Abstract
Abstract. Understanding the spatiotemporal dynamics of global urbanization over a long time series is increasingly important for sustainable development goals. The harmonized nighttime light (NTL) time-series composites created by fusing multi-source NTL observations provide a long and consistent record of the nightscape for characterizing and understanding global urban dynamics. In this study, we generated a global dataset of annual urban extents (1992–2020) using consistent NTL observations and analyzed the spatiotemporal patterns of global urban dynamics over nearly 30 years. The urbanized areas associated with locally high intensity human activities were mapped from the global NTL time-series imagery using a new stepwise-partitioning framework. This framework includes three components: (1) clustering of NTL signals to generate potential urban clusters, (2) identification of optimal thresholds to delineate annual urban extents, and (3) check of temporal consistency to correct pixel-level urban dynamics. We found that the global urban land area percentage of the Earth's land surface rose from 0.22 % to 0.69 % between 1992 and 2020. Urban dynamics over the past 3 decades at the continent, country, and city levels exhibit various spatiotemporal patterns. Our resulting global urban extents (1992–2020) were evaluated using other urban remote sensing products and socioeconomic data. The evaluations indicate that this dataset is reliable for characterizing spatial extents associated with intensive human settlement and high-intensity socioeconomic activities. The dataset of global urban extents from this study can provide unique information to capture the historical and future trajectories of urbanization and to understand and tackle urbanization impacts on food security, biodiversity, climate change, and public well-being and health. This dataset can be downloaded from https://doi.org/10.6084/m9.figshare.16602224.v1 (Zhao et al., 2021).
Highlights
A better understanding of global urban dynamics over the long term is crucial for sustainable development goals in a changing world experiencing complex human–environment interactions (Li and Gong, 2016; Li et al, 2019; Zhu et al, 2019)
We generated a global dataset of annual urban extents (1992–2020) using consistent nighttime light (NTL) observations and analyzed the spatiotemporal patterns of global urban dynamics over nearly 30 years
To further extend the applications of NTL observations for delineating, understanding, and predicting pathways of global urban growth associated with socioeconomic activities, as well as to better support future sustainable development, we generated a global dataset of annual urban extents from 1992 to 2020 using long-term and consistent nighttime lights
Summary
A better understanding of global urban dynamics over the long term is crucial for sustainable development goals in a changing world experiencing complex human–environment interactions (Li and Gong, 2016; Li et al, 2019; Zhu et al, 2019). The issues of consistency and comparability in the derived urban results of different global maps inevitably hinder the applications of global change studies (Yu et al, 2018) To address these challenges, several-decades-long global maps of annual artificial impervious areas were systematically developed, using the improved automatic mapping algorithms and massive Landsat time-series imagery on Google Earth Engine (GEE) platform (Gong et al, 2020; Liu et al, 2020; Huang et al, 2021). A salient advantage of this framework is to map the urban extents over different spaces and time by effectively characterizing the diverse patterns of NTL spatial gradients at the local scales from urban to surrounding non-urban areas (Zhao et al, 2020b) Far, this approach has been initially applied to Southeast Asia for monitoring its annual urban extents (1992–2018), showing great potential in further applications for studies over large regions and long periods. The remainder of this article describes the datasets and pre-processing (Sect. 2), details of the stepwise methods for NTL-based urban mapping (Sect. 3), a discussion of the results and findings (Sect. 4), and conclusions (Sect. 5)
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