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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: [email protected]
K. B. Jones
U.S. Geological Survey, 755 East Harmon Road,
Las Vegas, NV 89119, USA
e-mail: [email protected]
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.
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