# Simulating interactions between land-use and land-cover changes for prospective scenarios with FORESCEM

Gaëtan Palka, Thomas Houet

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Gaëtan Palka, Thomas Houet. Simulating interactions between land-use and land-cover changes for prospective scenarios with FORESCEM. 2025. hal-05121081

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# Simulating interactions between land-use and land-cover changes for prospective scenarios with FORESCEM.

Gaëtan Palka<sup>1,2,3,*</sup>, Thomas Houet<sup>1,3</sup>

1. LETG-Rennes COSTEL UMR 6554 CNRS / Université Rennes 2, Place du Recteur Henri Le Moal, 35043 Rennes Cedex, France

2. UR 1210 CEDETE, Université d'Orléans, 10 rue de Tours, 45065 Orléans, France

3. LTSER "Zone Atelier Armorique", CNRS, Rennes, F-35043, France

* Corresponding author: gaetan.palka@univ-orleans.fr

## Abstract

Anticipating socio-environmental impacts of spatial planning strategies is a prerequisite for sustainable development design. Land change models are increasingly employed to evaluate the impacts of spatial planning on land use and land cover (LULC), and their subsequent effects on ecosystem services and environmental resources. This paper presents a process-based model—the FORecasting landscapE SCEnarios Model (FORESCEM)—designed to spatially simulate future LULC changes based on narrative scenarios developed through participatory or expert-driven approaches. The model enables the assessment of the effects of interactions among LULC changes within human-managed landscapes. Validation results demonstrate the model's capability to replicate both natural and anthropogenic dynamics, effectively simulating trend-based and trend-breaking LULC trajectories under contrasting scenarios.

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## 1 Introduction

Both land use and land cover changes (LUCC) critically affect land-atmosphere exchanges and exert significant influences on climate regulation, water and soil quality, biogeochemical cycles, biodiversity, and ecosystem services (UNEP, 2002; Lambin & Geist, 2008; Akber et al., 2018). Evaluating potential future LUCC and their associated impacts has been recognized as a key priority in land change science, given their central role in global environmental change (Lambin &

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Geist, 2008). At the same time, effectively integrating considerations of climate, water and soil quality, biogeochemical processes, biodiversity, and ecosystem services into spatial planning remains a complex and persistent challenge (Wilson & Piper, 2008; Edman et al., 2011; Albert et al., 2020; Wang, 2001).

Exploring medium- to long-term future land-use and land-cover changes (LUCC) has become essential for the development of resilient and adaptive strategies. The integration of dynamic, spatially explicit models with scenario-based approaches has emerged as a widely adopted and effective method for projecting future LUCC (Dorning et al., 2015; Xiang & Clarke, 2003; Couclelis, 2005). These approaches are frequently employed to evaluate the implications of current or short-term land management plans and policies, particularly in identifying high-risk areas for natural hazard mitigation and in supporting decision-making processes for stakeholders. A persistent challenge in LUCC modelling lies in adequately capturing the interactions between land use and land cover changes (Verburg et al., 2016). While the concepts of land use and land cover are interrelated, they differ in fundamental ways that are critical when analyzing, modeling, and simulating socio-ecological systems. Two key differences are that "[land cover] refers to the composition of the features of the Earth's surface, while [land use] refers to the type of human activity taking place at or near the Earth's surface" (Cihlar & Jansen, 2001, p. 278) and the relationship between land-use and land-cover is not a one-to-one relationship. Modeling one (e.g., land cover) inevitably leads to a loss of information about the other (e.g., land use). However, if interactions between land use and land cover changes are prime features of land change model (Verburg et al. 2004; Verburg 2006; Güneralp et al. 2012), most of research in the literature consider land uses and land covers as a whole, within one single land classification map, avoiding the possibility to account for their interactions.

Simulating both land use and land cover changes, along with their interactions, requires the integration of scenario-based frameworks with modeling tools—a methodology aligned with the Story-And-Simulation (SAS) approach (Alcamo, 2008). In this context, scenarios are developed through participatory processes or expert knowledge and are formalized as narratives that specify where and how land-use changes may occur in the future, driven by anticipated policies or land management strategies (Houet et al., 2017). These narratives, once formalized and translated into quantitative variables, serve as inputs to parameterize land change models. The resulting outputs—LUCC maps—not only visualize the narratives but also quantify the diversity of potential future landscapes, enabling the construction of imaginative yet data-informed scenarios (Houet et al., 2016). This approach aligns with the foresight paradigm (Godet, 1986), which emphasizes the exploration of multiple plausible futures rather than a single deterministic projection.
In addition to simulating both land use and land cover changes and their interactions, the spatial resolution of the model is a critical consideration. Fine-scale modeling of LUCC is essential for accurately assessing the impacts of these changes on natural processes such as vegetation dynamics (Bartha et al., 2004; Delheimer et al., 2023), as well as for informing the development of context-specific and effective policy interventions (Mertens & Lambin, 1999). Nevertheless, simulating LUCC at fine spatial resolutions remains a considerable challenge due to data availability, computational demands, and the complexity of processes operating at multiple scales (Houet & Verburg, 2022).

Furthermore, when coupled with scenario-based approaches, the use of LUCC models raises critical questions regarding their validation (see the review by van Vliet et al., 2016). Commonly employed validation techniques—such as assessments of location accuracy and spatial pattern conformity—are particularly challenging to apply in land systems that are heavily influenced by human activity. These methods generally assume that land systems evolve along current trends, particularly in terms of spatial patterns, surface extents, and underlying drivers. While such techniques are effective when applied to retrospective simulations—where modelled outputs can be compared against observed historical LUCC data—they are inherently limited in the context of mid- to long-term future projections due to the deep uncertainty associated with future developments. In such cases, evaluating the accuracy of modelled outcomes becomes virtually impossible, as there is no empirical reference against which to compare them.

Moreover, no LUCC model can perfectly reproduce reality (Mas et al., 2014). As suggested by Houet et al. (2016), model validation should instead aim to enhance the plausibility of simulations by assessing whether the modelled landscapes exhibit spatial patterns and dynamics that are coherent, credible, and consistent with the underlying scenarios. This form of validation primarily seeks to build stakeholder confidence in the modelling process. Although stochastic processes may yield multiple plausible outcomes from the same parameter set (i.e., scenario), such variability can actually foster stakeholder engagement by emphasizing the exploratory, rather than predictive, nature of scenario-based modeling (Rigo & Houet, 2023; Rigo et al., 2022).

