A Robust Technique for MappingVegetation Condition Across a Major

River System

S. C. Cunningham,1* R. Mac Nally,1 J. Read,2 P. J. Baker,1 M. White,3

J. R. Thomson,1 and P. Griffioen4

1Australian Centre for Biodiversity, School of Biological Sciences, Monash University, Melbourne, Victoria 3800, Australia;2School of Biological Sciences, Monash University, Melbourne, Victoria 3800, Australia; 3Arthur Rylah Institute for Environmental

Research, Department of Sustainability and Environment, Heidelberg, Melbourne, Victoria 3084, Australia; 4acroMap,

37 Gloucester Drive, Heidelberg, Melbourne, Victoria 3084, Australia

ABSTRACT

Ecologists need to develop tools that allow char-

acterization of vegetation condition over scales that

are pertinent to species’ persistence and appropri-

ate for management actions. Our study shows that

stand condition can be mapped accurately over the

floodplain of a major river system (ca 100,000 ha of

forest over 1600 km of river)—the Murray River in

southeastern Australia. It demonstrates the value of

using quantitative ground surveys in conjunction

with remotely sensed data to model vegetation

condition over very large spatial domains. A com-

parison of four modelling methods found that stand

condition was best modelled using the multivariate

adaptive regression spline (MARS) method

(R2 = 0.85), although there was little difference

among the methods (R2 = 0.77–0.85). However, a

subsequent validation survey of condition at new

locations showed that use of artificial neural net-

works had substantially higher predictive power

(R2 = 0.78) than the MARS model (R2 = 0.28). This

discrepancy demonstrates the value of using sev-

eral modelling approaches to determine relation-

ships among vegetation responses and environ-

mental variables, and stresses the importance of

validating ecological models with predictive sur-

veys conducted after model building. The artificial

neural network was used to produce a stand con-

dition map for the whole floodplain, which pre-

dicted that only 30% of the area containing

Eucalyptus camaldulensis stands is currently in good

condition. There is a downstream decline in stand

condition, which is related to more extreme de-

clines in flooding, due to water harvesting, and

drier climate found in the Lower Murray region.

Rigorous surveying and modelling approaches,

such as those used here, are necessary if vegetation

health is to be effectively monitored and managed.

Key words: Eucalyptus camaldulensis; floodplains;

neural networks; regression splines; regression

trees; remote sensing; river regulation; validation;

vegetation condition.

INTRODUCTION

Much has been made of adverse effects of loss and

fragmentation of natural vegetation (Sala and

others 2000). Substantial work has also been

undertaken to relate biodiversity decline to degra-

dation of remnant vegetation (Saunders and others

Received 18 February 2008; accepted 19 October 2008;

published online 22 November 2008

Author Contribution: SCC wrote the paper and was involved in all parts

of the research; RM conceived and designed the study, and contributed to

analysis and writing; JR designed the study, and contributed to writing;

PJB designed the study and contributed to writing; MW contributed to

design and modelling; JRT contributed to modelling; PG contributed new

methods and modelling.

*Corresponding author; e-mail: shaun.cunningham@sci.monash.edu.au

Ecosystems (2009) 12: 207–219DOI: 10.1007/s10021-008-9218-0

� 2008 Springer Science+Business Media, LLC

207

1991), but this typically is described for relatively

small locations (hectare scale, Parkes and others

2003). There has been much less work on linking

loss and degradation of vegetation to declines in

biota over very extensive spatial extents (land-

scapes to regions). Ecologists need to develop tools

that allow characterization of vegetation condition

over scales that are pertinent to species’ persistence

and over which management actions are or need to

be undertaken.

Extensive dieback has been reported in many

forest types since the 1960s (Huettl and Mueller-

Dombois 1993). Forest dieback is characterized by a

progressive reduction in the crowns of individual

trees that leads to widespread mortality. Forest

dieback affects the quantity and quality of forest

and water resources, the flora and fauna dependent

on these ecosystems, and beyond into associated

terrestrial and aquatic ecosystems. Causes of forest

dieback include changes in chemical composition

of the atmosphere (McLaughlin and Percy 1999),

soils (Czerniakowski and others 2006) and water

(Ohrui and Mitchell 1997), climate change (Bour-

que and others 2005), succession (Mueller-Dom-

bois 2006), and impacts of exotic insects (Bonneau

and others 1999).

Potential indicators of forest condition include

compositional (for example, species richness),

structural (for example, shrub cover) and func-

tional (for example, recruitment, Noss 1999) attri-

butes. Various assessment procedures have been

devised to estimate tree condition and these in-

clude a range of indicators, such as crown vigor,

crown size and foliage density (for example, Innes

1990; USDA Forest Service 2004). Many of these

assessments are based on subjective classifications

of crown condition, which are difficult to use in

temporal and statistical comparisons (Ferretti

1997). However, more objective measures such as

percentage live basal area, plant area index and

crown vigor can provide reliable indicators of stand

condition (Cunningham and others 2007).

Ground surveys are time consuming, costly and

geographically limited, whereas remote sensing,

which uses satellite or airborne data, provides wide

spatial coverage of an area at high resolution

(<1 m to 1 km) and at a lower cost per unit area.

Consequently, there has been increased use of re-

mote sensing in assessment of forest condition.

Remote sensing research in forests has often fo-

cused on estimation of leaf area index (LAI), which

is an indicator of forest productivity and water use

(for example, Nemani and others 1993). Assess-

ments of forest dieback using remote sensing have

concentrated on insect defoliation in boreal and

coniferous forests of Northern America (for exam-

ple, Bonneau and others 1999; Hall and others

2003) and rarely on other types of dieback or on

angiosperms (for example, Coops and others 2004).

Most studies relate forest dieback to vegetation

indices that are ratios of red and near infrared

reflectance bands, with Normalized Difference

Vegetation Index (NDVI, Rouse and others 1973)

being the most widely used.

Forest dieback has been modelled from remotely

sensed data with varying success (R2 = 0.2–0.9, for

example, Diem 2002; Pontius and others 2005).

Models showing strong relationships between for-

est dieback and remotely sensed data (R2 > 0.75)

were based on very limited surveys (N < 20, for

example, Hall and others 2003) and, therefore, are

unlikely to predict well beyond the original sites.

Previous forest dieback models were derived using

simple statistical methods (for example, linear

regression and correlation) and were not validated

with subsequent surveys, and some were not even

related to ground surveys (for example, Maselli

2004). Validation surveys comparing actual to

predicted conditions at new locations provide the

most compelling demonstration of the usefulness of

models (Mac Nally and Fleishman 2004; Thomson

and others 2007).

Floodplain ecosystems are of great ecological and

economic importance due to their high biodiversity

and productivity (Tockner and Stanford 2002).

