Un panorama de la télédétection de l'étalement urbain

The objective of this review paper is to provide an overview of remote sensing based research tackling urban sprawl issue. 113 articles were indexed and analyzed after research on bibliographical databases. These 113 articles are presented in the form of summary table giving highlights of the listed publications. Articles are divided into 6 categories (F, A, B, C, D, E) according to whether they are articles of methodology, characterization, prospective modeling-simulation, retrospective modeling-simulation, analysis of impacts or monitoring of urban sprawl. The summary table is conceived as a tool which can help researchers interested by the measurement and the analysis of urban sprawl.Cette note rend compte d'une recherche bibliographique dont l'objectif est de fournir un panorama des recherches utilisant la télédétection pour aborder la problématique de l'étalement urbain. 113 articles ont été répertoriés et analysés à la suite de recherches dans des bases de données bibliographiques. Ces 113 articles sont présentés sous forme de tableau récapitulatif donnant un aperçu général des publications recensées. Les articles sont répartis en 6 catégories (F, A, B, C, D, E) suivant qu'il s'agit d'articles de méthodologie, de caractérisation, de modélisation-simulation prospective, de modélisation-simulation rétrospective, d'analyse d'impacts ou de monitorage de l'étalement urbain. Le panorama est conçu comme un outil d'aide aux chercheurs qui s'intéressent à la mesure et à l'analyse de l'étalement urbain

TRENDS IN URBAN MORPHOLOGICAL DATA CAPTURE: A REVIEW OF THEORETICAL PERSPECTIVES ON UTILITY OF GEOSPATIAL TECHNOLOGY

Aim: The purpose of this paper is to review the trends in the application of geospatial technology in urban morphology data capture and studies. Methodology and Results: This review was guided by critical thinking research approach, which involves analysis of relevant literature on a phenomenon to enable to draw conclusion(s) on whether a claim is true, sometimes true, partly true or false and using methods to applied in critical thinking include broad intellectual criteria such as clarity, credibility, accuracy, precision, relevance, depth, significance and fairness. The results show that increasing urbanization and sub-optimal locations of urban amenities and utilities has resulted in many cities facing environmental, land use and socio-economic challenges. This can be mitigated by the implementation of cost-effective urban development plans and policies together with an in-depth understanding of the interactions existing between urban natural and human systems, an undertaking reliably aided by geospatial technologies notably Remote Sensing, Geographical Information Systems, Global Positioning System and Photogrammetry. Conclusion, significance, and impact study: This paper is therefore anchored on an understanding of urban morphology, factors determining its changes over time and demonstrated achievements of the utility of geospatial technology in the study of the same with policy implications for the applications of the geospatial technology in urban studies

Mapping regional land cover and land use change using MODIS time series

Coarse resolution satellite observations of the Earth provide critical data in support of land cover and land use monitoring at regional to global scales. This dissertation focuses on methodology and dataset development that exploit multi-temporal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve current information related to regional forest cover change and urban extent. In the first element of this dissertation, I develop a novel distance metric-based change detection method to map annual forest cover change at 500m spatial resolution. Evaluations based on a global network of test sites and two regional case studies in Brazil and the United States demonstrate the efficiency and effectiveness of this methodology, where estimated changes in forest cover are comparable to reference data derived from higher spatial resolution data sources. In the second element of this dissertation, I develop methods to estimate fractional urban cover for temperate and tropical regions of China at 250m spatial resolution by fusing MODIS data with nighttime lights using the Random Forest regression algorithm. Assessment of results for 9 cities in Eastern, Central, and Southern China show good agreement between the estimated urban percentages from MODIS and reference urban percentages derived from higher resolution Landsat data. In the final element of this dissertation, I assess the capability of a new nighttime lights dataset from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) for urban mapping applications. This dataset provides higher spatial resolution and improved radiometric quality in nighttime lights observations relative to previous datasets. Analyses for a study area in the Yangtze River Delta in China show that this new source of data significantly improves representation of urban areas, and that fractional urban estimation based on DNB can be further improved by fusion with MODIS data. Overall, the research in this dissertation contributes new methods and understanding for remote sensing-based change detection methodologies. The results suggest that land cover change products from coarse spatial resolution sensors such as MODIS and VIIRS can benefit from regional optimization, and that urban extent mapping from nighttime lights should exploit complementary information from conventional visible and near infrared observations

Spatial and Temporal Dust Source Variability in Northern China Identified Using Advanced Remote Sensing Analysis

