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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

Abstract

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

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)


Acknowledgment

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.


Supplement

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.


References

[1] Voogt J A, Oke T R. Thermal remote sensing of urban climates. Remote Sens Environ, 2003, 86: 370-384 CrossRef ADS Google Scholar

[2] Stewart I D. A systematic review and scientific critique of methodology in modern urban heat island literature. Int J Climatol, 2011, 31: 200-217 CrossRef ADS Google Scholar

[3] Mills G, Bechtel B, Ching J, et al. An introduction to the WUDAPT project. In: Proceedings of the 9th International Conference on Urban Climates. Toulouse, 2015. Google Scholar

[4] Stewart I D, Oke T R. Local climate zones for urban temperature studies. Bull Amer Meteor Soc, 2012, 93: 1879-1900 CrossRef ADS Google Scholar

[5] Stewart I D. Redefining the Urban Heat Island. Dissertation for Dcotoral Degree. Vancouver: The University of British Columbia, 2011. Google Scholar

[6] Geletič J, Lehnert M. GIS-based delineation of local climate zones: The case of medium-sized central European cities. Moravian Geograph Rep, 2016, 24: 2--12. Google Scholar

[7] Nyamadzawo G, Wuta M, Chirinda N, et al. Greenhouse gas emissions from intermittently flooded (Dambo) rice under different tillage practices in chiota smallholder farming area of zimbabwe george. Atmos Clim Sci, 2013, 03: 13-20 CrossRef Google Scholar

[8] Stewart I, Oke T. Classifying urban climate field sites by “local climate zones”: The case of Nagano, Japan. In: Proceddings of the 7th International Conference on Urban Climate. Yokohama, 2009. Google Scholar

[9] Houet T. Pigeon G. Mapping urban climate zones and quantifying climate behaviors: An application on Toulouse urban area (France). Environ Pollution, 2011, 159: 2180--2192. Google Scholar

[10] Leconte F, Bouyer J, Claverie R, Pétrissans M. Using local climate zone scheme for UHI assessment: Evaluation of the method using mobile measurements. Building Environ, 2015, 83: 39--49. Google Scholar

[11] Thomas G, Sherin A P, Ansar S, et al. Analysis of urban heat island in Kochi, India, using a modified local climate zone classification. Procedia Environ Sci, 2014, 21: 3-13 CrossRef Google Scholar

[12] Zapata C E, Jiménez J F, Ramiréz M, et al. Relocation of the air quality monitoring stations network for Aburrá valley based on local climatic zones. Int J Environ Chem Ecol Geol Geophys Eng, 2016, 10: 842–847. Google Scholar

[13] Zheng Y, Ren C, Xu Y, et al. GIS-based mapping of local climate zone in the high-density city of Hong Kong. Urban Clim, 2018, 24: 419-448 CrossRef Google Scholar

[14] Bechtel B, Alexander P, Böhner J, et al. Mapping local climate zones for a worldwide database of the form and function of cities. ISPRS Int J Geo-Inf, 2015, 4: 199-219 CrossRef ADS Google Scholar

[15] Montanges A P, Moser G, Taubenböck H, et al. Classification of urban structural types with multisource data and structured models. In: Proceedings of 2015 Joint Urban Remote Sensing Event (JURSE). Lausanne, 2015. Google Scholar

[16] Gamba P, Lisini G, Liu P, et al. Urban climate zone detection and discrimination using object-based analysis of VHR scenes. In: Proceedings of the 4th GEOBIA. Rio de Janeiro, 2012. Google Scholar

[17] Verdonck M L, Okujeni A, van der Linden S, et al. Influence of neighbourhood information on “local climate zone” mapping in heterogeneous cities. Int J Appl Earth Observation GeoInf, 2017, 62: 102-113 CrossRef ADS Google Scholar

[18] Xu Y, Ren C, Cai M, et al. Classification of local climate zones using ASTER and LANDSAT data for high-density cities. IEEE J Sel Top Appl Earth Observations Remote Sens, 2017, 10: 3397-3405 CrossRef ADS Google Scholar

[19] Bechtel B, See L, Mills G, et al. Classification of local climate zones using SAR and multispectral data in an arid environment. IEEE J Sel Top Appl Earth Observations Remote Sens, 2016, 9: 3097-3105 CrossRef ADS Google Scholar

[20] Bechtel B, Daneke C. Classification of local climate zones based on multiple earth observation data. IEEE J Sel Top Appl Earth Observations Remote Sens, 2012, 5: 1191-1202 CrossRef ADS Google Scholar

