Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment

Brian Alan Johnson,Shahab Eddin Jozdani

doi:10.3390/rs11202420

Brian Alan Johnson, Shahab Eddin Jozdani

Open Access

https://doi.org/10.3390/rs11202420

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Abstract

Local climate zone (LCZ) maps are increasingly being used to help understand and model the urban microclimate, but traditional land use/land cover map (LULC) accuracy assessment approaches do not convey the accuracy at which LCZ maps depict the local thermal environment. 17 types of LCZs exist, each having unique physical characteristics that affect the local microclimate. Many studies have focused on generating LCZ maps using remote sensing data, but nearly all have used traditional LULC map accuracy metrics, which penalize all map classification errors equally, to evaluate the accuracy of these maps. Here, we proposed a new accuracy assessment approach that better explains the accuracy of the physical properties (i.e., surface structure, land cover, and anthropogenic heat emissions) depicted in an LCZ map, which allows for a better understanding of the accuracy at which the map portrays the local thermal environment.

Highlights

Mapping and analysis of local climate zones (LCZs), i.e., landscape units with distinct screen-height temperature regimes [1], has become a popular research topic in the fields of remote sensing and urban climatology in recent years
Comparing the cell values of the two error matrices, it is evident that the differences between the producer’s accuracy (PA)/weighted producer’s accuracy (wPA) values and user’s accuracy (UA)/weighted user’s accuracy (wUA) values were greater for LCZ types that had been mostly misclassified as other physically similar LCZ types
We presented a new approach for evaluating the accuracy of local climate zone (LCZ) maps

Summary

Introduction

Mapping and analysis of local climate zones (LCZs), i.e., landscape units with distinct screen-height temperature regimes [1], has become a popular research topic in the fields of remote sensing and urban climatology in recent years. LCZ maps have become an important source of data for studies on urban climatology [2,3,4,5] and urban planning [6]. In a more general context, the LCZ classification scheme has been suggested as a standardized framework for the mapping of urban form at the global scale, as it can provide information on the basic physical properties of any urban area [7]. LCZ maps are typically generated by analyzing satellite images using semi-automated image classification algorithms. Other studies have proposed incorporating ancillary GIS datasets in the image classification workflow, e.g., OpenStreetMap data [11,14], and/or the use of alternative classification algorithms (e.g., convolutional neural networks [11])

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