Nicolas Dendoncker

Spatial analysis and modelling of land use distributions in Belgium

When statistical analyses of land use drivers are performed, they rarely deal explicitly with spatial autocorrelation. Most studies are undertaken on autocorrelation-free data samples. By doing this, a great deal of information that is present in the dataset is lost. This paper presents a spatially explicit, cross-sectional analysis of land use drivers in Belgium. It is shown that purely regressive logistic models only identify trends or global relationships between socio-economic or physico-climatic drivers and the precise location of each land use type. However, when the goal of a study is to obtain the best statistical model fit of land use distribution, a purely autoregressive model is appropriate. It is shown that this type of model deals appropriately with spatial autocorrelation as measured by the lack of autocorrelation in the deviance residuals of the model. More specifically, three types of autoregressive models are compared: (1) a set of binomial logistic regression models (one for each modelled land use) accounting only for the proportion of the modelled land use within the neighbourhood of a cell; (2) a multinomial autologistic regression that accounts for the composition of a cells neighbourhood; and (3) a stateof-the-art Bayesian Maximum Entropy (BME) based model that accounts fully for the spatial organization of the land uses within the neighbourhood of a cell. The comparative analysis shows that the BME approach has no advantages over the other methods, for our specific application, but that accounting for the composition of a cells neighbourhood is essential in obtaining an optimal fit

Exploring spatial data uncertainties in land-use change scenarios

This paper evaluates errors and uncertainties in representing landscapes that arise from different data rasterization methods, spatial resolutions, and downscaled land‐use change (LUC) scenarios. A vector LU dataset for Luxembourg (minimum mapping unit: 0.15 ha; year 2000) was used as the baseline reference map. This map was rasterized at three spatial resolutions using three cell class assignment methods. The landscape composition and configuration of these maps were compared. Four alternative scenarios of future LUC were also generated for the three resolutions using existing LUC scenarios and a statistical downscaling method creating 37 maps of LUC for the year 2050. These maps were compared in terms of composition and spatial configuration using simple metrics of landscape fragmentation and an analysis of variance (ANOVA). Differences in landscape composition and configuration between the three cell class assignment methods and the three spatial resolutions were found to be at least as large as the differences between the LUC scenarios. This occurred in spite of the large LUC projected by the scenarios. This demonstrates the importance of the rasterization method and the level of aggregation as a contribution to uncertainty when developing future LUC scenarios and in analysing landscape structure in ecological studies.

Assessing uncertainties in land cover projections

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.

Conceptualising the analysis of socio-ecological systems through ecosystem services and agent-based modeling

In this article we present a conceptual model for analysing socio-economic systems using agent-based modelling, with ecosystem services as the focus of analysis. This is designed to allow the development of integrated models of human land managers, the landscapes which they manage and certain species of interest which live in these landscapes. We argue that in order to understand the effect of humans on the landscape and ES provision, we must take into account the preferences and priorities which they have; it is necessary to firmly embed their models into a rich socio-ecological model context, while taking into account the idiosyncrasies of human decision making. This requires a rich representation of plant and animal responses to human actions, in order to provide dynamic feedback on the results of courses of action and move beyond the static indicator frameworks commonly used. After exploring possible implementations of parts of the conceptual model, we conclude that it will provide a useful tool for analysing the effects of human behaviour on ecosystem services.

Towards Participatory Integrated Valuation and Modelling of Ecosystem Services under Land-use Change

The lack of consideration for ecosystem services (ES) values in current decision making is recognised as one of the main reasons leading to an intense competition and arguably unsustainable use of well-located available land. In this article, we present a framework for the Valuation Of Terrestrial Ecosystem Services (VOTES), aiming at structuring a methodology that is applicable for valuing ES in a given area through a set of indicators that are both meaningful for local actors and scientifically constructed. Examples from a case study area in central Belgium are used to illustrate the methodology: a stepwise procedure starting with the valuation of ES at present. The valuation of the social, biophysical and economic dimensions of ES is based on current land-use patterns. Subsequently, scenarios of land-use change are used to explore potential losses (and/or gains) of ES in the future of the study area. With the VOTES framework, we aim at (1) incorporating stakeholders’ inputs to widen the valuation process and increase trust in policy-oriented approach; (2) integrating valuation of ES with a sustainable development stance accounting for land-use change and (3) developing suggestions to policy-makers for integrating ES monitoring in policy developments.