[21] Kaloustian N, Bechtel B. Local climatic zoning and urban heat island in Beirut. Procedia Eng, 2016, 169: 216-223 CrossRef Google Scholar

[22] Qiu C, Schmitt M, Mou L, et al. Feature importance analysis for local climate zone classification using a residual convolutional neural network with multi-source datasets. Remote Sens, 2018, 10: 1572 CrossRef ADS Google Scholar

[23] See L, Perger C, Duerauer M, et al. Developing a community-based worldwide urban morphology and materials database (WUDAPT) using remote sensing and crowdsourcing for improved urban climate modelling. In: Proceedings of 2015 Joint Urban Remote Sensing Event (JURSE). Lausanne, 2015. Google Scholar

[24] Danylo O, See L, Bechtel B, et al. Contributing to WUDAPT: A local climate zone classification of two cities in Ukraine. IEEE J Sel Top Appl Earth Observations Remote Sens, 2016, 9: 1841-1853 CrossRef ADS Google Scholar

[25] Xu Y, Ren C, Cai M, et al. Issues and challenges of remote sensing-based local climate zone mapping for high-density cities. In: Proceedings of 2017 Joint Urban Remote Sensing Event (JURSE). Dubai, 2017. Google Scholar

[26] Shi Y, Zhang Y. Remote sensing retrieval of urban land surface temperature in hot-humid region. Urban Clim, 2018, 24: 299-310 CrossRef Google Scholar

[27] Brousse O, Martilli A, Foley M, et al. WUDAPT, an efficient land use producing data tool for mesoscale models? Integration of urban LCZ in WRF over Madrid. Urban Clim, 2016, 17: 116-134 CrossRef Google Scholar

[28] Geletič J, Lehnert M, Dobrovolný P. Land surface temperature differences within local climate zones, based on two central European cities. Remote Sens, 2016, 8: 788 CrossRef ADS Google Scholar

[29] Lelovics E, Unger J, Gál T, et al. Design of an urban monitoring network based on local climate zone mapping and temperature pattern modelling. Climate Res, 2014, 60: 51--62. Google Scholar

[30] Unger J, Lelovics E, Gál T. Local climate zone mapping using GIS methods in Szeged. Hungarian Geograph Bull, 2014, 63: 29--41. Google Scholar

[31] Lehnert M, Geletič J, Husák J, et al. Urban field classification by “local climate zones” in a medium-sized central European city: The case of Olomouc (Czech republic). Theor Appl Climatol, 2015, 122: 531-541 CrossRef ADS Google Scholar

[32] Šećerov I, Savić S, Milošević D, et al. Development of an automated urban climate monitoring system in Novi Sad (Serbia). Geographica Pannonica, 2015, 19: 174--183. Google Scholar

[33] Emmanuel R. Performance standard for tropical outdoors: A critique of current impasse and a proposal for way forward. Urban Clim, 2018, 23: 250-259 CrossRef Google Scholar

[34] Picone N, Campo A. Preparing urban climate maps using the LCZ methodology for improving communication with urban planners: The case of Tandil city, Argentina. In: Proceedings of the 9th International Conference on Urban Climate (ICUC9). Toulouse, 2015. Google Scholar

[35] Koc C B, Osmond P, Peters A, et al. Mapping local climate zones for urban morphology classification based on airborne remote sensing data. In: Proceedings of 2017 Joint Urban Remote Sensing Event (JURSE). Dubai, 2017. Google Scholar

[36] Mitraka Z, Frate F D, Chrysoulakis N, et al. Exploiting earth observation data products for mapping local climate zones. In: Proceedings of 2015 Joint Urban Remote Sensing Event (JURSE). Lausanne, 2015. Google Scholar

[37] Nassar A K, Blackburn G A, Whyatt J D. Dynamics and controls of urban heat sink and island phenomena in a desert city: Development of a local climate zone scheme using remotely-sensed inputs. Int J Appl Earth Observation GeoInf, 2016, 51: 76-90 CrossRef ADS Google Scholar

[38] Kotharkar R, Bagade A. Local climate zone classification for indian cities: A case study of Nagpur. Urban Clim, 2018, 24: 369-392 CrossRef Google Scholar

[39] Kotharkar R, Bagade A. Evaluating urban heat island in the critical local climate zones of an indian city. Landscape Urban Planning, 2018, 169: 92-104 CrossRef Google Scholar

[40] Hammerberg K, Brousse O, Martilli A, et al. Implications of employing detailed urban canopy parameters for mesoscale climate modelling: A comparison between WUDAPT and GIS databases over Vienna, Austria. Int J Climatol, 2018, 38: e1241-e1257 CrossRef ADS Google Scholar

