URBAN DATABASE ANALYSIS FOR MAPPING MORPHOLOGY AND AERODYNAMIC PARAMETERS: THE CASE OF ST JEROME SUB-URBAN

AREA, IN MARSEILLE DURING ESCOMPTE

Nathalie Long* (1), Patrice G. Mestayer*, Claude Kergomard ** *École Centrale de Nantes, Nantes, France; ** Université des Sciences et Technique de Lille,

(1) Société SIRIATECH, Villeneuve d’Ascq, France. Abstract Urban fabric and terrain generate important and complex interferences with local meteorology and climatology. BDTopo is an urban database, produced by the French national geographic institute (IGN), which describes the elements of the urban fabric in details. It includes an inventory of buildings, vegetation areas, roads and hydrographic networks, topography, among other objects. The software DFMap has been developed to transform initial information in vector mode into grid mesh averaged statistical variables describing building morphology and cover modes density. From these geometrical factors, DFMap further computes aerodynamic parameters like roughness length, displacement height, … The results are validated for the area of St Jerome, the measurement station in the north of Marseille during ESCOMPTE, by comparison with aerial photographs and cadastral database. Key words: urban database, urban fabric, aerodynamic parameters. 1. INTRODUCTION

The presence of city modifies the earth surface properties and influences the local meteorology. To carry out numerical simulations with soil and atmospheric models, it is therefore needed to know with a high spatial resolution the city structure and its components. Here, the study area, called here St Jerome, is a specific case of urban space, located in the north of Marseille, and composed of several types of districts. One a collective building area, individual buildings, sparse buildings, vegetation area (trees, grass, cultures, …) and water surfaces may be identified (figure 1). This area measures 4 km * 4 km and is marked by the diversity of urban fabric. An urban database was selected to analyze the urban fabric of Marseille in order to bring out different urban structures, characterized by the morphology of the settlements and the land coverage. It was first necessary to analyze the representativeness of this database compared to ground reality and then to estimate the precision of the descriptive parameters computed for each district.

Figure 1: St Jerome sub-urban area represented by buildings ( ), vegetation ( ), roads network (one way: , two ways: , three ways: , four ways: ) and water surface ( )

* Corresponding author address: Nathalie Long, Laboratoire de Mécanique des Fluides, 1 rue de la Noé, BP 92101, 44321 Nantes, Cedex 3, France; e-mail: nathalie.long@ec-nantes.fr

2. DATABASES The selected database is the topographic database (BDTopo), produced by the French national geographic institute (IGN). BDTopo is built by photogrammetry from aerial photographs taken in 1994 and its precision is metric. This database was retained for this study because it gives information both on the land cover and on elevation of the listed objects. These objects are drawn by a polyline, their outline, and grouped by themes: buildings, vegetation, road and hydrographic networks, among other. Contour lines and points are both used to represent the topography. The DFMap software was developed in a partnership with the company SIRIATECH, to transform the initial information in raster mode. Variables describing the morphology of the buildings and the land covers are computed onto a grid with statistical methods and form a Geographic Information System (GIS). This GIS is composed of building average height, perimeter, volume, compactness (the compactness is the ratio between surface and perimeter normalized by that of a circle), and plan area density, vegetation and pavement densities, … and aerodynamic parameters like roughness length and displacement height. Three models may be selected after Grimmond and Oke, 1999: Bottema (1995), Raupach (1992) and Macdonald (1998). Each function is computed for each cell, which size is selected by the user. Here, the cell size is 200 m * 200 m. A 4 ha cell makes it possible to bring out the urban structure of each district without representing a fragment only of the urban fabric. The information of the data contained in the GIS is further compared with other sources of information like aerial photographs provided by NGI with a resolution of 1: 25 000 and taken in 1999, and images from SPOT 4 satellite. Another urban GIS (U-GIS), produced by the geographical information service of the city hall of Marseille, is also used. U-GIS is a very complete and update set of data. 3. BDTOPO REPRESENTATIVENESS

We have compared the initial information of BDTopo to that of the U-GIS to estimate the percentage of surface where there is no information in the BDTopo, which has no evidence due to its vector mode. First, we noted that all buildings are not listed in the BDTopo. In St Jerome quarter, the underestimation is 0.5% of the built surface. This surface corresponds generally to garden sheds or huts, since these constructions were too small to be listed in BDTopo or were hidden by trees. Nevertheless, they exist for the administrative services. Other absent buildings were built after 1994.

