Nothing Special   »   [go: up one dir, main page]
Next Article in Journal
Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
Previous Article in Journal
ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 14
Issue 20
10.3390/rs14205255
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Improved Landscape Expansion Index and Its Application to Urban Growth in Urumqi

by
Yuhang Tian
1,2,3,4,
Yanmin Shuai
2,3,4,*,
Xianwei Ma
2,
Congying Shao
2,
Tao Liu
1,3,4 and
Latipa Tuerhanjiang
1,4,5
1
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2
College of Surveying and Mapping and Geographic Science, Liaoning Technical University, Fuxin 123000, China
3
College of Resources and Environmental, University of Chinese Academy of Sciences, Beijing 100049, China
4
CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China
5
College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(20), 5255; https://doi.org/10.3390/rs14205255
Submission received: 3 September 2022 / Revised: 17 October 2022 / Accepted: 17 October 2022 / Published: 20 October 2022
Browse Figures
Graphical abstract
"> Figure 1
<p>Schematic diagram of the three landscape expansion patterns. (<b>a</b>–<b>c</b>) represents <span class="html-italic">infilling</span> growth, (<b>d</b>) represents <span class="html-italic">edge-expansion</span> growth, and (<b>e</b>) represents <span class="html-italic">outlying</span> growth.</p> "> Figure 2
<p>Process framework for landscape expansion pattern identification and compactness calculation.</p> "> Figure 3
<p>Cases of misclassification of landscape expansion index based on the envelope and the buffer method. (<b>a</b>,<b>b</b>) is the envelop box method. (<b>c</b>,<b>d</b>) is the buffer method.</p> "> Figure 4
<p>Overview map of the study area. (<b>a</b>,<b>b</b>) The spatial location of the study area. (<b>c</b>) The spatial distribution of expansion patches in Urumqi from 1990–2015.</p> "> Figure 5
<p>Percentage of area and number of patches with different landscape expansion patterns. (<b>a</b>) Percentage of the number of different patches. (<b>b</b>) Percentage of area of different patches.</p> "> Figure 6
<p>Spatial distribution of landscape expansion patterns in Urumqi from 1990 to 2015. (<b>a</b>–<b>e</b>) denote the spatial distribution of landscape expansion patterns for each of the five 5-year intervals from 1990–2015.</p> "> Figure 7
<p>Eight-directional map of the urban expansion area of Urumqi from 1990 to 2015.</p> ">
Review Reports Versions Notes

Abstract

:
Automatic determination of quantitative parameters describing the pattern of urban expansion is extremely important for urban planning, urban management and civic resource configuration. Though the widely adopted LEI (landscape expansion index) has exhibited the potential to capture the evolution of urban landscape patterns using multi-temporal remote sensing data, erroneous determination still exists, especially for patches with special shapes due to the limited consideration of spatial relationships among patches. In this paper, we improve the identification of urban landscape expansion patterns with an enhancement of the topological relationship. We propose MCI (Mean patch Compactness Index) and AWCI (Area-Weighted Compactness Index) in terms of the moment of inertia shape index. The effectiveness of the improved approach in identifying types of expansion patches is theoretically demonstrated with a series of designed experiments. Further, we apply the proposed approaches to the analysis of urban expansion features and dynamics of urban compactness over Urumqi at various 5-year stages using available SUBAD-China data from 1990–2015. The results achieved by the theoretical experiments and case application show our approach effectively suppressed the effects induced by shapes of patches and buffer or envelope box parameters for the accurate identification of patch type. Moreover, the modified MCI and AWCI exhibited an improved potential in capturing the landscape scale compactness of urban dynamics. The investigated 25-year urban expansion of Urumqi is dominated by edge-expansion patches and supplemented by outlying growth, with opposite trends of increasing and decreasing, with a gradual decrease in landscape fragmentation. Our examination using the proposed MCI and AWCI indicates Urumqi was growing more compact in latter 15-year period compared with the first 10 years studied, with the primary urban patches tending to be compacted earlier than the entire urban setting. The historical transformation trajectories based on remote sensing data show a significant construction land gain—from 1.06% in 1990 to 6.96% in 2015—due to 289.16 km2 of recently established construction, accompanied by fast expansion northward, less dynamic expansion southward, and earlier extension in the westward direction than eastward. This work provides a possible means to improve the identification of patch expansion type and further understand the compactness of urban dynamics.