Regulation of rivers using dams, channels and le-

vees has led to the decline of floodplain forests

throughout the world (for example, Busch and

Smith 1995). Rivers in semi-arid landscapes are

among the most susceptible to fluctuations in water

availability through decreases in precipitation, in-

creases in water extraction and changes to the sea-

sonality of flows. The Murray River, in southeastern

Australia, is the country’s longest river (2520 km),

providing water for extensive irrigation farming,

human consumption and power generation.

Increasing regulation of the Murray River since the

1920s has significantly reduced peak flows, and re-

duced frequency (35–62% of historical) and dura-

tion (40–84% of historical) of floods large enough to

connect anabranches to the river (MDBC 2005a).

Climate change is predicted to further reduce water

availability in southeastern Australia by raising

temperatures and decreasing annual rainfall

(CSIRO, BOM 2007), thereby increasing the sever-

ity of droughts (Cai and Cowan 2008).

River red gum (Eucalyptus camaldulensis Dehnh.)

is the dominant floodplain tree along the Murray

River and has the widest natural distribution of all

eucalypts, occupying watercourses throughout

208 S. C. Cunningham and others

mainland Australia (Boland and others 1984).

Qualitative surveys of the health of E. camaldulensis

forests suggest a decline in stand condition has

occurred in the lower Murray River over the past

20 years (Margules and Partners 1990; MDBC

2005b). Here, we present the first work to quantify

the decline of a keystone tree species by building a

robust map of stand condition over a very large

spatial domain (ca 1600 km of river, ca 100,000 ha

of forest) using an extensive ground survey

(N = 140 sites), remotely sensed data, a range of

advanced modelling methods and a predictive val-

idation survey (N = 42 new sites).

METHODS

Study Area

The study area included forests and woodlands of

Eucalyptus camaldulensis in Victoria, Australia, on the

floodplains of the Murray River from the Hume

Dam (36º06¢ S 147º01¢ E) to the South Australian

border (34º01¢ S 141º00¢ E), the lower Ovens River

downstream of Wangaratta and the lower Goulburn

River downstream of Shepparton (Figure 1). A GIS

layer of the current extent of E. camaldulensis on

these floodplains (30 9 30 m pixels) was deter-

mined by cropping a GIS layer of the inferred dis-

tribution of ecological vegetation classes (EVCs)

prior to European settlement (EVC1750) with a

layer of the extant distribution of woody vegetation

(Tree25), and only including EVCs that are domi-

nated by E. camaldulensis. EVCs are a classification of

vegetation types used by land managers in Victoria,

Australia, based on plant communities (for exam-

ple, life form, genera, vegetation structure), geo-

morphology and climate (DNRE 1997). Tree25 is a

layer derived from a combination of digital classifi-

cation and visual interpretation of SPOT panchro-

matic imagery to determine the presence of woody

vegetation. All patches of E. camaldulensis within

15 km of the Murray River in Victoria, the lower

Ovens River or the lower Goulburn River were in-

cluded. The study area comprised 103,550 ha of E.

camaldulensis forest and woodland. This included

open forests (10–30 m tall, 30–45% projective foli-

age cover) and woodlands (10–30 m tall, 20–25%

projective foliage cover (Specht 1981)) with

shrubby, sedgy and grassy understoreys (Margules

and Partners 1990). The climate across this area is

temperate and covers a wide range of annual pre-

cipitation (270–715 mm y-1), annual evaporation

(120–1790 mm y-1), mean annual maximum

temperatures (22.1–24.5�C) and mean annual

minimum temperatures (8.9–15.2�C, BOM 2007).

Site Selection

A random-stratified approach was used to select

sites covering a range of stand conditions. First,

sites were restricted to public land, which provided

an adequate coverage of the study area, to mini-

mize access problems. Second, the area was divided

into the five Bioregions (Victorian Riverina, Upper

Murray Fans, Lower Murray Fans, Robinvale Plains

and Murray Scroll Belt), which are defined by dif-

ferences in climate, geomorphology, lithology and

biodiversity (Environment Australia 2000). This

ensured good spatial coverage of the extensive

survey area and an ability to standardize data

within different ecological units. Third, selection

was restricted within each Bioregion to the pre-

dominant E. camaldulensis communities (EVCs) that

covered the largest area. EVCs were added

sequentially, starting with the EVC with the largest

area, until 65% or more of the total forest area

within the Bioregion was included. This ensured

that both riverine and floodplain stands were se-

lected and, therefore, included stands with a range

of flooding frequencies and potential stand condi-

tions. Finally, sites were selected within each EVC

according to a classification of reflectance values

from Landsat7 imagery from 2004 to ensure a

range of reflectance values and potentially a range

of stand conditions were included.

To classify the Landsat7 imagery from 2004,

10,000 pixels were randomly selected from each

Bioregion using Hawth’s Tools (http://www.spatia-

lecology.com). For each Bioregion, principal com-

ponents analysis (PCA) was used to determine the

relationship of Landsat7 data (six reflectance bands)

among these pixels. PCA loadings from the primary

axis were used to produce a combined reflectance

layer. The reflectance layer was then divided into the

25%, 25–75% and 75% quartiles of reflectance

values to ensure sites with low and high reflectance

values were well represented. GIS layers of the 45

stratification groups [Bioregions (5) 9 EVC (2–

4) 9 reflectance quartile (3)] were produced.

Twenty-eight point locations were selected from

each Bioregion (a total of 140 sites), with equal

numbers of sites randomly selected from each

stratification group. Each point location was at the

center of a 30 9 30 m pixel of the Landsat7 imagery.

Stand Condition Assessment

Previous research showed that percentage live ba-

sal area, plant area index and crown vigor were the

best indicators of condition in E. camaldulensis

stands (Cunningham and others 2007). Percentage

Mapping Vegetation Condition Across Regions 209

live basal area is the percentage of a stand’s basal

area that is live trees. Plant area index (PAI) is the

area of leaves and stems per unit ground area

without adjustment for clumping of canopy com-

ponents. Crown vigor is the percentage of the po-

tential crown, which is determined by the extent of

the existing branching structure that contains foli-

age. Epicormic growth, which was estimated as the

percentage of live foliage that contained epicormic

shoots, was included to discriminate between per-

sistent crown and potentially transient growth re-

sponses to recent watering events. Crown vigor and

epicormic growth were measured from 30 repre-

sentative trees. We measured these indicators in a

survey of 140 sites. Details of measurement pro-

cedures for these indicators are given in Cunning-

ham and others (2007). The condition of stands of

E. camaldulensis was surveyed between June and

October 2006, during the wettest months of the

year when the canopy should be at a maximum. A

0.25 ha plot was established at each point location.

Most plots were 50 9 50 m but rectangular plots

(width = 20–40 m) were used to assess linear

stands along watercourses.

Environmental Predictor Variables

Twenty-five environmental variables were assem-

bled as GIS layers, with a 30 9 30 m pixel resolu-

tion, using ArcInfo (ESRI, Redlands, California).

