Access the full text.
Sign up today, get DeepDyve free for 14 days.
Landscape Ecol (2018) 33:861–878 https://doi.org/10.1007/s10980-018-0643-y REVIEW ARTICLE Assessing and coping with uncertainties in landscape planning: an overview . . Felix Neuendorf Christina von Haaren Christian Albert Received: 5 January 2017 / Accepted: 2 April 2018 / Published online: 9 April 2018 The Author(s) 2018 Abstract statistical methods for the assessment of uncertainties Context Although uncertainties are ubiquitous in in planning approaches that help to cope with uncer- landscape planning, so far, no systematic understand- tainties. The integration of uncertainty assessments ing exists regarding how they should be assessed, into landscape planning results is lacking. appropriately communicated and what impacts they Conclusions The assessment of uncertainties in yield on decision support. With increasing interest in landscape planning have been addressed by science, the role of uncertainties in science and policy, a but what is missing are considerations and ideas on synthesis of relevant knowledge is needed to further how to use this knowledge to foster uncertainty promote uncertainty assessment in landscape planning analysis in landscape planning practice. More research practice. is needed on how the application of identiﬁed Objectives The aim of this paper is to synthesize approaches into landscape planning practice can be knowledge about types of uncertainties in landscape achieved and how these results might affect decision planning, of methods to assess these uncertainties, and makers. of approaches for appropriately coping with them. Methods The paper is based on a qualitative litera- Keywords Uncertainty Landscape planning ture review of relevant papers identiﬁed in the ISI Web Environmental planning Uncertainty assessment of Knowledge and supplemented by frequently cited Communication of uncertainty publications. The identiﬁcation and synthesis of relevant information was guided by a developed framework concerning uncertainty in landscape planning. Introduction Results The main types of uncertainties identiﬁed in landscape planning are data-, model-, projection- and Uncertainties are ubiquitous in spatial planning in evaluation uncertainty. Various methods to address general and landscape planning in particular. Knowl- these uncertainties have been identiﬁed, including edge of ecological, social and economic interrelations is fragmented and this strongly affects the certainty of projections about future landscape developments (von F. Neuendorf (&) C. von Haaren C. Albert Haaren 2004). In addition, the sensitivity of stake- Institute of Environmental Planning, Leibniz Universita¨t holders and the public for uncertainties in projections Hannover, Herrenha¨user Str. 2, 30419 Hannover, has been increasing since the introduction of more Germany e-mail: email@example.com 123 862 Landscape Ecol (2018) 33:861–878 complex scientiﬁc models, especially climate models. planning theory and methods in three ways: by Climate change reports now usually work with enhancing awareness of the role of uncertainties in probability indicators and have a section devoted to planning, by identifying potential approaches for the evaluation of the suitability of used models for minimizing uncertainties in planning practice, and different tasks. This is even the case in the summary by highlighting areas for further research, to better for policy makers (IPCC 2013). Methods for land- address uncertainty that is especially important for scape planning are generally aimed at practicability, landscape planning. not necessarily completeness and have inherent The aim of this paper is to synthesize current uncertainties in their results and management propos- scientiﬁc knowledge on uncertainty assessment for als. Uncertainty consideration in environmental application in landscape planning. To fulﬁll this aim, assessments has been a rarity (Fischer 2007; Lees we developed a conceptual framework for uncertainty et al. 2016) and landscape planning has up to now—if in landscape planning and conducted a literature at all—only indirectly addressed uncertainties or review to answer the following questions: included general uncertainty considerations into ﬂex- – What relevant types of uncertainties and likely ible management design (Kato and Ahern 2008). What effects on landscape planning results are described is missing is a systematic and transparent approach for in scientiﬁc literature? identifying and describing uncertainties in a way that – What suitable methods for addressing uncertain- enables them to be appropriately included into plan- ties in landscape planning can be identiﬁed? ning propositions and communicated to decision – How can uncertainties be appropriately integrated makers. It is evident that uncertainties in landscape into planning practice? planning will have to be addressed in the future to avoid an impairment of the credibility of results with The remainder of the paper is structured as follows. the public and stakeholders. First, we introduce a conceptual framework for Some publications have already begun to assess uncertainty in landscape planning as derived from uncertainties in planning or have explored approaches leading publications. This framework is subsequently for dealing with them in planning proposals (Gallo and used to structure a review of relevant literature concerning the above named questions. We then Goodchild 2012; Gret-Regamey et al. 2013a). A review by Refsgaard et al. (2007) summarizes meth- discuss the ﬁndings and possible implications, as well ods for the assessment of uncertainties in environ- as challenges to further promote the use of uncertainty mental modelling processes but misses the important assessment in landscape planning practice. Finally, we consideration of communicating uncertainties in plan- identify knowledge gaps and opportunities for further ning. In a similar vein, Hou et al. (2013) provide a research. detailed overview of uncertainties in ecosystem ser- vice assessments and general strategies on how to address uncertainties in assessments processes. How- A conceptual framework concerning uncertainties ever, they do not propose application options in in landscape planning planning. Hamel and Bryant (2017) explore possible Landscape planning takes a multitude of different reasons for a lack of uncertainty analysis in ecosystem services analysis and propose solutions. Although not forms (Selman 2006). This paper follows the Euro- speciﬁcally addressed at landscape planning, their pean Landscape Convention (ELC) in understanding insights could also help to ﬁnd solutions for uncer- landscape planning as a ‘forward-looking action to tainty handling in planning practice. However, up until enhance, restore or create landscapes’ (Council of now, no comprehensive overview exists of the state of Europe 2000, Art. 1). The tasks to be executed by knowledge in assessing uncertainties and dealing with landscape planning differ with respect to the degree of them in landscape planning. This is especially the case its formalization and how landscape planning is for knowledge which may be useful for introducing embedded in a formalized planning system. In general, uncertainty analysis into actual landscape planning the tasks of landscape planning are relatively broad, practice. A review of relevant knowledge could also such as including the generation of environmental information for decision making and the protection contribute to the further development of landscape 123 Landscape Ecol (2018) 33:861–878 863 and redevelopment of natural and cultural assets into scales for the evaluation of different landscape (Margules and Pressey 2000; Leita˜o and Ahern functions (or ecosystem services), or prioritizing 2002). Some countries, including many member states objectives with incomplete knowledge about the of the European Union, have well-established plan- preferences of all affected people, give room for ning systems in which landscape planning proposals evaluation uncertainties. may have direct or indirect legal consequences for land use decisions. In other countries or planning systems, as for example in many states of the US, Methods landscape planning is applied more on a case basis and focuses on inﬂuencing decisions through information The following results section is based on a qualitative and persuasion. Regardless of the planning system in literature review of publications that have been place, landscape planning recently is moving from a published from 1996 to the end of 2016 and been purely expert-based approach to more transdisci- identiﬁed using the scientiﬁc literature database ISI plinarity, involving different experts, stakeholders Web of Knowledge, supplemented by much-cited and decision-makers in participatory processes. These publications not included in the search results. participatory approaches to landscape planning not The search parameters were as follows: only aim at generating relevant content but