Integration of Environmental Processes into Land-use Management Decisions

1. Introduction

Land-use management decisions are confronted since ever with the challenge to consider complex interactions of different land-use types - natural ecosystems and man-made systems - and to balance at the same time various needs of different land-users (Dragosits et al., 2006; Kallioras et al., 2006; Letcher & Giupponi, 2005; Niemelä et al., 2005). Changing frame conditions such as Climate Change, changing intensity of land-use, changing impact by deposition, etc. impact eco- or man made systems, lead to a severe disturbance of system specific processes and lower in consequence the system stability and resilience (see e.g. Goetz et al., 2007; Metzger et al., 2006; Callaghan et al., 2004).

Taking the impact of Climate Change on European forest ecosystems as an example, biomass production and drinking water supply are severely affected by growing biotic and abiotic risks as a result of longer vegetation periods, higher annual mean temperature and lower annual mean precipitation with shift to the winter period (see e.g. Lindner & Kolström, 2009; Kellomäki et al., 2008; Bytnerowicz et al., 2007; Garcia-Goncalo et al., 2007). Respective observations were also made for agricultural land-use (see e.g. Miraglia et al., 2009; Olesen & Bindi 2002; Bonsall et al., 2002).

Back-coupled on landscape level, the effects of changing frame conditions on individual eco- or man-made systems impact neighbouring systems and might endanger the fulfilment of socially requested functions, goods and services (Fürst et al., 2007a) such aus Carbon sequestration (Schulp et al., 2008), water balance and provision of drinking water (Tehunen et al., 2008). These back-coupling effects must be considered in a holistic land-use management planning approach (Jessel & Jacobs, 2005; Bengtsson et al., 2000).

This becomes even more important with regard to changes in land-use philosophy and intensity such as the increased biofuel crop production and its multi-facetted environmental impact (Demirbas, 2009; Stoeglehner & Narodoslawsky, 2009).

To ensure a sustainable environmental development on the one hand and a sustainable provision of socially requested goods and services on the other, process knowledge must be an integral part of management planning decisions.

A process knowledge oriented land-use management demands:

1. for the identification of process-sensible indicators and for pathways how to make them accessible, understandable and usable for decision makers. (Castella & Verburg, 2007; Fürst et al., 2007a; Mendoza & Martins, 2006; Botequilha Leitao & Ahern, 2002).

2. Furthermore, instruments are demanded which are apt to deal with challenges such as the sectoral fragmentation of information on landscape level, missing data communication standards and which allow for complex knowledge and experience management (Mander et al., 2007; Van Delden et al., 2007; Wiggering et al., 2006).

3. Last but not least, such tools and instruments must fullfill the criterion of being designed in a user-friendly way to ensure their use in practice (Uran & Jansen, 2003).

The book chapter gives an introduction on process-integration into management decisions, starting with the choice of adequate process-indicators and a condensed overview on process-oriented management support approaches.

Focus is laid on the presentation of the software “Pimp your landscape” (P.Y.L.) and its application areas including some examples. The potential of P.Y.L. to support the integration of processes into land-use management decisions are discussed and remaining development tasks are identified.

2. Integration of environmental processes in land-use management decisions

The landscape is the integrative platform, where interactions and processes meet. Interactions are given between the land-users and decide upon land-use pattern changes. The land-use types interact between themselves and with their environment, with impact on environmental processes. These are pre-adjusted by the (regionally specific) environmental frame conditions, but the latter, such as regional climatic frame conditions or site potentials can be impacted again by land-use pattern changes. Figure 1 proposes a respective conceptual framework for process-oriented land-use management.

A process-oriented land-use management must consider this network of processes and interactions and is furthermore confronted with the challenge to bring together the three pillars of sustainability (i) the ecological view emphasizing environmental and ecosystem processes. On the other hand, also (ii) the economic view must be kept to optimize land-use management planning and decision making. And (iii) the (regionally specific) societal demands and frame conditions must be considered (Fürst et al., 2007a).

The DPSIR approach discussed e.g. by Mander et al. (2005) is a suitable and widely spread methodological framework for dealing with environmental management processes in a feedback loop, which controls the interactions within the cycle of Drivers–Pressures–State–Impact–Responses. The DPSIR-approach, demands (i) for a set of suitable indicators and (b) for process-models, which provide information on eco- and man-made system reactions under changing (environmental) frame conditions. Climate change as an example is one of the most important challenges for the future. Its complex impact on land-use management and the potential of single land-use types to contribute in the future to socially requested services and functions on landscape level are still under debate (Harrison et al., 2009; Prato, 2008, Metzger et al., 2006; Hitz & Smith, 2004). For supporting the integration of climate change induced processes into sustainable land-use management decisions, both - indicators and models - must be integrated into intelligent system solutions, which help to come to a common understanding and acceptance of process-based management decisions.

