- Email: [email protected]
Quantitative assessment of land use dynamic variation using remote sensing data and landscape pattern in the Yangtze River Delta, China
Sustainable Computing: Informatics and Systems 23 (2019) 111–119
Contents lists available at ScienceDirect
Sustainable Computing: Informatics and Systems journal homepage: www.elsevier.com/locate/suscom
Quantitative assessment of land use dynamic variation using remote sensing data and landscape pattern in the Yangtze River Delta, China Shiguang Shen a,∗ , Pei Yue a , Chenjing Fan b a b
College of Landscape Architecture, Nanjing Forestry University, Nanjing, 210037, China School of Architecture, Tsinghua University, Beijing, 100084, China
a r t i c l e
i n f o
Article history: Received 8 January 2019 Received in revised form 1 May 2019 Accepted 16 July 2019 Available online 18 July 2019 Keywords: Land use Landscape pattern Urban expansion Ecological space Interference of human activity Urban agglomeration of Yangtze River Delta
a b s t r a c t The urban agglomeration of the Yangtze River Delta is one of the fastest expansions of urban regions in China. In this study, the variation of urban construction land and landscape patterns were studied based on the remote sensing image classification data of 2005, 2010, and 2015. The results show that: 1) In the region, the change rate of urban construction land in 2010–2015 is significantly higher than that of 2005–2010.; 2) The rapid expansion of urban landscape leads to the reduction of farmland, while the total area of ecological space such as that of forest and waters is almost unchanged.; 3) The farmland fragmentation is enhanced and the corresponding shape is more complicated, but the shape of urban landscape is more continuous and regular during the urban expansion process.; and 4) The interference of human activities to ecological space is increasing. Therefore, the conclusion of this study provides the basic data for the spatial planning and ecological environment protection of urban agglomeration in Yangtze River Delta. Not only that, but it also promotes the sustainable development of ecological resources in the Yangtze River Delta urban agglomeration, as well as makes social and economic contributions. © 2019 Elsevier Inc. All rights reserved.
1. Introduction The urban agglomeration in the Yangtze River Delta is located in the alluvial plain before the Yangtze River flows into the sea. It includes 29 cities such as Shanghai, Nanjing, and Hangzho, and it is an important convergence of the Yangtze River Economic Zone and ¨ ¨ the strategy of One Belt, One Road.Further, it is a region with an important strategic position in the modernized construction and comprehensive opening of China. Since the 21 st century, the rapid economic development of urban agglomeration in the Yangtze River Delta has brought about the rapid urbanization development and expansion of land construction, which has also caused the eco¨ logical space to be squeezed. As for the Yangtze River Delta Urban ¨ Agglomeration Development Planapproved in 2016, it was specifically noted that the development of urban agglomeration should firmly establish and practice the concept of ecological civilization. This suggests that one should rely on the ecological background, give full play to the unique advantages of landscape resources, and optimize the pattern of land spatial development to form the ecological urban agglomeration with green hills, clear water, and fresh
∗ Corresponding author. E-mail addresses: [email protected] (S. Shen), [email protected] (P. Yue), [email protected] (C. Fan). https://doi.org/10.1016/j.suscom.2019.07.006 2210-5379/© 2019 Elsevier Inc. All rights reserved.
air. In the process of urbanization, the problem of urban ecological environment is serious. It is necessary to integrate the concept of ecological civilization into urban development and improve the efficiency of urban space utilization [1]. To analyze the variations of urban space and ecological space, an increasing number of scholars use landscape pattern measurement as a research method to study the arrangement characteristics and the variation of landscape patch with different shapes and sizes [2]. As for this research, attention has been given to the pattern index measurement and the spatial characteristic statistics [3]. Moreover, the conclusion has great significance for reviewing the effects of urbanization policies, strengthening ecological protection, as well as for optimizing and developing the environment [4–7]. In the field of urban planning and geography, the research of urban construction land variation in the Yangtze River Delta region has garnered much attention. Many studies have been conducted for the dynamic pattern variation of the whole Yangtze River Delta and the inner metropolitan region among Suzhou and Wuxi, as well as Changzhou [8–10], Shanghai, Nanjing, and Hangzhou [11–14]. Furthermore, these studies provided detailed and fundamental data to support the formulation of an environmentally friendly plan. The existing research mainly focused on the quantification of the pattern and the variation analyses of different landscape types, while simultaneously lacking the impact of land-use landscape pattern variation on the surrounding ecological
112
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
environment [15,16]. In addition, these studies focused more on the entire land area of the region [17–20], but ignored the horizontal inter-comparison between cities within the region. The quantitative studies on the spatial pattern variation of ecology caused by urban expansion were not conducted [21,22]. The urban agglomeration of the Yangtze River Delta is selected as an example in this study, and the remote sensing image data in 2005, 2010, and 2015 are analyzed to reveal the land development, the utilization of construction land, the conversion of farmland, as well as the process and characteristics of green ecological pattern variation, which is represented by the forest in the entire delta area and internal cities. It is of great significance for reviewing the urban development process of the Yangtze River Delta urban agglomeration, especially in terms of analyzing the effects of sustainable development policies, assessing the influences of ecological space restoration, and formulating measures for sustainable socioeconomic development. 2. Research method
information. At the same time, assist remote sensing images are used to verify and quantitatively analyze the acquired landscape categories and images. 2.3. Research methods This research is divided into four steps. First, the dynamic variation transfer matrix analysis of different lands is conducted to study the area variation at different stages based on the entire Yangtze River Delta urban agglomeration. Second, the characteristic index calculation is performed on the landscape pattern of the entire Yangtze River Delta urban agglomeration, so as to analyze the variations of landscape matrix, uniformity, landscape fragmentation, and shape. Third, the interference process between the land for human activities (town and farmland) and the ecological green space is described by using the correlation index of adjacent urban construction land probability. Finally, the driving force of the change described in this paper is analyzed by principal component analysis.
2.1. Case overview The Yangtze River Delta urban agglomeration composed of 26 cities is located in the alluvial plain before the Yangtze River flows into the sea. These cities include Shanghai, Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yancheng, Yangzhou, Zhenjiang, Taizhou, Hangzhou, Ningbo, Jiaxing, Huzhou, Shaoxing, Jinhua, Zhoushan, Taizhou, Hefei, Wuhu, Ma anshan Tongling, Anqing, Zhangzhou, Chizhou, and Xuancheng. Further, the corresponding coordinate ranges from 32◦ 34 to 29◦ 20 north latitude, and 115◦ 46 to 123◦ 25 east longitude (Fig. 1), with plains covering the north of the Yangtze River and hills found in the south of the Yangtze River. Moreover, the rainfall is abundant, the soil is fertile, and the water system is well developed. To continue, the land area is 21.17 million square miles – accounting for 2.2% of China’s total area – and the population is approximately 150 million. On account of its unique development advantages, the region has become one of the most developed regions in China. In 2015, the total GDP of urban agglomeration was 1355.2 billion yuan, reaching 20.02% of the national GDP. According to the research demand and the adjustment of administrative division, this paper uniformly adopts the above 26 municipal administrative division boundaries as the basis for delineating the urban administrative scope.
2.3.1. Urban construction land transition matrixes The urban construction land transition matrixes can reflect the process of mutual transformation between urban construction land types at the beginning and the end of period:
⎡ ⎢
···
aa1n
..
.. .
aij = ⎢ .. ⎣. an1
.
···
⎤ ⎥ ⎥ ⎦
(1)
ann
where n is the total number of urban construction land types; i is the land type before transformation; j is the land type after transformation; and aij is the area of i transferring to j. 2.3.2. Landscape pattern indices In this study, Shannon diversity index (SHDI) the proportion of the largest patch to the landscape area, the number of plaques, the landscape shape index, and the spatial correlation index were used to reflect the balance of urban construction land, the strength of dominant landscape, the landscape fragmentation, the shape diversity, as well as the interference of human activities on forests. The calculation method of the five indexes is shown as follows: 1. Shannon diversity index:
2.2. Data source The original data of this study come from LANDSAT remote sensing image data of 2005, 2010, and 2015, which originates from National Resource Environment Database and respectively corresponds with the urban construction land situation of “Tenth Five-Year Plan,” “Eleventh Five-Year Plan,” and “Twelfth Five-Year Plan.” Further, the data from the years 2005 and 2010 come from LANDSAT4/5 data, while the data from the year 2015 come from LANDSAT8 data. The remote sensing data are manually interpreted to obtain a digital distribution map of urban construction land, which is divided into six types: farmland, woodland, grassland, waters, construction land, and unutilized land. After proofreading the national land survey data, it is suggested that the correct classification rate of farmland and construction land (including rural residential area) is not less than 95%, and the correct rates of grassland, woodland, and waters are not less than 90%. In this paper, Remote Sensing and Geographic Information System technology can be applied to study the changes of landscape patterns. RS technology can study the process of dynamic change of landscape pattern while simultaneously classifying it. GIS assists in the research of landscape pattern in the classification of landscape categories, the extraction, and analysis of landscape index statistical
a11
SHDI =
m
(pi ln pi )
i=1
where Pi is the area ratio of landscape i, and m is the total number of landscape types. The SHDI is usually used to reflect the unbalanced distribution of plaque types. If the SHDI is close to 0, the entire landscape simply consists of one type-dominated landscape. When the value of the SHDI increases, the plaque type is increased or the landscape presents a balanced distribution. In this study, this index is used to reflect the overall land-use equalization variation in the Yangtze River Delta. 2. Largest patch index max aj
LPI =
1≤j≤n
A
(100)
(3)
where A is the total area of the entire landscape, and aj is the area of landscape patch. The largest patch index (LPI) is essentially equal to the proportion of the largest plaque to the entire landscape area, which is helpful in determining the advantaged type of landscape. The index variation can reflect the direction and strength of human activities. In this study, this index is used to determine the dom-
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
113
Fig. 1. Study Area.
