A systematic method for selecting suitable locations for an urban climatological sensor observation network

Urban climate observation through a network of sensors provides important information for climate change adaptation and climate-resilient planning. The effectiveness of such networks depends largely on the appropriate identification and spatial placement of urban climatological sensor sites. The heterogeneity in urban surface, building heights, land use and land cover patterns, along with sensor quality differences, makes it a complicated process. Multiple approaches are in practice to cover the spatial variabilities within an urban area through a dense network of observation stations. In this study, we proposed a GIS-based systematic method for the identification of possible location points for sensor placement in an urban area using a 250m×250 m gridded resolution local climate zone (LCZ) map through a multivariate clustering method. We processed a total of 1524 grid cells for Kassel city through LCZ accuracy assessment and extracted 532 cells as homogeneous LCZ cells. The fractional area comparison with the 10 m land use and land cover (LULC) dataset for Kassel showed that the extracted LCZ cells covered less area (0.02%) of the flooded vegetation class and a larger area (>55%) of the built class. We applied the k-mediods multivariate cluster analysis method using input variables (elevation, aspect and slope) layers with extracted LCZ cells for identification of urban climatological sensor locations. We identified 16 location cells to be the optimal sensor placement locations based on multivariate cluster analysis with 16 defined cluster classes. The statistical comparison of location cells starting from 1524 city grid cells, 532 homogeneous cells, 47 identified cells and 16 finalized cells with Land Surface Temperature (LST) for spring, summer, autumn and winter seasons showed that standard deviation (SD) and absolute deviation (AD) for all seasons tend to decrease with a decrease in the number of cells (n). The statistical analysis indicated that the identified sensor location cells are in the corresponding Climatope zones of the urban climate map (UCM) of Kassel, validating the characteristics of that LCZ class and the distribution of sensor location points in the urban area. Our proposed method will enable urban climate researchers, city governments and citizens to observe the urban environment effectively.