RESEARCH ARTICLE

Highlighting order and disorder in social–ecologicallandscapes to foster adaptive capacity and sustainability

Giovanni Zurlini • Irene Petrosillo •

K. Bruce Jones • Nicola Zaccarelli

Received: 11 December 2011 / Accepted: 21 May 2012

� Springer Science+Business Media B.V. 2012

Abstract Landscape sustainability can be consid-

ered in terms of order and disorder, where order

implies causality, well-defined boundaries and pre-

dictable outcomes, while disorder implies uncertain

causality, shifting boundaries and often-unpredictable

outcomes. We address the interplay of order and

disorder in social–ecological landscapes (SELs) using

spatiotemporal analysis of entropy-related indices of

Normalized Difference Vegetation Index time-series.

These indices can provide insights for complex

systems analysis for the evaluation of adaptive

capacity in SELs. In particular, our overarching aim

is to help interpret what an increase of order/disorder

means with regards to SELs and the underlying drivers

and causes of conditions in SELs. The approach can be

used to increase spatially explicit anticipatory capa-

bility in environmental science and natural resource

management based on how the system has responded

to stress in the past. Such capability is crucial to

address SEL adaptive capacity and for sustainable

planning given that surprises may increase as a

consequence of both climate change and multiple

interacting anthropogenic stressors. These advance-

ments should greatly contribute to the application of

spatial resilience strategies in general, and to sustain-

able landscape planning in particular, and for the

spatially explicit adaptive comanagement of ecosys-

tem services.

Keywords Spectral entropy � Order and disorder �Adaptive capacity � Sustainability �NDVI-related indices

Introduction

Sustainability science is an emerging interdisciplinary

field that addresses issues such as self-organizing

complexity, resilience, inertia, thresholds, complex

responses to multiple interacting stresses, adaptive

management, and social learning, and is committed to

place-based and solution-driven research encompass-

ing local, regional, and global scales (Kates et al.

2001; Clark and Dickson 2003; Levin and Clark

2010). Thus, sustainability science shares principles,

goals, knowledge and operating methods with com-

plex adaptive system and resilience theory.

To face the challenge of sustainable development,

an effective interdisciplinary integration has to be

achieved by embodying the complexities of societies

and economies into landscape ecology analyses (Wu

G. Zurlini � I. Petrosillo (&) � N. Zaccarelli

Landscape Ecology Laboratory, Department of Biological

and Environmental Sciences and Technologies, University

of Salento, Ecotekne (Campus), Strada per Monteroni,

73100 Lecce, LE, Italy

e-mail: irene.petrosillo@unisalento.it

K. B. Jones

U.S. Geological Survey, 755 East Harmon Road,

Las Vegas, NV 89119, USA

e-mail: kbjones@usgs.gov

123

Landscape Ecol

DOI 10.1007/s10980-012-9763-y

2006). This integration will help to operationalize the

multi-faced performances of both already designed

and future landscapes as sustainable landscapes in an

urbanizing world (Musacchio 2009). In the face of

planetary boundaries with regard to the functioning of

the Earth System, this is crucial for estimating a safe

operating space for humanity (Rockstrom et al. 2009).

Additionally, the approach should be socially fair,

enhancing human and social capital, and economic

prosperity (Daly and Cobb 1989; Costanza et al.

2009).

While the links between landscape ecology and

planning should be natural and almost immediate

(Leitao Botequilha and Ahern 2002; Opdam et al.

2002), we have yet to achieve the long awaited

integration of landscape ecology and landscape plan-

ning in operation (McAlpine et al. 2010). Such integra-

tion is getting far more complex today as landscape

ecology is expanding its scope to respond to the

challenges of sustainable development of human–

environmental systems (Wu 2006, 2010; Naveh 2007).

Landscape ecologists are escalating their thinking to

embrace and connect landscape planning and manage-

ment with the theory of complex adaptive systems

(Berkes and Folke 1998; Levin 1999), framing scien-

tific questions that can guide different scenarios of

landscape change to be perceived and evaluated both as

beneficial and environmentally sustainable (Opdam

and Wascher 2004; Musacchio 2009; Wu 2010). New

emerging applied fields such as resilience thinking

(Berkes et al. 2003; Walker and Salt 2006; Folke et al.

2010), vulnerability studies (Adger 1999; Petrosillo

et al. 2010a), environmental security (Petrosillo et al.

2008, 2010b), and sustainability science seek to inform

managers and policy makers.

Most of this research has moved beyond the tradi-

tional separation of social and ecological components in

social–ecological systems (SESs), toward studying

SESs as whole co-evolving and historically interdepen-

dent systems of humans-in-nature (Berkes and Folke

1998; Costanza et al. 2007a), where it is often impos-

sible to distinguish what is ‘‘natural’’ and what is not.

The systems approach is already implicit in land-

scape ecological analyses, but while expanding into an

interdisciplinary ground for sustainable landscape

development, landscape ecology is faced with the

challenge of recognizing SESs in a coherent geo-

graphical space of the real world (Cumming 2011). A

contribution of particular relevance is the ‘‘land-

change science’’ (Wu 2006; Turner et al. 2007) that

focuses on observing and monitoring land use and

land cover changes, assessing the impacts of such

transformations on ecosystem processes, goods and

services, and understanding biophysical and socio-

economic drivers and mechanisms of interaction.

However, such increasing scientific recognition of

the complex and adaptive nature of human–envi-

ronmental systems has not been matched by the

corresponding effort in understanding the explicit

spatiotemporal variation in SESs.

The visioneering—the engineering of a lucid and

shared vision (e.g., Meadows 2008)—has started to

emerge as a framework in the science of sustainability

for problem solving (Kim and Oki 2011). To meet the

challenges of sustainability, landscape ecology needs

to strengthen its capacity to develop spatially explicit

problem solving related to landscape sustainability

issues (McAlpine et al. 2010). In this respect,

addressing SESs as social–ecological landscapes

(SELs) (Berkes and Folke 1998; Zaccarelli et al.