Figure 2: Example of buildings from BDTopo ( ) and additional buildings from U-GIS ( ) on St Jerome area.

Vegetation coverage is largely underestimated in this type of quarters: only 9% of the surface are covered by vegetation according to BDTopo analysis while the analysis of SPOT images shows 59% is covered by vegetation among which 29% do correspond to the vegetation types listed in the BDTopo. It appears nevertheless, that some vegetation areas are taken into account neither BDTopo, nor in the satellite image analysis, or partially but not systematically. They correspond, e.g., to private gardens close to individual buildings (on the bottom left of Figure 3). Moreover the classification, created in the analysis of the satellite images, must be improved since the sparse vegetation in combination with bare soil, is not well represented. Higher resolution could also be used to identify the residual vegetation from satellite images obtained when the chlorophyllous activity is more intense.

Figure 3: Example of vegetation area from BDTopo (wood: , brushwood: ), from SPOT images (sparse vegetation: , herbaceous vegetation: ) with aerial photography in the background (dark grey is vegetation and light grey / white is mineral surface). Road network is well represented: BDTopo lists all roads but only a few ones with one way in individual building areas. On the other hand, BDTopo technical document specifies that the average width of one way is 3.5 m. Comparison with aerial photography, shows that the road width varies with the urban fabric type: in individual building area, the average width of 2 ways roads is 7.8 m, while it is 8.8 m in areas where building density. Generally, the width of 3 ways-roads is 18.5 m and that of 4 ways-roads is 20 m. The computation of the difference between the measured width and the width of roads (estimating each way to 3.5 m) and weighted by the length of roads according to the number of ways, shows that road width is underestimated by 1.5 m in St Jerome area. The width of one-way roads cannot be verified because the aerial photography resolution does not allow measurement with sufficient precision.

4. RESULTS 4.1 Building morphology and land cover Statistical parameters were computed with DFMap to describe the urban fabric and cover modes of St Jerome area. The building average height is 7.7 m and 50% are between 4.8 and 9.7 m (i. e., between 15% and 75% of the set). The buildings are small: in the average, their perimeter is 62.5 m, and their volume 3192 m3. The maximum volume is 98514 m3 and 50% are between 620 and 3277 m3. The building density and vegetation density are nearly the same, 0.1 and 0.09 respectively. The pavement density is 0.08. We may note an opposition between the north and the south areas (Figure 4). In the north, the building density and the building height are lower than in the south of St Jerome. In this part, the buildings are taller and higher and the vegetation density is lower with higher standard deviations of these variables. Several urban structures constitute St Jerome quarter. On the histogram, the distribution is very asymmetric, with maximum values corresponding to cells in the southern part of the study area, the northern one being the extreme limit between urban and rural spaces (see Figure 1). Figure 4: Average building height (left) and building plan area density (right) on St Jerome area with a resolution of 200 m. 4.2 Roughness length DFMap computes canopy roughness parameters. The models are used to estimate a roughness length, from the morphology of buildings, the building density and the frontal surface density. Figure 5 presents the distribution of the roughness values for each model (indice B for Bottema’s model, R for Raupach’s model and M for Macdonald’s model) and for two wind directions: north and west. The difference between models is low but we can note an extreme value of roughness with a west wind direction. Values of z0R are a little bit lower with an average roughness length of 0.44, while the average z0B is 0.52 and z0M 0.56. 50% of z0B and z0M values are contained between 0.06 or 0.09 and 0.63, and between 0.08 and 0.57 for z0R. Then the standard deviation is higher and putting in light the heterogeneity of urban fabric. The stronger values of z0 are computed in the south of the area, where buildings are taller and the building plan area density higher.

Figure 5: Roughness length obtained with the models of Bottema (left), Raupach (middle), and Macdonald (right) for St Jerome area with north (1) and west (2) wind directions, with a resolution of 200 m.