2.1. Process-indicators

Suitable process indicators must be apt to describe course, direction and progress of processes in single eco- or man-made systems. Furthermore, they should allow for an upscaling of such processes on landscape level (Fürst et al., 2009; Zirlewagen, 2009;

Figure 1.

Conceptual framework of process-oriented land-use management: land-use management decisions consider the close connection of interactions and processes on landscape level and are based on indicators, which reflect environmental processes and on decision criteria resulting from the interacting land-users.

Zirlewagen & von Wilpert, 2009; Fürst et al., 2007b, Zirlewagen et al., 2007; Mander et al., 2005). Finally, such indicators should also enable a comparative evaluation of processes in different eco- or man-made systems to come to a holistic view on landscape level. (Wrbka et al., 2004).

Herrick et al. (2006) highlightened the weakness of single indicators such as vegetation composition to conclude on ongoing ecosystem processes and proposed to combine the indicator vegetation composition with other process-indicators such as soil and site stability, hydrologic function and biotic integrity. Fürst et al. (2007b) propose a framework of change-ratio oriented indicators in forest ecosystems, which includes information on the natural frame conditions, man-made changes and temporal development. Nigel et al. (2005) analysed existing sets of criteria and indicators for biodiversity management impact in forests and agricultural land-use and propose a landscape oriented approach how to evaluate changes.

Concluding from research on appropriate process-indicators leads to the problem that process-indicator-based management planning is not yet realizable in practice, because the necessary holistic aggregation of single indicators or indicator sets from single ecosystems or land-use types with focus on single landscape services is still in progress (Therond et al., 2008).

2.2. Process-oriented management support tools and systems

To support the integration of environmental processes into management decisions, several scientific and technological approaches are used. The challenge to integrate manifold indicators and information as output of process-models into process-oriented decisions is picked up by computer-based management and decision support systems (MSS, DSS). They are drawing high attention as a means of improving the quality and transparency of decision making in natural resource management (Rauscher, 1999). Beyond, an increasing number of stakeholders, which are involved in natural resource management and the resulting necessity to consider multiple interests and preferences in the decision-making process led to the use of Multi-Criteria Decision Making (MCDM) techniques in DSS development. Collaborative technologies such as Group Decision Support Systems (GDSS) might help to avoid the consequences of knowledge fragmentation and will extend that support to decision-making processes involving several individuals. Mendoza & Martins (2006) remarked however that a paradigm shift is necessary in existing MCDM approaches to come from methods for problem solving to methods for problem structuring to ensure better support for the user.

Riolo et al. (2005) e.g. propose a combination of agent-based models and GIS to come to an integration of spatio-temporal processes into management decisions. Castella & Verburg (2007) tested a combination of process- and pattern-oriented models for decisions related to land-use changes. Le et al. (2008) used a multi-agent based model for simulating spatio-temporal processes in a coupled human–landscape system. From a review of existing multi-agent models (MAS), Bousquet & Le Page (2004) came to the conclusion that these mostly interdisciplinary approaches are helpful in complex decision situations.

However, Malczewski (2004) analysed appropriate systems for supporting the integration of processes and process-knowledge into management decision and compared different tools for GIS-based land-use suitability analysis. His analysis comprised methods such as GIS-based modelling and overlay mapping, multicriteria decision making and artificial intelligence methods (fuzzy logic, neural networks, cellular automatons, etc.). He highlightened, that the major limitation of GIS-based modelling and overlapping is the lack of well defined mechanisms for incorporating decision-makers preferences. Uran & Jansen (2003) found additionally that the lack of user friendliness is the reason, why most of these systems fail to be used in practice. According to Malczewski (2004), the main problem of multicriteria decision making consists in the high variability of methods, which are applied and the fact that the selection of different methods may produce different results. Considering artificial intelligence methods, Malczewski (2004) criticised in general their ‘black box’ style, which makes it difficult for the user to understand how spatial problems are analysed and how the results are produced.