Graphical Abstract">

Graphical Abstract

1. Introduction

Automatic determination of urban expansion patterns is essential for urban planning, urban management and civic resource configuration. Cities and towns represent the epicenter in terms of politics, the economy, culture and population and maintain routine civic affairs of regions and countries. Accurate determination of the spatial distribution and expansion features of different cities is an important prerequisite to understand the situation of the investigated cities and serves to support government decision making to optimize urban resources and improve urban ecology [1,2]. With economic development and residents’ living requirements, global urbanization has increased with an enhanced velocity in recent decades. In 2021, about 56% of the global population lived in cities and towns, a rate expected to reach 68% in 2050 [3,4]. Compared with developed countries, with a rate higher than 80%, the percentage of urban dwelling in developing countries is as low as 20% [5]. China, a developing country, rapidly increased its percentage from 10.64% in the 1970s to 65% in 2021 [6]. Large populations continually aggregated in cities and towns, prompting the requirement of work positions, education, transportation and living facilities, hence inducing the fast expansion of urban areas. Issues thus frequently emerge from land cover changes, disturbance of the ecological environment and difficulty in the deployment of urban resources, with the process of urbanization [7,8,9]. Therefore, extracting urban expansion information and describing it with quantitative parameters is an urgent priority.
Numerous studies have focused on the evolution of urban spatial pattern dynamics to improve our understanding. The early investigation of urbanization mostly focuses on urban morphology. It examines the social or physical form of cities as well as human urban perceptions from sociology, economics, geography or psychology for improving the recognition of urban development and functioning mechanisms [10,11]. Subsequently, models constructed from metrology and system dynamics are frequently adopted to perform “top–bottom” empirical or mathematical statistical analyses of the relationship between urban morphology at different stages and various driving factors. The rapid development of satellite remote sensing has made it possible to analyze the structure, function and evolution rules of urban landscapes at the pixel scale. Cities and towns are considered as the scene-synthesis, consisting of different surface endmembers, used to explore urban dynamics qualitatively or semi-quantitatively. Next, ecological landscape parameters are introduced to describe the structure of urban landscapes [10]. For instance, the landscape index and landscape fragmentation are often used to reflect the components and configuration patterns of ecosystems, urban patches or functional units within the investigated region [12,13,14]. Further challenges are still met in accurately extracting spatial and temporal urban expansion information.
Models used to monitor the dynamics of urban landscape patterns have been updated with the development of remote sensing technology and increased requirements. The first urban growth model developed by the University of Connecticut can quantify the urban expansion and dynamics using land cover thematic maps [15]. Next, remote sensing–based urban process models have been widely applied to capture the landscape pattern changes of urban patches [16,17]. Three types of urban expansion patterns (infilling, edge-expansion and outlying) gradually emerged with the increasing accumulation of research on urban landscape change and dynamic evolution [18,19]. Infilling is defined as “ the filling of non-urban area embraced by urban region or undeveloped land in urban built-up area“ [20,21]. Liu et al. [18], based on the relationship among patches in remote sensing thematic maps, suggests that infilling is the pattern in which gaps (or holes) between or within old patches are filled with newly grown patches. Edge-expansion is considered as a pattern of the gradual expansion outwards from the urban fringes. Outlying is the expansion pattern in which the new patch has no adjacent relationship with existing patches. In the urban planning community, infilling can mostly increase the rate and compactness of urban land use, especially for the utilization of in-city undeveloped land [22,23]. The bid rent theory indicates that the cost decreases with increasing distance away from the center of the urban district [24]. Although both infilling and edge-expansion patterns increase the compactness of urban morphology, the cost of edge-expansion is lower than infilling. Therefore, accurate extraction of expansion patterns is a prerequisite for evaluating urban dynamic features.
The accumulation of multiple urban landscape data has made it possible to develop related models and quantitatively capture urban landscape pattern changes. Two kinds of indices, named as reference-based and non-reference models, are designed to describe the dynamic changes in urban landscape patterns. Reference-based methods frequently adopt empirical thresholds to categorize urban expansion patterns, such as using the ratio of common boundary lengths between new and old patches to the perimeter of new patches [25]. The Landscape Expansion Index (LEI) is defined in terms of two empirical parameters, the patch envelope box (LEIbox) and patch buffer (LEIbuffer), to aid quantifying the situation of urban expansion patterns [18]. Evaluation of the urban compactness over Dongguan has captured the major features at the landscape scale using the modified LEI, Mean Expansion Index (MEI) and Area-Weighted Mean Expansion Index (AWMEI) [18,26]. The Proximity Expansion Index (PEI) is presented to enhance the ability in capturing the newly added outlying patches, depending on the distance between new and old patches and the shared ratio of boundaries, followed by a special emphasis in determining the parameters of buffer distance [27]. Jiao et al. [27] pointed out that it is more reasonable to set the buffer distance as the average scale of geographic entities that separate urban neighborhoods, such as the width of urban roads. The shape-weighted landscape evolution index (SWLEI) uses image pixel neighborhoods to quantify bidirectional changes in urban landscapes and to improve the ability to identify the spatial expansion patterns of specially shaped patches [28]. Xia et al. [28] pointed out that LEI is susceptible to the effects of patch shape and buffer distance. The non-reference method generally adopts topology relationships between old and new patches to identify urban expansion patterns. Wu et al. [29] defined a new landscape expansion index (LEIw) according to the topographic relationship between new and old patches to recognize the adjacent expansion or exterior expansion. A further effort is performed to distinguish the relationship of “inclusion”, ”intersection”, and “disjoint” between new and old patches [23]. Ghani and Abidin [22] modified the definition of LEIbuffer depending on the detection of “inclusion” and “intersection” between the buffers of new and old patches to reduce the misclassification of infilling and edge-expansion patterns. The Landscape-Adjacency Index (LAI) was proposed to measure the regional ecological pressure of urban sprawl on natural landscapes based on the spatial adjacency between urban landscape patches and natural landscape patches [30].
Various LEIs exhibit different potential in identifying patterns of urban expansion patches as that shown for the three LEIs widely used, as shown in Table 1. These indices have made efforts to help improve the accuracy of capturing the dynamic information of landscape patterns at different periods, while limitations still exist with the requirements of these applications, as follows: (1) Parameters of the reference-based method, such as the buffer distance or the expansion multiple of the envelope box, can affect the expanded patch area, original patch area and blank area within the reference object, which may increase the uncertainty of the LEI. (2) LEIbox and LEIbuffer methods have detectable uncertainty, especially for patches with multilateral shape. (3) Non-reference methods such as LEIw can avoid the issues in the above two items due to its non-reference, but only two landscape expansion patterns can be identified. In addition, LEIbuffer and LEIbox enable the quantitative estimation of the compactness of urban expansion at the landscape scale, while LEIw does not.
To address the above issues, we aimed to upgrade the identification method of landscape expansion pattern depending on the existing topological relationship among patches. Further, a stable approach is modified from the classical one for the evaluation of urban compactness. The city of Urumqi, which has undergone a dramatic urban evolution, is chosen as an example to demonstrate the effectiveness of our methodology. Our study enriches the methodological system of landscape pattern index research, and the relevant conclusions are available to government policy makers to manage urban sprawl optimally.

2. Methodology

2.1. Effective Identification of Landscape Expansion Patterns

Urban landscape expansion patterns can be classified as infilling, edge-expansion and outlying. In Figure 1a–c, infilling refers to the type where undeveloped patches (holes) in built-up urban areas are filled with newly grown urban patches; in Figure 1d, edge-expansion refers to the kind that extends outward from the edge of the urban built-up area; in Figure 1e outlying refers to the type of new urban patches that are separated from the built-up area.
This paper improves the identification of urban landscape expansion patterns with an enhancement of the topological relationship among patches. In Figure 2, the new urban patches and the old urban patches are available by the difference analysis of built-up area vector boundaries in the period before and after; the holes inside the built-up area vector can be accessed through the topology examination function. These patches are input to the topological relationship analysis module. The key to identification of landscape expansion patterns is to analyze the topological relationships among the old patches, the holes and the new patches. Specifically, if the holes contain or overlap the new patch, the new patch is identified as infilling; if the new patch is disjointed from the hole and touches to the original patch, the new patch is identified as an edge-expansion; and if the new patch is disjointed from the hole and old patch, the new patch is identified as outlying. In this paper, the analysis of spatial topological relationship is implemented based on the Python 3.8 GeoPandas project, and the hole identification is implemented based on the ArcGIS 10.6 topology examination function.

2.2. Comparison Verification

To verify the effectiveness of the improved landscape expansion pattern identification method, several specific shape patches were selected to compare the improved method with the reference-based landscape expansion pattern identification method.
Figure 3a,b belong to the envelope box method, and the calculation results are in Table 2. According to the landscape expansion pattern identification rules of the envelope box method, the new patch in case (a) with an LEI equal to 100 is identified as infilling, which is actually an edge-expansion, expanding outward along the fringe of the built-up area. The new patch can be correctly identified by topological touch with the old patch. The new patch in case (b) with an LEI equal to 27.45 is identified as edge-expansion, which is an outlying, and can be correctly identified by new patch disjoint from the old patch.
Figure 3c,d belongs to the buffer method, and the buffer distance is set to 1 m; Table 3 describes calculation results. According to the landscape expansion pattern identification rules of the buffer method, the new patch in case (c) with an LEI equal to 20.93 is identified as edge-expansion, which is actually the infilling of the interior of the built-up area of the city; similarly, case (d), with an LEI equal to 0, is identified as outlying, which is actually also infilling. Both can be correctly identified by the new patch overlap with the hole.
In addition to the above cases, the buffer-based LEI is influenced by the buffer distance; the final LEI is calculated based on the buffer distance of 1 m to obtain a robust outcome. The magnification of the envelope also influences the envelop method. However, the buffer distance for different cities needs to be optimized by experimentation, and there is limited generalizability of setting it to 1 m [27]. Figure 3d represents filling interior urban gaps; however, different buffer distances will result in different expansion patterns. A too-small buffer distance will be identified as outlying, while a buffer distance above a specific value will be identified as infilling. In summary, the improved landscape extension pattern identification method depends on the existing spatial topological relationship among patches, which is not influenced by the shape of the patch and the reference object parameters, thus solving the misclassification problem to a certain extent.