In this paper, we introduce a fine-resolution LUCC model specifically designed to simulate future, spatially explicit, and contrasting scenarios. The model emphasizes a transparent and traceable translation of stakeholder-informed narratives into model inputs, enabling the simulation of interactions between land use and land cover changes. In this framework, land cover changes refer to natural vegetation dynamics (e.g., transitions from meadows to forested states) as well as changes in agricultural or forestry cover (e.g., crop rotations, transitions from planted to mature
95 forests). Land use changes, by contrast, are defined as land changes resulting from human
96 decisions— eg. deforestation, urbanization or abandonment. The model is applied to two case
97 studies: the Couesnon catchment in France, which employs participatory-based scenarios, and
98 the Pas, Miera, and Asón catchments in Spain, where scenarios are based on expert knowledge.
99 For model evaluation, we propose a two-pronged assessment approach: (1) a series of unitary
100 tests to verify rule compliance, and (2) a retrospective simulation of a past period to assess the
101 model's capacity to reproduce observed LUCC dynamics. The paper is structured as follows: first,
102 the model's architecture and underlying principles are described; second, the case study areas
103 and scenario characteristics are introduced; third, simulation results are presented; and finally,
104 the discussion addresses key features of the model and the implications of the validation
105 framework.

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# 2 Materials and methods

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## 2.1 Model design

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### 2.1.1 Principles

109 The FORecasting landscapE SCEnarios Model (FORESEM) has been developed as a tool for
110 simulating land change processes at a fine spatial resolution, based on scenario narratives co-
111 constructed with stakeholders. To achieve this objective, the model integrates several key
112 features that underpin its conceptual and methodological framework (Figure 1):

113 (1) Stakeholder narratives can provide information on both land uses (e.g., urbanization, agriculture) and land covers (e.g., wheat fields, permanent meadows, natural recolonization), as well as on the changes and interactions between them. Accordingly, the
114 model explicitly distinguishes land use and land cover as separate data. It incorporates a set of
115 transition rules that govern changes within land uses, within land covers, and between land uses
116 and land covers, allowing for the simulation of complex and interdependent LUCC processes.
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119 (2) Stakeholder narratives may incorporate temporal variations in land-use and land-cover changes, including shifts in rates, directions, or durations. These changes may diverge from
120 historical trends (e.g., a reduction in the expansion of urbanized areas), involve abrupt disruptions
121 (such as the creation of a new housing estate resulting from planning decisions, or a randomly
122 located land-use shift due to deforestation or reforestation), or stem from unexpected events
123 (e.g., wildfires). To account for such dynamics, the model is implemented in Python to ensure
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flexibility, adaptability, and ease of evolution. It includes mechanisms that allow for the punctual and localized enforcement of specific land-use or land-cover changes. In FORESCEM, deviations from LUCC trends are operationalized through two distinct mechanisms: *events* and *policies*. Events represent sudden, discrete changes. They forcibly assign a specific land cover or land use to a targeted location and, where necessary, update the corresponding land cover or land use. For instance, a silvicultural project—modelled as an event in FORESCEM—applied to agricultural land will force the land cover to shift to one compatible with silvicultural use. However, if the same project is implemented in an abandoned area where land cover types (e.g., encroachment) are already compatible with silviculture, a change in land cover is not required, and only the land use is updated. Policies, by contrast, represent broader spatial constraints that either facilitate or restrict the allocation of specific land uses or covers over the long term. These may function as soft constraints—for example, guiding the gradual relocation of certain crops, thereby shaping landscape evolution—or as hard constraints, such as zoning regulations that prohibit urbanization outside designated areas. Unlike events, policies do not enforce immediate change but influence land-use dynamics in response to evolving land demand.

(3) **Stakeholders seek to understand the mechanisms within the model that drive land-use and land-cover changes.** This necessitates the use of explainable simulation models—such as logistic regression or cellular automata—based on algorithms widely adopted in well-established land change models (Van Vliet et al., 2016; Silva & Wu, 2012). FORESCEM builds upon this foundation by employing transparent, interpretable modelling techniques and relying on commonly available and easily understandable data sources, such as raster-based land cover maps and spatially explicit, weighted explanatory variables.

(4) **Stakeholder narratives typically refer to changes occurring at the scale of coherent elementary spatial units, often linked to perceived landscape structures.** In response, FORESCEM operates at a fine spatial resolution, using *parcels* as the fundamental units of simulation—reflecting the scale at which land development decisions are commonly made. Parcels can be derived from existing spatial datasets (e.g., cadastral maps, or the LPIS database for European agricultural land), or generated through landscape segmentation based on environmental criteria such as slope, elevation, aspect, or geology. In FORESCEM, a parcel is defined as a spatial object composed of a set of contiguous pixels (e.g., 10 m × 10 m) that undergo change collectively. This object-based approach allows the model to generate spatially coherent and realistic landscape patterns, enhancing its relevance for ecological assessments and land planning applications (Houet et al., 2022).
<figure><img src="image_2.png" /><figcaption>Figure 1: Conceptual description of the framework design accounting for interactions between land-use and land-cover changes: (1) if encroachment [as a land cover] appears or grows accordingly to the initialization, it converts the [land use of the] parcel as abandonment; (2) if urbanization or sylvicultural lands appears or grows accordingly to the initialization, it modifies the land-cover changes of parcels located within the concerned area; (3) if new scenario-based land-use changes appear, it modifies the land-cover change processes.</figcaption></figure>

### 2.1.2 Architecture and functioning

FORESCEM is built on a modular architecture that integrate all necessary processing components required to perform LUCC simulations —including transition, calibration, and simulation modules— while offering flexibility for users to customize the latter with their own functions (Figure 2).

The Transition module supports the definition of all land cover transitions based on two reference maps from different time points. When not explicitly defined by stakeholder narratives, future land-cover demand (i.e., the quantity of each land cover to be simulated) can be estimated using
a Markov Chain approach, similar to that employed in the Land Change Modeler (LCM) (Eastman, 2012). This module also assists users in identifying unrealistic or biologically implausible transitions and in preparing the transition matrix for simulation. For example, a direct transition from meadows to forest—though observed over a long interval—may be flagged if it omits intermediate stages such as shrub encroachment, which are ecologically necessary for realism.

