Article

Patch-Based Local Climate Zones Mapping and Population
Distribution Pattern in Provincial Capital Cities of China
Liang Zhou 1, Lei Ma 1,*, Brian Alan Johnson 2, Ziyun Yan 1, Feixue Li 1 and Manchun Li 1
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory forLand Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography andOcean Science, Nanjing University, Nanjing 210023, China; mg20270122@smail.nju.edu.cn (L.Z.);
mg21270109@smail.nju.edu.cn (Z.Y.); lifeixue@nju.edu.cn (F.L.); limanchun@nju.edu.cn (M.L.)
2 Natural Resources and Ecosystem Services, Institute for Global Environmental Strategies, 2108-11,
Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan; johnson@iges.or.jp* Correspondence: maleinju@nju.edu.cn
1

Citation: Zhou, L.; Ma, L.;
Johnson, B.A.; Yan, Z.; Li, F.; Li, M.
Patch-Based Local Climate Zones
Mapping and Population
Distribution Pattern in Provincial

Abstract: Accurate urban morphology provided by Local Climate Zones (LCZ), a universal surface
classification scheme, offers opportunities for studies of urban heat risk, urban ventilation, andtransport planning. In recent years, researchers have attempted to generate LCZ maps worldwide
with the World Urban Database and Access Portal Tools (WUDAPT). However, the accuracy of LCZ
mapping is not satisfactory and cannot fulfill the quality demands of practical usage. Here, weconstructed a high-quality sample dataset from Chinese cities and presented a patch-basedclassification framework that employs chessboard segmentation and multi-seasonal images for LCZ
mapping. Compared with the latest WUDAPT method, the overall accuracy for all LCZ types (OA)
and urban LCZ types (OAu) of our framework increased by about 10% and 9%, respectively.
Furthermore, based on the analysis of population distribution, we first gave the population densityof different built-up LCZs of Chinese cities and found a hierarchical effect of population density
among built-up LCZs in different size cities. In summary, this study could serve as a valuablereference for producing high-quality LCZ maps and understanding population distribution
patterns in built-up LCZ types.

Capital Cities of China. ISPRS Int. J.
Geo-Inf. 2022, 11, 420. https://doi.org/
10.3390/ijgi11080420

Keywords: local climate zones; patch-based classification; multi-seasonal images; WUDAPT;
population distribution pattern; Chinese cities

Academic Editors: Mingshu Wang
and Wolfgang Kainz
Received: 18 May 2022

1. Introduction

Accepted: 7 July 2022
Published: 25 July 2022
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In recent decades, the world has experienced an unprecedented process ofurbanization. According to the latest report of the United Nations Habitat (UNH),
although urban areas account for only 3% of the world’s land area, 55% of the population
lives in urban areas; this number is expected to increase to 68% by 2050 [1]. Rapid andunplanned urbanization, coupled with the challenges brought by climate change, can leadto environmental and health problems [2,3] in many cities, thereby affecting thesustainable development of cities. The lack of globally consistent data on cities makes it
difficult to understand the overall impact of urbanization. However, remote sensing
technology provides a source of data for this challenge; especially, the Landsat-8 satelliteof the National Aeronautics and Space Administration (NASA) and the Sentinel-2 satelliteof the European Space Agency (ESA) provide continuous, reliable, and quality-controlled
earth observation data that are free and open.
Through remote sensing data and artificial intelligence techniques, some scholars
have generated several datasets on the extent of global urban boundaries, such as Global
Urban Footprint (GUF) [4], Global Human Settlement Layer (GHSL) [5], and Global
Artificial Impervious Area (GAIA) [6], which provide a binary mask of urban and non-

ISPRS Int. J. Geo-Inf. 2022, 11, 420. https://doi.org/10.3390/ijgi11080420

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urban surfaces. In addition, there are some land use/land cover (LULC) datasets, such as
the global land cover map (GLC) [7,8] and ESA Climate Change Initiative Land Cover(CCI-LC) [9], which represent urban areas through impervious surfaces. Through these
global urban boundary datasets and LULC datasets, the growth and rate of global
urbanization can be monitored at the city scale. However, many international efforts toaddress the issues of urbanization, such as the United Nation’s call for “Sustainable Cities
and Communities”, are based on accurate measurements of urban morphological anddemographic figures that can provide key scientific foundations for allocating valuable
resources for a wide range of stakeholders [10]. These binary urban masks and LULC
datasets cannot provide sufficient spatial details to describe cities’ internal structures andfunctions.
To address this issue, Stewart and Oke [11] proposed a Local Climate Zone (LCZ)
classification system, which is composed of ten built-up, or artificial, LULC types (LCZs
1–10; Figure 1) and seven natural LULC types (LCZs A-G; Figure 1). The LCZ types aredefined as areas with uniform surface cover, structure, material, and human activity,
spanning hundreds of meters to several kilometers on the horizontal scale [11]. Since the
LCZ scheme was originally designed for standardizing the exchange of urban
temperature observations, urban heat island studies under the LCZ classification scheme
have been conducted in more than 130 cities worldwide in recent years [12]. At the sametime, the LCZ scheme also provides a numerical description of urban canopy parametersthat are rare in traditional LULC classification but are key in the urban ecosystem. Thishas made LCZ data attractive for a wide range of applications, including urban planning[13,14], urban energy demand modeling [15,16], urban ventilation simulation [17–19], andoutdoor thermal comfort monitoring [20,21].
The LCZ classification scheme was originally designed for urban climatic studies[11,22], and the reference population range for each LCZ type was not given [23].
However, LCZ maps can also be used to better understand the urban population
distribution, as the built-up LCZs have relatively distinct traits in building size, height,
density, and usage. Demuzere et al. [23] found the population data in the LCZs agreed
with their classification when they mapped the continental United States into LCZ types.
In addition, Hu et al. [24] combined LCZ maps with population density to analyze urban
land consumption at an intra-urban spatial scale over 40 cities worldwide. However, few
studies have examined the relationship between LCZs and population data on a national
scale, especially in China, the world’s most populous country. Furthermore, it is unknown
how different city sizes affect population distribution in LCZs.
There are many methods for generating LCZ maps, including traditional samplingthrough field surveys, geographic information system (GIS) approaches, and remotesensing image classification approaches [25–30]. Classification based on remote sensing
images provides a fast and economical method for LCZ mapping and has been widelyused in prior studies [31–36]. Bechtel et al. [26] designed a pixel-based supervisedclassification approach, which uses Landsat-8 images and the random forest classification
algorithm [37], to generate an LCZ map with a resolution of 100 m. The method was usedby the World Urban Database and Access Portal Tools (WUDAPT) project to create a
database for uploading LCZ samples and then generated LCZ maps of different cities
around the world [38]. Demuzere et al. [39] then designed an online platform (LCZGenerator) to simplify the default WUDAPT classification workflow further. Only 150
cities worldwide have had their LCZ map and corresponding samples uploaded to the
WUDAPT website [39], and these maps may be quickly outdated due to rapid LULC
changes occurring in/around urban areas. Although more and more training area sets
have been submitted to the LCZ Generator recently, the samples from different cities
cannot be consistent. Currently, only Europe [40] and the continental United States [23]have a complete LCZ map. Thus, there is a need for a high-quality training sample set
with label information to generate a complete LCZ map of China.

