Remote Sensing-Based Analysis of Landscape Pattern Evolution in Industrial Rural Areas: A Case of Southern Jiangsu, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection and Pre-Processing
2.3. Landscape Pattern Indexes
3. Results
3.1. Land Classification Results
3.2. Patch-Class-Level Landscape Pattern Results
3.3. Landscape-Level Landscape Pattern Results
4. Discussion
- (1)
- At the patch-class level, the landscape pattern evolution characteristics of industrial rural areas in Southern Jiangsu were that construction land had continuously encroached on green and cultivated land in all four study areas, exhibiting trends of expansion, centralization and the continuous consolidation of small patches into large patches during evolution and ultimately replacing cultivated land as the dominant landscape category. The result was in accordance with those in the studies of Yang, Sun et al., Ma et al., Xu et al. and Chuai et al. [31,32,33,34,35]. The reasons might be as follows: (1) Relevant studies showed that under the background of rapid socio-economic development, urban master planning is constantly updated, and relevant policies and regulations constantly promote human beings to expand the scope of urban built-up areas through deforestation, farmland reclamation and civil construction. Therefore, this study inferred that these human activities were likely to be the main reason for the changes of the area with urban landscape patches and the PLAND index [31]; and (2) in recent years, the process of urbanization development had been observed to show a strong aggregation (that is, all kinds of land shrink to the city center), and all kinds of land in the urban area were forced to “squeeze” out of the core area of the city. This phenomenon reflected in the landscape pattern in that the number of urban land patches had been reduced and the complexity increased [32].
- (2)
- At the landscape-class level, the landscape patches were evenly distributed, and the landscape patterns exhibited increased fragmentation and uniformity during evolution. The evolution of landscape patterns in the four study areas was most intense during 1981–2001. Among the four study areas, Kunshan exhibited the most significant landscape pattern evolution characteristics. This result was also in accordance with the results of the studies by Yang, Sun et al., Ma et al., Xu et al. and Chuai et al. [31,32,33,34,35]. The reason for the results might be as follows: (1) The gradual improvement of a traffic network would cause disorderly cutting of the original patch landscape, which seriously affects the circulation of the ecological function of the regional landscape. From the perspective of landscape pattern, it is embodied in the increase of the SPILT index and the decrease of the CONTAG index [33,34]; and (2) for the rapid urbanization in China, the area of urban construction land had increased sharply, and the landscape tends to be homogeneous and fragmented. These might mainly explain the decline of the SHDI index and the SHEI index [35].
- (1)
- Conserve existing cultivated land patches, appropriately expand grassland and forest land patches
- (2)
- Respect the four decades of historical progress, reasonably modify industrial landscapes
- (3)
- Capitalize on the economic and positional advantage, optimize urban–rural landscape spaces
5. Conclusions
- (1)
- At the patch-class level, construction land had continuously encroached on green and cultivated land in all four study areas, exhibiting trends of expansion, centralization and the continuous consolidation of small patches into large patches during evolution and ultimately replacing cultivated land as the dominant landscape category. Concurrently, green and cultivated land patches, which are high-quality landscape resources, were continuously segmented into a large number of small patches, which led to fragmentation during the evolution of green and cultivated land. The effects of human disturbance on water bodies should receive attention, even though they are less severe than those experienced by green and cultivated land. Urban master planning was being constantly updated, and relevant policies and regulations were constantly promoting human beings to expand the scope of urban built-up areas through deforestation, farmland reclamation and civil construction. The processes of urbanization development had been observed to show strong aggregation. And all kinds of lands in the urban area were forced to “squeeze” out of the core area of the city.
- (2)
- At the landscape level, the number of small patches and degree of landscape fragmentation generally increased in all four study areas. The landscape patches were evenly distributed and the landscape patterns exhibited increased fragmentation and uniformity during evolution. The evolution of landscape patterns in the four study areas was most intense during 1981–2001. Among the four study areas, Kunshan exhibited the most significant landscape pattern evolution characteristics. The gradual improvement of traffic network would cause disorderly cutting of the original patch landscape, which seriously affected the circulation of the ecological function of the regional landscape. For rapid urbanization in China, the areas of urban construction land had increased sharply, and the landscape tends to be homogeneous and fragmented.