The *Calibration module* estimates the influence of spatially explicit drivers on the location of each land cover. There is no predefined limit on the number of explanatory variables that can be used.

By default, logistic regression is employed to generate statistical models, which are then used to compute probability maps as used by Verburg and Overmars (2007) and Mas et al. (2014). The *Simulation module* loads all required inputs, including spatial masks, probability maps and policy and event layers, and runs the simulation using user-defined functions. In this study, land cover allocation is performed by selecting, for each parcel, the land cover type that maximizes the computed probability while respecting the annual land demand and transition rules.

The simulation begins with an initialization phase during which all necessary data and configuration parameters are loaded, including land cover, land use, parcel age, policy settings, events, demand, and user-defined functions. The simulation then proceeds through a series of iterations. Each iteration starts with the computation of target land areas based on current demand and the previous system state. Subsequently, probability maps are generated for each land cover and land use category. Next, various constraint masks are applied to adjust the probability maps. The age mask enforces persistence or change according to parcel age. Land cover and land use masks constrain possible transformations based on predefined transition matrices. Policy masks weight the probabilities according to policy influence, while event masks restrict potential changes to specific land covers and modulate demand accordingly. Following the adjustment of probabilities, land cover allocations required to meet demand are determined.

This begins with non-competitive situations, where only one land cover is possible, followed by competitive cases involving multiple candidates. Allocations not driven by demand are resolved next. Land uses are then assigned. Finally, parcel ages are updated: they are reset to one if a change has occurred or incremented by one if the parcel remains unchanged.
<figure><img src="image_3.png" /></figure>

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203 2.2 Study sites

204 The application and validation of FORESCEM were conducted using two contrasting study areas: 205 the Couesnon catchment in Brittany, France, and the Pas, Miera, and Asón catchments in 206 Cantabria, Spain. The French case study covers an area of 1,711 km², encompassing the 207 Couesnon catchment itself along with a 3 km buffer zone (Figure 3). This region represents a highly 208 anthropogenic landscape dominated by intensive agricultural land uses, particularly dairy and 209 livestock production, which occupy approximately 80% of the study area. The predominant land 210 covers include grasslands, maize, and wheat, arranged within a characteristic bocage landscape
211 (Baudry et al., 2000). Maize and wheat are typically found on flat or gently sloping areas with large 212 plots and deep soils, whereas grasslands are more commonly situated on granitic plateaus, 213 slopes with shallow soils, valley bottoms, or are integrated into crop rotations. Agricultural 214 intensity varies spatially: areas with higher densities of hedgerows, smaller plots, and a greater 215 prevalence of grasslands generally exhibit lower-intensity agricultural practices. In the northern 216 part of the catchment, adjacent to the coast, a polder zone is primarily used for cash crops and 217 vegetable production. Forestry activities are limited (covering ~8% of the area) and are mostly 218 confined to public forests. The principal urban center, Fougères, is a medium-sized town located 219 in the southeast of the catchment, with a population exceeding 50,000 inhabitants. Between 2006 220 and 2018, key land use trends included a significant expansion of urban areas (+2,300 ha) and a 221 slight reduction in agricultural land (-850 ha). Agricultural intensification was observed, marked 222 by an increase in crop-dominated areas (+3,100 ha, including +2,300 ha of maize) at the expense 223 of meadows (-4,000 ha). The Couesnon site offers a diverse set of conditions well-suited for 224 testing the capabilities of FORESCEM. These include highly heterogeneous land cover mosaics, a 225 wide range of land cover durations—from very short (1–2 years) to very long (>100 years)—and 226 varying spatial structures. These characteristics provide a robust context for evaluating the 227 model’s ability to represent complex landscape dynamics and simulate realistic land cover 228 trajectories.
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<caption>Figure 3: Presentation of the French anthropogenic case study: Land cover map of the Couesnon catchment in 2018.</caption>

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<figure><img src="image_4.png" /></figure>

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The Spanish case study covers an area of 2,958 km² and encompasses the Pas, Miera, and Asón catchments, including their terrestrial, riverine, and estuarine components in the Cantabria region. This area is further extended with a 3 km buffer (Figure 4). The northern coastal zone is predominantly urbanized, interspersed with fragmented agricultural land uses. In contrast, the southern portion of the study area is characterized by a semi-natural mountainous landscape, with elevations exceeding 3,000m a.s.l. These high-altitude zones are primarily composed of bare soil, grasslands, shrublands, and forests, and are subject to extensive pastoral activities and frequent wildfires. Between these two extremes lies a hilly landscape consisting of a heterogeneous mosaic of silvicultural plantations, unmanaged forests, shrublands, and agricultural lands. This spatial heterogeneity, combined with dynamic ecological processes such as vegetation succession and disturbance events (particularly wildfires), makes the region a compelling case for LUCC modelling. Furthermore, land cover changes in this context are not
always confined to delineated landscape features such as cadastral parcels. Thus, integrating
such events is essential to ensure the representativeness and realism of the model outputs.
Observed land use trends between 2008 and 2018 include a modest increase in urban areas
(+148 ha) and a substantial decrease in agricultural land (-6,500 ha), primarily due to grassland
abandonment in the mountainous regions. These dynamics, driven by rural depopulation and
land abandonment, contribute to spontaneous reforestation processes that are often interrupted
or redirected by fire events. This study area offers valuable conditions for testing the FORESCEM
framework, particularly regarding: (1) Variable land cover durations, ranging from medium-term
(~5 years) to long-term (>100 years), which the model must accurately represent; and (2)
Disturbance-driven LUCC, where fire events act as exogenous drivers that disrupt natural
succession processes.

