A combined Remote Sensing and GIS-based method for Local Climate Zone mapping using PRISMA and Sentinel-2 imagery

• Published in: International journal of applied earth observation and geoinformation 2024-07, Vol.131, p.103944, Article 103944
• Main Authors: Vavassori, Alberto, Oxoli, Daniele, Venuti, Giovanna, Brovelli, Maria Antonia, Siciliani de Cumis, Mario, Sacco, Patrizia, Tapete, Deodato
• Format: Article
• Language: English
• Subjects: Free and open-source software; Hyperspectral; Local Climate Zone; PRISMA; Sentinel-2
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2024.103944

•Hyperspectral PRISMA and multispectral Sentinel-2 imagery are used for LCZ mapping.•PRISMA provides higher spectral separability between LCZ classes compared to Sentinel-2.•The method yields better results than LCZ Generator benchmark tool (+16 % mean OA).•PRISMA shows better classification performance than Sentinel-2 (+5% mean OA increase).•Reproducibility owing to exclusive use of free and open-source data and software. In the last decade, several methods have been developed for Local Climate Zone (LCZ) mapping, encompassing Remote Sensing and Geographic Information Systems (GIS) −based procedures. Combined approaches have also been proposed to compensate for intrinsic limitations that characterized their separate application. Recent work has disclosed the potential of hyperspectral satellite imagery for improving LCZ identification. However, the use of hyperspectral data for LCZ mapping is yet to be fully unfolded. A combined Remote Sensing and GIS-based method for LCZ mapping is proposed to exploit the integration of hyperspectral PRISMA and multispectral Sentinel-2 images with ancillary urban canopy parameter layers. Random Forest algorithm is applied to the feature sets to obtain the LCZ classification. The method is tested on the Metropolitan City of Milan (Italy), for the period from February to August 2023. A spectral separability analysis is carried out to investigate the improvement in LCZ identification using PRISMA in comparison to Sentinel-2 data, as well as improvements in LCZ spectral separability on PRISMA pan-sharpened images. The resulting maps’ quality is evaluated by extracting accuracy metrics and performing inter-comparisons with maps computed from the LCZ Generator benchmark tool. Inter-comparisons yield promising results with a mean Overall Accuracy increase of 16% using PRISMA for each LCZ class. Furthermore, we find that PRISMA improves the detection of LCZs compared to Sentinel-2, with a mean Overall Accuracy increase of 5%, in line with the higher spectral separability of PRISMA spectral signatures computed on the training samples.