ISPRS Int. J. Geo-Inf. 2022, 11, 420

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Figure 1. Classification of local climate zones and range of urban canopy parameters based on [11].
(Note: The top-left corner of each LCZ type is the schematic drawings given by [41]. The top-rightcorner of each LCZ type is the representative high-resolution images from Google Earth. SVF means
sky view factor; BSF means building surface fraction; SA means surface admittance; BH means
height of roughness elements; ISF means impervious surface fraction; A means surface albedo; ARmeans aspect ratio; PSF means pervious surface fraction; AH means anthropogenic heat.).

Different quality evaluation results show that the accuracy of LCZ mapping obtainedby the WUDPAT method is often not high [41,42]. The WUDAPT method is pixel based,
which relies heavily on spectral information of image pixels, i.e., electromagneticreflectance at different wavelengths. However, due to the differences in cultural andphysical environmental factors (e.g., construction materials and vegetation types), LCZISPRS Int. J. Geo-Inf. 2022, 11, 420

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types in different cities often have different spectral characteristics. Therefore, LCZ
classification approaches based only on spectral information of pixels alone cannot
achieve high accuracy. Remote sensing researchers have found that texture information is
helpful for image classification tasks [43], particularly when image pixels are smaller thanthe LULC features of interest (as individual pixels represent only a portion of the featureof interest in this case). Patch-based classification approaches can better take advantage ofcontextual information provided by neighboring pixels, e.g., local spectral heterogeneity.
Some scholars have put forward deep learning methods such as CNN and FCN [35,36,44],
which also incorporate patch-based features, or utilized auxiliary data such as buildingheight data [32,45] to improve LCZ mapping accuracy. However, because of limitationsin the training data or image data available in most cities, these methods are not universal
at present. At the same time, the classification results vary significantly with the spatial
scale at which LCZ maps are produced [26,41]. Stewart and Oke [11] suggested that theminimum radius of each LCZ should be between 200–500m. Liu et al. [44] used Sentinel2 imagery with a 10 m spatial resolution for LCZ mapping and found that the optimal
image sizes were from 32 to 64. Zheng et al. [28] conducted LCZ mapping in Hong Kongand found that the optimal mapping scale was 300 m. Aside from the use of contextual
information from neighboring pixels, many studies have shown that multi-seasonal
information from remote sensing imagery can help optimize the mapping results of land
cover classification [46,47] and significantly improve the cartographic accuracy of LCZclassification [36,48,49]. Based on the above information from previous works, it is evident
that the 300 m resolution, multi-temporal and patch-based LCZ classification framework
could be successful for LCZ mapping across multiple cities.
This study aimed to: (1) construct a high-quality training sample set with label
information in 34 provincial capital cities of China, (2) propose a classification framework
to improve LCZ mapping accuracy, (3) study population distribution in LCZs on anational scale, and (4) further explore how different city sizes affect population
distribution in LCZs. To achieve these objectives, we outlined and labeled sample
polygons using high-resolution images from Google Earth. Then, we proposed an LCZ
classification framework based on chessboard segmentation and multi-seasonal Sentinel2 images on a 300 m grid and used this framework to generate LCZ maps in 34 provincial
capital cities of China. Then, an overlay analysis was performed to analyze the relation
between LCZ classification results with the population data on a national scale. Finally,
the influence of city size on population distribution in LCZ was analyzed by classifying
cities based on the number of urban permanent populations.
2. Materials and Methods
2.1. Study Aera
China is a vast and populous country located in the east of Asia and on the west coastof the Pacific Ocean (73°33′–135°05′ E; 3°51′–53°33′ N), as shown in Figure 2. Due to thehuge differences in geographical conditions, China’s climate is complex and diverse.
Climatic types include tropical climates in the south to cold temperate climates in thenorth and plateau mountain climates in the Qinghai-Tibet Plateau. The southeastern partof China is densely populated, and the northwestern part is sparsely populated. The
diversity of climate and the uneven distribution of population lead to huge differences inurban landscapes in China.
Chinese cities were classified according to the new city size classification standard
issued by China [50] and were divided into four sizes according to the urban permanent
residents in the study area (Table S1). In the analysis of Section 3.3, we excluded Macao
and Lasa because these two cities differ greatly in urban form apart from the similarities
in terms of population. Meanwhile, the number of medium-sized cities is too small for the
results to be representative.

