Bauwens, W.

Salvadore, E.

Verbeiren, B.

Wirion, C.

I-Revues

NOVATECH 2016 
1 
Simulating urban interception based on detailed in-
situ monitoring and remote sensing data 
Simulation des pertes d’eau de pluie liées à l’interception 
par la végétation urbaine, basée sur l’acquisition de 
données in situ et de télédétection 
 
Charlotte Wirion, Elga Salvadore, Willy Bauwens, Boud Verbeiren  
 
Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Belgium 
 
RÉSUMÉ 
Cette étude propose une méthode intégrale pour la simulation des pertes d’eau de pluie liées à 
l’interception par la végétation urbaine. La méthode est basée sur l’acquisition détaillée de données in 
situ et de télédétection. 
Nous nous basons sur une image hyperspectrale APEX pour construire une carte d’occupation du sol 
à haute résolution spatiale (2m). Nous catégorisons la variation saisonnière de la végétation urbaine 
avec les nouveaux produits de Proba-V (100m). Les cartes dérivées des données de Proba-V sont 
validées avec une cartographie détaillée de la couverture végétale pendant toute la saison. A cette fin, 
nous avons collectionné l’indice foliaire avec le système SS1 Sunscan de tous les arbres et arbustes 
inclus dans un pixel de Proba-V. Le résultat consiste en une série chronologique de cartes validées 
qui permettent de caractériser la dynamique de la végétation urbaine en détail.  
La carte d’occupation du sol, ainsi que les cartes des indices foliaires sont introduites dans un modèle 
hydrologique distribué, le ‘Wetspa-Python model’, pour simuler le bilan hydrologique sur une année 
dans le bassin de Watermaelbeek à Bruxelles. Les cartes des indices foliaires permettent d’améliorer 
le paramétrage des pertes d’eau de pluie liées à l’interception par la végétation urbaine et donc les 
dynamiques spatio-temporelles de la distribution de pluie arrivant sur la surface et de 
l’évapotranspiration quittant la surface. 
 
ABSTRACT 
This study proposes an integrated methodology to simulate urban interception storage using detailed 
and consistent in-situ measurements along with remote sensing data.  
We derive an urban land-cover map at a high spatial resolution (2m) using a hyperspectral APEX 
image, and we characterize the seasonal variation of urban green with the new daily Proba-V products 
(100m). We validate distributed LAI maps, derived from Proba-V data, with a detailed mapping of 
vegetation cover throughout the season. We collected, for this purpose, LAI measurements with the 
SS1 Sunscan system of all trees and shrubs within a Proba-V pixel. The outcome is a time series of 
validated LAI maps which enable a detailed characterization of urban vegetation dynamics.  
The distributed land-cover map, as well as LAI maps are used as input data for the process-based and 
spatially-distributed WetSpa-Python model to simulate the urban water balance over one year for the 
Watermaelbeek catchment in Brussels. The LAI maps allow improving the parameterization of 
interception storage of urban vegetation and thus the dynamics in spatio-temporal distribution of 
precipitation reaching and evapotranspiration leaving the urban land surface. 
 