## 2.3 Dataset

### 2.3.1 List of input data for FORESCEM

FORESCEM needs a minimal input dataset to run simulations effectively. These include : two land
cover maps (at time t_n and t_0), two land use maps (at time t_n and t_0), a set of driver maps, an initial
age map for each land cover unit, a land cover duration table, a table linking drivers to land cover,
a table linking land covers to land uses, a land-cover priority table, and the annual land cover
demand. To support scenario customization and enhance simulation realism, several optional
inputs can also be integrated. These include updated drivers during the simulation period, event
and policy maps for land uses and land covers, parcels map (table 1). Additionally, other optional
data for customized land allocation functions can be added.
<figure><img src="image_5.png" /><figcaption>Figure 4: Presentation of the Spanish case study: Land cover map of the Pas, Miera and Asón watershed case study in 2018.</figcaption></figure>
<table><caption>Table 1: Mandatory (M) and Optional (O) data in FORESCEM</caption><thead><tr><th>Data</th><th>Type</th><th>M/O</th><th>Description</th></tr></thead><tbody><tr><td>Land cover at time t-n</td><td>Map</td><td>M</td><td>Land cover map used to compute past transitions, past trend, and the calibration</td></tr><tr><td>Land cover at time t</td><td>Map</td><td>M</td><td>Land cover map used to compute past transitions, past trend, and the calibration. It is also the starting landscape of the simulation</td></tr><tr><td>Land use at time t-n</td><td>Map</td><td>M</td><td>Land use map used to calibrate the model</td></tr><tr><td>Land use at time t</td><td>Map</td><td>M</td><td>Land use map as starting date of the simulation</td></tr><tr><td>Set of drivers</td><td>Maps</td><td>M</td><td>Maps used for the calibration and the simulation. Driver maps at time t+x can be included if drivers evolve during the simulation</td></tr><tr><td>Cover duration table</td><td>Table</td><td>M</td><td>Duration for each cover that FORESCEM will intend to respect. In some situations, cover duration can be exceeded to find a solution.</td></tr><tr><td>Cover type</td><td>Table</td><td>M</td><td>Table defining for each land cover if demand is a constraint or not.</td></tr><tr><td>Drivers</td><td>Table</td><td>M</td><td>Table to define which driver need to be used for the calibration for each land cover</td></tr><tr><td>Demand</td><td>Table</td><td>M</td><td>Table used during the simulation and gathering annual demand for each land cover</td></tr><tr><td>Parcels</td><td>Map</td><td>O</td><td>Simulation is processed at pixel unit if this map is not provided</td></tr><tr><td>Land cover's age at time t</td><td>Map</td><td>O</td><td>If no map is provided, a random age map respecting land cover duration is generated</td></tr><tr><td>Policy</td><td>Map</td><td>O</td><td>Map used to influence land allocation according to a narrative. It can be a hard (binary) or soft map. Policy is used from the year for which it is defined. For example, urban sprawl policy.</td></tr><tr><td>Event</td><td>Map</td><td>O</td><td>Map used to create unexpected change such as an urban project (housing estate, new lake), a forest project (new forest). It occurs from the date it is defined.</td></tr></tbody></table>

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## 2.3.2 Data for the case studies

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Land-cover maps were derived from remote sensing data. Multitemporal Sentinel-2 imagery was used to produce the 2018 land-cover maps for both study sites. For historical land cover data, multitemporal Landsat archives were employed: 1990 and 2006 for the French site, and 2008 for the Spanish site. Supervised random forest classification was applied to enhance vegetation discrimination, achieving overall accuracies (OA) exceeding 85% in each case. Subsequent post-processing, informed by local expert knowledge, was conducted to correct minor misclassifications—particularly those resulting from inconsistent transitions between land covers caused by spatial and temporal resolution differences between Sentinel-2 and Landsat datasets. Land-use maps were generated using validated land cover maps, supplemented by local expert input and ancillary geographical data, such as areas of exploited forests.

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The LUCC drivers incorporated into the model include elevation, slope, aspect, geological characteristics, travel time to major cities, and land planning maps of the Blue and Green Infrastructure Network (BEGIN). Elevation, slope, and aspect were derived from Digital Elevation
Models (DEMs), specifically the national BD ALTI 50 m dataset for France and the SRTM 90 m dataset for Spain. Both DEMs were resampled to align with data consistency (10 x 10m).

Geological data were sourced from institutional databases and simplified to reduce the number of geological classes. Travel time to major urban centers was calculated using OpenStreetMap road network data. The BEGIN maps, provided by local land planners, represent ecological corridors with continuous values ranging from 0 (absence of corridor) to 1 (highest corridor importance).

Five tables summarize key model inputs and parameters: (1) minimum and maximum land cover duration, (2) drivers used for each land cover regression model, (3) drivers to be used for each land use regression model, (4) annual land cover demands, (5) land cover types.

Landscape patterns are incorporated through the use of rasterized cadastral parcels for urban and forested areas, and the Land Parcel Identification System (LPIS) parcels (data.europa.eu) for agricultural lands. In mountainous regions or where parcel data are unavailable, homogeneous units are derived by discretizing the landscape based on slope, elevation, and aspect classes.

These raster-based parcels aggregate contiguous 10 m resolution pixels that evolve as cohesive units within the model. Employing parcels instead of individual pixels enhances the representation of realistic landscape patterns in land change simulations (Sohl et al., 2017; Houet et al., 2015). For the French and Spanish case studies, the parcel counts are approximately XX and YY, respectively. All spatial data are pre-processed by aggregating values within each parcel.

## 2.4 Validation tests

The validation process is grounded on the premise that, given the inherent unpredictability of the future, modelling efforts should primarily aim to maximize end-users' confidence in FORESCEM's outputs. To achieve this, the validation tests are designed to demonstrate the model's capacity to accurately reproduce historical LUCC and to incorporate policies and trend-breaking assumptions present in future scenarios (Houet, 2014). Six distinct tests are conducted to do so.

The resulting simulations are benchmarked against a trend-based simulation calibrated on observed LUCC data from 2006 to 2018 for France and from 2010 to 2018 for Spain. By applying these tests across both study sites, the validation emphasizes FORESCEM's adaptive capability in simulating diverse LUCC trajectories.