Inclusive Ecosystem Services Valuation

Abstract This chapter discusses the concept of ecosystem services valuation. It argues that beyond monetary valuation, ES valuation should also take into account ecological and social values. Valuation should be geared toward strong sustainability in order to improve the well-being of every individual and society, now and in the future. Following a systemic approach, bundles of ES should be valued together. When systems are far from critical thresholds, valuing changes through various alternatives is appropriate. Deliberative multicriteria decision tools could be appropriate to collectively value ES. However, when systems are close to thresholds or tipping points, ecosystem service valuation will need to switch from choosing among resources or alternatives to valuing the avoidance of catastrophic ecosystem change. Finally, it is important to remember that valuation is merely a tool and not a solution in itself. When one is valuing for sustainability, the questions of politics, governance, and institutions cannot be ignored.

Empirically derived suitability maps to downscale aggregated land use data

Understanding mechanisms that drive present land use patterns is essential in order to derive appropriate models of land use change. When static analyses of land use drivers are performed, they rarely explicitly deal with spatial autocorrelation. Most studies are undertaken on autocorrelation-free data samples. By doing this, a great deal of information that is present in the dataset is lost. This paper presents a spatially explicit, cross-sectional, logistic analysis of land use drivers in Belgium. It is shown that purely regressive logistic models can only identify trends or global relationships between socio-economic or physico-climatic drivers and the precise location of each land use type. However, when the goal of a study is to obtain the best model of land use distribution, a purely autoregressive (or neighbourhood-based) model is appropriate. Moreover, it is also concluded that a neighbourhood based only on the 8 surrounding cells leads to the best logistic regression models at this scale of observation. This statement is valid for each land use type studied – i.e. built-up, forests, cropland and grassland.

CICES going Local : Ecosystem Services Classification Adapted for a Highly Populated Country

Abstract Multiple classification systems for ecosystem services (ES) make comparison and integration between studies and assessments very difficult. With the fast-growing number of ecosystem services assessment and valuation studies, there is a need to identify generally agreed definitions and to design a common base that will enable comparisons between ecosystem services assessments at different places. The recently developed Common International Classification for Ecosystem Services (CICES) is aiming to fill this gap. One advantage of the CICES approach is that it allows adjustment to local conditions. Through an iterative consultation round with Belgian experts from administrations, policy support units, and research centers CICES has been adapted to the needs of a highly populated country, where multifunctional land use is very common. The goal of CICES-Be is to introduce a common reference base for ecosystem services in Belgium, which is locally adapted and compatible with an international standard.

Negotiated Complexity in Ecosystem Services Science and Policy Making

It’s a long way from scientific knowledge to concrete policy action. Along the way many decisions have to be made. A lot of these decisions relate to setting priorities. With regard to policy uptake of scientific knowledge on ecosystem services, the need for an integrated decision-making framework is crucial. Framing complexity is a crucial aspect of any ecosystem services approach: How do we deal with ecological and social complexity? The complexity to be taken into account and the approach for dealing with that complexity are part of context-specific negotiation among actors involved in the process of investigation and interpretation, and as such becomes negotiated complexity. We propose an analytical deliberative multicriteria decision-support framework for ecosystem services decision making. We illustrate the practicalities of the framework by referring to its application in the field of environmental health in Belgium, and we reflect on the opportunities for a similar approach regarding ecosystem services.

Ecosystem services, social interdependencies, and collective action: a conceptual framework

The governance of ecosystem services (ES) has been predominantly thought of in terms of market or state-based instruments. Comparatively, collective action mechanisms have rarely been considered. This paper addresses this gap by proposing a conceptual framework that brings together ES, social interdependencies, and collective action thinking. We use an ES conceptual lens to highlight social interdependencies among people so as to reflect on existing or potential collective actions among them. This framework can also contribute to increasing people’s awareness of their mutual interdependencies and thereby fostering, framing, or enriching collective action, in ways that take into account the diversity and complexity of ecological processes underlying human activities. Our approach can contribute in particular to agroecological transitions that require landscape level innovations and coordination mechanisms among land users and managers. The framework distinguishes three types of social interdependencies: (i) between ES beneficiaries and ES providers, (ii) among beneficiaries, and (iii) among providers. These social interdependencies are in turn analyzed according to four main dimensions that are critical for collective action: (i) cognitive framing of interdependencies, (ii) levels of organization, (iii) formal and informal institutions, and (iv) power relations. Finally, we propose a strategy to turn this framework into action in contexts of participatory action research, a strategy grounded on a number of methodological principles and tools that convey complexity and increase people’s awareness of interdependencies in agrarian social-ecological systems.