Evolution of landscape dynamics in the Yangtze River Delta from 2000 to 2020

Zhou, Zhe Chen; Wang, Jun

doi:10.2166/wcc.2022.307

Cited by 12 publications

(9 citation statements)

References 35 publications

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“…The regional green space outside the built-up area of Shanghai, which is the most seriously disturbed area in the process of rapid urbanization [35], is important in the study of its landscape spatial pattern characteristics. Previous studies on the spatial and temporal evolution of the green space landscape pattern in Shanghai were mostly based on the built-up area or the overall green space identified using remote sensing image data [36]. The green space data cover the built-up area inside and outside of the city, and it is difficult to determine the unique characteristics of the changes in the spatial pattern of the green space landscape in the areas outside the built-up area.…”

Section: Discussionmentioning

confidence: 99%

Study on the Spatial and Temporal Evolution of Regional Green Space Morphology Outside Built-Up Areas based on the Google Earth Engine and Biophysical Component Modeling

Ji,

Zhang,

et al. 2023

Land

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The spatial pattern of regional green space is an important dimension to describe and quantitatively express the characteristics of regional green spaces outside the built-up area of a city. With the expansion of urban and rural construction land, regional green space has been continuously encroached upon. This leads to a decline in regional ecological well-being and the loss of biodiversity. Based on the remote sensing data of Shanghai city from 2000 to 2020, we quantitatively studied the spatial morphological change characteristics of regional green space outside the built-up area of Shanghai city. Firstly, with the help of the GEE platform, the optimal decoding accuracy classification method was selected through machine learning (random forest, support vector machine, classification regression tree); then, based on the biophysical component (BCI) and CA binarization, the built-up area ranges for up to five time nodes were obtained; finally, through GIS spatial data analysis and processing technology, the regional green space dynamic data of Shanghai for five time nodes were extracted. Based on the above data, an analysis index system was constructed to quantitatively analyze the spatial morphology characteristics of the regional green space outside the built-up area of Shanghai. The results show that (1) the area of regional green space outside the built-up area of Shanghai had a fluctuating growth pattern of “decreasing and then increasing”. The arable land and water areas in Shanghai decreased, and the woodland area increased steadily, while the wetland and grassland areas showed a trend of first decreasing and then increasing. (2) The regional green patch fragmentation shows a fluctuating development trend of increasing, decreasing, and increasing. (3) The change in the spatial center of gravity of the regional green space in Shanghai had a high degree of consistency with the overall green space change. The center of gravity of the grasslands in the regional green space moved substantially to the northwest, while the center of gravity of the other types remained basically unchanged. This study reveals the spatial morphology characteristics of regional green spaces and provides a research method to study the dynamic changes in regional ecological resources. The results of this study can provide a scientific basis for the identification, protection, and development of regional ecological resources.

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Section: Discussionmentioning

confidence: 99%

Study on the Spatial and Temporal Evolution of Regional Green Space Morphology Outside Built-Up Areas based on the Google Earth Engine and Biophysical Component Modeling

Ji,

Zhang,

et al. 2023

Land

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“…The Yangtze River Delta is one of the most economically developed regions in China. The population was stated to be more than 200 million residents at the end of 2019 [ 38 ]. However, affected by the subtropical monsoon, there are many typhoons and rainstorms in the summer, which are characterized by the high intensity and concentrated range.…”

Section: Methodsmentioning

confidence: 99%

“…Meanwhile, the main surface cover of the Yangtze River Delta has transferred from cropland to built-in land. Shanghai, especially, has experienced a fast increase in the proportion of built-in land, from 23% in 2000 to 39% in 2020 [ 38 ]. Along with the rapid economic development and urbanization, the affected population, deaths, crops, and direct economic loss by rainstorms disasters are increasing, and the risk of rainstorm disasters in Jiangsu, Anhui, and Zhejiang has an upward tendency [ 40 , 41 ].…”

Section: Methodsmentioning

confidence: 99%

Rainstorm Disaster Risk Assessment and Influence Factors Analysis in the Yangtze River Delta, China