ISPRS Int. J. Geo-Inf. 2022, 11, 420

5 of 20In this study, we conducted LCZ mapping and population distribution analysis in 34
major Chinese cities. These 34 cities represent municipalities or provincial capitals locatedin different climatic zones and contain a variety of complex urban forms and landscapes.
The extent of the study area of each city considered for our analysis was determined by
the GUB dataset and the city’s administrative boundary to reduce the non-urban areas
significantly. We show the boundary derived for Wuhan in Figure 2 as an example.

Figure 2. The geographic locations of the 34 Chinese cities. (Note: the gray line indicates theprovincial boundary; the green line indicates the municipal boundary; the blue line indicates theglobal urban boundary; we take Wuhan city as an example to illustrate the determination of theresearch boundary, the red line.)

2.2. Datasets
In this study, we used Sentinel-2 surface reflectance data [51,52] for remote sensing
image analysis and LCZ mapping. At present, Sentinel-2 is the highest resolution freely
available satellite imagery, with spatial resolutions ranging from 10 to 60 m depending on
the spectral band. Another benefit of Sentinel-2 data is that imagery is acquired of most
locations on the Earth’s surface every 5 days with two complementary satellites. Thus,
these data are suitable for LCZ mapping. As previously mentioned, multi-seasonal remotesensing data is likely to improve LCZ mapping accuracy. Cloud cover, however, often
obstructs the sensor’s view of the ground in individual Sentinel-2 images. To mitigate
cloud contamination effects, we generated seasonal mosaic images from the originalSentinel-2 images. The dates of multi-seasonal composite images used in this study are
shown in Table S2 (136 seasonal composite images in total).
In addition to Sentinel-2 imagery, we also used additional geospatial datasets foranalysis of the 34 urban areas. As mentioned previously, the GUB data and theadministrative boundaries of cities are used to determine the research boundaries of eachcity. Population count data [53] was used (in combination with the LCZ maps wegenerated) to analyze the population distribution of each city at the intra-urban scale. The
detailed methods for preprocessing Sentinel-2 and population data are presented in
Section 2.3.1.
2.3. Methods
The flow chart of this study is shown in Figure 3. We first preprocessed sentinel-2
images for LCZ mapping and population counts for population distribution analysis in
Section 2.3.1. The process of constructing the LCZ sample dataset for Chinese cities is
described in Section 2.3.2. To achieve high-quality mapping of LCZ in 34 cities, a patchbased LCZ classification scheme (Scheme 1, S1) based on chessboard segmentation andmulti-seasonal images is proposed in Section 2.3.3. To verify the effectiveness of ourISPRS Int. J. Geo-Inf. 2022, 11, 420

6 of 20scheme, the latest classification methodology (Scheme 2, S2) from the WUDAPT website
was chosen for comparison, and the details are shown in Section 2.3.4. Finally, the analysisof population distribution based on LCZ types is carried out (Supplementary Materials
Figure S1). The details are shown in Section 2.3.5.

Figure 3. The workflow of LCZ classification method (S1 and S2), sample data processing, accuracy
assessment, and application analysis.

2.3.1. Data Preprocessing
To obtain Sentinel-2 images of China’s provincial capital cities, Sentinel-2 L2A data
was acquired using the Google Earth Engine web platform [54]. First, the Sentinel-2 L2A
images were filtered by the regions of interest, dates, and cloud threshold to obtain theinitial image datasets. The result of this was a dense stack of Sentinel-2 images having lessthan 10% cloud cover. Then, clouds remaining in these initial images were removed using
the QA60 band of Sentinel-2, resulting in a set of cloud-filtered images. Finally, the cloudfiltered image dataset for each season was used to generate Sentinel-2 composite imagesby taking the median pixel value at each location (for each image band). As a result, a total

ISPRS Int. J. Geo-Inf. 2022, 11, 420

7 of 20of 136 Sentinel-2 seasonal composite images were generated for the 34 cities, which were
utilized for subsequent LCZ mapping.
To keep the resolution of population counts consistent with the results of LCZ
mapping of Chinese cities, the population data are resampled to 300 m, and then, the pixel
values are divided by the area represented by each grid. The population density data of
Chinese cities with a resolution of 300 m are obtained for subsequent population
distribution analysis.
2.3.2. LCZ Sample Dataset Construction
To solve the lack of LCZ samples in China, we manually collected training area data
for the 34 provincial capital cities. First, following the definitions of each LCZ type [11],we digitized and labeled LCZ sample polygons by visual analysis of high-resolution
images from Google Earth. In the process of sample labeling, we followed a few generalcriteria (https://www.wudapt.org/digitize-training-areas, accessed on 1 May 2022). Then,the initial mapping results were obtained using the random forest classifier based on theinitial LCZ sample dataset and the Sentinel-2 seasonal composite imagery. Then, theclassification maps were checked by us on Google Earth to ensure that the classification
results were consistent with the ground truth. If there is an error, the boundary of sample
polygons was modified, or additional sample polygons were added to facilitateclassification again. The process of sample modification and classification needs to berepeated until the classification maps can accord with the actual condition. Finally, we
have built a high-quality training sample set of LCZ in 34 Chinese cities. The number of
LCZ sample polygons collected from each city is shown in Figure 4. Due to the differencein urban development level and urban landscape, the number of LCZ types varies fromcity to city.
450

1

2

3

4

5

6

7

8

9

10

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B

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350

300

250

200

150

100

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Figure 4. The number of sample polygons for different LCZ types in 34 Chinese cities.