also at (i) The title, the keywords or the abstract of the facilitating deliberation and social learning as well as paper should include the words ‘landscape moving towards landscape governance (cf. Beunen planning’ or ‘environmental planning’ or and Opdam 2011; Scott 2011; Albert et al. 2012; ‘ecosystem services’. Opdam et al. 2015; Westerink et al. 2017). In all (ii) The word ‘uncertainty’ or ‘uncertainties’ must described variations of landscape planning, knowl- also appear in the title, the keywords or the edge of uncertainty would be beneﬁcial for improving abstract of the paper. transparent plausible decisions about landscape development. We reduced the amount of publications by con- In order to structure the literature review, we ducting a relevance check of the title and the keywords and a subsequent review of the abstract of remaining developed a conceptual framework for uncertainty in landscape planning (Fig. 1). Our framework shows the publications to determine their relevance for land- assessment and evaluation process as well as the scape planning. An in-depth qualitative review was forward-looking approach amended by its respective then conducted on the ﬁnal publications with the aim types of uncertainties. We decided to use the terms to identify different focal points of the publications ‘data uncertainty’, ‘model uncertainty’ and ‘projection and to gain an overview of the concepts of uncertainty uncertainty’ as they complement the classiﬁcations research. The review was conducted on the basis of the used in landscape planning practice. These terms are previously mentioned understanding of landscape recognizable and similar to the terms already used and planning. found in the majority of approaches for standardizing uncertainty typologies in planning and ecology Results (Walker et al. 2003; Refsgaard et al. 2007; Kato and Ahern 2008; Hou et al. 2013; Larsen et al. 2013). We supplemented this set of uncertainties with ‘evaluation The search returned 707 unique records with the uncertainty’ as the evaluation process is an important majority of papers being published in recent years (see step in landscape planning and inevitable for priori- Fig. 2). After the relevance check and the abstract tizing the value and endangerment of landscape review, papers that did not focus on uncertainty in functions as well as the need for action. Evaluation combination with tasks of landscape planning were uncertainty occurs when a descriptive statement about excluded, reducing the amount of publications to 65 pressure or the landscape state is changed into a value for an in-depth review. The majority of the identiﬁed judgement, which may be based on either legally papers were published from the year 2010 and on, prescribed standards (thresholds) or political, public showing the emerging interest in uncertainty analysis in planning processes. Potential sources of uncertainty preferences. The process of converting information 123 864 Landscape Ecol (2018) 33:861–878 D M Applicaon State of landcape funcons / Elicitaon and collecon of data ecosystem services Valuaon Method selecon and model Frame development condions Response Measures Data uncertainty D D Model uncertainty M M Projected future scenarios of Evaluaon Uncertainty landscape funcons / ecosystem services Projecon Uncertainty P P D M P Fig. 1 Conceptual framework for uncertainty consideration in landscape planning. Origin (ﬁlled) and propagation (hollow) of relevant types of uncertainty in the frame of a simpliﬁed landscape planning process Sources of uncertainty The literature review identiﬁed a great diversity of sources of uncertainty that are of relevance in landscape planning. The framework presented earlier is used as the conceptual basis to structure the results of the literature review for potential sources of uncertainty in landscape planning. In more detail, possible sources of uncertainty in scaling and mod- elling are systemized in Fig. 4, which gives a comprehensive overview of essential elements of the more technical uncertainty in assessments. This Fig. 2 Publications identiﬁed by the literature search by framework is particularly suitable for landscape number (y-axis) and year of publication (x-axis) planning because it displays different levels of detail that can be transferred into different planning and the communication of uncertainty were addressed by more than half of the publications (Fig. 3). Over concepts. 60% of the identiﬁed papers have ecosystem services as a focal point, with the majority of them also published within the last 5 years. Origin Propagaon Projecon Landscape Ecol (2018) 33:861–878 865 Fig. 3 Thematic focal points (y-axis) and their share (x-axis) of the total number of identiﬁed publications distinguished between types of papers Fig. 4 Sources of uncertainty in scaling and modeling. Adapted from Li and Wu (2006) 123 866 Landscape Ecol (2018) 33:861–878 Data uncertainty location of objects (geometric uncertainty), others are the values attributed to these objects (thematic Data uncertainty is of importance in landscape plan- uncertainty) (Schulp and Alkemade 2011). Geometric ning practice because of its implications in the uncertainties arise mainly from spatial data resolution, transformation of information during the planning for example, when using global land cover data that process. Data is usually illustrated spatially in land- aims to map and monitor general spatial distribution. scape planning, allowing stakeholders to easily iden- The resolution of such data can still be coarse. tify data ﬂaws in those areas they are particularly Consequently, there might be poor representation of knowledgeable of. If diverging perspectives and small landscape elements (Ozdogan and Woodcock conﬂicts emerge in the planning process, stakeholders 2006) or the disappearance of minor land cover types sometimes draw upon identiﬁed data ﬂaws in order to (Ozdogan and Woodcock 2006; Verburg et al. 2011). question the appropriateness of the overall planning Thematic uncertainties arise in the classiﬁcation of proposals and to promote their interests (cf. Maxim land cover types from remote sensing data, for and van der Sluijs 2007). example from satellite images (Fang et al. 2006; Data uncertainty has been identiﬁed in the literature Bargiel and Herrmann 2011). Land cover maps as a prevailing source of uncertainty in environmental derived from remote sensing data also face accuracy assessments (Rae et al. 2007; Hou et al. 2013; Schulp issues that can be traced back to the satellite imagery et al. 2014). Potential uncertainties in the data will equipment used for the data acquisition (Schulp and spread to the output, which in some cases leads to an Alkemade 2011). Additionally, land use changes lead outcome that can be rendered questionable (Heuvelink to the possibility of deviations between different maps 2000; Huang et al. 2005). In science, the scale of the that have been generated at different times (Schulp and data should ideally match the processes that are being Alkemade 2011). A ﬁtting description for this is ‘‘no studied (Schulp and Alkemade 2011). This represents matter how visually convincing digital spatial data can a condition that, although worth aspiring to, cannot be be, it should always be noted that these data are in fact achieved in all cases of landscape planning. Com- a model of the real world’’ (Rae et al. 2007, p. 216). monly, data from data services, such as ofﬁcial soil maps, are being used as a substantial segment of the Model uncertainty information for environmental assessments (Smith et al. 2011) and respective assessments of landscape One feature that all modelling approaches have in functions. This data combined with acquired data can common is that the variables have been chosen by the include sources of uncertainties, for example, mea- model’s developers whose knowledge limits the surement imprecisions and potential errors in subse- validity of the model (Foley 2010). Many ecosystem quent data processing (Rae et al. 2007; Zhang et al. processes are still poorly understood, regardless of the 2015). These sources of uncertainty will not only be considerable scientiﬁc effort over the past decades conﬁned to land cover data or biophysical data (see (Barnaud and Antona 2014; Seidl 2014). The appro- Schulp et al. 2014), but also be associated with social priate selection of indicators and variables for the data (see Lechner et al. 2014). The development of assessment of landscape functions and ecosystem models and the assessment of landscape functions is services is, therefore, a vast challenge in the develop- also often limited by the availability of adequate land ment of new models (Foley 2010; Hou et al. 2013; cover data (Alvarez Martinez et al. 2011; Hou et al. Schulp et al. 2014). Some general limitations of 2013; Blennow et al. 2014; Schulp et al. 2014). In models have long been identiﬁed and can be seen in landscape planning the acquisition of additional data is Fig. 4. Empirical models, used to assess landscape