Preface to the selected papers on spatially explicit landscape modelling: Current practices and challenges

Gaucherel, Cédric; Houet, Thomas

doi:10.1016/j.ecolmodel.2009.06.025

Cited by 18 publications

(10 citation statements)

References 37 publications

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“…Indeed, there is a critical common point between these expanding and non-expanding dynamics: they change their inner structure and the spatial relationships between their constitutive entities. In other words, the intrinsic topology of the structure is rapidly evolving in both cases, and this is what strongly suggests context-dependent formalization to model non-expanding systems [8], [19]. To formalize changes with a formal grammar opens the way to a finer analysis of dynamic properties such as: regularities (invariants), equilibrium if ever (trajectories and asymptotical behaviours), and coherency (conflicts and redundancies).…”

Section: Discussionmentioning

confidence: 99%

Understanding Patchy Landscape Dynamics: Towards a Landscape Language

et al. 2012

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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.

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Section: Discussionmentioning

confidence: 99%

Understanding Patchy Landscape Dynamics: Towards a Landscape Language

et al. 2012

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“…Currently, landscape dynamics is addressed in two contexts: (1) In remote sensing (Liu & Yang, 2015;Olmedo et al, 2018;Verbürg et al, 2004) land use/cover change (LUCC) models provide short-time predictions of land use/cover change and (2) in landscape ecology (Gaucherel et al, 2014;Gaucherel & Houet, 2009) landscape models (LMs) predict how change in landscape pattern influences the ecological process. LUCC models (Mas et al, 2014;NRC, 2014) are designed to predict land change mostly on a pixel-by-pixel basis though some polygonal or patch-based models have also been proposed (Meentemeyer et al, 2013;Xu & Brown, 2017).…”

Section: Introductionmentioning

confidence: 99%

Stochastic, Empirically Informed Model of Landscape Dynamics and Its Application to Deforestation Scenarios

Nowosad

Stepinski

2019

Geophysical Research Letters

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Land change including deforestation undermines the sustainability of the environment.Using data on 1992-2015 pattern change in over 1.7 million mesoscale landscapes worldwide we developed a stochastic model of long-term landscape dynamics. The model suggests that observed heterogeneous landscapes are short-lived stages in a transition between quasi-stable homogeneous landscapes of different themes. As a case study we used Monte Carlo simulations based on our model to derive a probability distribution for evolutionary scenarios of landscapes that undergo a forest-to-agriculture transit, a prevalent element of deforestation. Results of simulations show that most likely and the fastest deforestation scenario is through the sequence of highly aggregated forest/agriculture mosaics with a decreasing share of the forest. Simulations also show that once forest share drops below 50% the remainder of the transit is rapid. This suggests that possible conservation policy is to protect mesoscale tracts of land before the forest share drops below 50%. Plain Language SummaryLand change across the world undermines the sustainability of the environment. Understanding the dynamics of landscape change would help to find trade-offs between the development and sustainability of the environment. We developed a data-driven model capable of providing plausible scenarios of long-term evolution of landscapes. Using this model, we proposed a general principle of landscape evolution: heterogeneous landscapes are short-lived stages in transit between two homogeneous landscapes of different land cover. As a case study we applied our model to identify the most likely scenarios of the forest-to-agriculture transit, a dominant source of deforestation. The model suggests that scenarios of the forest-to-agriculture transits that proceed through a sequence of aggregated mosaics are more frequent and more damaging to an environment than transits proceeding through a sequence of disaggregated mosaics. It also suggests that preserving mesoscale (∼100 km 2 ) tracts of land before they lose 50% of forest share may be a good conservation strategy.

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“…Currently, landscape dynamics is addressed in two contexts; (1) in remote sensing (Verburg et al, 2004;Liu & Yang, 2015;Olmedo et al, 2018) land use/cover change (LUCC) models provide short-time predictions of land use/cover change, and (2) in landscape ecology (Gaucherel & Houet, 2009;Gaucherel et al, 2014) landscape models (LM) predict how change in landscape pattern influences the ecological process. LUCC models (NRC, 2014;Mas et al, 2014) are designed to predict land change mostly on a pixel-by-pixel basis though some polygonal or patch-based models have also been proposed (Meentemeyer et al, 2013;Xu & Brown, 2017).…”

Section: Introductionmentioning

confidence: 99%

Stochastic, empirically‐informed model of landscape dynamics and its application to deforestation scenarios

Nowosad¹,

Stepinski²

2019

Preprint

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Land change including deforestation undermines the sustainability of the environment. Using data on 1992‐2015 pattern change in over 1.7 million meso‐scale landscapes worldwide we developed a stochastic model of long‐term landscape dynamics. The model suggests that observed heterogeneous landscapes are short‐lived stages in a transition between quasi‐stable homogeneous landscapes of different themes. As a case study we used Monte Carlo simulations based on our model to derive a probability distribution for evolutionary scenarios of landscapes that undergo a forest‐to‐agriculture transit, a prevalent element of deforestation. Results of simulations show that most likely and the fastest deforestation scenario is through the sequence of highly aggregated forest/agriculture mosaics with a decreasing share of the forest. Simulations also show that once forest share drops below 50% the remainder of the transit is rapid. This suggests that possible conservation policy is to protect meso‐scale tracts of land before the forest share drops below 50%.

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Preface to the selected papers on spatially explicit landscape modelling: Current practices and challenges

Abstract: To cite this version:C. Gaucherel, Thomas Houet. Preface to the selected papers on spatially explicit landscape modelling: current practices and challenges. Ecological Modelling, Elsevier, 2009, 220 (24), pp

Cited by 18 publications

References 37 publications

Understanding Patchy Landscape Dynamics: Towards a Landscape Language

Understanding Patchy Landscape Dynamics: Towards a Landscape Language

Stochastic, Empirically Informed Model of Landscape Dynamics and Its Application to Deforestation Scenarios

Stochastic, empirically‐informed model of landscape dynamics and its application to deforestation scenarios

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