Contribution à l’analyse de l’érosion intra-urbaine à Kinshasa (R.D.C.)

Cette étude contribue à améliorer la compréhension de l’érosion en milieu urbain. Elle propose une clef d’interprétation visuelle des ravins actifs ou non sur une image satellitaire à très haute résolution spatiale. Les ravins étant délimités, le contexte urbain est analysé afin d’identifier sur l’image l’origine du ravinement. Ces résultats sont confrontés à un levé de terrain exhaustif effectué par DGPS sur le terrain.This study contributes to improve the understanding of the erosion process in an urban context. It proposes a visual interpretation key of the gullies, be they active or not, on a very high resolution satellite image. While the gullies are delineated, the urban context is analyzed on the image to identify the origin of each gully. These results are confronted with an exhaustive ground survey achieved by DGPS

Editorial: Special theme issue “Mapping, monitoring and modelling of urban areas”

Today 54% of the world’s population is living in urban areas, a number that is expected to rise to 66% by 2050 (United Nations, 2014). The increase in urban population over the last decades has led to a rapid growth of urban areas. This urban land consumption, if not managed well, puts strong pressure on open spaces and increases the demand for ecosystem services while endangering their supply (Elmqvist et al., 2015; Burkhard et al., 2012). Uncontrolled urban growth may also lead to higher co..

Evolution de l’utilisation du sol le long du littoral belge

Utilisant la base de données LACOAST, l’article retrace l’évolution de l’utilisation du sol d’une bande de 10 km de profondeur correspondant à l’espace littoral belge (cordon dunaire et polders). Proportionnellement, le tissu urbain n’y occupe pas un espace plus important que dans d’autres portions du littoral nord européen, mais il se concentre fortement à proximité immédiate du rivage, l’arrière pays étant très peu urbanisé. L’emprise des différentes phases d’urbanisation est comparée et la relative parcimonie foncière du modèle d’urbanisation régénératif de la période d’après guerre mise en évidence. Enfin, le glissement récent de l’urbanisation vers les polders, longtemps préservés, et l’importance de l’emprise des infrastructures non directement liées au tourisme (Zeebrugge, notamment) sont soulignés. Ces constatations sont établies sur des bases quantitatives, en termes de superficies utilisées, et non pas selon une vision paysagère qualitative, plus communément répandue.Using the data from the LACOAST database, the paper traces the land-use evolution of a 10 km broad land strip forming the Belgian coast (dune cordon and polders). Proportionately, the urban fabric is not denser here than in other parts of the coasts of northern Europe, but it is highly concentrated at the immediate proximity of the coastline, while inland urbanization is few important. We will compare the results of the successive urbanization stages, and highlight the relative parsimony of the regenerative urbanization model of the post-war period. Finally, we will look into the recent urbanization trend towards the polders area, for a long time protected, and the significance of the infrastructures non directly related to tourism (e.g. Zeebrugge). These statements are produced from quantitative bases in terms of used surfaces, and do not proceed from a more usual qualitative landscape analysis

Cartographie de la croissance urbaine de Kinshasa (R.D. Congo) entre 1995 et 2005 par télédétection satellitaire à haute résolution

La croissance spatiale de Kinshasa (RDC) est cartographiée par classification des espaces bâtis au départ d’images satellitaires SPOT datant de 1995 et 2005. D’après les résultats, la croissance de la ville est moins rapide que l’évolution démographique ; elle s’effectue désormais dans les espaces interstitiels en dépit de leurs fortes pentes et d’un certain éloignement des principales voies de communication (au delà d’1 km).The urban growth of Kinshasa (DRC) is mapped by classification of built-up areas using SPOT images dating from 1995 and 2005. From the results, the city growth is slower than the population growth; it is taking place within interstitial areas despite their steep slopes and their distance (1 km) from the main communication axes

The use of ORFEO ToolBox in the context of map updating

This paper presents experiments with the ORFEO ToolBox (OTB) developed by the CNES in the context of the Brussels project ARMURS about map updating. Depending on the availability of required functionalities, the project either considered the use of OTB or the development of proprietary or open source code. Since the project includes the development of a demonstrator for map updating from image analysis, the different aspects of data format, image processing for remote sensing and graphical interface are key points for the success of the system integration. As OTB addresses these topics, remains opened for extensions and is available as a freeware, it has been envisaged as a possible basic component.info:eu-repo/semantics/publishe

Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale

Extending Data for Urban Health Decision-Making : a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs

Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1 × 1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data—ideally to be made free and publicly available—and offer lay descriptions of some of the difficulties in generating such data products

site grand public présentant Bruxelles à partir d’images satellitaires et de photographies aériennes, à différentes échelles et à différentes époques

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Système d'information géographique

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