Version 1
: Received: 23 August 2016 / Approved: 24 August 2016 / Online: 24 August 2016 (10:19:40 CEST)
Version 2
: Received: 21 October 2021 / Approved: 26 October 2021 / Online: 26 October 2021 (13:11:23 CEST)
How to cite:
Borfecchia, F.; Rosato, V.; Caiaffa, E.; Pollino, M.; De Cecco, L.; La Porta, L.; Ombuen, S.; Barbieri, L.; Benelli, F.; Camerata, F.; Pellegrini, V.; Filpa, A. Remote Sensing and Data Mining Techniques for Assessing the Urban Fabric Vulnerability to Heat Waves and UHI. Preprints2016, 2016080202. https://doi.org/10.20944/preprints201608.0202.v1
Borfecchia, F.; Rosato, V.; Caiaffa, E.; Pollino, M.; De Cecco, L.; La Porta, L.; Ombuen, S.; Barbieri, L.; Benelli, F.; Camerata, F.; Pellegrini, V.; Filpa, A. Remote Sensing and Data Mining Techniques for Assessing the Urban Fabric Vulnerability to Heat Waves and UHI. Preprints 2016, 2016080202. https://doi.org/10.20944/preprints201608.0202.v1
Borfecchia, F.; Rosato, V.; Caiaffa, E.; Pollino, M.; De Cecco, L.; La Porta, L.; Ombuen, S.; Barbieri, L.; Benelli, F.; Camerata, F.; Pellegrini, V.; Filpa, A. Remote Sensing and Data Mining Techniques for Assessing the Urban Fabric Vulnerability to Heat Waves and UHI. Preprints2016, 2016080202. https://doi.org/10.20944/preprints201608.0202.v1
APA Style
Borfecchia, F., Rosato, V., Caiaffa, E., Pollino, M., De Cecco, L., La Porta, L., Ombuen, S., Barbieri, L., Benelli, F., Camerata, F., Pellegrini, V., & Filpa, A. (2016). Remote Sensing and Data Mining Techniques for Assessing the Urban Fabric Vulnerability to Heat Waves and UHI. Preprints. https://doi.org/10.20944/preprints201608.0202.v1
Chicago/Turabian Style
Borfecchia, F., Valeria Pellegrini and Andrea Filpa. 2016 "Remote Sensing and Data Mining Techniques for Assessing the Urban Fabric Vulnerability to Heat Waves and UHI" Preprints. https://doi.org/10.20944/preprints201608.0202.v1
Abstract
Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.