# Co-designing geoprospective private and public urban greening scenarios: A data-driven workflow for planning decisions and research

Mélissa Poupelin, Ludovic Granjon, Guillaume Huteau, Thomas Thevenin, Julien Pergaud, Yves Richard, Benjamin Pohl

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## To cite this version:

Mélissa Poupelin, Ludovic Granjon, Guillaume Huteau, Thomas Thevenin, Julien Pergaud, et al. Co-designing geoprospective private and public urban greening scenarios: A data-driven workflow for planning decisions and research. Urban Forestry & Urban Greening, 2025, 113, pp.129021. 10.1016/j.ufug.2025.129021. hal-05223528

HAL Id: hal-05223528

https://hal.science/hal-05223528v1

Submitted on 26 Aug 2025

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Original article

# Co-designing geoprospective private and public urban greening scenarios: A data-driven workflow for planning decisions and research

Mélissa Poupelin<sup>a,b,*</sup>, Ludovic Granjon<sup>c,1</sup>, Guillaume Huteau<sup>b</sup>, Thomas Thevenin<sup>a</sup>, Julien Pergaud<sup>b</sup>, Yves Richard<sup>b</sup>, Benjamin Pohl<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Geoprospective

Urban greening

Urban planning

Scenarios

Adaptation

Decision-support tool

Co-design

## ABSTRACT

In the context of climate change, adapting cities to extreme heat is a key challenge for urban planning research. Evaluating how urban greening affects urban climate at the metropolitan scale is crucial for promoting sustainable land-use planning research and practice. Here, we present a co-designed, GIS-automated workflow grounded in geoprospective to quantify where and how much greening can be added, while preserving essential urban functions rather than simply replacing impervious surfaces with greenery. Applied to Dijon (France), the Green Scenario, anchored in local urban planning rules adds 46,000 new trees and over 9 km² of low vegetation within the city (130,000 trees and 20 km² metropolitan-wide), covering nearly one-quarter of Dijon with new greenery. To benchmark the added value of co-design, a normative baseline that applies uniform greening targets regardless of planning constraints was also generated. Compared with the baseline, the Green Scenario shifts vegetation towards the most feasible locations: industrial and commercial zones on the periphery receive about two-thirds of the added low vegetation, whereas the historic core shows limited potential beyond streets and squares. These findings identify priority areas for mitigating heat stress, guide private and public greening policies, and provide actionable datasets for integrating nature-based solutions into urban planning and climate adaptation strategies. The transferable methodology, available through open-access computational tools, also provides layers suitable for use in climate models to estimate cooling benefits.

## 1. Introduction

Due to their morphology, size, and growth, urban areas tend to be warmer than the surrounding non-urbanized regions (Rosenzweig et al., 2018; Krayenhoff et al., 2021; Zhao et al., 2021), a phenomenon known as the urban heat island (UHI; Oke, 1982). Under climate change, heat waves are becoming more intense and more frequent. Therefore, adapting urban spaces is essential to mitigate their increasingly adverse effects on human health, thermal comfort, and energy consumption (VijayaVenkataRaman et al., 2012). Among the available adaptation approaches, land-use planning offers viable pathways (Biesbroek et al., 2009; Desjardins, 2011).

Numerous studies have demonstrated the beneficial impact of adaptive planning, particularly through greening measures (Bowler et al., 2010; Knight et al., 2021). At the European Union (EU) level,

urban greening is now framed by the Biodiversity Strategy 2030, which calls for “Urban Greening Plans” in every large city (Marei et al., 2024), and by the New European Bauhaus initiative, which links climate, aesthetics and inclusion (European Commission, 2024). Greening has significant effects on reducing air temperature and alleviating thermal stress. Research on this topic is abundant (Bowler et al., 2010; Krayenhoff et al., 2021; VijayaVenkataRaman et al., 2012; Knight et al., 2021; Quaranta et al., 2021), spanning scales from the micro level to the international scale, and employs varied assessment methods (observational and/or numerical modelling studies) as well as examining different types of green infrastructure (e.g., street trees, lawns, green walls and roofs, parks and private gardens). Yet, most existing methods prioritize public spaces and tree canopies, leaving private parcels and herbaceous cover underrepresented and limiting the usefulness of current decision-support tools (Neyns and Canters, 2022). These surfaces

<footnote><sup>a</sup> Laboratoire ThéMA, Université Bourgogne Europe, UMR 6049, CNRS, Dijon, France</footnote>

<footnote><sup>b</sup> Centre de recherches de climatologie, Laboratoire Biogéosciences, UMR 6282 CNRS, Université Bourgogne Europe, Dijon, France</footnote>

<footnote><sup>c</sup> MSH Dijon, Université Bourgogne Europe, UAR 3516, CNRS, Dijon, France</footnote>

<footnote>* Corresponding author at: Laboratoire ThéMA, Université Bourgogne Europe, UMR 6049, CNRS, Dijon, France. E-mail address: poupelinmelissa.pro@gmail.com (M. Poupelin).</footnote>

<footnote n="1">¹ present address: LEEISA, Université de Guyane, CNRS, Cayenne, France</footnote>
are explicitly targeted by EU green-infrastructure policy as key levers for stormwater control and biodiversity corridors (European Commission, 2024).

To our knowledge, only a few studies, such as Masson et al. (2013) and Lemonsu et al. (2015) for Paris, have attempted urban-scale scenario building. Yet they apply uniform vegetation-increase rates and largely omit low vegetation, resulting in spatially coarse outputs. Studies assessing the potential cooling impact of greening solutions should provide valuable insights for decision makers, taking into account different types of spaces and vegetation.

