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Citation: Pan, Y.; Wu, Y.; Xu, X.;
Zhang, B.; Li, W. Identifying
Terrestrial Landscape Character
Types in China. Land 2022,11, 1014.
https://doi.org/10.3390/
land11071014
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Received: 18 May 2022
Accepted: 30 June 2022
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land
Article
Identifying Terrestrial Landscape Character Types in China
Yulian Pan 1,2,† , Yunong Wu 1,2, † , Xi Xu 1,2, Bin Zhang 1,2 ,* and Weifu Li 3
1Department of Landscape Architecture, College of Horticulture and Forestry, Huazhong Agricultural
University, Wuhan 430070, China; landscape@webmail.hzau.edu.cn (Y.P.);
yunong@webmail.hzau.edu.cn (Y.W.); xuxi11@webmail.hzau.edu.cn (X.X.)
2Key Laboratory of Urban Agriculture in Central China, Ministry of Agriculture and Rural Affairs,
Wuhan 430070, China
3College of Science, Huazhong Agricultural University, Wuhan 430070, China; liweifu@mail.hzau.edu.cn
*Correspondence: zhangbin@mail.hzau.edu.cn
† These authors contributed equally to this work.
Abstract:
Landscape character assessment (LCA) is a widely used tool that integrates natural, cultural,
and perceptual attributes to identify and portray landscape. In this study, we used the LCA method to
identify the landscape characteristics of China at the national scale. Furthermore, we applied cultural
and landscape structural factors along with spatial transmission to improve the identification system.
First, we incorporated all the parameters in the assessment. We selected 15 landscape character factors
from four factor types including nature, culture, spatial geographic co-ordinates, and landscape
structure. These parameters were analysed using multilevel overlay and spatial connection tools in
ArcGis 10.2, which resulted in 2307 landscape description units (LDUs). Second, the spatial structure
properties of the LDUs were determined using a semivariogram and the moving window method in
ArcGis 10.2 and Fragstats 4.2 software, respectively. Third, for visualisation, we applied the principal
component analysis (PCA) using the SPSS software and elbow and k-means clustering methods using
MATLAB to determine 110 landscape character types (LCTs) of China’s entire terrestrial landscape.
Finally, we determined 1483 landscape character areas through semiautomatic segmentation and
manual visual correction using eCognition. Based on the unique characteristics of the entire terrestrial
landscape of China, a set of ideas and methods for the overall identification of LCTs was proposed.
Our findings can be used to optimise territorial spatial planning and landscape protection and
management, and promote multiscale land-use studies in China.
Keywords:
China; cluster analysis; landscape character areas (LCAs); landscape character assessment
(LCA); landscape structure; landscape character types (LCTs)
1. Introduction
Landscape character is a relatively consistent and distinguishable pattern of elements
determined by the identification of landscape resources [
1
]. Landscape character can pro-
mote a better understanding of landscape resources [
2
] and help avoid the homogeneous
evolution of the landscape that leads to the loss of uniqueness and a sense of place [
3
,
4
].
Identifying landscape characteristics, mapping their types, making judgments based on
landscape character assessment (LCA), specific development, and utilisation methods are
accepted core methods of landscape characterisation in Europe [
5
–
8
]. These approaches
provide information for research on the landscape, territorial planning, and land manage-
ment [
9
], and provide a basic framework for the systematic monitoring and assessment of
landscape changes [3,7,10–12].
In recent decades, China’s rapid economic development, large-scale urbanisation, and
agricultural intensification have changed and deformed the structure of several natural
landscapes, resulting in the gradual alienation and loss of cultural landscapes. Thus, the
landscape faces the risk of sameness. Since 2019, China has introduced relevant policies
Land 2022,11, 1014. https://doi.org/10.3390/land11071014 https://www.mdpi.com/journal/land
Land 2022,11, 1014 2 of 19
to promote the use of ‘national territory’ as a carrier for the construction of an ecological
civilisation [
13
,
14
]. These policies emphasise the overall planning of all regional and
elemental resources to achieve sustainable development [
15
], and landscapes have received
considerable attention and considered vital resources. LCA is applied in China to address
both risk and development and provide special support for the planning of territorial
spaces [
13
]. In recent years, many scholars have conducted studies on LCA [
16
,
17
]; however,
a complete and systematic LCA method is still lacking. Existing studies mostly focus on
provinces, cities, and rural areas that are delineated by administrative regions [
16
–
20
],
while national-scale studies are absent.
Multidisciplinary studies on landscape character have resulted in the identification and
classification of various methods with different emphases. Simensen et al. [
21
] categorised
these methods into ensemble, automatic segmentation, and parametric methods. The
ensemble method is an intuitive and interpretive method [
1
,
21
], which is conducive for
comprehensive identification [
16
] as it can directly obtain landscape character types (LCTs)
and landscape character areas (LCAs) in a top-down approach; however, it requires strong
comprehensive thinking, abstraction, and application of expert knowledge and judgment,
and it is subjective and difficult to replicate [3,22]. The automatic segmentation method is
highly dependent on machines for image processing and segmentation and are commonly
used for LCA over large areas (e.g., at the national and transnational scales) [
11
] and has
the advantages of rapid identification and objective and accurate boundary demarcation. It
is mostly used for research on the classification of LCAs. However, this method requires
a prior definition of key variables [
23
,
24
] and does not involve factors such as cultural
attributes [
25
]. The parametric method is based on geographic information systems (GIS)
and statistical methods that can classify and cluster landscape samples [
5
,
10
,
26
], choose the
detailed characteristics of landscape elements to reflect more comprehensive relationships
among landscape elements, and produce perfect recognition results while reducing the
influence of human subjectivity [
3
]. However, this method cannot directly access LCAs,
and grid plaques must be integrated for interpretation. The GIS approach has been used
as a tool for the large-scale assessment of LCA [
1
], and ensures consistency at the national
scale, while manual techniques at finer scales capture local subtleties [10].
Notably, combining a top-down holistic approach and bottom-up clustering analysis
can accurately identify LCTs [
16
]. Comparisons revealed that the three LCA methods
implemented at the transnational and national scales focused primarily on biophysical
features to determine landscape character factors (LCFs), whereas human factors were
mainly based on land use/cover [
3
,
11
,
27
]. A majority of these studies are based on the
perspective of landscape composition and thus cannot reflect the overall appearance of
landscape characters. To address this issue, Belgian researchers introduced landscape
structure factors (LSFs) [
5
]; however, the scale effect of landscape structure has not yet been
clearly defined.
Our study describes a refined framework combing the ensemble, automatic segmenta-
tion, and parametric methods for efficient and flexible identification of LCTs. We considered
the entire terrestrial landscape of China as our study area and introduced the spatial distri-
bution types of Chinese settlements based on the contextual characteristics of the country.