- (3)
- The direct cause of landscape pattern evolution in the industrial rural areas of Southern Jiangsu was the encroachment and segmentation of green and cultivated land patches by construction land patches, and the dominant factors driving the changes in construction land patches in the industrial rural areas of Southern Jiangsu were the effects of land and population aggregation exerted by the development of township enterprises and rural industries.
- (4)
- This study concluded the landscape pattern and evolution dynamic of industrial rural areas, providing relevant fields with methods to investigate the evolution dynamic of urban–rural industry during urbanization and propose strategies for improving the landscape pattern and promoting the development of the ecological environment and tourism. It would also serve as a reference for other developing countries in Asia for sustainability of urban and rural development during industrialization, which is helpful to achieve sustainability for this region.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Prefectural-Level | County-Level | Year | Population/Million | GDP/Billion | Area/km² |
---|---|---|---|---|---|
Wuxi | Jiangyin | 1990 | 1.10 | 3.67 | 987.53 |
2001 | 1.15 | 36.50 | |||
2011 | 1.21 | 233.59 | |||
2017 | 1.25 | 348.83 | |||
Suzhou | Zhangjiagang | 1990 | 0.83 | 2.78 | 999.00 |
2001 | 0.85 | 30.68 | |||
2011 | 0.90 | 186.03 | |||
2017 | 0.92 | 260.61 | |||
Changshu | 1990 | 1.03 | 3.63 | 1264.00 | |
2001 | 1.04 | 30.30 | |||
2011 | 1.06 | 171.05 | |||
2017 | 1.07 | 227.96 | |||
Kunshan | 1990 | 0.56 | 2.01 | 927.68 | |
2001 | 0.60 | 23.08 | |||
2011 | 0.72 | 243.23 | |||
2017 | 0.86 | 352.03 |
Name | Calculation Formula | Notes |
---|---|---|
Proportion of landscape types (PLAND) [30] | PLAND = = (100) | represents the area of patches numbered ij, and A represents the total area of all patches. |
Number of patches (NP) [30] | NP = | represents the total number of patches contained in type I of the entire landscape. |
Patch density (PD) [30] | PD = | A represents the total area of all patches M represents the total number of landscape element types at a spatial resolution within the scope of the study |
Largest patch index (LPI) [30] | LPI = (100) | represents the area of patches numbered ij, and A represents the total area of all patches. |
Contagion index (CONTAG) [30] | CONTAG = (100) | represents the percentage of area occupied by type I; represents the number of IK adjacent to the plaque type. M represents the total number of patch types in the landscape |
Splitting Index (SPLIT) [30] | = | represents the number of patches. represents the total area of all patches. |