The translation of these findings into urban planning policies often remains slow, and the scientific literature still lacks studies that deliver decision-support tools at planning scales relevant to local authorities (Bowler et al., 2010). The literature agrees that tree canopies are effective during daytime because they provide shading, and open grassy areas are more effective at night to cool air temperature and mitigate the urban heat island effect (Knight et al., 2021). This review concludes by calling for studies on the “optimal design of urban green areas for public-health benefit”. This highlights the need for methods that can precisely locate where each vegetation type can realistically be added while preserving essential urban functions (such as pedestrian, bicycle, and vehicle circulation, as well as public gatherings). The goal is therefore not to green every paved surface indiscriminately, but rather to distinguish between areas that can be depaved and those that must remain functional.

Given these gaps, this study develops a method to generate greening scenarios at the local, decision-making scale. The approach directly addresses the “Urban Greening Plan” requirement of the EU Biodiversity Strategy by quantifying where and how much vegetation can realistically be added (Costadone and Vierikko, 2023). Considerations regarding the anticipation of territorial evolutions based on multiple scenarios fall within the realm of geoprospective (Houet and Gourmelon, 2014), which provides a framework for co-designing and evaluating adaptation scenarios. It calls for a critical examination of the scenario-building and modelling tools used to develop the scenarios. In this context, our objective is to deliver a transferable, GIS-automated geoprospective workflow that quantifies greening potential at the parcel scale across both public and private spaces, producing policy-ready layers for planners.

The method inventories existing vegetation in private and public areas and integrates local urban planning documents. Spatial data generated by this work will enable the analysis of land-use planning based on territorial greening potential.

The Methods section presents the reference land-use database, which provides the initial, realistic foundation for scenario development. Secondly, it details the methodological approach, which spans from the action-research context to the translation of local planning documents into an automated Geographic Information System (GIS) tool. The Results section presents and analyses the generated scenario through an urban-scale approach. This is followed by a discussion that questions the relevance of presenting scenarios as raw data or in aggregated formats (such as the hectometric scale) among other possibilities.

# 2. Methods

## 2.1. Study site

With nearly 160,000 inhabitants (INSEE, 2020), Dijon is the administrative centre of the Bourgogne-Franche-Comté region, located in northeastern France along the Paris–Lyon axis. It is the main city of Dijon Metropolis, which, in 2020, comprised 256,758 inhabitants distributed across 23 municipalities (Fig. 1.A). Dijon serves as the economic and industrial hub, as well as the primary provider of employment for the metropolitan area, representing 42 % of its urbanized land (Fig. 1.B). The remaining municipalities become increasingly rural with distance from the city centre, typically organized around small towns or

villages. Urban areas account for 20 % of the metropolitan area and are bordered by commercial or industrial zones that cover around 10 % of the metropolitan area. The remainder consists of various types of natural areas (Fig. 1.B).

Since the 2010s, regular collaborations have been established between climatology and urban planning researchers and Dijon metropolitan services (Jegou et al., 2024). These partnerships led to the creation of the MUSTARDijon<fnref n="1" /> temperature observation network (Richard et al., 2018), which comprised 92 connected stations as of 2022 (Jegou et al., 2024). They have also promoted joint reflection on territorial planning and launched several action-research projects. Within this context, the RESPONSE<fnref n="2" /> (H2020) project began in 2020. The first phase improved understanding of local climatic risk and the space-time UHI variability through climatic interpolations (Crétat et al., 2024) while the second focused on adaptation solutions and planning scenarios. This context provided an ideal platform for co-designing urban greening scenarios. Furthermore, data from the MUSTARDijon network indicate that Dijon Metropolis experiences the urban heat island effect during extreme heat periods, with intensities up to 6°C (Richard et al., 2021). The city centre and the western part of the metropolis are particularly vulnerable due to architectural, geomorphological, and atmospheric factors (Crétat et al., 2024). In this context, research projects on urban greening are strongly supported by local stakeholders, who are willing to actively engage in their development.

## 2.2. Reference land use database

Urban planning scenarios need metric-resolution, realistic land-use databases that describe vegetation, built-up areas, and water bodies. This baseline prevents duplicate planting, avoids misplacing greenery, and ensures high reliability of the obtained scenarios. Since the 1990s, GIS and spatial databases have transformed urban analysis (González et al., 2011). Comprehensive, component-level land-use databases now support in-depth territorial studies.

To address this need, the French government provides a national database known as BD TOPO, produced by the National Institute of Geographic and Forest Information (IGN). This vector-based dataset offers metric-resolution information on the surface area and location of buildings and hydrographic features. However, it has severe limitations for urban vegetation: it only includes clusters of trees larger than 500 m<fnref n="2" /> in public spaces, omitting street-lining and garden trees as well as grassy areas. Therefore, a dedicated urban-vegetation dataset must be created using high- or very-high-resolution satellite imagery and remote-sensing (Nabucet, 2018; Neyns and Canters, 2022).

In this study, the urban vegetation database developed for the Dijon area is based on a mosaic of very high-resolution (2 m) images acquired by the PLEIADES satellite on 29 May 2021 at 10:37 AM. These clear-sky images cover the entire Dijon Metropolis and were captured in mid-spring, when urban vegetation peaks. Among available vegetation indices, the Modified Soil Adjusted Vegetation Index (MSAVI) was selected because it is particularly well-suited for identifying vegetation in urban environments (Qi et al., 1994; Van Delm and Gulinck, 2011; Puissant et al., 2014). MSAVI was applied to imagery pre-processed to top-of-atmosphere reflectance in the red and near-infrared bands. The resulting data layer provides detailed information on all vegetated surfaces across Dijon Metropolis and its surroundings (Fig. 2). Further methodological and photo-interpretation validation showing very satisfactory vegetation detection, with the main remaining uncertainty due to tree crowns masked by building shadows, is provided in Diarlo-Dudek et al. (2023).

This database was subsequently cleaned after a photo-validation process revealed that deteriorated rooftops and tree shadows on roofs

<footnote n="1">¹ Measuring Urban Sensitive Temperature ARound Dijon</footnote>

<footnote n="2">² integRatEd Solutions for POsitive eNergy and reSilient CitiEs</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Dijon Metropolis maps. A: Main watercourses crossing Dijon Metropolis and elevation. B: Land use in Dijon Metropolis and its surroundings.</figcaption></figure>

could be mistaken for vegetation. The cleaning step removed vegetated pixels overlapping buildings in QGIS (version 3.14). Such refinement is required (i) to obtain a more precise estimation of the fraction of impervious surfaces across the metropolis and (ii) for applications with climate models because current models cannot represent canopy-roof overlap.