We constructed a basic ArcGIS information database by performing a structural attribute
analysis of the landscape description units (LDUs) and by scientifically calculating the scale
transitivity of landscape structure using the quantitative statistical method. The overall
process is shown in Figure 1. Our study was aimed at improving the selection of index
factors and analysing the structural configuration at the LCA stage and establishing a sys-
tematic method to create an overall, comprehensive, and relatively objective classification
system of LCTs that is suitable for China. Additionally, our aim was to fill the gap in the
current literature on China’s LCA at the national scale. Our results can provide a reliable
reference for feature identification in provinces and cities at the mesolevel.
Land 2022,11, 1014 3 of 19
Land 2022, 11, x FOR PEER REVIEW 3 of 19
Figure 1. Flowchart depicting the process used to determine China’s terrestrial landscape character
types and identify landscape character areas.
2. Materials and Methods
2.1. Research Area
China’s terrestrial area covers approximately 9.6 million km2, with over 23 provinces,
four municipalities, and two special administrative and five autonomous regions. Fifty-
six ethnic groups are widely distributed throughout the country. The contiguous land area
runs for approximately 5500 km from north to south and 5200 km from east to west. The
overall terrain has a high and low altitude in the west and east, respectively. The vast area
of mountains and plateaus in China include the Tianshan, Gangdisi, and Hengduan
mountains, North China Plain, middle and lower reaches of the Yangtze River Plains and
Sichuan Basin, and parts of the Himalayas; the natural landforms are diverse, with sub-
stantial altitude differences. In addition, the country consists of several major climate
types: the subtropical, temperate, and warm temperate climate zones. Notably, regional
precipitation also varies considerably throughout China. In line with these factors and
human resources, the country has diverse landscape resources and rich biodiversity.
2.2. Data Collection
Natural and biophysical attributes provide a factual background for the development
of landscapes, society, and culture, which, in turn, provide an important basis for LCA.
Thus, identifying LCTs is of great significance for determining the dynamic evolution of
the landscape and optimising land management and planning [28,29].
Based on the accuracy and availability of data, we selected 15 LCFs from four factor
types, namely, nature, culture, spatial geographic co-ordinates, and landscape structures.
Among them, the natural factors included (1) altitude and topographic relief, which was
measured by the Shuttle Radar Topography Mission (SRTM 4.1) with a digital elevation
model (1 × 1 km resolution) using the ArcGIS 10.2 spatial analysis tool
(http://srtm.csi.cgiar.org, accessed on 5 March 2021), and (2) climate zone obtained from
Figure 1.
Flowchart depicting the process used to determine China’s terrestrial landscape character
types and identify landscape character areas.
2. Materials and Methods
2.1. Research Area
China’s terrestrial area covers approximately 9.6 million km
2
, with over 23 provinces,
four municipalities, and two special administrative and five autonomous regions. Fifty-six
ethnic groups are widely distributed throughout the country. The contiguous land area
runs for approximately 5500 km from north to south and 5200 km from east to west. The
overall terrain has a high and low altitude in the west and east, respectively. The vast
area of mountains and plateaus in China include the Tianshan, Gangdisi, and Hengduan
mountains, North China Plain, middle and lower reaches of the Yangtze River Plains
and Sichuan Basin, and parts of the Himalayas; the natural landforms are diverse, with
substantial altitude differences. In addition, the country consists of several major climate
types: the subtropical, temperate, and warm temperate climate zones. Notably, regional
precipitation also varies considerably throughout China. In line with these factors and
human resources, the country has diverse landscape resources and rich biodiversity.
2.2. Data Collection
Natural and biophysical attributes provide a factual background for the development
of landscapes, society, and culture, which, in turn, provide an important basis for LCA.
Thus, identifying LCTs is of great significance for determining the dynamic evolution of
the landscape and optimising land management and planning [28,29].
Based on the accuracy and availability of data, we selected 15 LCFs from four factor
types, namely, nature, culture, spatial geographic co-ordinates, and landscape structures.
Among them, the natural factors included (1) altitude and topographic relief, which was
measured by the Shuttle Radar Topography Mission (SRTM 4.1) with a digital elevation
model (1
×
1 km resolution) using the ArcGIS 10.2 spatial analysis tool (http://srtm.csi.
cgiar.org, accessed on 5 March 2021), and (2) climate zone obtained from the ‘China Climate
Type Figure’ vectorised distribution map (http://map.ps123.net/, accessed on 5 March
2021), with a total of five climate zones.
Land 2022,11, 1014 4 of 19
The human factors included (1) land-use data obtained from the Institute of Geo-
graphic Sciences and Natural Resources Research, Chinese Academy of Sciences (http:
//www.resdc.cn/, accessed on 5 March 2021, 2018; 1
×
1 km resolution), and (2) the
distribution of rural settlements obtained from the settlement division vector diagram in
China [30] consisting of a total of 11 settlement divisions.
To comprehensively analyse landscape at the regional level and determine the struc-
ture of each LCT, eight commonly used indexes were selected from four aspects, namely,
clustering and dispersion, density and difference, shape complexity, and diversity. The
indexes applied were artificial intelligence (AI), contiguity mean (CONTIG-MN), landscape
division index (DIVISION), area-weighted fractal dimension (FRAC-AM), land sustain-
ability index (LSI), patch density (PD), Shannon’s diversity index (SHDI), and Shannon’s
evenness index (SHEI). In addition, the boundaries of China’s administrative map were
downloaded from the BIGEMAP platform.
2.3. Research Methods
2.3.1. Identification of Landscape Description Units
We divided the LCFs into distinguishing factors with typical boundaries (altitude,
topographic relief, and land use) and descriptive factors (settlement and climate). The
latter factors were not used to delineate the LDU boundaries; instead, they were used to
supplement the description.
We used a resolution of 1
×
1 km as the statistical unit for the following reasons:
(1) this resolution is often used to analyse large-scale study objects [11] and national-scale
character recognition [
5
,
25
,
31
], and (2) China’s national-scale mapping mainly uses this
resolution expansion [32–35].
To ensure the uniqueness of the calculation results of the distinguishing factors in
the process of applying the spatial overlay technique in ArcGIS 10.2, the number theory
was adopted to encode the classification of different LCFs. Moreover, to ensure that
no number exceeded the upper limit of integer data, similar values were grouped to
ensure the lowest possible variance within the groups. Thus, a dataset of the LCFs of the
entire landscape of China was constructed. Table A1 (Appendix A, Table A1) illustrates
the categorical attributes [
36
,
37
] and the number of variables considered in our study.