2.3. Calculation of the Landscape Scale Compactness

To better understand the trend of urban morphological changes, we introduced the moments of inertia shape index and calculated the landscape scale compactness of the urban area with the developed variants MCI and AWCI [31]. The landscape scale urban compactness reflected the changes in urban morphology due to the changes in the three landscape expansion patterns, which is an effective complement and validation of the results of landscape expansion pattern identification. The isoperimetric quotient (CIPQ) is a commonly used compactness algorithm, which is the ratio of the area of a patch to the area of a circle patch with the same perimeter (the tightest patch) and is calculated by the formula in Equation (1).
C I P Q = 4 π A P 2
where A is the area of the patch, P is patch’s perimeter, and CIPQ is the patch compactness index, which ranges from (0, 1) and is equal to 1 only if the patch is a circle. It is easily calculated and insensitive to size change, but the compactness algorithm is not robust enough to handle irregularly contoured patches and patches with holes [31,32].
The moment of inertia shape index has the following advantages over the conventional isoperimetric quotient method: (1) It can easily deal with holed and multi-part elements and (2) it has robustness in dealing with irregular boundary patches [31]. These advantages made it possible to quantify the difference in compactness that the holes bring to the overall patch when they are filled and also allow quantification of robust compactness for expansion patches with different shapes. The moment of inertia shape index is calculated as the ratio of the moment of inertia of a circle with same area about its center, to the moment of inertia of the shape about its centroid; see Equation (2). However, the compactness of the above index is only represented at the scale of individual patches, so we defined MCI and AWCI based on the moment of inertia shape index to reflect the compactness changes caused by urban expansion at the landscape scale; the formulae are given in Equations (3) and (4).
C M I = A 2 2 π I g
where, A is the area of the patch, Ig is the moment of inertia of the patch about its centroid, and CMI is the shape index based on the moment of inertia, ranging from (0, 1), with higher values meaning that the patch is more compact.
M C I = ∑ i = 1 N C M I i N
where MCI is the mean compactness index of each urban patch in a certain period, N is the number of patches, and CMIi is the moment of inertia shape index of the patch i. The higher the MCI, the more compact the urban landscape tends to be in that period.
A W C I = ∑ i = 1 N C M I i × a i A
where AWCI is the area-weighted compactness of each urban patch in a certain period, ai is the area of patch i, A is the total area of urban patches during that period, and CMIi is the moment of inertia shape index of the patch i. AWCI further explains the changes in compactness brought about by main urban patches.
In addition, the Largest Patch Index (LPI) indicates the percentage of area occupied by the largest patches of urban built-up land. Increasing LPI means the expansion of urban cores and can be used to supplement the explanation of changes in urban compactness caused by main patches.
L P I = M A X a i A × 100
where MAX(ai) is the area of the largest patch in a period, and A is the total area of the patch in that period.

3. Application over Urumqi

In this paper, we applied the improved method to identify the landscape expansion patterns of different patches and calculated the compactness of urban patches in each period, to objectively evaluate the spatial and temporal expansion characteristics of the city. For our work, we chose the period between 1990 and 2015 to understand the urban expansion process of Urumqi city.

3.1. Study Area and Data

The city of Urumqi, located in northwest China, is the largest city in Central Asia. It currently has seven districts and one county under its administration, with mountains in its southwest and northeast regions as well as the alluvial plains of the Urumqi river and Toutunhe river in the north. The total area of Urumqi is approximately 14,577 km², whose impervious surface increased from 51.61 km2 to 340.25 km2 between 1990 and 2015, an increase of 559.27%. Urumqi is a typical city experiencing dramatic landscape pattern evolution; the specific location of the city is shown in Figure 4.
The data used in this paper mainly include the following: (1) The standardized urban built-up area dataset (SUBAD) of Urumqi for the five 5-year time periods involving the years 1990, 1995, 2000, 2005, 2010 and 2015 was selected as the basic data. The dataset was extracted from multi-source remote sensing data (time-series of Landsat and Sentinel images), and vectorized data were provided. The address of the dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00004, accessed on 1 August 2022 [33]. The years between 1990–2015 were chosen because the dataset was not developed for the 2020 built-up area data. After the difference analysis of the built-up area patches in the two periods before and after, the new patches in each time period were captured respectively. The spatial distribution of the new patches of urban built-up land in the study area between 1990 and 2015 is shown in Figure 4. (2) The urban built-up areas in 1990, 1995, 2000, 2005, 2010 and 2015, obtained from data from the Ministry of Housing and Urban-Rural Development of the People’s Republic of China; the address of the data is available at http://www.mohurd.gov.cn, accessed on 1 August 2022.

3.2. Landscape Expansion Pattern

We extracted the landscape expansion patterns of urban expansion patches in Urumqi for five periods from 1990, 1995, 2000, 2005, 2010 and 2015. Figure 4c shows the spatial distribution of new patches in the built-up area of Urumqi city from 1990 to 2015. The core district of Urumqi in 1990 was mainly located in the southern Tianshan district and the northeastern part of the Saybagh district, while smaller patches were scattered in the Xinshi district, Tutunhe district, Shuimogou district and Midong district. The urban built-up area extended to the north and expanded in the east–west direction every five years between 1990 and 2015; the urban area of Urumqi in general formed a “T” shape with a long north–south direction and a narrow east–west direction in 2015.
Figure 5a shows the number percentage of the three landscape expansion patterns in the five periods. An overview of the five periods shows that the number percentage of edge-expansion is greater than outlying, and outlying is greater than infilling, among which the number of edge-expansion patches percentage was 87.77%, 80.01%, 81.34%, 84.64% and 88.08% in the five periods, showing a decreasing trend followed by an increasing trend. The number of outlying patches percentage was 9.65%, 14.08%, 11.71%, 11.51%, and 10.51% in the five periods, with a sharp increase from 1995 to 2000 and an overall trend of first increase and then decrease. The percentage of the number of infilling patches was 2.58%, 5.83%, 6.95%, 3.85%, and 1.41% in the five periods, showing an overall trend of first increase and then decrease. Figure 5b shows the area percentage of the three landscape expansion pattern patches in the five periods. Edge-expansion accounted for 84.69% of the total area of new patches in 1990–1995, outlying accounted for 13.99% of the total area, and infilling accounted for the least amount of area at 1.32%. The edge-expansion patches area percentage decreased significantly to 53.95% from 1995 to 2000; on the contrary, the outlying and infilling patches area percentage increased significantly to 35.34% and 10.71%, respectively. The infilling patches area percentage continued to increase to 17.28% from 2000 to 2005, which started to be higher than the outlying patches area percentage, while the percentage of edge-expansion patch area also increased to 75.44%; on the contrary, the percentage of outlying patch area decreased to 7.28%. The area percentage of the three landscape expansion patterns from 2005 to 2010 was 5.80% for infilling, 80.76% for edge-expansion, and 13.44% for outlying. The area percentage of the three landscape expansion patterns from 2010 to 2015 was 2.42% for infilling, 75.23% for edge-expansion, and 22.33% for outlying.
Table 4 shows the overall number and area of the three landscape expansion patterns over the five 5-year periods extracted using the improved method. The number and area of edge-expansion patches were consistently higher than outlying and infilling patches in all time periods. The number of outlying patches was higher than that of infilling patches in all five time periods, but the area of outlying patches was smaller than that of infilling patches for the first time between 2000–2005.
Figure 6 shows the spatial distribution of different landscape expansion pattern patches in Urumqi during five periods from 1990 to 2015. Incorporating Table 4 and Figure 6, there were 1316 expansion patches in Urumqi between 1990 and 1995, and 34 patches belonged to infilling, with the total area of 0.68 km2, mainly located in the Saybagh and Tianshan districts. There were 127 patches belonging to outlying, mainly located in the towns near Midong and Xinshi districts, totaling about 7.22 km2. Edge-expansion accounts for the majority, 1155 patches in total, mostly located in the southern part of the Xinshi district, Tianshan district and Saybagh district, totaling about 43.71 km2. There were 1577 expansion patches from 1995 to 2000, and 222 belonged to outlying, with a total area of about 18.58 km2, mainly located in the Xinshi district, Midong district and the northern part of the Tianshan district. The outlying and infilling patches were much higher in both area and number than in the previous period. There were 92 infilling patches with a total area of about 5.63 km2, mostly located in the Xinshi district, Saybagh district and the north of Tianshan district. The number of edge-expansion patches increased slightly and the area decreased significantly during this period, but edge-expansion was still the main expansion pattern during this period and was widely distributed, mainly located in districts other than Wulumuqi county and the Durban district. The number of new patches increased but the area decreased significantly between 2000 and 2005. Among these patches, there were 199 outlying patches with a total area of about 37.1 km2, scattered in Saybagh district, Xinshi district and Midong district. There were 118 infilling patches with a total area of about 6.41 km2, mainly located in the Saybagh district, Xinshi district and Tianshan district. There were 1382 edge-expansion patches with a total area of about 27.99 km2, with the edge-expansion patches in this period mainly located in the Xinshi district, Shuimogou district and Tianshan district. Between 2005 and 2010, there were 2233 expansion patches with a total area of about 73.12 km2, and the number of expansion patches reached the peak of the five periods. Among these patches, there were 1890 edge-expansion patches with a total area of 59.05 km2, representing the majority, mainly in the Toutunhe district, Xinshi district and Midong district. There were 257 outlying, with a total area of 9.83 km2, mainly in the outskirts of the urban areas of the Midong district and Toutunhe district. The infilling expansion consisted of 86 patches with a total area of 4.24 km2, mainly in the outskirts of the Saybagh district, Shuimogou district and Tianshan district. Between 2010 and 2015, there were 1485 expansion patches with a total area of about 85.44 km2, and the area of expansion patches reached the peak of the five periods. Among these patches, there were 1308 edge-expansion patches with a total area of 64.28 km2, mainly in the Midong district. There were 156 outlying patches with a total area of 19.08 km2, mainly in the Toutunhe and Midong districts. The infilling expansion consisted of 21 patches with a total area of 2.07 km2, mainly in the south of the Xinshi district.