Shannon’s diversity index (SHDI) [30] | SHDI = − | represents the probability of patch type K appearing in landscape. m represents the total number of patch types in the landscape. |
Shannon’s evenness index (SHEI) [30] | SHEI = = | represents the probability of patch type K appearing in landscape. m represents the total number of patch types in the landscape. |
City | Category | Year | PLAND | NP | PD | LPI |
---|---|---|---|---|---|---|
Jiangyin | Construction Land | 1981 | 9.59 | 2186.00 | 2.24 | 0.20 |
1991 | 17.56 | 1269.00 | 1.30 | 1.23 | ||
2001 | 28.90 | 1897.00 | 1.95 | 6.42 | ||
2011 | 46.76 | 923.00 | 0.95 | 32.83 | ||
2018 | 57.61 | 228.00 | 0.23 | 56.50 | ||
Green Land | 1981 | 23.27 | 2201.00 | 2.26 | 1.97 | |
1991 | 29.23 | 797.00 | 0.82 | 1.23 | ||
2001 | 23.04 | 2836.00 | 2.91 | 0.44 | ||
2011 | 10.11 | 956.00 | 0.98 | 0.64 | ||
2018 | 18.71 | 2234.00 | 2.29 | 0.95 | ||
Water Bodies | 1981 | 9.18 | 1056.00 | 1.08 | 5.31 | |
1991 | 10.75 | 873.00 | 0.90 | 6.09 | ||
2001 | 10.97 | 480.00 | 0.49 | 5.20 | ||
2011 | 10.60 | 315.00 | 0.32 | 4.45 | ||
2018 | 10.71 | 1441.00 | 1.48 | 4.71 | ||
Cultivated Land | 1981 | 57.96 | 245.00 | 0.25 | 55.75 | |
1991 | 42.46 | 690.00 | 0.71 | 30.91 | ||
2001 | 37.09 | 359.00 | 0.37 | 47.25 | ||
2011 | 32.53 | 1010.00 | 1.04 | 19.91 | ||
2018 | 12.98 | 2365.00 | 2.42 | 0.52 |
City | Category | Year | PLAND | NP | PD | LPI |
---|---|---|---|---|---|---|
Zhangjiagang | Construction Land | 1981 | 8.86 | 1615.00 | 1.61 | 0.31 |
1991 | 10.33 | 884.00 | 0.45 | 0.48 | ||
2001 | 21.24 | 1446.00 | 1.44 | 4.54 | ||
2011 | 36.69 | 739.00 | 0.73 | 22.18 | ||
2018 | 45.40 | 411.00 | 0.41 | 38.06 | ||
Green Land | 1981 | 29.08 | 1140.00 | 1.13 | 16.94 | |
1991 | 15.98 | 1250.00 | 1.24 | 2.34 | ||
2001 | 12.71 | 2414.00 | 2.40 | 0.11 | ||
2011 | 6.98 | 869.00 | 0.86 | 0.54 | ||
2018 | 13.68 | 2033.00 | 2.02 | 0.17 | ||
Water Bodies | 1981 | 21.80 | 592.00 | 0.59 | 20.16 | |
1991 | 22.69 | 459.00 | 0.46 | 20.55 | ||
2001 | 21.03 | 281.00 | 0.28 | 20.09 | ||
2011 | 21.22 | 182.00 | 0.18 | 17.53 | ||
2018 | 21.09 | 943.00 | 0.94 | 17.40 | ||
Cultivated Land | 1981 | 40.25 | 456.00 | 0.45 | 28.14 | |
1991 | 51.01 | 161.00 | 0.16 | 49.08 | ||
2001 | 45.02 | 226.00 | 0.22 | 41.77 | ||
2011 | 35.11 | 608.00 | 0.60 | 31.55 | ||
2018 | 19.83 | 1430.00 | 1.42 | 1.92 |
City | Category | Year | PLAND | NP | PD | LPI |
---|---|---|---|---|---|---|
Changshu | Construction Land | 1981 | 7.15 | 1987.00 | 1.64 | 0.28 |
1991 | 16.11 | 1258.00 | 1.04 | 0.83 | ||
2001 | 24.26 | 2110.00 | 1.74 | 4.72 | ||
2011 | 36.69 | 964.00 | 0.80 | 25.08 | ||
2018 | 52.70 | 412.00 | 0.34 | 48.41 | ||
Green Land | 1981 | 34.12 | 1655.00 | 1.37 | 12.42 | |
