is associated with larger areas of high NDVI.Groundwater level is not an important variable in splitting NDVI forriparian zones with sparse trees (assets 3 and 4). This could be theeffect of fewer trees in these riparian zones, as non-tree vegetatione especially grasses e are unlikely to be effected by changes ofgroundwater levels at the depths observed in our study area.

5.4. Limitations and implications for management

Regression tree analysis allowed us to classify NDVI and identifycritical explanatory variables without the assumption about theirlinear relationships (as is the case for linear regression analysisused in other literature). It provides insight into the roles of oneexplanatory variable in relation to other explanatory variables. Forexample, inter-flood dry period often becomes important whenrainfall is below certain thresholds. The results of regression treeanalysis are sensitive to the dataset used. However, in our study aconsistent story emerged in terms of the relationships betweenNDVI and climatic, surface water and groundwater variables. This

B. Fu, I. Burgher / Journal of Arid Environments 113 (2015) 59e68 67

demonstrates the robustness of the findings. The thresholds iden-tified in the regression trees apply to the dataset used in this study.Therefore, these thresholds should be seen as indicative and theexact values should not be used for water planning purposes.

Lack of field studies to cross-verify remotely-sensed observa-tions is arguably a limitation of this study. Field studies would haveallowed us to make physiological observations of vegetation stressbrought on by low water availability as well as observations of landclearing and grazing which would affect NDVI values in ways notaccounted for in this study. This said, remotely sensed NDVI hasallowed this study to rapidly collect data not only across a sizeablelandscape, but more importantly, across a time span of 23 years, forwhich field data on vegetation vigour in our study region does notexist. In this way the use of remote sensing can be viewed as aparticular strength especially for this exploratory analysis: the largenumber of data points generated by remote sensing data givesstrength to the data mining technique of decision tree analysis.

Another limitation is that NDVI does not account for soilreflectance which can make NDVI difficult to interpret whenvegetation cover is low and the surface substrate is unknown. It ispossible that NDVI can indicate a range of values when soils in thebackground are different, despite vegetation vigour being effec-tively the same (Rondeaux et al., 1996; Sims and Colloff, 2012). Carewas taken to demarcate dense patches of trees for tree patch NDVI,so as to minimise the problem of soil reflectance.

Among the variables we investigated, flow and groundwaterlevels are manageable through human intervention. Though not asinfluential as temperature and rainfall, our study indicates thesevariables are important for assisting the maintenance of healthyriparian vegetation communities. Inter-flood dry period is shown tobe important for maintenance of NDVI levels, particularly in sum-mer when high temperatures can reduce the amount of availablewater in the soil fed from rain. Our study suggests that environ-mental flows which allowwater to overflow the bank in summer orin particularly dry winters will assist to maintain healthy riparianvegetation.

6. Conclusion

Regression tree analysis was used to investigate how wateravailability affects vegetation vigour in riparian zone in nineecological assets in the Namoi catchment, Australia. This was ach-ieved by relating a range of climatic and hydrological variables to 23years of NDVI data. Of these variables, the regression trees pre-sented here consistently indicate that maximum temperature is thevariable that primarily splits NDVI values, followed by antecedent28-day rainfall and then the surface water variable (inter-flood dryperiod) and groundwater levels. Maximum temperature is thedominant variable and is negatively related to NDVI. This can bedue to lower soil moisture caused by higher temperature, especiallyin semi-arid regions when rainfall is depleted. More rain is requiredin the warmer months compared to cooler months to achievesimilar mean NDVI values in tree patches or areas of high NDVI inriparian zones, presumably because of higher evaporation. Inter-flood dry period was identified to be the only significant surfacewater variable in regression trees. Generally this variable becomesimportant when rainfall is limited. Our study also suggested thatshallower groundwater levels sustain the NDVI and hence vegeta-tion greenness when cooler and wetter.

Acknowledgement

We would like to thank Geoscience Australia for providingLandsat 5 data between 1987 and 1998. This study was funded bythe Cotton Research and Development Corporation and the

National Centre for Groundwater Research and Training (Grant ID22654), an Australian Government initiative supported by theAustralian Research Council and the National Water Commission.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jaridenv.2014.09.010.

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