The ArcGIS 10.2 platform was used to apply the spatial overlay technique. The spatial
connection tool was used to extract the attribute values of the settlement and climate
factors into the grid attribute table obtained after stacking, thus producing a database of
five factors. Finally, with reference to the European Landscape Classification (LANMAP)
method [
11
], the elimination tool was used to merge the patches of land that had an area of
<10 km
2
, along with adjacent surfaces that had the largest common boundary or areas, to
identify the LDUs of China’s entire territory.
2.3.2. Quantification of the Structure of Landscape Description Units
Landscape pattern is a concrete manifestation of the composition and spatial hetero-
geneity of perceptible landscape elements [
38
] that reflect the structural characteristics in
relation to the biophysical, social, and cultural conditions. Notably, landscape patterns are
important data required to determine the type of landscape characteristics [
39
,
40
]. The
LSF is often used to make quantitative analyses of the structural characteristics of the
landscape [
41
]. According to the overall structure of the LDU and to avoid redundancy, we
applied the properties of the eight abovementioned LSFs (Appendix A, Table A2) while
considering the operability of the units and the complexity of the spatial structure. Among
them, AI and CONTIG-MN characterised the aggregation and dispersion of LDUs, DI-
VISION and PD characterised the fragmentation degree of spatial distribution, and LSI
and FRAC characterised the complexity and regularity of shapes and reflected the degree
of disturbance to the landscape. Meanwhile, SHDI and SHEI characterised the degree of
heterogeneity and uniform distribution of the landscape, providing a thorough understand-
ing of landscape diversity in the region. Considering that scale changes have different
Land 2022,11, 1014 5 of 19
degrees of influence on the results of quantitative analysis of spatial structures [
39
,
42
,
43
],
semivariograms are widely used to study landscape pattern scales [
44
–
47
]. Therefore, we
used a semivariogram to identify the suitable study scales.
Semivariograms describe the distribution characteristics of elements by measuring
the relationship between the degree of variation in the spatial attributes of two points and
the distance between them [
48
]. The semivariogram curve contains four corresponding pa-
rameters: nugget value (C
0
), partial abutment value (C), variable range (A
0
), and abutment
value (C
0
+ C). Among these, the ratio of nugget value to abutment value [C
0
/(C
0
+ C)]
is also called the basal effect, and it is used to conduct comparative analyses for different
window sizes. A lower value of the degree of variation of the LSF indicates a higher spatial
autocorrelation and a stable landscape pattern [
49
–
51
]. Previous studies have indicated
that a stable C
0
/(C
0
+ C) ratio can be identified as the characteristic scale of the landscape
of the study area [52,53].
r(h) = 1
2n(h)
n(h)
∑
i=1[z(xi)−z(xi+h)]2(1)
where the regional change amount,
z(xi)
, is the measured value obtained at position
xi
,
n(h)
is the logarithm separated by h units in the vector direction, and
r(h)
is called the
semivariogram [54,55].
We used the moving window tool in Fragstats 4.2 to set the radius at the landscape
level to 8, 10, 12, 14, 16, 18, 20, 22, and 24 km, combined with the semivariogram model
that was set to an isotropic condition [
25
]. We then calculated the base effect of each LSF
and obtained the line graphs, C
0
/(C
0
+ C), for each window size (Figure 2). As shown in
Figure 2, the spatial pattern reached a stable state at 14 km and the grid distribution of each
pattern index can be calculated.
Land 2022, 11, x FOR PEER REVIEW 5 of 19
the degree of disturbance to the landscape. Meanwhile, SHDI and SHEI characterised the
degree of heterogeneity and uniform distribution of the landscape, providing a thorough
understanding of landscape diversity in the region. Considering that scale changes have
different degrees of influence on the results of quantitative analysis of spatial structures
[39,42,43], semivariograms are widely used to study landscape pattern scales [44–47].
Therefore, we used a semivariogram to identify the suitable study scales.
Semivariograms describe the distribution characteristics of elements by measuring
the relationship between the degree of variation in the spatial attributes of two points and
the distance between them [48]. The semivariogram curve contains four corresponding
parameters: nugget value (C0), partial abutment value (C), variable range (A0), and abut-
ment value (C0 + C). Among these, the ratio of nugget value to abutment value [C0/(C0 +
C)] is also called the basal effect, and it is used to conduct comparative analyses for differ-
ent window sizes. A lower value of the degree of variation of the LSF indicates a higher
spatial autocorrelation and a stable landscape pattern [49–51]. Previous studies have indi-
cated that a stable C0/(C0 + C) ratio can be identified as the characteristic scale of the land-
scape of the study area [52,53].
(1)
where the regional change amount, , is the measured value obtained at position ,
is the logarithm separated by h units in the vector direction, and is called the
semivariogram [54,55].
We used the moving window tool in Fragstats 4.2 to set the radius at the landscape
level to 8, 10, 12, 14, 16, 18, 20, 22, and 24 km, combined with the semivariogram model
that was set to an isotropic condition [25]. We then calculated the base effect of each LSF
and obtained the line graphs, C0/(C0 + C), for each window size (Figure 2). As shown in
Figure 2, the spatial pattern reached a stable state at 14 km and the grid distribution of
each pattern index can be calculated.
Figure 2. Line chart of variation value of landscape structure factors C0/(C0 + C) for different window
sizes.
2.3.3. Identification and Nomination of Landscape Character Types
As a multivariate statistical algorithm, principal component analysis (PCA) can
achieve ‘noise reduction’ and ‘redundancy’ [56,57]. Furthermore, previous studies have
proved that combining PCA with clustering algorithms can ensure data accuracy and
streamlining [16,58]. After PCA, eight LSFs were fused and extracted into two
Figure 2.
Line chart of variation value of landscape structure factors C
0
/(C
0
+ C) for different window
sizes.
2.3.3. Identification and Nomination of Landscape Character Types
As a multivariate statistical algorithm, principal component analysis (PCA) can achieve
‘noise reduction’ and ‘redundancy’ [
56
,
57
]. Furthermore, previous studies have proved
that combining PCA with clustering algorithms can ensure data accuracy and streamlin-
ing
[16,58]
. After PCA, eight LSFs were fused and extracted into two independent variables
and one mixed variable, explaining 84.581% of the data variation. Combined with the longi-
tude and latitude of the grid centroid and the LDU database, 10 variables and
1.39 million
landscape patches were determined.
The 10 LCFs included continuous and categorical variables. Since k-means cluster
analysis can only be applied to continuous variables, one-hot encoding was introduced
Land 2022,11, 1014 6 of 19
to convert the categorical variables in the LCFs into multiple dummy binary variables,
which can be conveniently compared with other variables. The combination can intuitively
reflect the influence of different attributes of the independent variables on the dependent
variables, thus improving the accuracy of the model. Notably, if the dummy binary variable
corresponded to a category, its value was set to 1; otherwise, it was set to 0 [59].