MSAVI indicates photosynthetic activity but cannot distinguish vegetation height, so an additional processing step was needed. A 2015 Digital Heights Model (DHM) of Dijon Metropolis was used to distinguish vegetation strata. Intersecting the remote-sensed vegetation layer with the DHM in GIS made it possible to distinguish “tall” from “low” vegetation (Fig. 2). Although the DHM predates the 2021 imagery by six years, any tree growth during that interval is insufficient to move vegetation across the 2-meter threshold for the Dijon Metropolis case. The binary “low” versus “tall” classification, therefore, remains valid. Users who require finer height accuracy should employ a DHM contemporaneous with their imagery or adjust heights using local growth rates. Although urban-tree growth models are scarce (Rust, 2014), representative annual increments can be derived from local and regional studies (Semenzato et al., 2011; Rust, 2014; Vaz Monteiro et al., 2016). Trees planted after 2015 are not included in the DHM; for these, we assign a constant height of 3 m, corresponding to the average size of newly planted street trees in the Dijon Metropolis. “Low vegetation” is defined as all vegetation elements under 2 m (hedges, shrubs, scrub, and grass). This threshold reflects the 2022 Dijon Metropolis planning document (PLu<fnref n="3" />), which caps hedge height at 2 m in urban areas (PLU revision, (Dijon Métropole, 2022)). The resulting layer, combining tall and low vegetation, forms the Dijon Metropolis Vegetation Database (VGDB). This database provides highly detailed information on urban vegetation across the Dijon Metropolis. Comparison with IGN's BD

TOPO reveals a threefold larger vegetated surface at the metropolitan scale. The final product was validated through photo-interpretation. For example, the Place de la République neighbourhood near the city centre (Fig. 2) illustrates its added value: it captures vegetation in public (squares, streets), but also private (gardens) spaces. The information is more detailed and accurate. It also corrects BD TOPO errors; for instance, the central fountain in Place de la République was misclassified as green space.

Thanks to the BD TOPO classification, it is possible to distinguish private parcels from public land. The VGDB maps 22.18 km² of vegetation within Dijon, about 3.5 times the amount detected in BD TOPO. A total of 4.79 km² lies on private land (Table 1), thereby quantifying private vegetation, which is a key input for co-designing greening strategies with planners from Dijon Metropolis.

The reference land use database for the study area is thus constructed using a combination of vegetation data from the VGDB, building footprints and water surfaces from the BD TOPO. “Undefined surfaces” refer to roadways, sidewalks, and public squares, areas that are typically paved or gravel-covered within Dijon Metropolis. While the BD TOPO also defines roads, it does so using line features only, providing attributes such as road width, lane count, and classification (e.g., highways, dead-end streets, private roads, bicycle lanes, or stairs). These data are essential for identifying surfaces required for human mobility and for distinguishing them from areas that could potentially be vegetated.

## 2.3. Scenario design method and data

Scenario co-design was carried out in partnership with Dijon Metropolis as part of the H2020 RESPONSE European project. The sessions were organized as brainstorming meetings and structured debates. Balancing political priorities, research objectives, legislative and technical constraints, the joint objective emerged: to formulate a scenario for maximal, yet realistic urban greening.

<footnote n="3">³ in french for "Plan Local d'Urbanisme"</footnote>
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Sources: Google Maps Satellite, BD TOPO V3, OCS GE V1.1.

<caption>**Fig. 2.** Land cover databases and aerial imagery, focus on the city centre of Dijon. 1: Aerial photograph from Google Maps (2024), 2: BD TOPO v.3 (IGN, 2021), 3: OCSOL GE V1.1 (IGN, 2017), 4: VGDB (2021, Biogéosciences, ThéMA, MSH Dijon).</caption>

**Table 1**

Surface statistics from the national database (BD TOPO v.3, IGN) and the vegetation database (VGDB, 2021, Biogéosciences, ThéMA, MSH Dijon, UBE) for the territories of Dijon and Dijon Metropolis. This cross-tabulated table provides information on parcel types as well as vegetation height classes.

<table><thead><tr><th rowspan="2">(Areas expressed<br>in km²)</th><th colspan="2">Dijon</th><th colspan="2">Dijon Metropolis</th></tr><tr><th>total</th><th>Only private<br>residential parcels</th><th>total</th><th>Only private<br>residential parcels</th></tr></thead><tbody><tr><td>Area surface</td><td>41,73</td><td>11,14</td><td>240,62</td><td>36,89</td></tr><tr><td>BDTOPO</td><td>6,24</td><td>0,62</td><td>66,84</td><td>2,60</td></tr><tr><td>Surfaces</td><td>Low</td><td>2,56</td><td>121,33</td><td>11,30</td></tr><tr><td>VGDB</td><td>Tall</td><td>2,27</td><td>57,27</td><td>5,97</td></tr><tr><td></td><td></td><td>mean height: 4,68 m</td><td></td><td>mean height: 4,46 m</td></tr><tr><td>total</td><td>22,18</td><td>4,83</td><td>178,6</td><td>17,27</td></tr></tbody></table>

The question arose as to whether aerial (e.g., power lines) and underground (e.g., water, gas, or district heating) easements should be taken into account. It was ultimately decided that such constraints are highly context-dependent and that, in this case, the goal is to identify

potentially greenable surfaces, rather than to provide detailed construction plans. As such, the method is designed to produce a preliminary working basis that can be further refined by urban planners. At the metropolitan scale, the outputs still offer valuable insight into the amount of vegetation that could be added. These results can inform urban climate models, which typically operate using simulation grids at the hectometric scale, where extremely high spatial precision is not required.