1) **Respecting landscape dynamics.** This test evaluates the model's ability to reproduce historical LUCC dynamics. A simulation is conducted over the period 2006–2018 for the French case study. The simulated land cover map is then compared to the observed classification derived
from remote sensing data. Model performance is quantified using the Figure of Merit (Pontius et al., 2008) and Kappa indices as defined by van Vliet et al. (2011). (2011). Kappa, kappa<sub>histogram</sub>, kappa<sub>location</sub>, kappa<sub>simulation</sub>, kappa<sub>transition</sub>, kappa<sub>transloc</sub> indicates respectively the similarity of the two compared maps, the quantitative similarity of the two compared maps, the similarity of the spatial allocation of the categories of the two compared maps, the similarity between simulated and real transitions, the quantitative similarity between simulated and real transitions, and the similarity of spatial allocation between simulated and real transitions. Analysis is made for all land cover classes separately, and for all crops reclassified as a single class ‘agricultural land’ as crops rotations show a high degree of randomness inhering from individual farmers’ decision.

2) **Respecting the duration of land covers.** This feature is particularly important for land covers characterized by specific temporal dynamics, such as agricultural crop rotations. Most of commonly used land change models are unable to accurately simulate these types of land cover dynamics (Houet and Verburg, 2022). FORESCEM's capacity to respect such constraints enables the simulation of realistic agricultural practices. For example, in the French case study with an intensive agricultural system, wheat cultivation is restricted to a maximum of two consecutive years on the same plot. Maize cultivation generally does not (1) exceed three consecutive years and (2) typically follows wheat. Temporary meadows have durations ranging from one to seven years. However, under certain conditions, the full set of constraints—including land use duration—may result in no feasible solution that satisfies all constraints simultaneously. In these cases, FORESCEM is designed to override the duration constraints to ensure the model can find a viable solution.

3) **Simulating unexpected land cover transitions resulting from land use changes.** Land development projects, such as afforestation initiatives, can drive land cover changes that are not predicted by historical trends. To simulate such scenarios, a new silvicultural zone is introduced in 2028 in the French case study, without prior information on expected land cover changes. FORESCEM is designed to enforce these unexpected land cover transitions triggered by land use changes defined as events in the stakeholder narratives, while striving to maintain the overall proportions of initial land cover demand. For instance, transitions initially prohibited—such as from wheat land cover to recolonization land cover—are allowed to occur. Subsequently, the recolonization land cover progresses to forest cover after 10 years, as dictated by the configured land cover duration parameters.

4) **Simulating variations in land cover demands.** Compared to historical trends, a substantial shift in crop demand is anticipated following a reform of the Common Agricultural Policy. Based on a narrative simulating this policy shift in 2026, which triggers agricultural intensification, this test examines a marked increase in wheat and maize at the expense of
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grasslands and other crops. During the calibration period, the respective annual increases for wheat and maize were +25.6 ha and +192.5 ha. Post-2026, these rates are projected to rise to +43.2 ha and +496.5 ha per year, respectively.

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5) **Simulating trend-breaking policies.** Urban planning policies evolve over time. For instance, the French urban management plan, defined at the municipal level, is expected to become more restrictive in the future to limit artificialization. This test evaluates how the FORESCEM framework responds when areas previously designated as suitable for urban development become temporarily unavailable due to policy restrictions, and are later reopened following local policy changes. Specifically, it assesses how the model handles annual urban land demand when no suitable areas are available during a certain period, but new areas become accessible later.

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6) **Including case-specific land change patterns.** Countries experiencing warm and dry summers are prone to recurrent fires caused by natural hazards or human activities. In the Spanish case study, fires frequently occur in mountainous areas, significantly affecting land cover changes such as afforestation. To address this, a module generating random fire events from past events is added in the FORESCEM framework to demonstrate its ability to integrate specific rules / modules adapted to specific case studies. The module analyzes, prior to the start of the simulation and based on historical fire records, both the frequency of fire occurrences on each plot and the fire size distribution. In the Spanish case study, fire recurrence is annual, thus one fire map is produced per year. During each step of the simulation, the module incorporates a pre-solver with specific land-use assignment rules (e.g., forced transition to one-year-old shrubland or to bare soil, thereby limiting shrubland recolonization).

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# 3 Results

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## 3.1 Respecting landscape patterns

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The simulated map exhibits some differences when compared to the observed map derived from remote sensing (Figure A in annexe). While the Figure of Merit and Kappa indices highlight these discrepancies, the Similarity and Quantity indices indicate that the overall landscape pattern remains reasonably consistent with observed conditions. The Figure of Merit, which is based on the proportion of correctly predicted changes relative to all predicted and observed changes (Figure 5), reaches 12.7% for the simulation, compared to 24.44% for the null model (i.e., assuming no change). When agricultural land covers involved in crop rotations are aggregated into
a single class, the Figure of Merit decreases to 11.15% for the simulation and increases to 32.38% for the null model.

<figure><img src="image_6.png" /><figcaption>Figure 5: Figure of merit</figcaption></figure>

The kappa index shows a rather low correspondence (kappa = 0.35) when all classes are considered (Table 2). This low value is due to crop rotations (Table 3). Grouping the covers involved in crop rotations (wheat, maize, other crop, and temporary meadow) into a single class leads to a higher kappa value (kappa = 0.80). The similarity indicators for quantities and locations are also high (kappa<sub>histogram</sub> = 0.95 and kappa<sub>location</sub> = 0.84 respectively). Moreover, if we look more specifically at the similarities between simulated and real transitions the values remain very high (kappa<sub>simulation</sub> = 0.81; kappa<sub>transition</sub> = 0.92 and kappa<sub>transloc</sub> = 0.88).