1991 | 19.98 | 2627.00 | 0.86 | 0.64 | ||
2001 | 22.79 | 2293.00 | 0.70 | 2.07 | ||
2011 | 13.68 | 996.00 | 0.82 | 2.83 | ||
2018 | 15.29 | 2510.00 | 2.07 | 1.13 | ||
Water Bodies | 1981 | 13.36 | 1349.00 | 1.11 | 5.52 | |
1991 | 12.88 | 1038.00 | 0.86 | 5.30 | ||
2001 | 11.61 | 850.00 | 0.70 | 5.02 | ||
2011 | 12.79 | 484.00 | 0.40 | 3.88 | ||
2018 | 13.62 | 1414.00 | 1.17 | 3.84 | ||
Cultivated Land | 1981 | 45.36 | 811.00 | 0.67 | 37.50 | |
1991 | 51.04 | 143.00 | 0.12 | 59.43 | ||
2001 | 41.34 | 862.00 | 0.71 | 28.91 | ||
2011 | 36.85 | 919.00 | 0.76 | 26.93 | ||
2018 | 18.40 | 2008.00 | 1.66 | 0.97 |
City | Category | Year | PLAND | NP | PD | LPI |
---|---|---|---|---|---|---|
Kunshan | Construction Land | 1981 | 9.87 | 2475.00 | 2.60 | 0.25 |
1991 | 15.37 | 862.00 | 0.90 | 0.66 | ||
2001 | 42.79 | 1118.00 | 1.17 | 24.39 | ||
2011 | 41.40 | 932.00 | 0.98 | 26.52 | ||
2018 | 53.96 | 312.00 | 0.33 | 50.99 | ||
Green Land | 1981 | 12.03 | 2201.00 | 2.31 | 0.70 | |
1991 | 30.47 | 1965.00 | 2.06 | 6.04 | ||
2001 | 12.07 | 2787.00 | 2.92 | 0.25 | ||
2011 | 22.13 | 1236.00 | 1.30 | 3.60 | ||
2018 | 18.87 | 2243.00 | 2.35 | 0.69 | ||
Water Bodies | 1981 | 15.58 | 1510.00 | 1.58 | 3.48 | |
1991 | 13.43 | 944.00 | 0.99 | 3.02 | ||
2001 | 13.69 | 1472.00 | 1.54 | 1.86 | ||
2011 | 13.62 | 773.00 | 0.81 | 1.87 | ||
2018 | 14.77 | 1727.00 | 1.81 | 1.92 | ||
Cultivated Land | 1981 | 62.52 | 190.00 | 0.20 | 60.98 | |
1991 | 40.72 | 352.00 | 0.37 | 46.08 | ||
2001 | 31.45 | 1270.00 | 1.33 | 5.27 | ||
2011 | 22.85 | 1942.00 | 2.04 | 1.82 | ||
2018 | 12.39 | 2086.00 | 2.19 | 0.20 |
City | Category | Year | PLAND | NP | PD | LPI |
---|---|---|---|---|---|---|
Jiangyin | Construction Land | 1981–1991 | 83.22% | −41.95% | −41.95% | 518.57% |
1991–2001 | 64.55% | 49.49% | 49.49% | 423.80% | ||
2001–2011 | 61.80% | −51.34% | −51.34% | 411.29% | ||
2011–2018 | 23.19% | −75.30% | −75.30% | 72.09% | ||
1981–2018 | 500.94% | −89.57% | −89.57% | 28,407.87% | ||
Green Land | 1981–1991 | 32.51% | 73.44% | 73.45% | 60.33% | |
1991–2001 | 1.13% | −57.99% | −57.98% | −80.91% | ||
2001–2011 | −50.73% | 207.26% | 207.25% | −98.65% | ||
2011–2018 | −82.45% | 319.30% | 319.25% | −98.86% | ||
1981–2018 | −19.62% | 1.50% | 1.50% | −51.44% | ||
Water Bodies | 1981–1991 | 17.11% | −17.33% | −17.33% | 14.70% | |
1991–2001 | 2.07% | −45.02% | −45.01% | −14.63% | ||
2001–2011 | −3.45% | −34.38% | −34.38% | −14.41% | ||
2011–2018 | 1.03% | 357.46% | 357.43% | 5.98% | ||
1981–2018 | 16.61% | 36.46% | 36.46% | −11.17% | ||
Cultivated | 1981–1991 | 26.71% | −64.69% | −64.70% | 74.39% | |
1991–2001 | −26.75% | 181.63% | 181.65% | −44.55% | ||
2001–2011 | −12.65% | −47.97% | −47.97% | 52.85% | ||
