Urban Intensities : the Urbanization of the Iberian Mediterranean Coast in the Light of Nighttime Satellite Images of the Earth

The contribution shares the approach of critical urban studies that have conceptualized urbanization more as a process than as a sum of spatial forms. Thus, the contribution studies the urbanization process not only from the point of view of the physical occupation of land but also considers changes in the intensity of the uses of space. To fulfill this aim, the new sources of nocturnal satellite images are particularly useful. These allow us to observe the intensity of urban uses both in terms of their distribution over space and their recurrence over time. The research focuses on the Iberian Mediterranean coast and permits the verification of the intensity of the urban uses of the space for the whole of this area and their seasonal variations throughout the year. The source of the study are the nighttime satellite images of the Earth for the 2012-2017 period from the NASA SNPP satellite equipped with the VIIRS-DNB instrument. By establishing a threshold of urban light the research shows that those districts with the greatest extensions of urban light do not necessarily correspond with the most densely populated areas. Similarly the absence of urban light does not necessarily indicate the absence of urban uses. Finally, the variations of intensity of light prove to be a good indicator of seasonal variations of activity in tourist areas

Evaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areas

This paper introduces a novel top-down approach to geospatially identify and distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Under the framework of the World Bank’s Central American Country Disaster Risk Profiles (CDRP) initiative, a disaggregated property stock exposure model has been developed as one of the key elements for disaster risk and loss estimation. Global spatial datasets are therefore used consistently to ensure wide-scale applicability and transferability. Residential and mixed use areas need to be identified in order to spatially link accordingly compiled property stock information. In the presented study, multi-sensor nighttime Earth Observation data and derivative products are evaluated as proxies to identify areas of peak human activity. Intense artificial night lighting in that context is associated with a high likelihood of commercial and/or industrial presence. Areas of low light intensity, in turn, can be considered more likely residential. Iterative intensity thresholding is tested for Cuenca City, Ecuador, in order to best match a given reference situation based on cadastral land use data. The results and findings are considered highly relevant for the CDRP initiative, but more generally underline the relevance of remote sensing data for top-down modeling approaches at a wide spatial scale

Evaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areas

This paper introduces a novel top-down approach to geospatially identify and distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Under the framework of the World Bank’s Central American Country Disaster Risk Profiles (CDRP) initiative, a disaggregated property stock exposure model has been developed as one of the key elements for disaster risk and loss estimation. Global spatial datasets are therefore used consistently to ensure wide-scale applicability and transferability. Residential and mixed use areas need to be identified in order to spatially link accordingly compiled property stock information. In the presented study, multi-sensor nighttime Earth Observation data and derivative products are evaluated as proxies to identify areas of peak human activity. Intense artificial night lighting in that context is associated with a high likelihood of commercial and/or industrial presence. Areas of low light intensity, in turn, can be considered more likely residential. Iterative intensity thresholding is tested for Cuenca City, Ecuador, in order to best match a given reference situation based on cadastral land use data. The results and findings are considered highly relevant for the CDRP initiative, but more generally underline the relevance of remote sensing data for top-down modeling approaches at a wide spatial scale

Exposure and vulnerability for seismic risk evaluations

To make effective decisions for earthquake risk reduction, accurate seismic risk evaluations are required. Substantial data, methods, and tools from the field of structural engineering are used in the seismic risk assessment process, including the collection and interpretation of building data and the estimation of seismic vulnerability, for which there are numerous sources of inefficiency and inaccuracy. Compiling building exposure datasets in an effective manner for use in seismic vulnerability and risk assessments requires methods that collect applicable or useful data whilst balancing accuracy and cost. This thesis investigates this three-way balance. First, a systematic review of the literature is completed to ascertain the most useful building data for estimating seismic vulnerability. Useful building characteristics are determined by: (1) investigating the frequency of building characteristics used in published seismic vulnerability assessment methods, and (2) reviewing studies that explore the sensitivity of inputs to vulnerability assessments; the more sensitive the input, the more useful the data. Second, a range of building data collection methods are tested in the urban centre of Guatemala City. A series of desk-based studies are used to collate published and available information, such as housing censuses, existing studies, the history of urban development, and construction practices and trends. Field-based methods are then employed including established methods such as street-level rapid visual surveys and detailed internal surveys, and newer methods such as virtual surveys using omnidirectional imagery and three-dimensional models derived from unmanned aerial vehicle imagery. The resources required by each method are calculated from the actual costs encountered in the desk study, fieldwork, and post-trip analysis. The accuracy of collected data is determined by justifying assumptions of accurate data and comparing results for individual buildings across the methods using inter-rater agreement statistical methods. The balance of data usefulness, cost and accuracy is examined in detail to highlight the effectiveness of the tested data collection methods. It is found that the building data collection methods that employ newer technology have great potential in this field, although some struggle to collect all of the necessary data to classify building typologies and assess seismic vulnerability, so are most effective when combined with other datasets. Using the collected data, the seismic vulnerability and risk of the study area are estimated, and a preliminary study starts to investigate the impacts of uncertainties in building data when propagated through to loss ratios. Further work is required, but the preliminary result indicate that range the in losses is significant, highlighting the need for accurate building data collection to feed into seismic exposure and vulnerability assessments and, in turn, seismic risk evaluations