In this regard, the theoretical framework of geosimulation-based foresight (geoprospective) was identified as particularly suitable for working with spatial planning stakeholders on future scenarios (Dodane et al., 2014). As defined by Houet et al. (Houet, 2023), geoprospective involves the co-design of urban planning scenarios based on rules developed jointly with planners. We hereafter refer to this exploratory pathway as the Green Scenario. It seeks to answer two questions: (1) Where and how can the city be greened, and (2) What cooling benefits would this greening provide during extreme-heat events?

The scenarios were subsequently developed using GIS, which also served as a platform for discussion and feedback during meetings. The
method relies on a typology that differentiates urban spaces based on
governance:

* Public spaces, such as squares, roads, pavements, and cycling lanes;

* Publicly accessible spaces within **industrial and commercial areas** (including parking lots, roads, and pavements);

* **Private spaces** such as gardens, courtyards, and residential parks.

A concise overview of all greening rules and editable parameters referenced in the Methods section is provided in Supplementary Table S1.

Geographic Information System (GIS) tools were used to develop a realistic street-scale method for identifying areas suitable for greening while preserving essential urban functions. In residential areas, a minimal road network was maintained to ensure accessibility. The same principle was applied to industrial and commercial zones, where essential circulation areas were defined as follows:

* Roadways: 3.5 m wide for single lanes or 2.5 m per lane for multilane roads;

* Sidewalks: 2 m wide along each street;

* Circulation lanes: 2 m wide around buildings in industrial and commercial zones.

Outside these essential circulation areas, all remaining surfaces were covered with low vegetation, including parking areas, which could be replaced with suitable low vegetative cover. Tree trunks were randomly added above this ground layer at a density of one tree every 15 m. Their canopies were generated with QGIS, with a radius of:

* 2.5 m in public spaces,

* 3 m in industrial and commercial zones.

These values were derived from a photo-interpretation of 1500 trees within the metropolitan area, providing average canopy dimensions for these urban contexts. Trees whose trunks or canopies extended over buildings were automatically removed, thereby excluding plantings on streets that were too narrow.

The greening of private spaces was defined according to the regulatory framework established in the local urban planning document. This context-sensitive methodology was strongly supported by the metropolitan teams. Dijon Metropolis is among the first French cities to incorporate specific greening requirements for private parcels in its local land-use plan (PLU for "Plan local d'urbanisme"), a document updated roughly once per decade that consolidates all urban development for both existing and future constructions.

As in several other French cities, Dijon applies a Biotope Coefficient (CBS) inspired by the Berlin model (Berlin Natur & Stadtgrün, 2016) is applied. Two regulatory values must be met: the minimum proportion of unsealed soil (PLT) and CBS itself. The CBS calculation in Dijon includes a tree planting bonus (+0.02 per tree) and requires at least one tree per 100 m² of permeable ground. Fig. 3 summarizes the input variables, calculation rules, and output values used to compute PLT and CBS at the parcel scale.

For example, for a parcel with a required CBS of 0.6, the bonus contribution is capped at 0.3, meaning a maximum of 15 bonus trees can be counted if no other greening measures (e.g., green roofs or vegetated walls) are implemented.

The regulation's structure promotes maximum greening of private

<figure><img src="image_6.png" /><figcaption>Fig. 3. Workflow for computing PLT and CBS indicators.</figcaption></figure>
lots. PLT and CBS thresholds increase from the city centre to the periphery, reflecting higher parcel density near the centre. The historic centre of Dijon (the densest part of the city) is exempt: its planning is governed not by the PLU but by a PSMV (Plan de Sauvegarde et de Mise en Valeur; Fig. 4), which contains only limited greening requirements

PLT/CBS regulations apply only when a private parcel is built, renovated, or newly created. This scenario therefore, quantifies the permeable area and tree numbers that owners must provide, a question previously unanswered by Dijon Metropolis planners.

The Green Scenario simulates residential parcels that comply as closely as possible with existing regulatory requirements. To build this component of the scenario, the CBS and PLT calculation rules applied to each parcel by Dijon Metropolis were embedded in a QGIS plug-in developed specifically for the project (Poupelin et al., 2025). A zoning map that specifies the CBS and PLT targets for every parcel in Dijon Metropolis (Fig. 4) is publicly available in vector format and is fully GIS-compatible. The plug-in combines this map with the existing low and tall vegetation layers. All technical details are open-access to encourage replication in other territories. The required inputs are standard GIS layers; parcel-level calculations merely require cadastral

data. Default thresholds and buffers are listed in Supplementary Table S1; users can overwrite them to reflect local street morphologies, regulatory values, or alternative climate hazards (see 4.4).

The plug-in runs in three steps:

1) Initial assessment: Plantable area is determined by subtracting the building footprint from each cadastral parcel. VGDB data quantify existing tall and low vegetation; low vegetation is assumed beneath tree canopies.

2) Computation of additional surface requirements: PLT/CBS zoning values define the additional greening required.

3) Validation and final output: Calculated greening is capped by the maximum plantable area established in Step 1.

Thus, implementing this method requires three datasets:

* Building, road, and parcel footprints (in this case: BD TOPO v.3, IGN, vector polygon format).

* An urban-vegetation layer separating tall from low strata (here: VGDB, vector polygon format).

<figure><img src="image_7.png" /></figure>

Sources : Dijon Metropolis, cadastre, IGN.

<caption>**Fig. 4.** PLT and CBS zones defined in the 2019 Dijon Metropolis local land-use plan (PLU, revised in 2022, (Dijon Métropole, 2022)), displayed over cadastral parcels.</caption>
* A zoning map reflecting local regulations on private greening (here: Dijon Metropolis's PLU zoning).

Once the processing workflows are complete (Fig. 5), the final step is to assemble the outputs for their intended use. Vector layers support visualisation, a crucial aspect of action-oriented research. These results can also be used as input to climate models, which requires an additional processing step to convert the data into a gridded (raster) format (Fig. 5).