limited by factors such as high costs or dependence on functions, are parameterized on existing data and are the responsible agents. Remote sensing data is some- generally limited to a smaller range of explanatory times an alternative to ﬁeld work and has been used for variables (Smith et al. 2011). They only contain ecosystem service assessments, for example data from information about conditions that have been observed the CORINE Land Cover Project (cf. Burkhard et al. in the past (Seidl 2014). These might not necessarily 2010). Land cover maps, however, can contain depict current conditions with high certainty. Addi- uncertainties. One uncertainty is the shape and tionally, empirical information can be deﬁcient for 123 Landscape Ecol (2018) 33:861–878 867 precise parameterization of a model (Williams and in which multiple processes, for instance the diversity Johnson 2013), which adds to the overall uncertainty. of system components, temporal or spatial evolutions Dynamic process models often aim to reﬂect a higher and other ecological arguments, should be considered range of variables in order to give a more compre- by the planner and eventually the decision makers hensive overview of a wide variety of physical and (You et al. 2014). Despite many decision problems in biochemical processes. They also include a time landscape planning and conservation practice having a component, for example when modeling plant growth dynamic nature with often long time horizons, they or groundwater cleaning en route to a waterbody. This often are formulated as static problems and repre- can translate into a tradeoff between precision and sented by plans that, for example, identify conserva- usefulness. Such a tradeoff can be seen when models tion areas or predicted future extension of habitats increase in complexity because the data requirements (Kubiszewski et al. 2013; Williams and Johnson will also become more demanding (McVittie et al. 2013). The understanding of the various components 2015). With ongoing technological evolution, the of a dynamic environmental system does not neces- development of dynamic process models has increased sarily imply understanding of the future behavior of while the validation of these models has become more the overall system (Foley 2010). Unfortunately plan- scientiﬁcally challenging (Callaghan et al. 2004). ning results often give the contrary impression. Additionally, when evaluating the accuracies of land Furthermore, long-term projections are prone to time-scale mismatches, when short policy lifespans use based environmental assessments, researchers reach the conclusion that a ‘‘classiﬁcation system meet with longer term perspectives for implementing with a very high level of detail is impractical and may policies (Dockerty et al. 2006). Changes in general lead to erroneous interpretations when the classiﬁca- drivers, in the form of political, technological, demo- tion system is used by people other than its develop- graphical and economic developments, make the ers’’ (van der Biest et al. 2015, p. 42). In addition to the prediction of land use change difﬁcult and therefore potential sources of uncertainty that have been men- introduces a large number of uncertainties into the tioned, emerging concepts like ecosystem services and planning process (Carter and White 2012; Gret- their respective assessment methods may include Regamey et al. 2013b; Seidl 2014). The introduction additional sources of uncertainty, particularly in the or the neglect of such drivers into landscape planning form of economic values. The quantiﬁcation and always relies on assumptions about the future that are ultimately the monetization of ecosystem service bound to personal judgement and which may amplify values can be relatively easily understood when uncertainty (Metzger et al. 2010). In addition, factors looking at provisioning services. However, a valuation like climate change, land degradation and biodiversity can be difﬁcult and linked with high uncertainty when loss are increasingly challenging environmental man- looking at non-market values such as regulation or agement (Schroeter et al. 2006). Projecting climate cultural services (Smith et al. 2011; Johnson et al. change, for example, can lead to very different results 2012) presenting challenges for landscape planners when using various climate models for the same area when trying to use such concepts. Despite the best and time scale (Williams and Johnson 2013). This efforts to eliminate uncertainty in the development of phenomenon can also be found in several other methods for spatial modelling, it will always be planning results (see Schulp et al. 2014). present to some extent (Rae et al. 2007). Therefore, it Assuming that there always will be knowledge is important that landscape planners acknowledge gaps, uncertainty may not only pose problems for these uncertainties. management options but also may provide opportuni- ties to rethink or reinvent them, leading to outcomes Projection uncertainty that are robust against a variety of possible futures (Bohensky et al. 2006; Seidl 2014). Landscape planning as a forward-looking approach is inherently laden with a multitude of uncertainties regarding the projection of future states of the environment (Shearer 2005; Gret-Regamey et al. 2013a). Ecosystems include a number of subsystems 123 868 Landscape Ecol (2018) 33:861–878 Methods for the assessment of and coping programming has its limitations when it comes to with uncertainties accounting for uncertainty in a non-fuzzy decision space (Li et al. 2009). Nevertheless, the modelling of Several methods for the assessment of uncertainties uncertainty ‘can (still) be considered to be the most and multiple approaches to cope with uncertainties in important goal of fuzzy set theory’ (Zimmermann landscape planning have been identiﬁed while con- 2001, p. 6) and can be seen in recent studies, where FA ducting this review (see Table 1). These methods has been used to analyze uncertainty in pollination differ in their aim and in their overall strategy. maps based on land cover data of different quality (see Statistical methods, summarized under ‘Uncertainty Schulp and Alkemade 2011 for detail). This concurs assessment’, aim to assess uncertainties and often give with the statement that ‘Fuzzy numerical similarity’, numerical results on which the uncertainty can be another derivate of FA, is considered one of the best measured. Other strategies or planning approaches, methods when analyzing spatial differences or errors summarized in the following under ‘coping with between different maps (Hagen-Zanker 2006). uncertainty’, address uncertainties within the planning Sensitivity analysis (SA) can be used to investigate process and offer opportunities to deal with them how sensitive a model output is towards changes in the within the process. different input variables. With this, it is possible to understand the importance of different input param- Uncertainty assessment eters and their inﬂuence on the accuracy of the model output (Refsgaard et al. 2007). Rae et al. (2007) Monte Carlo analysis (MCA) is one of, if not, the most conducted a SA with the aim to add information on commonly used technique for uncertainty analysis (Li reliability for planning questions in the frame of area and Wu 2006) and is used in both commercial and free protection. The results of the SA show that with software packages. It utilizes random samples of input different assumptions and variations of parameters, data and variable model parameters as basis to results of the ‘seemingly’ same planning exercise can generate output statistics through repeated simulations differ a lot (see Rae et al. 2007 for detail). SA can be (Rastetter et al. 1992; Jansen 1998; Katz 2002). A used to communicate model behavior to experts and main advantage of such a simulation is its general thus, can aid in the transparency of an assessment applicability and its possibility to be linked to any method for landscape functions or ecosystem services model code (Refsgaard et al. 2007). MCA may permit making it an important awareness raising tool for an effective mechanism for the assessment of model landscape planners. performance but lacks the ability to provide best system solutions (Huang et al. 2005). An example for Coping with uncertainty the use of MCA in landscape planning would be the work by Pearson and Dawson (2005), which included The review identiﬁed structured planning methodolo- a number of 10,000 Monte Carlo runs to create gies with an integrated assessment and general plan- probability values on plant dispersal for the identiﬁ- ning proposals for coping with uncertainties. cation of conservation goals under climate change. In recent landscape planning and especially ecosys- Fuzzy analysis (FA) approaches have been used for tem service literature, Bayesian belief networks addressing uncertainties in situations where environ- (BBNs) have become increasingly popular when mental decisions need to be made despite only