2008), represents a more pragmatic basis for envi-

sioning how the real world works and how we would

like the world to be, as SELs represent the spatially

explicit integration of social–political and ecological

scales in the geographical world. Yet, there is the need

to go beyond the traditional views embraced by

landscape and urban planning where sustainability has

been envisioned as a durable, stable condition that,

once achieved, could persist for generations (Ahern

2011).

The problem we face is how an apparent ‘‘static’’

and ‘‘ordered’’ landscape condition in SELs, resulting

from cross-scale intersections of land-use, biophysical

conditions, and socio-political plans can be made

sustainable in face of unpredictable disturbance and

change. The science of sustainability is emerging as a

dynamic process that requires adaptive capacity in

resilient SELs to deal with change and resilience

theory (with an emphasis on spatiotemporal patterns)

can offer a new perspective, or possibly a solution, to

this paradox of sustainability.

In this respect, one of the most critical challenges is

an understanding of how the historical dynamic profile

of SELs evolved in response to internal processes

(e.g., plant succession, management practices) and

external drivers (e.g., rainfall, temperature, climate

change, exchange rates). Those profiles can tell us a

great deal about past and current SEL dynamics

Landscape Ecol

123

(Walker et al. 2002; Antrop 2005; Zurlini et al. 2006a)

and how the system might respond in the future

(Walker et al. 2002).

Landscape sustainability problems can be

addressed in terms of order and disorder: where order

implies causality, well-defined boundaries and pre-

dictable outcomes to ensure continuous provision of

functions and ecosystem services for human use, while

disorder designates the circumstance of not knowing

which of the four conditions (simple, complicated,

complex, chaotic) is dominant at a given moment,

implying hazy causality, shifting boundaries and

often-unpredictable outcomes due to complex process

interactions and higher uncertainty (Snowden and

Boone 2007). Forces operating at larger scales like

climate change can drive disorder in SELs, as well as

social–ecological activities that generally occur at

local or regional scales.

In this perspective we address the interplay of order

and disorder in SELs by using spatiotemporal landscape

analysis through entropy-related indices of time series to

provide insights for complex systems analysis for the

evaluation of adaptive capacity. We provide examples

of this approach and discuss what an increase of disorder

could mean, what it says about the condition of SELs,

and what could be the underlying causality of observed

conditions in SELs. The approach implements a

spatially explicit anticipatory capability in environmen-

tal science, and natural resource management based on

how the system has historically responded to stress and

to anticipate how it might respond in the future. Such a

capability is critical given that the pervasiveness of

surprises is predicted to increase as a consequence of

climate change and from the effects of multiple

interacting anthropogenic stressors.

The interplay of order and disorder

Anthropogenic disturbances are typically imposed by

groups of people who are organized at different levels

(e.g., from household to global) in a panarchy

(Gunderson and Holling 2002; Zaccarelli et al. 2008;

Petrosillo et al. 2010a), with differing views as to

which system states are desirable or which ecosystem

services are to be exploited. Land-use change depends

on individual and social responses to changing eco-

nomic conditions, mediated by institutional factors

(Lambin et al. 2001), such as markets and policies, and

increasingly influenced by global markets (Foley et al.

2005). There may be circumstances where landscapes

don’t change due to social and cultural drivers like

new reserves based on nature or cultural values. Yet,

climate change and weather extremes could occasion-

ally trigger further change resulting in ecological

surprises (Williams and Jackson 2007). The difficulty

of controlling or predicting the biophysical effects of

all those forces has resulted in failed attempts to

closely manage and regulate the dynamics of ecosys-

tems (Holling and Meffe 1996).

The usual state of affairs in living systems like

SELs is one of systems fluctuating around some trend

or stable average; however, sporadically, this condi-

tion is interrupted by an abrupt shift to a radically

different regime (Fig. 1). Disturbance can be deemed

as an event causing departure of a living system from

the ‘‘normal range’’ of conditions typical of its basin of

attraction (Carpenter et al. 2001; Scheffer and Car-

penter 2003; Scheffer et al. 2009).

The apparent paradox that disruption of the existing

order (i.e., disorder) and persistence (i.e., order, stabil-

ity) always coexist in living systems such as SELs is

addressed by the concept of resilience, defined as the

amount of disturbance a system can absorb without

shifting into an alternative state and losing function and

services (Carpenter et al. 2001; Walker and Salt 2006).

Fig. 1 A simple representation of system’s response to

disturbances with the three tests of the definition of disturbance:

abruptness (E), duration (G), and magnitude (F). Systems are

usually fluctuating around some trend or stable average (old

basin of attraction, A); however, sporadically, this condition is

interrupted by disturbances (d2) causing an abrupt shift

eventually to a radically different regime (new basin of

attraction, B) (after White and Jentsch 2001)

Landscape Ecol

123

Such a concept seeks to explain how disorder and order

usually work together, allowing living systems to

assimilate disturbance, innovation, and change, while

at the same time maintaining characteristic structures

and processes (Westley et al. 2006).

In analyzing SELs for simulating their behavior

into the future, biophysical laws that govern aspects of

nature can reveal a set of regularities (Holling 2001).

This occurs even though complex adaptive systems

are typically characterized by strong nonlinearities,

tipping points and dramatic regime shifts (Scheffer

and Carpenter 2003). This broad set of regularities

(order) is defining the ambit of certainties for every-

day living and decision-making, and represents the

usual space within which human societies operate.

Many disturbances can have a strong climate

forcing; nevertheless the relative importance of dif-

ferent drivers varies among systems and can even vary

through time within the same system. To understand

the disturbance regime of biophysical systems like

SELs and reveal possible regularities we must con-

sider the variable frequency of forcing due to the

physical environment and its historical role in shaping

biophysical systems.