2.3.3. LCZ Mapping Based on Chessboard Segmentation and Multi-Seasonal Images

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To generate the LCZ maps of the 34 provincial capital cities of China, we proposed apatch-based LCZ classification framework (Scheme 1, S1) that uses chessboard
segmentation and multi-seasonal images. Initially, to utilize the spatial and contextual
information of neighboring pixels, the Sentinel-2 composite images were divided into
patches of 30 × 30 pixels in size through chessboard segmentation. Several patch-level
spectral and texture features were then calculated. Patch-level spectral features include
the average spectral value of each image band, maximum difference in pixel values foreach image band, and brightness (i.e., average spectral values of all bands combined) fora total of 18 features. The texture features calculated included: homogeneity, contrast,
dissimilarity, entropy, angular second moment, mean, standard deviation, and correlation
calculated from the gray level co-occurrence matrix (GLCM), with eight features in total,
as described in [55].
Next, the LCZ sample polygons obtained by manual sampling were used to label thesegmentation patches, and the labeling criterion was set according to an overlap rate
greater than 50%. To be consistent with the sampling rate of the latest WUDAPT method,
70% of the labeled patches were used as training samples, and 30% were used asvalidation samples. Here, a stratified random sampling strategy was used to obtaintraining samples and validation samples. The random forest classifier was chosen for LCZ
mapping using these spectral/texture features because it is the state-of-the-art, nonparametric and efficient classifier, which is widely used in local climate zone classification
tasks [26,29,56,57]. In keeping with the latest WUDAPT method, the classification process
is repeated 25 times. We have also calculated several specialized accuracy metrics for LCZ
mapping proposed in recent years, namely: overall accuracy (OA), the overall accuracy ofurban LCZ types (OAu), the overall accuracy of built-up LCZ types (OAbu), and weighted
accuracy (OAw). The specific definitions of these indexes can be found in [41,58].
2.3.4. Latest WUDAPT Processing Method
To generate globally consistent LCZ maps more conveniently and quickly, Demuzere
et al. [39] proposed a web application, LCZ Generator (Scheme 2, S2), which is based on
the default WUDAPT classification workflow. LCZ Generator uses samples uploaded byresearchers to carry out LCZ mapping and finally shares the corresponding LCZ
classification maps on the website. Specifically, in addition to Landsat-8, other Earthobservations are used, in combination with the LCZ samples, as input to the random forestclassifier. A total of 33 features were used, including 16 features from Landsat-8, 5 featuresfrom Sentinel-1, 8 features from Sentinel-2, and 4 additional features, all of which havebeen resampled to a resolution of 100 m, as seen in detail [39].To ensure the quality of the
LCZ map, an automated cross-validation approach using 25 bootstraps is applied [41].
Then, 70% of the sample polygons in each bootstrap are used to train and 30% to test. The
LCZ Generator outputs several LCZ map accuracy metrics, including OA, OAu, OAbu, andOAw.
2.3.5. Population Distribution Analysis Based on LCZ Classification Results
Although the LCZ types are primarily a description of land cover, some of them canbe linked to land use and population. The built LCZs have obvious features in buildingheight, openness, and usage. Generally speaking, the higher and more compact thebuilding is, the higher the population per unit area will be (assuming the buildings aremainly used for housing). As a result, the population density will theoretically vary
between LCZs. To study the population distribution in LCZs in China, an overlay analysisof the LCZ results and population data was performed. In addition, the 32 cities (except
for Macao and Lasa) in China are divided into four sizes, namely super city, megacity,
type Ⅰ large city, and type Ⅱ large city according to the number of permanent residents.
Then, the impact of city size on the population distribution in LCZ is analyzed.

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3. Results
3.1. LCZ Mapping Results and Accuracy Evaluation
3.1.1. LCZ Mapping Results Analysis among Cities
Using the last modified training area samples, we produced LCZ maps for all 34
Chinese cities, as shown in Figure S1. To show the situation of LCZ classification, a box
plot of the overall accuracy of each city is shown in Figure 5. The overall accuracy of LCZ
mapping results in the 32 cities, except for Lasa and Macao, ranged from 75% to 94%, witha mean value of 85%. The overall accuracy of most cities fluctuated within 10%, and there
are no apparent outliers, indicating that the mapping results were relatively stable. At thesame time, we noticed that Lasa and Macao have a low average overall accuracy of 54%
and 65%, respectively. This was due to the small research area in these two cities, resulting
in the smallest sample size. Previous studies have found that RF achieves less accurate
results when a small number of training samples is used [59,60]. In summary, the averageoverall accuracy for all cities exceeded 50%, and most cities far exceeded this value.
According to Bechtel et al. [41], the quality of the LCZ data we generated was acceptable
for subsequent applied analysis.
1.0

Overall Accuracy (OA)

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0.8

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0.6

0.5

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Figure 5. The box plot of overall accuracy in all selected cities. (Note: X-axis shows the 34 Chinese
cities; Y-axis shows the overall accuracy ranging from 0–1; a legend is given at the lower-left corner.)