inant landscape variation and the dominant level of the Yangtze River Delta region. 3. Number of patch NP = n
(4)
where n is the total number of plaques. The number of patch (NP) is equal to the total number of landscape plaques, which is usually used to describe the heterogeneity and fragmentation of the entire landscape. As NP increases, the fragmentation enhances, and the unfavorable species migration also increases. In this study, this index is used to reflect the variation of urban construction land fragmentation in the overall and internal cities of the Yangtze River Delta. 4. Landscape Shape Index LSI =
0.25 × E √ A
(5)
where E is the total length of all plaque edges, and A is the total area of landscape. The Landscape Shape Index (LSI) is an index of the plaque discretization or aggregation. As LSI increases, the complexity and dispersion enhances. This index is used to reflect the urban construction landscape variation of cities within the Yangtze River Delta urban agglomeration. 5. Association Index AI =
Ef −i Ef
and reproduction are hindered, as the interference index is much higher as AI reaches 100%. The ecological isolated island can be observed (Fig. 2.b, c). As a result, the number of species of wild animals and plants declines, and the construction of cities and towns takes up a large amount of land and the ecological environment of wild animals is seriously affected. In this study, this index is used to reflect the impact of human activities in farmland and towns on the forests within the Yangtze River Delta urban agglomeration. 2.3.3. Analysis of driving force Principal Component Analysis (PCA) was used to analyze the driving force of urban construction land change in the Yangtze River Delta urban agglomeration from 2005 to 2015. As an important tool in multivariate statistical analysis, PCA could reduce the dimension of several correlated factor variables and reflect most information of the original variables. This paper will use this method to find several principal component factors (F1, F2. . .). Following this, the principal component factors and the interpolated land use and landscape pattern change dependent variable (Y) can be used for regression analysis (Eq. 7), so as to determine which factors are the main driving forces of changes. Y = b1F1 + b2F2 + b3F3 + . . . + c
(7)
3. Results and analyses × 100%
(6)
where Ef -i is the total perimeter of land, which is the total perimeter of the farmland associated with the forest and the urban land in this paper. Ef is the total perimeter of the forest plaque. The Association Index (AI) can reflect the effect of human disturbance on the forests [23–25], as well as the intensity of mutual interference between two types of landscapes with the same scale and landscape index. As for (a) the forest land and (b) the town depicted in Fig. 2, when urban construction is developing, towns are gradually directly connected with the surrounding forest. The biological activities in the forest are unstable, and the migration
The interpretation results of three remote sensing images in 2005, 2010, and 2015 are shown in Fig. 3. Owing to space constraints, this paper mainly discusses the indexes of farmland, town, and forest landscape. 3.1. Analyses of urban construction land transfer The transfer matrixes of urban construction land type for the Yangtze River Delta region during 2005–2010 and 2010–2015 are shown in Table 1 and Table 2, respectively. Meanwhile, the variations of urban, farmland, and forest space are shown in Fig. 4 and
114
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
Fig. 2. The woodland interference index (AI) for different urban expansion modes.
Fig. 3. The interpretation results of remote sensing images for the Yangtze River Delta urban agglomeration in (a) 2005, (b) 2010, and (c) 2015.
Table 1 Yangtze River Delta urban agglomeration area of urban construction land transition matrixes during.2005–2010.
Farmland Forest Grassland Water Construction land Unutilized land Area in 2010
Farmland
Forest
Grassland
Water
Construction land
Unutilized land
Area in 2005
106240.02 2.17 0.21 7.81 0.94 0.00 106251.15
22.43 57896.53 0.20 0.49 0.69 0.08 57,920.41
2.90 24.80 7619.90 1.46 0.00 0.30 7649.36
115.84 2.18 13.24 17096.39 1.68 0.00 17,229.32
2493.98 104.88 13.37 46.82 19955.09 0.43 22,614.57
0.00 0.39 0.00 0.00 0.00 35.07 35.46
108875.17 58030.94 7646.92 17152.96 19958.40 35.87
Table 2 Urban construction land type area transfer matrix of Yangtze River Delta region during 2010–2015.
Farmland Forest Grassland Water Construction land Unutilized land Area in 2015
Farmland
Forest
Grassland
Water
Construction land
Unutilized land
Area in 2010
100258.57 72.53 121.25 386.79 427.97 0.03 101267.14
73.24 57383.20 5.60 3.80 23.22 1.82 57,490.88
37.08 22.31 7143.79 139.00 32.64 0.00 7374.82
570.64 22.16 265.26 16288.84 317.12 0.94 17,464.96
5273.02 387.13 110.23 330.17 21796.87 1.89 27,899.30
38.60 33.08 3.23 80.72 16.76 30.78 203.17
106251.15 57,920.41 7649.36 17,229.32 22,614.57 35.46
Fig. 5. According to the results, the following observations can be noted. 1. Since 2005, the urban construction land of the Yangtze River Delta region has experienced large-scale variation. During the years 2005–2010, the area variation of urban construction land reached 2857.29 km2 , accounting for 1.35% of the total land area. From 2010–2015, the area variation of urban construction land reached 879.23 km2 , which accounted for 4.16% of the total land area. Above
all, the transformation speed is significantly higher than that of 2005–2010. 2. The farmland has the largest area reduction, which is mainly converted into urban area. From 2005–2010, the farmland decreased by 2653.15 km2 , and again decreased by 5992.58 km2 in the next five years with a faster reduction rate. On the other hand, the town scale gradually increased. From 2005–2010, the town scale increased by 2569.48 km2 , and then increased again
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
115
Fig. 4. Variations of urban, farmland, and forest space during.2005–2010.
Fig. 5. Variations of urban, farmland, and forest space during 2010–2015.
by 6102.44 km2 during 2010–2015. According to the statistics, it can be seen that the expansion speed continuously increases. The increased population, the urban construction, and the large influx of foreign capital are the main factors for the rapid growth of construction land. Although some forest land and digging water surface have become urban land, the conversion of cropland to forest or digging fish ponds has made the total area of waters and forest space change little. Moreover, the urban expansion is basically consistent with the farmland reduction. 3. As for the city scale, the newly added construction land is concentrated in Shanghai, the region surrounding Suzhou, Wuxi, Changzhou, and Nanjing, as well as other plain areas in the lower Yangtze River during the years 2005–2010. By 2010–2015, the Yangtze River Delta urban agglomeration experienced large-scale expansion, and the farmland with the same size also converted into the construction land. It is worth noting that a large number of urban land types (mainly Yantian) in the eastern part of Yancheng have become water landscapes (culture ponds), grasslands (wetlands), and farmlands within the last ten years [26]. As for Hangzhou, a land-building project was carried out to convert some waters into farmland along the bank of Qiantang River (Fig. 4 and Fig. 5). 3.2. The dynamic variations in landscape patterns General variations of landscape pattern in the Yangtze River Delta region
Table 3 Landscape pattern index value.
LPI SHDI NP
2005
2010
2015
13.66 1.24 138104
13.48 1.27 138679
13.06 1.30 138700
The general variations of landscape pattern in the Yangtze River Delta region in 2005, 2010, and 2015 are exhibited in Table 3. As for these three periods of landscape pattern data, the farmland area is the largest with the largest LPI patch. Therefore, the farmland is the base of urban construction land pattern for the entire region. The LPI index decreases from 13.66 in 2005 to 13.06 in 2015, indicating that the farmland matrix area degraded and the human interference constantly increased. The NP index increased from 138,104 in 2005 to 138,700 in 2015, signifying that the general landscape pattern was constantly fragmented. The Shannon’s diversity index decreased from 13.66 in 2005 to 13.06 in 2015, denoting that human activities have an increased impact on urban construction land in the Yangtze River Delta. Further, the landscape diversity has increased and the overall urban construction land has become more balanced. Landscape pattern variations in farmland, forest, and urban areas of the Yangtze River Delta region urban agglomeration. Table 4 presents the general situation of landscape fragmentation and shape index variations of farmland, forest, and urban types
116
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
Fig. 6. The NP values of farmland, forest, and urban landscape fragmentation index in different cities of the Yangtze River Delta urban agglomeration.
Fig. 7. The LSI values of farmland, forest, and urban landscape shape indices in different cities of the Yangtze River Delta urban agglomeration.
Table 4 Farmland, forest, and urban landscape index values for different periods in the Yangtze River Delta.