In this respect, Steel (1985) has shown that, if

regular physical cycles are removed from historical

geophysical records, there is a residual variability that

can be considered inherently unpredictable. This

allows distinguishing between signal and noise of

temporal variation that corresponds to the distinction

between predictable (ordered) and unpredictable (dis-

ordered) fluctuations in the physical environment. In

marine environments, spectral distributions for vari-

ations in many physical forces exhibit patterns that are

inversely related to the square of the frequency (‘‘red

noise’’) (Barnes and Allan 1966), which makes them

inherently unpredictable at very long time scales. On

the contrary in terrestrial environments, for higher

frequency events that occur within the lifetime of

many organisms, variations in physical forces tend to

be distributed independently of the frequency of

occurrence, producing a pattern of ‘‘white noise’’

(constant variance per unit frequency). One ecological

consequence is that organisms in terrestrial environ-

ments (as opposed to marine) are better able to adapt

their behaviors and physiologies to the more predict-

able physical variations (Steel 1985).

However, climate–fire–vegetation interactions can

produce ecological changes that might differ in

direction from those expected from the effects of

physical forcing alone like climate change, resulting in

‘‘ecological surprises’’. For instance, prolonged or

repeated droughts after ca. AD 1265 reduced the

biomass and connectivity of fine fuels (grasses) within

the woodlands in Minnesota, and as a result, regional

fire severity declined and allowed tree populations to

expand (Shuman et al. 2009).

As such, uncertainty is normal; therefore, distur-

bance and disturbance regimes are no longer thought as

rare, external events, but rather as intrinsic and inherent

features of system dynamics. Uncertainty allows the

future to be open but it cannot be easily controlled even

through improved analytical procedures, therefore,

prudent management will require precautionary and

adaptive approaches (Doak et al. 2008).

Which state variables for SELs?

Humans trying to understand the current state or

predict the future condition of SELs regularly resort to

simple, easily interpreted surrogates as parts of the

whole complexity that can be understood and used by

non-scientists to make planning and management

decisions. Yet, the overall information we can gain

from a set of indicators will never match that of the

whole system, since each individual indicator carries

only partial information. Thus, the set of indicators

needs to be constantly re-evaluated and re-interpreted

in the light of the increasing understanding of the

whole organization and functioning of systems.

Fortunately, the complexity of living systems of

people and nature emerges not from a random

association of a large number of interacting factors

but rather from a smaller number of key-controlling

processes (Holling 2001; Gunderson and Holling

2002). Much of the fundamental nature of systems

can often be captured and described by single key-

variables, as many features of the system’s state tend

to shift in concert with a few important key-state

variables (Holling 2001). Examples of such variables

are total plant biomass per unit area, turbidity of lake

water (Scheffer et al. 2000), actual precipitation, or

phosphorous concentration in shallow lakes (Scheffer

and Carpenter 2003). Clearly, many more aspects of

SEL state are of importance to human users, and even

more factors are essential for the sustainable func-

tioning of SELs (e.g., Musacchio 2009).

Landscape Ecol

123

Remote sensing is a primary source of information

to study the complexity of SESs at the landscape level

in terms of dynamics at multiple spatial and temporal

scales. It has become a proven tool for scientists to

monitor synoptically, and to understand major distur-

bance events and their historical regimes at regional

and global scales (Kerr and Ostrovsky 2003; Potter

et al. 2003; Zurlini et al. 2006b). It has provided

valuable indices to describe and quantify natural and

human-related land-cover transformations and pro-

cesses, and one, in particular, has been widely used:

the Normalized Difference Vegetation Index (NDVI).

For a comprehensive review of NDVI applications,

see Kerr and Ostrovsky (2003) and Pettorelli et al.

(2005). Briefly, NDVI can be used to quantify annual

net primary productivity (Young and Harris 2005),

which is a main supporting ecosystem service (MEA

2005; Costanza et al. 2007b). NDVI is broadly

recognized as a spatially explicit robust indicator of

vegetation photosynthesis related to social–ecological

processes such as habitat-land use conversion (e.g.,

urban sprawling) or crop rotation (Guerschman et al.

2003; Potter et al. 2003; Young and Harris 2005). It is

used to identify and assess the impact of disturbances

such as drought, fire, flood, frost (Potter et al. 2003;

Mildrexler et al. 2007), or other human-driven distur-

bances (Guerschman et al. 2003; Zurlini et al. 2006b;

Wylie et al. 2008; Zaccarelli et al. 2008).

Reducing the complexity of SELs to one-dimen-

sional representation of state might seem oversimpli-

fied. Nonetheless, vegetation cover is the primary

determinant of landscape mosaics and quite respon-

sive to land cover transformations at all scales, as all

climate changes and anthropogenic influences have a

spatial outcome. As a result, synoptic and recurrent

representations of vegetation cover indices like NDVI

are the most frequently used as indicators of under-

performing SELs (Pettorelli et al. 2005; Young and

Harris 2005; Wylie et al. 2008).

An example of NDVI time series for primary land

use/land cover (LULC) categories of the Apulia region

(south Italy) (Fig. 2) demonstrates the differences in

the inter-annual periodicities of NDVI related to both

human controls (arable lands and olive groves) and

natural balancing feedback loops (natural grasslands,

broad-leaved forests, coniferous forests), whereas

urban areas show a disordered behavior. Mean NDVI

paths of LULC categories separate most during the hot

and dry summers when drought has its greatest effects

on semi-arid grasslands and olive groves, even if they

are moderately irrigated. Trends also highlight the

stronger vegetation activity of large natural forests in

summer, and the larger variation of arable land series

due to agricultural practices such as sowing, growing,

harvesting, and fire (Zaccarelli et al. 2008). Cross-

correlation analyses with temperature and precipita-

tion (Fig. 2) demonstrate that time-series are consis-

tent with a climatic forcing. This corroborates with

well-known vegetation cover changes in Apulia

associated with seasonal variations in climate and

water regimes as well as agricultural practices.