The aim of this research is to provide a detailed characterization of spatial patterns and temporal trends in the regional and local dust source areas within the desert of the Alashan Prefecture (Inner Mongolia, China). This problem was approached through multi-scale remote sensing analysis of vegetation changes. The primary requirements for this regional analysis are high spatial and spectral resolution data, accurate spectral calibration and good temporal resolution with a suitable temporal baseline. Landsat analysis and field validation along with the low spatial resolution classifications from MODIS and AVHRR are combined to provide a reliable characterization of the different potential dust-producing sources. The representation of intra-annual and inter-annual Normalized Difference Vegetation Index (NDVI) trend to assess land cover discrimination for mapping potential dust source using MODIS and AVHRR at larger scale is enhanced by Landsat Spectral Mixing Analysis (SMA). The combined methodology is to determine the extent to which Landsat can distinguish important soils types in order to better understand how soil reflectance behaves at seasonal and inter-annual timescales. As a final result mapping soil surface properties using SMA is representative of responses of different land and soil cover previously identified by NDVI trend. The results could be used in dust emission models even if they are not reflecting aggregate formation, soil stability or particle coatings showing to be critical for accurately represent dust source over different regional and local emitting areas

An integrative approach using remote sensing and social analysis to identify different settlement types and the specific living conditions of its inhabitants

Someday in 2007, the world population reached a historical landmark: for the first time in human history, more than half of the world´s population was urban. A stagnation of this urbanization process is not in sight, so that by 2050, already 70 percent of humankind is projected to live in urban settlements. Over the last few decades, enormous migrations from rural hinterlands to steadily growing cities could be witnessed coming along with a dramatic growth of the world’s urban population. The speed and the scale of this growth, particularly in the so called less developed regions, are posing tremendous challenges to the countries concerned as well as to the world community. Within mega cities the strongest trends and the most extreme dimensions of the urbanization process can be observed. Their rapid growth results in uncontrolled processes of fragmentation which is often associated with pronounced poverty, social inequality, socio-spatial and political fragmentation, environmental degradation as well as population demands that outstrip environmental service capacity. For the majority of the mega cities a tremendous increase of informal structures and processes has to be observed. Consequentially informal settlements are growing, which represent those characteristic municipal areas being subject to particularly high population density, dynamics as well as marginalization. They have quickly become the most visible expression of urban poverty in developing world cities. Due to the extreme dynamics, the high complexity and huge spatial dimension of mega cities, urban administrations often only have an obsolete or not even existing data basis available to be at all informed about developments, trends and dimensions of urban growth and change. The knowledge about the living conditions of the residents is correspondingly very limited, incomplete and not up to date. Traditional methods such as statistical and regional analyses or fieldwork are no longer capable to capture such urban process. New data sources and monitoring methodologies are required in order to provide an up to date information basis as well as planning strate¬gies to enable sustainable developments and to simplify planning processes in complex urban structures. This research shall seize the described problem and aims to make a contribution to the requirements of monitoring fast developing mega cities. Against this background a methodology is developed to compensate the lack of socio-economic data and to deduce meaningful information on the living conditions of the inhabitants of mega cities. Neither social science methods alone nor the exclusive analysis of remote sensing data can solve the problem of the poor quality and outdated data base. Conventional social science methods cannot cope with the enormous developments and the tremendous growth as they are too labor-, as well as too time- and too cost-intensive. On the other hand, the physical discipline of remote sensing does not allow for direct conclusions on social parameters out of remote sensing images. The prime objective of this research is therefore the development of an integrative approach − bridging remote sensing and social analysis – in order to derive useful information about the living conditions in this specific case of the mega city Delhi and its inhabitants. Hence, this work is established in the overlapping range of the research topics remote sensing, urban areas and social science. Delhi, as India’s fast growing capital, meanwhile with almost 25 million residents the second largest city of the world, represents a prime example of a mega city. Since the second half of the 20th century, Delhi has been transformed from a modest town with mainly administrative and trade-related functions to a complex metropolis with a steep socio-economic gradient. The quality and amount of administrative and socio-economic data are poor and the knowledge about the circumstances of Delhi’s residents is correspondingly insufficient and outdated. Delhi represents therefore a perfectly suited study area for this research. In order to gather information about the living conditions within the different settlement types a methodology was developed and conducted to analyze the urban environment of the mega city Delhi. To identify different settlement types within the urban area, regarding the complex and heterogeneous appearance of the Delhi area, a semi-automated, object-oriented classification approach, based on segmentation derived image objects, was implemented. As the complete conceptual framework of this research, the classification methodology was developed based on a smaller representative training area at first and applied to larger test sites within Delhi afterwards. The object-oriented classification of VHR satellite imagery of the QuickBird sensor allowed for the identification of five different urban land cover classes within the municipal area of Delhi. In the focus of the image analysis is yet the identification of different settlement types and amongst these of informal settlements in particular. The results presented within this study demonstrate, that, based on density classes, the developed methodology is suitable to identify different settlement types and to detect informal settlements which are mega urban risk areas and thus potential residential zones of vulnerable population groups. The remote sensing derived land cover maps form the foundation for the integrative analysis concept and deliver there¬fore the general basis for the derivation of social attributes out of remote sensing data. For this purpose settlement characteristics (e.g., area of the settlement, average building size, and number of houses) are estimated from the classified QuickBird data and used to derive spatial information about the population distribution. In a next step, the derived information is combined with in-situ information on socio-economic conditions (e.g., family size, mean water consumption per capita/family) extracted from georeferenced questionnaires conducted during two field trips in Delhi. This combined data is used to characterize a given settlement type in terms of specific population and water related variables (e.g., population density, total water consumption). With this integrative methodology a catalogue can be compiled, comprising the living conditions of Delhi’s inhabitants living in specific settlement structures – and this in a quick, large-scaled, cost effective, by random or regularly repeatable way with a relatively small required data basis.The combined application of remotely sensed imagery and socio-economic data allows for the mapping, capturing and characterizing the socio-economic structures and dynamics within the mega city of Delhi, as well as it establishes a basis for the monitoring of the mega city of Delhi or certain areas within the city respectively by remote sensing. The opportunity to capture the condition of a mega city and to monitor its development in general enables the persons in charge to identify unbeneficial trends and to intervene accordingly from an urban planning perspective and to countersteer against a non-adequate supply of the inhabitants of different urban districts, primarily of those of informal settlements. This study is understood to be a first step to the development of methods which will help to identify and understand the different forms, actors and processes of urbanization in mega cities. It could support a more proactive and sustainable urban planning and land management – which in turn will increase the importance of urban remote sensing techniques. In this regard, the most obvious and direct beneficiaries are on the one hand the governmental agencies and urban planners and on the other hand, and which is possibly the most important goal, the inhabitants of the affected areas, whose living conditions can be monitored and improved as required. Only if the urban monitoring is quickly, inexpensively and easily available, it will be accepted and applied by the authorities, which in turn enables for the poorest to get the support they need. All in all, the listed benefits are very convincing and corroborate the combined use of remotely sensed and socio-economic data in mega city research