NP of Farmland NP of Forest NP of Urban LSI of Farmland LSI of Forest LSI of Urban
2005
2010
2015
13910 16279 92840 265.65 170.54 315.66
14879 16442 90445 269.71 171.32 305.47
16394 16618 87298 276.14 172.38 287.88
in the Yangtze River Delta urban agglomeration in 2005, 2010, and 2015. Further, the city statistics are shown in Fig. 6 and Fig. 7. As for the NP index of fragmentation, the farmland in each city has a constant broken state. This is attributable to the fact that urban development blocks the continuity of farmland. As for most of the areas, the fragmentation of forests either does not change or just increases a little, while the fragmentation of Nantong, Suzhou, and Yancheng decreased because the number of original forest plaques in these cities is not large, and the development of urban areas leads to the disappearance of small forest plaques. The continuous development of towns makes most fragmentation of urban landscapes unchanged or continuously increases. However, the fragmentation increase seen in Xuancheng, Zhoushan, Taizhou, Ningbo, and Huzhou is likely attributable to the number of mountains found in ¨ ¨ phenomenon of development within these areas and the enclave these cities [7,8]. As for the LSI, the complexity of farmland interference of urban development is increasing. However, the complication of forest increased a little, while the landscape shape index of Nantong, Suzhou, Yangzhou, and Yancheng decreased. The disappearance of small forest patch and the newly added forest land being adjacent to
Table 5 The AI value of the forest association index of farmland and urban landscape in the Yangtze River Delta in different periods.
Farmland AI Urban AI
2005
2010
2015
74.95% 4.93%
74.12% 5.74%
72.04% 7.59%
the original forest land also causes these phenomena. To continue, the expansion of urban scale simultaneously develops, reducing the general complexity of urban area but increasing the complexity of urban landscape shapes in Zhoushan and Taizhou. Hangzhou, Huzhou, Ningbo, and Shaoxing completed the increasing process and reduced the shape complexity because of the many mountains within these areas and cities. The enclave type development is the first stage, and the development of being “filled” is the one that follows [8]. Human activities interfere with the forest Table 5 shows the AI value for the forest association index of farmland and urban landscape in the Yangtze River Delta urban agglomeration in the years 2005, 2010, and 2015, whereas the city situation is shown in Fig. 8. Although the total area of forests is not severely reduced, the correlation index AI between farmland and forest decreases as the area of farmland shrinks, indicating that the human activities in farmland have a reduced degree of disturbance to forests. However, many urban fringe areas are directly adjacent to the forests where the AI was increasing, and the interference intensity was also increasing. The above conclusions can also be observed for the city scale, but it should be noted that Yancheng differs from other cities where the AI value for the impact of urban landscape on forests decreases. This is because a large number of
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
117
Fig. 8. The AI value of the forest association index of farmland and urban landscape in the Yangtze River Delta urban agglomeration during different periods.
Table 6 The principal component contribution rate of driving factors. Indicators Population
Economic income
Economic spending Environment
X1 total population X2 urbanization rate X3GDP X4 Secundiparity proportion X5 Tertiary industry proportion X6 fiscal revenue X7 fiscal spending X8 fixed assets investment X9 the temperature X10 rainfall
salt pans in the eastern part of Yancheng change into other types of landscape, reducing the interference on surrounding woodland. As for the horizontal comparison between cities, the urban AI value of Suzhou reached 39.85% in 2015, which is the highest among the Yangtze River Delta urban agglomeration. It is suggested that many green ecological spaces in Suzhou are directly adjacent to the city and are disturbed by the city. Further, the forest correlation index of cities in the central part of the Yangtze River Delta urban agglomeration – such as Maanshan, Nanjing, Taizhou, Wuxi, Yancheng, Zhenjiang, Shanghai, and Jiaxing – is also high, indicating that the activities of humans in the built-up areas in these cities are distributed to the forest. The reason may be that plains dominate these cities, and urban expansion will “wrap” the forest in them. The increase in the degree of disturbance AI caused by urban expansion to forests will result in the enhancement of the impact of human activities on biological habitats, and thus impeding the migration of animals. For example, in the process of urban development, by building expressway passages in forest areas or edges, the migration passages of some organisms will be cut off and then the reproduction of organisms will be affected. In the future space planning process, the urban expansion weight cannot be directly connected with the ecological green space. There should be buffer space between urban construction space and green ecological space ¨ t¨ he forest. At the same time, to avoid urban development wrapping constructing the ecological corridor connecting the isolated green ecological space can effectively reduce the interference degree. 3.3. Driving force impact analysis Quantitative method for the influence of driving force factors on land change and landscape pattern From the perspective of land changes and landscape pattern changes in the Yangtze River Delta urban agglomeration, urban and farmland changes are the most significant. In this paper, the interpolation of land size and landscape pattern index of urban, farmland, and forest land between 2005 and 2015 is used as a dependent variable to analyze the quantitative driving force of its
The first principal component F1
Second principal component F2
0.976 0.869 0.993 −0.978 0.994 0.972 0.998 0.993 0.094 0.205
−0.212 −0.207 −0.091 −0.169 0.075 0.001 −0.019 0.088 0.99 0.677
change. According to the research of relevant scholars, ten factors that may affect the land use change are selected to analyze the driving force. Among them, there are two social driving factors (X1 total population and X2 urbanization rate), four regional economic income factors (X3GDP, X4 ratio of production, X5 ratio of three production, and X6 fiscal revenue), two economic input factors (X7 fiscal expenditure and X8 fixed assets investment), and two environmental driving factors (X9 temperature and X10 rainfall) (Table 6). Because there is a high correlation between the ten factors, the main driving force cannot be found through simple multivariate correlation analysis. As such, the principal component dimensionality reduction is first carried out for the ten factors. The analysis results showed that dimensionality reduction Kaiser-Meyer-Olkin was 0.67, which was suitable for principal component analysis. There were two principal component eigenvalues greater than 1, which could explain 89.9% factor information. The social and economic factors of X2-X8 in the principal component F1 contribute to most of the information, while the principal component F2 is mainly composed of natural environmental indicator factors. Completion of principal component analysis, the urban scale, the scale of farmland and urban LSI, farmland LSI, farmland, and town AI for standardization are the dependent variables. The first principal component F1 and the second principal component F2 are used as independent variables to establish a linear regression equation (Eq. 1).Table 7 presents the results of driving factor and the Yangtze River Delta urban land use and landscape pattern index of linear regression analysis. It can be seen that the principal component F1 of the driving factor has a very high significant correlation with the dependent variables of urban land use and landscape pattern change in the Yangtze River Delta. However, in the principal component F1, the urbanization rate of X2, X3GDP, proportion of tertiary industry of X5, fiscal revenue of X6, fiscal expenditure of X7, and fixed asset investment of X8 have a very high principal component contribution rate. Therefore, it is demonstrated that the driving force for the change of urban land use and landscape pattern in the Yangtze River Delta mainly comes from the increase
118
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
Table 7 Linear regression analysis of driving factors, land use, and landscape pattern index in the Yangtze River Delta.
F1 coefficient b1 F2 coefficient b2 Adjust R2
Urban scale
Farmland scale
Forest land scale
Cities and towns LSI
Farmland LSI
Woodland LSI
Cities and towns AI
Farmland AI
0.997* 0.055 0.972
−0.987* −0.066 0.980
−0.833* −0.218 0.800
−0.997* −0.15 0.993
0.997* −0.05 0.993
0.996* −0.42 0.993
0.995* −0.075 0.994
−0.993* −0.094 0.991
Label:*p < 0.001.
