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Item type:Publication, Vulnerabilities and exposure of recent informal urban areas in Lima, Peru(Elsevier Ltd, 2024-10-01)Urban areas are experiencing rapid growth, accompanied by significant disorder in Lima Metropolitan area and many other cities in South America. Due to decades of uncontrolled construction practices, it is imperative to identify and better understand the types of informalities prevalent in these recent urban areas. Addressing this lack of information is crucial for implementing appropriate countermeasures and developing new policies that benefit the communities residing in such areas. It is worth noting that understanding disaster risk aligns with the first priority of the Sendai Framework for Disaster Risk Reduction. In this study, we propose the use of radar satellite imagery recorded by the Sentinel-1 constellation since 2017 to identify clusters of urban growth in Lima Metropolitan area. Then, the informal urban clusters can be depicted by visual inspection of the last recorded high-resolution optical image. With good spatial and temporal resolution, we identified 25 clusters informal areas. Among our findings, we observed that several of these clusters are situated in landfills comprised of construction and other waste, increasing their vulnerability to debris flow, landslides, and earthquakes. Additionally, we noted that some new urban areas mainly consist of temporarily empty houses, highlighting the feasibility of implementing countermeasures, such as relocations, in the early stages of informal occupation. These results underscore the significant contribution of satellite radar imagery in identifying recent informal urban areas. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Informal urban growth monitoring in earthquake-prone areas using SAR satellite images(International Association for Earthquake Engineering, 2024-01-01)In recent decades, Peru’s primary mode of urban growth has been the informal occupation of bare lands. These urban areas are characterized by their lack of essential services, such as electricity and water. With the lack of suitable land for urban development, informal urban has grown into unsafe areas against earthquakes. Due to the lack of resources in developing countries, detecting recent informal occupations in unsafe areas cannot be performed, which is an important task for relocation purposes. This article reports the performance of machine learning applied in synthetic aperture radar (SAR) satellite images for the early detection of informal settlements in hazardous areas. The methodology uses a set of temporally SAR images of a specific area, binary pixel classification, and post-processing techniques to improve the prediction performance. Two informal occupations that occurred in the districts of Chorrillos and Villa el Salvador, Lima, Peru, in April 2021 were used as experimental evaluation. A set of SAR images of the constellation Sentinel-1 was used with a resolution of 10m. The results show that time series analysis of SAR images can identify recent informal occupations. However, the geometrical distortions in SAR images reduce the accuracy of the spatial extent of the occupations. We conclude that SAR images are a valuable source for a sustainable informal urban growth monitoring system.