Abstract
Urbanisation processes inherently influence land cover (LC) and have dramatic impacts on the amount, distribution and quality of vegetation cover. The latter are the source of ecosystem services (ES) on which humans depend. However, the temporal and thematical dimensions are not documented in a comparable manner across Europe and China. Three cities in China and three cities in Europe were selected as case study areas to gain a picture of spatial urban dynamics at intercontinental scale. First, we analysed available global and continental thematic LC products as a data pool for sample selection and referencing our own mapping model. With the help of the Google Earth Engine (GEE) platform and earth observation data, an automatic LC mapping method tailored for more detailed ES features was proposed. To do so, differentiated LC categories were quantified. In order to obtain a balance between efficiency and high classification accuracy, we developed an optimal classification model by evaluating the importance of a large number of spectral, texture-based indices and topographical information. The overall classification accuracies range between 73% and 95% for different time slots and cities. To capture ES related LC categories in great detail, deciduous and coniferous forests, cropland, grassland and bare land were effectively identified. To understand inner urban options for potential new ES, dense and dispersed built-up areas were differentiated with good results. In addition, this study focuses on the differences in the characteristics of urban expansion witnessed in China and Europe. Our results reveal that urbanisation has been more intense in the three Chinese cities than in the three European cities, with an 84% increase in the entire built-up area over the last two decades. However, our results also show the results of China’s ecological restoration policies, with a total of 963 km2 of new green and blue LC created in the last two decades. We proved that our automatic mapping can be effectively applied to future studies, and the monitoring results will be useful for consecutive ES analyses aimed at achieving more environmentally friendly cities.
Highlights
IntroductionIncreased urbanisation and the expansion of urban areas cause radical changes to and the eradication and fragmentation of ecosystems and green infrastructure— in Remote Sens
To make our approach reproducible for other cities, we develop this processing workflow based on Google Earth Engine (GEE)
The results show that the seasonal index was one of the most significant variables for prediction, suggesting the value of temporal information
Summary
Increased urbanisation and the expansion of urban areas cause radical changes to and the eradication and fragmentation of ecosystems and green infrastructure— in Remote Sens. Europe is already one of the most urbanised continents in the world with about 74% of Europeans living in urban areas, while the level of urbanisation in Asia is around 50% [1]. Since intense interdependencies exist regarding the use of resources, urban growth has a tremendous impact on resource regimes and ecosystems which make urban dwellers simultaneously culprits and victims of urbanisation processes [4]. It is for this reason that the United Nations Sustainable
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