3.1.2. Accuracy Variation among LCZ Types
To analyze the differences in mapping accuracy between LCZ types, a producer’s
accuracy heatmap (as shown in Figure 6) was produced for each city for each LCZ type.
From the perspective of urban and rural LCZ types, it was observed that the average
accuracy of the built LCZs ranged from 68% to 86%, which was generally lower than the
average accuracy of the natural LCZs from 84% to 99%. Specifically, it can be seen that nomatter where the city is located and how developed the city is, the accuracy of LCZ A(dense trees) and LCZ G (water) is consistently high, with a mean accuracy of 96% and99%. However, the accuracy of LCZ 1–6 is low, with a mean accuracy of 80%, 68%, 79%,
77%, 68%, and 79%, respectively. In addition, due to differences in urban morphology, thesurface of some cities did not have certain LCZ categories, mainly including LCZ 7(lightweight low-rise), LCZ 9 (sparsely built), LCZ 10 (heavy industry), and LCZ C (Bush).
Therefore, there is no accuracy score for an LCZ type when it does not exist.

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0.94
0.81

0.85
1.00
0.83
0.82
0.62
0.94
1.00
0.91
0.94
0.86
0.96
0.81
0.82
0.92
0.85
0.87
0.97
0.96
0.82
0.81
0.72
0.61
0.86
0.76
0.77
0.73
0.78
0.87
0.88
0.83
0.91
0.89
0.88
0.82
0.77

0.84
-0.36
-1.00
1.00
0.71
0.47
1.00
1.00
1.00
0.78
1.00
--0.67
1.00
0.97
1.00
0.49
-0.61
1.00
0.92
--0.84
0.71
-0.83
0.88
0.89
1.00
0.77
--

0.86
-0.61
0.79
1.00
0.69
------0.56
1.00
0.92
0.76
0.47
0.95
--0.74
0.89
-0.93
1.00
1.00
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0.82
-1.00
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0.96
0.93

0.96
1.00
0.99
0.96
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1.00
1.00
0.91
1.00
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1.00
0.98
0.96
0.85
0.98
0.77
0.82
1.00
0.98
0.98
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0.99
1.00
0.97
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0.96
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0.96
0.84
0.96
0.87
0.99

0.84
0.67
0.93
0.85
1.00
0.96
1.00
0.83
0.83
0.90
0.69
1.00
0.82
0.80
0.74
0.82
0.68
0.86
0.74
0.88
1.00
0.57
0.85
0.77
-0.68
0.82
0.59
0.90
0.77
1.00
1.00
0.97
0.71
0.96

0.92
----1.00-1.00--0.76----0.81
1.00
0.98------0.80
1.00
1.00
-0.91
0.90
----0.88

0.91
-1.00
0.95
0.76
0.96
0.94
0.82
0.89
0.73
0.97
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0.91
0.92
0.99
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0.97
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0.92
0.98
0.91
0.99
0.91
0.63
0.98
0.98
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0.99
0.98

0.84
0.59
0.65
0.90
0.64
0.85
1.00
0.93
0.43
0.39
-1.00
0.97
0.95
0.94
0.96
-1.00
0.78
0.42
0.64
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0.83
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0.94
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0.97
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0.99
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0.99
0.98
1.00
0.99
1.00

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

LC
Z1
LC
Z2
LC
Z3
LC
Z4
LC
Z5
LC
Z6
LC
Z7
LC
Z8
LC
Z9
LC
Z1
0
LC
ZA
LC
ZB
LC
ZC
LC
ZD
LC
ZE
LC
ZF
LC
ZG

Average
Macao
Hongkong
Guangzhou
GuiyangKunming
Taibei
Hefei
Nanchang
HaikouLasa
Nanning
Lanzhou
ChengduChongqing
Wulumuqi
XiningYinchuan
Xian
ChangshaWuhan
FuzhouHangzhouNanjingShanghai
HuhehaoteTaiyuanZhengzhou
TianjinBeijingChangchun
HaerbinShenyang
Jinan
Shijiazhuang

Figure 6. The accuracy of each LCZ type for the 34 cities and the average accuracy is given at the
top line for all cities. Dashed lines indicate no accuracy score.

3.1.3. Accuracy Comparison between Our Method and WUDAPTThe LCZ training area samples with label information from 34 Chinese cities were
uploaded to the WUDAPT website. Then, the mapping accuracy and results of LCZ for
the corresponding cities were obtained after processing on the website platform. The
mapping results are shown in Figure S1, and the relevant data can be found anddownloaded at https://lcz-generator.rub.de/, accessed on 1 May 2022. To compare the LCZ
classification accuracy of the two schemes, the four accuracy indicators of 34 cities under
the two classification schemes were calculated and counted, as shown in Figure 7. Then,the four indicators of 34 cities were averaged to obtain Table 1.
1.0

Accuracy

0.8

0.6

OA - WUDAPTOA - Our method
OAu - WUDAPT

0.4

OAu - Our method
OAbu - WUDAPT
OAbu - Our method
OAw - WUDAPT

Sh
iji
az

hu
Sh Ji angen na
C Ha yannha er g
ng bi
n
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n
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en an gjin
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uh yu uSh eha anan otg e
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gz ng
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z u
C Wuhouha hng an
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H
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H nge
K Ta fei
un ib
G Gu minei
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on zh
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ac g
ao

OAw - Our method

Figure 7. Comparison of four indicators (OA, OAu, OAbu, and OAw) under two schemes (ourmethod, S1; WUDAPT method, S2) for the 34 cities.

ISPRS Int. J. Geo-Inf. 2022, 11, 420

11 of 20

Table 1. The average accuracy of four indicators under the two schemes.