5. DISCUSSION First, the sum of the densities is not equal to 1; in the average only 26% of the cell surface is documented for the land cover: this is very low and no cell has a sum of densities equal to 1. Few inaccuracies were already noted in section 2, to estimate the representativeness of BDTopo, but, these differences do not appear significant in the computation of the building and pavement densities. On the other hand, vegetation areas are strongly underestimated by BDTopo. When replacing the vegetation density from BDTopo analysis by its counterpart in SPOT images analysis, the sum of densities is close to 0.7. It is difficult to further estimate and recognize the last 30% since the surface corrections for these remaining land covers is highly dependant on the type of urban quarters. Therefore to implement land cover the 30% is needed to identify clearly the different types of structure of the urban fabric. An automatic classification method is used to identify urban structure as function of building morphology and land cover. This k-means method first constructs an initial partition of the data and then exchanges the class members to obtain a new partition minimizing the intra-class variability. This method is applied to a matrix of 10 GIS variables and 6300 observations, the 200 m * 200 m cells. As a result, 3 classes define the “natural” land cover, vegetation areas, water surfaces and bare soil, and 4 classes define the urban fabric. Over the area of St Jerome, the urban fabric is divided in 3 classes: collective buildings (CB), high density of individual buildings (HIB), and low density of individual buildings (LIB). The land coverage obtained from BDTopo analysis being underestimated, aerial photographs from IGN or recorded during ESCOMPTE campaign, the cover modes were identified/corrected for each urban fabric class and include in the GIS. The percentage of each cover mode is presented in Table 1. Four cover modes are not listed in the BDTopo: herbaceous vegetation area (Herb. veg.), arable land, bare soil and mixed surface with asphalt and trees (Asph. + veg.). Two cover modes are underestimated because some types of surfaces are not taken into account in BDTopo: impervious surfaces (Imperv. surface) and some vegetation areas (wood / brushwood). Impervious surfaces include asphalt surfaces of roads, which appear in the BDTopo, and parking and concrete surfaces, which do not.

CL HIB LIB Surface (%) Correction Surface (%) Correction Surface (%) Correction

Built surface 20 0 18 - 3 4 0 Imperv. surf. 14 + 18 12 + 16 5 0 Asph. + veg. NaN + 12 NaN 0 NaN 0 Wood / Brush 8 + 17 8 + 37 33 + 46 Herb. veg. NaN + 5 NaN + 7 NaN + 4 Arable land NaN 0 NaN 0 NaN + 4 Bare soil NaN + 6 NaN + 5 NaN + 4 NaN: no values calculated from BDTopo; Table 1: Surfaces of cover modes according to urban quarters, and proposed correction. 6. Conclusion The built surfaces are relatively well estimated from the information contained in the BDTopo as analyzed by DFMap. This is not the case for the other land covers like woods, brushwoods and impervious surfaces which are not roads and constructions. However, from the analysis of test zones, it is possible to determine a set of corrections adapted to each type of urban districts. The complexity of the structure and morphology of urban fabric is brought out through the St Jerome case. The analysis is applied to city of Marseille and gives satisfactory results. Nevertheless, it remains adapted and restricted to French cities, perhaps to European cities but this requires further tests. References Bottema M., 1995, Aerodynamic roughness parameters for homogeneous buildings groups – part 1 : Theory, document SUBMESO 18, Ecole Centrale de Nantes, France, 40 pages. Grimmond CSB., Oke T., 1999, Aerodynamic properties of urban area derived from analysis of surface form, in Journal Applied Meteorology, 38, 1261-1292 Macdonald R.W., Griffiths R.F., Hall D.J., 1998, An improved method for estimation of surface roughness of obstacle arrays, Atmospheric Environment, 32, 1857-1864 Raupach, M.R., 1994, Simplified expressions for vegetation roughness length and zero-plane displacement as functions of canopy height and area index, Boundary-Layer Meteorology, 71, 211-216 Raupach, M.R., 1995, Corrigenda, Boundary-Layer Meteorology, 76, 303-304.