vague considering uncertainty in modeling and planning information (You et al. 2014). FA is the grouping term tasks (Gret-Regamey et al. 2013a, b; Landuyt et al. for a variety of different methods based on the fuzzy 2014; McVittie et al. 2015). BBN present a strategy to set theory introduced by Zadeh (1965). A fuzzy set is a reduce and simultaneously communicate uncertainty set of variables, in that for each variable, there is no within the observed system parameters and eventually ‘true’ or ‘false’ statement but anything between true the outputs. BBN are multivariate statistical models and false (see Zimmermann 2001 for detail). This consisting of a causal network (nodes and connectors) presents additional challenges for planners when and conditional probability tables that quantify rela- results of FA need to be prepared for decision making tions within the network. The creation of a BBN is a exercises, as it has been identiﬁed that fuzzy balance between complexity and intelligibility, when 123 Landscape Ecol (2018) 33:861–878 869 Table 1 Overview of identiﬁed methodologies for the handling of uncertainties in landscape planning processes and identiﬁed potentials as well as challenges for the use in landscape planning practice Methods Uncertainty handling approach Uncertainty Potentials and challenges for the use in addressed landscape planning practice Uncertainty assessment Monte Carlo Quantitative approach for assessing uncertainty values by DU, MU ? General applicability analysis generating output statistics with varying input and ? Viable assessment of model (MCA) model parameters performance - can be time consuming with very high number of runs Fuzzy Quantitative approach for assessing uncertainty values DU, MU ? Addressing of uncertainty analysis using fuzzy parameters in situations where only vague (FA) information is available - No general applicability because different planning tasks might require different fuzzy analysis approaches - Problems when handling multiple data formats Sensitivity Quantitative approach to assess model behavior and MU ? Communication of model analysis associated uncertainties by analyzing dependencies performance and uncertainties (SA) between input and output (better transparency) Coping with uncertainty in planning Bayesian Semi-quantitative approach to make informed decisions in DU, PU ? Communication and reduction of belief uncertain environments using stakeholder information uncertainty networks and values ? Integration of spatial and temporal (BBNs) scales ? Combination of empirical data and expert knowledge - Generation of a BBN can be bound to uncertainty itself and can lack transparency - Limited comparability of the results between different regions/planning tasks Adaptive Qualitative approach to cope with uncertainties by using DU, MU, ? Better informed management planning planning results of the past for adapted decision making PU decisions based on decisions observed in the past ? Encouragement to act ? Interdisciplinary approach, promoting innovative measures Requires a mind shift in decision making, accepting failure - Requires monitoring Scenario Qualitative approach to cope with uncertainties by PU ? Incorporation of projection planning exploring different possible future states uncertainty through the exploring of multiple futures ? Aids in robust decision making in cases where future states are uncertain - Can lack transparency if not conducted well 123 870 Landscape Ecol (2018) 33:861–878 Table 1 continued Methods Uncertainty handling approach Uncertainty Potentials and challenges for the use in addressed landscape planning practice Participation Qualitative approach to cope with uncertainties by DU, MU, ? Local knowledge can help in coping activating local knowledge PU with uncertainty especially in task oriented landscape planning ? Improved transparency and acceptance of landscape planning results - Can also hinder the implementation of planning measures if not conducted well Types of uncertainty: DU (data uncertainty), MU (model uncertainty), PU (projection uncertainty) selecting variables that try to provide a realistic (Holling 1978; Kato and Ahern 2008). The adaptive representation and while also keeping the model as management approach expands traditional approaches simple as possible (McVittie et al. 2015). The core by adding feedback loops where the effectiveness of capability of a BBN lies in the possibility to use planning decisions made and measures taken is empirical data and expert estimations to deﬁne nodes monitored, generating new data to structure alternative and integrate uncertainties directly in the probability or future decisions (Walters and Holling 1990; Ahern tables. Despite the usefulness of BBN when working 2012). This approach has been commonly used in in an environment where knowledge and data is of ecosystem and environmental management (Walters different quality or lacking (Landuyt et al. 2015), there and Holling 1990; Rist et al. 2013), but has been rarely are also some limitations with this approach. Knowl- integrated into landscape planning (Ahern 2006). The edge based models can be seen as subjective and are use of adaptive planning can be beneﬁcial because of often hard to validate (Landuyt et al. 2015). Addi- the ability to cope with uncertainties that are inherent tionally, the development of a BBN may be a difﬁcult in natural and social systems (Kato and Ahern 2008). and time consuming procedure when integrating the In recent years, the adaptive management concept has knowledge of multiple experts or even stakeholder been expanded to include design principles into knowledge (Gret-Regamey et al. 2013b). Results of planning processes (Ahern 2012; Ahern et al. 2014). BBN are nevertheless seen less as providing ﬁnal Adaptive design implies intentional, often experimen- management suggestions but more as potential con- tal changes with a multifocal view on environmental tributions to a more complete assessment or as part of a and societal needs (see Nassauer and Opdam 2008). decision support tool that aims to develop a better The changes, or ‘‘designed experiments’’ often take understanding of processes and their interactions place in a small spatial extent with the aim to test (Landuyt et al. 2014; McVittie et al. 2015). Studies innovative approaches in a ‘‘safe to fail’’ environment investigating the effectiveness of the approach in (Ahern et al. 2014). Designed experiments are devel- mapping future ecosystem service provisions and oped in transdisciplinary processes including scien- coupled uncertainties are, however, still lacking (Gret- tists, planners, design professionals and other Regamey et al. 2013a). stakeholders (Felson and Pickett 2005) amongst whom Adaptive planning approaches have their origins in the risk of failure of such approaches is recognized the adaptive management concept that was introduced (Ahern 2011). These principles of adaptive design ﬁt and has been used since the late 1970s. Adaptive especially well with the concept of resilience, which is management was developed to deal with uncertainties deﬁned by Walker and Salt (2006, p. 1) as the ‘‘ability that occur in complex systems (Holling 1978). In of a system to absorb disturbance and still retain its contrary to traditional management decisions that are basic function and structure’’. According to Ahern often based on empirical studies, adaptive manage- (2011), resilience capacity of a system can be achieved ment treats decisions as experiments to promote a through multiple ways, amongst others being the ‘‘learning by doing’’ process in a proactive way multifunctionality of measures, and the adaptive 123 Landscape Ecol (2018) 33:861–878 871 planning approach. To be effective, ecological limits, Emerging concepts like landscape stewardship specif- as well as economic and social limits need to be ically aim at the activation of local knowledge and considered when constructing designed experiments involvement of local land owners in environmental (Pickett et al. 2004). protection for both, nature and people (see Brown and When envisioning future states of ecosystems, Mitchell 2000; Plieninger et al. 2015 for detail). To scenarios are a commonly used tool in landscape achieve better informed decisions and a reduction of planning for coping with projection uncertainty. uncertainties through collective knowledge, it is of Scientists agree that scenarios are best used when high importance that inherent uncertainties are explic- information about the future under different policies is itly addressed within the participatory process (Newig poorly deﬁned and the knowledge is, at best, precar- et al. 2005). ious (Shearer 2005; Biggs et al. 2007; Foley 2010; Metzger et al. 2010). Scenarios allow us to explore Integration of uncertainty in planning possible futures and illustrate how different policies can alter the landscape (Shearer 2005). Based on There has been a lack of integration of uncertainty information of current and past conditions, scenarios information