SchemeS1
S2

OA (%)
85.45
74.94

OAu (%)
75.99
66.94

OAbu (%)
97.66
97.06

OAw (%)
96.33
93.88

First, it can be seen from Figure 7 that the OA, OAu, OAbu, and OAw of 34 cities
obtained by S1 are higher than those obtained by S2 in varying degrees. Second, it can beseen from Table 1 that the OA, OAu, OAbu, and OAw obtained by S1 are 85.45%, 75.99%,
97.66%, and 96.33%, respectively. The OA, OAu, OAbu, and OAw obtained by S2 are 74.94%,
66.94%, 97.06%, and 93.88%, respectively. The four indicators of S1 are 10.51%, 9.05%,
0.6%, and 2.45% higher than those of S2, indicating that the classification accuracy of S1 isbetter than that of S2 on the whole. The OAu of the two schemes is 8% and 9.46% less thanthat of OA, indicating that the classification accuracy of built types remains improved.
Finally, more than 300 cities have uploaded LCZ samples to the WUDAPT website, andthe average overall accuracy of LCZ mapping in all cities is 65.61%. By uploading LCZ
samples from 34 Chinese cities to the WUDAPT website, the average overall accuracy of
34 cities is 74.94%, which meets the requirement of WUDAPT automatic quality control
with an average minimum accuracy of 50% [41], indicating that the quality of LCZ
samples constructed by us is high.
To more specifically compare the LCZ mapping results under different schemes,
Fuzhou city is taken as an example to illustrate, as shown in Figure 8. First, on the whole,
the LCZ classification result of S1 (our method) is more consistent with the remote sensing
images in Google Earth than that of S2 (WUDAPT). In addition, it can be seen from regions
1 and 2 that there are many LCZ A misclassified into LCZ 9 under S2, and the classification
results under S1 can more accurately describe the surface coverage. This is because thespectral information of some natural surface cover changes with seasons, and multiseasonal images can introduce the phenological characteristics of vegetation to improve
the accuracy of classification. Second, from the local point of view, it can be seen fromregion 3 and region 4 that the classification results of S2 can distinguish some smaller
objects, such as LCZ A and LCZ G, but the classification results are more fragmented, andthe classification effect is not as good as that of S1. Although S2 can describe the internal
differences in urban morphology and surface characteristics in more detail, the noise isalso larger and cannot represent homogeneous urban areas. In summary, the classification
framework S1 was more robust, attained higher classification accuracy, and was easier tointerpret (due to reducing the “salt-and-pepper” effect).

ISPRS Int. J. Geo-Inf. 2022, 11, 420

12 of 20

Figure 8. Comparison of LCZ mapping results under two schemes (S1 and S2): A case study of
Fuzhou. The enlarged results of each scheme for the four subset areas (Box 1, 2, 3, and 4) arepresented. Google Earth images of each region are also presented.

3.2. Relationship between LCZ TYPES and population
To obtain an overview of the 34 cities in China, the proportions of area andpopulation for different built LCZ classes were counted, as shown in Figure 9. Amongthese classes, the large low-rise (LCZ 8) has the largest share of area, being 23.17% of thetotal built-up area and 14.43% of the total population. The sparsely built (LCZ 9) occupied
12.51% of the total built-up area, only accommodating 2.54% of the total population in 34
cities, and the lightweight low-rise (LCZ 7) held 6.11% of the total built-up area,accommodating 2.09% of the total population. Furthermore, 51.37% of the population
lived in the open areas (LCZ 4–6), accounting for 41.62% of the total built-up area.
However, the 13.26% compact area (LCZ 1–3) accommodated 26.74% of the total
population. The difference in population density between compact high-rise (LCZ 1) andsparsely built (LCZ 9) was more than eighteen times.

ISPRS Int. J. Geo-Inf. 2022, 11, 420

13 of 20

Figure 9. The proportions of area (left) and population (right) of the ten built-up classes (LCZ 1–10)
for the 34 cities. A legend for the 3D colored pie chart is given at the bottom.

To further analyze the relationship between built-up LCZ types and population
density, the heat map and histogram of population density (Figure 10a,b) at the LCZ scale
were obtained by overlaying the LCZ results with population data. It can be seen from
Figure 10a that LCZs 1–3 had the highest population density of 1372–83200 people/km2.
LCZs 4–6 corresponded to a moderate level of population density, 384–21682 people/km2.
LCZs 7–9 had the lowest population density of 0–5650 people/km2. Furthermore, there
exists a decreasing trend of population density in LCZs 1–3 and LCZs 4–6, respectively.
From Figure 10b, we found that the overall variation trend of population density in thebuilt-up LCZ type was: LCZ 1 > LCZ 2 > LCZ 4 > LCZ 5 > LCZ 3 > LCZ 8 > LCZ 6 > LCZ 7> LCZ 9. Among them, LCZ 1 had the highest population density of 19,222 people/km2,
while LCZ 9 corresponded to the lowest population density of 1030 people/km2.
Population density(people/km2)(a)83,20042,473
21,682
11,068
5650
2884
1472
752
384
196
100

Sh
iji
az
hu
a
Sh Ji ngen nany
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LCZ9
LCZ8
LCZ7
LCZ6
LCZ5
LCZ4
LCZ3
LCZ2
LCZ1

Population density(people/km2)

20,00019,222

16,00012,000(b)

11,078

10,113
7223

8000
8,000
4485

4000
4,000

2673

3166
1737

1030

0
LCZ1 LCZ2 LCZ3 LCZ4 LCZ5 LCZ6 LCZ7 LCZ8 LCZ9

Figure 10. (a) Heat map of population density at the LCZ scale for 34 cities. (b) Histogram of average
population density for each LCZ.

