Long-term evaluation on urban intensive land use in five fast-growing cities of northern China with GEE support

Abstract

Intensive land use (ILU) is a multi-objective optimization process that aims to simultaneously improve the economic, social, and ecological benefits, as well as the carrying capacity of the land, without increasing additional land, and evaluation of the ILU over long time series has a guiding significance for rational land use. To tackle inefficient extraction of information, subjective selection of dominant factor, and lack of prediction in previous evaluation studies, this paper proposes a novel framework for evaluation and analysis of ILU by, first, using Google Earth Engine (GEE) to extract cities’ built-up land information, second, by constructing an index system that links economic, social and ecological aspects to evaluate the ILU degree, third, by applying Geodetector to identify the dominant factor on the ILU, finally, by using the S-curve to predict the degree. Based on the case study data from northern China’s five fast-growing cities (i.e., Beijing, Tianjin, Shijiazhuang, Jinan, Zhengzhou), the findings show that the ILU degree for all cities has increased over the past 30 years, with the highest growth rate between 2000 and 2010. Beijing had the highest degree in 2018, followed by Tianjin, Zhengzhou, Jinan, and Shijiazhuang. In terms of the time dimension, the dominant factor for all cities shifted from the output-value proportion of secondary and tertiary industries in the early stage to the economic density in the late stage. In terms of the space dimension, the dominant factor varied from cities. It is worth noting that economic density was the dominant factor in the two high-level ILU cities, Beijing and Tianjin, indicating that economic strength is the main driver of the ILU. Moreover, cities with high-level ILU at the current stage will grow slowly in the ILU degree from 2020 to 2035, while Zhengzhou and Jinan, whose ILU has been in the midstream recently, will grow the most among the cities.

Evaluation of Urban Intensive Land Use Degree with GEE Support: A Case Study in the Pearl River Delta Region, China

“…The entropy weighting method determines weights based on the dispersion of index values, which is relatively objective compared to other methods. Hence, this paper refers to the steps of previous studies [9,46] to calculate the indices' weights. The steps are as follows:…”

“…Here, because the land use information used to evaluate ILU in this case is primarily built-up land, only "built-up land" and "not built-up land" were labeled as samples in the classification process. According to previous studies [9,31], the NTL data can be used to select samples for both "built-up land" and "not built-up land", assuming that pixels with illumination are related to artificial structures that emit light. Obviously, this sample selection strategy is convenient and avoids the past labor-intensive manual selection of samples.…”

“…More and more scholars are currently devoted to scientifically evaluating and analyzing the ILU level, which has become a popular topic in recent years, with the ultimate aim of supporting the Sustainable Development Goals (SDG) [4,[7][8][9]. Researchers have conducted many studies on ILU evaluation.…”

“…Land use information has been incorporated into the evaluation system as a result of the advancement of satellite imagery technology and machine learning (ML). For instance, Shang [9] classified Landsat images by implementing the random forest classifier to obtain land use information, then combined the land use information with social statistics to assess five cities' ILU levels from 1990 to 2018. Wang [18] chose the Black River Basin as a typical area and calculated the Biological Abundance Index (BAI), Vegetation Cover Index (VCI), and Water Density Index (WDI) based on land use information, as well as the statistical data, to make a comprehensive evaluation of the ecological environment from 2010 to 2030.…”

Impact of Iran’s Forest Nationalization Law on Forest Cover Changes over Six Decades: A Case Study of a Zagros Sparse Coppice Oak Forest

“…Large-scale and accurate land use/cover mapping often requires a high degree of processing to obtain a large number of spectral-temporal characteristics that properly reflect the spectral variance of different land use/cover classes [ 25 ]. New advances, including the Google Earth Engine (GEE) computing platform, time series feature extraction methods, and machine learning algorithms, may enable a more reliable tool for large-scale forest cover change monitoring (e.g., degradation, deforestation, and recovery) [ 26 ]. A detailed understanding of forest cover dynamics may be achieved by using geospatial techniques, historical aerial photography, and long-term archives of satellite images (such as Landsat).…”