Environmental variables included mean annual

precipitation, mean annual temperature, combined

radiometric data for thorium and potassium (used

for mapping soil types, Cook and others 1996), a

tree density layer (Tree9) and Landsat7 data. Tree9

was derived from the tree cover layer (Tree25) by

calculating the number of 10 9 10 m pixels in the

3 9 3 array centered on a 30 9 30 m pixel that

contained woody vegetation. Landsat7 data layers,

which included six spectral bands (0.45–0.52, 0.52–

0.60, 0.63–0.69, 0.76–0.90, 1.55–1.75, 2.08–

2.35 lm), were obtained for 1989, 1991, 1992,

1995, 1998, 2000, 2002, 2004 and 2005. GIS layers

of the Normalized Difference Vegetation Index

(NDVI, Rouse and others 1973) were calculated for

each year. Layers of mean values and standard er-

rors were calculated for each band and NDVI from

the above nine years of data. The Landsat7 layers

used in the modelling included the six spectral

bands and NDVI for 2005 (7 layers) and for the

means and standard errors over the nine years of

data (14 layers). Estimates of flooding history were

not included because hydrological models and

digital elevation models with the resolution (sub-

metre) necessary in areas of low topography were

not available for the majority of the floodplain.

Model Building

A two-stage approach was taken to determine how

stand condition assessed during the ground survey

could be best modelled using spatial variation in

the above environmental (predominantly remotely

sensed) data, which had coverage across the study

area. First, artificial neural networks were used to

determine which combination of the condition

indicators was best modelled by the environmental

data and, therefore, was the best stand condition

index to map the floodplain. Second, we deter-

mined whether this stand condition index could be

better modelled from the environmental data by

other advanced modelling methods.

Feed-forward, multilayer perceptron artificial

neural networks learned by a back-propagation

Figure 1. Distribution of

Eucalyptus camaldulensis forests

on the Victorian Murray River

floodplain showing the extent of

the Bioregions (/).

210 S. C. Cunningham and others

algorithm (ANN, Rumelhart and others 1986) were

used to determine the best stand condition indicator

because they are a powerful technique for finding

patterns in ecological data, which rarely meet para-

metric statistical assumptions and commonly in-

volve nonlinear relationships. Artificial neural

networks make no prior assumptions about the form

of relationship between input variables and the dis-

tributions of the data (Ozesmi and others 2006).

An artificial neural network was built for each

condition indicator using the Neural Networks

module within Statistica (StatSoft Inc., Tulsa,

Oklahoma). PAI was standardized by Bioregional

maxima to detrend the natural downstream decline

in PAI owing to the decline in productivity associ-

ated with reduced rainfall and increased evapora-

tion. Compound indices were also calculated to

determine if these were better modelled by the

environmental data than the individual indicators.

A series of compound indices (for example, PAI

+ crown vigor) was calculated by adding indicators

in order of how well they were modelled by the

environmental data. Each condition indicator

contributed a maximum of five points to the total

score of a compound index.

For each condition indicator, the modelling

procedure involved the following steps. Input data

(25 environmental variables) and output data

(condition indices) were standardized to unit

maxima. The dataset was divided into training (70

sites), selection (35 sites) and test data (35 sites).

The training data were used to build the artificial

neural network, the selection data were used to

check for over-fitting (that is, the model is too

specific to the idiosyncrasies of the training data)

and the test data were used to test the final fit of the

model. Models were built from 200 random starts

and the 50 models with the best statistical fit

(highest R2 values) were retained. Selection of the

best model was based on parsimony, with a model

being considered the best fit when increasing

numbers of variables did not increase the amount

of variance explained by more than 2%. An upper

limit of 14 variables was set because it is recom-

mended to have at least ten times more samples

than there are variables to avoid overly complex

models (Burham and Anderson 2002).

In the past decade, modelling approaches based

on machine learning developed by computer sci-

entists, including artificial neural networks, have

been used successfully to predict the nonlinearities

and interactions of ecological data (for example,

Leathwick and others 2005; Moisen and others

2006). Three of these recent modelling advances

were used to assess if they could better model the

chosen stand condition index from the environ-

mental variables than by artificial neural networks.

First, boosted regression trees (BRT) were used be-

cause these select relevant environmental variables

and can also model interactions among variables.

BRTs overcome the inherent inaccuracies in seek-

ing a single parsimonious model by constructing an

ensemble of regression trees, which relate values of

a response (leaves) to its predictors through a series

of binary decisions or branches (Friedman 2001).

Second, Bayesian additive regression trees (BART)

modelling was used, which uses Bayesian model

averaging to determine the average regression tree.

BART has the qualities of BRT but also allows full

assessment of prediction uncertainty, and is

thought to be effective at finding simple models to

explain multivariate data (Chipman and others

2006). Finally, multivariate adaptive regression

spline (MARS) modelling combines linear regres-

sion, construction of splines and binary, recursive

partitioning to fit nonlinear functions with a series

of linear segments. MARS is related to regression

methods, such as generalized additive models, but

allows quicker fitting and simultaneous analysis of

multiple responses (Friedman 1991).

Model Validation

Given that internal validation procedures generally

are optimistic (Mac Nally 2000), a predictive vali-

dation survey was performed in April 2007 to assess

the ability of the models to predict stand condition

beyond the original survey sites. We chose to vali-

date the model in stands from two contrasting areas:

Gunbower Island in the Middle Murray (average

rainfall of 395 mm y-1, 36o45¢ S 144o15¢ E) and

Wallpolla Island in the Lower Murray (285 mm y-1,

34o15¢ S 141o45¢ E). The artificial neural network

model, which predicts stand condition scores (SCS)

calculated from PAI, percentage live basal area and

crown vigor, was used to produce stand condition

maps for these two areas. Each condition indicator

contributed up to five points to a total score of 15

points. These maps were classified into areas of high

(SCS = 10.1–15.0), medium (SCS = 5.1–10.0) and

low (SCS = 0.2–5.0) conditions using ArcGIS (ESRI,

Redlands, California). In each area, seven sites were

randomly selected from each condition class using

Hawth’s Tools, giving 42 validation sites across the

two areas. The condition assessment procedure was

the same as the original survey except for the

exclusion of epicormic growth due to its poor pre-

dictive power. Eighteen sites from the original sur-

vey were resurveyed, with nine sites in each region,

to determine if the stand condition had declined

Mapping Vegetation Condition Across Regions 211

during the six months since the original survey. The

resurveyed sites were chosen to cover the full range

of stand condition within an area.