Concluding from the research and comparison of existing tools and systems, (a) transparency how environmental processes and interactions are handled in the approach and how the results are produces, (b) user friendliness and (c) allowance for user dialog and user interactions seem to be the most important features (see also Diez & McIntosh, 2009).

3. Pimp your landscape - a process-oriented management support tool

3.1. Idea and conception

“Pimp your landscape” (P.Y.L.) was designed to support the understanding of complex interactions between various land-use types on landscape level and to provide a basis to evaluate the impact of user-made land-use pattern changes on most important land-use services. Therefore, the continuous spatial problem “landscape” must have been divided into spatially distinct units, which can interact and communicate with each other and to which different attributes can be assigned.

The mathematical approach, which has been chosen to reflect complex spatial interactions, was a cellular automaton with Moore-neighbourhood ship. Cellular automata were first introduced by Ulam (1952) and their potential to support the understanding of the origin and role of spatial complexity was highlightened by Tobler (1979). The approach was e.g. used to model urban structures and land-use dynamics (Barredo et al., 2003; White et al., 1996; White & Engelen, 1994, 1993), regional spatial dynamics (White & Engelen, 1997), or the development of strategies for landscape ecology in metropolitan planning (Silva et al., 2008). Nowadays, cellular automata are broadly used to simulate the impact of land-use (pattern) changes and landscape dynamics (e.g. Moreno, et al., 2009; Wickramasuriya et al., 2009; Yang et al., 2008; Holzkämper & Seppelt, 2007; Soares-Filho et al., 2002).

The starting point in P.Y.L. are land cover datasets, which are taken from Corine Landcover (CLC) 2000 or national level (biotope type / land-use type maps). The smallest unit in the P.Y.L. maps is the cell, which represents an area of 100x100 m² (CLC 2000) or 10x10 m² (only special test sites based on land register maps). A cell can only be attributed with one land-use type. Land-use types with a small share within a cell are assigned to the dominating land-use type. Furthermore, multiple other attributes can be imported as geo-referenced information layer (text or shape files) and can be assigned to the cells, such as geo-pedological information, topographical parameters and climate characteristics. Also, linear elements such as rivers, roads, railways or point-shaped elements of less than 100x100 m² such as power plants can be assigned to a cell. Regarding point-shaped elements, the extent of their spatial impact (e.g. deposition impact gradient) can be defined in the system.

Either it is possible to assign manually additional attributes to a cell, if digital information is not available. In opposite direction, information from P.Y.L. can be exported as geo-referenced text or shape file to a GIS.

The core of P.Y.L. is a hierarchical approach to evaluate the impact of land-use pattern changes, which are induced by the user, on land-use services and functions (Fig. 2).

The evaluation starts by selecting the land-use types (biotope types / ecosystem types), which are of regional relevance and by defining the land-use services and functions of regional interest. The land-use classification standards of CLC 2000 and the land-use services and functions (LUF) set described by Perez-Soba et al. (2008) are available as initial settings. The user can modify these initial settings or adopt completely different settings according to the regional application targets.

In a next step, indicator sets are identified, which provide information on the impact of the land-use types on land-use services and functions. This step requires several feed-back loops with regional experts: a major problem in the holistic evaluation on landscape level consists (a) in the different scales and dimensions of indicator sets at the different land-use types (Fürst et al., 2009) and (b) in the regional availability of respective knowledge sources. Therefore, a meaningful selection and weighting of the indicators is requested, which respects also regional expert knowledge and experiences to compensate existing knowledge gaps.

Based on the indicator sets, the impact of each land-use type on each land-use service or function is evaluated on a relative scale from 0 (worst case) to 100 (best case). The introduction of this relative scale enables (a) to compare the impact of different land-use

Figure 2.

Hierarchical evaluation of the impact of land-use pattern changes.

types on an individual land-use service or function. (b) The setting of a relative scale as reference supports also a multifunctional evaluation, which faces the challenge to make comparable reactions of different land-use services and functions on land-use pattern changes.

The resulting (regional) value table represents initial impact values of the land-use types on the services and functions. These must be regionalized to consider (a) the cell specific environmental frame conditions (e.g. height above sea level, mean annual precipitation and temperature, soil type and exposition) and (b) the neighbourhood of different land-use types. This step is supported by rule-sets, which offer the user the possibility to specify a possible increase or decrease of the initial value in dependence from neighbourhood type (homogeneous land-use types different land-use types, edge to edge corner to corner) and in dependence from the (available) environmental attributes.