<table><caption>Table 2: Kappa indices considering all land cover class (first column) and reclassified land cover (agricultural, forest, urban, water classes)</caption><thead><tr><td>Index</td><td>Meaning</td><td>Land cover</td><td>Reclassified cover</td></tr></thead><tbody><tr><td>Kappa</td><td>similarity of the two compared maps</td><td>0,35</td><td>0,80</td></tr><tr><td>Kappa<sub>histogram</sub></td><td>quantitative similarity of the two compared maps</td><td>0,95</td><td>0,95</td></tr><tr><td>Kappa<sub>location</sub></td><td>similarity of the spatial allocation of the categories of the two compared maps</td><td>0,37</td><td>0,84</td></tr><tr><td>Kappa<sub>simulation</sub></td><td>similarity between simulated and real transitions</td><td>0,42</td><td>0,81</td></tr><tr><td>Kappa<sub>transition</sub></td><td>quantitative similarity between simulated and real transitions</td><td>0,82</td><td>0,92</td></tr><tr><td>Kappa<sub>transloc</sub></td><td>similarity of spatial allocation between simulated and real transitions</td><td>0,51</td><td>0,88</td></tr></tbody></table>
<table><caption>Table 3: Kappa index per land cover</caption><thead><tr><th>Land cover</th><th colspan="2">kappa</th><th>Reclassified land cover</th></tr></thead><tbody><tr><td>Broadlead forest</td><td>0,92</td><td>0,92</td><td>Broadlead forest</td></tr><tr><td>Mixed forest</td><td>0,83</td><td>0,83</td><td>Mixed forest</td></tr><tr><td>Shrubland</td><td>0,06</td><td>0,06</td><td>Shrubland</td></tr><tr><td>Water</td><td>1,00</td><td>1,00</td><td>Water</td></tr><tr><td>Urban</td><td>0,87</td><td>0,87</td><td>Urban</td></tr><tr><td>Wheat</td><td>0,09</td><td></td><td></td></tr><tr><td>Maize</td><td>0,14</td><td rowspan="3">0,61</td><td rowspan="3">Land covers as part of crops' rotations</td></tr><tr><td>Other crop</td><td>0,08</td></tr><tr><td>Temporary meadow</td><td>0,13</td></tr><tr><td>Permanent meadow</td><td>0,61</td><td>0,86</td><td>Permanent meadow</td></tr></tbody></table>

## 3.2 Respecting the duration of land covers: Crop occurrences / maximum duration

We focus here on agricultural land covers—wheat, maize, other crops, permanent and temporary meadows—that exhibit specific temporal dynamics, including varying durations and frequencies of occurrence. Results demonstrate that FORESCEM successfully respects land cover duration in most cases (>98% for all agricultural land covers; Figure 6, top left and bottom left). Less than 2.5% of the total agricultural area exceeds the maximum duration threshold, while still meeting overall land cover demand. This level of non-compliance is particularly limited for temporary meadows (under 0.03% of their total area), wheat (less than 1%), and other crops (less than 2%). For maize, which accounts for more than 30% of the agricultural land use at the initial time step and over 35% by the final year, 3–6% of the area surpasses the three-year duration limit. However, in practice, maize can be cultivated on the same plot annually for 10 to 15 years in certain locations. This observed deviation highlights the influence of diverse and localized agricultural practices, which are challenging to model precisely. In essence, the generalization and simplification of land cover change rules in FORESCEM can explain some of the localized discrepancies. The shorter the maximum allowed duration, the more difficult it becomes for the model to reconcile this constraint with annual agricultural land cover demands. For other crops, non-compliance is mainly concentrated in a specific region—namely, the polder areas north of Couesnon catchment—where pedological conditions favour more frequent transitions between wheat and other crops such as vegetables and orchards. When the duration threshold is relaxed
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by one year (e.g., allowing wheat to be maintained for up to four years), overall non-compliance
drops to just 0.1% of the total agricultural area. The highest rate of non-compliance under this
scenario is observed for wheat, reaching only 0.4% in the year 2043 (Figure 6, top right). This
indicates that most violations stem from minor extensions in crop duration to maintain optimal
land use efficiency on the most suitable soils.

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<figure><img src="image_7.png" /><figcaption>Figure 6: proportion of crops respecting duration</figcaption></figure>

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## 3.3 Simulating unexpected land cover transitions resulting from land use changes: influence of an event

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Figure 7 illustrates a scenario-based silvicultural project initiated in 2028. Prior to this intervention, urban sprawl and agricultural land cover changes follow their usual patterns. However, once the silvicultural land use project begins, urban expansion halts, and selected agricultural land covers are converted to shrubland—representing areas planted with young trees. In this scenario, a specific event triggers a local shift in land cover dynamics: for example, maize cultivated in 2027 transitions directly to shrubland in 2028, bypassing typical transitions such as grassland or wheat. This illustrates FORESCEM's ability to override predefined constraints in the transition matrix when necessary to accommodate scenario-based land use changes. The model
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adapts to event-driven shifts in land cover by prioritizing narrative-defined land-use objectives,
demonstrating its flexibility and responsiveness to local planning decisions.

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Figure 7: Land cover in 2018 and land cover changes in and out the sylvicultural project area that occurs in 2028

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<figure><img src="image_8.png" /></figure>

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## 3.4 Simulating variations in land cover demands

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Figure 8 shows that the evolution of agricultural land covers aligns well with the land cover demand changes introduced in 2026. Maize surfaces increase significantly, while wheat expands at a more moderate rate. In contrast, areas covered by other crops and permanent meadows experience a notable decline. The model adheres closely to the specified demand, with a maximum deviation of only 246 hectares—equivalent to 1.13% of the total study area—between simulated and expected land cover surfaces. These discrepancies are largely attributed to crop
rotation constraints. At the beginning of the simulation, the initial age of all land covers is 450 arbitrarily set to one year. During the first seven years—matching the maximum duration allowed 451 for permanent meadows—land cover options remain limited due to uniform age distributions and 452 competition between covers. Beyond this period, the diversification of land cover ages across 453 parcels reduces these constraints, enabling greater flexibility to meet the demand. However, 454 minor variations persist due to the influence of parcel size, as illustrated in the lower graphs of 455 Figure 8. 456

Figure 8: Land-cover change with change in agricultural demand. On the left, land-cover surface. On the top-right, difference between simulated and expected agricultural surfaces. On the bottom-right, percentage of the surface difference related to the expected agricultural surface.

<figure><img src="image_9.png" /></figure>

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## 3.5 Simulating trend-breaking policies

Figure 9 illustrates the impact of changing urbanization policies in a municipality. In the witness 462 scenario, areas in purple are open from 2018 to 2050 and areas in blue are open for urban 463 development from 2030 to 2050. In the trend-breaking scenario, where urban policy evolves, 464 purple areas remain also open to urbanization from 2018 to 2050, blue areas become restricted 465 after 2030, and red areas are newly opened to compensate for the closures. These red areas are 466 located in municipalities that have become more attractive due to proximity to a major 467 transportation axis. This test demonstrates FORESCEM's ability to manage spatial policy changes 468
(opening and closing accessible areas) and resolve land use conflicts. It ensures that new urban development does not occur in unauthorized areas while distinguishing between pre-existing urban land cover and potential expansion areas—urban sprawl typically occurs adjacent to existing urban zones. Moreover, this example reinforces FORESCEM's capability to handle complex policy-driven dynamics. By relying on the land use–land cover relationship table and the transition matrix, the model determines which land cover transitions are allowed based on the current land use and policy context of each parcel. This distinction allows FORESCEM to simulate realistic policy impacts on land development patterns.