Because k-means clustering is a simple classic clustering algorithm used for unsuper-
vised learning [
60
], it does not rely entirely on expert judgement and reduces the influence
of human subjectivity [
61
]. Therefore, it is widely used in large-scale data clustering [
62
,
63
].
In this study, we used the elbow method to calculate the sum square error (SSE) value of the
specified cluster. When the SSE value decreased sharply to a certain point and tended to be
flat, that point was designated as the ‘elbow point,’ indicating the optimal K value [
61
,
64
].
The SSE value was calculated using the following equation:
SSE =∑k
i=1∑p∈Li|P−qi|2, (2)
where
Li
is the i-th cluster,
P
is the sample point in
Li
,
qi
is the centroid of
Li
(mean of all
samples in Li), and krepresents the number of groups.
Since the sample size of our landscape (1.39 million landscape patches) was large,
the similarity matrix exceeded the limit of computer memory. To address this issue, we
randomly selected 100,000 samples from the original database. We calculated the cluster
values and cluster centre points for these samples before calculating for the entire data set.
We used a juxtaposition method to create names for the LCTs. This method was also
used in LANMAP [
11
]. The names of each LCT comprised five parts with respect to the
landscape codes: the letters indicated land use, altitude, undulation, climate, settlement,
and the numerical subscripts corresponding to the number of the factor classification
(Appendix A, Table A1). Since the spatial structure factor was mainly used to analyse the
complexity of the spatial configuration of LCTs, LSF was used as the subdivision type,
distinguished by a number added at the end. To improve the comprehensibility of the
LCTs map, we used the colour classification of the land-use type in China’s urban land
classification and planning and construction land standard. We combined the saturation
changes to vividly portray and define similar and dis-similar character types to improve
the application of the process, results, and achieve the visual display of different LCTs
intuitively [5,65].
2.3.4. Regional Division of Landscape Characters
eCognition, an image segmentation technology, integrates automatic and supervised
classification, manual editing, and object-based image segmentation. Notably, previous
studies have indicated that it can intuitively and instantaneously identify character at-
tributes and reduce the influence of subjective factors [
66
]. We used the LCT map, obtained
by cluster analysis, as the eCognition image segmentation base map for the spatial distribu-
tion position and appropriate regional division of the LCTs (through the control variable
method) to ensure information richness of LCFs. Then, we adjusted the three parameters
(scale, shape, and compactness) to realize the automatic or semiautomatic segmentation
of the LCTs, identify the LCAs, and adjust the boundaries by combining manual visual
inspection and remote sensing images.
3. Results
3.1. Types of Landscape Characters and Regional Delineation
Using the spatial analysis of five LCFs, we finally identified 2307 LDUs and 1,438,518 patches.
Each LDU had a unique number. As shown in Figure 3, the spatial pattern was stable at
14 km
, which is the optimal research scale for an LDU structure. As shown in Figure 4, the
eight LSF maps corresponded to a window size of 14 km. By integrating LDUs, LSFs, and
the spatial geographic co-ordinate factors, and after performing PCA and clustering, we
identified 110 LCTs (Figure 5). These LCTs were then ranked with respect to their land-use
Land 2022,11, 1014 7 of 19
(Appendix A, Table A3). For naming, we used the LSFs as a subdivision type, distinguished
by placing a number at the end. For example, the 26th and 27th types of LCFs: woodland,
middle altitude, middle topographic relief, subtropical monsoon climate, and southwestern
rural settlement region, were consistent in both natural and human factors. However, there
were differences in their spatial configuration; therefore, they were named L
2
A
2
R
4
C
3
S
8
_1
and L2A2R4C3S8_2, respectively.
Land 2022, 11, x FOR PEER REVIEW 7 of 19
3. Results
3.1. Types of Landscape Characters and Regional Delineation
Using the spatial analysis of five LCFs, we finally identified 2307 LDUs and 1,438,518
patches. Each LDU had a unique number. As shown in Figure 3, the spatial pattern was
stable at 14 km, which is the optimal research scale for an LDU structure. As shown in
Figure 4, the eight LSF maps corresponded to a window size of 14 km. By integrating
LDUs, LSFs, and the spatial geographic co-ordinate factors, and after performing PCA
and clustering, we identified 110 LCTs (Figure 5). These LCTs were then ranked with re-
spect to their land-use (Appendix A, Table A3). For naming, we used the LSFs as a subdi-
vision type, distinguished by placing a number at the end. For example, the 26th and 27th
types of LCFs: woodland, middle altitude, middle topographic relief, subtropical mon-
soon climate, and southwestern rural settlement region, were consistent in both natural
and human factors. However, there were differences in their spatial configuration; there-
fore, they were named L2A2R4C3S8_1 and L2A2R4C3S8_2, respectively.
Figure 3. Attribute classification of natural and human factors and 2307 kinds of landscape descrip-
tion units: (A) altitude type classification, (B) topographic relief type classification, (C) land-use type
classification, (D) settlement type classification, (E) climate type classification, and (F) showing 2307
kinds of landscape description units (LDUs).
Figure 3.
Attribute classification of natural and human factors and 2307 kinds of landscape description
units: (
A
) altitude type classification, (
B
) topographic relief type classification, (
C
) land-use type
classification, (
D
) settlement type classification, (
E
) climate type classification, and (
F
) showing
2307 kinds of landscape description units (LDUs).
We developed a base map consisting of the 110 LCTs for image segmentation using
eCognition. We adjusted the scale parameters, regardless of the administrative boundaries,
to an appropriate segmentation scale by combining satellite images and natural and human
attributes (which were based on the image colour and shape). Notably, the weights of shape
and tightness in the single-variable test homogeneity parameters were controlled. After
several experiments, we deduced that there were appropriate classification results when
scale, shape, and compactness were 80, 0.7, and 0.3, respectively. We identified 1483 LCAs
by combining satellite images and LCTs after manual division and adjustment in ArcGIS
10.2 (Figure 6).
Land 2022,11, 1014 8 of 19
Land 2022, 11, x FOR PEER REVIEW 8 of 19
Figure 4. Eight landscape structure factors (LSFs) corresponding to a scale of 14 km.
Figure 4. Eight landscape structure factors (LSFs) corresponding to a scale of 14 km.
Land 2022,11, 1014 9 of 19
Land 2022, 11, x FOR PEER REVIEW 9 of 19
Figure 5. Map of 110 landscape character types (LCTs) across China at a scale of 1 × 1 km.
