Preprint Article Version 1 NOT YET PEER-REVIEWED

Remote Sensing and Data Mining Techniques for Assessing the Urban Fabric Vulnerability to Heat Waves and UHI

Version 1 : Received: 23 August 2016 / Approved: 24 August 2016 / Online: 24 August 2016 (10:19:40 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. Preprints 2016, 2016080202 (doi: 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 (doi: 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.

Subject Areas

HR satellite remote sensing; urban fabric vulnerability; UHI & heat waves; landsat & MODIS sensors; LST & urban heating; segmentation & objects classification; data mining; feature extraction & selection; stepwise regression & model calibration

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