[41] Quan S J, Dutt F, Woodworth E, et al. Local climate zone mapping for energy resilience: A fine-grained and 3D approach. Energy Procedia, 2017, 105: 3777-3783 CrossRef Google Scholar

[42] Wang C, Middel A, Myint S W, et al. Assessing local climate zones in arid cities: The case of Phoenix, Arizona and Las Vegas, Nevada. ISPRS J Photogrammetry Remote Sens, 2018, 141: 59-71 CrossRef ADS Google Scholar

[43] Zhang Y, Gu Z, Zhou D. Simulation on urban wind environment based on local climate zones and its parameterization. J Earth Environ, 2016, 7: 780–786. Google Scholar

[44] Yang X, Yao L, Jin T, et al. Assessing the thermal behavior of different local climate zones in the Nanjing metropolis, China. Building Environ, 2018, 137: 171-184 CrossRef Google Scholar

[45] Gál T, Bechtel B, Unger J. Comparison of two different local climate zone mapping methods. In: Proceedings of the 9th International Conference on Urban Climate (ICUC9). Toulouse, 2015. Google Scholar

[46] Wicki A, Parlow E. Attribution of local climate zones using a multitemporal land use/land cover classification scheme. J Appl Remote Sens, 2017, 11: 026001 CrossRef ADS Google Scholar

[47] Alexander P, Mills G. Local climate classification and Dublin’s urban heat island. Atmosphere, 2014, 5: 755-774 CrossRef ADS Google Scholar

[48] Ching J, Mills G, Bechtel B, et al. WUDAPT: An urban weather, climate, and environmental modeling infrastructure for the anthropocene. Bull Amer Meteor Soc, 2018, 99: 1907-1924 CrossRef ADS Google Scholar

[49] Zhou J, Chen Y H, Li J, et al. A volume model for urban heat island based on remote sensing imagery and its application: A case study in Beijing. Int J Remote Sens, 2008, 12: 734–742. Google Scholar

[50] Wang P, Sneep M, Veefkind J P, et al. Evaluation of broadband surface solar irradiance derived from the Ozone Monitoring Instrument. Remote Sens Environ, 2014, 149: 88-99 CrossRef ADS Google Scholar

[51] Wang J, Wang K, Wang P. Urban heat (or cool) island over Beijing from MODIS land surface temperature. J Remote Sens, 2007, 11: 330–339. Google Scholar

[52] Zhao C, Fu G, Liu X, et al. Urban planning indicators, morphology and climate indicators: A case study for a north-south transect of Beijing, China. Building Environ, 2011, 46: 1174-1183 CrossRef Google Scholar

[53] Yu L, Wang J, Li X C, et al. A multi-resolution global land cover dataset through multisource data aggregation. Sci China Earth Sci, 2014, 57: 2317-2329 CrossRef Google Scholar

[54] Zakšek K, Oštir K, Kokalj Ž. Sky-view factor as a relief visualization technique. Remote Sens, 2011, 3: 398-415 CrossRef ADS Google Scholar

[55] Sobrino J A, Jiménez-Muñoz J C, Paolini L. Land surface temperature retrieval from Landsat TM 5. Remote Sens Environ, 2004, 90: 434-440 CrossRef ADS Google Scholar

[56] Zhan W, Chen Y, Zhou J, et al. An algorithm for separating soil and vegetation temperatures with sensors featuring a single thermal channel. IEEE Trans Geosci Remote Sens, 2011, 49: 1796-1809 CrossRef ADS Google Scholar

[57] Walde I, Hese S, Berger C, et al. From land cover-graphs to urban structure types. Int J Geographical Inf Sci, 2014, 28: 584-609 CrossRef Google Scholar

[58] Voltersen M, Berger C, Hese S, et al. Object-based land cover mapping and comprehensive feature calculation for an automated derivation of urban structure types at block level. Remote Sens Environ, 2014, 154: 192-201 CrossRef ADS Google Scholar

[59] Jiménez-Muñoz J C, Sobrino J A, Skoković D, et al. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geosci Remote Sens Lett, 2014, 11: 1840--1843. Google Scholar

[60] Quan J, Zhan W, Ma T, et al. An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes. Remote Sens Environ, 2018, 206: 403-423 CrossRef ADS Google Scholar

[61] Snyder W C, Wan Z, Zhang Y, et al. Classification-based emissivity for land surface temperature measurement from space. Int J Remote Sens, 1998, 19: 2753-2774 CrossRef Google Scholar

[62] Santer B D, Wigley T M L, Boyle J S, et al. Statistical significance of trends and trend differences in layer-average atmospheric temperature time series. J Geophys Res, 2000, 105: 7337-7356 CrossRef ADS Google Scholar