Mapping order and disorder (spectral entropy)

of NDVI time-series

Green et al. (2005) and Parrott (2010), in their

exhaustive review of methods for measuring com-

plexity of spatiotemporal dynamics, acknowledge

how, among all the fields of science, the field of the

information theory and entropy-related indices has

provided the deepest insights in complex systems

analysis. Entropy measures have a long tradition in

ecology (Ulanowicz 2001) and they have been fruit-

fully applied in biodiversity assessment (Magurran

2004), evolution analysis (Avery 2003), and species

interactions and spatial dynamics (Chen et al. 2005;

Parrott 2005). In the context of landscape ecology,

entropy-based indices like Shannon’s H or contagion

(Li and Reynolds 1994) are among the most com-

monly used metrics to represent landscape composi-

tion and configuration diversity, and are hypothesized

to reflect changes in the level of human impacts and

disturbance regimes (Johnson et al. 2001; Bogaert

et al. 2005), species diversity and habitat use (Wagner

et al. 2000; Hrabik et al. 2005), or biodiversity level

estimates from remotely sensed images (Rocchini

et al. 2005).

Research in ecological time-series analysis has

been focused on different aspects of temporal com-

plexity such as, for instance, intra-annual vegetation

dynamics (Zhang et al. 2003), the identification of

changes and discontinuities using principal compo-

nent or scale-dependent correlation analyses (Jassby

and Powell 1990; Rodrıguez-Arias and Rodo 2004),

and the identification of climate influences from

human disturbances through an integrated modelling

and remote sensing technique (Wylie et al. 2008).

Landscape Ecol

123

Nonetheless, only a few entropy-related measures

have been proposed to exploit the information content

and assess the regularity of a series, like the mean

information gain index (Wackerbauer et al. 1994), and

the fluctuation complexity index (Bates and Shepard

1993).

A recent further derivation is the ‘‘normalized

spectral entropy’’ (Hsn), an entropy-related index able

to describe the degree of regularity (orderliness)

within an ecological time-series based on its power

spectrum (Fig. 3) (Zaccarelli et al. 2012). Spectral

entropy has been suggested as a holistic indicator for

system level properties able to characterize heteroge-

neity in time and pointing to the system’s self-

organization strength (Li 2000). Thus, 1 - Hsn can

be calculated to emphasize the degree of regularity or

predictability of the series.

Adaptability (adaptive capacity) is the capacity of a

SEL to adjust to changing internal processes (plant

succession, management practices) and external forc-

ing (rainfall, temperature, climate change, exchange

rates) and thereby allow for development within the

current stability domain, along the current trajectory

(Carpenter and Brock 2008; Folke et al. 2010). If the

time series are regular (orderliness), then the system’s

responses have been effective either because of human

or natural controls through balancing feedback loops

that result in trajectories within preferred bounds.

The map of normalized spectral entropy (Hsn),

based on the trajectories for each pixel and calculated

from 10 year long time series of 16-day maximum

NDVI composite images—acquired by the two MO-

DIS platform for the Apulia region (south Italy)—

shows distinctive spatial patterns at 250 m resolution

(Fig. 4). Greener zones mean higher predictability

(1 - Hsn), i.e. more regular time series, while reddish

areas are more unpredictable. Clear coherent regions

of predictability and unpredictability emerge as well

as gradients of transition between the two. Large

predictability geographic regions arise in the map

(e.g., olive groves near Brindisi, or large farmlands

near Foggia) whereas unpredictability regions tend to

be associated with heterogeneous cultivation areas.

As an example of occasionally triggered distur-

bances, we can evaluate the response of the mean

NDVI and normalized spectral entropy to arsons that

took place during 2007 in an area of the Gargano

National Park (Fig. 5) mostly with Mediterranean

maquis habitats. Mean NDVI time series show an

abrupt decrease in July 2007 followed by a slow

Fig. 2 Mean NDVI 10-year

(2000–2010) time series for

different major LULC

categories in Apulia region

(south Italy) are computed

on 16-day maximum NDVI

composite images acquired

by the two MODIS platform

TERRA and AQUA from

2000 to 2010 (MOD13Q1

v.005 and MYD13Q1 v.005)

(see text)

Landscape Ecol

123

recovery trajectory lasting three years (cf. Fig. 1).

Normalized spectral entropy mirrored this change

going from 0.595 to 0.778.

The predictability (1 - Hsn) for major LULC

classes is shown in Fig. 6, in relation to their cover

percentage in the Apulia region. The degree of human

disturbance and regulation are different among LULC

classes in terms of intensity, temporal patterns and

spatial extent (Zaccarelli et al. 2008). Low predict-

ability is associated with urban areas and vineyards

(urban sprawl, land-use conversion and management

practices), whereas high predictability is related to

fruits orchards and olive groves as well as arable lands.

Landscapes that are intensively used, managed, con-

served, or restored are more predictable, and less

dependent on climate variability through the action

of self-correcting balancing feedback loops (e.g.,

drought-irrigation, soil impoverishment-fertilization)

to keep important stocks and flows of marketed

ecosystem services.

Natural land cover like grasslands, broad-leaved

forests, and coniferous forests exhibit an intermediate

predictability (Fig. 6). These are ‘‘naturally’’ adjusted,

as nature has evolved balancing feedback loops as

controls that keep stocks of natural capital within

certain bounds. The main vegetation types of the area

are characterized by differences in phenological

cycles and abilities to cope with weather constraints

such as water availability and maximum temperature.

NDVI is especially suited for vegetation monitoring,

and is strongly influenced by the amount of water, bare

soil or concrete and paved surfaces. In urban areas, NDVI

records changes in color, solar lighting or material types

of buildings and streets (Pettorelli et al. 2005).

When the map of normalized spectral entropy is

compared with the map of LULC categories (Fig. 4),

Fig. 3 To illustrate the meaning of normalized spectral entropy

(Hsn), consider two signals with the same mean and standard

deviation: a cosine wave (black curve) and a random permu-

tation of the sequence (in grey). The power spectrum of the

cosine wave shows a sharp peak accounting for the 97 % of the

total power, while the spectrum of permutations shows a

scattered distribution of power along all frequencies, with an

average value of 2 % of the total power. Hsn is near zero (more

ordered/predictable) for the cosine curve whose power spectrum

has one dominant frequency (Hsn = 0.016), while Hsn is near

unity (disordered/unpredictable) for the broader banded spec-

trum of the random permutation (Hsn = 0.907). Adapted from

Zaccarelli et al. (2012)

Landscape Ecol

123

the correspondence is much more complex as the

predictability expected for specific LULC categories

might differ in different places. This allows for a

specific identification of the local causes of deviation.