3.3. Landscape Scale Compactness

In Table 5, we calculated the MCI and AWCI of Urumqi for each period between 1990 and 2015 using Equations (3) and (4), respectively, to capture the compactness of urban expansion at the landscape scale. MCI showed a significant trend of decreasing and then increasing between 1990 and 2015, and it can be observed that 2000 was a turning point; the urban expansion after 2000 gradually tended to be compact and reached a peak of the five periods in 2015. The trend of AWCI is generally consistent with MCI, but there was a sharp increase between 1995 and 2000, in contrast to MCI showing a decreasing trend. The maximum connectivity patch compactness index (MAX-CM) is the moment of inertia shape index of the patch with the largest area, which is calculated as in Equation (2). The maximum connectivity patch area proportion LPI was further calculated for each period according to Equation (5). The maximum connectivity patch area for each period was also counted. The results showed that the change trends of MAX-CM and AWCI were highly consistent. In addition, the maximum connectivity patch area continued to increase, and LPI was consistent with this trend, except for 2015.

3.4. Urban Historical Transformation Trajectories and Expansion Direction

Previous studies have shown that describing historical transformation trajectories of land cover (including built-up land) is a fundamental step in the rational planning of landscape patterns [34,35]. To examine the changes that occurred during the investigated time interval, we used a post-classification comparison approach for historical analysis [36]. Two vectors of construction land data (1990–2015) derived from SUBAD were overlaid in a GIS environment, obtaining a unique vector that responds to the trajectory of construction land change. We built a complete transfer matrix to quantify the change in built-up land in the main urban zone (except for Wulumuqi county and the Durban district) of Urumqi, reporting in rows the value of changes in the 1990 category and in columns the number of changes in the 2015 category.
In Table 6, The built-up land increased from 51.61 km2 in 1990 to 340.25 km2 in 2015, representing a rise of the proportion of construction land to the main urban area from 1.06% to 6.96%. The overall trend observed shows an absolute increase of 559.27% (288.64 km2) in the built-up area land cover, 5.9% if referring to the main urban zone area growth. Overall, there was a built-up land gain of 289.16 km2, against a cover loss of 0.52 km2. Built-up cover persistence involved 51.09 km2 of construction land over the total built-up area of the main urban area of Urumqi.
To further understand the spatial and temporal patterns of urban development, we calculated the expansion area of the study area in eight major directions from 1990–2015 using zonal statistics. Table 7 shows the area statistics of the built-up area of Urumqi in the eight expansion directions of east, west, south, north, southeast, northwest, northeast and southwest for the five 5-year periods from 1990 to 2015. The main urban expansion directions during 1990–1995 were north, northwest and northeast, while the other new patches were primarily located in the south and southeast directions. The main expansion direction remained stable during 1995–2000, and the new area added in the northeast direction started to be larger than that in the northwest direction, while the expansion in the south and southeast was restrained, and the growth in the southwest direction increased. The development of the city slowed down in most directions between 2000 and 2005, but the northwest direction remained on a continuous expansion trend. Urban expansion accelerated significantly between 2005 and 2010, with north, northeast and west becoming the new growth poles for urban development, and the remaining expansion area concentrated in the northwest. The urban expansion continued during 2010–2015, with the west and northeast of the three growth poles remaining the main expansion direction. The northern expansion significantly decreased, but remained a vital expansion direction. According to Figure 7, the southwest and southeast regions of Urumqi are at a higher elevation; urban expansion area is lower in all five periods. The northern alluvial plain is under the main direction of expansion, being the most significant metropolitan expansion area in the 1990–2010 period and showing an increasing trend, remaining an important expansion direction in 2015. The western region has significantly increased its expansion area since 2005, becoming another major growth pole. The area of expansion in the southern region was relatively stable and at low values in all five periods. With the incorporation of Miquan city into Urumqi in 2007 to establish the Midong district, the city has been characterized by a significant expansion to the northeast.