We developed a base map consisting of the 110 LCTs for image segmentation using
eCognition. We adjusted the scale parameters, regardless of the administrative bounda-
ries, to an appropriate segmentation scale by combining satellite images and natural and
human attributes (which were based on the image colour and shape). Notably, the weights
of shape and tightness in the single-variable test homogeneity parameters were con-
trolled. After several experiments, we deduced that there were appropriate classification
results when scale, shape, and compactness were 80, 0.7, and 0.3, respectively. We identi-
fied 1483 LCAs by combining satellite images and LCTs after manual division and adjust-
ment in ArcGIS 10.2 (Figure 6).
Figure 6. Combined artificial adjustment of the regional map of 1483 landscape character areas
(LCAs) in China.
Figure 5. Map of 110 landscape character types (LCTs) across China at a scale of 1 ×1 km.
Land 2022, 11, x FOR PEER REVIEW 9 of 19
Figure 5. Map of 110 landscape character types (LCTs) across China at a scale of 1 × 1 km.
We developed a base map consisting of the 110 LCTs for image segmentation using
eCognition. We adjusted the scale parameters, regardless of the administrative bounda-
ries, to an appropriate segmentation scale by combining satellite images and natural and
human attributes (which were based on the image colour and shape). Notably, the weights
of shape and tightness in the single-variable test homogeneity parameters were con-
trolled. After several experiments, we deduced that there were appropriate classification
results when scale, shape, and compactness were 80, 0.7, and 0.3, respectively. We identi-
fied 1483 LCAs by combining satellite images and LCTs after manual division and adjust-
ment in ArcGIS 10.2 (Figure 6).
Figure 6. Combined artificial adjustment of the regional map of 1483 landscape character areas
(LCAs) in China.
Figure 6.
Combined artificial adjustment of the regional map of 1483 landscape character areas
(LCAs) in China.
3.2. Spatial Distribution Characteristics of Landscape Character Types
In our study, we identified 110 LCTs across China. Spatially, LCTs existed across all
administrative regions, and there were substantial differences in the LCTs in the north–
south and east–west geographic locations. Furthermore, the LCTs of adjacent administrative
regions were similar.
By considering the Aihui–Tengchong Line (Hu line), which divides China’s population
density and urbanisation level (Figure 7A), we deduced that the richness of LCTs was
negatively correlated with China’s population density and economic development. Notably,
our results also revealed that the LCTs west of the Hu Line were rich and distinct from each
Land 2022,11, 1014 10 of 19
other and were portrayed as independent areas on the map. The area east of the Hu Line
demonstrated a high degree of economic development; moreover, in large areas, the LCTs
were intertwined, and the homogeneity of the region was obvious.
Land 2022, 11, x FOR PEER REVIEW 10 of 19
3.2. Spatial Distribution Characteristics of Landscape Character Types
In our study, we identified 110 LCTs across China. Spatially, LCTs existed across all
administrative regions, and there were substantial differences in the LCTs in the north–
south and east–west geographic locations. Furthermore, the LCTs of adjacent administra-
tive regions were similar.
By considering the Aihui–Tengchong Line (Hu line), which divides China’s popula-
tion density and urbanisation level (Figure 7A), we deduced that the richness of LCTs was
negatively correlated with China’s population density and economic development. Nota-
bly, our results also revealed that the LCTs west of the Hu Line were rich and distinct
from each other and were portrayed as independent areas on the map. The area east of
the Hu Line demonstrated a high degree of economic development; moreover, in large
areas, the LCTs were intertwined, and the homogeneity of the region was obvious.
China’s provincial administrative boundaries (Figure 7B) indicated that the adminis-
trative region having the most abundant LCTs was Sichuan Province (64 categories), fol-
lowed by the Tibet Autonomous Region (62 categories) and Qinghai Province (57 catego-
ries); all these areas were located in Southwest China. Since these areas border each other,
they are suitable for integrated protection and planning and management across admin-
istrative boundaries. Additionally, Sichuan Province was the administrative region with
the most LCTs per unit area. It is located in the transition zone between the second and
third steps of altitude in China and has rich landform types. Furthermore, the province
has a pleasant climate, and several ethnic minorities have created a variety of natural and
cultural tourism regions in this area.
(a)
(b)
Figure 7. Landscape character types (LCTs) maps superimposed on (a) Hu Line and the (b) and
administrative boundaries of China.
An LSF can effectively reflect the composition and spatial configuration of the land-
scape structure and it is a classic method to describe landscape patterns and their changes
[67]. Among the eight indexes considered in our study, AI represents the degree of aggre-
gation and dispersion of patches in the spatial distribution of the landscape; the higher
the value of this index is, the higher the degree of aggregation is. On a national scale,
Xinjiang, Inner Mongolia, northeastern Heilongjiang, northern Tibet, and Chengdu Plain
in central Sichuan demonstrated the highest agglomeration of LDUs, followed by central
Tibet, Shandong, Jiangsu and Shanghai, Yunnan, northern Sichuan, and Shaanxi. In con-
trast, the agglomeration degree in Shanxi was low. The SHDI, LSI, DIVISION, and PD
indexes characterised the patch diversity, shape complexity, segmentation degree, and
patch density of the landscape, respectively. These four indexes demonstrated similar
trends with respect to their spatial distribution, but there were still differences in their
peaks and some properties of spatial structure. The values were low in the Tarim and
Junggar basins and western Inner Mongolia in Xinjiang, and high in southern Tibet, Yun-
nan, Sichuan, Gansu, Hebei, and other regions. However, the strengths and weaknesses
of these spatial distribution trends differed considerably, and there are prominent differ-
ences in regions such as the Altun Mountains in Xinjiang. The SHEI index characterised
Figure 7.
Landscape character types (LCTs) maps superimposed on (
a
) Hu Line and the (
b
) and
administrative boundaries of China.
China’s provincial administrative boundaries (Figure 7B) indicated that the administra-
tive region having the most abundant LCTs was Sichuan Province (64 categories), followed
by the Tibet Autonomous Region (62 categories) and Qinghai Province (57 categories); all
these areas were located in Southwest China. Since these areas border each other, they are
suitable for integrated protection and planning and management across administrative
boundaries. Additionally, Sichuan Province was the administrative region with the most
LCTs per unit area. It is located in the transition zone between the second and third steps
of altitude in China and has rich landform types. Furthermore, the province has a pleasant
climate, and several ethnic minorities have created a variety of natural and cultural tourism
regions in this area.
An LSF can effectively reflect the composition and spatial configuration of the landscape
structure and it is a classic method to describe landscape patterns and their changes [
67
].