Model Evaluation

We evaluated and compared model fit using con-

ventional and unconventional R2 relating modelled

to observed stand conditions. This evaluation was

done for four sets of models: (1) the artificial neural

networks built for the various condition indicators,

(2) the four models of the stand condition score

(ANN, BART, BRT, MARS) for the model-building

data, (3) the same four models but for resurveyed

data and (4) the same four models for the valida-

tion sites (previously unmeasured). For each of the

datasets, we fitted conventional (‘‘free’’) linear

regressions: Pi = a + bOi, where P and O are the

predicted and observed values, a is the intercept

and b is the slope. Model fit can be computed using

the conventional R2 approach (sums of deviations

from fitted values divided by sums of deviations

from �P). However, in the most useful models,

a ” 0 and b ” 1 (that is, there is perfect agree-

ment between the observed and predicted values, P

and O). Therefore, we also constrained regression

parameters to these values and recomputed pseu-

do-R2 values, which need not lie between 0 and 1

but will be very poor fits for values of 0 or below.

We fitted free and constrained regressions using

Bayesian models in the WinBUGS software pack-

age (Spiegelhalter and others 2003). Regression

parameters were assigned uninformative normal

priors for the free regressions, for which we com-

puted conventional R2 values. For the constrained

regressions, we assigned high-precision (SD =

0.001) priors with mean a ” 0 and mean b ” 1

and computed the pseudo-R2 values. Within each

set of comparisons, we simultaneously fitted the

four model regressions (all share the same O-values)

and constructed 95% credible intervals of differ-

ences in model fit (either R2 or pseudo-R2 values).

These were then used to rank the relativities among

models in relation to their fits.

Map Analysis

The artificial neural network that predicted stand

condition score was used because of its superior

performance (see Results) to produce a stand con-

dition map for E. camaldulensis forests of the Vic-

torian Murray River floodplain. Stand condition

scores of less than 0.2 were excluded from analysis

because they were considered to be noise and often

coincided with the edges of water bodies. For

analysis, the map was classified into five condition

classes: good (SCS = 12.1–15.0), declining (SCS

= 9.1–12.0), poor (SCS = 6.1–9.0), degraded (SCS

= 3.1–6.0) and severely degraded (SCS = 0.2–3.0)

using ArcGIS.

Percentage of the total area included in each

condition class was determined from the number of

pixels in each class. The resurveyed and validation

datasets were combined (N = 50) to determine the

field accuracy of model predictions. Bayesian

models were used to determine the 25–75% cred-

ible interval for the predictions of stand condition.

The equations for the 25% and 75% credible limits

were used to produce the ‘worst case’ and ‘best

case’ predictions for stand condition across the

study area.

Multiple linear regression was used to determine

if stand condition was related to water availability.

It was assumed that water availability would be

related to rainfall and flooding frequency, which

decreases with distance downstream and is likely to

decline with distance from permanent water. We

used ArcGIS to create (1) a ‘permanent water’ layer

from a layer that contained all water bodies shown

on 1:100,000 topographic maps and (2) a distance

from the Hume Dam layer. A random selection of

25,000 points from the study area was created

using Hawth’s Tools and values for the condition

index, distance from the Hume Dam and distance

from permanent water were extracted for each

point. Multiple linear regression was performed on

this dataset, with stand condition score as the re-

sponse variable, and distance from the Hume Dam,

distance from permanent water and the interaction

between these variables as potential predictor

variables.

RESULTS

Modelling

The 140 sites selected using the random-stratified

approach covered a representative range of stand

condition. Stand condition was negatively skewed

across the study area, with half of the sites having

more than 90% live basal area, a third of stands

having 80–100% of their potential crown and two-

thirds of stands having less than 11% epicormic

growth in their crown. Values of plant area index

(PAI) were normally distributed between 0 and

2 m2 m-2.

The four stand condition indicators were mod-

elled using artificial neural networks (ANN) with

different degrees of success using the 25 environ-

mental variables (Table 1). The best ANN among

the single-indicator models was for the PAI data

212 S. C. Cunningham and others

(R2 = 0.72). The PAI model used six environmental

variables and the variable contributing most to this

model was the Normalized Difference Vegetation

Index for 2005. In contrast, the best ANN for epi-

cormic growth used 13 environmental variables

and could not fit the data (R2 = 0.09). Stand con-

dition indices, which were calculated from several

stand condition indicators, were more reliably

modelled by the environmental variables than the

individual stand condition indicators (Table 1). The

best ANN (R2 = 0.77) was for a stand condition

index calculated from PAI, crown vigor and per-

centage live basal area. This model was found to be

most sensitive to reflectance in the near infrared

(0.76–0.90 lm) and far infrared (2.08–2.35 lm)

measured during the most recent year of available

data, 2005. The inclusion of epicormic growth in a

compound index using all stand condition indica-

tors resulted in a poorer model (R2 = 0.72).

Regression parameters for predicted and ob-

served condition for the model-building dataset

suggest that all four modelling methods fitted the

data well (R2 = 0.77–0.85), with multivariate

adaptive regression splines (MARS) fitting best and

ANN worst (Table 2). However, all models deviated

substantially from a perfect agreement between

observed and predicted values (a = 0 and b = 1),

with slopes well below unity. When regression

parameters were constrained (pseudo-R2), the

performance of ANN increased markedly relative to

Bayesian additive regression trees (BART) and

boosted regression trees (BRT). For resurvey sites,

MARS again performed better than the other three

modelling approaches for both free and uncon-

strained regressions (Table 2). Both MARS and

ANN had slopes exceeding unity (with inter-

cepts <0) whereas slopes for BART and BRT fell

well below 1.

Given the success of MARS with model-building

and resurvey data, a surprising outcome was the

poor performance of MARS (and BART, BRT) for

predicting conditions of new sites—slopes for

MARS, BART and BRT were less than 0.4 and

intercepts exceeded 5.8 (Table 2). ANN performed

exceedingly well for the new sites, with R2 similar

to the initial model-building data (ca 0.78), the

pseudo-R2 being similar to the free regression, the

slope being close to unity and the intercept close to

zero.

Map Characteristics

The artificial neural network model that predicted

stand condition score was used to produce a stand

condition map across the whole Victorian Murray

River floodplain (Figure 2). Any interpretation of

the map is contingent on two qualifications. First,

the map has been classified into five condition

classes and all classes other than good condition

should be considered suboptimal. Second, the map

does not distinguish, particularly downstream of

Robinvale, between areas that have poor condition

due to altered watering regimes and those that may

be in poor condition due to the natural limitations

of the environment (for example, infrequent

flooding, low rainfall, low soil nutrients).