4. Discussion

Different from the reference-based LEI, which identifies landscape expansion patterns by empirical thresholds, this paper improves the extraction method of landscape expansion patterns with an enhancement of the exiting topological relationship such as ‘’overlap’’, “touches”, and “disjoint” among new patches, old patches and holes inside old patches, which corresponds to infilling, edge-expansion and outlying. Compared with the traditional LEI (e.g., buffer method and envelope box method), the improved method is not affected by the shape of the patch and the parameters of the reference object. To a certain extent, it overcomes the problem of misclassification of landscape expansion patterns. The improved method was applied to Urumqi city, and the spatial and temporal distributions of landscape expansion patterns were extracted for five 5-year intervals between 1990 and 2015. The application results demonstrate that the area and number percentages of edge-expansion patches are always more significant than that of outlying and infilling, the number percentage of outlying is always larger than that of infilling, and the area percentage is lower than infilling only in 2000–2005. This indicates Urumqi’s landscape expansion pattern of built-up land is dominated by edge-expansion, supplemented by outlying. Although not the dominant expansion pattern, the outlying patches provide new growth points for urban expansion. The proportion of outlying area was lower than infilling area during 2000–2005, probably due to the significant increase of the outlying patch area in the previous five years, which provided a large amount of excavatable land within the urban area, prompting infilling expansion to occupy these spaces further. According to the definition of landscape expansion pattern, infilling and edge-expansion belong to compact growth, and outlying belongs to diffuse growth. With the year 2000 as the boundary, the number proportion of edge-expansion patches showed a trend of decreasing and then increasing, precisely the opposite of outlying. In addition the number proportion of infilling patches showed a trend of increasing and then decreasing, with 2005 as the boundary. The proportion of the number of patches with diffuse growth rose between 1990 and 2000, inducing an increasing landscape fragmentation of urban built-up land. The proportion of the number of compact growth patches rose between 2000 and 2015, indicating a gradual decrease in urban landscape fragmentation and an increase in aggregation. The changing trend of infilling patches further indicates that the rate of development in urban interior space weakened during 2005–2015. The above trend is more evidence in the area of different expansion pattern patches. Based on the above analysis, we further summarize the fragmentation of landscape expansion patterns. As a dynamic quantitative model of urban growth patterns, the identification of the landscape expansion index can also be used to explain landscape fragmentation. Previous studies proposed a widely used model to quantify the fragmentation of forest landscapes, defining six patterns of landscape expansion with increasing fragmentation, including interior, perforated, edge, transitional, and patch [37,38]. The three urban landscape expansion patterns are a streamlining of the above six patterns and also represent a gradual increase in landscape fragmentation. Infilling is surrounded by built-up land patches, so it is not fragmented. The edge-expansion is adjacent to construction land and non-construction land patches representing a small degree of fragmentation. The outlying is surrounded by non-construction land patches and has the highest disturbance to the landscape and the greatest fragmentation. In summary, analyzing the results of landscape expansion patterns provides a quick understanding of urban development patterns and landscape fragmentation trends and also provides guidelines for the macro adjustment and layout of urban development in the next stage, ultimately reasonably avoiding the disorderly sprawl of the city.
The MCI and AWCI can reflect the compactness of the city at the landscape scale, which compensates for the problem that the topology-based method cannot quantitatively serve to estimate the compactness of urban expansion. Meanwhile, it can validate whether landscape expansion pattern identification results are consistent with the rule of urban morphological change. The results of MCI show that the urban expansion of Urumqi took 2000 as a turning point, presenting a scattered growth to a gradually compact expansion pattern. The changing trend of its response is consistent with the identification results of the three landscape expansion patterns, which validates the reliability of the landscape expansion pattern results. The AWCI is significantly influenced by the primary patches; thus, the sharp rise in AWCI from 1995 to 2000 and the decline in MCI indicate that the rapid development of the city during the early years caused the primary patches to become compact in advance of the whole city, while the landscape fragmentation of the entire city also increased. The compactness of the maximum connectivity patches, the change in the area, and the area percentage also supports the tendency of the primary urban patches to be compact in advance. Correspondingly, infilling and outlying patches increased during this period, which is related to the existence of sufficient available land inside the urban area and the fragmentation of external expansion patches. In summary, MCI and AWCI can reflect the change of urban compactness more obviously and quantitatively at the landscape scale and complement the reliability of the extracted results of landscape expansion patterns. The quantitative metric provides decision makers with an intuitive overview of urban development compactness and allows them to learn from the development patterns of developed cities based on the comparison of landscape scale compactness changes in different cities.
Urban growth phase theory argues that urban development consists of two phases [39]: diffusion and coalescence. Corresponding to three landscape expansion patterns; outlying can be considered a diffusion process [18], edge-expansion and infilling can be regarded as a process of coalescence. The urban development level was lower in 1990, with the LPI being the weakest of any period, indicating that the city has no significant growth core. The relatively high MCI and AWCI further suggest that the city has not yet begun to expand significantly. The period of 1990–1995 was the initial stage of urban development; the rapid increase of outlying patches and infilling patches provided a large amount of new land for the city, while both MCI and AWCI decreased significantly, indicating that this stage corresponded to the diffusion stage of rapid urban development. From 1995 to 2000, the increment of outlying patches increased significantly. In addition, MCI dropped to the bottom and AWCI increased rapidly, indicating that the primary urban patches tended to aggregate, but the overall landscape fragmentation of the city was still increasing. Thus, this stage is still in the diffusion stage. During 2000–2005, the rate of urban expansion slowed down, the area of outlying patches decreased significantly, and the increment of infilling and edge-expansion remained basically unchanged. In addition, both MCI and AWCI increased. Therefore, the external expansion of the city slowed down and began to excavate available space internally, indicating a gradual transformation into a coalescence phase with reduced landscape fragmentation. The gradual increase in MCI between 2005 and 2015 indicates that the entire city tends to aggregate. The AWCI decreases slightly but is still at a high level, combined with the gradual decrease in the increment of infilling patches; this indicates that the primary patches are still in a compact development morphology. However, the space inside the built-up area is developed, and the city needs a new growth core. The results of landscape expansion pattern identification and urban compactness calculation showed that the urban development of Urumqi fits the development process of “diffusion-coalescence”. Such a phenomenon is also experienced in the Nanjing [40], Dongguan [18] and Huangpi district of Hubei Province [41]. From the perspective of landscape pattern, the landscape pattern of the construction land exhibits a reduction in landscape fragmentation as the city grows.
The results of construction land transformation trajectories in the main urban area of Urumqi over the past 25 years reveal that the study area has experienced a dramatic evolution of the landscape pattern. The increase and persistence of the built-up land cover dominates the reconstructed land transfer matrix. The reason for this is that urban development is generally recognized as an irreversible process, that is, there is no de-urbanization of existing urban areas [39]. However, it can be observed that there is still a loss of construction land. This may be due to the urban redevelopment process, including the transformation of old buildings to demolished land, with the aim of increasing the efficiency of land use within the city [28]. Previous studies have shown that China has undergone massive urban redevelopment in the past few decades [42,43]. To gain an in-depth understanding of the directions and driver factors of urban expansion, we further analyzed the area of built-up land expansion in eight major directions. The expansion direction of Urumqi in the 25 years shows a general development pattern of “south control, north expansion, west extension first, then east expansion“. The primary function of infilling growth is to explore the potential of the inner city, and the expansion direction is mainly determined by the edge-expansion and outlying. The area of urban sprawl in the south, southwest and southeast districts is at a stable low value in all periods. At the same time, the basic absence of edge-expansion and outlying patch distribution can be observed, which is due to the higher elevation limiting urban sprawl. The Xinshi district and the Midong district in the north have experienced high rates of expansion and area in all periods and are the major distribution regions for edge-expansion and outlying patches. This is due to the extensive alluvial plain in the north that facilitates urban expansion and agricultural development. During 2000–2005, the northwest region took the lead as another growth pole and gradually extended to the west; after 2005 edge-expansion and outlying patches began to expand in large numbers to the Toutunhe district in the west. The eastern Shuimogou district is higher and farther away from the central urban zone. Thus, the expansion area is lower in all five periods. The northeast region has gradually sprawled around Midong into the third growth polarity of the city with the incorporation of Midong into Urumqi City after 2007. In summary, the direction of urban expansion in Urumqi is influenced by various factors, such as terrain, administrative district adjustment, macro layout of resources, etc. By analyzing the construction land transformation trajectory and expansion direction, we can understand the development pattern of the city and then reasonably constrain the edge-expansion and outlying patches to guide the efficient layout of urban space.