KEYWORDS 
Field measurements, interception capacity, remote sensing, urban vegetation 
SESSION 
2 
1 INTRODUCTION 
Urban interception is defined as the part of rain that is stored on the urban vegetation canopy and 
subsequently evaporates before reaching the ground. In urban areas, interception plays an important 
role in controlling the water and energy balance of our cities (Asadian & Weiler 2009). The part of 
precipitation that is intercepted by urban vegetation is evaporated back to the atmosphere and thus 
does not contribute to surface runoff.  
In hydrological modelling, evaporation from interception storage is often simplified by combining it with 
the transpiration process or taking a fixed percentage of the rainfall. However, rainfall interception has 
a strong impact on evaporation, transpiration and surface runoff and should be considered as an 
important factor in hydrological modelling. The heterogeneity and complexity of the urban landscape 
makes it difficult to quantitatively assess the spatial and temporal distribution of rainfall interception in 
urban areas, which therefore is seldom available for modelling purposes (de Jong & Jetten 2007). 
Conventional mapping implies labour intensive and time consuming field work and thus the mapping of 
vegetation dynamics and its role in hydrological processes remains a major challenge. 
The categorization of surface types in heterogeneous areas can be supported by remote sensing (RS) 
techniques. Remote sensing measures the radiation that is reflected by the target.  The unique 
spectral response pattern enables the classification of surfaces into land-cover patterns as well as to 
monitor the changes of land-cover over time. The potential of satellite data for field and regional 
planning is however limited due to its relatively coarse resolution (30m – km scale) and the challenge 
of translating images into useful parameters (Carlson and Arthur, 2000). To further improve the 
parameterization of urban hydrological models, Mass et al. (2002) suggest the use of airborne high-
resolution imagery. Advances in high-resolution imaging spectroscopy over the last decade have 
increased the potential for a thematically detailed mapping of urban areas. Several studies indicate 
improvements in describing functional vegetation properties (Hermans et al., 2003; Weng et al., 2006). 
In this study, we characterize the land surface cover at a high spatial resolution (2m) with an airborne 
hyperspectral APEX (Airborne Prism Experiment) image, while the new daily Proba-V (Project for 
OnBoard Autonomy – Vegetation) products (100m) are used for a detailed characterization of the 
seasonal variation of urban green. The Proba-V satellite, which provides daily vegetation data with a 
100m resolution as NDVI maps contributes to an improved monitoring of the seasonal variation of 
vegetation characteristics. The Normalized Difference Vegetation Index (NDVI) is a numerical indicator 
that uses the visible and near-infrared bands of the electromagnetic spectrum to identify the amount of 
living green vegetation. 
Based on the high spatial resolution of the airborne image and the high temporal resolution of Proba-V 
we can create distributed leaf area index (LAI) maps to simulate interception storage in urban areas 
with the process-based and spatially distributed WetSpa-Python model. The Water and Energy 
Transfer between Soil Plants and Atmosphere tool (WetSpa) (Wang et al., 1997; Liu and De Smedt, 
2004) allows for a detailed simulation of the hydrological processes at the surface in a continuous and 
distributed manner. Its recent Python version (WetSpa-Python) was developed in view of application in 
urban areas (Salvadore et al., 2012, 2015). The WetSpa-Python model has increased flexibility as the 
different model components represent physical processes and every component can have a different 
spatial and temporal resolution. This allows to account for the heterogeneous distribution of urban 
green and the seasonal effects of the vegetation. 
 
2 METHODS 
2.1 Ground-truth validation for RS data 
The generation of the urban land-cover map is based on the airborne hyperspectral APEX image with 
a 2m resolution. There are two methods of image classification: supervised and unsupervised. With 
supervised classification, the user develops the spectral signatures of known categories and then the 
software assigns each pixel in the image to the cover type to which its signature is most similar. With 
unsupervised classification, the software groups pixels into categories of similar signatures, and then 
the user interprets what cover types those categories represent. Based on 200 known pixels, so-called 
training pixels, per class a supervised classifier, the Support Vector Machine (SVM) classifier, was 
applied to the APEX image to generate the high-resolution land-cover map of the Watermaelbeek 
catchment using 31 sub-classes such as impervious roofing and pavements (tiles, asphalt, concrete) 
NOVATECH 2016 
3 
or pervious vegetated (trees, shrubs, grasses) and non-vegetated surfaces (water, bare soil). The 
ground-truthing of RS data includes a detailed mapping of land-cover characteristics and more 
specifically vegetation cover throughout the seasons. Two Proba-V pixels on the VUB and ULB 
campus in Brussels, Belgium are selected to monitor the dynamics of the tree canopies from April to 
October 2015. Within the 100 x 100m Proba-V pixels, all 2x2 m pixels of the land-cover map without 
trees are assumed to be stable throughout the season. The seasonal variation of LAI within the Proba-
V pixel is thus only influenced by the dynamics of the tree canopies within that pixel. For assessing the 
leaf area index (LAI), we use the Sunscan system (Type SS1-COM-R4) to measure incident and 
transmitted photosynthetically active radiation. The SS1 Sunscan system is used to measure LAI of all 
trees within the pixel. To validate the Proba-V data we calculate the LAI based on the provided NDVI 
maps and using the method of Su (Su, 1996). 
Once the Proba-V pixels are validated, distributed LAI maps can be created and used as an input to 
the hydrological model to improve the calculation of interception storage. 
 
2.2 Calculating interception storage based on RS data 
To determine interception storage with optical imagery, the LAI and the fractional vegetation 
parameter V% are derived using spectral mixture analysis and spectral vegetation indices (de Jong & 
Jetten, 2007). Interception is calculated by the following equation (Aston, 1979): 
 
       Equation 1 
where I is the cumulative interception loss [mm], Imax is the canopy interception storage capacity [mm], 
P is the cumulative rainfall for one rainfall event [mm], and kc is the correction factor of canopy 
openness. Different values for the correction factor kc for most common urban trees, shrubs and lawns 
are found in literature (e.g.: Aston, 1979; Gomez et al., 2001).  
The canopy interception storage capacity Imax in urban areas is related to LAI using linear regression 
(de Jong & Jetten, 2007). 
 
     Equation 2 
 
2.3 Simulation with the WetSpa model 
The Wetspa-Python model has an adaptable structure as every hydrological process is implemented 
in a separate, generic module. This allows introducing equations 1 and 2 into a new interception 
module of the Wetspa-Python model. Further, modules are interchangeable and can have different 
spatial and temporal resolution according to the data availability. Therefore, we can use both the land-
cover fraction map at a 2m resolution derived from the APEX image and the LAI maps derived from 
Proba-V data (100m resolution) as inputs to the Wetspa-Python model. Based on the distributed land-
cover fraction map each 2m pixel is divided into different land cover types. For the vegetated land-
cover types, the seasonal LAI maps are used to calculate the interception storage capacity based on 
equation 2. The interception loss per pixel is derived from equation 1. However, this equation does not 
account for the fact that interception loss is higher during the initial phase of a storm and approaches 
zero thereafter. If the total rainfall during the first time increment is greater than the interception 
storage capacity, the rainfall rate is reduced by the same capacity. Otherwise, all rainfall is intercepted 
in the canopy and the remainder of the interception capacity is removed from the rainfall in the 
following time increments. 
 
          Equation 3 
where Ii(t) is the interception loss at cell i over the time interval (mm), Ii,0 is the cell interception storage 
capacity (mm), SIi(t-1) is the cell interception storage at time step t-1 (mm), and Pi(t) is the cell 
SESSION 
4 
precipitation amount (mm).The interception storage capacity is a function of the LAI and plant species 
as described above. 
 
3 RESULTS 
 
Figure 1: simplified land-cover map for VUB campus with the selected Proba-V pixels. 
The detailed land-cover map (Fig. 1) allows to define the area within the Proba-V pixel that is covered 
with trees versus the area covered with grass or urban materials which are assumed constant over the 
season. Results of seasonal LAI measurements indicate that different tree species have different 
seasonal canopy growth (Fig. 2) but also that LAI varies within tree species as canopy growth also 
depends on the amount of water and the hours of sun (shadow of buildings is important) reaching the 
tree. This motivates the development of seasonal LAI maps based on Proba-V data, where the LAI 
can be based on the spectral response of the vegetation within a Proba-V pixel.  
 
 
Figure 2: Seasonal dynamics of LAI for different tree species. 
Further we expect that by including the spatially distributed land-cover and the seasonal LAI maps into 
the WetSpa-Python model we will improve the spatio-temporal distribution of the precipitation reaching 
and evapotranspiration leaving the urban land surface and thus get a more realistic simulation of 
surface runoff in urban areas. 
4 CONCLUSIONS 
As interception by urban trees plays an important role to control the water balance in urban areas, it is 
important to characterize urban vegetation in detail. This study uses RS data validated with ground-
truth measurements to characterize urban green in a spatially distributed way throughout the season. 
The WetSpa-Python model allows to input spatially distributed land-cover and seasonal LAI maps in 
order to simulate the urban water balance in the Watermaelbeek catchment. We expect that the LAI 
maps will improve the parameterization of interception storage of urban vegetation and thus improve 
Legend: 
    Proba-V grid 
    Selected pixels 
    Water 
    Grass 
    Trees 
    Pavement 
    Roofing 
    Bare soil 
  
     
 
 
Apple Tree 
Walnut 
Oak 
Chestnut 
Maple 
Hornbeam 
Birch 
Ash 
NOVATECH 2016 
5 
the simulation of net precipitation and surface runoff.  
5 ACKNOWLEDGEMENT 
This research is co-funded within the frame of the BELAIR project SR/XX/170 (BELSPO STEREOII 
programme) and UrbanEARS project SR/00/307 (BELSPO STEREOIII). We want to thank Frederik 
Priem (CGIS Department, Vrije Universiteit Brussel) who produced the land-cover map for this study. 
 
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Carlson, T. N., & Arthur, S. T. (2000). The impact of land use - land cover changes due to urbanization on surface 
microclimate and hydrology: a satellite perspective. Global and Planetary Change, 25, 49–65. 
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Simulation des pertes d’eau de pluie liées à l’interception par la végétation urbaine, basée sur l’acquisition de données in situ et de télédétection

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Simulation des pertes d’eau de pluie liées à l’interception par la végétation urbaine, basée sur l’acquisition de données in situ et de télédétection

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