Deng

Tao

2022

IJERPH

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Rainstorm disasters have had a serious impact on the sustainable development of society and the economy. However, due to the complexity of rainstorm disasters, it is difficult to measure the importance of each indicator. In this paper, the rainstorm disaster risk assessment framework was systematically proposed based on the disaster system theory and a system of corresponding indicators was established. Furthermore, the genetic algorithm optimized projection pursuit and XGBoost were coupled to assess the rainstorm disaster risk and to measure the relative importance of each indicator. Finally, the Yangtze River Delta was taken as the case study area. The results show that: the rainstorm disaster risk in the eastern and southeast is higher than those in the central and northwest of the Yangtze River Delta; the total precipitation from June to September and the top ten indicators contribute 9.34% and 74.20% to the rainstorm disaster risk assessment results, respectively. The results can provide references for decision makers and are helpful for the formulation of rainstorm adaptation strategies.

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“…Landscape patterns can be quantified using the landscape pattern index, which is an effective way to reveal spatial characteristics of future land use under different simulation scenarios. Based on similar related studies [11,15,33,34], six landscape-level metrics were applied to evaluate the landscape patterns in 2035 under the four urban development scenarios (Table 2). Metrics included the number of patches (NP), landscape shape index (LSI), Simpson's diversity index (SIDI), Shannon's evenness index (SHEI), contagion (CONTAG), and aggregation index (AI).…”

Section: Landscape Pattern Analysismentioning

confidence: 99%

“…Though the YRDUA is the sixth-largest urban agglomeration in the world, it is undergoing rapid urbanization [10], which has negatively impacted the Yangtze River Delta's ecosystem value. Specifically, from 2000 to 2020, farmland area declined from 56% to 49% due to urban land expansion [11]. Moreover, the urban land-use intensity is still increasing in response to population growth and industrial agglomeration.…”

Section: Introductionmentioning

confidence: 99%

A CLUMondo Model-Based Multi-Scenario Land-Use Change Simulation in the Yangtze River Delta Urban Agglomeration, China

Zhao

Yang

et al. 2022

Sustainability

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Land-use changes have profound effects on both socio-economic development and the environment. As a result, to optimize land-use planning and management, models are often employed to identify land-use patterns and their associated driving forces. In this work, physical and socioeconomic factors within the Yangtze River Delta Urban Agglomeration (YRDUA) from 2000 to 2015 were identified, integrated, and used as the foundation for a CLUMondo model. Subsequently, the Markov model and the CLUMondo model were combined to predict land-use changes in 2035. Natural growth (NG), economic development (ED), ecological protection (EP), and coordinated social and economic development (CSE) scenarios were set according to the land-use date in the assessment. Results showed that: (1) From 2000 to 2015, urban land increased by 8139.5 km2 (3.93%), and the paddy field decreased by 7315.8 km2 (8.78%). The Kappa coefficient of the CLUMondo model was 0.86, indicating that this model can be used to predict the land-use changes of the YRDUA. (2) When this trend was used to simulate landscape patterns in 2035, the land-use structure and landscape patterns varied among the four simulated urban development scenarios. Specifically, urban land increased by 47.6% (NG), 39.6% (ED), 32.9% (EP), and 23.2% (CSE). The paddy field was still the primary landscape, with 35.85% NG, 36.95% ED, 37.01% EP, and 36.96% CSE. Furthermore, under all four scenarios, the landscape pattern tended to simplify and fragment, while connectivity and equilibrium diminished. The results provided herein are intended to elucidate the law of urban agglomeration development and aid in promoting urban sustainable development.

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Evolution of landscape dynamics in the Yangtze River Delta from 2000 to 2020

Cited by 12 publications

References 35 publications

Study on the Spatial and Temporal Evolution of Regional Green Space Morphology Outside Built-Up Areas based on the Google Earth Engine and Biophysical Component Modeling

Study on the Spatial and Temporal Evolution of Regional Green Space Morphology Outside Built-Up Areas based on the Google Earth Engine and Biophysical Component Modeling

Rainstorm Disaster Risk Assessment and Influence Factors Analysis in the Yangtze River Delta, China

A CLUMondo Model-Based Multi-Scenario Land-Use Change Simulation in the Yangtze River Delta Urban Agglomeration, China

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