Thomas Houet

Exploring subtle land use and land cover changes: a framework for future landscape studies

Land cover and land use changes can have a wide variety of ecological effects, including significant impacts on soils and water quality. In rural areas, even subtle changes in farming practices can affect landscape features and functions, and consequently the environment. Fine-scale analyses have to be performed to better understand the land cover change processes. At the same time, models of land cover change have to be developed in order to anticipate where changes are more likely to occur next. Such predictive information is essential to propose and implement sustainable and efficient environmental policies. Future landscape studies can provide a framework to forecast how land use and land cover changes is likely to react differently to subtle changes. This paper proposes a four step framework to forecast landscape futures at fine scales by coupling scenarios and landscape modelling approaches. This methodology has been tested on two contrasting agricultural landscapes located in the United States and France, to identify possible landscape changes based on forecasting and backcasting agriculture intensification scenarios. Both examples demonstrate that relatively subtle land cover and land use changes can have a large impact on future landscapes. Results highlight how such subtle changes have to be considered in term of quantity, location, and frequency of land use and land cover to appropriately assess environmental impacts on water pollution (France) and soil erosion (US). The results highlight opportunities for improvements in landscape modelling.

Monitoring and modelling landscape dynamics

Changes in land cover and land use are among the most pervasive and important sources of recent alterations of the Earths land surface.This special issue also presents new directions in modelling landscape dynamics. Agent-based models have primarily been used to simulate local land use and land cover changes processes with a focus on decision making (Le 2008; Matthews et al. 2007; Parker et al. 2003; Bousquet and Le Page 2001).

Understanding patchy landscape dynamics: Towards a landscape language

Patchy landscapes driven by human decisions and/or natural forces are still a challenge to be understood and modelled. No attempt has been made up to now to describe them by a coherent framework and to formalize landscape changing rules. Overcoming this lacuna was our first objective here, and this was largely based on the notion of Rewriting Systems, also called Formal Grammars. We used complicated scenarios of agricultural dynamics to model landscapes and to write their corresponding driving rule equations. Our second objective was to illustrate the relevance of this landscape language concept for landscape modelling through various grassland managements, with the final aim to assess their respective impacts on biological conservation. For this purpose, we made the assumptions that a higher grassland appearance frequency and higher land cover connectivity are favourable to species conservation. Ecological results revealed that dairy and beef livestock production systems are more favourable to wild species than is hog farming, although in different ways. Methodological results allowed us to efficiently model and formalize these landscape dynamics. This study demonstrates the applicability of the Rewriting System framework to the modelling of agricultural landscapes and, hopefully, to other patchy landscapes. The newly defined grammar is able to explain changes that are neither necessarily local nor Markovian, and opens a way to analytical modelling of landscape dynamics.

Mapping urban climate zones and quantifying climate behaviors – An application on Toulouse urban area (France)

Facing the concern of the population to its environment and to climatic change, city planners are now considering the urban climate in their choices of planning. The use of climatic maps, such Urban Climate Zone‑UCZ, is adapted for this kind of application. The objective of this paper is to demonstrate that the UCZ classification, integrated in the World Meteorological Organization guidelines, first can be automatically determined for sample areas and second is meaningful according to climatic variables. The analysis presented is applied on Toulouse urban area (France). Results show first that UCZ differentiate according to air and surface temperature. It has been possible to determine the membership of sample areas to an UCZ using landscape descriptors automatically computed with GIS and remote sensed data. It also emphasizes that climate behavior and magnitude of UCZ may vary from winter to summer. Finally we discuss the influence of climate data and scale of observation on UCZ mapping and climate characterization.

Combining narratives and modelling approaches to simulate fine scale and long-term urban growth scenarios for climate adaptation

Although climate scientists explore the effects of climate change for 2100, it is a challenging time frame for urban modellers to foresee the future of cities. The question addressed in this paper is how to improve the existing methodologies in order to build scenarios to explore urban climate impacts in the long term and at a fine scale. This study provides a structural framework in six steps that combines narratives and model-based approaches. The results present seven scenarios of urban growth based on land use strategies and technological and socio-economic trends. These contrasted scenarios span the largest possible world of futures for the city under study. Urban maps for 2010, 2040 and 2100 were used to assess the impacts on the Urban Heat Island. The comparison of these scenarios and related outputs allowed some levers to be evaluated for their capacity to limit the increase of air temperature. Narrative scenarios of urban change are flexible and highly imaginative.Model-based scenarios of urban change allow for quantitative environmental assessment.A six-step method provides a framework for combining and benefiting from both approaches.Contrasting scenarios of urban change and their impacts on Urban Heat Island are simulated.The comparison of scenarios provides insights into key triggers to improve urban adaptation.