and limitations of certain models into are plausible stories about future states (Biggs et al. planning results. Pe’er et al. (2014) believe that one 2007). They can be divided into different types main reason for this dearth is the simpliﬁcation of model outputs for decision makers. This may be regarding their focus. These types are (i) predictive scenarios with a narrow focus on future developments attributed to the belief that there is a considerable answering the question ‘‘what will happen’’ (forecast- mismatch between information outcomes that scien- ing), (ii) explorative scenarios with a broader focus tists and practitioners produce and the expertise that addressing potential impacts that can signiﬁcantly policy and decision makers have. Even if science were alter future states (forecasting) and (iii) normative freely available, it might remain inaccessible because scenarios that start with a desired future state and of the level of detail desired by scientists and explore pathways with conditions to achieve this practitioners, which often conﬂicts with the time future state (backcasting) (see Maier et al. 2016 for constraints imposed by policy makers (McInerny et al. detail). Well-developed scenarios can be seen as an 2014; McManus et al. 2015). Consequently, there is a awareness raising tool that can help challenge the call for simpler and more understandable decision views of individuals about how a possible future may supporting tools (Ruckelshaus et al. 2015). It is look like (Carter and White 2012). Through this it is important to communicate how information for deci- possible to improve the robustness of planning through sion support has been generated and address any the incorporation of increasing amounts of uncertainty potential uncertainties. If this does not occur, there is a on the range from predictive scenarios to unframed risk that information will be assumed to be part of exploratory scenarios (Maier et al. 2016). reality even if this is virtually impossible. In conven- To enhance understanding and raise the acceptance tional planning systems such uninformed decisions of landscape planning results, it is possible that could result in long-term risks to both the environment scenarios and management options are developed in and humans (Pe’er et al. 2014). In environmental a participatory process. When developing scenarios or assessment results uncertainties are usually not response measures, for example, it is possible to described. This is backed by the ﬁndings of studies involve local stakeholders in the development process. that have focused on the consideration of uncertainties This may lead to improved consensus building, in such assessments (Larsen et al. 2013; Lees et al. strengthen the communication and can ultimately lead 2016). The results of one study found that only 5 out of to decisions being made that are more accept- 87 planning cases considered climate change and able amongst the general public (Biggs et al. 2007; mentioned associated uncertainties (see Larsen et al. Beach and Clark 2015). Participation can also present 2013 for detail). Uncertainties can be described by a way to cope with sources of uncertainties in values of probability, error percentages or by other landscape planning, especially on a local level where formal concepts like the results of FAs (see Zhang generic scientiﬁc knowledge needs to be reinterpreted et al. 2015) and BBN (see Gret-Regamey et al. 2013a). to ﬁt the local context (Beunen and Opdam 2011). Other ways of displaying uncertainty information for 123 872 Landscape Ecol (2018) 33:861–878 decision makers could be through the utilization of assessment of the current state of the landscape or free graphical values or interactive representations in site. Landscape planners need to use the data that best thematic maps (Griethe and Schumann 2005). As ﬁt the scale and detail required for solving the visualizations are bound to potential biases in audi- problems of the speciﬁc landscape planning exercises. ences perception, it should be noted that particular When the standardized available input data from soil information might be advocated if it is given promi- maps, habitat maps, topographical maps, and remote nence (Pidgeon and Fischhoff 2011; McInerny et al. sensing seem to be incomplete many papers suggest 2014). Uncertainty values should be seen as additional acquiring further data through ﬁeld work or the information in a decision making process, one that consultation of experts. However, generating such does not detract from the actual thematic information new empirical data is often challenged by the limited (Ruckelshaus et al. 2015) but rather, contributes to the human, temporal and ﬁnancial resources available in overall information basis on which decisions are practical planning (Pourabdollah et al. 2014). Avail- made. able standard data usually misses information con- cerning its inherent uncertainty. Providing this uncertainty information could help planners in esti- Discussion mating the overall uncertainty of their planning proposals. In order to qualify landscape planning for this uncertainty integration, it is necessary to ﬁrst Our ﬁndings show that, in general, uncertainty anal- ysis in planning has gained increasing interest in investigate the degree to which the data uncertainty corresponding scientiﬁc literature, potentially spurred actually discredits the assessment results. Information also by emerging applications of more quantitative on the degree of uncertainty propagation throughout concepts such as ecosystem services. It seems that the the planning process could help landscape planners to development of new, and often more complex, address critiques from stakeholders who use identiﬁed methodologies in planning has given rise to an data ﬂaws to question the appropriateness of the increased interest to assess uncertainties. This also planning proposals. may have a positive effect on the evaluation and Model uncertainty is at best handled within the further development of existing methodologies and model development process, with special considera- processes in landscape planning. tion of the inﬂuence that model developers can have on While we are conﬁdent that we included the the ﬁnal model. Landscape planning often uses relevant publications in our review according to our methodologies that have been developed decades search criteria, we acknowledge that we may have ago and are still considered sufﬁcient, but validation missed some differently labeled papers that could have through uncertainty assessment has been missing potentially provided additional insights, if we had used (Gruehn 2010). It is conceivable that a validation of more search terms. However, we took additional these models, with identiﬁed methods for uncertainty measures to ensure a comprehensive review by also assessment, could aid in a more informed decision adding new and relevant papers to our database once making process. we became aware of them through reference lists. An Our review shows that projection uncertainties important challenge for the transferability of our have received less attention in uncertainty research. A discussion ﬁndings is that we adopted the ELC’s reason for this might be that projection uncertainty addresses sources of uncertainty emerging mostly understanding of landscape planning which might divert from understandings on different continents. from human actions/-behavior or the dynamic of However, we expect our ﬁndings to be instructive also nature that are difﬁcult to assess. It is seemingly easier for planners in other contexts. to develop viable assessment methods for data- and model uncertainty. Landscape planning as a forward Challenges for landscape planning derived looking action is especially prone to these kinds of from identiﬁed types of uncertainty uncertainties. Evaluation uncertainty that should be especially Data and model uncertainties can be found in almost prominent in more formalized landscape planning has every landscape planning process as part of the not been addressed explicitly by the identiﬁed 123 Landscape Ecol (2018) 33:861–878 873 publications. The evaluation of assessment results by BBN can be tailored to ﬁt speciﬁc landscape planners is prone to similar uncertainties as the actual planning tasks, which is a main advantage of this model development itself, mainly being the inﬂuence concept. Landscape planning according to the ELC is of the planner on the ﬁnal planning results through often only a part of the whole decision making process different evaluation methods. The legitimation of the where the results are not decisions themselves but different values used as starting point for evaluation propositions for realizing environmental friendly and could be used as reference for uncertainty assignment. resource sparing developments. This is where BBN can help to clarify interrelations between different Potential use of different methodologies functions. Due to validation limitations and concerns for