2011–2018 | −12.28% | 181.34% | 181.34% | −57.87% | ||
1981–2018 | −77.60% | 865.31% | 865.33% | −99.07% |
City | Category | Year | PLAND | NP | PD | LPI |
---|---|---|---|---|---|---|
Zhangjiagang | Construction Land | 1981–1991 | 16.49% | −45.26% | −71.76% | 54.54% |
1991–2001 | 105.65% | 63.57% | 217.10% | 844.20% | ||
2001–2011 | 72.75% | −48.89% | −48.89% | 389.01% | ||
2011–2018 | 23.75% | −44.38% | −44.38% | 71.64% | ||
1981–2018 | 412.16% | −74.55% | −74.55% | 12,147.14% | ||
Green Land | 1981–1991 | −45.06% | 9.65% | 9.65% | −86.21% | |
1991–2001 | −20.41% | 93.12% | 93.12% | −95.25% | ||
2001–2011 | −45.08% | −64.00% | −64.00% | 389.10% | ||
2011–2018 | 95.95% | 133.95% | 133.95% | −69.15% | ||
1981–2018 | −52.95% | 78.33% | 78.34% | −99.01% | ||
Water Bodies | 1981–1991 | 4.09% | −22.47% | −22.46% | 1.93% | |
1991–2001 | −7.33% | −38.78% | −38.79% | −2.21% | ||
2001–2011 | 0.93% | −35.23% | −35.23% | −12.77% | ||
2011–2018 | −0.63% | 418.13% | 418.19% | −0.70% | ||
1981–2018 | −3.26% | 59.29% | 59.29% | −13.66% | ||
Cultivated Land | 1981–1991 | 26.71% | −64.69% | −64.70% | 74.39% | |
1991–2001 | −11.74% | 40.37% | 40.44% | −14.89% | ||
2001–2011 | −22.02% | 169.03% | 168.98% | −24.46% | ||
2011–2018 | −43.52% | 135.20% | 135.19% | −93.92% | ||
1981–2018 | −50.74% | 213.60% | 213.59% | −93.18% |
City | Category | Year | PLAND | NP | PD | LPI |
---|---|---|---|---|---|---|
Changshu | Construction Land | 1981–1991 | 125.25% | −36.69% | −36.69% | 193.36% |
1991–2001 | 50.61% | 67.73% | 67.73% | 465.91% | ||
2001–2011 | 51.24% | −54.31% | −54.31% | 430.88% | ||
2011–2018 | 43.65% | −57.26% | −57.26% | 92.99% | ||
1981–2018 | 637.03% | −79.27% | −79.27% | 16,908.96% | ||
Green Land | 1981–1991 | −41.45% | 58.73% | −37.28% | −94.87% | |
1991–2001 | 14.09% | −12.71% | −18.11% | 225.15% | ||
2001–2011 | −40.01% | −56.56% | 17.17% | 36.67% | ||
2011–2018 | 11.78% | 152.01% | 152.01% | −60.26% | ||
1981–2018 | −55.20% | 51.66% | 51.66% | −90.94% | ||
Water Bodies | 1981–1991 | −3.65% | −23.05% | −23.05% | −3.86% | |
1991–2001 | −9.87% | −18.11% | −18.11% | −5.29% | ||
2001–2011 | 10.20% | −43.06% | −43.06% | −22.78% | ||
2011–2018 | 6.47% | 192.15% | 192.17% | −0.97% | ||
1981–2018 | 1.89% | 4.82% | 4.82% | −30.37% | ||
Cultivated Land | 1981–1991 | 12.51% | −82.37% | −82.37% | 58.49% | |
1991–2001 | −19.00% | 502.80% | 502.96% | −51.36% | ||
2001–2011 | −10.87% | 6.61% | 6.61% | −6.83% | ||
2011–2018 | −50.08% | 118.50% | 118.49% | −96.40% | ||
1981–2018 | −59.45% | 147.60% | 147.62% | −97.41% |
City | Category | Year | PLAND | NP | PD | LPI |
---|---|---|---|---|---|---|
Kunshan | Construction Land | 1981–1991 | 55.85% | −65.17% | −65.17% | 159.74% |