Among the eight indexes considered in our study, AI represents the degree of aggregation
and dispersion of patches in the spatial distribution of the landscape; the higher the value
of this index is, the higher the degree of aggregation is. On a national scale, Xinjiang, Inner
Mongolia, northeastern Heilongjiang, northern Tibet, and Chengdu Plain in central Sichuan
demonstrated the highest agglomeration of LDUs, followed by central Tibet, Shandong,
Jiangsu and Shanghai, Yunnan, northern Sichuan, and Shaanxi. In contrast, the agglomera-
tion degree in Shanxi was low. The SHDI, LSI, DIVISION, and PD indexes characterised
the patch diversity, shape complexity, segmentation degree, and patch density of the land-
scape, respectively. These four indexes demonstrated similar trends with respect to their
spatial distribution, but there were still differences in their peaks and some properties of
spatial structure. The values were low in the Tarim and Junggar basins and western Inner
Mongolia in Xinjiang, and high in southern Tibet, Yunnan, Sichuan, Gansu, Hebei, and
other regions. However, the strengths and weaknesses of these spatial distribution trends
differed considerably, and there are prominent differences in regions such as the Altun
Mountains in Xinjiang. The SHEI index characterised the uniformity of landscape patches;
the higher the value, the more even the distribution of the LCT. As shown in Figure 5, the
spatial trends of the SHEI and SHDI indexes portrayed similarity. However, the trends of
the former are more average, and there are differences in some regions, such as Qinghai
and Tibet, which have higher SHEI and lower SHDI values. The trends in Qinghai and
Tibet are different because the proportion of LCTs in these areas is average, and the number
of types is small. Meanwhile, CONTIG-MN represented the coherence of patches within
the LDU, and its spatial trend was unique, portraying high aggregation in Xinjiang and
Inner Mongolia, followed by Tibet, and low and uniform coherence values in other regions.
Land 2022,11, 1014 11 of 19
3.3. Description of Landscape Character Areas
As shown in Figure 6, the top three LCAs for the largest areas of terrestrial China are
summarised below:
LCA No. 1314: Located in the Xinjiang Autonomous Region of northwest China, this
area covers 7.60% of China. The hilly desert landscape, with sparse vegetation and wide
views, is adjacent to the Tianshan Mountains and Qilian Mountains and shows only a few
signs of human activities. It belongs to the ‘northern rural settlements’ category.
LCA No. 334: Located in Hebei, Shandong, Jiangsu, and Henan, the area accounts
for 5.73% of the land surface of China. This is a low-elevation area with a flat terrain and
expansive views. It mainly comprises cultivated land, including a large expanse with a
regular geometric texture, and built-up areas occur only sporadically. Human habitation
shows a scattered distribution, including the rural settlements of the Great Wall in the north
and the North China Plain in the south. Settlement patches are mainly distributed along
roads and on both sides of the river.
LCA No. 1333: Located northeast of the Inner Mongolia Autonomous Region, this area
accounts for 2.17% of China’s terrestrial surface. This region occupies a medium elevation
area, supports important grasslands, offers wide views, and is rich in geological, mineral,
animal, and vegetation resources. Human occupants are mainly nomadic pastoralists, and
the habitation type belongs to the northern rural settlements category.
4. Discussion
In this study, we introduced settlement zoning, which is widely recognized, as a
cultural factor for identifying terrestrial LCTs in China. Settlement zoning was classified
into 11 regional types from the perspective of structure, economic function, and relationship
with the natural environment [
30
] to reflect the diversity in physical geography and social
and cultural differences underlying the spatial structure, density, and housing form of
the rural settlements in the country [
68
–
70
]. Notably, the only cultural factor used by
the European LCA is land-use [
11
]. Land tenure, field size and pattern, farm type, and
settlement mode are mentioned in the LCA method used in the United Kingdom. However,
these indicators are used in a detailed scale, and only settlement patterns are used in
large-scale research [
1
]. In addition, we have tried to introduce ethnic elements, but the
‘Hanization’ of ethnic minorities in China is widespread. Moreover, the population of each
ethnic group is dispersed over large areas, and these groups have small settlements and
staggered living patterns [
71
]. Our study highlights the fact that ethnic factors are more
suitable for consideration in studies conducted at the meso- and microscales, and a large
amount of statistical analysis is still needed to obtain the spatial distribution characteristics
of ethnic minorities within each region. In terms of selection of LCFs, we prefer the visibility
of landscape that is affected by data accuracy, and hence, we chose altitude, topographic
relief, and land-use as distinguishing factors, and climate and settlement as descriptive
factors. We also considered vegetation coverage and other regionally common data with
the objective of covering the entire territory of China to arrive at broad patterns rather than
fine details [
1
]. Vegetation cover and other data will show more interesting results at the
regional scale.
In previous landscape classification studies, the selection of factors was mostly based
on natural and physical factors of landscape composition [
22
,
31
]. In addition to introduc-
ing settlement factors, our study demonstrates that LCFs depend on the composition of
elements (natural and human) and should also include structural features [
28
,
29
,
40
,
72
].
This, to a certain extent, creates a sharp distinction between different patches of the same
landscape and reflects the interaction between man and different elements of nature. At the
same time, the existence of culture has geographical characteristics, and there are potential
cultural connections in adjacent areas. However, due to the nonspatial characteristics of
landscape composition and structure, there may be LDUs with the same composition and
structure in different regions in a large range of research objects, but their cultural attributes
are different. The cultural factors that can be obtained at the same scale covering China
Land 2022,11, 1014 12 of 19
are limited by data availability and accuracy. We solved this problem using the spatial
co-ordinates of grid centroid points of supplementary LDUs in the clustering calculation.
Since the structure of landscape patches is a relatively fixed composition system,
the main difference and core concern of transmission at different scales lie in the more
significant granularity and amplitude effects [
73
]. Notably, generalised structural properties
result in false perceptions of landscape features at different scales [
42
,
43
], which may hinder
the accuracy of the LCA results. Therefore, we used the semivariogram to analyse the
relationship between the granularity and amplitude of the landscape structure and the
study object [
52
] and calculated the window size corresponding to the spatial attribute
reaching a stable state; this was used to quantify the structural characteristics of different
objects at different scales and provide scientific and objective calculation methods. Results
showed that the optimal window size for the calculation of the LSF was 14 km, which
reflected the unique spatial characteristics of landscape patches in China on a national
scale. Furthermore, this window size is similar to the one chosen by Li and Qi [
35
] and Zou,
Wang, and Bai [34] in their studies related to the global landscape structure in China.