of urbanization rate, economic development, and financial investment. Driving force of population urbanization Overall, the total population of the Yangtze River Delta urban agglomeration increased from 127.32 million in 2005 to 145.04 million in 2015, with an average annual growth rate of only 0.61%, which is essentially in line with the population growth level of China. However, the urbanization rate increased rapidly from 56% in 2005 to 85% in 2015. The urban population increased from 72.1 million in 2005 to 102.1 million in 2015, with an average annual growth rate of 3.56%, much higher than the population growth rate. The newly added urbanized population therefore needs new urban carrying space. The demand for land for various construction projects such as housing, public services and commerce is increasing, resulting in a strong demand for urban construction land and an increase in the size of the city. At the same time, urbanization has also attracted some of the original farming peasants to leave the farmland, and villages and cultivated land around some towns have also been levied as construction land. Economic driver Economic factors are also the main driving factors of urban land use change in the Yangtze River Delta urban agglomeration. From 2005 to 2015, the Yangtze River Delta urban agglomeration maintained the momentum of rapid growth and also promoted the influx of various industries. The regional GDP increased from 408.96 billion yuan in 2005 to 1369.68 billion yuan in 2015. By 2015, the primary, secondary and tertiary industries have become 6%, 43% and 51% respectively, and the proportion of the primary and secondary industries has decreased yearly [27], forming an industrial pattern with the tertiary industry as the leading industry, with the urban agglomeration entering the post-industrial era. In addition, high fiscal revenue brings about high fiscal expenditure and fixed asset investment. On the one hand, the inflow of these funds improves land use intensity and expands urban space. On the other hand, the connection between cities is closer, towns are developed in succession, and the construction of roads, highways, and railways organically connects distant towns. This reduces the complexity of urban shape and further increases the interference of cities into the green ecological space. 4. Conclusions The Yangtze River Delta urban agglomeration is one of the most developed regions in China, exhibiting the fastest urban expansion and the most developed economy. Moreover, its urban construction land variations are drastic, causing complex and diverse landscape ecological types and structures. Based on the remote sensing image interpretation data of 2005, 2010, and 2015, as well as the urban construction land variation matrix model and the landscape pattern indexes, the quantitative analyses are conducted for the variations of urban construction land and landscape patterns in the Yangtze River Delta. These tests reveal the impact of human activities on the landscape pattern and green ecology space. The main conclusions of this paper can be given as follows: 1 During the years 2005–2010, the area of urban construction land variation in the Yangtze River Delta region reached
2857.29 km2 , which accounted for 1.35% of the total land area. From 2010–2015, the urban construction land variation achieved 879.23 km2 , which accounted for 4.16% of the total area. Further, the transformation speed was significantly higher than that in 2005–2010. Among them, the farmland type had the largest area reduction, and the urban areas landscape increased the most. The newly added construction land was concentrated in Shanghai, Suzhou, Wuxi, and Nanjing from 2005 to 2010. Between 2010 and 2015, the cities in the Yangtze River Delta urban agglomeration underwent a massive expansion, and the scale of land in water and forest types did not change much. 2 The landscape pattern of the Yangtze River Delta has undergone some variations. The most prominent feature is the increase of the patch number, landscape diversity, and more balanced urban construction land. Farmland has always been the base of urban construction land pattern within the entire region, but, on account of the disturbance from human activities, the advantage decreased, the degree of fragmentation increased, and the landscape shape became irregular and complex. The fragmentation and complexity of forest landscape slightly increased, and the fragmentation of urban landscape was reduced. Thus, the corresponding shape became more regular and presented a more sturdy distribution. 3 The correlation of farmland landscape to forests is declining. The correlation between towns and forests is increasing, and the interference of human activities to the green ecological space is continuously increasing. This is attributable to the fact that the shrinkage of farmland and urban expansion causes many urban fringe areas to be directly adjacent to the forest. 4 Through the principal component analysis, the driving force of land use change in the Yangtze River Delta urban agglomeration over the past 16 years was analyzed. It was determined that the main driving force of the above changes was the increase of population urbanization rate, GDP, proportion of tertiary industry, fiscal revenue, fiscal expenditure, and fixed asset investment. According to the above conclusions, the urban expansion in the Yangtze River Delta region has slightly eroded the scale of ecological space in the past decade. However, the occupied ecological space has been restored through the Conversion of Cropland to Forest Policy. The disturbance to the forest of human activities in the construction land is gradually increasing because of the change of urban landscape pattern. Therefore, this paper suggests that it is necessary to control the total ecological land scale and optimize the shape of urban and green ecological space in the future urban development of the Yangtze River Delta urban agglomeration. In future urban green space ecological planning, the green space landscape should be rationally arranged, and enough buffer space should be reserved to establish ecological protection areas so as to reduce the interference of human activities on the green ecological space. At the same time, the formation of the overall green network should be made through the establishment of ecological greenways connecting the broken patches. More attention should be given to the fact that green corridors connecting to the ecological island in towns can also effectively play a role. The dynamic analyses of landscape pattern using historical data can directly reflect the temporal and spatial differences in the urban
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
construction land variations. The conclusions of this study provide historical data for the spatial planning and the ecological environment protection of the Yangtze River Delta urban agglomeration. With the increasingly rapid urbanization in China, it is of great significance to explore whether the ecological landscape pattern of other cities is the same as that of the Yangtze River Delta region, as well as to provide policy support for sustainable development. On the one hand, it is expected that future research should expand the research method and content to a larger research area so as to protect the green space and reduce the interference of urban development on ecological space. On the other hand, this study is expected to provide a reference for the sustainable development of the eco-environment and social economy of the Yangtze River Delta urban agglomeration. Declaration of Competing Interest The authors declare no conflicts of interest. Acknowledgments This research was funded by the National Natural Science Foundation of China (No. 31570703; and the Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (No. PPZY2015A063). References [1] W. Liang, M. Yang, Urbanization, economic growth and environmental pollution: evidence from China, Sustain. Comput. Inform. Syst. 21 (2019) 1–9. 2. [2] J.G. Wu, Landscape ecology-concepts and theories, Chinese Journal of Ecology 19 (01) (2000) 42–52. [3] Q.J. Zhang, B.J. Fu, L.D. Chen, Several problems about landscape pattern change research, Scientia Geographica Sinica 23 (03) (2003) 264–270. [4] Y.Z. Yang, D.D. Xue, H. Zhang, Analysis on dynamic development of landscape fragmentation for urban forest in fast-urbanization regions, Journal of South China Agricultural University 37 (04) (2016) 97–104. [5] M.S. Li, et al., Remote sensing based characterization of fragmentation patterns and trends of the collective forests in Southern China— a case study from Yuhang City of Zhejiang Province, Journal of Nanjing Forestry University (Natural Science Edition) 34 (04) (2010) 135–139. [6] S.Q. Che, Y.C. Song, Analysis of landscape pattern of park system in city of Shanghai, Journal of Shanghai Jiaotong University (Agriculture Science) 20 (04) (2002) 322–327. [7] J. Gao, Analysis the Pattern of Urban Greenery Features in Shanghai, Chinese Landscape Architecture, Chinese Landscape Architecture 1 (2000) 53–56. [8] M. Qian, L.J. Yan, J. Zhang, Urban spatial morphology evolution in Suzhou-Wuxi-Changzhou region based on improved landscape expansion index, Scientia Geographica Sinica 4 (37) (2015) 314–321. [9] D.Y. Liu, S. Chen, The growth process and the law of gathering and dispersing in Suzhou, Wuxi and Changzhou in recent 30 years, Geographical Science 1 (32) (2012) 47–54. [10] W.L. Xiong, J.Y. Yang, Quantitative research on the evolution features of Wuxi urban spatial morphology since 1949, Modern Urban Research (2) (2016) 56–61. [11] Q. Quan, G.J. Tian, J. Wang, Comparison of spatiotemporal DynamicChanges of urban landscape patterns in four cities in Yangtze River Delta During urbanization, Chinese Journal of Ecology 4 (28) (2009) 721–727. [12] F.F. Diao, Research on the Evolution and Optimization of Landscape Patterns O Hangzhou, Zhejiang Agriculture and Forestry University, Hangzhou, 2012. [13] C.X. Hu, et al., Effects of land use change on ecosystem service value in rapid urbanization areas in Yangtze River Delta–a case study of Jiaxing city, Resources and Environment in the Yangtze Basin 3 (26) (2017) 333–340. [14] J.J. Li, Study on the Evolution of Urban Landscape Pattern and Its Ecological Security Impact in Shanghai, Fudan University, Shanghai, 2007.
119
[15] Y.H. Lü, L.D. Chen, B.J. Fu, Analysis of the coupling pathway between landscape pattern and ecological process, Advances in Geography 3 (26) (2007) 1–10. [16] L.D.;R.H.Sun Chen, H.L. Liu, Eco-environmental effects of urban landscape pattern changes: progresses, problems, and perspectives, Acta Ecol. Sin. 4 (33) (2013) 1042–1050. [17] G.S. Yang, The process and driving forces of change in arable-land area in the Yangtze River Delta during the past 50 years, Journal of Natural Resources 2 (16) (2001) 121–127. [18] B. Zhang, et al., Land use Chang and driving mechanism research in city region—the Yangtze River Delta as an example, Resources and Environment in the Yangtze Basin 1 (14) (2005) 28–33. [19] M. Liu, et al., Dynamic variations and environmental effects of land use and land cover change in the Yangtze Delta region, J. Nat. Resour. Life Sci. Educ. 8 (2010) 1533–1537. [20] B.Z. Peng, Z.G. Gao, The studies on the changes of land use and policy making on the deltaic area of the Changjiang River, Quaternary Sciences 5 (24) (2004) 506–512. [21] Z.X. Cao, Analysis and Application of Landscape Pattern in the Yangtze River Delta., Nanjing Agricultural University, Nanjing, 2007. [22] N. Xu, et al., Dynamic changes of landscape pattern in the Yangtze River Delta from 2000 to 2010, Quaternary Sciences 4 (34) (2014) 856–864. [23] F. Sun, et al., Assessment of provincial-scale forest fragmentation in chinese mainland, Journal of Northwest A&F University (Nat. Sci. Ed.) 1 (2011) 43–51. [24] S.A. Abdullah, N. Nakagoshi, Changes in agricultural landscape pattern and its spatial relationship with forestland in the state of Selangor, Peninsular Malaysia, Landsc. Urban Plan. 87 (2) (2008) 147–155. [25] M.S. Li, et al., Comparing forest fragmentation and its drivers in China and the USA with globcover v2.2, J. Environ. Manage. 91 (12) (2010) 2572–2580. [26] D.Q. Gu, et al., Evaluation of coastal wetlands degradation in Yancheng City and zonal diagnosis, Soc. Wetl. Sci. Bull. 1 (10) (2012) 1–7. [27] C.X. Li, K.N. Wu, et al., Analysis on characteristics and driving forces of land use change in beijing-tianjin-hebei region. China population, Resour. Environ. (S1) (2016) 252–255. Shiguang Shen is an associate professor and master student supervisor in the College of Landscape Architecture, Nanjing Forestry University, Nanjing China. Research direction: application of GIS & RS technology in spatial planning.
Pei Yue is a graduate student in the College of Landscape Architecture, Nanjing Forestry University, Nanjing China. Research direction: urban and rural planning technology.
Chenjing Fan is a postdoctoral fellow at the School of Architecture at Tsinghua University, Beijing China. Research direction: urban planning and landscape.
