Annual maps of global artificial impervious area (GAIA) between 1985 and 2018

https://doi.org/10.1016/j.rse.2019.111510Get rights and content

Highlights

  • We improved the performance of “Exclusion/Inclusion” approach in arid regions.

  • We mapped global artificial impervious areas (GAIA) with Google Earth Engine.

  • The mean overall accuracy over multiple years is higher than 90%.

  • GAIA reached 797,076 km2 by 2018, more than 2.5 times that of 1990.

  • The top five countries are China, US, India, Russia, and Brazil.

Abstract

Artificial impervious areas are predominant indicators of human settlements. Timely, accurate, and frequent information on artificial impervious areas is critical to understanding the process of urbanization and land use/cover change, as well as of their impacts on the environment and biodiversity. Despite their importance, there still lack annual maps of high-resolution Global Artificial Impervious Areas (GAIA) with longer than 30-year records, due to the high demand of high performance computation and the lack of effective mapping algorithms. In this paper, we mapped annual GAIA from 1985 to 2018 using the full archive of 30-m resolution Landsat images on the Google Earth Engine platform. With ancillary datasets, including the nighttime light data and the Sentinel-1 Synthetic Aperture Radar data, we improved the performance of our previously developed algorithm in arid areas. We evaluated the GAIA data for 1985, 1990, 1995, 2000, 2005, 2010, and 2015, and the mean overall accuracy is higher than 90%. A cross-product comparison indicates the GAIA data are the only dataset spanning over 30 years. The temporal trend in GAIA agrees well with other datasets at the local, regional, and global scales. Our results indicate that the GAIA reached 797,076 km2 in 2018, which is 1.5 times more than that in 1990. China and the United States (US) rank among the top two in artificial impervious area, accounting for approximately 50% of the world's total in 2018. The artificial impervious area of China surpassed that of the US in 2015. By 2018, the remaining eight among the top ten countries are India, Russia, Brazil, France, Italy, Germany, Japan, and Canada. The GAIA dataset can be freely downloaded from http://data.ess.tsinghua.edu.cn.

Introduction

The change of global artificial impervious area (GAIA) is a critical indicator for understanding the impact of global urbanization on human society and the environment. Artificial impervious areas are mainly man-made structures that are composed of any material that impedes or prevents natural infiltration of water into the soil. They include roofs, paved surfaces, hardened grounds, and major road surfaces mainly found in human settlements (Angel et al., 2011; Bounoua et al., 2018). As an important proxy to built-up areas in the world, artificial impervious areas play a critical role in controlling the flows of energy and materials and reflecting various levels of human activities (Solecki et al., 2013; Zhu et al., 2019). The rapid urban growth worldwide has significant local and tele-connected impacts on biodiversity loss, agricultural production, and environmental quality (Li et al., 2019; Seto et al., 2012), all of which finally influence human health and well-being (Whitmee et al., 2015; Yang et al., 2018).

Although studies of mapping urban built-up areas and artificial impervious areas using satellite observations started more than four decades ago (Forster, 1985; Jensen, 1979; Lo and Welch, 1977), earlier efforts were mainly made at the local scale. Initial urban remote sensing efforts focused on the use of spatial features in mapping urban land use/cover change, in support of urban planning and social-economic studies (Chen et al., 2002; Gong and Howarth, 1989, 1992; Gong et al., 1992). Later, another branch of urban remote sensing emerged and emphasized on mapping artificial impervious areas (Phinn et al., 2002; Ridd, 1995; Wu and Murray, 2003), in support of hydrological, urban heat island, and other environmental studies in the urban domain (Clinton and Gong, 2013; Li and Zhou, 2017; Weng, 2012). Thus far most previous studies were devoted to local and regional scales (Andrade-Núñez and Aide, 2018; Lu et al., 2008; Ma et al., 2012; Wang et al., 2012, 2017; Xian et al., 2009).

Since 2005, the advent of freely accessible satellite data made it possible to map urban extent and artificial impervious areas at the global scale. Global urban extent maps were derived initially from 1000 m resolution night-time light data (Elvidge et al., 2007; Small et al., 2005; Zhou et al., 2018). Then artificial impervious areas were mapped at 250–500 m resolution using Moderate Resolution Imaging Spectrometer (MODIS) data (Potere et al., 2009; Schneider et al., 2010), and finally from 30 m resolution Landsat data (e.g., Gong et al., 2013; Pesaresi et al., 2013; Liu et al., 2018; Melchiorri et al., 2018), and 10 m resolution Sentinel-2 data (Gong et al., 2019b). Artificial impervious area is the essential cover type to characterize the built-up area and urban extent, especially at finer spatial resolutions (Liu et al., 2014).

Despite of many existing efforts in mapping GAIA, annual maps covering a relatively long period of time (i.e., more than 30 years) do not exist. In fact, accurate annual global land cover information including GAIA is needed in global climate change studies (Bontemps et al., 2012; Running, 2008). On the other hand, in urban growth modeling, planning, and administration, annual maps of artificial impervious areas are also highly desirable (Li and Gong, 2016b). Unfortunately, accuracies of GAIA from multiple-class general-purpose automatic land cover mapping are not enough to support these applications (Yu et al., 2018). Hence, there is an urgency to develop a relatively long-term annual GAIA dataset that is temporally consistent and accurate for various applications.

The purpose of this research was to develop a 34-year long annual GAIA using the full archive of Landsat data from 1985 to 2018. In the remaining of this paper, we introduce the method framework (Section 2), report the results and compare GAIA data with existing global urban products (Section 3), and draw some conclusions from this study (Section 4).

Section snippets

Methodology

We developed an automatic mapping procedure on the Google Earth Engine (GEE) platform to implement the planetary-scale mapping of annual artificial impervious areas at a 30-m resolution from 1985 to 2018. The performance of our previously developed procedures that form the foundation in this mapping project has been successfully examined at local (Li and Gong, 2016a; Li et al., 2015) and national (Gong et al., 2019a) scales with a time span covering more than three decades. Our previous studies

Overall accuracy at the global scale

Validation of our results using randomly selected sample units suggests the accuracies are consistently higher than 89% at the global scale (Table 1). All but one overall accuracies (OA) is below 90% over these seven years (i.e., 1985, 1990, 1995, 2000, 2005, 2010, and 2015). The OA of 2015 is a little bit lower than in previous years, which is mainly caused by the overestimation of artificial impervious area. Possible reasons behind this issue are likely attributed to training sample units for

Comparison with other global urban products

Overall, GAIA data agree with most existing global urban products of different spatial resolutions, showing a rational temporal trend over the past 34 years (Fig. 9). It can be seen that GAIA agrees with the other two shorter annual trend curves (i.e., the ESACCI and (Zhou et al., 2018)). During the 1990s and 2000s, GAIA results are closer to Liu et al., (2018), He et al., (2018), ESACCI, MODIS, and Zhou et al. (2018), whereas the GHSL is slightly higher than these products. In more recent

Conclusions

At the global scale, we first mapped the annual artificial impervious area dynamic at a 30 m resolution from 1985 to 2018, using the full archive of Landsat images on the GEE. This is the longest temporal coverage currently available for artificial impervious areas in the world. The approaches we used in this study are efficient in mapping artificial impervious areas at large scales. In addition, with the use of ancillary datasets (i.e., NTL and Sentinel SAR data), we improved the mapping

Declaration of competing interestCOI

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was partially supported by the National Research Program of the Ministry of Science and Technology of the People's Republic of China (2016YFA0600104), and donations from Delos Living LLC and the Cyrus Tang Foundation to Tsinghua University.

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