SCIENCE CHINA Technological Sciences, Volume 62 , Issue 12 : 2243-2260(2019) https://doi.org/10.1007/s11431-018-9417-6

Enhanced geographic information system-based mapping of local climate zones in Beijing, China

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  • ReceivedNov 23, 2018
  • AcceptedDec 19, 2018
  • PublishedJun 28, 2019


The vague urban-rural dichotomy severely restricts effective comparisons and communications among urban heat island studies. A local climate zone (LCZ) scheme has therefore been developed to classify urban and natural landscapes in a standardized and universal manner. Despite LCZ mapping efforts in worldwide cities, this study attempts to propose an enhanced geographic information system-based workflow to enable the hierarchical classification of LCZs with fewer indicators but higher accuracies while considering supplementary classes and subclasses. Specifically, five morphological and coverage indicators that were easily obtained and well differentiated among LCZs were derived from a city street map and satellite images, and 25 LCZs (including 16 standard, 3 supplementary, and 6 sub-classified zones) were determined at a block-level according to the indicator hierarchy and criteria. The method was performed over Beijing, China, and evaluations by field surveys and google earth images showed a high accuracy with little noise and sharp boundaries, outperforming the widely-used remote sensing-based method of the World Urban Database and Access Portal Tools, particularly in terms of building height and heavy industry. Results also demonstrate that the Beijing core was dominated by open (including extremely open) mid-rise buildings (28.7%) and open low-rise buildings (12.8%), forming an inner-low-middle-high-outer-low annular building-height pattern. Significant land surface temperature differences were detected among the LCZs, where the low-rise and compact LCZs had higher temperatures than the mid-/high-rise and open LCZs during daytime, and subclasses LCZ XB/C/D (LCZ XE/F) generated lower (higher) temperatures than their parent classes in May. This method was proposed to augment the LCZ mapping system and further support applications (e.g., urban planning/management and climate/weather modeling) in high-density cities similar to Beijing.

Funded by

National Natural Science Foundation of China(Grant,No.,41590845)

the Major State Basic Research Development Program of China(Grant,No.,2015CB954101)

the National Natural Science Foundation of China(Grant,Nos.,41601462,41421001)

the Key Research Project on Frontier Science

Chinese Academy of Sciences(CAS)

the Youth Science Funds of LREIS


the Key Laboratory of Space Utilization


and the National Key Research and Development Program of China(Grant,No.,2016YFB0502301)


This work was supported by National Natural Science Foundation of China (Grant Nos. 41590845, 41601462, 41421001), the Major State Basic Research Development Program of China (Grant No. 2015CB954101), the Key Research Project on Frontier Science, CAS (Grant No. QYZDY-SSW-DQC007-1), the Youth Science Funds of the State Key Laboratory of Resources and Environmental Information System (LREIS), Chinese Academy of Sciences (CAS) (Grant No. O8R8A083YA), the Key Laboratory of Space Utilization, CAS (Grant No. LSU-2016-06-03), and the National Key Research and Development Program of China (Grant No. 2016YFB0502301). The author thanks professor MA Ting (Institute of Geographic Sciences and Natural Resources Research, CAS), Professor ZHAN WenFeng (Nanjing University), and Professor LONG Di (Tsinghua University) for providing insightful suggestions on data processing and manuscript submission.


Supporting Information

The supporting information is available online at tech.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.


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  • Figure 1

    Study area. (a) Location of the study area; (b) landsat-8 OLI pseudo color composite image of the study area on May 4, 2016 (red: band 5, green: band 4, blue: band 3); (c) city street map of the subset area in (b).

  • Figure 2

    Flowchart of the local climate zone (LCZ) classification method. LCZ 1: compact high-rise; LCZ 2: compact mid-rise; LCZ 2.5: extremely compact low-rise; LCZ 3: compact low-rise; LCZ 3.5: extremely open high-rise; LCZ 4: open high-rise; LCZ 4.5: extremely open mid-rise; LCZ 5: open mid-rise; LCZ 6: open low-rise; LCZ 7: lightweight low-rise; LCZ 8: large low-rise; LCZ 9: sparsely built; LCZ 10: heavy industry; LCZ A/B: dense/scattered trees; LCZ C: bush, scrub; LCZ D: low plants; LCZ E: bare rock or paved; LCZ F: bare soil or sand; LCZ G: water.

  • Figure 3

    Indicators and LCZs. (a) Sky view factor (SVF); (b) height of roughness elements (H); (c) composite of building surface fraction (BF) in the red band, pervious surface fraction (Per) in the green band, and impervious surface fraction (ImP) in the blue band; (d) LCZs and sample blocks for evaluation.

  • Figure 4

    Google Earth images and photographs of field observations of LCZ sample areas (LCZ 1–LCZ 3.5).

  • Figure 5

    Google Earth images and photographs of field observations of LCZ sample areas (LCZ 4–LCZ 6).

  • Figure 6

    Google Earth images and photographs of field observations of LCZ sample areas (LCZ 6E/F–LCZ 10).

  • Figure 7

    Google Earth images and photographs of field observations of LCZ sample areas (LCZ A–LCZ F). Large areas of bare soil or sand were mostly surrounded by high walls/fences; thus, their photographs were unavailable in this study.

  • Figure 8

    Comparison of the GIS-based LCZ map with the WUDAPT LCZ map. (a) WUDAPT LCZ map, where the white lines indicate consistent LCZs between the two maps; (b)–(d) Google Earth images and photographs of field observations of the subset areas in (a).

  • Figure 9

    Thermal responses of LCZs on May 4 and December 14, 2016.

  • Figure 10

    Significance of the thermal differences in the average and standard deviation among LCZs on May 4 and December 14, 2016. The upper-right and lower-left plots are the results of t-tests of the average and F-tests of the standard deviation, respectively.

  • Table 1   Data used in this study





    City street map (CSM)

    Blocks, buildings, streets, water, green spaces


    Characterize urban morphology and land covers

    List of pollutant discharge units (2017)



    Locate heavy industries

    Landsat-8 image

    RED/NIR/thermal bands

    Raster, 15-/100-m resolution

    Characterize pervious surface fractions and thermal responses

    Global land cover (GLC) map

    Forest, cropland, grassland, shrubland, water, impervious land, bare land, and snow/ice

    Raster, 30-m resolution

    Characterize pervious surface fractions and determine vegetation types

    WUDAPT-based local climate zone (LCZ) map

    16 standard LCZ classes

    Raster, 30-m resolution

    Comparison to the GIS-based LCZ map

  • Table 2   Indicators adopted in this study



    Method and input data

    Sky view factor (SVF)

    Mean ratio of the amount of sky hemisphere visible from the ground in a block

    Relief Visualization Toolbox, digital elevation Model, buildings and blocks from CSM

    Height of roughness elements (H)

    Weighted mean height of buildings in a block

    Buildings and blocks from CSM

    Building surface fraction (BF)

    Percentage of building plan areas in a block

    Buildings and blocks from CSM

    Pervious surface fraction (Per)

    Percentage of vegetation, water, and bare soil surface areas in a block

    Water and blocks from CSM, Landsat, GLC

    Impervious surface fraction (ImP)

    Percentage of impervious plan areas in a block


    Each indicator was calculated at a block level in a raster format with a spatial resolution of 10 m.

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