The map predicted that across the Victorian

Murray River Floodplain only 30.1% of E. camal-

dulensis stands are currently in good condition

(Table 3). The remaining stands are in declining

Table 1. Results of Linear Regression Analyses of ‘Predicted’ Stand Condition Against ‘Observed’ Conditionfor Artificial Neural Network Models Relating Stand Condition Indicators and Compound Indices to Envi-ronmental Variables

Model Intercept Slope R2 Pseudo-R2*

Condition indicators

PAI 0.28 ± 0.04 0.71 ± 0.04 0.720 ± 0.023 0.601 ± 0.031

V 1.31 ± 0.16 0.66 ± 0.04 0.675 ± 0.006 0.500 ± 0.005

LBA 0.15 ± 0.06 0.85 ± 0.07 0.654 ± 0.051 0.632 ± 0.052

E 0.67 ± 0.03 0.16 ± 0.04 0.086 ± 0.136 -3.287 ± 0.620

Compound indices

PAI + V 1.81 ± 0.24 0.73 ± 0.04 0.744 ± 0.004 0.639 ± 0.001

PAI + V + LBA 2.55 ± 0.38 0.75 ± 0.04 0.771 ± 0.003 0.689 ± 0.002

PAI + V + LBA + E 3.67 ± 0.56 0.73 ± 0.04 0.716 ± 0.004 0.623 ± 0.003

Relativity PVL > PV > PAI

� PVLE > LBA � V > E

PVL > PV � LBA

> PVLE > PAI > V > E

Condition indicators included plant area index (PAI), crown vigor (V), % live basal area (LBA) and epicormic growth (E). Values are means ± standard deviations. SeeMethods for explanation of pseudo-R2. *Intercept constrained to 0 and slope constrained to 1.

Mapping Vegetation Condition Across Regions 213

(53.5%), poor (10.6%), degraded (4.1%) or se-

verely degraded (1.7%) condition. The ‘worst case’

prediction (25% credible limit) for stand condition

is that no stands are in good condition and that the

majority are declining (73.5%) whereas the ‘best

case’ prediction (75% credible limit) for stand

condition is that 60.2% of stands are in good con-

dition.

There is a general trend of decreasing stand

condition with increasing distance downstream of

the Hume Dam (Figure 2). In the Middle Murray,

only 22% of stands in Barmah Forest are in good

condition, 22% are declining and 3% are in poor

condition (Figure 2A). In Barmah Forest, good

condition stands are generally close to the Murray

River whereas declining and poor stands are fur-

ther inland on the floodplain. In the Lower Mur-

ray, 21% of stands on Lindsay Island are in good

condition, 45% are declining and 34% are poor to

severely degraded (Figure 2B). In this region,

stands are naturally close to the river and ephem-

eral creeks due to the lower frequency of floods

compared to upstream. Good condition stands tend

to be restricted to the river channel on Lindsay Is-

land, whereas declining to severely degraded stands

are found along the river channel and anabranches

and on the floodplain.

Multiple linear regression of predicted stand

condition against distance from the Hume Dam and

distance from permanent water found a weak

relationship (F = 6816, P < 0.001, N = 25 000,

R2 = 0.35) among these variables:

Stand condition ¼15:09 � 0:02� 0:01 � 0:00

Hume� 0:36 � 0:01water

where Stand condition is on a 15-point scale, Hume is

the linear distance from the Hume Dam (km), water

is the distance from permanent water (km) and

uncertainties are standard errors.

DISCUSSION

Modelling

Condition of E. camaldulensis stands was modelled

successfully from remotely sensed and spatially

modelled (for example, rainfall) environmental

variables using artificial neural networks. Plant area

index (PAI) was the condition indicator best mod-

elled by the environmental variables (Table 1). This

is not surprising because the model used the Nor-

malized Difference Vegetation Index (NDVI), which

is a measure of the amount of vegetation, and PAI,

Table 2. Results of Linear Regression Analyses of ‘Predicted’ Stand Condition Against ‘Observed’ Conditionfor the Four Models of the Original Survey, Resurvey and Validation Datasets

Model Intercept Slope R2 Pseudo-R2*

Original survey (model building, N = 140 sites)

ANN 2.54 ± 0.38 0.75 ± 0.03 0.771 ± 0.003 0.690 ± 0.002

BART 3.38 ± 0.32 0.67 ± 0.03 0.798 ± 0.003 0.612 ± 0.003

BRT 3.13 ± 0.32 0.70 ± 0.03 0.806 ± 0.003 0.658 ± 0.003

MARS 2.02 ± 0.31 0.80 ± 0.03 0.846 ± 0.002 0.797 ± 0.002

Relativity MARS > BRT

> BART > ANN

MARS > ANN

> BRT > BART

Resurvey (N = 18 resurveyed sites)

ANN -2.75 ± 1.87 1.22 ± 0.19 0.691 ± 0.039 0.678 ± 0.001

BART 1.63 ± 1.03 0.79 ± 0.11 0.744 ± 0.032 0.702 ± 0.004

BRT 2.30 ± 1.35 0.74 ± 0.14 0.607 ± 0.047 0.564 ± 0.003

MARS -1.27 ± 1.14 1.08 ± 0.12 0.824 ± 0.022 0.817 ± 0.002

Relativity MARS > BART

> ANN > BRT

MARS > BART

> ANN > BRT

Validation survey (N = 42 new sites)

ANN 0.62 ± 0.63 0.88 ± 0.07 0.782 ± 0.011 0.771 ± 0.002

BART 5.87 ± 0.48 0.38 ± 0.05 0.546 ± 0.023 -1.115 ± 0.007

BRT 6.29 ± 0.52 0.35 ± 0.06 0.456 ± 0.027 -1.461 ± 0.007

MARS 6.01 ± 0.76 0.36 ± 0.08 0.278 ± 0.035 -0.772 ± 0.004

Relativity ANN � MARS

> BART > BRT

ANN � MARS

> BART > BRT

Modelling methods included artificial neural networks (ANN), Bayesian additive regression trees (BART), boosted regression trees (BRT), and multivariate adaptive regressionsplines (MARS). Values are means ± standard deviations. See Methods for explanation of pseudo-R2. *Intercept constrained to 0 and slope constrained to 1.

214 S. C. Cunningham and others

Figure 2. Condition map for Eucalyptus camaldulensis stands along the Victorian Murray River floodplain predicted from

the neural network model, with insets from (A) Lindsay Island and (B) Barmah Forest. Permanent water bodies are

indicated in blue.

Mapping Vegetation Condition Across Regions 215

which is similar to leaf area index, is a measure of

the amount of canopy. Leaf area index of forests has

been modelled successfully (R2 = 0.7–0.9) from re-

motely sensed data (for example, Coops and others

1997; Heiskanen 2006) but success differs widely

among forest types (R2 = 0.5–0.8, for example,

Eklundh and others 2003). Epicormic growth was

not modelled well by the environmental variables

(R2 = 0.23) because it was unrelated to stand con-

dition (r = -0.06, P = 0.48, N = 140). Epicormic

growth is an indicator of recent stress in eucalypts,

so it appears that many stands of good condition are

experiencing water stress that is mitigated by

watering events (rainfall or flooding) or other

environmental stresses such as structural damage,

disease or insect defoliation (Burrows 2002). The

best model of stand condition of E. camaldulensis was

built using a condition index calculated from three

indicators (PAI, crown vigor and percentage live

basal area) previously shown to be reliable, objec-

tive indicators of stand condition (Cunningham and

others 2007). Reflectance in the near infrared and

far infrared measured during 2005, which are sen-

sitive to vegetation structure and vegetation mois-

ture, respectively, was found to explain the most

variance in this model of stand condition.