Figure 9: Urban sprawl in 2019, 2029, 2039 and 2049. On left, areas open to urbanization for the witness scenario (purple from 2018 to 2029, then purple and blue from 2030 to 2025) and trend-breaking scenario (purple and blue from 2018 to 2029, then purple and red from 2030 to 2050). On the right, comparison of urbanized areas between the witness and trend-breaking scenarios.

<figure><img src="image_10.png" /></figure>

## 3.6 Case-specific land change patterns: accounting for fire events

Fires in Spain do not appear to have had a significant impact on overall land cover during the simulation period (Figure 10). However, while fire events may not drastically alter land cover
485 classes, they do influence the age of parcels, which is reset to zero following a burn event. In the

486 Spanish case study, simulated fires produce localized effects with subtle land cover transitions.

487 Specifically, they facilitate the conversion from shrubland to bare soil, while limiting the natural

488 succession from bare soil to shrubland and from shrubland to forest. As a result, fire disturbances

489 contribute to maintaining open landscapes in mountainous areas, aligning with the persistence

490 of traditional pastoral land-use practices.
<figure><img src="image_11.png" /><figcaption>Figure 10: Land cover and age in 2050 without fire event (on the left) and with fire events (on the right).</figcaption></figure>
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# 4 Discussion

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Simulating future land use and land cover changes (LUCC) based on narratives requires anticipating a wide range of possible changes. Many of these changes cannot be validated against past data, as they are speculative or unprecedented. At the same time, relying too heavily on past trends for calibration may overly constrain the model, limiting its ability to explore and simulate contrasting future scenarios. A robust model must therefore strike a balance: it should be capable of handling exploratory, narrative-based simulations while also producing business-as-usual scenarios calibrated on observed LUCC, assuming a stationary system—an approach commonly adopted in many existing LUCC models (Mas et al., 2014). In this context, two central issues are discussed in the paper: (1) The characteristics and functionalities of the model implemented within the FORESCEM framework and (2) the validation process of a LUCC model in the context of future land planning.

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## 4.1 Characteristics of FORESCEM for future land planning

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FORESCEM introduces several key advancements in the modelling and simulation of land use and land cover changes (LUCC). These include: (1) The ability to simulate changes at a fine spatial resolution, enabling a detailed representation of landscape patterns and their temporal dynamics; (2) A clear distinction between land use and land cover, along with explicit modelling of their interactions and dependencies; (3) The capacity to easily incorporate policies and scenario-based events, such as afforestation projects or non-linear urban development trajectories; (4) The integration of complementary, case-specific modules that allow adaptation to local contexts and narrative-driven assumptions. Together, these features make FORESCEM well-suited for supporting and simulating spatially explicit, contrasting prospective scenarios, as emphasized by Houet et al. (2022).

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Furthermore, the use of parcel-based elementary units strengthens the model's capacity to preserve key landscape structures in scenario outcomes. As shown by Houet et al. (2015) and Sohl et al. (2017), parcel-based modelling not only provides a more realistic depiction of the spatial heterogeneity and organization of land cover classes, but also enhances downstream applications such as biodiversity assessments based on land cover maps.

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Differentiating land use and land cover processes and dynamics enhances both the flexibility and genericity of FORESCEM when constructing a wide range of scenarios. From a modelling perspective, it avoids the need to multiply land cover types to represent every possible transition. Instead, FORESCEM maintains a consistent logic of land cover transitions while modulating them
according to the land use context. For instance, it distinguishes shrublands resulting from agricultural abandonment from shrublands established as part of a forestry land use. The model further incorporates other spatial parameters, such as landscape units or soil quality (treated as sub-regions), to authorize or restrict specific transitions or to locally modulate land cover duration. This design reduces the burden of converting stakeholder and expert knowledge into a large number of complex transition rules. Instead, stakeholders can focus on defining scenario assumptions, which are then translated into land use or land cover change rules via event or policy layers, or transition matrices. This approach highlights the importance of involving the modeller early in the scenario co-construction process, working closely with stakeholders to ensure that assumptions are accurately and meaningfully translated into simulation parameters and spatial rules. FORESCEM's ability to easily incorporate policy changes, unexpected events, or narrative-driven developments makes it well suited to support a wide spectrum of future scenarios. As described by the IPBES (IPBES, 2016), this flexibility supports the co-construction of exploratory, target-seeking or policy-screening scenarios. Even within exploratory contexts, scenario assumptions may define desired landscape compositions, include nature-based solutions, or evaluate the effectiveness of landscape projects (e.g., enhancing connectivity or biodiversity). FORESCEM's structure allows some inputs to remain loosely defined, facilitating iterative dialogue with stakeholders and enabling the model to adapt to local needs and knowledge.

The FORESCEM framework combines parameterized yet generic sub-models, built on core mandatory modules—calibration, transitions, and simulation—with optional functions that can be developed and integrated based on the specific case study. This adaptability and its explicit treatment of land use–land cover interactions make FORESCEM particularly effective for modelling fine-scale, prospective scenarios in highly anthropogenic landscapes. Crucially, FORESCEM accommodates two types of dynamics often found in real-world systems: (1) Trend-based changes, following gradual and often predictable evolutions (e.g., urban expansion or land abandonment), and (2) Abrupt changes, triggered by events or decisions such as policy shifts, land reclassification, or natural hazards (e.g., wildfires or floods).

As Hersperger et al. (2018) point out, spatial planning operates under both progressive transformations and sudden deviations from past trends. This duality exists in urban contexts and in natural systems—such as the rapid designation of protected areas. As emphasized by Koomen et al. (2011), the capacity to accommodate diverse planning and land management approaches is essential. FORESCEM's structure meets this need, making it a versatile tool for LUCC modelling in support of integrated, participatory, and adaptive spatial planning.
Nonetheless, FORESCEM has some limitations. As revealed by conventional land change modelling metrics, its spatial allocations occasionally diverge from observed patterns. Enhancing the model's ability to better preserve landscape structure remains a challenge. One promising direction is to incorporate intermediate organizational levels—such as farms, municipalities, or inter-municipal groupings—to improve the simulation of landscape patterns and dynamics. However, the availability and accessibility of detailed data on these units, particularly farm boundaries, remain challenging, independent from FORESCEM.