With respect to terrestrial China, a comparison of the landscape structure and com-
position revealed that land-use type had a significant impact on the AI, SHDI, LSI, PD,
and DIVISION values. The AI was high in cultivated and unused land and low in forest
land and grassland; in these areas, the values of DIVISION, LSI, PD, and SHDI were also
high. At the same time, there were differences in the structural trend of cultivated and
unused land. Concerning AI aggregation, the AI value of unused land in the northwest
region was significantly higher than that of the cultivated land in the central and eastern
regions. This was because other land types were scattered among the cultivated land due
to the influence of the natural geographical environment and the intensity of development
and utilisation. Furthermore, among the types of cultivated land, there were also differ-
ences in the LSPs of the Chengdu Plain, the North China Plain, and the middle and lower
reaches of the Yangtze River. We believe that this is related to the altitude, fluctuation,
climate, and settlement type of the area, indicating that the landscape was the result of
the long-term interaction of natural abiotic, biotic, and human activities, creating a terrain
that people perceived as distinctive [
9
,
74
,
75
]. Notably, for this region, the composition is
not only determined by land-use but also by other driving factors, such as biophysical
factors, landscape structural characteristics, and human attributes (e.g., settlements) [
28
,
40
].
Several scholars have carried out quantitative analyses on landscape patterns from the
perspective of land-use type [
34
,
76
,
77
]. Our study provides a new perspective for landscape
structure analysis. Furthermore, our results revealed that the spatial distribution trends of
diversity (SHDI) and fragmentation (PD and DIVISION) were highly consistent and are
important for defining fragmentation and diversity thresholds of landscape structures in
future studies.
To evaluate the effectiveness of different research methods, we compared the short-
comings of the ensemble, automatic segmentation, and parametric methods. We considered
the parameter method as the main method of LCA and selected the detailed character of
landscape elements to reflect on the more comprehensive combination of relationships of
LCFs [
5
]. In addition, we introduced the automatic segmentation method to quickly and
objectively divide the LCAs and reduced the subjective impact of human interpretation [
66
].
Finally, we supplemented the expert knowledge and judgment of the ensemble method
to correct and verify the LCAs [
3
]. Compared with satellite images, we found that the
accuracy of LCTs and LCAs obtained using this method provided better results. Our
research provides a systematic and logical approach for LCA, and the developed system
can be applied to other countries and regions. We performed cluster value calculation using
the elbow method to solve the a priori definition of the number of clusters [
25
,
26
], which
are considered unavoidable defects [
5
,
78
]. Furthermore, by comparing k-prototype, affinity
propagation, second-order clustering, and k-means clustering algorithms, we deduced
that the k-means clustering algorithm had high efficiency in processing large samples and
effects [62,63].
Land 2022,11, 1014 13 of 19
Our study has two major limitations, which are as follows: (1) The completeness
and availability of cultural history and socioeconomic data are limited as only a selection
of factors for cultural attributes were considered. (2) As the scale of the study area is
extremely large, manual field correction was almost impossible to implement. The LCA
of the national-scale region was mainly based on satellite images for manual verification.
Therefore, future studies must consider different cultural factors while exploring and
expanding their zoning types, such as language, place-name, ethnic elements, historical
memory, and so on. Additionally, with the improvement of computer software technology
and the optimisation of the popularisation rate, research results can be shared on the
public participatory GIS platform in the later stages, thus promoting extensive and easy-
to-understand popular scientific studies. Strengthening public participation can further
supplement the national LCA results, thereby strengthening the landscape character and
collecting and preserving interpretations of landscapes by the people, which can be used to
supplement social and cultural elements.
5. Conclusions
Guided by the value of neutrality, we selected nature, culture, spatial geographic
co-ordinates, and landscape structure to identify LCTs across China. Thus, we filled the
gap in the literature regarding the identification of LCTs at the national scale against
the current background of China’s territorial and spatial planning. Notably, our study
has constructed a system for China’s terrestrial LCA using an approach that combines
the ensemble, automatic segmentation, and parametric methods. This system provides
a comprehensive and relatively objective classification of LCTs, which can serve as a
reference at the mesoscale. Furthermore, through data processing, statistical clustering,
automatic image segmentation, and manual correction, we improved the LCA system
and provided a solution for the transmission of landscape structure. These data can be
further supplemented and optimised at the meso- and microlevel in future research, which
can evolve into a multilevel nested landscape management and protection system at the
national, regional, and local scales. Additionally, our study can provide a scientific basis and
support for national land-space planning and cross-administrative regional development,
protection, enhancement, and management strategies.
Author Contributions:
Conceptualisation, Y.P., Y.W., and B.Z.; methodology, Y.P. and Y.W.; software,
Y.P., Y.W., X.X., and W.L.; formal analysis, Y.P.; data curation, X.X.; writing—original draft preparation,
Y.P.; writing—review and editing, Y.P. and Y.W.; visualisation, X.X.; supervision, B.Z.; funding
acquisition, B.Z. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was supported by the Chinese Fundamental Research Funds for the Central
Universities (HZAU:2662018YJ017), the National Natural Science Foundation of China (No. 51678269),
and by the National Key Research and Development Program of China (No. 2019YFD1100401).
Data Availability Statement: Not applicable.
Acknowledgments: The authors would like to thank the anonymous reviewers for their comments,
which helped to improve this work.
Conflicts of Interest: The authors declare no conflict of interest.
Land 2022,11, 1014 14 of 19
Appendix A
Table A1. Thirty-one landscape characteristic factors.
Categories Subcategories Digital Number Landscape Code
Altitude
Low <1000 m 59 A1
Middle 1000–3500 m 61 A2
High 3500–5000 m 67 A3
Extremely high >5000 m 71 A4
Land use/cover
Farmland 2 L1
Woodland 3 L2
Grassland 5 L3
Water 7 L4
Construction land 11 L5
Unused land 13 L6
Topographic relief
Plain <30 m 17 R1
Hills 30–200 m 19 R2
Small topographic relief 200–500 m 23 R3
Middle topographic relief 500–1000 m 29 R4
High topographic relief >1000 m 31 R5
Climate
Temperate continental climate 37 C1
Tropical monsoon climate 41 C2
Subtropical monsoon climate 43 C3
Plateau alpine climate 47 C4
Temperate monsoon climate 53 C5
Settlement
Northern China Rural Settlement
Region 73 S1
Great Wall Rural Settlement Region 79 S2
Loess Plateau Rural Settlement Region 83 S3
North China Plain Rural Settlement
Region 89 S4
Middle-Lower Yangzi River Rural
Settlement Region 97 S5
South Yangzi River Hill Areas Rural
Settlement Region 101 S6
Southeast Coastal Rural Settlement
Region 103 S7
Southwest Rural Settlement Region 107 S8
Northern Rural Settlement Region 109 S9
Northwestern Rural Settlement Region 113 S10
Qinghai-Xizang (Tibet) Rural
Settlement Region 127 S11
Land 2022,11, 1014 15 of 19
Table A2. Landscape structure factor and its structural meaning.
Index Theoretical Extent Meaning
Aggregation index
(ai) 0≤AI ≤100 The degree of aggregation or dispersion
of LCTs.