This kind of map can provide a new spatially explicit

description of the efficacy of balancing feedback loops

as controls in SELs, according to the spatial and

temporal resolution of the spectral entropy of different

time series.

Natural areas and permanent cultivations like fruit

orchards and olive groves result in most of ecosystem

service providers in the study area (Petrosillo et al.

2010a). However, the overall provision of services

does not so much depend on the features of the

Fig. 4 Map of normalized spectral entropy (Hsn) (a) and map of major LULC classes (b) for the Apulia region (south Italy). Hsn is

based on the same composite images used for Fig. 2. The LULC categories are derived from a CORINE land cover map of the year 2006

Fig. 5 Arsons in an area of

the Gargano National Park

(north Apulia) and relative

mean NDVI time series

(cf. Fig. 2)

Landscape Ecol

123

individual LULC patches, but rather on the spatial

interactions of the mosaic elements generated from

natural and human-managed patches, and by human

elements, such as footpaths and roads (Termorshuizen

and Opdam 2009), causing synergies and trade-offs

between services across multiple scales. For instance,

natural areas and permanent cultivations in the Apulia

region interact with disturbance patterns within SELs,

regulating landscape mosaic dynamics and mitigating

disturbances across scales (Petrosillo et al. 2010a).

This disturbance regulation service across scales has

consequences for regional SEL since it may govern if

and how disturbance drivers like land-use intensifica-

tion and climate change will affect the provisioning of

ecosystem services.

Perspectives for operationalizing SEL

sustainability

Adaptability captures the capacity of an SEL to learn,

combine experience and knowledge, adjust its

responses to changing external drivers and internal

processes, and continue developing within the current

stability domain or basin of attraction (Berkes et al.

2003). The probability that such state will persist is a

measure of its resilience (Peterson 2002). The overall

adaptive capacity of a SEL is about people and nature

as interdependent systems concerning all forms of

capital (natural, human, social, built) (Costanza et al.

2009). Such capacity would likely depend not so much

on the features of the individual LULC patches, but

rather on the pattern and spatiotemporal interactions of

the mosaic elements represented by natural and

human-managed patches, and by human and natural

networks. These patterns are strongly influenced by

actor groups, social learning, networks, organizations,

institutions, governance structures, incentives, politi-

cal and power relations or ethics (Folke et al. 2005),

which are often harder to map. Nonetheless, most of

the features above have spatial attributes interacting

with landscapes (Cumming 2011).

In this respect, spectral entropy of NDVI-related

indices appears to be a good indicator of a fundamental

synoptic SEL state variable. The knowledge of

causality, however, could be relatively weak in the

face of uncertainty and emerging complex systems,

unless the future is within the parameters of knowl-

edge of the past. Even so, the role of NDVI related

indices as a sign of SEL condition should not be

underestimated. Through spectral entropy of NDVI

time series we can, as in our examples, derive in any

case important lessons from recent historical trends

and the collection of different case studies to guide

both existing studies and new investigations to better

foresee unusual phenomena, and take proactive steps

to plan for and alleviate ‘‘undesirable’’ surprises

(Lindenmayer et al. 2010). In this context, temporal

sensitivity is crucial, as one may perceive a drop in

NDVI well before the loss of biomass or of vegetation

cover, providing enough time to allow for a treatment

if the system is not performing within expected

tolerances. According to Snowden and Boone

(2007), with complex situations one begins by ‘‘prob-

ing’’ the environment, and NDVI could act as such a

probe given that its spatiotemporal sensitivity is

susceptible to considerable improvements through

new remote sensing platforms and technologies.

This could be the basis for the further integration

with other key-state variables in a much broader

framework, depending on development of suitable

metrics related to other sustainability issues.

However, to anticipate surprises and their primary

drivers, and to take proactive steps to avoid undesir-

able transitions to different states, we must develop

Fig. 6 Predictability (1 - Hsn) versus regional percentage

cover of main CORINE LULC categories in the Apulia region.

Circles and triangles represent median values of natural and

human-managed categories respectively. Vertical bars indicate

the first and third quartiles of the LULC category distribution of

predictability values

Landscape Ecol

123

indicators of the primary causes and drivers of spectral

entropy change. For boreal forests, for example, which

are exposed to more pronounced warming effects, an

integrated modelling and remote sensing technique for

NDVI can be used to distinguish climate influences

from disturbances by fires (Wylie et al. 2008).

However, in more heterogeneous and human-man-

aged regions, where the effects of multiple and

interacting stressors are common and persistently

patterned in association with periurban areas and

arable fields (Petrosillo et al. 2010a), search for

causality could be rather intricate. To evaluate the

effect of non-contagious disturbances like climate

change, one approach could be analyzing spectral

entropy across multiple spatial and temporal scales

focusing on natural areas that are the most vulnerable

to climate change (Petrosillo et al. 2010a). This would

help identify the scales of operation of non-contagious

disturbances and their possible cross-scale interactions

with contagious disturbances like, for example, when

temperature rise can affect the extent and magnitude

of contagious disturbances (e.g., a fire made worse

over larger area due to greater temperature extremes).

A combination of the entropy analyses plus the

approach of Wylie et al. (2008) could make for a

powerful way to identify what has caused the trajec-

tory change.

The visioneering for problem solving in SELs

requires integration of three processes (Costanza

2003): (1) creation of a shared vision of both how

the world works and how we want it to be, (2)

systematic analysis conforming to the vision, and (3)

implementation appropriate to the vision. In this

respect, Musacchio (2009) suggested operationalizing

the difference features (six Es) of landscape sustain-

ability for designed landscapes for human/health

security, ecosystem service, and resource manage-

ment. Such a framework can include goal setting,

indicator setting, indicator measurement, causal chain

analysis, forecasting, back casting, and problem–

solution chain analysis manifested as governance,

management, and monitoring (Kim and Oki 2011).