[63] Heiden U, Heldens W, Roessner S, et al. Urban structure type characterization using hyperspectral remote sensing and height information. Landscape Urban Planning, 2012, 105: 361-375 CrossRef Google Scholar

[64] Stewart I D, Oke T R, Krayenhoff E S. Evaluation of the “local climate zone” scheme using temperature observations and model simulations. Int J Climatol, 2014, 34: 1062-1080 CrossRef ADS Google Scholar

[65] Skarbit N, Stewart I D, Unger J, et al. Employing an urban meteorological network to monitor air temperature conditions in the “local climate zones” of Szeged, Hungary. Int J Climatol, 2017, 37: 582--596. Google Scholar

[66] Yang X, Jin T, Yao L, et al. Assessing the impact of urban heat island effect on building cooling load based on the local climate zone scheme. Procedia Eng, 2017, 205: 2839-2846 CrossRef Google Scholar

[67] Quanz A J, Ulrich S, Fenner D, et al. Micro-scale variability of air temperature within a local climate zone in Berlin, Germany, during summer. Climate, 2018, 6: 5. Google Scholar

[68] Xiang L, Ren C. Effects of the building typology on PET value in different local climate zones: A case study in Beijing, China. In: Proceedings of the Passive and Low Energy Architecture (PLEA). Edinburgh, 2017. Google Scholar

[69] Kato S, Matsunaga T, Yamaguchi Y. Influence of shade on surface temperature in an urban area estimated by ASTER data. In: Proceedings of the International Archives of the Photogammetry, Remote Sensing and Spatial Information Science. Kyoto, 2010. Google Scholar

[70] Middel A, Häb K, Brazel A J, et al. Impact of urban form and design on mid-afternoon microclimate in Phoenix local climate zones. Landscape Urban Planning, 2014, 122: 16-28 CrossRef Google Scholar

[71] Cai H, Xu X. Impacts of built-up area expansion in 2D and 3D on regional surface temperature. Sustainability, 2017, 9: 1862 CrossRef Google Scholar

[72] Liu Y, Hu C, Zhan W, et al. Identifying industrial heat sources using time-series of the VIIRS nightfire product with an object-oriented approach. Remote Sens Environ, 2018, 204: 347-365 CrossRef ADS Google Scholar

[73] Xia H, Chen Y, Quan J. A simple method based on the thermal anomaly index to detect industrial heat sources. Int J Appl Earth Observation GeoInf, 2018, 73: 627-637 CrossRef ADS Google Scholar

[74] Quan J, Chen Y, Zhan W, et al. A hybrid method combining neighborhood information from satellite data with modeled diurnal temperature cycles over consecutive days. Remote Sens Environ, 2014, 155: 257-274 CrossRef ADS Google Scholar

[75] Mackey C W, Lee X H, Smith R B. Remotely sensing the cooling effects of city scale efforts to reduce urban heat island. Building Environ, 2012, 49: 348--358. Google Scholar

[76] Quan J, Zhan W, Chen Y, et al. Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands. J Geophys Res Atmos, 2016, 121: 2638-2657 CrossRef ADS Google Scholar

[77] Streutker D R. A remote sensing study of the urban heat island of Houston, Texas. Int J Remote Sens, 2002, 23: 2595-2608 CrossRef Google Scholar

[78] Beck C, Straub A, Breitner S, et al. Air temperature characteristics of local climate zones in the Augsburg urban area (Bavaria, southern Germany) under varying synoptic conditions. Urban Clim, 2018, 25: 152-166 CrossRef Google Scholar

[79] Sailor D J. A review of methods for estimating anthropogenic heat and moisture emissions in the urban environment. Int J Climatol, 2011, 31: 189-199 CrossRef ADS Google Scholar

[80] Zhou D, Zhao S, Liu S, et al. Surface urban heat island in China’s 32 major cities: Spatial patterns and drivers. Remote Sens Environ, 2014, 152: 51-61 CrossRef ADS Google Scholar

[81] Davenport A, Grimmond S, Oke T R, et al. Estimating the roughness of cities and sheltered country. In: Proceedings of the 12th Conference of Applied Climatology. Asheville, 2000. Google Scholar

  • 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

    Data

    Contents

    Format

    Use

    City street map (CSM)

    Blocks, buildings, streets, water, green spaces

    Vector

    Characterize urban morphology and land covers

    List of pollutant discharge units (2017)

    Industries

    Text

    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

    Indicatora)

    Definition

    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

    100%-BF-Per

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

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