The availability of gridded, screen-level air temperature data at an effective spatial and temporal resolution is important for many fields such as climatology, ecology, urban planning and design. This study aims at providing such data in a data-scarce, arid city within the greater Cairo region (Egypt), namely the Sixth of October, where, to our knowledge, no such data are available. By using (i) air temperature data, collected from mobile measurements, (ii) multiple spectral indices, (iii) spatial analysis techniques and (iv) random forest regression modelling, we produced air temperature maps (for both daytime and nighttime) at 30-m spatial resolution for the entire city. The proposed method is systematic and relies on low-cost instrumentation and freely-available satellite data and hence it can be replicated in similar data-scarce, arid areas to allow for better spatial and temporal monitoring of air temperature.

Climate change and global warming requires a strong boost to sustainable growth strategies. In particular, urban green management and planning is becoming a crucial and at the same time critical aspect. Therefore, urban green requires being accurately mapped, quantified and monitored over time. In this study we propose a cost-effective but reliable approach for the automatic classification and quantification of the tree canopy cover over extended geographical areas. The classification can also be used for estimating the number of trees, based on land use land cover (LULC) and the corresponding planting layout. The case study application is the Metropolitan City of Milan. Data used for classifying the tree canopy are based on high-resolution satellite imagery provided by the PlanetScope constellation. Based on the latter information, the work relies on the use of radiometric Vegetation Indices (VIs) to quantify the tree canopy. However, because the use of VIs can cause mixing of different types of vegetation, such as tree and grass, we used a stack of multi-temporal data from PlanetScope to retrieve per-pixel statistics for Red band and Normalized Difference Vegetation Index (NDVI). The hypothesis here is that during spring-summer season tree canopy provides less variability than grass and/or agricultural fields. The approach provides an improved vegetation index capable of separating potential canopy-tree from other vegetation types. The result of the accuracy assessment shows an overall accuracy of 78.33% and 71.5% for the whole Metropolitan City of Milan and the City of Milan respectively.

The Local Climate Zone (LCZ) classification scheme, introduced by Stewart and Oke (2012), offers promising opportunities for better studying the urban climate phenomena at the micro-and local scale (e.g. the urban heat island effect). However, although several methods have been introduced to apply the concept of LCZs to cities, only few utilize publicly available data, like, for instance, the World Urban Database and Access Portal Tools (WUDAPT). However, to date, results are relatively rough, and frequent quality assessments demonstrate moderate overall accuracy. This paper proposes an approach for improving the quality of LCZ automatic classification, combining freely available multispectral satellite imagery together with morphological features of the urban environment. An overall accuracy of 67% was achieved for the Metropolitan City of Milan with an improvement of 12% with respect to using only Landsat 8 multispectral and thermal data. This ascertains the physic-morphological nature of the LCZs and opens the possibility for mapping more accurate LCZs without the need for additional thermal information.