To enable a side-by-side comparison with a so-called Baseline Scenario built without stakeholder input and thereby quantify the added value of our co-designed workflow, we generated a normative baseline applying uniform greening targets across the metropolis. In geo-prospective terms, this scenario addresses the question: *Could the city be vegetated uniformly, and if so, where would the vegetation be added?* We imposed a city-wide target of 50 % low vegetation and 25 % tall vegetation in every 150 m grid cell. The same 150 m grid is applied to the Green scenario so that the two scenarios can be compared directly. This allows for the quantification and scenario comparison of the additional tall and low vegetation that can be introduced across Dijon Metropolis and maps its distribution from the city centre to the periphery, spanning public, industrial, and private spaces.

The obtained vector layers are released as an open-access database (see Section 6). This open release reflects our commitment to free access for researchers and, above all, to sharing the data with planning practitioners.

# 3. Results

## 3.1. Comparison with the normative baseline scenario

Table 2 summarizes the low and tall vegetation areas for both Dijon and the wider metropolitan area, in the current (reference) situation and the two scenarios. In the reference dataset, low vegetation occupies more than twice the area of tall vegetation, a pattern that partly reflects our database protocol, which automatically classifies the ground beneath tree canopies as low vegetation.

Scenario-reference comparisons reveal two key contrasts: (i) the relative balance between low and tall additions, and (ii) the divergence between the normative baseline and the co-designed Green Scenario. In the normative baseline, additions within Dijon amount to 4.10 km² of

<table><caption>**Table 2**
Statistical summaries of urban vegetation areas (tall and low) in the reference dataset and the two scenarios.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Dijon<br>(41,73 km²)</th><th colspan="2">Dijon Metropolis<br>(240,62 km²)</th></tr><tr><th>Low<br>vegetation</th><th>Tall<br>vegetation</th><th>Low<br>vegetation</th><th>Tall<br>vegetation</th></tr></thead><tbody><tr><td>Reference<br>database</td><td></td><td></td><td></td><td></td></tr><tr><td>total surfaces</td><td>11,26 km²</td><td>4,69 km²</td><td>29,79 km²</td><td>10,53 km²</td></tr><tr><td>Normative<br>baseline<br>scenario</td><td></td><td></td><td></td><td></td></tr><tr><td>Total surfaces</td><td>15,36 km²</td><td>7,67 km²</td><td>37,36 km²</td><td>17,99 km²</td></tr><tr><td>Added area<br>relative to<br>reference</td><td>+ 4,10 km²</td><td>+ 2,98 km²</td><td>+ 7,57 km²</td><td>+ 7,46 km²</td></tr><tr><td>Net increase over<br>reference</td><td>+ 36 %</td><td>+ 64 %</td><td>+ 25 %</td><td>+ 71 %</td></tr><tr><td>Green scenario</td><td></td><td></td><td></td><td></td></tr><tr><td>Total surfaces</td><td>20,26 km²</td><td>5,71 km²</td><td>46,42 km²</td><td>11,72 km²</td></tr><tr><td>Added area<br>relative to<br>reference</td><td>+ 9 km²</td><td>+ 1,02 km²</td><td>+ 16,63 km²</td><td>+ 1,19 km²</td></tr><tr><td>Net increase over<br>reference</td><td>+ 80 %</td><td>+ 22 %</td><td>+ 56 %</td><td>+ 11 %</td></tr></tbody></table>

low vegetation and 2.98 km² of tall vegetation, broadly comparable figures. The same balance is found at the metropolitan scale (+7.57 km² vs. +7.46 km²).

The Green Scenario exhibits a markedly different signature: 9.00 km² of low vegetation but only 1.02 km² of tall vegetation are added within Dijon, and 16.63 km² versus 1.19 km² across the metropolis, implying that just 0.17 km² of tall vegetation are introduced outside the municipal boundary. Because the Green Scenario is grounded in local regulations and feasibility constraints, these numbers suggest that large-scale expansion of tall vegetation is difficult to achieve, whereas substantial gains in low vegetation are realistic. Indeed, the co-designed scenario more than doubles the low-vegetation additions relative to the uniform baseline. In statistical terms, a purely normative approach therefore underestimates the potential for low vegetation and overestimates the potential for tall vegetation.

Spatial distribution shows that, in the normative baseline scenario, additions of low and tall vegetation are spatially similar (Fig. 6. A/B).

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Scenario-Building workflow: datasets, methods, resulting layers and uses for operational and research purposes.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 6. Low and tall vegetation addition rates in each scenario (150 m grid resolution).</figcaption></figure>

Many 150 m grid cells in Dijon's historic core and in the north-east/south-west industrial corridors exceed + 20 % relative to the reference, because the uniform algorithm preferentially greens the city's most impervious areas (dense blocks and industrial estates) while largely ignoring the already-green residential fringe. Although this highlights vegetation deficits, it is detached from actual planning rules.

The Green Scenario shows a much different picture. Low-vegetation potential clusters in industrial zones, with some cells approaching + 90 % (Fig. 6.C). Central Dijon shows more modest gains (≤ +40 %) because the scenario preserves existing buildings and rights-of-way. By contrast with the normative baseline scenario, peripheral residential districts receive substantial low-vegetation additions, often bringing cells to ≈ 50 % coverage. Tall-vegetation gains (Fig. 6. D) remain modest (≈ +10 % per cell on average) and are scarce in the historic core, consistent with regulatory and spatial constraints.

In sum, a purely normative approach highlights need rather than feasible potential. Grounding the scenario in current planning regulations not only reduces the total quantity of added vegetation but also yields a more realistic spatial distribution, which is crucial for decision-support applications.