Contents lists available at ScienceDirect
Sustainable Computing: Informatics and Systems journal homepage: www.elsevier.com/locate/suscom
Quantitative assessment of land use dynamic variation using remote sensing data and landscape pattern in the Yangtze River Delta, China Shiguang Shen a,∗ , Pei Yue a , Chenjing Fan b a b
College of Landscape Architecture, Nanjing Forestry University, Nanjing, 210037, China School of Architecture, Tsinghua University, Beijing, 100084, China
a r t i c l e
i n f o
Article history: Received 8 January 2019 Received in revised form 1 May 2019 Accepted 16 July 2019 Available online 18 July 2019 Keywords: Land use Landscape pattern Urban expansion Ecological space Interference of human activity Urban agglomeration of Yangtze River Delta
a b s t r a c t The urban agglomeration of the Yangtze River Delta is one of the fastest expansions of urban regions in China. In this study, the variation of urban construction land and landscape patterns were studied based on the remote sensing image classification data of 2005, 2010, and 2015. The results show that: 1) In the region, the change rate of urban construction land in 2010–2015 is significantly higher than that of 2005–2010.; 2) The rapid expansion of urban landscape leads to the reduction of farmland, while the total area of ecological space such as that of forest and waters is almost unchanged.; 3) The farmland fragmentation is enhanced and the corresponding shape is more complicated, but the shape of urban landscape is more continuous and regular during the urban expansion process.; and 4) The interference of human activities to ecological space is increasing. Therefore, the conclusion of this study provides the basic data for the spatial planning and ecological environment protection of urban agglomeration in Yangtze River Delta. Not only that, but it also promotes the sustainable development of ecological resources in the Yangtze River Delta urban agglomeration, as well as makes social and economic contributions. © 2019 Elsevier Inc. All rights reserved.
1. Introduction The urban agglomeration in the Yangtze River Delta is located in the alluvial plain before the Yangtze River flows into the sea. It includes 29 cities such as Shanghai, Nanjing, and Hangzho, and it is an important convergence of the Yangtze River Economic Zone and ¨ ¨ the strategy of One Belt, One Road.Further, it is a region with an important strategic position in the modernized construction and comprehensive opening of China. Since the 21 st century, the rapid economic development of urban agglomeration in the Yangtze River Delta has brought about the rapid urbanization development and expansion of land construction, which has also caused the eco¨ logical space to be squeezed. As for the Yangtze River Delta Urban ¨ Agglomeration Development Planapproved in 2016, it was specifically noted that the development of urban agglomeration should firmly establish and practice the concept of ecological civilization. This suggests that one should rely on the ecological background, give full play to the unique advantages of landscape resources, and optimize the pattern of land spatial development to form the ecological urban agglomeration with green hills, clear water, and fresh
∗ Corresponding author. E-mail addresses: [email protected] (S. Shen), [email protected] (P. Yue), [email protected] (C. Fan). https://doi.org/10.1016/j.suscom.2019.07.006 2210-5379/© 2019 Elsevier Inc. All rights reserved.
air. In the process of urbanization, the problem of urban ecological environment is serious. It is necessary to integrate the concept of ecological civilization into urban development and improve the efficiency of urban space utilization [1]. To analyze the variations of urban space and ecological space, an increasing number of scholars use landscape pattern measurement as a research method to study the arrangement characteristics and the variation of landscape patch with different shapes and sizes [2]. As for this research, attention has been given to the pattern index measurement and the spatial characteristic statistics [3]. Moreover, the conclusion has great significance for reviewing the effects of urbanization policies, strengthening ecological protection, as well as for optimizing and developing the environment [4–7]. In the field of urban planning and geography, the research of urban construction land variation in the Yangtze River Delta region has garnered much attention. Many studies have been conducted for the dynamic pattern variation of the whole Yangtze River Delta and the inner metropolitan region among Suzhou and Wuxi, as well as Changzhou [8–10], Shanghai, Nanjing, and Hangzhou [11–14]. Furthermore, these studies provided detailed and fundamental data to support the formulation of an environmentally friendly plan. The existing research mainly focused on the quantification of the pattern and the variation analyses of different landscape types, while simultaneously lacking the impact of land-use landscape pattern variation on the surrounding ecological
112
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
environment [15,16]. In addition, these studies focused more on the entire land area of the region [17–20], but ignored the horizontal inter-comparison between cities within the region. The quantitative studies on the spatial pattern variation of ecology caused by urban expansion were not conducted [21,22]. The urban agglomeration of the Yangtze River Delta is selected as an example in this study, and the remote sensing image data in 2005, 2010, and 2015 are analyzed to reveal the land development, the utilization of construction land, the conversion of farmland, as well as the process and characteristics of green ecological pattern variation, which is represented by the forest in the entire delta area and internal cities. It is of great significance for reviewing the urban development process of the Yangtze River Delta urban agglomeration, especially in terms of analyzing the effects of sustainable development policies, assessing the influences of ecological space restoration, and formulating measures for sustainable socioeconomic development. 2. Research method
information. At the same time, assist remote sensing images are used to verify and quantitatively analyze the acquired landscape categories and images. 2.3. Research methods This research is divided into four steps. First, the dynamic variation transfer matrix analysis of different lands is conducted to study the area variation at different stages based on the entire Yangtze River Delta urban agglomeration. Second, the characteristic index calculation is performed on the landscape pattern of the entire Yangtze River Delta urban agglomeration, so as to analyze the variations of landscape matrix, uniformity, landscape fragmentation, and shape. Third, the interference process between the land for human activities (town and farmland) and the ecological green space is described by using the correlation index of adjacent urban construction land probability. Finally, the driving force of the change described in this paper is analyzed by principal component analysis.
2.1. Case overview The Yangtze River Delta urban agglomeration composed of 26 cities is located in the alluvial plain before the Yangtze River flows into the sea. These cities include Shanghai, Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yancheng, Yangzhou, Zhenjiang, Taizhou, Hangzhou, Ningbo, Jiaxing, Huzhou, Shaoxing, Jinhua, Zhoushan, Taizhou, Hefei, Wuhu, Ma anshan Tongling, Anqing, Zhangzhou, Chizhou, and Xuancheng. Further, the corresponding coordinate ranges from 32◦ 34 to 29◦ 20 north latitude, and 115◦ 46 to 123◦ 25 east longitude (Fig. 1), with plains covering the north of the Yangtze River and hills found in the south of the Yangtze River. Moreover, the rainfall is abundant, the soil is fertile, and the water system is well developed. To continue, the land area is 21.17 million square miles – accounting for 2.2% of China’s total area – and the population is approximately 150 million. On account of its unique development advantages, the region has become one of the most developed regions in China. In 2015, the total GDP of urban agglomeration was 1355.2 billion yuan, reaching 20.02% of the national GDP. According to the research demand and the adjustment of administrative division, this paper uniformly adopts the above 26 municipal administrative division boundaries as the basis for delineating the urban administrative scope.
2.3.1. Urban construction land transition matrixes The urban construction land transition matrixes can reflect the process of mutual transformation between urban construction land types at the beginning and the end of period:
⎡ ⎢
···
aa1n
..
.. .
aij = ⎢ .. ⎣. an1
.
···
⎤ ⎥ ⎥ ⎦
(1)
ann
where n is the total number of urban construction land types; i is the land type before transformation; j is the land type after transformation; and aij is the area of i transferring to j. 2.3.2. Landscape pattern indices In this study, Shannon diversity index (SHDI) the proportion of the largest patch to the landscape area, the number of plaques, the landscape shape index, and the spatial correlation index were used to reflect the balance of urban construction land, the strength of dominant landscape, the landscape fragmentation, the shape diversity, as well as the interference of human activities on forests. The calculation method of the five indexes is shown as follows: 1. Shannon diversity index:
2.2. Data source The original data of this study come from LANDSAT remote sensing image data of 2005, 2010, and 2015, which originates from National Resource Environment Database and respectively corresponds with the urban construction land situation of “Tenth Five-Year Plan,” “Eleventh Five-Year Plan,” and “Twelfth Five-Year Plan.” Further, the data from the years 2005 and 2010 come from LANDSAT4/5 data, while the data from the year 2015 come from LANDSAT8 data. The remote sensing data are manually interpreted to obtain a digital distribution map of urban construction land, which is divided into six types: farmland, woodland, grassland, waters, construction land, and unutilized land. After proofreading the national land survey data, it is suggested that the correct classification rate of farmland and construction land (including rural residential area) is not less than 95%, and the correct rates of grassland, woodland, and waters are not less than 90%. In this paper, Remote Sensing and Geographic Information System technology can be applied to study the changes of landscape patterns. RS technology can study the process of dynamic change of landscape pattern while simultaneously classifying it. GIS assists in the research of landscape pattern in the classification of landscape categories, the extraction, and analysis of landscape index statistical
a11
SHDI =
m
(pi ln pi )
i=1
where Pi is the area ratio of landscape i, and m is the total number of landscape types. The SHDI is usually used to reflect the unbalanced distribution of plaque types. If the SHDI is close to 0, the entire landscape simply consists of one type-dominated landscape. When the value of the SHDI increases, the plaque type is increased or the landscape presents a balanced distribution. In this study, this index is used to reflect the overall land-use equalization variation in the Yangtze River Delta. 2. Largest patch index max aj
LPI =
1≤j≤n
A
(100)
(3)
where A is the total area of the entire landscape, and aj is the area of landscape patch. The largest patch index (LPI) is essentially equal to the proportion of the largest plaque to the entire landscape area, which is helpful in determining the advantaged type of landscape. The index variation can reflect the direction and strength of human activities. In this study, this index is used to determine the dom-
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
113
Fig. 1. Study Area.
