Mapping and Monitoring Urban Environment through Sentinel-1 SAR Data: A Case Study in the Veneto Region (Italy)
| Autor: | Daniele Codato, Salvatore Pappalardo, Andrea Semenzato, Massimo De Marchi, Silvano De Zorzi, Umberto Trivelloni, Matteo Massironi, Sabrina Ferrari |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Process (engineering) Geography Planning and Development 0211 other engineering and technologies lcsh:G1-922 SDG 02 engineering and technology Land cover 01 natural sciences urban planning Footprint land cover Urban planning Radar imaging Earth and Planetary Sciences (miscellaneous) Computers in Earth Sciences Sentinel 021101 geological & geomatics engineering 0105 earth and related environmental sciences Sustainable development Strategic urban planning business.industry Environmental resource management 2030 Agenda urban environment SAR urban footprint Statistical classification Geography business lcsh:Geography (General) |
| Zdroj: | ISPRS International Journal of Geo-Information, Vol 9, Iss 375, p 375 (2020) ISPRS International Journal of Geo-Information Volume 9 Issue 6 |
| Popis: | Focusing on a sustainable and strategic urban development, local governments and public administrations, such as the Veneto Region in Italy, are increasingly addressing their urban and territorial planning to meet national and European policies, along with the principles and goals of the 2030 Agenda for the Sustainable Development. In this regard, we aim at testing a methodology based on a semi-automatic approach able to extract the spatial extent of urban areas, referred to as &ldquo urban footprint&rdquo from satellite data. In particular, we exploited Sentinel-1 radar imagery through multitemporal analysis of interferometric coherence as well as supervised and non-supervised classification algorithms. Lastly, we compared the results with the land cover map of the Veneto Region for accuracy assessments. Once properly processed and classified, the radar images resulted in high accuracy values, with an overall accuracy ranging between 85% and 90% and percentages of urban footprint differing by less than 1%&ndash 2% with respect to the values extracted from the reference land cover map. These results provide not only a reliable and useful support for strategic urban planning and monitoring, but also potentially identify a solid organizational dataflow process to prepare geographic indicators that will help answering the needs of the 2030 Agenda (in particular the goal 11 &ldquo Sustainable Cities and Communities&rdquo ). |
| Databáze: | OpenAIRE |
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