## 3.2. Local-scale distribution in public and industrial/commercial areas

The method generates a schematic view of public and industrial/commercial surfaces to be vegetated. These maps serve as draft plans to be refined with metropolitan planners. These results involve identifying and removing vegetation surfaces already present in the VGDB from the scenario. For instance, on the central square of Dijon, the Place de la Libération, up to 18 trees could potentially be added under the random

placement rules described in the method (Fig. 7.A). In this example, the trees are distributed randomly, but they could just as well be aligned in rows. A 15 m spacing retains design flexibility and eases communication with planners. In practice, many alignment trees in Dijon stand only 7 m apart.

In industrial and commercial areas (Fig. 7.B), the scenario produces extensive low-vegetation patches and many new trees in parking lots—an assertive, idealised outcome that sparked debate during feedback sessions. Interestingly, this criticism was mainly raised in response to maps of commercial zones, even though the same rules were applied to streets and squares. It highlights the underlying issue of impervious surface coverage in industrial and commercial zones and the dominance of parking lots in peri-urban landscapes. Beyond their geometric compatibility with hectometric-scale climate models, industrial and commercial areas are of scientific interest due to their extensive, contiguous surfaces, which can collectively contribute to urban cooling if partially vegetated. However, real-world implementation must consider technical constraints (e.g., underground utilities, logistical access) and social acceptability, particularly about parking and daily uses by workers and residents.

## 3.3. Local-scale distribution on private parcels

The algorithm outputs the area (m²) of tall and low vegetation that must be added to each parcel. It flags parcels that require greening, both with trees and grass, and quantifies those amounts. This representation is less concrete than the public-space maps, but it matches the metropolitan view: private garden design and implementation remain the owner's responsibility, so planners mainly need numerical targets at the
<figure><img src="image_10.png" /></figure>

Sources: BD TOPO V3 (IGN), BD VG (ThéMA & Biogéosciences).

<caption>Fig. 7. Result of the Green scenario in vector format. A: the Place de la Libération area in Dijon (historic city centre). B: a section of the commercial and industrial area in northern Dijon.</caption>

parcel's resolution.

A centre-periphery gradient proves useful for interpreting low vegetation additions (green and purple parcels, Fig. 8). The parcels most affected are those closest to the city centre, where available space is limited. This is important because Dijon's centre experiences a strong night-time urban-heat-island effect, and added ground vegetation could reduce night-time temperatures (Bowler et al., 2010; Knight et al., 2021).

The third step of the plug-in checks whether the total vegetation calculated by the scenario (existing + added) fits within each parcel's plantable area (parcel area - building footprint). If not, tall and/or low vegetation is adjusted proportionally.

Among the 34,950 private parcels analysed in Dijon Metropolis, 2959 (8%) required adjustment, almost entirely for tall vegetation (2936 parcels) and, to a lesser extent, low vegetation (717 parcels). Most of these parcels lie near the historic centre or in the old cores of surrounding villages (Fig. 9). A distance-to-centre analysis (Fig. 9, top) confirms a general “historic centre” effect explained by smaller parcel and higher built density.

While the regulations are broadly adapted to different parts of the metropolitan area, denser zones could benefit from finer thresholds. Several factors explain the required adjustments: overly ambitious greening targets in the PLU and methodological limitations. For PLT (see Section 2.3), the shortfall is related to parcel density. For CBS, however, the calculation counts only added tall vegetation and therefore, assumes the availability of open surface area. In densely built parcels, eco-constructed surfaces (such as green roofs and walls) could raise CBS values, but they are excluded from both the baseline and subsequent calculations. To date, they remain rare in Dijon.

Ultimately, this step identifies parcels that cannot meet PLT/CBS

thresholds by adding grass and/or trees alone. These parcels require alternative strategies, notably eco-constructed surfaces.

### 3.4. Metropolitan scale distribution of added vegetation

Fig. 10 provides a metropolitan-scale view of added vegetation surfaces by space type, derived from vector data. This cartographic output enables precise localisation of additions, which is a key asset in the context of co-design with stakeholders. It highlights the large areas of tall and low vegetation added in industrial and commercial zones on the urban periphery (Table 3), as well as the greening potential of private gardens, particularly to the north of the city centre. By contrast, the historic centre—strongly affected by the urban heat island effect—can be vegetated only in its streets, squares and sidewalks, because it lacks industrial land and private greening rules do not apply there (see PSMV in Methods).

Overall, the Green Scenario covers nearly one-quarter of Dijon with new low vegetation (9.03 km² over a total of 41.73 km², Table 3), whereas the added tall vegetation is lower (1.12 km², Table 3). Expressed as tree count, the scenario proposes planting 46,321 trees within the city—an ambitious yet achievable goal according to metropolitan staff. For the wider metropolis, 130,351 trees would be required within ten years. Their placement is crucial for heat-stress mitigation; the maps produced (Fig. 7) will guide future planning.

Because other municipalities already contain extensive rural vegetation, the metropolitan ratios of added low and tall vegetation are lower. Additions within Dijon (9.03 km² low; 1.12 km² tall) represent almost half of the totals for the entire metropolis (20.26 km² low; 3.17 km² tall; Table 3).
<figure><img src="image_11.png" /></figure>

Sources: BD TOPO V3 (IGN), BD VG (ThéMA & Biogéosciences).

<caption>**Fig. 8.** Vegetation surfaces to be added in residential parcels in central Dijon to meet the PLT/CBS targets set by the Dijon Metropolis Local land-use plan (PLU).</caption>

In Dijon, two-thirds of the added low vegetation lies in industrial and commercial zones (accounting for three-quarters of the vegetation added in the metropolis).

These zones also offer the greatest potential for tree planting, reflecting their large impervious areas, especially parking lots. Public-space additions are smaller in area but more central, where heat stress is greatest. Private parcels already contain substantial low vegetation, and the method does not aim for maximal greening; even so, they still require nearly as many new trees as public spaces. This may indicate either limited resident uptake of tree planting or the need to rely on eco-constructed solutions to meet PLU targets.