Land change modelling: moving beyond projections

During the last decades, there has been an increasing interest from the academic and policy communities to monitor and model changes of the earth surface. Modeling environmental dynamics helps to understand changes that are taking place currently and to anticipate future evolutions. Prospective simulation supports decision-making for environmental management and land planning. This special issue is dedicated to advances in land change modeling. Land Use / Land Cover Change, also called LUCC, is certainly a prominent interface between natural and social dynamics because anthropogenic LUCC has a profound impact on Earth. LUCC impacts a large amount of highly relevant topics such as resource exploitation, climate change, biodiversity loss, etc. Land change modeling can provide transparent, efficient and sustainable decision support to these current and rising environmental problems

Improving the simulation of fine-resolution landscape changes by coupling top-down and bottom-up land use and cover changes rules

Modelling land use and cover changes (LUCC) at local and landscape scales simultaneously, in terms of composition and configuration, remains today highly challenging. Agricultural landscapes offer an illustrative context for this purpose. This article presents a modelling platform (DYPAL) able to simulate LUCC at both local (agricultural parcel, farm) and landscape levels by combining LUCC processes with an optimization algorithm. The efficiency of this approach is assessed by comparing it with an approach applying the same LUCC processes without optimization. Simulations have been developed for two representative case studies of temperate intensive agricultural mosaics: (1) neutral landscapes with simple and theoretical rules and (2) observed landscapes with realistic crop successions rules. Results show that this modelling platform improves the simulation of LUCC achieved at fine resolution, although not systematically. Improvements are observed when compared to theoretical farming practices. But, when compared with an observed landscape, it is true for one type of (arable) farms only. Several hypotheses are discussed, such as the fact that farmers possibly do not follow optimized rules. Finally, this study illustrates that the use of several indices is crucial to assess whether a simulated landscape is realistic or not, because it does not rely to the assessment of the predictive power of the model.

Modelling regional land change scenarios to assess land abandonment and reforestation dynamics in the Pyrenees (France)

Over the last decades and centuries, European mountain landscapes have experienced substantial transformations. Natural and anthropogenic LULC changes (land use and land cover changes), especially agro-pastoral activities, have directly influenced the spatial organization and composition of European mountain landscapes. For the past sixty years, natural reforestation has been occurring due to a decline in both agricultural production activities and rural population. Stakeholders, to better anticipate future changes, need spatially and temporally explicit models to identify areas at risk of land change and possible abandonment. This paper presents an integrated approach combining forecasting scenarios and a LULC changes simulation model to assess where LULC changes may occur in the Pyrenees Mountains, based on historical LULC trends and a range of future socio-economic drivers. The proposed methodology considers local specificities of the Pyrenean valleys, sub-regional climate and topographical properties, and regional economic policies. Results indicate that some regions are projected to face strong abandonment, regardless of the scenario conditions. Overall, high rates of change are associated with administrative regions where land productivity is highly dependent on socio-economic drivers and climatic and environmental conditions limit intensive (agricultural and/or pastoral) production and profitability. The combination of the results for the four scenarios allows assessments of where encroachment (e.g. colonization by shrublands) and reforestation are the most probable. This assessment intends to provide insight into the potential future development of the Pyrenees to help identify areas that are the most sensitive to change and to guide decision makers to help their management decisions.

Evaluating the Spatial Uncertainty of Future Land Abandonment in a Mountain Valley（Vicdessos, Pyrenees-France）： Insights from Model Parameterization and Experiments

European mountains are particularly sensitive to climatic disruptions and land use changes. The latter leads to high rates of natural reforestation over the last 50 years. Faced with the challenge of predicting possible impacts on ecosystem services, LUCC models offer new opportunities for land managers to adapt or mitigate their strategies. Assessing the spatial uncertainty of future LUCC is crucial for the definition of sustainable land use strategies. However, the sources of uncertainty may differ, including the input parameters, the model itself, and the wide range of possible futures. The aim of this paper is to propose a method to assess the probability of occurrence of future LUCC that combines the inherent uncertainty of model parameterization and the ensemble uncertainty of the future based scenarios. For this purpose, we used the Land Change Modeler tool to simulate future LUCC on a study site located in the Pyrenees Mountains (France) and two scenarios illustrating two land use strategies. The model was parameterized with the same driving factors used for its calibration. The definition of ’static vs. dynamic’ and ’quantitative vs. qualitative (discretized)’ driving factors, and their combination resulted in four parameterizations. The combination of model outcomes produced maps of the spatial uncertainty of future LUCC. This work involves adapting the definition of spatial uncertainty in the literature to future-based LUCC studies. It goes beyond the uncertainty of simulation models by integrating the uncertainty of the future to provide maps to help decision makers and land managers.

From meta-studies to modeling: Using synthesis knowledge to build broadly applicable process-based land change models

This paper explores how meta-studies can support the development of process-based land change models (LCMs) that can be applied across locations and scales. We describe a multi-step framework for model development and provide descriptions and examples of how meta-studies can be used in each step. We conclude that meta-studies best support the conceptualization and experimentation phases of the model development cycle, but cannot typically provide full model parameterizations. Moreover, meta-studies are particularly useful for developing agent-based LCMs that can be applied across a wide range of contexts, locations, and/or scales, because meta-studies provide both quantitative and qualitative data needed to derive agent behaviors more readily than from case study or aggregate data sources alone. Recent land change synthesis studies provide sufficient topical breadth and depth to support the development of broadly applicable process-based LCMs, as well as the potential to accelerate the production of generalized knowledge through model-driven synthesis.