Contiguity index_Mean
(contig_mn) 0≤CONTIG ≤1The degree of
coherence of the same LCT patches.
Landscape division index
(division) 0≤DIVISION ≤1The degree of fragmentation of the
distribution of LCT.
Fractal dimension index_Area-weighted
mean
(frac_am)
1≤FRAC ≤2The shape complexity of the LCTs at the
spatial scale.
Landscape shape index
(lsi) LSI ≥1
The complexity or regularity of the shape
of the LCT.
Patch density
(pd) PD > 0 The degree of fragmentation of LCTs.
Shannon’s diversity index
(shdi) SHDI ≥0
Indicates the heterogeneity of LCTs and
emphasises the contribution of scarce
patch types to information.
Fractal dimension index_Area-weighted
mean
(frac_am)
1≤FRAC ≤2The shape complexity of the LCTs at the
spatial scale.
Shannon’s evenness index
(shei) 0≤SHEI ≤1
Indicates whether there are obvious
dominant types in the landscape and the
degree to which each patch type is evenly
distributed in the landscape.
Table A3. Nomenclature of 1 km * 1 km scale LCTs.
Serial
Number
Landscape
Code
Area
Percentage
(%)
Serial
Number
Landscape
Code
Area
Percentage
(%)
1-1 L1A1R1C1S10 0.9912 1-56
L
3
A
2
R
3
C
3
S
8
_2
0.1882
1-2 L1A1R1C1S20.4983 1-57 L3A2R4C3S80.0311
1-3
L
1
A
1
R
1
C
5
S
1
_1
0.8502 1-58 L3A2R1C4S10 2.5549
1-4
L
1
A
1
R
1
C
5
S
1
_2
0.2943 1-59 L3A2R3C4S11 0.209
1-5 L1A1R1C3S61.7655 1-60 L3A2R3C4S10 0.66
1-6 L1A1R1C3S70.8299 1-61 L3A2R4C4S11 1.2264
1-7 L1A1R1C3S51.5249 1-62 L3A3R1C4S11 4.2387
1-8 L1A1R2C3S70.3936 1-63
L
3
A
3
R
2
C
4
S
11
_1
1.2107
1-9 L1A1R2C3S80.7315 1-64
L
3
A
3
R
2
C
4
S
11
_2
0.7532
1-10 L1A2R1C1S25.7585 1-65
L
3
A
3
R
2
C
4
S
11
_3
0.9384
1-11 L1A2R1C5S30.9316 1-66 L3A3R3C4S10 0.3671
1-12 L1A2R2C3S80.9143 1-67
L
3
A
3
R
3
C
4
S
11
_1
0.4491
1-13
L
1
A
2
R
3
C
3
S
8
_1
0.1975 1-68
L
3
A
3
R
3
C
4
S
11
_2
0.8617
1-14
L
1
A
2
R
3
C
3
S
8
_2
0.1735 1-69 L3A3R4C4S11 0.1134
1-15 L1A2R4C3S80.0124 1-70 L3A4R2C4S11 2.4811
1-16 L1A3R2C4S11 0.0677 1-71
L
3
A
4
R
3
C
4
S
11
_1
0.4293
Land 2022,11, 1014 16 of 19
Table A3. Cont.
Serial
Number
Landscape
Code
Area
Percentage
(%)
Serial
Number
Landscape
Code
Area
Percentage
(%)
1-17 L2A1R1C1S21.0025 1-72
L
3
A
4
R
3
C
4
S
11
_2
0.2449
1-18 L2A1R1C1S10 0.1623 1-73 L4A1R1C5S20.0981
1-19
L
2
A
1
R
1
C
5
S
1
_1
3.0789 1-74 L4A1R1C5S10.2433
1-20
L
2
A
1
R
1
C
5
S
1
_2
1.5621 1-75 L4A1R1C5S40.4337
1-21 L2A1R1C5S30.7715 1-76
L
4
A
1
R
1
C
3
S
5
_1
0.292
1-22 L2A1R1C3S52.8842 1-77
L
4
A
1
R
1
C
3
S
5
_2
0.2174
1-23 L2A1R1C3S74.8023 1-78 L4A1R1C3S70.4169
1-24 L2A1R3C3S70.6338 1-79 L4A2R1C1S90.1574
1-25 L2A2R1C3S81.4807 1-80 L4A2R1C4S10 0.118
1-26
L
2
A
2
R
3
C
3
S
8
_1
0.805 1-81 L4A2R3C4S11 0.0519
1-27
L
2
A
2
R
3
C
3
S
8
_2
1.3185 1-82 L4A3R1C4S11 0.724
1-28 L2A2R3C4S10 0.0545 1-83 L4A4R3C4S10 0.1684
1-29 L2A2R4C4S11 0.237 1-84 L4A4R2C4S11 0.0554
1-30 L2A3R1C5S10.3932 1-85 L5A1R1C1S20.2905
1-31 L2A3R1C5S10 0.855 1-86 L5A1R1C1S10 0.0864
1-32 L2A3R1C3S70.3581 1-87 L5A1R1C3S50.4385
1-33 L2A3R1C3S81.6915 1-88 L5A1R1C3S70.2978
1-34 L2A3R1C4S10 0.0725 1-89 L5A1R1C5S10.2288
1-35 L2A3R3C4S11 0.9679 1-90 L5A1R1C5S40.7722
1-36 L2A3R2C4S11 0.6626 1-91 L5A2R1C5S30.2017
1-37 L2A4R2C4S11 0.2609 1-92 L6A1R1C1S10 11.9268
1-38 L3A1R1C1S91.3274 1-93
L
6
A
1
R
1
C
5
S
1
_1
0.4567
1-39 L3A1R1C5S30.4226 1-94
L
6
A
1
R
1
C
5
S
1
_2
0.593
1-40 L3A1R1C5S10.2525 1-95 L6A1R1C3S50.0344
1-41 L3A1R1C1S20.3639 1-96 L6A2R1C1S90.9843
1-42 L3A1R1C1S90.6308 1-97 L6A2R2C1S11 1.6022
1-43 L3A1R1C3S50.1284 1-98 L6A2R3C4S10 0.2345
1-44 L3A1R1C3S70.2421 1-99 L6A2R4C1S10 0.0142
1-45 L3A1R2C3S80.3587 1-100 L6A3R1C4S11 2.0671
1-46 L3A1R1C3S73.307 1-101
L
6
A
3
R
2
C
4
S
11
_1
1.2224
1-47 L3A2R1C5S30.7049 1-102
L
6
A
3
R
2
C
4
S
11
_2
0.6958
1-48 L3A2R2C5S10 1.067 1-103 L6A3R2C4S10 0.3492
1-49 L3A2R3C5S10 0.1692 1-104
L
6
A
3
R
3
C
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