# 4. Discussion

## 4.1. *Perspective for method refinement*

The methodology presented here was developed with a focus on generating realistic greening scenarios for a mid-latitude European city, primarily to inform climate modelling and strategic urban planning at the metropolitan scale. As such, several local constraints were deliberately set aside during the co-design process. These include aerial and underground easements—such as power lines, gas pipelines, water and heating networks—as well as physical limitations related to rooftops and walls, and regulatory setbacks required near protected buildings or historical monuments. At the hectometric resolution used in urban climate models, these details are not required for estimating cooling potential. However, they become essential when translating greening

scenarios into actionable, site-specific interventions. Future developments of the tool could integrate such constraints, allowing for the generation of operationally viable greening scenarios at finer spatial scales. This would enhance its relevance for implementation by urban planners, landscape architects, and infrastructure managers. Taken together, these points already signal some strengths of the approach (co-design, open data, dual treatment of public and private space) as well as its most salient limitations, which are detailed in Section 4.4.

## 4.2. *Application to other case studies*

Results confirm that the method is operational and ready for replication in other cities, enabling inter-urban comparisons of vegetation distribution and, ultimately, urban climate-risk exposure. This paper also highlights the importance of collaboration between practitioners and researchers in producing geoprospective scenarios. However, it is important to emphasize that the rules derived from such collaboration ultimately reflect the implementation, whether directly or indirectly, of existing urban planning regulations in the territory. Therefore, researchers working outside a co-design framework with local planners may find it useful to draw on the methodological principles developed here and adapt them to other contexts, in France (where similar regulations apply) or beyond (with appropriate adaptations to the local context).

This collaborative approach was also examined in a comparative study of several cities, focusing on the conditions of researcher/practitioner collaboration around the links between urban planning and
<figure><img src="image_12.png" /><figcaption>Fig. 9. Histograms of distances between parcels and the centre of Dijon. TOP: all parcels included in the study. BOTTOM: parcels for which vegetation additions under the PLT/CBS method had to be reduced to the available plantable area.</figcaption></figure>

climate change (Mhedhbi et al.). The study highlights that scenario-based workshops are a key pathway for translating such collaborations into actionable outcomes, contributing to urban planning research and the development of tools aligned with climate adaptation goals, particularly for extreme heat.

### 4.3. Potential applications

This method allows for the analysis of results according to urban-space typologies, aligned with the responsibilities of different actors involved in urban planning: public authorities, public or private companies and residents. Industrial and commercial areas receive the largest share of added vegetation (6.01 km² in Dijon; 15.97 km² metropolitan-wide), a finding that future planning documents should take into account.

These zones lie outside city centres, raising the still-debated question of how peripheral greening influences the UHI effect in central areas. While some studies have begun to address this issue using mobile measurements (Richard et al., 2021; Crétat et al., 2024 in Dijon; Heusinkveld et al., 2014 in Amsterdam), further research is needed to generalize their findings across different urban contexts. This approach is also particularly relevant to the “zero net land take” objective, as it helps identify areas that could be greened within the existing urban footprint.

Next steps involve coupling the greening scenario with a climate model to quantify the cooling effect provided by each type of greened space. A first-order appraisal, using empirical relationships reported in synthesis studies of urban greening benefits (Bowler et al., 2010; Knight et al., 2021) suggests that the additional vegetation could lower summer

night-time air temperatures by roughly 0.5–1 °C in the most densely built districts. Although indicative, this estimate underlines the potential climatic value of the scenario. Particular attention should be paid to the combined effects of tall and low vegetation and to the distinct responses of public, industrial, and residential areas, as cooling efficiency is expected to vary with both vegetation structure and urban morphology. Combining the scenario with IPCC projections would enable an assessment of how these cooling benefits might evolve under increasing heat stress. Additionally, testing irrigation scenarios would allow for estimating water needs and the potential amplification of cooling through evapotranspiration. Beyond thermal regulation, such greening interventions may also offer collateral benefits for urban biodiversity by enhancing ecological continuity. The spatial configuration of added vegetation can contribute to the emergence of a functional green infrastructure, potentially supporting habitat connectivity and offering a complementary framework to traditional ecological corridor planning. At present, these findings are mainly used as a diagnostic and discussion layer; formal incorporation into statutory documents is planned once the refinements listed below are completed.

### Transferability, calibration needs and methodological limits

The Dijon case study highlights four substantial strengths. First, the workflow is rooted in a co-design process with stakeholders, which guarantees regulatory realism and political legitimacy. Second, every script and the QGIS plug-in are open-source and fully documented (CeCILL-B licence), allowing external teams to audit, reproduce and extend the method. Third, the approach treats public space and private parcels in a single framework, thereby revealing greening opportunities that are usually overlooked. Fourth, it operates seamlessly at two complementary scales: parcel-level vectors support dialogue with planners,
<figure><img src="image_13.png" /><figcaption>Fig. 10. Map of the spatial distribution of added tall and low vegetation by urban space type (private gardens, activity zones, roads, and public squares).</figcaption></figure>

<caption>**Table 3**</caption>

Summary of tall and low vegetation surfaces added in the Green Scenario by urban space typology and for total surfaces of urban spaces of Dijon Metropolis (France).