Stand condition, estimated using the above in-

dex, was modelled from the environmental vari-

ables with varying success using a range of

contemporary statistical methods (Table 2). Multi-

variate adaptive regression splines (MARS) were

marginally best at modelling the original stand

condition data. Recent statistical methods (for

example, MARS and artificial neural networks)

have been shown to outperform more commonly

used modelling methods (for example, logistic

regression and generalized additive models) for

predicting species distributions (Elith and others

2006; Munoz and Felicisimo 2004). However, dif-

ferences among contemporary methods such as

those used here (MARS, artificial neural networks

and boosted regression trees) are often small (for

example, Elith and others 2006; Moisen and Fres-

cino 2002).

The real test of predictive power is how well a

model predicts samples beyond the original dataset,

especially samples collected after model construc-

tion (Mac Nally and Fleishman 2004). The artificial

neural network was a much better predictor of

stand condition in the validation dataset than the

other modelling methods. We found that models

with marginally better fits of the original survey

data (for example, MARS) were substantially worse

at predicting stand condition in the validation

survey (Table 2). This emphasizes the importance

of validating models with predictive surveys. This

suggests all methods besides artificial neural net-

works were over-fitting the models by incorporat-

ing information representative of all stands as well

as idiosyncrasies of the original survey data. It was

surprising that artificial neural network modelling

had the highest predictive power because one of its

major criticisms is over-fitting datasets (Paruelo and

Tomasel 1997). Over-fitting may have been pre-

vented here by restricting the number of predictor

variables to a tenth of the number of samples and

by the use of the quasi-independent ‘selection’

dataset, which was from the original survey data

but only used to test the model and not to build the

model.

To our knowledge, this stand condition model for

E. camaldulensis is the only forest dieback model to

have been rigorously validated. The majority of

forest dieback models are based on single surveys,

which often have few sample plots (n < 20, for

example, Hall and others 2003), and some were not

tested against field measurements (for example,

Maselli 2004). Although many forest diebacks have

been modelled successfully using remotely sensed

data (for example, Pontius and others 2005), the

predictive power of these models cannot be known

without a subsequent survey. We are very confi-

dent of the predictions of our stand condition

model for E. camaldulensis due to the high predictive

Table 3. Predictions of the Artificial Neural Network for Stand Condition Across the Victorian MurrayFloodplain

Stand condition Mean

prediction (%)

Worst case

(25% CI)

Best case

(75% CI)

Good 30.1 0 60.2

Declining 53.5 73.5 31.1

Poor 10.6 18.4 7.1

Degraded 4.1 6.0 1.7

Severely degraded 1.7 2.1 0

CI = credible interval.

216 S. C. Cunningham and others

power (R2 = 0.78) shown by the validation con-

ducted in new sites.

Map Characteristics

The stand condition map predicts that only 30.1%

of E. camaldulensis stands are currently in good

condition along the Victorian Murray River flood-

plain (Figure 2, Table 3). Past surveys of the con-

dition of E. camaldulensis forests have shown an

apparently substantial decline in stand condition in

the lower Murray River (downstream of Swan Hill)

over the past 20 years (Margules and Partners

1990; MDBC 2005b). These surveys used qualita-

tive assessments of crown condition from aerial

photographs and field observations at point loca-

tions. Our study is the first rigorous quantifica-

tion—stand condition assessed by an extensive,

quantitative survey and predicted by a validated

model—of the extent of the crisis faced by this

floodplain ecosystem.

The cause of the decline in condition of E. cam-

aldulensis stands along the Victorian Murray River

is complex but is ultimately due to river regulation.

The biomass of E. camaldulensis forests is consider-

ably higher than would be predicted from the

rainfall of their distribution, suggesting the forests

are dependent on additional water from ground-

water and surface flooding. This is supported by the

increased growth rate of E. camaldulensis after

flooding (Bacon and others 1993) and the pre-

dominant use of groundwater by trees growing

next to ephemeral streams or on floodplains

(Mensforth and others 1994; Thorburn and others

1993). Regulation of flows along the Murray River

has decreased the frequency and extent of floods,

which has lowered groundwater depths and re-

duced leaching of salts drawn up from naturally

saline groundwater, and has raised river levels near

dams, which has consequently raised adjacent

groundwater depths (Jolly 1996). Declines in con-

dition of E. camaldulensis have been observed in

association with these declines in water availability

and quality (DEH 2005; MDBC 2005b).

The map shows a downstream decline in stand

condition (that is, condition decreases from east to

west) of E. camaldulensis forests and woodlands

(Figure 2). This trends supports previous observa-

tions of substantial declines in stand condition in

the Lower Murray (for example, Margules and

Partners 1990). The downstream decline in stand

condition probably is related to the larger declines

in flooding frequency in the Lower Murray (from

76 to 35 y century-1) compared with the Middle

Murray (from 92 to 57 y century-1, MDBC 2005a).

The substantially lower rainfall (270 mm y-1) and

higher evaporation (1790 mm y-1) in the Lower

Murray compared with upstream areas (ca

700 mm y-1 and 1400 mm y-1, respectively)

exacerbates water stress in stands of this region.

Climate change is predicted to further reduce water

availability by raising temperatures (1–1.5�C and

consequently increasing evaporative demand) and

decreasing annual rainfall (2–5%) in southeastern

Australia by 2030 (CSIRO, BOM 2007). Therefore,

the substantial declines currently observed in the

Lower Murray may progress upstream in the near

future as flooding frequencies are further reduced

by climate change.

In many areas, the map shows that good condi-

tion stands are found near permanent water (for

example, Murray River, Figure 2A, B). A weak

relationship was found between predicted stand

condition and distance downstream from the Hume

Dam and distance from permanent water. Al-

though these two variables probably are good

indicators of flooding frequency, the relationship

explained only 35% of the predicted variation in

stand condition. At the local scale, stand condition

is also likely to be related to other variables such as

topography, soil type, soil salinity, groundwater

depth and salinity, and disturbance history (for

example, logging and grazing).

The map suggests that current watering regimes

(rainfall and flooding) are insufficient to maintain

the majority of E. camaldulensis stands in good

condition. If the condition of these forests is to be

maintained and potentially improved, the fre-

quency of floods needs to approach that of unreg-

ulated rivers. In recent years, there have been

artificial floods through overbank pumping and

opening regulators in limited areas. Our map pro-

vides a tool for land managers to prioritize available

water allocation and to monitor success of watering

programs.