1991–2001 | 178.30% | 29.70% | 29.71% | 3587.65% | ||
2001–2011 | −3.23% | −16.64% | −16.64% | 8.73% | ||
2011–2018 | 30.34% | −66.52% | −66.52% | 92.29% | ||
1981–2018 | 447.01% | −87.39% | −87.39% | 19,926.20% | ||
Green Land | 1981–1991 | 153.33% | −10.72% | −10.72% | 761.93% | |
1991–2001 | −60.39% | 41.83% | 41.83% | −95.84% | ||
2001–2011 | 83.36% | −55.65% | −55.65% | 1334.49% | ||
2011–2018 | −14.74% | 81.47% | 81.48% | −80.76% | ||
1981–2018 | 56.86% | 1.91% | 1.91% | −1.00% | ||
Water Bodies | 1981–1991 | −13.84% | −37.48% | −37.49% | −13.26% | |
1991–2001 | 1.96% | 55.93% | 55.94% | −38.29% | ||
2001–2011 | −0.52% | −47.49% | −47.48% | 0.28% | ||
2011–2018 | 8.46% | 123.42% | 123.41% | 2.74% | ||
1981–2018 | −5.21% | 14.37% | 14.37% | −44.84% | ||
Cultivated Land | 1981–1991 | −34.86% | 85.26% | 85.25% | −24.43% | |
1991–2001 | −22.77% | 260.80% | 260.83% | −88.57% | ||
2001–2011 | −27.37% | 52.91% | 52.91% | −65.41% | ||
2011–2018 | −45.74% | 7.42% | 7.42% | −89.22% | ||
1981–2018 | −80.17% | 997.89% | 997.94% | −99.68% |
Year | CONTAG | SPLIT | SHDI | SHEI | |
---|---|---|---|---|---|
Jiangyin | 1981 | 25.93 | 3.18 | 1.10 | 0.79 |
1991 | 22.88 | 8.02 | 1.17 | 0.84 | |
2001 | 24.24 | 4.33 | 1.15 | 0.83 | |
2011 | 28.84 | 6.53 | 1.12 | 0.81 | |
2018 | 22.25 | 3.11 | 1.14 | 0.82 | |
Zhangjiagang | 1981 | 22.75 | 2.56 | 1.27 | 0.92 |
1991 | 31.06 | 3.52 | 1.16 | 0.84 | |
2001 | 24.29 | 4.59 | 1.28 | 0.92 | |
2011 | 28.37 | 5.50 | 1.23 | 0.89 | |
2018 | 18.72 | 5.67 | 1.28 | 0.92 | |
Changshu | 1981 | 20.15 | 2.25 | 1.18 | 0.85 |
1991 | 29.92 | 2.80 | 1.06 | 0.76 | |
2001 | 13.46 | 11.13 | 1.30 | 0.93 | |
2011 | 24.21 | 16.98 | 1.20 | 0.87 | |
2018 | 17.31 | 14.23 | 1.21 | 0.87 | |
Kunshan | 1981 | 28.57 | 2.68 | 1.07 | 0.77 |
1991 | 26.55 | 4.56 | 1.11 | 0.80 | |
2001 | 15.20 | 14.90 | 1.25 | 0.90 | |
2011 | 16.25 | 13.43 | 1.29 | 0.93 | |
2018 | 19.99 | 13.83 | 1.18 | 0.85 |
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Zhu, Y.; Wang, C.; Sakai, T. Remote Sensing-Based Analysis of Landscape Pattern Evolution in Industrial Rural Areas: A Case of Southern Jiangsu, China. Sustainability 2019, 11, 4994. https://doi.org/10.3390/su11184994
Zhu Y, Wang C, Sakai T. Remote Sensing-Based Analysis of Landscape Pattern Evolution in Industrial Rural Areas: A Case of Southern Jiangsu, China. Sustainability. 2019; 11(18):4994. https://doi.org/10.3390/su11184994
Chicago/Turabian StyleZhu, Yifan, Chengkang Wang, and Takeru Sakai. 2019. "Remote Sensing-Based Analysis of Landscape Pattern Evolution in Industrial Rural Areas: A Case of Southern Jiangsu, China" Sustainability 11, no. 18: 4994. https://doi.org/10.3390/su11184994