inant landscape variation and the dominant level of the Yangtze River Delta region. 3. Number of patch NP = n
(4)
where n is the total number of plaques. The number of patch (NP) is equal to the total number of landscape plaques, which is usually used to describe the heterogeneity and fragmentation of the entire landscape. As NP increases, the fragmentation enhances, and the unfavorable species migration also increases. In this study, this index is used to reflect the variation of urban construction land fragmentation in the overall and internal cities of the Yangtze River Delta. 4. Landscape Shape Index LSI =
0.25 × E √ A
(5)
where E is the total length of all plaque edges, and A is the total area of landscape. The Landscape Shape Index (LSI) is an index of the plaque discretization or aggregation. As LSI increases, the complexity and dispersion enhances. This index is used to reflect the urban construction landscape variation of cities within the Yangtze River Delta urban agglomeration. 5. Association Index AI =
Ef −i Ef
and reproduction are hindered, as the interference index is much higher as AI reaches 100%. The ecological isolated island can be observed (Fig. 2.b, c). As a result, the number of species of wild animals and plants declines, and the construction of cities and towns takes up a large amount of land and the ecological environment of wild animals is seriously affected. In this study, this index is used to reflect the impact of human activities in farmland and towns on the forests within the Yangtze River Delta urban agglomeration. 2.3.3. Analysis of driving force Principal Component Analysis (PCA) was used to analyze the driving force of urban construction land change in the Yangtze River Delta urban agglomeration from 2005 to 2015. As an important tool in multivariate statistical analysis, PCA could reduce the dimension of several correlated factor variables and reflect most information of the original variables. This paper will use this method to find several principal component factors (F1, F2. . .). Following this, the principal component factors and the interpolated land use and landscape pattern change dependent variable (Y) can be used for regression analysis (Eq. 7), so as to determine which factors are the main driving forces of changes. Y = b1F1 + b2F2 + b3F3 + . . . + c
(7)
3. Results and analyses × 100%
(6)
where Ef -i is the total perimeter of land, which is the total perimeter of the farmland associated with the forest and the urban land in this paper. Ef is the total perimeter of the forest plaque. The Association Index (AI) can reflect the effect of human disturbance on the forests [23–25], as well as the intensity of mutual interference between two types of landscapes with the same scale and landscape index. As for (a) the forest land and (b) the town depicted in Fig. 2, when urban construction is developing, towns are gradually directly connected with the surrounding forest. The biological activities in the forest are unstable, and the migration
The interpretation results of three remote sensing images in 2005, 2010, and 2015 are shown in Fig. 3. Owing to space constraints, this paper mainly discusses the indexes of farmland, town, and forest landscape. 3.1. Analyses of urban construction land transfer The transfer matrixes of urban construction land type for the Yangtze River Delta region during 2005–2010 and 2010–2015 are shown in Table 1 and Table 2, respectively. Meanwhile, the variations of urban, farmland, and forest space are shown in Fig. 4 and
114
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
Fig. 2. The woodland interference index (AI) for different urban expansion modes.
Fig. 3. The interpretation results of remote sensing images for the Yangtze River Delta urban agglomeration in (a) 2005, (b) 2010, and (c) 2015.
Table 1 Yangtze River Delta urban agglomeration area of urban construction land transition matrixes during.2005–2010.
Farmland Forest Grassland Water Construction land Unutilized land Area in 2010
Farmland
Forest
Grassland
Water
Construction land
Unutilized land
Area in 2005
106240.02 2.17 0.21 7.81 0.94 0.00 106251.15
22.43 57896.53 0.20 0.49 0.69 0.08 57,920.41
2.90 24.80 7619.90 1.46 0.00 0.30 7649.36
115.84 2.18 13.24 17096.39 1.68 0.00 17,229.32
2493.98 104.88 13.37 46.82 19955.09 0.43 22,614.57
0.00 0.39 0.00 0.00 0.00 35.07 35.46
108875.17 58030.94 7646.92 17152.96 19958.40 35.87
Table 2 Urban construction land type area transfer matrix of Yangtze River Delta region during 2010–2015.
Farmland Forest Grassland Water Construction land Unutilized land Area in 2015
Farmland
Forest
Grassland
Water
Construction land
Unutilized land
Area in 2010
100258.57 72.53 121.25 386.79 427.97 0.03 101267.14
73.24 57383.20 5.60 3.80 23.22 1.82 57,490.88
37.08 22.31 7143.79 139.00 32.64 0.00 7374.82
570.64 22.16 265.26 16288.84 317.12 0.94 17,464.96
5273.02 387.13 110.23 330.17 21796.87 1.89 27,899.30
38.60 33.08 3.23 80.72 16.76 30.78 203.17
106251.15 57,920.41 7649.36 17,229.32 22,614.57 35.46
Fig. 5. According to the results, the following observations can be noted. 1. Since 2005, the urban construction land of the Yangtze River Delta region has experienced large-scale variation. During the years 2005–2010, the area variation of urban construction land reached 2857.29 km2 , accounting for 1.35% of the total land area. From 2010–2015, the area variation of urban construction land reached 879.23 km2 , which accounted for 4.16% of the total land area. Above
all, the transformation speed is significantly higher than that of 2005–2010. 2. The farmland has the largest area reduction, which is mainly converted into urban area. From 2005–2010, the farmland decreased by 2653.15 km2 , and again decreased by 5992.58 km2 in the next five years with a faster reduction rate. On the other hand, the town scale gradually increased. From 2005–2010, the town scale increased by 2569.48 km2 , and then increased again
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
115
Fig. 4. Variations of urban, farmland, and forest space during.2005–2010.
Fig. 5. Variations of urban, farmland, and forest space during 2010–2015.
by 6102.44 km2 during 2010–2015. According to the statistics, it can be seen that the expansion speed continuously increases. The increased population, the urban construction, and the large influx of foreign capital are the main factors for the rapid growth of construction land. Although some forest land and digging water surface have become urban land, the conversion of cropland to forest or digging fish ponds has made the total area of waters and forest space change little. Moreover, the urban expansion is basically consistent with the farmland reduction. 3. As for the city scale, the newly added construction land is concentrated in Shanghai, the region surrounding Suzhou, Wuxi, Changzhou, and Nanjing, as well as other plain areas in the lower Yangtze River during the years 2005–2010. By 2010–2015, the Yangtze River Delta urban agglomeration experienced large-scale expansion, and the farmland with the same size also converted into the construction land. It is worth noting that a large number of urban land types (mainly Yantian) in the eastern part of Yancheng have become water landscapes (culture ponds), grasslands (wetlands), and farmlands within the last ten years [26]. As for Hangzhou, a land-building project was carried out to convert some waters into farmland along the bank of Qiantang River (Fig. 4 and Fig. 5). 3.2. The dynamic variations in landscape patterns General variations of landscape pattern in the Yangtze River Delta region
Table 3 Landscape pattern index value.
LPI SHDI NP
2005
2010
2015
13.66 1.24 138104
13.48 1.27 138679
13.06 1.30 138700
The general variations of landscape pattern in the Yangtze River Delta region in 2005, 2010, and 2015 are exhibited in Table 3. As for these three periods of landscape pattern data, the farmland area is the largest with the largest LPI patch. Therefore, the farmland is the base of urban construction land pattern for the entire region. The LPI index decreases from 13.66 in 2005 to 13.06 in 2015, indicating that the farmland matrix area degraded and the human interference constantly increased. The NP index increased from 138,104 in 2005 to 138,700 in 2015, signifying that the general landscape pattern was constantly fragmented. The Shannon’s diversity index decreased from 13.66 in 2005 to 13.06 in 2015, denoting that human activities have an increased impact on urban construction land in the Yangtze River Delta. Further, the landscape diversity has increased and the overall urban construction land has become more balanced. Landscape pattern variations in farmland, forest, and urban areas of the Yangtze River Delta region urban agglomeration. Table 4 presents the general situation of landscape fragmentation and shape index variations of farmland, forest, and urban types
116
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
Fig. 6. The NP values of farmland, forest, and urban landscape fragmentation index in different cities of the Yangtze River Delta urban agglomeration.
Fig. 7. The LSI values of farmland, forest, and urban landscape shape indices in different cities of the Yangtze River Delta urban agglomeration.
Table 4 Farmland, forest, and urban landscape index values for different periods in the Yangtze River Delta.