We have developed a powerful tool for mapping

stand condition over very large areas, but we need

to establish the process-based links to stand health

(for example, physiological stress, productivity,

nutrient cycling). We are also in an excellent po-

sition to establish whether stand condition is con-

sistently linked to effects on biodiversity. Are biotic

components consistently altered in stands of com-

parable condition? Poor condition clearly is asso-

ciated with reduced leaf area, which may affect

occurrence of herbivores and understorey plants.

Other processes such as flowering may also be re-

duced, affecting recruitment and capacity to sustain

populations of nectar-feeding animals. If such links

can be established, then remotely sensed data,

Mapping Vegetation Condition Across Regions 217

which provides knowledge of vegetation condition,

may also provide important information on biodi-

versity status.

CONCLUSIONS

Our study shows that stand condition can be

mapped accurately over the floodplain of a major

river system (ca 100,000 ha). It reinforces the value

of using ground surveys in conjunction with re-

motely sensed data to model vegetation condition

across landscapes and regions. It demonstrates the

value of using several modelling approaches to

determine relationships between vegetation re-

sponses and environmental variables. The study

also illustrates the importance of validating eco-

logical models with a predictive survey, which is

rarely done (for example, Fleishman and others

2002). If the health of ecosystems is to be moni-

tored and managed effectively across large geo-

graphic areas, such rigorous modelling approaches

are necessary.

ACKNOWLEDGEMENTS

This research was funded by an ARC linkage Grant

(LP0560518, which was partially funded by the

Victorian Department of Sustainability and Envi-

ronment (DSE) and four Catchment Management

Authorities (Mallee CMA, North Central CMA,

Goulburn-Broken CMA, and North East CMA)).

We thank Rachael Nolan for assistance with field-

work. Environmental data layers were supplied by

the DSE’s Corporate Geospatial Data Library and

the Australian Greenhouse Office (Landsat7 data).

This is publication No. 146 from the Australian

Centre for Biodiversity: Analysis, Policy and Man-

agement.

REFERENCES

Bacon PE, Stone C, Binns DL, Leslie DJ, Edwards DW. 1993.

Relationships between water availability and Eucalyptus cam-

aldulensis growth in a riparian forest. J Hydrol 150:541–61.

Boland DJ, Brooker MIH, Chippendale GM, Hall N, Hyland

BPM, Johnston RD, Kleinig DA, Turner JD. 1984. Forest trees

of Australia. Melbourne: CSIRO Publications, p 687.

BOM. 2007. Climate data online: Australian Government Bu-

reau of Meteorology. http://www.bom.gov.au/climate/avera-

ges/. Accessed 24 Oct 2007.

Bonneau LR, Shields KS, Civco DL. 1999. A technique to iden-

tify changes in hemlock forest health over space and time

using satellite image data. Biol Invasions 1:269–79.

Bourque CPA, Cox RM, Allen DJ, Arp PA, Meng FR. 2005.

Spatial extent of winter thaw events in eastern North Amer-

ica: historical weather records in relation to yellow birch de-

cline. Glob Change Biol 11:1477–92.

Burham KP, Anderson DR. 2002. Model selection and multi-

model inference: a practical information-theoretic approach.

New York: Springer, p 488.

Burrows GE. 2002. Epicormic strand structure in Angophora,

Eucalyptus and Lophostemon (Myrtaceae)—implications for fire

resistance and recovery. New Phytol 153:111–31.

Busch DE, Smith SD. 1995. Mechanisms associated with decline

of woody species in riparian ecosystems of the southwestern

United States. Ecol Monogr 65:347–70.

Cai W, Cowan T. 2008. Evidence of impacts from rising tem-

perature on inflows to the Murray-Darling Basin. Geophys

Res Lett 35:L07701.

Chipman HA, Geogre EI, McCulloch RE. 2006. BART: Bayesian

Additive Regression Trees. Chicago: Graduate School of

Business, University of Chicago, p 31.

Cook SE, Corner RJ, Groves PR, Grealish GJ. 1996. Use of air-

borne gamma radiometric data for soil mapping. Aust J Soil

Res 34:183–94.

Coops N, Delahaye A, Pook E. 1997. Estimation of eucalypt

forest leaf area index on the South Coast of New South Wales

using Landstat MSS data. Aust J Bot 45:757–69.

Coops NC, Stone C, Culvenor DS, Chisholm L. 2004. Assessment

of crown condition in eucalypt vegetation by remotely sensed

optical indices. J Environ Qual 33:956–64.

CSIRO, BOM. 2007. Climate change in Australia. Melbourne:

CSIRO, p 148.

Cunningham SC, Read J, Baker PJ, Mac Nally R. 2007.

Quantitative assessment of stand condition and its relation-

ship to physiological stress in stands of Eucalyptus camaldul-

ensis (Myrtaceae) in southeastern Australia. Aust J Bot

55:692–9.

Czerniakowski B, Crnov R, Smith IW, Luck JE. 2006. Soil

properties associated with the tree decline ‘Mundulla Yel-

lows’. Plant Soil 285:197–206.

DEH. 2005. Floristic vegetation and tree health mapping, River

Murray floodplain, South Australia. Adelaide: South Austra-

lian Government Department of Environment and Heritage.

Diem JE. 2002. Remote assessment of forest health in southern

Arizona, USA: evidence for ozone-induced foliar injury.

Environ Manage 29:373–84.

DNRE. 1997. Victoria’s biodiversity: directions in management.

Melbourne: Victorian Department of Natural Resources and

Environment.

Eklundh L, Hall K, Eriksson H, Ardo J, Pilesjo P. 2003. Investi-

gating the use of Landsat thematic mapper data for estimation

of forest leaf area index in southern Sweden. Can J Remote

Sens 29:349–62.

Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A,

Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J,

Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M,

Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richard-

son K, Scachetti-Pereira R, Schapire RE, Soberon J, Williams

S, Wisz MS, Zimmermann NE. 2006. Novel methods improve

prediction of species’ distributions from occurrence data.

Ecography 29:129–51.

Environment Australia. 2000. Revision of the Interim Biogeo-

graphic Regionalisation of Australia (IBRA) and the develop-

ment of version 5.1—summary report. Canberra: Department

of Environment and Heritage. p 37.

Ferretti M. 1997. Forest health assessment and monitor-

ing—issues for consideration. Environ Monit Assess 48:45–72.

218 S. C. Cunningham and others

Fleishman E, Mac Nally R, Fay JP. 2002. Validation tests of

predictive models of butterfly occurrence based on environ-

mental variables. Conserv Biol 17:806–17.

Friedman JH. 1991. Multivariate adaptive regression splines.

Ann Stat 19:1–141.

Friedman JH. 2001. Greedy function approximation: a gradient

boosting machine. Ann Stat 29:1189–232.