handling uncertainty in landscape planning regarding the time it can take to set up an appropriate BBN it might be, at this point, still questionable if Although each of the above mentioned methods for BBN can be used in landscape planning practice the assessing uncertainty have been used in scientiﬁc way they have been used in science. studies, some of the identiﬁed approaches seem to be The concept of adaptive management (Holling better suited for the practical application on landscape 1978) is a promising approach for incorporating and planning uncertainties. coping with uncertainty in planning processes and also When reviewing statistical methods for the assess- for the support of management implementation to reduce uncertainty. One reason for the still limited use ment, MCA has the major advantage of a general applicability, but further interpretation and reprocess- of adaptive planning in landscape planning practice ing of the results from such an analysis might open up may be that in former times analog maps, approved by new sources of uncertainty. It is conceivable that local or regional governments, were not easy to uncertainty can be avoided when predeﬁned proce- change. Therefore, the biggest challenge may be the dures and interpretation guidelines are being used. implementation of adaptive planning approaches into This would add to the transparency of uncertainty existing planning systems. Such implementation also assessments with MCA. However, this would require requires the rethinking of planning traditions that further research as such guidelines do not already might have already been in existence for decades exist. Simpler fuzzy methods, like fuzzy numerical (Kato and Ahern 2008). One way to apply adaptive similarity statistics, could be used for uncertainty planning in existing landscape planning procedures analysis in landscape planning processes when dealing would be to intensify monitoring of management with different forms of spatial data but we do not implementations and communicating associated ﬁnd- foresee the integration of more complex fuzzy ings and limitations for other planning processes. This programming into landscape planning practice as we feedback loop would make it possible to enrich the struggle to see a ﬁt for purpose. further planning processes with knowledge derived When developing models and assessment methods from success or failure of earlier decisions. Integration for landscape planning purposes, a SA should not only can be achieved by municipalities through monitoring be recommended, but should be mandatory in our (at least periodically) and by decisions on adaptive opinion. The different indicators deﬁne the method planning approvals. In situations where development and should therefore be checked for inﬂuence on the measures can be more freely chosen, it is conceivable model outcome. If this is the case and the results of the to accompany them by small scale designed experi- ments to test novel and innovative approaches for SA are communicated together with the model output, a major contribution to the transparency of assessment generating knowledge and coping with uncertainty. methods in landscape planning could be achieved. It is Although multifunctional approaches are not new to also imaginable that additional information is gathered landscape planning, it is conceivable that in combina- using SA to assess the uncertainty of established tion with the resilience approach, adaptive design can methods and document the results in databases. be used to especially promote innovative ways of However, the question remains of how such databases establishing multifunctional measures that are robust need to be structured and which methods should be against a wide variety of uncertainties. included for the assessment of different landscape Scenarios are already commonly used in landscape functions. planning and provide an easy to use approach for 123 874 Landscape Ecol (2018) 33:861–878 making uncertainties transparent within the landscape that is easily understood. This should help landscape planning process. The integration of uncertainties planning results to maintain credibility amongst happens implicitly in the majority of cases but without stakeholders and the public. It becomes obvious that mentioning actual sources of uncertainty and solely in order to appropriately cope with inherent uncer- addresses projection uncertainties. Uncertainties in tainties, a dialogue between scientists, planners and data sources and assessment are still bound to the data decision makers is needed. This is important, for and methods used, which makes an additional assess- example, for the deriving of criteria to judge which ments of these uncertainties mandatory to cover the uncertainty is acceptable with respect to the conse- majority of the systems uncertainty. Nevertheless, we quences of the implementation of planning proposals. see in scenario planning a very good approach for When considered in innovative planning approaches buffering projection uncertainty by showing different like adaptive design, ‘‘uninformed decisions’’ can act results for different preconditions whilst not pretend- as a starting point for the generation of knowledge ing to be a prognosis but instead a picture of possible through designed experiments. In such cases uncer- future states. tainty can be seen as a catalyst. Participation concepts as presented in recent liter- ature (Beunen and Opdam 2011) are increasingly discussed in case oriented landscape planning tasks. Conclusion Participation and co-generation of knowledge on the local level can be seen as both, assessment and coping Our ﬁndings show that assessing uncertainties in with uncertainty and therefore presents a good way for landscape planning could provide highly relevant landscape planning to handle uncertainty and for the information for supporting discussions and decision results to be transparent. It should be noted that making. Landscape ecological modelling can already participatory systems have limitations as well and that provide much experience in approaches for uncer- participatory processes can also have a negative effect tainty estimation. However, little guidance can be on the broad acceptance of landscape planning results, found in the literature so far of how this information for example when the structure of the process and the can be appropriately transferred to the ﬁeld of time frame makes it unlikely for participants to attend landscape planning for decision support. Conse- every meeting (see Beach and Clark 2015). The aim in quently, further research on uncertainty in landscape knowledge generation for landscape planning should planning should focus on the challenges of actual be a balance between scientiﬁc research and partici- implementation options (cf. Hamel and Bryant 2017). pation, which is well perceived by the public, stake- Potential reservations of practitioners against the holders, politicians and planners altogether. communication of uncertainties should be identiﬁed, and ways to reduce these should be explored. Another Principles for the integration of uncertainties area for research could be to investigate politicians’ in landscape planning perceptions of uncertainty information and potential impacts of these perceptions on their decision-making. As identiﬁed in the literature, the integration of Finally, it should be explored how different kinds of uncertainty information in planning is lacking (Lees uncertainty descriptions and illustrations inﬂuence et al. 2016). The communication of uncertainty is the decision support processes. most important part in the process of integrating As a way forward, we propose landscape planners uncertainties into planning practice. Whether we use to collaborate with landscape ecologists and other methods or innovative planning approaches to assess natural scientists to explore ways for assessing and and cope with uncertainties, the usefulness of this integrating uncertainty information in planning prac- uncertainty analysis is deﬁned by the appropriate tice in ways that are both accurate enough to better integration of the associated uncertainties by decision- inform decision making and doable given the limited makers. It is therefore mandatory to present uncer- temporal and ﬁnancial resources in planning pro- tainty information in a way that both ﬁts planning cesses. It is conceivable that this might be achieved, results while keeping a scientiﬁc standard and also for example, through readily available uncertainty fulﬁlling the desire of decision-makers for information information for different types of input data and 123 Landscape Ecol (2018) 33:861–878 875 Wageningen UR Frontis Series, vol 12. Springer, Dor- different models. This would enable landscape plan- drecht, pp 119–131 ners to use predeﬁned values for the estimation of Ahern J (2011) From fail-safe to safe-to-fail: sustainability and inherent uncertainties with little additional effort. The resilience in the new urban world. Landsc