Governance stands as the process of providing a

shared vision and resolving trade-offs, while manage-

ment entails operationalizing this vision. Monitor-

ing—such as the spectral entropy of NDVI discussed

above—synthesizes the observations to a narrative

and provides feedback, which serves as the source

for adaptive design (Nassauer and Opdam 2008),

co-management and learning (Olsson et al. 2004)

toward sustainability.

In this respect, the map of normalized spectral

entropy of NDVI time series can be the basis for a

strategic adaptive planning and design (Ahern 2011;

Musacchio 2011) of both desirable order and disorder

patterns. As conservation, for instance, is primarily

focused on persistence, we could determine under which

conditions landscape networks allow persistence (pre-

dictability) of ecosystem service flow (Opdam et al.

2002). Through a combination of ‘‘predictable’’ spatial

patterns with landscape network connectivity analysis,

it would be possible to design a subset of core areas and

connectors that contribute the most to the persistence of

the overall network connectivity and functioning to act

as more effective corridors.

Such strategies could involve the design and man-

agement of landscape elements and structure through

the strategic placement of managed land uses and

natural ecosystems, so the services of natural ecosys-

tems (e.g., pest control, pollination, reduced land

erosion) can be maintained and even enhanced across

the landscape. Additionally, these strategies should also

consider landscape pattern design for the deliberate

placement and confinement of local contagious distur-

bances (disorder) that humans can manage at certain

scale ranges to control, for instance, biological inva-

sions, e.g., through multifractal patterns (Zurlini et al.

2007), and to mitigate cross-scale impacts on ecosystem

service flow (Petrosillo et al. 2010a).

All these advancements could greatly contribute to

the application of spatial resilience strategies (Cum-

ming 2011) in general, and to sustainable landscape

planning in particular, especially in the perspective of

the consequences of climate change and for the

spatially explicit adaptive co-management of ecosys-

tem’s services.

Acknowledgments The paper benefited from many

conversations with Bai-Lian Li and Felix Muller over several

years. We also thank three anonymous reviewers and the editor

for their helpful thought-provoking comments and suggestions

that much improved the original version of the paper.

References

Adger N (1999) Social vulnerability to climate change and

extremes in coastal Vietnam. World Dev 2:249–269

Ahern JF (2011) From fail-safe to safe-to-fail: sustainability and

resilience in the new urban world. Landscape architecture

Landscape Ecol

123

& regional planning graduate research and creative activ-

ity. Paper 8 (online). http://scholarworks.umass.edu/larp_

grad_research/8

Antrop M (2005) Why landscapes of the past are important for

the future. Landsc Urban Plan 70(1–2):21–34

Avery J (2003) Information theory and evolution. World Sci-

entific, London

Barnes JA, Allan DW (1966) A statistical model of flicker noise.

Proc IEEE 54(2):176–178

Bates JE, Shepard HK (1993) Measuring complexity using

information fluctuation. Phys Lett A 172:416–425

Berkes F, Folke C (eds) (1998) Linking social and ecological

systems: management practices and social mechanisms for

building resilience. Cambridge University Press, Cambridge

Berkes F, Colding J, Folke C (eds) (2003) Navigating social–

ecological systems: building resilience for complexity and

change. Cambridge University Press, Cambridge

Bogaert J, Farina A, Ceulemans R (2005) Entropy increase of

fragmented habitats: a sign of human impact? Ecol Ind

5:207–212

Carpenter SR, Brock WA (2008) Adaptive capacity and traps.

Ecol Soc 13(2):40 (online). http://www.ecologyandsociety.

org/vol13/iss2/art40/

Carpenter SR, Walker BH, Anderies JM, Abel N (2001) From

metaphor to measurement: resilience of what to what?

Ecosystems 4:765–781

Chen X, Li B-L, Collina SL (2005) Multiscale monitoring of a

multispecies case study: two grass species at Sevilleta.

Plant Ecol 179:149–154

Clark WC, Dickson NM (2003) Sustainability science: the

emerging research program. PNAS 100(14):8059–8061

Costanza R (2003) A vision of the future science: reintegrating the

study of human and the rest of nature. Futures 35: 651–671

Costanza R, Graumlich L, Steffen W, Crumley C, Dearing J,

Hibbard K, Leemans R, Redman C, Schimel D (2007a)

Sustainability or collapse: what can we learn from inte-

grating the history of humans and the rest of nature? Ambio

36(7):522–527

Costanza R, Fisher B, Mulder K, Shuang L, Treg C (2007b)

Biodiversity and ecosystem services: a multi-scale empir-

ical study of the relationship between species richness and

net primary production. Ecol Econ 61:478–491

Costanza R, Hart M, Posner S, Talberth J (2009) Beyond GDP:

the need for new measures of progress. The Pardee papers

no. 4. The Frederick S. Pardee Center for the Study of the

Longer-Range Future, Boston University, Boston

Cumming GS (2011) Spatial resilience: integrating landscape

ecology, resilience, and sustainability. Landscape Ecol

26:899–909

Daly H, Cobb J (1989) For the common good. Beacon Press,

Boston

Doak DF, Estes JA, Halpern BS, Jacob U, Lindberg DR,

Lovvorn J, Monson DH, Tinker MT, Williams TM,

Wootton JT, Carroll I, Emmerson M, Micheli F, Novak M

(2008) Understanding and predicting ecological dynamics:

are major surprises inevitable? Ecology 89:952–961

Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter

SR, Chapin FS, Coe MT, Daily GC, Gibbs HK, Helkowski

JH, Holloway T, Howard EA, Kucharik CJ, Monfreda C,

Patz JA, Prentice JC, Ramankutty N, Snyder PK (2005)

Global consequences of land use. Science 309(5734):

570–574

Folke C, Hahn T, Olsson P, Norberg J (2005) Adaptive gover-

nance of social–ecological systems. Annu Rev Environ

Resour 30:441–473

Folke C, Carpenter SR, Walker B, Scheffer M, Chapin T,

Rockstrom J (2010) Resilience thinking: integrating resil-

ience, adaptability and transformability. Ecol Soc 15(4):20

(online). http://www.ecologyandsociety.org/vol15/iss4/art20/

Green JL, Hastings A, Arzberger P, Ayala FJ, Cottingham KL,

Cuddington K, Davis F, Dunne JA, Fortin M-J, Gerber L,

Neubert M (2005) Complexity in ecology and conserva-

tion: mathematical, statistical, and computational chal-

lenges. Bioscience 55:501–510

Guerschman JP, Paruelo JM, Burke IC (2003) Land use impacts

on the Normalized Difference Vegetation Index in tem-

perate Argentina. Ecol Appl 13:616–628

Gunderson LH, Holling CS (eds) (2002) Panarchy: under-

standing transformations in human and natural systems.