5. Conclusions

The classical landscape expansion index has the potential to capture the dynamics of the urban landscape, with limitations in identifying urban expansion patches showing as special shapes. In this paper, we modified the automatic identification method of landscape expansion patterns with topology theory, introduced the moment of inertia shape index to upgrade the method of shape-compactness, and validated these approaches using designed experiments and the application of the urban expansion evolution over Urumqi. The major conclusions are drawn and summarized as follows.
(1) The modified landscape expansion index we defined exhibits improved potential to capture the change process of urban landscape patterns accurately. The designed experiments demonstrated that both reference-based LEIs, the envelope box and the buffer methods, show apparent inaccuracy in identifying the type of urban expansion patches with special shapes, which our modified approaches can distinguish. The introduction of the topological relationship into the modified approaches provides the chance to effectively avoid misidentification of patches with special shapes.
(2) We defined MCI and AWCI in terms of the moment of inertia shape index to improve the capture of urban compactness within dynamics of urban expansion. The qualitative analysis and application over Urumqi indicate that the compactness trend is consistent with the trend of landscape expansion patterns; MCI and AWCI can catch the variation of compactness at the landscape scale for urban dynamics with a stable and high-quality evaluation.
(3) Both modified LEI and compactness indexes are applied in the urban expansion of Urumqi using data from the period 1990–2015. Our results show it is dominated by edge-expansion compact growth with secondary outlying growth. The results of MCI imply an elevated divergence and then convergence of urban compactness over Urumqi, with the turning point of year 2000, and AWCI indicates an earlier coalescence of main urban patches, with the direction of expansion influenced by factors such as terrain, administrative zoning policies and resource layout. This is consistent with the “diffusion-coalescence” development pattern of urban growth phase theory. After several decades of dramatic urban expansion, Urumqi has formed a basic pattern of “south control, north expansion, west extension first, then east expansion”, as a “T” shape with a long north–south direction and a narrow east–west direction. This case application implies the potential of the modified approaches in identifying patterns of urban expansion patches and capturing the compactness of urban dynamics.

Author Contributions

Conceptualization, Y.T. and Y.S.; methodology, Y.T. and Y.S.; data organization, Y.T., L.T. and T.L.; writing—original draft preparation, Y.T. and Y.S.; writing—review and editing, X.M. and C.S.; supervision, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Key Research and Development Program of China (no. 2020YFA0608501), Talent recruited program of the Chinese Academy of Science (no. Y938091), National Natural Science Foundation of China (no. 42071351), and the project-supporting discipline innovation team of Liaoning Technical University (no. LNTU20TD-23).

Data Availability Statement

The standardized urban built-up area dataset (SUBAD) from 1990–2015 is available at (http://www.doi.org/10.11922/sciencedb.j00076.00004), accessed on 1 August 2022. The rest of the data presented in this study are available on request from the corresponding author.