## 4.2 How validating LUCC models when combined with prospective scenarios?

The validation of land change models typically relies on metrics such as the ROC curve, AUC, or Kappa index (van Vliet et al., 2016). However, these indicators can be challenging to apply in certain contexts, especially for prospective scenarios. This difficulty arises from the fundamentally different paradigms underlying land change modelling versus scenario-building. While traditional modelling strives for predictive accuracy, the prospective approach recognizes that the future is inherently uncertain and may follow multiple possible trajectories. Consequently, scenarios cannot be validated solely based on prediction accuracy but rather through criteria such as relevance, transparency, creativity, plausibility, and consistency. A land change model designed to simulate future narratives—where predictive validation is impossible—does not fit within current validation standards. Nonetheless, model calibration remains a critical step with strong implications for the model's capacity to simulate contrasted, trend-breaking scenarios. FORESCEM thus contributes to shifting the validation paradigm in land change science, complementing the predictive approach by emphasizing flexibility and scenario relevance.

To address this challenge, we propose a combination of commonly used validation approaches aimed at increasing end-user confidence in the model's outputs and capabilities. Some tests assess the model's ability to reproduce known past dynamics and quantify the similarity between simulated and observed landscapes. Others evaluate whether the model can fulfill stakeholder requirements and constraints, including evolving ones like minimum land use durations. A final category of tests examines the model's capacity to simulate abrupt shifts triggered by specific projects (e.g., forestry or urban development), policy changes (e.g., urban expansion restrictions), or external events (e.g., wildfires). This validation approach complements conventional unitary tests and indices. We therefore advocate for applying such a comprehensive approach to land change models designed to simulate contrasted, participatory narratives. To our knowledge, most
593 existing land change model literature has focused on predictive validation methods, which raises
594 questions about their suitability for simulating disruptive scenarios—including anthropogenic
595 projects, temporal inversions or variations in land demand relative to past trends, and new drivers
596 influencing land change allocation.

597 Furthermore, when working at fine spatial scales, simulations become increasingly sensitive to
598 data availability and the spatial representation of outcomes. For example, access to past urban
599 planning documents and information on intermediate organizational levels—such as farms or
600 municipalities—can significantly influence land dynamics. These factors directly impact the
601 model’s predictive performance and its ability to simulate realistic land change dynamics and
602 patterns, which in turn affects end-user confidence. Crop rotation modelling illustrates this
603 complexity, as it depends on parameters like initial crop age, minimum and maximum rotation
604 durations, multiple possible land cover transitions, and farm boundaries—data that are often
605 difficult to obtain. Consequently, this can lead to variations during the simulation period and
606 result in lower scores on standard performance indicators, even when the landscape
607 composition, spatial configuration, and dynamics are accurately represented at the case study
608 scale.

609 Beyond functional validation tests, local stakeholders’ expertise plays a crucial role in prospective
610 modelling. While local actors pay close attention to the model’s ability to respect predefined
611 constraints, the plausibility of the simulated dynamics—especially given their inherent
612 uncertainty—strongly influences stakeholder acceptance and the perceived relevance of the
613 results. Stakeholders typically prioritize the model’s capacity to realistically simulate the spatial
614 allocation and dynamics of land use and land cover. Notably, FORESCEM’s spatially explicit
615 outcomes have never been challenged by stakeholders, whereas certain scenario assumptions
616 (such as urban land demand) were questioned and subsequently adjusted to enhance
617 stakeholder confidence and engagement, as documented in Houet et al. (2022).

## 5 Conclusion

619 This paper presents the FORESCEM scenario-based LUCC modelling framework. Its innovative
620 strength lies in its ability to simulate interactions between land use and land cover changes, while
621 accounting for different land planning processes. These processes can influence both land use
622 and land cover through gradual land planning policies or sudden land development projects. The
623 simulation considers two types of interactions between land use and land cover: (1) direct
interactions, where a land use change drives land cover changes, and (2) indirect interactions, where land cover acts as a driving factor for land use changes—for example, land abandonment.

The validation focused on the key mechanisms of the FORESCEM modelling framework that enable it to capture interactions and diverse processes embedded in narratives. Each mechanism was validated through unitary tests conducted with local stakeholders and experts, assessing their ability to reproduce the expected scenario-based changes. This validation approach also challenges conventional methods commonly used in land change science. When simulating prospective scenarios, the validation paradigm shifts from evaluating predictive accuracy toward assessing the model's capacity to simulate realistic land patterns and dynamics. This aligns with the perspective of Burgi et al. (2022), who advocate rethinking determinant analysis and emphasize the importance of validating land change models in collaboration with local stakeholders.

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763 Wilson, E., & Piper, J. (2008). Spatial planning for biodiversity in Europe's changing climate. *European Environment*, 18(3), 135-151.

765 Xiang, W. N., & Clarke, K. C. (2003). The use of scenarios in land-use planning. *Environment and planning B: planning and design*, 30(6), 885-909.

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768 Annexe

769 Figure A illustrates these results: although agricultural land covers are not allocated exactly as 770 expected, the overall landscape pattern remains consistent. One key limitation is that FORESCEM 771 does not currently account for intermediate organizational levels such as farms or municipalities, 772 which hinders finer-scale allocation of land covers. As highlighted by Houet et al. (2014), 773 modelling medium- to long-term crop rotations remains a complex task and is not yet predictive. 774 Some discrepancies may also stem from differences in input data sources. The 2006 map was 775 derived from Landsat 8 imagery with a 30 m resolution, whereas the 2018 map uses Sentinel-2 776 data at 10 m resolution. This difference results in the appearance of many small built structures 777 in 2018 that were either absent or undetected in earlier imagery. In the case of the urban class, it 778 becomes particularly difficult to distinguish between actual new developments around existing 779 settlements and previously undetected buildings. 780

<figure><img src="image_12.png" /><figcaption>Figure A: Land cover in 2018 from remote sensing (left) and from simulation (right)</figcaption></figure>

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