Building upon the regionalized evaluation basis, landscape structure indices (landscape metrics) are introduced to adopt the evaluation of “soft” land-use services and functions referring to biodiversity or services related to the aesthetical value of a landscape. The indices help to integrate the heterogeneity of the land- use pattern, the size and connectivity of patches and the form of patches from the holistic landscape view (e.g Uuemaa et al., 2009).

In addition, the user is offered various options to insert regional planning rules and restrictions. These limit the degree of freedom to which the land-use pattern can be modified.

The user can specify (a) rules in dependence from the land-use pattern, such as if a land-use type can be converted into another, if a land-use type restricts the conversion of a neighbouring land-use type or if a linear element (street, water body) restricts the conversion of the land-use type at the cell to which this element is assigned.

Also rules for the spatial development of a land-use type can be defined, such as minimum or maximum thresholds and growth trends, i.e. if the share of a land-use type can increase, decrease or should remain equal.

(b) Rules depending from environmental frame conditions can be specified, such as if a land-use type is allowed to be converted into another in dependence from pedo-geological, topographical or climatic attributes. Here, the user can choose between the definition of value ranges of the attributes and the definition of upper or lower thresholds.

(c) Thresholds for the selected land-use services and functions can be defined. According to the evaluation logic, these must adopt a value between 0 and 100.

Taking the rules into account, the user can start the simulation and can start to modify the land-use pattern. He receives a feed-back on the impact of his changes on the land-use services and functions in real time: the system sums up the value of each cell for each land-use type and divides these sums by the total number of cells, which are displayed in the simulation. A mean value is calculated for each land-use service and the evaluation result is displayed as star diagram.

The evaluation result is based on the assumption that each land-use type as soon as it is established has its full impact on the land-use services and functions (time point tn). To come to a more realistic evaluation, the possibility to switch between the evaluation results at different time slots of 10, 30, 50 and 100 years is actually integrated into the system (time points tn-m, … tn-p).

3.2. Application areas and examples

P.Y.L. allows the user to test the complex and various effects of land-use pattern changes and the establishment of linear and point-shaped infrastructural elements on land-use services and functions by simple mouse click (Fig. 3).

The user can conduct local changes (cell by cell, freehand shape, establishment of a point-shaped element) or regional changes (changing all cells of a land-use type / changing all cells of a land-use type, which are spatially connected, establishment of linear elements).

In the philosophy of the system, natural transition processes between land-use types or ecosystems are not considered: the vision of the system is to teach the user the understanding of the effects of his actions on landscape level without additional impact factors, which he cannot influence.

In land-use management planning, P.Y.L. is adapted and tested for different application areas:

Figure 3.

Graphical user interface of P.Y.L. with variable options to modify the land-use pattern and to introduce linear / point-shaped elements (icons).

1. testing the effects of a regional application of rules and restrictions derived from EU regulations, such as EU Water Framework Directive (2000/60/EC) and Natura 2000 (79/409/EEC and 92/43/EEC) on regionally important land-use services and functions

2. testing different planning alternatives for the spatial development of urban areas and the establishment of infrastructural facilities, such as highways, railways and roads and deriving the extent of possible compensation measures to keep a politically / socially requested level of land-use services and functions such as live quality, biodiversity, etc.

3. testing the effects of flooding in the frame of open cast mining area restoration and of participatory elements in landscape planning (recreation areas and areas reserved for natural succession vs. establishment of touristic infrastructure)

4. testing the effects of climate change on regional risks and potentials and on possible mitigation strategies through changes in the land-use.

In case (a) - (c), additional effects of changing climatic frame conditions are considered, while responses to climate change are the focal point in case (d).

Considering (d), the impact of different climate change scenarios is currently tested at the model region “Dresden” (Saxony /Germany) in the project REGKLAM (Development and Testing of an Integrated Regional Climate Change Adaptation Programme for the Model Region of Dresden, www.regklam.de). Regionalized climate change scenarios are combined with soil and topographical data to derive scenario specific risk maps for erosion and drought. These are used as layers in P.Y.L. instead of primary climate, geological and topographical parameters. In a first step and based on a region specific evaluation, it is tested, how the actual land-use pattern increases or decreases the drought and erosion risk. In a next step, planning scenarios for urban growth, spatial development of forestry and agriculture are combined with the risk maps to get (a) information on possible range of responses to regional climate change impact by land-use pattern changes and (b) on areas, where additionally land-use type specific changes in management are demanded.