Acknowledgments

We thank the relevant teams and organizations for providing the datasets used in this study. The standardized urban built-up area dataset (SUBAD) was provided by Science Data Bank and is available at http://www.doi.org/10.11922/sciencedb.j00076.00004/, accessed on 30 July 2021. The General Land Use Plan of the City of Urumqi (2006–2020) was provided by the Bureau of Statistics of the Government of Urumqi and is available at http://www.wlmq.gov.cn/. The Python code for calculating shape compactness of geographic features was provided by Lee Hachadoorian and is available at https://github.com/leehach/geocompactness/, accessed on 30 January 2022.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Liu, Y.; Zuo, R.; Dong, Y. Analysis of temporal and spatial characteristics of urban expansion in Xiaonan District from 1990 to 2020 using time series Landsat imagery. Remote Sens. 2021, 13, 4299. [Google Scholar] [CrossRef]
  2. Yang, C.; Li, Q.; Zhao, T.; Liu, H.; Gao, W.; Shi, T.; Guan, M.; Wu, G. Detecting spatiotemporal features and rationalities of urban expansions within the Guangdong–Hong Kong–Macau Greater Bay area of China from 1987 to 2017 using time-series Landsat images and socioeconomic data. Remote Sens. 2019, 11, 2215. [Google Scholar] [CrossRef] [Green Version]
  3. United Nations. World Urbanization Prospects 2018: Highlights (ST/ESA/SER.A/421); United Nations: New York, NY, USA, 2019. Available online: http://esa.un.org/unpd/wup/index.htm2018 (accessed on 1 August 2022).
  4. United Nations. The World’s Cities in 2018; United Nations: New York, NY, USA, 2018. Available online: https://population.un.org/wup/Publications/ (accessed on 1 August 2022).
  5. National Development and Reform Commission (NDRE). Key Tasks of New Urbanization and Urban-Rural Integration Development in 2022; NDRE: Beijing, China, 2022. Available online: http://www.gov.cn/zhengce/zhengceku/2022-03/22/content_5680416.htm (accessed on 1 July 2022).
  6. National Bureau of Statistics of China. Statistical Bulletin on National Economic and Social Development of the People’s Republic of China in 2021. 2022. Available online: http://www.gov.cn/shuju/2022-02/28/content_5676015.htm (accessed on 1 July 2022).
  7. Rifat, S.A.A.; Liu, W. Quantifying spatiotemporal patterns and major explanatory factors of urban expansion in Miami metropolitan area during 1992–2016. Remote Sens. 2019, 11, 2493. [Google Scholar] [CrossRef] [Green Version]
  8. Seto, K.C.; Guneralp, B.; Hutyra, L.R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. USA 2012, 109, 16083–16088. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Guan, D.; Li, H.; Inohae, T.; Su, W.; Nagaie, T.; Hokao, K. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol. Model. 2011, 222, 3761–3772. [Google Scholar] [CrossRef]
  10. Chaolin, G.; Liya, W.U.; Cook, I. Progress in research on Chinese urbanization. Front. Arch. Res. 2012, 1, 101–149. [Google Scholar] [CrossRef] [Green Version]
  11. Burgi, M.; Hersperger, A.M.; Schneeberger, N. Driving forces of landscape change—Current and new directions. Landsc. Ecol. 2005, 19, 857–868. [Google Scholar] [CrossRef]
  12. Uuemaa, E.; Mander, Ü.; Marja, R. Trends in the use of landscape spatial metrics as landscape indicators: A review. Ecol. Indic. 2013, 28, 100–106. [Google Scholar] [CrossRef]
  13. Gustafson, E.J. Minireview: Quantifying landscape spatial pattern: What is the state of the art? Ecosystems 1998, 1, 143–156. [Google Scholar] [CrossRef]
  14. McGarigal, K.; Marks, B.J. FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure; Gerneral Technical Report PNW-GTR-351; U.S. Department of Agriculture, Forest Service: Washington, DC, USA; Pacific Northwest Research Station: Oregon, Porland, 1995; 122p. [Google Scholar] [CrossRef]
  15. Civco, D.L.; Hurd, J.D.; Wilson, E.H.; Arnold, C.; Prisloe, M.P. Quantifying and describing urbanizing landscapes in the northeast United States. Photogramm. Eng. Remote Sens. 2002, 68, 1083–1090. [Google Scholar]
  16. Herold, M.; Scepan, J.; Clarke, K.C. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ. Plan. A Econ. Space 2016, 34, 1443–1458. [Google Scholar] [CrossRef] [Green Version]
  17. Lausch, A.; Blaschke, T.; Haase, D.; Herzog, F.; Syrbe, R.-U.; Tischendorf, L.; Walz, U. Understanding and quantifying landscape structure—A review on relevant process characteristics, data models and landscape metrics. Ecol. Model. 2015, 295, 31–41. [Google Scholar] [CrossRef]
  18. Liu, X.; Li, X.; Chen, Y.; Tan, Z.; Li, S.; Ai, B. A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data. Landsc. Ecol. 2010, 25, 671–682. [Google Scholar] [CrossRef]
  19. Angel, S.; Sheppard, S.C.; Civco, D.L.; Buckley, R.; Chabaeva, A.; Gitlin, L.; Kraley, A.; Parent, J.; Perlin, M. The Dynamics of Global Urban Expansion; The World Bank: Washington, DC, USA, 2005. [Google Scholar]
  20. Hoffhine Wilson, E.; Hurd, J.D.; Civco, D.L.; Prisloe, M.P.; Arnold, C. Development of a geospatial model to quantify, describe and map urban growth. Remote Sens. Environ. 2003, 86, 275–285. [Google Scholar] [CrossRef]
  21. Forman, R. Land Mosaics: The ecology of landscapes and regions (1995). In The Ecological Design and Planning Reader; Island Press: Washington, DC, USA, 2014; pp. 217–234. [Google Scholar]
  22. Ghani, N.L.A.; Abidin, S.Z.Z. A modified landscape expansion index algorithm for urban growth classification using satellite remote sensing image. Adv. Sci. Lett. 2018, 24, 1843–1846. [Google Scholar] [CrossRef]
  23. Qian, M.; Pu, L.; Zhang, J. Urban spatial morphology evolution in Suzhou-Wuxi-Changzhou region based on improved landscape expansion index. Sci. Geogr. Sin. 2015, 35, 314–321. [Google Scholar]
  24. Alonso, W. Location and land use. In Location and Land Use; Harvard University Press: Cambridge, MA, USA, 2013. [Google Scholar]
  25. Deng, J.S.; Wang, K.; Hong, Y.; Qi, J.G. Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landsc. Urban Plan. 2009, 92, 187–198. [Google Scholar] [CrossRef]
  26. Liu, X.; Xia, L.i.; Yimin, C.; Yan, Q.I.N.; Shaoying, L.I.; Minghui, C. Landscape expansion index and its applications to quantitative analysis of urban expansion. Acta Geogr. Sin. 2009, 64, 1430–1438. [Google Scholar]
  27. Jiao, L.; Liu, J.; Xu, G.; Dong, T.; Gu, Y.; Zhang, B.; Liu, Y.; Liu, X. Proximity expansion index: An improved approach to characterize evolution process of urban expansion. Comput. Environ. Urban Syst. 2018, 70, 102–112. [Google Scholar] [CrossRef]
  28. Xia, C.; Zhang, A.; Yeh, A.G.-O. Shape-weighted landscape evolution index: An improved approach for simultaneously analyzing urban land expansion and redevelopment. J. Clean. Prod. 2020, 244, 118836. [Google Scholar] [CrossRef]
  29. Wu, P.; Zhou, D.; Gong, H. A new landscape expansion index: Definition and quantification. Acta Ecol. Sin. 2012, 32, 4270–4277. [Google Scholar]
  30. Lin, M.; Lin, T.; Jones, L.; Liu, X.; Xing, L.; Sui, J.; Zhang, J.; Ye, H.; Liu, Y.; Zhang, G.; et al. Quantitatively assessing ecological stress of urbanization on natural ecosystems by using a landscape-adjacency index. Remote Sens. 2021, 13, 1352. [Google Scholar] [CrossRef]
  31. Li, W.; Goodchild, M.F.; Church, R. An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems. Int. J. Geogr. Inf. Sci. 2013, 27, 1227–1250. [Google Scholar] [CrossRef]
  32. Montero, R.S.; Bribiesca, E. State of the art of compactness and circularity measures. Proc. Int. Math. Forum 2009, 4, 1305–1335. [Google Scholar]
  33. Jiang, H.; Sun, Z.; Guo, H.; Xing, Q.; Du, W.; Cai, G. A standardized dataset of built-up areas of China’s cities with populations over 300,000 for the period 1990–2015. Big Earth Data 2021, 6, 103–126. [Google Scholar] [CrossRef]
  34. Solano, F.; Praticò, S.; Piovesan, G.; Chiarucci, A.; Argentieri, A.; Modica, G. Characterizing historical transformation trajectories of the forest landscape in Rome’s metropolitan area (Italy) for effective planning of sustainability goals. Land Degrad. Dev. 2021, 32, 4708–4726. [Google Scholar] [CrossRef]
  35. Lavorel, S.; Grigulis, K.; Leitinger, G.; Kohler, M.; Schirpke, U.; Tappeiner, U. Historical trajectories in land use pattern and grassland ecosystem services in two European alpine landscapes. Reg. Environ. Chang. 2019, 17, 2251–2264. [Google Scholar] [CrossRef]
  36. Modica, G.; Praticò, S.; Di Fazio, S. Abandonment of traditional terraced landscape: A change detection approach (a case study in Costa Viola, Calabria, Italy). Land Degrad. Dev. 2017, 28, 2608–2622. [Google Scholar] [CrossRef]
  37. Riitters, K.H.; Wickham, J.D.; O’Neill, R.V.; Jones, K.B.; Smith, E.R.; Coulston, J.W.; Wade, T.G.; Smith, J.H. Fragmentation of continental United States forests. Ecosystems 2002, 5, 0815–0822. [Google Scholar] [CrossRef]
