Multiscale mapping of local climate zones in Tokyo using airborne LiDAR data, GIS vectors, and Sentinel-2 imagery

ABSTRACT

Multisource remote sensing and geographic information system (GIS) data have contributed powerfully to the large-scale automated mapping of local climate zones (LCZs). However, the accessibility of high-resolution height data, the applicability of standard thresholds to local contexts, and the dependence of mapping scales have limited LCZ classification studies. In this study, we combined airborne LiDAR data, Sentinel-2 imagery, and GIS vector (buildings and roads) data to develop a multiscale automated LCZ classification scheme in the 23 special wards of Tokyo. Based on the optimized thresholds of seven LCZ properties, GIS-based LCZ mapping was implemented using fuzzy logic classifiers at the block scale and at different grid-cell scales (100 m–1000 m). In addition to assessing accuracy using reference samples, multidate thermal infrared data (Landsat-8 and ASTER data) were used to understand the LCZ-LST (land surface temperature) relationship at multiple scales. The results showed that the overall accuracies of LCZs could be significantly increased by threshold optimization at all scales. Significant differences in LCZs and LSTs among different mapping units were observed. The highest overall accuracy was greater than 80% at the 100-m grid-cell scale. As the size of grid cells increased, the overall accuracy of LCZ classification decreased. For each LCZ, the mean daytime/nighttime LST exhibited more variation by date than by scale. This study provides a promising picture of GIS-based LCZ mapping and LCZ-LST relationships at multiple scales.

KEYWORDS:

• Local climate zones
• Urban form
• Multisource data fusion
• Fuzzy logic
• Threshold optimization
• Land surface temperature

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, H. Bagan, upon reasonable request. The data are not publicly available due to legal restrictions.

Additional information

Funding

The work was supported by the  National Key R&D Program of China [2022YFE0119500] and by the Science and Technology Commission of Shanghai Municipality, China [22010503600].