Urban Plan uncertainty assessments should also be ﬁt for purpose 100(4):341–343 Ahern J (2012) Urban landscape sustainability and resilience: in different landscape planning applications, ranging the promise and challenges of integrating ecology with from simple estimations up to more speciﬁc assess- urban planning and design. Landscape Ecol ments that deliver reliable results, especially in cases 28(6):1203–1212 where a high level of transparency in the actual Ahern J, Cilliers S, Niemela¨ J (2014) The concept of ecosystem services in adaptive urban planning and design: a frame- planning information is needed. In the end, landscape work for supporting innovation. Landsc Urban Plan planning needs to act, no matter how uncertain the 125:254–259 system under investigation might be. Uncertainties in Albert C, Zimmermann T, Knieling J, von Haaren C (2012) a decision can be well accepted, if the consequences of Social learning can beneﬁt decision-making in landscape planning: Gartow case study on climate change adaptation, a wrong decision will not be serious, if the decision Elbe Valley Biosphere Reserve. Landsc Urban Plan can be easily readjusted after some time and if there is 105:347–360 a lot of experience available as to the outcome. To Alvarez Martinez J, Suarez-Seoane S, de Luis Calabuig E maintain credibility in times of increased public (2011) Modelling the risk of land cover change from environmental and socio-economic drivers in heteroge- sensitivity to uncertainties in expert recommenda- neous and changing landscapes: the role of uncertainty. tions, landscape planners should take active measures Landsc Urban Plan 101(2):108–119 to ensure a transparent and understandable communi- Bargiel D, Herrmann S (2011) Multi-temporal land-cover cation of landscape planning proposals to its audi- classiﬁcation of agricultural areas in two European regions with high resolution spotlight TerraSAR-X data. Remote ences, including an appropriate communication of the Sens. https://doi.org/10.3390/rs3050859 inherent uncertainties as well as pointing out their Barnaud C, Antona M (2014) Deconstructing ecosystem ser- meaning for the projected landscape development, vices: uncertainties and controversies around a socially ﬁnancial risks and the life of people. constructed concept. Geoforum 56:113–123 Beach DM, Clark DA (2015) Scenario planning during rapid ecological change: lessons and perspectives from work- Acknowledgements We would like to thank the two shops with southwest Yukon wildlife managers. Ecol Soc. anonymous reviewers and the editor who provided very https://doi.org/10.5751/ES-07379-200161 helpful guidance and advice for the revision. Funding for the Beunen R, Opdam P (2011) When landscape planning becomes study was provided by the German Research Foundation (DFG) landscape governance, what happens to the science? by Research Grant for the Project ‘‘250763334 - Charting the Landsc Urban Plan 100(4):324–326 Unknown: Assessing and Communicating Uncertainties for Biggs R, Raudsepp-Hearne C, Atkinson-Palombo C, Bohensky Decision-Support in Landscape Planning’’. CA acknowledges E, Boyd E, Cundill G, Fox H, Ingram S, Kok K, Spehar S, further funding from the German Ministry for Education and Tengo M, Timmer D, Zurek M (2007) Linking futures Research (BMBF) for a Junior Research Group Grant (Funding across scales: a dialog on multiscale scenarios. Ecol Soc Code 01UU1601A). 12(1):17–20 Blennow K, Persson J, Wallin A, Vareman N, Persson E (2014) Open Access This article is distributed under the terms of the Understanding risk in forest ecosystem services: implica- Creative Commons Attribution 4.0 International License (http:// tions for effective risk management, communication and creativecommons.org/licenses/by/4.0/), which permits unre- planning. Forestry 87(2):219–228 stricted use, distribution, and reproduction in any medium, Bohensky EL, Reyers B, van Jaarsveld AS (2006) Future provided you give appropriate credit to the original ecosystem services in a Southern African river basin: a author(s) and the source, provide a link to the Creative Com- scenario planning approach to uncertainty. Conserv Biol mons license, and indicate if changes were made. 20(4):1051–1061 Brown J, Mitchell B (2000) The stewardship approach and its relevance for protected landscapes. George Wright Forum 17:70–79 References ¨ Burkhard B, Kroll F, Muller F (2010) Landscapes’ capacities to provide ecosystem services—a concept for land-cover based assessments. LO. https://doi.org/10.3097/LO. Ahern J (2006) Theories, methods and strategies for sustainable landscape planning. In: Tress B, Tress G, Fry G, Opdam P Callaghan TV, Bjorn Lo, Chernov Y, Chapin T, Christensen TR, (eds) From landscape research to landscape planning: Huntley B, Ims RA, Johansson M, Jolly D, Jonasson S, aspects of integration, education and application. Matveyeva N, Panikov N, Oechel W, Shaver G (2004) 123 876 Landscape Ecol (2018) 33:861–878 Uncertainties and recommendations. Ambio IPCC (2013) In: Stocker TF, Qin D, Plattner G-K, Tignor M, 33(7):474–479 Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley Carter JG, White I (2012) Environmental planning and man- PM (eds)Climate change 2013: the physical science basis. agement in an age of uncertainty: the case of the Water Contribution of Working Group I to the Fifth Assessment Framework Directive. J Environ Manag 113:228–236 Report of the Intergovernmental Panel on Climate Change. Council of Europe (2000) European Landscape Convention. Cambridge University Press, Cambridge European Treaty Series No. 176, Florence Jansen MJ (1998) Prediction error through modelling concepts Dockerty T, Lovett A, Appleton K, Bone A, Su¨nnenberg G and uncertainty from basic data. Nutr Cycl Agroecosyst (2006) Developing scenarios and visualisations to illustrate 50(1/3):247–253 potential policy and climatic inﬂuences on future agricul- Johnson KA, Polasky S, Nelson E, Pennington D (2012) tural landscapes. Agric Ecosyst Environ 114(1):103–120 Uncertainty in ecosystem services valuation and implica- Fang S, Gertner G, Wang G, Anderson A (2006) The impact of tions for assessing land use tradeoffs: an agricultural case misclassiﬁcation in land use maps in the prediction of study in the Minnesota River Basin. Ecol Econ 79:71–79 landscape dynamics. Landscape Ecol 21(2):233–242 Kato S, Ahern J (2008) ‘Learning by doing’: adaptive planning Felson AJ, Pickett STA (2005) Designed experiments: new as a strategy to address uncertainty in planning. J Environ approaches to studying urban ecosystems. Front Ecol Plan Manag 51(4):543–559 Environ 3(10):549–566 Katz RW (2002) Techniques for estimating uncertainty in cli- Fischer TB (2007) The theory and practice of strategic envi- mate change scenarios and impact studies. Clim Res ronmental assessment: towards a more systematic 20(2):167 approach. Earthscan, London Kubiszewski I, Costanza R, Paquet P, Halimi S (2013) Hydro- Foley AM (2010) Uncertainty in regional climate modelling: a power development in the lower Mekong Basin: alternative review. Prog Phys Geogr 34(5):647–670 approaches to deal with uncertainty. Reg Environ Change Gallo J, Goodchild M (2012) Mapping uncertainty in conser- 13(1):3–15 vation assessment as a means toward improved conserva- Landuyt D, Lemmens P, D’hondt R, Broekx S, Liekens I, de Bie tion planning and implementation. Soc Nat Resour T, Declerck SAJ, de Meester L, Goethals PLM (2014) An 25(1):22–36 ecosystem service approach to support integrated pond Gret-Regamey A, Brunner SH, Altwegg J, Bebi P (2013a) management: a case study using Bayesian belief net- Facing uncertainty in ecosystem services-based resource works—highlighting opportunities and risks. J Environ management. J Environ Manag 127:S145–S154 Manag 145:79–87 Gret-Regamey A, Brunner SH, Altwegg J, Christen M, Bebi P Landuyt D, van der Biest K, Broekx S, Staes J, Meire P, Goe- (2013b) Integrating expert knowledge into mapping thals PL (2015) A GIS plug-in for Bayesian belief net- ecosystem services trade-offs for sustainable forest man- works: towards a transparent software framework to assess agement. Ecol Soc 18(3):34 and visualise uncertainties in ecosystem service mapping. Griethe H, Schumann H (2005) Visualizing uncertainty for Environ Model Softw 71:30–38 improved decision making. In: Proceedings of the 4th Larsen SV, Kørnøv L, Driscoll P (2013) Avoiding climate international conference on business informatics research change uncertainties in Strategic Environmental Assess- BIR 2005 ment. Environ Impact Assess Rev 43:144–150 Gruehn D (2010) Validity in landscape function assessment Lechner AM, Raymond CM, Adams VM, Polyakov M, Gordon methods—a scientiﬁc basis for landscape and environ- A, Rhodes JR, Mills M, Stein A, Ives CD, Lefroy EC mental planning in Germany. Probl Landsc Ecol (2014) Characterizing spatial uncertainty when integrating XXVIII:191–200 social data in conservation planning. Conserv Biol Hagen-Zanker A (2006) Map comparison methods that simul- 28(6):1497–1511 taneously address overlap and structure. J