  38. Riitters, K.; Wickham, J.; O’Neill, R.; Jones, B.; Smith, E. Global-scale patterns of forest fragmentation. Conserv. Ecol. 2000, 4, art3. [Google Scholar] [CrossRef]
  39. Dietzel, C.; Herold, M.; Hemphill, J.J.; Clarke, K.C. Spatio-temporal dynamics in California’s Central Valley: Empirical links to urban theory. Int. J. Geogr. Inf. Sci. 2005, 19, 175–195. [Google Scholar] [CrossRef]
  40. Xu, C.; Liu, M.; Zhang, C.; An, S.; Yu, W.; Chen, J.M. The spatiotemporal dynamics of rapid urban growth in the Nanjing metropolitan region of China. Landsc. Ecol. 2007, 22, 925–937. [Google Scholar] [CrossRef]
  41. Liu, Y.; He, Q.; Tan, R.; Liu, Y.; Yin, C. Modeling different urban growth patterns based on the evolution of urban form: A case study from Huangpi, Central China. Appl. Geogr. 2016, 66, 109–118. [Google Scholar] [CrossRef]
  42. Yu, T.; Liang, X.; Shen, G.Q.; Shi, Q.; Wang, G. An optimization model for managing stakeholder conflicts in urban redevelopment projects in China. J. Clean. Prod. 2019, 212, 537–547. [Google Scholar] [CrossRef]
  43. Jianguo, W.; Wei-Ning, X.; Jingzhu, Z. Urban ecology in China: Historical developments and future directions. Landsc. Urban Plan. 2014, 125, 222–233. [Google Scholar] [CrossRef]
Remotesensing 14 05255 g001 550
Figure 1. Schematic diagram of the three landscape expansion patterns. (a–c) represents infilling growth, (d) represents edge-expansion growth, and (e) represents outlying growth.
Figure 1. Schematic diagram of the three landscape expansion patterns. (a–c) represents infilling growth, (d) represents edge-expansion growth, and (e) represents outlying growth.
Remotesensing 14 05255 g001
Remotesensing 14 05255 g002 550
Figure 2. Process framework for landscape expansion pattern identification and compactness calculation.
Figure 2. Process framework for landscape expansion pattern identification and compactness calculation.
Remotesensing 14 05255 g002
Remotesensing 14 05255 g003 550
Figure 3. Cases of misclassification of landscape expansion index based on the envelope and the buffer method. (a,b) is the envelop box method. (c,d) is the buffer method.
Figure 3. Cases of misclassification of landscape expansion index based on the envelope and the buffer method. (a,b) is the envelop box method. (c,d) is the buffer method.
Remotesensing 14 05255 g003
Remotesensing 14 05255 g004 550
Figure 4. Overview map of the study area. (a,b) The spatial location of the study area. (c) The spatial distribution of expansion patches in Urumqi from 1990–2015.
Figure 4. Overview map of the study area. (a,b) The spatial location of the study area. (c) The spatial distribution of expansion patches in Urumqi from 1990–2015.
Remotesensing 14 05255 g004
Remotesensing 14 05255 g005 550
Figure 5. Percentage of area and number of patches with different landscape expansion patterns. (a) Percentage of the number of different patches. (b) Percentage of area of different patches.
Figure 5. Percentage of area and number of patches with different landscape expansion patterns. (a) Percentage of the number of different patches. (b) Percentage of area of different patches.
Remotesensing 14 05255 g005
Remotesensing 14 05255 g006 550
Figure 6. Spatial distribution of landscape expansion patterns in Urumqi from 1990 to 2015. (a–e) denote the spatial distribution of landscape expansion patterns for each of the five 5-year intervals from 1990–2015.
Figure 6. Spatial distribution of landscape expansion patterns in Urumqi from 1990 to 2015. (a–e) denote the spatial distribution of landscape expansion patterns for each of the five 5-year intervals from 1990–2015.
Remotesensing 14 05255 g006
Remotesensing 14 05255 g007 550
Figure 7. Eight-directional map of the urban expansion area of Urumqi from 1990 to 2015.
Figure 7. Eight-directional map of the urban expansion area of Urumqi from 1990 to 2015.
Remotesensing 14 05255 g007
Table
Table 1. Comparison of three different landscape expansion indexes.
Table 1. Comparison of three different landscape expansion indexes.
LEIboxLEIbufferLEIw
Formula L E I box = { 100 ∗ A O A E − A N new   patch   is   not   rectangular 100 ∗ A LO A LE − A N new   patch   is   rectangular L E I buffer = 100 ∗ A c A c + A d L E I buffer = 100 ∗ A c A c + A d L E I w = A n − A o A n + A o
ParameterAE is the envelope box area of the new patch, AN is the area of the new patch, AO is the area of the old patch inside the new patch envelope box, ALE is the expanded envelope box area of the new patch, ALO is the area of the old patch inside the new patch expanded envelope box.AC is the overlap area between the new patch buffer and the old patch, Ad is the difference between the buffer area and AC.An is the area of new patches, and Ao is the area of patches that are adjacent to the new patches.
RationaleEnvelope box based on new patchBuffer based on new patchBased on the adjacency of the new patch to the old patch
Value
range		[0, 100][0, 100](−1, 1]
Influence
factors		New patch shape, envelope box expanded multiplesNew patch shape, buffer distanceNothing
Expansion
pattern		(50, 100], infilling
[2, 50], edge-expansion
[0, 2), outlying		(50, 100], infilling
(0, 50], edge-expansion
0, outlying		(−1, 1), adjacency expansion
1, external expansion
Table
Table 2. Calculation results of the envelope box method. Area unit: km2.
Table 2. Calculation results of the envelope box method. Area unit: km2.
CaseEnvelope Area (AE)New Patch Area (AN)Old Patch in Envelope (AO)LEI
(a)151.60128.9122.69100
(b)1989.34459.30420.0527.45
Table
Table 3. Calculation results of the buffer method. Area unit: m2.
Table 3. Calculation results of the buffer method. Area unit: m2.
CaseOverlap Area between Buffer and Old Patch (AC)Overlap Area between Buffer and Blank Area (Ad)LEI
(c)47.33178.8020.93
(d)0455.490
Table
Table 4. Increment of three landscape expansion patterns between 1990–2015.
Table 4. Increment of three landscape expansion patterns between 1990–2015.
YearsNumber of New PatchesArea of New Patches (km2)
InfillingEdge-ExpansionOutlyingTotal NumberInfillingEdge-ExpansionOutlyingTotal Area
1990–199534115512713160.6843.717.2251.61
1995–200092126322215775.6328.3618.5852.57
2000–2005118138219916996.4127.992.7037.1
2005–201086189025722334.2459.059.8373.12
2010–201521130815614852.0764.2819.0885.44
Table
Table 5. Landscape scale compactness and related indexes in Urumqi between 1990 and 2015.
Table 5. Landscape scale compactness and related indexes in Urumqi between 1990 and 2015.
199019952000200520102015
MCI52.3952.2951.6352.7253.7755.39
AWCI50.8539.1949.6954.4752.4652.08
MAX-CM52.9832.1549.8455.9252.6352.17
MAX-Area (km2)23.0762.7095.97138.79220.54261.17
LPI44.6960.7561.6171.9683.0876.72
Table
Table 6. Contingency matrix showing the transition between different built-up and non-built-up land cover (values in km2) from 1990 to 2015 in the main urban zone of Urumqi.
Table 6. Contingency matrix showing the transition between different built-up and non-built-up land cover (values in km2) from 1990 to 2015 in the main urban zone of Urumqi.
Built-Up/Non-Built-Up Land Cover2015
Non-Built-UpBuilt-UpTotal
1990
Non-built-up4548.47289.164837.63
Built-up0.5251.0951.61
Total4549.00340.254889.25
Table
Table 7. Area of urban expansion in eight directions in Urumqi from 1990–2015. Area unit: km2.
Table 7. Area of urban expansion in eight directions in Urumqi from 1990–2015. Area unit: km2.
1990–19951995–20002000–20052005–20102010–2015
West4.623.462.5614.6825.10
Southwest2.394.731.373.225.19
South5.514.283.234.377.80
Southeast6.755.887.571.643.67
East2.093.743.190.552.84
Northwest8.827.288.639.955.99
Northeast7.7810.193.7716.8923.44
North13.6513.006.7721.8111.42
Sum51.6152.5637.0973.1185.45
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Tian, Y.; Shuai, Y.; Ma, X.; Shao, C.; Liu, T.; Tuerhanjiang, L. Improved Landscape Expansion Index and Its Application to Urban Growth in Urumqi. Remote Sens. 2022, 14, 5255. https://doi.org/10.3390/rs14205255

AMA Style

Tian Y, Shuai Y, Ma X, Shao C, Liu T, Tuerhanjiang L. Improved Landscape Expansion Index and Its Application to Urban Growth in Urumqi. Remote Sensing. 2022; 14(20):5255. https://doi.org/10.3390/rs14205255

Chicago/Turabian Style

Tian, Yuhang, Yanmin Shuai, Xianwei Ma, Congying Shao, Tao Liu, and Latipa Tuerhanjiang. 2022. "Improved Landscape Expansion Index and Its Application to Urban Growth in Urumqi" Remote Sensing 14, no. 20: 5255. https://doi.org/10.3390/rs14205255

APA Style

Tian, Y., Shuai, Y., Ma, X., Shao, C., Liu, T., & Tuerhanjiang, L. (2022). Improved Landscape Expansion Index and Its Application to Urban Growth in Urumqi. Remote Sensing, 14(20), 5255. https://doi.org/10.3390/rs14205255

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop