Coraline Wyard

Urban heat waves pose increasing challenges to cities worldwide, with Brussels experiencing significant thermal stress events over the past two decades. This study investigates the relationship between heat wave occurrence, vegetation cover, and built-up density in the Brussels Capital Region using remote sensing data from 2004 to 2024. Land surface temperature (LST) data derived from Landsat 8 satellite imagery were analyzed alongside normalized difference vegetation index (NDVI) and built-up indices to understand their spatial and temporal correlations during heat wave events.The analysis revealed that areas with vegetation cover below 30% consistently experienced elevated temperatures, with surface temperatures during heat wave events reaching up to 2.5°C higher compared to well-vegetated neighborhoods. In regions where built-up density exceeded 75%, LST values were on average 2.0-3.2°C higher than in areas with moderate urbanization (40-60% built-up cover). Temporal analysis indicated a 20% increase in the frequency of heat wave events and a 15% rise in their average duration from 2004 to 2024, with the most significant impacts observed in high-density urban districts. Furthermore, neighborhoods with vegetation loss exceeding 10% over the 20-year period saw an additional 0.8-1.2°C rise in surface temperatures during heat waves, highlighting the critical role of green spaces in moderating UHI intensities. These findings underscore the disproportionate thermal stress faced by densely urbanized areas, emphasizing the necessity for targeted urban greening interventions.

The exploitation of urban-material spectral properties is of increasing importance for a broad range of applications, such as urban climate-change modeling and mitigation or specific/dangerous roof-material detection and inventory. A new spectral library dedicated to the detection of roof material was created to reflect the regional diversity of materials employed in Wallonia, Belgium. The Walloon Roof Material (WaRM) spectral library accounts for 26 roof material spectra in the spectral range 350–2500 nm. Spectra were acquired using an ASD FieldSpec3 Hi-Res spectrometer in laboratory conditions, using a spectral sampling interval of 1 nm. The analysis of the spectra shows that spectral signatures are strongly influenced by the color of the roof materials, at least in the VIS spectral range. The SWIR spectral range is in general more relevant to distinguishing the different types of material. Exceptions are the similar properties and very close spectra of several black materials, meaning that their spectral signatures are not sufficiently different to distinguish them from each other. Although building materials can vary regionally due to different available construction materials, the WaRM spectral library can certainly be used for wider applications; Wallonia has always been strongly connected to the surrounding regions and has always encountered climatic conditions similar to all of Northwest Europe.

Earth observation technologies offer non-intrusive solutions for monitoring complex and risky sites, such as landfills. In particular, unmanned aerial vehicles (UAVs) offer the ability to acquire data at very high spatial resolution, with full control of the temporality required for the desired application. The versatility of UAVs, both in terms of flight characteristics and on-board sensors, makes it possible to generate relevant geodata for a wide range of landfill monitoring activities. This study aims to propose a robust tool and to provide data acquisition guidelines for the land cover mapping of complex sites using UAV multispectral imagery. For this purpose, the transferability of a state-of-the-art object-based image analysis open-source processing chain was assessed and its sensitivity to the segmentation approach, textural and contextual information, spectral and spatial resolution was tested over the landfill site of Hallembaye (Wallonia, Belgium). This study proposes a consistent open-source processing chain for the land cover mapping using UAV data with accuracies of at least 85%. It shows that low-cost red-green-blue standard sensors are sufficient to reach such accuracies and that spatial resolution of up to 10 cm can be adopted with limited impact on the performance of the processing chain. This study also results in the creation of a new operational service for the monitoring of the active landfill sites of Wallonia.

Urban planning is a challenge, especially when it comes to limiting land take. In former industrial regions such as Wallonia, the presence of a large number of brownfields, here called “redevelopment sites”, opens up new opportunities for sustainable urban planning through their revalorization. The Walloon authorities are currently managing an inventory of more than 2200 sites, which requires a significant amount of time and resources to update. In this context, the Sentinel satellites and the Terrascope platform, the Sentinel Collaborative Ground Segment for Belgium, enabled us to deploy SARSAR, an Earth observation service used for the automated monitoring of redevelopment sites that generates regular and automatic change reports that are directly usable by the Walloon authorities. In this paper, we present the methodological aspects and implementation details of the service, which combines two well-known and robust methods: the Pruned Exact Linear Time method for change point detection and threshold-based classification, which assigns the detected changes to three different classes (vegetation, building and soil). The overall accuracy of the system is in the range of 70–90%, depending on the different methods and classes considered. Some remarks on the advantages and possible drawbacks of this approach are also provided.

Sub-daily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration extreme rainfall in a warmer future climate. Traditionally, regional climate models (RCMs) are run at a coarse resolution using deep-convection parameterization for these extreme events. As computational resources are continuously ramping up, these models are run at convection-permitting resolution, thereby partly resolving the small-scale precipitation events explicitly. To date, a comprehensive evaluation of convection-permitting models is still missing. We propose an evaluation strategy for simulated sub-daily rainfall extremes that summarizes the overall RCM performance. More specifically, the following metrics are addressed: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling and spatio-temporal clustering. The aim of this paper is: (i) to provide a statistical modeling framework for some of the metrics, based on extreme value analysis, (ii) to apply the evaluation metrics to a micro-ensemble of convection-permitting RCM simulations over Belgium, against high-frequency observations, and (iii) to investigate the added value of convection-permitting scales with respect to coarser 12-km resolution. We find that convection-permitting models improved precipitation extremes on shorter time scales (i.e, hourly or two-hourly), but not on 6h-24h time scales. Some metrics such as the diurnal cycle or the Clausius-Clapeyron rate are improved by convection-permitting models, whereas the seasonal cycle appears robust across spatial scales. On the other hand, the spatial dependence is poorly represented at both convection-permitting scales and coarser scales. Our framework provides perspectives for improving high-resolution atmospheric numerical modeling and datasets for hydrological applications. Sub-daily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration extreme rainfall in a warmer future climate. Traditionally, regional climate models (RCMs) are run at a coarse resolution using deep-convection parameterization for these extreme events. As computational resources are continuously ramping up, these models are run at convection-permitting resolution, thereby partly resolving the small-scale precipitation events explicitly. To date, a comprehensive evaluation of convection-permitting models is still missing. We propose an evaluation strategy for simulated sub-daily rainfall extremes that summarizes the overall RCM performance. More specifically, the following metrics are addressed: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling and spatio-temporal clustering. The aim of this paper is: (i) to provide a statistical modeling framework for some of the metrics, based on extreme value analysis, (ii) to apply the evaluation metrics to a micro-ensemble of convection-permitting RCM simulations over Belgium, against high-frequency observations, and (iii) to investigate the added value of convection-permitting scales with respect to coarser 12-km resolution. We find that convection-permitting models improved precipitation extremes on shorter time scales (i.e, hourly or two-hourly), but not on 6h-24h time scales. Some metrics such as the diurnal cycle or the Clausius-Clapeyron rate are improved by convection-permitting models, whereas the seasonal cycle appears robust across spatial scales. On the other hand, the spatial dependence is poorly represented at both convection-permitting scales and coarser scales. Our framework provides perspectives for improving high-resolution atmospheric numerical modeling and datasets for hydrological applications.

The Walloon Region is currently managing a database of more than 2000 ‘redevelopment sites', i.e. urban sites that were previously used for industrial activities and/or housing and that are now abandoned. The administration needs to keep this inventory up-to-date so that the necessary urban planning could be done; however, at the moment, this information is obtained via time-consuming on field campaigns. Thanks to the launch of the Copernicus programme, free satellite data are now provided at high temporal resolution, and new monitoring approaches can be implemented. Leveraging a well-established changepoint detection method, this paper shows some preliminary results on how time series of Sentinel-1 and Sentinel-2 data could be jointly used to automatically detect changes in urban areas, thus providing the Walloon Region with a tool that can be exploited for a more efficient management of the ‘redevelopment sites'.

Landfill managers are subject to obligations which include the regular monitoring of the topographical and land cover (LC) evolution of the site. This research aims at developing a cost-effective non-intrusive methodology for mapping landfill LC features. To this end, a state-of-the-art OBIA open-source workflow based on an integration of GRASS GIS and Python programming environment was adapted and applied to 3-cm optical UAV image acquired over the landfill site of Hallembaye (Belgium). The results of this 8-class supervised classification are promising with an overall accuracy of 80.5%. This study shows that existing open-source processing chain can be adapted to UAV imagery. In addition, the added value of feature selection and of textural information provided by very high-resolution optical data is also highlighted. Finally, this study illustrates the potential of machine learning for the monitoring of landfill sites.

In Belgium, most flood events occur in winter as a result of intense precipitation events but also through the abrupt melting of the snowpack that covers the Ardennes summits. These conditions favourable to floods exhibit a decreasing trend over 1959–2010 resulting from the reduction in snow accumulation, although extreme precipitation events show a positive, albeit non‐significant signal. In this study, the evolution of these trends in warmer climates is investigated by using future projections performed with the regional climate model MAR (‘Modèle Atmosphérique Régional’) forced by two global models NorESM1‐M and MIROC5 under the RCP8.5 scenario. These models were selected from the CMIP5 archive after evaluation of their ability to represent the current (1976–2005) mean climate over Europe. By the end of the century, the results show an acceleration of the snow depletion resulting in fewer snowmelt‐associated flood risk days depending on the warming rate from the global models forcing MAR. Regarding the impact of the evolution of extreme precipitation events on hydroclimatic conditions favouring floods, no significant change was found although these trends are subject to uncertainties due to model physics and natural variability.

The first aim of this study is to determine if changes in precipitation and more specifically in convective precipitation are projected in a warmer climate over Belgium. The second aim is to evaluate if these changes are dependent on the convective scheme used. For this purpose, the regional climate model Modèle Atmosphérique Régional (MAR) was forced by two general circulation models (NorESM1-M and MIROC5) with five convective schemes (namely: two versions of the Bechtold schemes, the Betts–Miller–Janjić scheme, the Kain–Fritsch scheme, and the modified Tiedtke scheme) in order to assess changes in future precipitation quantities/distributions and associated uncertainties. In a warmer climate (using RCP8.5), our model simulates a small increase of convective precipitation, but lower than the anomalies and the interannual variability over the current climate, since all MAR experiments simulate a stronger warming in the upper troposphere than in the lower atmospheric layers, favoring more stable conditions. No change is also projected in extreme precipitation nor in the ratio of convective precipitation. While MAR is more sensitive to the convective scheme when forced by GCMs than when forced by ERA-Interim over the current climate, projected changes from all MAR experiments compare well.

INSPIRE and PSI directives induced major changes in the Walloon geomatics ecosystem. INSPIRE aims at establishing an infrastructure for spatial information in the European community for environmental policies. PSI is the European legislation on the re-use of public sector information, including all government-held geodatasets. For compliance purposes, Wallonia proposed to develop a Georeferential aiming at providing free, open-access and consistent reference geodatasets to the Walloon stakeholders. Some of the current data being outdated, the Georeferential proposes pilot projects for a collaborative and user-oriented design of new maps. One of the categories being identified for the Georeferential is land cover and land use. Build on the knowledge of past researches, the Walous project aims at combining very high spatial with very high temporal resolution Earth observation and thematic data in a classification scheme. To ensure that the new data covers the general interest and to ensure his appropriation by the users, gathering requirements is one of the most vital pieces. Poor gathering of user needs could lead to an unused final product. Walous carried out interviews for clarifying data requirements. This paper introduces this methodology and illustrates how the results impacted the priorities and the specifications of the land cover and land use data.