Geospatial analysis of urban sprawl in Ile-Ife city, Nigeria

This paper assesses the effects of urban expansion of Ile-Ife city and appraises the changes that occurred in the landscape from 1986 to 2009. The study made use of information extracted from Landsat TM 1986, ETM+ 2002 and Ikonos 2009 images. GPS observations of notable points within and around the city were overlaid on the topographic map and on the satellite imageries in order to appraise the changes in the land use and land cover over the period. The land consumption was analyzed using the computed land consumption rate (LCR) and land absorption coefficient (LAC) from 1986 to 2009. The average annual rate of urban expansion of Ile-Ife is 3.5% which translated to a gain of 2256.7 ha in the surrounding rural areas from 1986 to 2002 and about 1434.7 ha between 2002 and 2009. The land is being transformed as shown by the land consumption rate which progressed from 1.1% in 1986 to 1.2% in 2002 and remained stable till 2009. The land absorption coefficient has increased from 0.013 to 0.014, confirming the high demand for land both within the city and in the suburbs.Keywords: Urban Sprawl; Land Consumption Rate; Land Absorption Coefficient, Ile-Ife; Remote Sensin

Achieving genuinely dynamic road user charging : issues with a GNSS-based approach

Peer reviewedPostprin

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Integrating spatial and temporal approaches for explaining bicycle crashes in high-risk areas in Antwerp (Belgium)

The majority of bicycle crash studies aim at determining risk factors and estimating crash risks by employing statistics. Accordingly, the goal of this paper is to evaluate bicycle-motor vehicle crashes by using spatial and temporal approaches to statistical data. The spatial approach (a weighted kernel density estimation approach) preliminarily estimates crash risks at the macro level, thereby avoiding the expensive work of collecting traffic counts; meanwhile, the temporal approach (negative binomial regression approach) focuses on crash data that occurred on urban arterials and includes traffic exposure at the micro level. The crash risk and risk factors of arterial roads associated with bicycle facilities and road environments were assessed using a database built from field surveys and five government agencies. This study analysed 4120 geocoded bicycle crashes in the city of Antwerp (CA, Belgium). The data sets covered five years (2014 to 2018), including all bicycle-motorized vehicle (BMV) crashes from police reports. Urban arterials were highlighted as high-risk areas through the spatial approach. This was as expected given that, due to heavy traffic and limited road space, bicycle facilities on arterial roads face many design problems. Through spatial and temporal approaches, the environmental characteristics of bicycle crashes on arterial roads were analysed at the micro level. Finally, this paper provides an insight that can be used by both the geography and transport fields to improve cycling safety on urban arterial roads