Island Press, Washington, DC

Holling CS (2001) Understanding the complexity of economic,

ecological, and social systems. Ecosystems 4:390–405

Holling CS, Meffe GK (1996) Command and control, and the

pathology of natural-resource management. Conserv Biol

10:328–337

Hrabik TR, Greenfield BK, Lewis DB, Pollard AI, Wilson KA,

Kratzet TK (2005) Landscape-scale variation in taxonomic

diversity in four groups of aquatic organisms: the influence

of physical, chemical, and biological properties. Ecosys-

tems 8:301–317

Jassby AD, Powell TM (1990) Detecting changes in ecological

time series. Ecology 71:2044–2052

Johnson GD, Myers WL, Patil GP, Taillie C (2001) Character-

izing watershed delineated landscapes in Pennsylvania

using conditional entropy profiles. Landscape Ecol 16:

597–610

Kates RW, Clark WC, Corell R (2001) Sustainability science.

Science 292(5517):641–642

Kerr JT, Ostrovsky M (2003) From space to species: ecological

applications for remote sensing. Trends Ecol Evol 18:

299–305

Kim J, Oki T (2011) Visioneering: an essential framework in

sustainability science. Sustain Sci 6:247–251

Lambin EF, Turner BL, Geist HJ (2001) The causes of land-use

and land-cover change: moving beyond the myths. Glob

Environ Chang 11:261–269

Leitao Botequilha A, Ahern J (2002) Applying landscape eco-

logical concepts and metrics in sustainable landscape

planning. Landsc Urban Plan 59:65–93

Levin SA (1999) Fragile dominions: complexity and the com-

mons. Basic Books, New York

Levin SA, Clark WC (2010) Toward a science of sustainability,

CID working paper No. 196. Center for International

Development, Harvard University, Cambridge, MA

Li B-L (2000) Fractal geometry applications in description and

analysis of patch patterns and patch dynamics. Ecol Model

132:33–50

Li H, Reynolds JF (1994) A simulation experiment to quantify

spatial heterogeneity in categorical maps. Ecology 75(8):

2446–2455

Landscape Ecol

123

Lindenmayer DB, Likensa GE, Krebsc CJ, Hobbs RJ (2010)

Improved probability of detection of ecological ‘‘sur-

prises’’. PNAS 107(51):21957–21962

Magurran AE (2004) Measuring biological diversity. Blackwell

Science, Oxford

McAlpine CA, Seabrook LM, Rhodes JR, Maron M, Smith C,

Bowen ME, Butler SA, Powell O, Ryan JG, Fyfe CT,

Adams-Hosking C, Smith A, Robertson O, Howes A,

Cattarino L (2010) Can a problem-solving approach

strengthen landscape ecology’s contribution to sustainable

landscape planning? Landscape Ecol 25:1155–1168

Meadows D (2008) Thinking in systems: a primer. Chelsea

Green, Vermont

Mildrexler DJ, Zhao M, Heinsch FA, Running SW (2007) A

new satellite-based methodology for continental scale

disturbance detection. Ecol Appl 17(1):235–250

Millennium Ecosystem Assessment [MEA] (2005) Ecosystems

and human wellbeing: synthesis. Island Press, Washington,

DC

Musacchio L (2009) The scientific basis for the design of

landscape sustainability: a conceptual framework for

translational landscape research and practice for designed

landscapes and the six Es of landscape sustainability.

Landscape Ecol 24:993–1013

Musacchio L (2011) The grand challenge to operationalize

landscape sustainability and the design-in-science para-

digm. Landscape Ecol 26:1–5

Nassauer JI, Opdam P (2008) Design in science: extending the

landscape ecology paradigm. Landscape Ecol 23:633–644

Naveh Z (2007) Landscape ecology and sustainability. Land-

scape Ecol 22:1437–1440

Olsson P, Folke C, Berkes F (2004) Adaptive comanagement for

building resilience in social–ecological systems. Environ

Manag 34:75–90

Opdam P, Wascher D (2004) Climate change meets habitat

fragmentation: linking landscape and biogeographical

scale levels in research and conservation. Biol Conserv

117:285–297

Opdam P, Foppen R, Vos CC (2002) Bridging the gap between

ecology and spatial planning in landscape ecology. Land-

scape Ecol 16:767–779

Parrott L (2005) Quantifying the complexity of simulated spa-

tiotemporal population dynamics. Ecol Complex 2:175–184

Parrott L (2010) Measuring ecological complexity. Ecol Ind

10:1069–1076

Peterson GD (2002) Estimating resilience across landscapes.

Conserv Ecol 6(1):17 (online). http://www.consecol.org/vol6/

iss1/art17/

Petrosillo I, Muller F, Jones KB, Zurlini G, Krauze K, Victorov

S, Li B-L, Kepner WG (2008) Use of landscape sciences

for the assessment of environmental security. Springer,

Dordrecht

Petrosillo I, Zaccarelli N, Zurlini G (2010a) Multiscale vulner-

ability of natural capital. Ecol Complex 7:359–367

Petrosillo I, Vassallo P, Valente D, Mensa JA, Fabiano M,

Zurlini G (2010b) Mapping the environmental risk of a

tourist harbor in order to foster environmental security:

objective vs. subjective assessments. Marine Pollut Bull

60:1051–1058

Pettorelli N, Vik JO, Mysterud A, Gaillard J-M, Tucker CJ,

Stenseth NC (2005) Using the satellite-derived NDVI to

assess ecological responses to environmental change.