3.3. Population Density of LCZs in Different Size Cities
To further reveal the population distribution pattern of LCZs, the average population
density of LCZs under different size cities was plotted. As shown in Figure 11, thehierarchical effect of population density in cities of different sizes was significant. In

ISPRS Int. J. Geo-Inf. 2022, 11, 420

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relatively larger cities, the population density of all LCZs was high. The highest average
population density was 7909 people/km2 in super cities (1495 people/km2 higher than thatin megacities), 6414 people/km2 in megacities (399 people/km2 higher than that in type I
large cities), and 6015 people/km2 in type I large cities (864 people/km2 higher than that in
Type II large cities at 5151 people/km2). The difference in average population density
between LCZs in megacities and type I large cities was the smallest, whereas that between
LCZs in type II large cities and super cities was the largest. Consequently, there was ahierarchical effect of population density in LCZs of cities of different sizes in China in thefollowing order: super cities > megacities > type I large cities > type II large cities. In
addition, in the LCZ categories that had a relatively high population density, such as LCZ
1, LCZ 2, LCZ 3, LCZ 4, and LCZ 5, the difference in population density was relatively
higher in different size cities, while in the LCZ categories that were with relatively low
population density, such as LCZ 6, LCZ 7, and LCZ 9, the difference in population density
was relatively lower in cities of different sizes.
LCZ 1

20,000
Average

LCZ 2

15,00010,000
5000

LCZ 9

LCZ 3

0
−5000

LCZ 8

LCZ 4

LCZ 7

LCZ 5
LCZ 6

Super city

MegacityType Ⅰ large city

Type Ⅱ large city

Figure 11. The radar chart of population density of LCZ types for different size cities. (Note: the redbox represents supercity, the orange circle represents megacity, the green diamond represents typeI large city, and the blue triangle represents type II large city.)

4. Discussion
4.1. Comparison with Traditional LCZ Classification Method
In the previous sections, we compared the proposed LCZ classification frameworkwith the traditional method (S2) in terms of classification accuracy and visualization
aspect. In this section, we further compare the two methods and present the limitations.
The built LCZ types often correspond to complex urban scenes containing many
artificial structures with high spectral heterogeneity, and therefore, their classification
accuracy is usually not high [41,42]. The results obtained by the S2 only through the pixelfeatures of remote sensing images are not satisfactory, as the spatial information andphenological features are not fully utilized. However, our proposed method generates
objects through chessboard segmentation and can make full use of spatial relationships
and contextual information by calculating the texture features (GLCM) of the objects[36,61]. In addition, the stacked multi-seasonal images are used to exploit the phenologicalfeatures carried by the surface objects. These steps are easily implemented by the softwareof eCognition [62]. In this way, the complex scenes consisting of multiple land-cover types

ISPRS Int. J. Geo-Inf. 2022, 11, 420

15 of 20can be more effectively represented by adding neighborhood information andphenological characteristics.
The resolution adopted by the WUDAPT method is 100 m; however, this value may
not be suitable for LCZ mapping. Considering the applicability of LCZ to climate research,
Stewart and Oke [11] suggested that the minimum radius of each LCZ should be 200 to
500 m. By analyzing the correlation between LCZ and climatic parameters, Zheng et al.
[28] found that the optimal resolution for LCZ classification in urban areas of Hong Kongis around 300 m. Research showed that neighborhood information had a positive
influence on LCZ mapping [63], and the larger image size (320–640 m) was more suitablefor LCZ mapping [44]. In addition, many scholars have adopted a resolution of around
300 m for LCZ mapping [36,64–66].
There still exist limitations to LCZ mapping based on remote sensing images. The
accuracy of the built LCZ types is generally lower than that of the natural LCZ categories,
as can be seen from Figure 6. The accuracy of LCZ A and LCZ G is generally high, whichis due to the large distribution area and the difficulty to appear mixed ground objects.
However, the accuracy of the classification of LCZs 1–6 is lower. This is due to thesimilarity of building materials in the same area and the lack of information on the 3D
structure of buildings in the LCZ classification, making it difficult to distinguish between
LCZs 1–3 or LCZs 4–6 only with a single data source of remote sensing imagery. Relevant
urban data available at small scales, combined with the use of multiple sources (e.g.,building morphology data, LiDAR data), will further improve the classification accuracy[24,42,67]. However, accurate and consistent data of height information at large scale
scales (national or global) are still scarce. Even with these limitations, the accuracy of our
proposed framework is still sufficient for subsequent population analysis.
4.2. Application of LCZs in Combination with Population
The built LCZ types inherently had distinct characteristics in building scale, height,
density, and usage; thus, they will have different features in terms of population density.
We analyzed the relationship between LCZs and population in 34 Chinese cities, and theresults showed that the compact LCZs have a higher population density than open LCZs,
with a maximum difference of 18 times (Figure 9). The huge differences in population
density in LCZ reflected, to some extent, the inequalities in the living conditions of theChinese people. Moreover, the compactness and height of the different LCZ classes weredifferent, and thus, their population density was also different (Figure 10). Specifically,
when comparing the population density of LCZs 1–3, we found that the higher thebuilding is, the higher the population density of the corresponding LCZ type is. When
comparing the population density in LCZs 1–3 and LCZs 4–6, we found that the morecompact the building is, the higher the population density of the corresponding LCZ type
is. The buildings are high and compact in LCZ 9, which has the highest population
density. However, the buildings are low and sparse in LCZ 9, which has the lowest
population density. In general, population density in built LCZs varies by type and ismainly affected by building height and density.
By analyzing the effect of different city sizes on population density in the LCZ, ourresults show that there was a hierarchical effect of population density in LCZs of cities ofdifferent sizes in China, in the following order: super cities > megacities > type I large cities> type II large cities (Figure 11). This is because the greater the scale of the city, the more
people have; thus, the utilization efficiency of the land is higher. Furthermore, we foundthat the difference in population density between cities of different sizes is greater whenin the LCZ class with higher population density. Since the height and density of thebuildings in LCZ 6, 7, and 9 are low, the upper limit of the population density in thesecategories is not high; thus, the population density between different sizes of cities issmall. Conversely, in the high-rise LCZ type (LCZ 1 and LCZ 4), the upper limit ofbuilding height is high, and larger cities tend to have more and taller buildings, whichmakes the difference in population density the largest.