Figs. 4-5 show a typical run at the model region Leipzig (Saxony / Germany), where the effects of building a highway are evaluated on regional level (4) and with local focus (5) and where a compensation measure (increase of regional forests from 12 to 30 %, Fig. 6) and finally the possible impact of the construction of a lignite power plant with well described gradient (7) are tested. The star diagram displays the effects of the planning measures for five regionally selected landscape services, the drinking water quality, the aesthetical value of the landscape, climate change sensitivity (based upon regionalized climate change scenarios), regional economy and human health.

The example reveals also a still existent problem in the evaluation: the impact of linear elements on a region (based on the model of a cellular automaton) is hardly appraisable. Here, the switch between two evaluation perspectives, the regional one (Fig. 4,5,6,7) and the local one (Fig. 5) helps to approximate to the impact of this planning measure. On the other hand, the increase of the forest area seems to overcompensate the highway construction and also the power plant construction. Here, the adjustment of the evaluation result by landscape metrics is still outstanding.

Figure 4.

Test of the impact of a highway construction on regional level.

Figure 5.

Switch to the local impact of the highway with focus on a planned motorway junction.

Figure 6.

Test of a large scale compensation measure by increasing the share of forest land from 12 to ca. 30 %.

Figure 7.

Testing of the sensitivity of the compensation measure “afforestation” against the additional establishment of a power plant with western deposition gradient.

4. Discussion and conclusions

“Pimp your landscape (P.Y.L.)” was developed since 2007 to support process-knowledge integration into land-use management planning decisions on landscape level (Fürst et al., 2008). The integration of process-knowledge is realized by several characteristics of the system:

1. the mathematical approach of a cellular automaton enables to simulate by a set of rules dynamic interactions between land-use types and to consider the spatial complexity at landscape level (White et al., 1997).

2. GIS features of P.Y.L. enable to overlay various land-use pattern scenarios with various environmental parameters, which can also be scenario-driven, such as e.g. climate data (as primary data set) or risk maps (as secondary data set) etc.

3. The evaluation approach comprises a complex bundling process of indicators and expert knowledge, which is highly sensible for specific regional demands, changing evaluation targets and variable societal demands.

4. The process of changing the land-use pattern and adding linear of point-shaped elements with their resulting impact on land-use services and functions is strictly driven and defined by the user on the basis of his planning questions and the planning alternatives, he wants to test. Therefore, the criterion transparency is given and as requested by Mendoza & Martins (2006), “decision making” is replaced by support in “problem structuring and testing”.

Compared to complex spatial decision or management support approaches, P.Y.L. is based on knowledge, which might be derived from modelling, but takes its results not by coupling of models as e.g. done by Le et al. (2008) or Castella et al. (2007). Therefore, also no transition probabilities between different land-use types and historical land-use development can be simulated. This shortcoming in applicability to real world was tolerated with regard to the intention to make better understandable the effects of user-driven land-use pattern changes.

The requested complex process of knowledge bundling and the identification and selection of indicators and their combination with expert knowledge and experiences must be moderated by science individually for each region and can only build upon results from comparable regions. Furthermore, the use of a relative scale from 0 to 100 to evaluate the impact of land-use changes on land-use services and functions gives no quantitative, but only qualitative information. A resulting risk, which is not specific for P.Y.L. but applies for all knowledge management and decision support systems, is the improper parameterization and use and hereby derived inaccurate decisions (Richardson et al., 2006). However, if the evaluation process is managed well under close participation of regional experts and with detailed documentation of the knowledge sources, the evaluation results in P.Y.L. can experience a high regional acceptance. The easy adaptation of the evaluation base and the rule systems supports also testing how the “system landscape” reacts under variable assumptions on the future value of land-use types for land-use services and functions.

Finally, a possible problem can occur in the case that P.Y.L. is used at different scale levels in a region, as actually tested in the frame of the REGKLAM project. Moreno et al. (2009) e.g., highlighten the sensitivity of cellular automata to cell size and neighbourhood configuration. Furthermore, problems in the classification logic can appear, when assigning land-use types over different scale levels to the dominating land-use type in a cell. Last but not least, also landscape metrics react sensible on scale level changes (Pascual-Hortal & Saura, 2007; Uuema et al., 2005). Here, approaches how to bridge scale level problems and recommendations for the proper use of the system are actually under development.