NP of Farmland NP of Forest NP of Urban LSI of Farmland LSI of Forest LSI of Urban
2005
2010
2015
13910 16279 92840 265.65 170.54 315.66
14879 16442 90445 269.71 171.32 305.47
16394 16618 87298 276.14 172.38 287.88
in the Yangtze River Delta urban agglomeration in 2005, 2010, and 2015. Further, the city statistics are shown in Fig. 6 and Fig. 7. As for the NP index of fragmentation, the farmland in each city has a constant broken state. This is attributable to the fact that urban development blocks the continuity of farmland. As for most of the areas, the fragmentation of forests either does not change or just increases a little, while the fragmentation of Nantong, Suzhou, and Yancheng decreased because the number of original forest plaques in these cities is not large, and the development of urban areas leads to the disappearance of small forest plaques. The continuous development of towns makes most fragmentation of urban landscapes unchanged or continuously increases. However, the fragmentation increase seen in Xuancheng, Zhoushan, Taizhou, Ningbo, and Huzhou is likely attributable to the number of mountains found in ¨ ¨ phenomenon of development within these areas and the enclave these cities [7,8]. As for the LSI, the complexity of farmland interference of urban development is increasing. However, the complication of forest increased a little, while the landscape shape index of Nantong, Suzhou, Yangzhou, and Yancheng decreased. The disappearance of small forest patch and the newly added forest land being adjacent to
Table 5 The AI value of the forest association index of farmland and urban landscape in the Yangtze River Delta in different periods.
Farmland AI Urban AI
2005
2010
2015
74.95% 4.93%
74.12% 5.74%
72.04% 7.59%
the original forest land also causes these phenomena. To continue, the expansion of urban scale simultaneously develops, reducing the general complexity of urban area but increasing the complexity of urban landscape shapes in Zhoushan and Taizhou. Hangzhou, Huzhou, Ningbo, and Shaoxing completed the increasing process and reduced the shape complexity because of the many mountains within these areas and cities. The enclave type development is the first stage, and the development of being “filled” is the one that follows [8]. Human activities interfere with the forest Table 5 shows the AI value for the forest association index of farmland and urban landscape in the Yangtze River Delta urban agglomeration in the years 2005, 2010, and 2015, whereas the city situation is shown in Fig. 8. Although the total area of forests is not severely reduced, the correlation index AI between farmland and forest decreases as the area of farmland shrinks, indicating that the human activities in farmland have a reduced degree of disturbance to forests. However, many urban fringe areas are directly adjacent to the forests where the AI was increasing, and the interference intensity was also increasing. The above conclusions can also be observed for the city scale, but it should be noted that Yancheng differs from other cities where the AI value for the impact of urban landscape on forests decreases. This is because a large number of
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
117
Fig. 8. The AI value of the forest association index of farmland and urban landscape in the Yangtze River Delta urban agglomeration during different periods.
Table 6 The principal component contribution rate of driving factors. Indicators Population
Economic income
Economic spending Environment
X1 total population X2 urbanization rate X3GDP X4 Secundiparity proportion X5 Tertiary industry proportion X6 fiscal revenue X7 fiscal spending X8 fixed assets investment X9 the temperature X10 rainfall
salt pans in the eastern part of Yancheng change into other types of landscape, reducing the interference on surrounding woodland. As for the horizontal comparison between cities, the urban AI value of Suzhou reached 39.85% in 2015, which is the highest among the Yangtze River Delta urban agglomeration. It is suggested that many green ecological spaces in Suzhou are directly adjacent to the city and are disturbed by the city. Further, the forest correlation index of cities in the central part of the Yangtze River Delta urban agglomeration – such as Maanshan, Nanjing, Taizhou, Wuxi, Yancheng, Zhenjiang, Shanghai, and Jiaxing – is also high, indicating that the activities of humans in the built-up areas in these cities are distributed to the forest. The reason may be that plains dominate these cities, and urban expansion will “wrap” the forest in them. The increase in the degree of disturbance AI caused by urban expansion to forests will result in the enhancement of the impact of human activities on biological habitats, and thus impeding the migration of animals. For example, in the process of urban development, by building expressway passages in forest areas or edges, the migration passages of some organisms will be cut off and then the reproduction of organisms will be affected. In the future space planning process, the urban expansion weight cannot be directly connected with the ecological green space. There should be buffer space between urban construction space and green ecological space ¨ t¨ he forest. At the same time, to avoid urban development wrapping constructing the ecological corridor connecting the isolated green ecological space can effectively reduce the interference degree. 3.3. Driving force impact analysis Quantitative method for the influence of driving force factors on land change and landscape pattern From the perspective of land changes and landscape pattern changes in the Yangtze River Delta urban agglomeration, urban and farmland changes are the most significant. In this paper, the interpolation of land size and landscape pattern index of urban, farmland, and forest land between 2005 and 2015 is used as a dependent variable to analyze the quantitative driving force of its
The first principal component F1
Second principal component F2
0.976 0.869 0.993 −0.978 0.994 0.972 0.998 0.993 0.094 0.205
−0.212 −0.207 −0.091 −0.169 0.075 0.001 −0.019 0.088 0.99 0.677
change. According to the research of relevant scholars, ten factors that may affect the land use change are selected to analyze the driving force. Among them, there are two social driving factors (X1 total population and X2 urbanization rate), four regional economic income factors (X3GDP, X4 ratio of production, X5 ratio of three production, and X6 fiscal revenue), two economic input factors (X7 fiscal expenditure and X8 fixed assets investment), and two environmental driving factors (X9 temperature and X10 rainfall) (Table 6). Because there is a high correlation between the ten factors, the main driving force cannot be found through simple multivariate correlation analysis. As such, the principal component dimensionality reduction is first carried out for the ten factors. The analysis results showed that dimensionality reduction Kaiser-Meyer-Olkin was 0.67, which was suitable for principal component analysis. There were two principal component eigenvalues greater than 1, which could explain 89.9% factor information. The social and economic factors of X2-X8 in the principal component F1 contribute to most of the information, while the principal component F2 is mainly composed of natural environmental indicator factors. Completion of principal component analysis, the urban scale, the scale of farmland and urban LSI, farmland LSI, farmland, and town AI for standardization are the dependent variables. The first principal component F1 and the second principal component F2 are used as independent variables to establish a linear regression equation (Eq. 1).Table 7 presents the results of driving factor and the Yangtze River Delta urban land use and landscape pattern index of linear regression analysis. It can be seen that the principal component F1 of the driving factor has a very high significant correlation with the dependent variables of urban land use and landscape pattern change in the Yangtze River Delta. However, in the principal component F1, the urbanization rate of X2, X3GDP, proportion of tertiary industry of X5, fiscal revenue of X6, fiscal expenditure of X7, and fixed asset investment of X8 have a very high principal component contribution rate. Therefore, it is demonstrated that the driving force for the change of urban land use and landscape pattern in the Yangtze River Delta mainly comes from the increase
118
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
Table 7 Linear regression analysis of driving factors, land use, and landscape pattern index in the Yangtze River Delta.
F1 coefficient b1 F2 coefficient b2 Adjust R2
Urban scale
Farmland scale
Forest land scale
Cities and towns LSI
Farmland LSI
Woodland LSI
Cities and towns AI
Farmland AI
0.997* 0.055 0.972
−0.987* −0.066 0.980
−0.833* −0.218 0.800
−0.997* −0.15 0.993
0.997* −0.05 0.993
0.996* −0.42 0.993
0.995* −0.075 0.994
−0.993* −0.094 0.991
Label:*p < 0.001.
