Abstract
Urban land use mapping is critical to understanding human activities in space. The first national mapping result of essential urban land use categories of China (EULUC-China) was released in 2019. However, the overall accuracies in some of the plain cities such as Beijing, Chengdu, and Zhengzhou were lower than 50% because many parcel-based mapping units are large with mixed land uses. To address this shortcoming, we proposed an area of interest (AOI)-based mapping approach, choosing Beijing as our study area. The mapping process includes two major steps. First, grids with different sizes (i.e., 300 m, 200 m, and 100 m) were derived from original land parcels to obtain classification units with a suitable size. Then, features within these grids were extracted from Sentinel-2 spectral data, point of interest (POI), and Tencent Easygo crowdedness data. These features were classified using a random forest (RF) classifier with AOI data, resulting in a 10-category map of EULUC. Second, we superimposed the AOIs layer on classified units to do some rectification and offer more details at the building scale. The overall accuracy of the AOI layer reached 98%, and the overall accuracy of the mapping results reached 77%. This study provides a fast method for accurate geographic sample collection, which substantially reduces the amount of fieldwork for sample collection and improves the classification accuracy compared to previous EULUC mapping. The detailed urban land use map could offer more support for urban planning and environmental policymaking.
Highlights
Simple/multiple linear regression, random forest (RF), and support vector regression (SVR) were used to estimate canopy nitrogen weight of maize leaves, and the results showed that both machine learning models performed much better than linear regression [4]
We developed a classification system of urban land use categories suitable for Beijing, which can be cross-walked to essential urban land use categories (EULUC)-China
area of interest (AOI) data were used as training samples, whose size was huge, and land use within the same unit was purer
Summary
Urban areas are the places where humankind has dramatically transformed the surface, affecting the Earth’s biochemical cycles and climate from local to global scales. The concentration of transportation and industry in urban centers means that cities are point sources of CO2 and other greenhouse gases, affecting Earth’s climate. Urbanization usually reduces both species richness and evenness for most biotic communities within cities as well as native species diversity at regional and global scales. To better support the development of adaptation planning to respond to climate change, it is critical to acquire high-quality (accurate and high resolution) urban land-use data. It is difficult to meet the requirements of efficiency and accuracy of land-use mapping through traditional mapping methods of visual interpretation and mathematical statistics in rapidly urbanizing areas [3]
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