Geogr Syst Lees J, Jaeger JAG, Gunn JAE, Noble BF (2016) Analysis of 8(2):165–185 uncertainty consideration in environmental assessment: an Hamel P, Bryant BP (2017) Uncertainty assessment in ecosys- empirical study of Canadian EA practice. J Environ Plan tem services analyses: seven challenges and practical Manag 59(11):2024–2044 responses. Ecosyst Serv 24:1–15 Leita˜o AB, Ahern J (2002) Applying landscape ecological Heuvelink GBM (2000) Error propagation in environmental concepts and metrics in sustainable landscape planning. modelling with GIS. Taylor et Francis, London Landsc Urban Plan 59(2):65–93 Holling CS (1978) Adaptive environmental assessment and Li YP, Huang GH, Yang ZF, Chen X (2009) Inexact fuzzy- management. International series on applied systems stochastic constraint-softend programming—a case study analysis, vol 3. Wiley, Chichester for waste management. Waste Manag 29(7):2165–2177 Hou Y, Burkhard B, Mueller F (2013) Uncertainties in land- Li H, Wu J (2006) Uncertainty analysis in ecological studies: an scape analysis and ecosystem service assessment. J Envi- overview. In: Wu J, Jones KB, Li H, Loucks OL (eds) ron Manag 127:117–131 Scaling and uncertainty analysis in ecology. Springer, Huang GH, Linton JD, Yeomans JS, Yoogalingam R (2005) Dordrecht, pp 45–66 Policy planning under uncertainty: efﬁcient starting pop- Maier HR, Guillaume JHA, van Delden H, Riddell GA, Haas- ulations for simulation–optimization methods applied to noot M (2016) An uncertain future, deep uncertainty, municipal solid waste management. J Environ Manag scenarios, robustness and adaption: how do they ﬁt toge- 77(1):22–34 ther. Environ Model Softw 81:154–164 123 Landscape Ecol (2018) 33:861–878 877 Margules CR, Pressey RL (2000) Systematic conservation on fuzzy systems (FUZZ-IEEE). IEEE, Piscataway, planning. Nature 405(6783):243–253 pp 2230–2237 Maxim L, van der Sluijs JP (2007) Uncertainty: cause or effect Rae C, Rothley K, Dragicevic S (2007) Implications of error and of stakeholders’ debates? Analysis of a case study: the risk uncertainty for an environmental planning scenario: a for honeybees of the insecticide Gaucho. Sci Total Environ sensitivity analysis of GIS-based variables in a reserve 376(2007):1–17 design exercise. Landsc Urban Plan 79(3–4):210–217 McInerny GJ, Chen M, Freeman R, Gavaghan D, Meyer M, Rastetter EB, King AW, Cosby BJ, Hornberger GM, O’Neill Rowland F, Spiegelhalter DJ, Stefaner M, Tessarolo G, RV, Hobbie JE (1992) Aggregating ﬁne-scale ecological Hortal J (2014) Information visualisation for science and knowledge to model coarser-scale attributes of ecosystems. policy: engaging users and avoiding bias. Trends Ecol Evol Ecol Appl 2(1):55–70 29(3):148–157 Refsgaard JC, van der Sluijs JP, Højberg AL, Vanrolleghem PA McManus MC, Taylor CM, Mohr A, Whittaker C, Scown CD, (2007) Uncertainty in the environmental modelling pro- Borrion AL, Glithero NJ, Yin Y (2015) Challenge clusters cess—a framework and guidance. Environ Model Softw facing LCA in environmental decision-making—what we 22(11):1543–1556 can learn from biofuels. Int J Life Cycle Assess Rist L, Felton A, Samuelsson L, Sandstro¨m C, Rosvall O (2013) 20(10):1399–1414 A new paradigm for adaptive management. Ecol Soc. McVittie A, Norton L, Martin-Ortega J, Siameti I, Glenk K, https://doi.org/10.5751/ES-06183-180463 Aalders I (2015) Operationalizing an ecosystem services- Ruckelshaus M, McKenzie E, Tallis H, Guerry A, Daily G, based approach using Bayesian Belief Networks: an Kareiva P, Polasky S, Ricketts T, Bhagabati N, Wood SA, application to riparian buffer strips. Ecol Econ 110:15–27 Bernhardt J (2015) Notes from the ﬁeld: lessons learned Metzger MJ, Rounsevell MDA, van den Heiligenberg H, Perez- from using ecosystem service approaches to inform real- Soba M, Soto Hardiman P (2010) How personal judgment world decisions. Ecol Econ 115:11–21 inﬂuences scenario development: an example for future Schroeter D, Zebisch M, Grothmann T (2006) Climate change in rural development in Europe. Ecol Soc 15(2). http://www. Germany—vulnerability and adaption of climate-sensitive jstor.org/stable/26268151 sectors. In: Deutscher Wetterdienst (ed) Klimastatus- Nassauer J, Opdam P (2008) Design in science: extending the bericht 2005. Klimastatusbericht, vols2005. Deutscher landscape ecology paradigm. Landscape Ecol 23:633–644 Wetterdienst, Offenbach am Main Newig J, Pahl-Wostl C, Sigel K (2005) The role of public par- Schulp CJE, Alkemade R (2011) Consequences of uncertainty ticipation in managing uncertainty in the implementation in global-scale land cover maps for mapping ecosystem of the Water Framework Directive. Eur Environ functions: an analysis of pollination efﬁciency. Remote 15(6):333–343 Sens 3(9):2057–2075 Opdam P, Westerink J, Vos C, de Vries B (2015) The role and Schulp CJE, Burkhard B, Maes J, van Vliet J, Verburg PH evolution of boundary concepts in transdisciplinary land- (2014) Uncertainties in ecosystem service maps: a com- scape planning. Plan Theory Pract 16(1):63–78 parison on the European scale. PLoS ONE. https://doi.org/ Ozdogan M, Woodcock CE (2006) Resolution dependent errors 10.1371/journal.pone.0109643 in remote sensing of cultivated areas. Remote Sens Environ Scott A (2011) Beyond the conventional: meeting the challenges 103(2):203–217 of landscape governance within the European Landscape Pe’er G, Mihoub J, Dislich C, Matsinos YG (2014) Towards a Convention? J Environ Manag 92(10):2754–2762 different attitude to uncertainty. Nat Conserv Bulg Seidl R (2014) The shape of ecosystem management to come: 8:95–114 anticipating risks and fostering resilience. Bioscience Pearson RG, Dawson TP (2005) Long-distance plant dispersal 64(12):1159–1169 and habitat fragmentation: identifying conservation targets Selman PH (2006) Planning at the landscape scale. The RTPI for spatial landscape planning under climate change. Biol library series, vol 12. Routledge, London Conserv 123(3):389–401 Shearer AW (2005) Approaching scenario-based studies: three Pickett STA, Cadenasso ML, Grove JM (2004) Resilient cities: perceptions about the future and considerations for land- meaning, models, and metaphor for integrating the ecolo- scape planning. Environ Plan B 32(1):67–87 gical, socio-economic, and planning realms. Landsc Urban Smith RI, Dick JM, Scott EM (2011) The role of statistics in the Plan 69:369–384 analysis of ecosystem services. Environmetrics Pidgeon N, Fischhoff B (2011) The role of social and decision 22(5):608–617 sciences in communicating uncertain climate risks. Nat van der Biest K, Vrebos D, Staes J, Boerema A, Bodi MB, Clim Change 1(1):35–41 Fransen E, Meire P (2015) Evaluation of the accuracy of Plieninger T, Kizos T, Bieling C, Le Du-Blayo L, Budniok M-A, land-use based ecosystem service assessments for different Burgi M, Crumley CL, Girod G, Howard P, Kolen J, thematic resolutions. J Environ Manag 156:41–51 Kuemmerle T, Milcinski G, Palang H, Trommler K, Ver- Verburg PH, Neumann K, Nol L (2011) Challenges in using land burg PH (2015) Exploring ecosystem-change and society use and land cover data for global change studies. Glob through a landscape lens: recent progress in European Change Biol 17(2):974–989 landscape research. E&S 20(2):5 von Haaren C (ed) (2004) Landschaftsplanung. UTB Land- Pourabdollah A, Wagner C, Miller S, Smith M, Wallace K schaftsplanung, Okologie, Biologie, Geographie, vol 8253. (2014) Towards data-driven environmental planning and Ulmer, Stuttgart policy design—leveraging fuzzy logic to operationalize a planning framework. 2014 IEEE international conference 123 878 Landscape Ecol (2018) 33:861–878 Walker B, Salt D (2006) Resilience thinking: sustaining eco- Williams BK, Johnson FA (2013) Confronting dynamics and systems and people in a changing world. Island Press, uncertainty in optimal decision making for conservation. Washington Environ Res Lett 8(2):025004 Walker WE, Harremoes J, Rotmans J, van der Sluijs J, van You L, Li YP, Huang GH, Zhang JL (2014) Modeling regional Asselt MB, Janssen P, Krayer von Krauss MP (2003) ecosystem development under uncertainty—a case study Deﬁning uncertainty: a conceptual basis for uncertainty for New Binhai District of Tianjin. Ecol Model management in model-based decision support. Integr 288:127–142 Assess 4(1):5–17 Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353 Walters CJ, Holling CS (1990) Large-scale management Zhang K, Li YP, Huang GH, You L, Jin SW (2015) Modeling experiments and learning by doing. Ecology for regional ecosystem sustainable development under 71(6):2060–2068 uncertainty—a case study of Dongying, China. Sci Total Westerink J, Opdam P, van Rooij S, Steingro¨ver E (2017) Environ 533:462–475 Landscape services as boundary concept in landscape Zimmermann H-J (2001) Fuzzy set theory—and its applica- governance: building social capital in collaboration and tions. Springer, Dordrecht adapting the landscape. Land Use Policy 60:408–418
Landscape Ecology – Springer Journals
Published: Apr 9, 2018
Access the full text.
Sign up today, get DeepDyve free for 14 days.