of urbanization rate, economic development, and financial investment. Driving force of population urbanization Overall, the total population of the Yangtze River Delta urban agglomeration increased from 127.32 million in 2005 to 145.04 million in 2015, with an average annual growth rate of only 0.61%, which is essentially in line with the population growth level of China. However, the urbanization rate increased rapidly from 56% in 2005 to 85% in 2015. The urban population increased from 72.1 million in 2005 to 102.1 million in 2015, with an average annual growth rate of 3.56%, much higher than the population growth rate. The newly added urbanized population therefore needs new urban carrying space. The demand for land for various construction projects such as housing, public services and commerce is increasing, resulting in a strong demand for urban construction land and an increase in the size of the city. At the same time, urbanization has also attracted some of the original farming peasants to leave the farmland, and villages and cultivated land around some towns have also been levied as construction land. Economic driver Economic factors are also the main driving factors of urban land use change in the Yangtze River Delta urban agglomeration. From 2005 to 2015, the Yangtze River Delta urban agglomeration maintained the momentum of rapid growth and also promoted the influx of various industries. The regional GDP increased from 408.96 billion yuan in 2005 to 1369.68 billion yuan in 2015. By 2015, the primary, secondary and tertiary industries have become 6%, 43% and 51% respectively, and the proportion of the primary and secondary industries has decreased yearly [27], forming an industrial pattern with the tertiary industry as the leading industry, with the urban agglomeration entering the post-industrial era. In addition, high fiscal revenue brings about high fiscal expenditure and fixed asset investment. On the one hand, the inflow of these funds improves land use intensity and expands urban space. On the other hand, the connection between cities is closer, towns are developed in succession, and the construction of roads, highways, and railways organically connects distant towns. This reduces the complexity of urban shape and further increases the interference of cities into the green ecological space. 4. Conclusions The Yangtze River Delta urban agglomeration is one of the most developed regions in China, exhibiting the fastest urban expansion and the most developed economy. Moreover, its urban construction land variations are drastic, causing complex and diverse landscape ecological types and structures. Based on the remote sensing image interpretation data of 2005, 2010, and 2015, as well as the urban construction land variation matrix model and the landscape pattern indexes, the quantitative analyses are conducted for the variations of urban construction land and landscape patterns in the Yangtze River Delta. These tests reveal the impact of human activities on the landscape pattern and green ecology space. The main conclusions of this paper can be given as follows: 1 During the years 2005–2010, the area of urban construction land variation in the Yangtze River Delta region reached
2857.29 km2 , which accounted for 1.35% of the total land area. From 2010–2015, the urban construction land variation achieved 879.23 km2 , which accounted for 4.16% of the total area. Further, the transformation speed was significantly higher than that in 2005–2010. Among them, the farmland type had the largest area reduction, and the urban areas landscape increased the most. The newly added construction land was concentrated in Shanghai, Suzhou, Wuxi, and Nanjing from 2005 to 2010. Between 2010 and 2015, the cities in the Yangtze River Delta urban agglomeration underwent a massive expansion, and the scale of land in water and forest types did not change much. 2 The landscape pattern of the Yangtze River Delta has undergone some variations. The most prominent feature is the increase of the patch number, landscape diversity, and more balanced urban construction land. Farmland has always been the base of urban construction land pattern within the entire region, but, on account of the disturbance from human activities, the advantage decreased, the degree of fragmentation increased, and the landscape shape became irregular and complex. The fragmentation and complexity of forest landscape slightly increased, and the fragmentation of urban landscape was reduced. Thus, the corresponding shape became more regular and presented a more sturdy distribution. 3 The correlation of farmland landscape to forests is declining. The correlation between towns and forests is increasing, and the interference of human activities to the green ecological space is continuously increasing. This is attributable to the fact that the shrinkage of farmland and urban expansion causes many urban fringe areas to be directly adjacent to the forest. 4 Through the principal component analysis, the driving force of land use change in the Yangtze River Delta urban agglomeration over the past 16 years was analyzed. It was determined that the main driving force of the above changes was the increase of population urbanization rate, GDP, proportion of tertiary industry, fiscal revenue, fiscal expenditure, and fixed asset investment. According to the above conclusions, the urban expansion in the Yangtze River Delta region has slightly eroded the scale of ecological space in the past decade. However, the occupied ecological space has been restored through the Conversion of Cropland to Forest Policy. The disturbance to the forest of human activities in the construction land is gradually increasing because of the change of urban landscape pattern. Therefore, this paper suggests that it is necessary to control the total ecological land scale and optimize the shape of urban and green ecological space in the future urban development of the Yangtze River Delta urban agglomeration. In future urban green space ecological planning, the green space landscape should be rationally arranged, and enough buffer space should be reserved to establish ecological protection areas so as to reduce the interference of human activities on the green ecological space. At the same time, the formation of the overall green network should be made through the establishment of ecological greenways connecting the broken patches. More attention should be given to the fact that green corridors connecting to the ecological island in towns can also effectively play a role. The dynamic analyses of landscape pattern using historical data can directly reflect the temporal and spatial differences in the urban
S. Shen et al. / Sustainable Computing: Informatics and Systems 23 (2019) 111–119
construction land variations. The conclusions of this study provide historical data for the spatial planning and the ecological environment protection of the Yangtze River Delta urban agglomeration. With the increasingly rapid urbanization in China, it is of great significance to explore whether the ecological landscape pattern of other cities is the same as that of the Yangtze River Delta region, as well as to provide policy support for sustainable development. On the one hand, it is expected that future research should expand the research method and content to a larger research area so as to protect the green space and reduce the interference of urban development on ecological space. On the other hand, this study is expected to provide a reference for the sustainable development of the eco-environment and social economy of the Yangtze River Delta urban agglomeration. Declaration of Competing Interest The authors declare no conflicts of interest. Acknowledgments This research was funded by the National Natural Science Foundation of China (No. 31570703; and the Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (No. PPZY2015A063). References [1] W. Liang, M. Yang, Urbanization, economic growth and environmental pollution: evidence from China, Sustain. Comput. Inform. Syst. 21 (2019) 1–9. 2. [2] J.G. Wu, Landscape ecology-concepts and theories, Chinese Journal of Ecology 19 (01) (2000) 42–52. [3] Q.J. Zhang, B.J. Fu, L.D. Chen, Several problems about landscape pattern change research, Scientia Geographica Sinica 23 (03) (2003) 264–270. [4] Y.Z. Yang, D.D. Xue, H. Zhang, Analysis on dynamic development of landscape fragmentation for urban forest in fast-urbanization regions, Journal of South China Agricultural University 37 (04) (2016) 97–104. [5] M.S. Li, et al., Remote sensing based characterization of fragmentation patterns and trends of the collective forests in Southern China— a case study from Yuhang City of Zhejiang Province, Journal of Nanjing Forestry University (Natural Science Edition) 34 (04) (2010) 135–139. [6] S.Q. Che, Y.C. Song, Analysis of landscape pattern of park system in city of Shanghai, Journal of Shanghai Jiaotong University (Agriculture Science) 20 (04) (2002) 322–327. [7] J. Gao, Analysis the Pattern of Urban Greenery Features in Shanghai, Chinese Landscape Architecture, Chinese Landscape Architecture 1 (2000) 53–56. [8] M. Qian, L.J. Yan, J. Zhang, Urban spatial morphology evolution in Suzhou-Wuxi-Changzhou region based on improved landscape expansion index, Scientia Geographica Sinica 4 (37) (2015) 314–321. [9] D.Y. Liu, S. Chen, The growth process and the law of gathering and dispersing in Suzhou, Wuxi and Changzhou in recent 30 years, Geographical Science 1 (32) (2012) 47–54. [10] W.L. Xiong, J.Y. Yang, Quantitative research on the evolution features of Wuxi urban spatial morphology since 1949, Modern Urban Research (2) (2016) 56–61. [11] Q. Quan, G.J. Tian, J. Wang, Comparison of spatiotemporal DynamicChanges of urban landscape patterns in four cities in Yangtze River Delta During urbanization, Chinese Journal of Ecology 4 (28) (2009) 721–727. [12] F.F. Diao, Research on the Evolution and Optimization of Landscape Patterns O Hangzhou, Zhejiang Agriculture and Forestry University, Hangzhou, 2012. [13] C.X. Hu, et al., Effects of land use change on ecosystem service value in rapid urbanization areas in Yangtze River Delta–a case study of Jiaxing city, Resources and Environment in the Yangtze Basin 3 (26) (2017) 333–340. [14] J.J. Li, Study on the Evolution of Urban Landscape Pattern and Its Ecological Security Impact in Shanghai, Fudan University, Shanghai, 2007.
119
[15] Y.H. Lü, L.D. Chen, B.J. Fu, Analysis of the coupling pathway between landscape pattern and ecological process, Advances in Geography 3 (26) (2007) 1–10. [16] L.D.;R.H.Sun Chen, H.L. Liu, Eco-environmental effects of urban landscape pattern changes: progresses, problems, and perspectives, Acta Ecol. Sin. 4 (33) (2013) 1042–1050. [17] G.S. Yang, The process and driving forces of change in arable-land area in the Yangtze River Delta during the past 50 years, Journal of Natural Resources 2 (16) (2001) 121–127. [18] B. Zhang, et al., Land use Chang and driving mechanism research in city region—the Yangtze River Delta as an example, Resources and Environment in the Yangtze Basin 1 (14) (2005) 28–33. [19] M. Liu, et al., Dynamic variations and environmental effects of land use and land cover change in the Yangtze Delta region, J. Nat. Resour. Life Sci. Educ. 8 (2010) 1533–1537. [20] B.Z. Peng, Z.G. Gao, The studies on the changes of land use and policy making on the deltaic area of the Changjiang River, Quaternary Sciences 5 (24) (2004) 506–512. [21] Z.X. Cao, Analysis and Application of Landscape Pattern in the Yangtze River Delta., Nanjing Agricultural University, Nanjing, 2007. [22] N. Xu, et al., Dynamic changes of landscape pattern in the Yangtze River Delta from 2000 to 2010, Quaternary Sciences 4 (34) (2014) 856–864. [23] F. Sun, et al., Assessment of provincial-scale forest fragmentation in chinese mainland, Journal of Northwest A&F University (Nat. Sci. Ed.) 1 (2011) 43–51. [24] S.A. Abdullah, N. Nakagoshi, Changes in agricultural landscape pattern and its spatial relationship with forestland in the state of Selangor, Peninsular Malaysia, Landsc. Urban Plan. 87 (2) (2008) 147–155. [25] M.S. Li, et al., Comparing forest fragmentation and its drivers in China and the USA with globcover v2.2, J. Environ. Manage. 91 (12) (2010) 2572–2580. [26] D.Q. Gu, et al., Evaluation of coastal wetlands degradation in Yancheng City and zonal diagnosis, Soc. Wetl. Sci. Bull. 1 (10) (2012) 1–7. [27] C.X. Li, K.N. Wu, et al., Analysis on characteristics and driving forces of land use change in beijing-tianjin-hebei region. China population, Resour. Environ. (S1) (2016) 252–255. Shiguang Shen is an associate professor and master student supervisor in the College of Landscape Architecture, Nanjing Forestry University, Nanjing China. Research direction: application of GIS & RS technology in spatial planning.
Pei Yue is a graduate student in the College of Landscape Architecture, Nanjing Forestry University, Nanjing China. Research direction: urban and rural planning technology.
Chenjing Fan is a postdoctoral fellow at the School of Architecture at Tsinghua University, Beijing China. Research direction: urban planning and landscape.