Hall RJ, Fernandes RA, Hogg EH, Brandt JP, Butson C, Case BS,

Leblanc SG. 2003. Relating aspen defoliation to changes in leaf

area derived from field and satellite remote sensing data. Can

J Remote Sens 29:299–313.

Heiskanen J. 2006. Estimating aboveground tree biomass and

leaf area index in a mountain birch forest using ASTER sa-

tellite data. Int J Remote Sens 27:1135–58.

Huettl RF, Mueller-Dombois D. 1993. Forest decline in the

Atlantic and Pacific region. Berlin: Springer-Verlag, p 366.

Innes JL. 1990. Assessment of tree condition. London: Her

Majesty’s Stationery Office, p 96.

Jolly ID. 1996. The effects of river management on the hydrol-

ogy and hydroecology of arid and semi-arid floodplains. In:

Anderson MG, Walling DE, Bates PD, Eds. Floodplain pro-

cesses. Chichester: John Wiley and Sons. p 577–609.

Leathwick JR, Rowe D, Richardson J, Elith J, Hastie T. 2005.

Using multivariate adaptive regression splines to predict the

distribution of New Zealand’s freshwater diadromous fish.

Freshw Biol 50:2034–52.

Mac Nally R. 2000. Regression and model-building in conser-

vation biology, biogeography and ecology: the distinction

between—and reconciliation of—‘predictive’ and explana-

tory’ models. Biodivers Conserv 9:655–71.

Mac Nally R, Fleishman E. 2004. A successful predictive model

of species richness using ‘indicator’ species. Conserv Biol

18:646–54.

Margules and Partners. 1990. Riparian vegetation of the River

Murray. Report prepared by Margules and Partners Pty. Ltd.,

P. & J. Smith Ecological Consultants and Department of

Conservation Forests and Lands. Canberra: Murray-Darling

Basin Commission. p 187.

Maselli F. 2004. Monitoring forest conditions in a protected

Mediterranean coastal area by the analysis of multiyear NDVI

data. Remote Sens Environ 89:423–33.

McLaughlin S, Percy K. 1999. Forest health in North America:

some perspectives on actual and potential roles of climate and

air pollution. Water Air Soil Pollut 116:151–97.

MDBC. 2005a. The Living Murray Foundation Report on the

significant ecological assets targeted in the First Step Decision.

Canberra: Murray-Darling Basin Commission, p 327.

MDBC. 2005b. Survey of River Red Gum and Black Box Health

along the River Murray in New South Wales, Victoria and

South Australia—2004. Canberra: Murray-Darling Basin

Commission, p 24.

Mensforth LJ, Thorburn PJ, Tyerman SD, Walker GR. 1994.

Sources of water used by riparian Eucalyptus camaldulensis

overlying highly saline groundwater. Oecologia 100:21–8.

Moisen GG, Frescino TS. 2002. Comparing five modelling

techniques for predicting forest characteristics. Ecol Model

157:209–25.

Moisen GG, Freeman EA, Blackard JA, Frescino TS, Zimmer-

mann NE, Edwards TCJ. 2006. Predicting tree species presence

and basal area in Utah: a comparison of stochastic gradient

boosting, generalized additive models, and tree-based meth-

ods. Ecol Model 199:176–87.

Mueller-Dombois D. 2006. Long-term rain forest succession and

landscape change in Hawai’i: the ‘Maui Forest Trouble’

revisited. J Veg Sci 17:685–92.

Munoz J, Felicisimo AM. 2004. Comparison of statistical meth-

ods commonly used in predictive modelling. J Veg Sci 15:285–

92.

Nemani R, Pierce L, Running S, Band L. 1993. Forest ecosystem

processes at the watershed scale—sensitivity to remotely-

sensed leaf-area index estimates. Int J Remote Sens 14:2519–

34.

Noss RF. 1999. Assessing and monitoring forest biodiversity: a

suggested framework and indicators. For Ecol Manag

115:135–46.

Ohrui K, Mitchell MJ. 1997. Nitrogen saturation in Japanese

forested watersheds. Ecol Appl 7:391–401.

Ozesmi SL, Tan CO, Ozesmi U. 2006. Methodological issues in

building, training and testing artificial neural networks in

ecological applications. Ecol Model 195:83–93.

Parkes D, Newell G, Cheal D. 2003. Assessing the quality of

native vegetation: the ‘habitat hectares’ approach. Ecol

Manage Restoration 4:S29–38.

Paruelo JM, Tomasel F. 1997. Prediction of functional charac-

teristics of ecosystems: a comparison of artificial neural net-

works and regression models. Ecol Model 98:173–86.

Pontius J, Hallett R, Martin M. 2005. Using AVIRIS to assess

hemlock abundance and early decline in the Catskills, New

York. Remote Sens Environ 97:163–73.

Rouse JW, Haas RH, Schell JA, Deering DW. 1973. Monitoring

vegetation systems in the Great Plains with ERTS. In: Third

Earth Resources Technology Satellite-1 Symposium, 10–14

December 1973. Washington, DC, United States of America:

NASA. p 309–17.

Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning repre-

sentations by back-propagating errors. Nature 323:533–6.

Sala O, Chapin F, Armesto J, Berlow E, Bloomfield J, Dirzo R,

Huber-Sanwald E, Huenneke L, Jackson R, Kinzig A, Leemans

R, Lodge D, Mooney H, Oesterheld M, Poff N, Sykes M,

Walker B, Walker M, Wall D. 2000. Global biodiversity sce-

narios for the year 2100. Science 287:1770–4.

Saunders DA, Hobbs RJ, Margules CR. 1991. Biological conse-

quences of ecosystem fragmentation: a review. Conserv Biol

5:18–32.

Spiegelhalter D, Thomas A, Best N. 2003. WinBUGS version 1.4

user manual. Bayesian inference using Gibbs sampling.

Cambridge: MRC Biostatistics Unit, Institute for Public Health.

USDA Forest Service. 2004. Forest Inventory and Analysis South-

ern Research Station Field Guide. Version 1.71, Phase 3. Volume

2: field data collection procedures for Phase 3 plots. Washington:

United States Department of Agriculture, Forest Service.

Specht RL. 1981. Major vegetation formations in Australia. In:

Keast A, Ed. Ecological biogeography of Australia. The Haag:

Junk. p 163–298.

Thomson J, Mac Nally R, Fleishman E, Horrocks G. 2007. Pre-

dicting bird species distributions in reconstructed landscapes.

Conserv Biol 21:752–66.

Thorburn PJ, Hatton TJ, Walker GR. 1993. Combining mea-

surements of transpiration and stable isotopes of water to

determine groundwater discharge from forests. J Hydrol

150:563–87.

Tockner K, Stanford JA. 2002. Riverine flood plains: present

state and future trends. Environ Conserv 29:308–30.

Mapping Vegetation Condition Across Regions 219