Trends Ecol Evol 20(9):503–510

Potter C, Tan PN, Steinbach M, Klooster S, Kumar V, Myneni

R, Genovese V (2003) Major disturbance events in ter-

restrial ecosystems detected using global satellite data sets.

Glob Change Biol 9:1005–1021

Rocchini D, Andreini Butini S, Chiarucci A (2005) Maximizing

plant species inventory efficiency by means of remotely

sensed spectral distances. Glob Ecol Biogeogr 14:431–437

Rockstrom J, Steffen W, Noone K, Persson A, Chapin FS III,

Lambin E, Lenton TM, Scheffer M, Folke C, Schellnhuber

HJ, Nykvist B, de Wit CA, Hughes T, van der Leeuw S,

Rodhe H, Sorlin S, Snyder PK, Costanza R, Svedin U,

Falkenmark M, Karlberg L, Corell RW, Fabry VJ, Hansen

J, Walker B, Liverman D, Richardson K, Crutzen P, Foley J

(2009) Planetary boundaries: exploring the safe operating

space for humanity. Ecol Soc 14(2):32 (online). http://

www.ecologyandsociety.org/vol14/iss2/art32/

Rodrıguez-Arias M, Rodo X (2004) A primer on the study of

transitory dynamics in ecological series using the scale-

dependent correlation analysis. Oecologia 138:485–504

Scheffer M, Carpenter SR (2003) Catastrophic regime shifts in

ecosystems: linking theory to observation. Trends Ecol

Evol 18(12):648–656

Scheffer M, Brock WA, Westley F (2000) Socioeconomic

mechanisms preventing optimum use of ecosystem ser-

vices: an interdisciplinary theoretical analysis. Ecosystems

3:451–471

Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR,

Dakos V, Held H, van Nes EH, Rietkerk M, Sugihara G

(2009) Early-warning signals for critical transitions. Nat-

ure 461:53–59

Shuman B, Henderson AK, Plank C, Stefanova I, Ziegler SS

(2009) Woodland-to-forest transition during prolonged

drought in Minnesota after ca. AD 1300. Ecology 90:

2792–2807

Snowden DJ, Boone ME (2007) A leader’s framework for

decision making. Harv Bus Rev 85:68–76

Steel JH (1985) A comparison of terrestrial and marine eco-

logical systems. Nature 313:355–358

Termorshuizen JW, Opdam P (2009) Landscape services as a

bridge between landscape ecology and sustainable devel-

opment. Landscape Ecol 24:1037–1052

Turner BL II, Lambin EF, Reenberg A (2007) The emergence of

land change science for global environmental change and

sustainability. PNAS 104(52):20666–20671

Ulanowicz RE (2001) Information theory in ecology. Comput

Chem 25:393–399

Wackerbauer R, Witt A, Atmanspacher H, Kurths J, Scheingr-

aber H (1994) A comparative classification of complexity

measures. Chaos Soliton Fract 4(1):133–173

Wagner HH, Wildi O, Ewald KC (2000) Additive partitioning of

plant species diversity in an agricultural mosaic landscape.

Landscape Ecol 15:219–227

Walker BH, Salt D (2006) Resilience thinking: sustaining eco-

systems and people in a changing world. Island Press,

Washington, DC

Walker BH, Carpenter SR, Anderies J, Abel N, Cumming G,

Janssen M, Lebel L, Norberg J, Peterson GD, Pritchard R

(2002) Resilience management in social–ecological sys-

tems: a working hypothesis for a participatory approach.

Landscape Ecol

123

Conserv Ecol 6(1):14 (online). http://www.consecol.org/

vol6/iss1/art14

Westley F, Zimmerman B, Patton M (2006) Getting to maybe.

Random House of Canada, Toronto

White PS, Jentsch A (2001) The search for generality in studies

of disturbance and ecosystem dynamics. Prog Bot 62:

399–450

Williams JW, Jackson ST (2007) Novel climates, no-analog

communities, and ecological surprises. Front Ecol Environ

5:475–482

Wu J (2006) Landscape ecology, cross-disciplinarity, and sus-

tainability science. Landscape Ecol 21:1–4

Wu J (2010) Urban sustainability: an inevitable goal of land-

scape research. Landscape Ecol 25:1–4

Wylie BK, Zhang L, Bliss N, Ji L, Tieszen LL, Jolly WM (2008)

Integrating modelling and remote sensing to identify eco-

system performance anomalies in the boreal forest, Yukon

River Basin, Alaska. Int J Digit Earth 1(2):196–220

Young SS, Harris R (2005) Changing patterns of global-scale

vegetation photosynthesis, 1982–1999. Int J Remote Sens

26(20):4537–4563

Zaccarelli N, Petrosillo I, Zurlini G, Riitters KH (2008) Source/

sink patterns of disturbance and cross-scale effects in a

panarchy of social–ecological landscapes. Ecol Soc 13(1):

26. http://www.ecologyandsociety.org/vol13/iss1/art26/

Zaccarelli N, Li B-L, Petrosillo I, Zurlini G (2012) Order and

disorder in ecological time-series: introducing normalized

spectral entropy. Ecol Ind. doi:10.1016/j.ecolind.2011.

07.008

Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF,

Gao F, Reed BC, Huete A (2003) Monitoring vegetation

phenology using MODIS. Remote Sens Environ 84:

471–475

Zurlini G, Zaccarelli N, Petrosillo I (2006a) Indicating retro-

spective resilience of multi-scale patterns of real habitats in

a landscape. Ecol Ind 6:184–204

Zurlini G, Riitters KH, Zaccarelli N, Petrosillo I, Jones KB,

Rossi L (2006b) Disturbance patterns in a social–ecologi-

cal system at multiple scales. Ecol Complex 3(2):119–128

Zurlini G, Riitters KH, Zaccarelli N, Petrosillo I (2007) Patterns

of disturbance at multiple scales in real and simulated

landscapes. Landscape Ecol 22:705–721

Landscape Ecol

123