ISPRS Int. J. Geo-Inf. 2022, 11, 420

16 of 204.3. Possible Application Based on LCZ
LCZs were originally developed for metadata communication in temperature observation of a field site; thus, the urban heat island study is its main application [68]. Due totheir detailed description of urban areas, a few studies have also applied the LCZ scheme
to the studies of urban energy budgets [15], urban ventilation [17], and outdoor thermal
comfort [20]. In recent years, the future land use simulation (FLUS) model was proposedby Liu et al. [69] and was successfully applied to the future spatial change simulation in
the LCZ [70]. The urban morphological structure provided by LCZs is closely related tothe transportation network, which is an important factor influencing the urban land use
pattern, and its planning guides the direction of urban development [71]. By considering
different transport planning policies, such as the sustainable urban mobility plan [72], we
could simulate the spatial change pattern of the LCZ using the FLUS model for ex-ante
assessment of the impact of transportation planning policies. Furthermore, by combining
population data, the LCZ forecast results will provide credible data support for the adjustments of planning policy.
5. Conclusions and Future Work
This study first constructed a high-quality training sample set with label information.
Then, we proposed an LCZ classification framework based on chessboard segmentationand multi-seasonal Sentinel-2 images at a 300 m grid and used this framework to generate
LCZ maps in 34 provincial capital cities of China. Subsequently, we evaluated the accuracy of the LCZ mapping results. In addition, we conducted an overlay analysis of the
LCZ classification results and population density. The key findings of this study are summarized as follows:(1) The values of overall accuracy in the 34 cities of China ranged from 75% to 94%,
with an average of 85%. Compared with the latest WUDAPT method, the OA, OAu, OAbu,
and OAw values of our proposed framework increased by an average of 10%, 9%, 1%, and
2%, respectively. Meanwhile, the LCZ reference training sample set has been uploaded to
the WUDAPT, which is available at https://lcz-generator.rub.de/submissions, accessed on
1 May 2022. Please cite the paper to use the dataset.
(2) The accuracy of different LCZ types varies. In general, the accuracy of natural
LCZ types was higher than that of built LCZ types. The accuracy of the built LCZ typewill be further improved by combining remote sensing images with multi-source data
(e.g., building morphology data, LiDAR data, etc.) when relevant data are available.
(3) Population density in built LCZs varies by type and is mainly affected by buildingheight and density. The overall variation trend of population density in the built-up LCZ
type was: LCZ 1 > LCZ 2 > LCZ 4 > LCZ 5 > LCZ 3 > LCZ 8 > LCZ 6 > LCZ 7 > LCZ 9.
Among them, the mean population density of LCZ 1 (compact high-rise) was highest(19,222 people/km2), whereas that of LCZ 9 (sparsely built) was lowest (1030 people/km2).
(4) There was a hierarchical effect of population density in LCZs of cities of different
sizes in China in the following sequence: super cities > megacities > type I large cities >type II large cities. In addition, the difference in population density was higher in the LCZ
types (LCZ 1, LCZ 2, LCZ 3, LCZ 4, and LCZ 5) that had relatively high population density, while the difference in population density was lower in the LCZ types (LCZ 6, LCZ
7, and LCZ 9) that had relatively low population density.
In conclusion, the high-quality LCZ training samples not only contribute to the production of LCZ maps at national or even global scales but also make sample transferability
possible. Meanwhile, the proposed classification framework provided a valuable reference for improving the accuracy of LCZ mapping. Moreover, this study gave the population density in different built LCZs of Chinese cities and explored the influence of city sizeon it, which helps to understand population distribution patterns in China.
In the future, the Earth Engine (e.g., Google Earth Engine) with huge computing
power will be used to map the whole of China into LCZ types with these high-quality

ISPRS Int. J. Geo-Inf. 2022, 11, 420

17 of 20training sample. Furthermore, we intend to integrate multi-source data to improve theclassification accuracy of LCZ, especially for built LCZ types. When the entire LCZ mapof China is available, we could estimate the population of China by population density in
LCZs of cities of different sizes in a low-cost way.
Supplementary materials: Comparison of LCZ classification results for 34 cities between
the proposed method (Scheme 1) and the latest WUDAPT method (Scheme 2).Supplementary materials to this article can be found online at www.mdpi.com/article/10.3390/ijgi11080420/s1.
Author Contributions: Conceptualization: Liang Zhou and Lei Ma; Data curation, Liang Zhou; Formal analysis, Liang Zhou and Ziyun Yan; Funding acquisition, Lei Ma; Methodology, Liang Zhou
and Lei Ma; Project administration, Lei Ma; Resources, Liang Zhou; Software, Lei Ma; Supervision,
Lei Ma, Feixue Li, and Manchun Li; Validation, Lei Ma; Visualization, Liang Zhou; Writing—original draft, Liang Zhou; Writing—review and editing, Liang Zhou, Lei Ma, Brian Alan Johnson, andZiyun Yan. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the National Natural Science Foundation of China (Grant
Number: 42171304, 41701374) and the Fundamental Research Funds for the Central Universities.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The LCZ sample dataset generated by this study can be found here:[https://lcz-generator.rub.de/submissions, accessed on 1 May 2022].
Acknowledgments: Sincere thanks to the anonymous reviewers and members of the editorial teamfor the comments and contributions.
Conflicts of Interest: The authors declare no conflict of interest.

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