<table><thead><tr><th></th><th>Dijon</th><th></th><th>Dijon Metropolis</th><th></th></tr></thead><tbody><tr><td>Area surface →<br>Vegetation<br>addition zones↓</td><td>41,73 km<sup>2</sup></td><td>Tall<br>vegetation</td><td>240,62 km<sup>2</sup></td><td>Tall<br>vegetation</td></tr><tr><td>Public spaces</td><td>2,68 km<sup>2</sup></td><td>0,36 km<sup>2</sup><br>18 664 trees</td><td>3,82 km<sup>2</sup></td><td>0,98 km<sup>2</sup><br>51 352 trees</td></tr><tr><td>Industrial and<br>commercial areas</td><td>6,01 km<sup>2</sup></td><td>0,32 km<sup>2</sup><br>11 609 trees</td><td>15,97 km<sup>2</sup></td><td>1,36 km<sup>2</sup><br>49 027 trees</td></tr><tr><td>Private spaces</td><td>0,34 km<sup>2</sup></td><td>0,44 km<sup>2</sup><br>16 048 trees</td><td>0,47 km<sup>2</sup></td><td>0,83 km<sup>2</sup><br>29 972 trees</td></tr><tr><td><strong>Added vegetation<br>(total)</strong></td><td><strong>9,03 km<sup>2</sup></strong></td><td><strong>1,12 km<sup>2</sup><br>46 321<br>trees</strong></td><td><strong>20,26 km<sup>2</sup></strong></td><td><strong>3,17 km<sup>2</sup><br>130 351<br>trees</strong></td></tr><tr><td>Total vegetation<br>surfaces:<br>Green scenario<br>additions<br>+ existing<br>vegetation<br>(Overlap<br>removed)</td><td>20,26 km<sup>2</sup></td><td>5,71 km<sup>2</sup></td><td>46,42 km<sup>2</sup></td><td>11,72 km<sup>2</sup></td></tr></tbody></table>

while aggregation to a 150 m grid meets the resolution requirements of contemporary urban-climate models.

Several limitations must nonetheless be noted. The current scenario is a static snapshot: neither vegetation growth nor phasing of works is modelled. Trees heights depend on a 2015 digital-height model, leading to a potential slight underestimation of recent canopy expansion. Irrigation demand, maintenance costs and social acceptance are not yet parameterised, and fine-scale constraints (e.g. underground utilities, heritage setbacks, aerial easements) are absent. If these constraints are deemed necessary, the outputs will require post-processing before shovel-ready designs can be issued.

Regarding transferability, most cities already possess the three core datasets that drive the workflow—building/road footprints, a tall/low vegetation layer, and a parcel zoning map—so the tool is largely plug-and-play. Only a limited set of parameters demand local calibration: street-buffer widths, trunk spacing, canopy radii and greening-ratio thresholds must be tuned to reflect local morphology, species and regulations. Where drought or flooding is the dominant hazard, an irrigation cap or a permeable-surface target can be added as an extra layer; all numeric values are exposed in the plug-in's interface and can be modified without altering the Python backend.
## 5. Conclusion

This scenario, designed to be nature-based and grounded in existing planning documents and regulations, quantifies greening potential across public and private urban spaces and reveals spatial inequalities in vegetation cover. This information is essential for analysing stakeholder dynamics and planning climate adaptation strategies.

The outputs can be integrated into climate models after rasterisation, while the vector format supports parcel or street-scale visualisation for planners. Together, these two formats enable collaborative action research and inform future urban-planning documents.

Co-designed with local stakeholders, the Green Scenario proposes planting 46,321 new trees within Dijon and 130,351 across the wider metropolis, including 18,664 trees along Dijon's public roads. Local stakeholders confirmed this target's feasibility given the current annual tree planting rate. It also adds 9.03 km² of low vegetation and 1.12 km² of tall vegetation within Dijon (respectively 20.26 km² and 3.17 km² metropolitan-wide), corresponding to over 16 km² of grass, roughly equivalent to 4000 average-sized soccer pitches.

The spatial typology shows that peripheral industrial and commercial zones account for nearly two-thirds (6.01 km²) of the added low vegetation in Dijon, whereas Dijon proper contains half of all public and private greening opportunities. Parcels near the historic centre emerge as the most vegetation-deficient. Although private-garden potential appears lower, this partly reflects stricter assessment criteria and suggests a need to encourage residents or incentivise eco-constructed solutions.

Importantly, benchmarking against a uniform, normative baseline shows the added value a regulatory co-design. Indeed, the Green Scenario roughly doubles low-vegetation gains while constraining tall-vegetation additions to locations where they are feasible, yielding a more realistic and actionable spatial distribution for planning.

Beyond cooling, the method may also support biodiversity and ecological connectivity by identifying opportunities for green infrastructures within the existing urban fabric. Together with the open-access datasets and code, this provides a replicable basis for operational uptake and for inter-city comparison.

Altogether, this approach delivers a replicable framework and operational tools for mitigating urban heat through context-sensitive greening. Future work should (i) couple the scenario with climate-impact models to quantify cooling, (ii) evaluate irrigation demand and maintenance costs, and (iii) test the methodology in other cities to compare adaptation capacity.

## Funding statement

This work was funded by the Bourgogne-Franche-Comté region (France) through a doctoral scholarship (IRRIFU), as well as by the European Union through the RESPONSE H2020 project.

## Data and code availability

The QGIS plug-in and Python scripts used to generate the private-parcel greening scenario are hosted on GitLab (https://gitlab.in2p3.fr /JulienPergaud/urban-greening-scenarios-application.git) and distributed under a CeCILL-B Licence.

The complete **Green Scenario** database for Dijon Metropolis is openly available on dataUBFC (v1.0, DOI: 10.25666/DATAUBFC-2025-07-04) under a CC BY-NC-SA licence.

## CRediT authorship contribution statement

**Poupelin Mélissa:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
**Granjon Ludovic:** Writing – review & editing,

Software, Resources, Methodology, Data curation. **Huteau Guillaume:** Formal analysis, Data curation. **Thévenin Thomas:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Conceptualization. **Pergaud Julien:** Writing – review & editing, Supervision, Software, Methodology, Conceptualization. **Richard Yves:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Conceptualization. **Pohl Benjamin:** Writing – review & editing.

## Declaration of Competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Melissa Poupelin reports financial support was provided by Bourgogne-Franche-Comté Region. Yves Richard, Benjamin Pohl, Melissa Poupelin reports financial support was provided by Europe. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

We would like to thank the Dijon Metropolis teams involved in this action-research project for regularly welcoming us into their offices and for their availability. Calculations were performed using HPC resources from DNUM CCUB (Centre de Calcul de l'Université de Bourgogne). This work is a contribution to the RESPONSE programme (ERC H2020 Grant #957751:https://h2020response.eu/).

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ufug.2025.129021.

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