Elsevier

Science Bulletin

Volume 65, Issue 12, 30 June 2020, Pages 1039-1048
Science Bulletin

Article
High-resolution urban land-cover mapping and landscape analysis of the 42 major cities in China using ZY-3 satellite images

https://doi.org/10.1016/j.scib.2020.03.003Get rights and content

Abstract

Detailed and precise urban land-cover maps are crucial for urban-related studies. However, there are limited ways of mapping high-resolution urban land cover over large areas. In this paper, we propose an operational framework to map urban land cover on the basis of Ziyuan-3 satellite images. Based on this framework, we produced the first high-resolution (2 m) urban land-cover map (Hi-ULCM) covering the 42 major cities of China. The overall accuracy of the Hi-ULCM dataset is 88.55%, of which 14 cities have an overall accuracy of over 90%. Most of the producer’s accuracies and user’s accuracies of the land-cover classes exceed 85%. We further conducted a landscape pattern analysis in the 42 cities based on Hi-ULCM. In terms of the comparison between the 42 cities in China, we found that the difference in the land-cover composition of urban areas is related to the climatic characteristics and urbanization levels, e.g., cities with warm climates generally have higher proportions of green spaces. It is also interesting to find that cities with higher urbanization levels are more habitable, in general. From the landscape viewpoint, the geometric complexity of the landscape increases with the urbanization level. Compared with the existing medium-resolution land-cover/use datasets (at a 30-m resolution), Hi-ULCM represents a significant advance in accurately depicting the detailed land-cover footprint within the urban areas of China, and will be of great use for studies of urban ecosystems.

Introduction

Urbanization, which is characterized by a population shift from rural to urban and a transformation in the surface physical and geometric properties, has dramatically changed human habitats [1]. The evolution of the urban landscape has had a pronounced influence on the energy balance, carbon cycle, hydrological process, and climate of the urban systems [2], [3]. Meanwhile, the accompanying problems include air and water pollution, the urban heat island (UHI) effect, environmental noise, and biodiversity reduction, which are threatening our health and future development [4], [5], [6]. Measuring and analyzing the urban landscape is thus essential for many applications in climatology, hydrology, environmental planning, and resource management, where detailed and precise urban land-cover maps are indispensable [7], [8], [9].

It is widely recognized that the satellite remote sensing technique can provide an effective tool for land-cover mapping at local, regional, and global scales [10]. Several global land-use/cover datasets have been developed based on satellite images, with spatial resolutions of 300 m to about 1 km [11], [12], [13], [14]. However, the drawbacks of these datasets are the coarse spatial resolutions, low accuracies, and poor agreement concerning time, space, and theme [15], [16]. With the free availability of medium-resolution satellite images such as the Landsat and Sentinel series, more precise land-cover datasets with 10–30 m resolutions have become feasible. At the global level, the state-of-the-art products include the Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) maps [17], the Global Land Cover Dataset (GlobeLand30) [18], FROM-GLC-agg [19], and FROM-GLC10 [20]. At the regional level, the products include the European CORINE dataset [21], the United States National Land Cover Dataset (NLCD) [22], and the National Land Cover/Use Database of China (NLUD-C) [23]. Nevertheless, restricted by their spatial resolutions, none of these datasets can offer the necessary level of details within urban areas, such as individual buildings, sidewalks, and urban green spaces (e.g., roadside trees), which are crucial for the in-depth interpretation of the urban landscape. In this context, although the existing products are effective for the large-scale measurement and analysis of urban areas, they are still far from satisfactory for some urban planning and environmental analysis applications, due to the dearth of spatial details [8].

High-resolution satellite images can minimize the mixed-pixel phenomenon, and can thus provide abundant information in the spatial domain. Given their potential in delineating the structure and shape of individual objects (e.g., buildings, trees), high-resolution satellite images have been successfully applied in many urban-related studies, e.g., monitoring subtle urban dynamics [24], interpreting urban scenes [25], and investigating the effects of the landscape on the environment, such as the UHI effect [26]. Nevertheless, these studies were usually carried out in several small-size urban areas, for the purpose of classification algorithm analysis. To the best of our knowledge, to date, there has been very little practical research on techniques for high-resolution urban land-cover mapping over a large number of cities, and the related urban map products are also lacking.

The Ziyuan-3 (ZY-3) satellites are the first high-resolution civilian stereo mapping satellites in China, and the ZY-3 01 and ZY-3 02 satellites were successfully launched in 2012 and 2016, respectively. With a large swath width (50 km), the ZY-3 satellites can cover a large spatial extent with a spatial resolution of 2.1 m. The satellites are equipped with three high-resolution panchromatic cameras positioned 22° from each other to provide forward, nadir, and backward imaging modes, which enables them to provide abundant three-dimensional (3D) information to support accurate classification in complex urban scenes. Compared with other multi-angle high-resolution satellites (e.g., the WorldView series and the Cartosat series), the unique imaging mode of the ZY-3 satellites (along-track stereo mode with stable nadir-view cameras) enables them to provide nearly orthographic images, which can effectively minimize the angular effects, and therefore delineate urban surface cover more precisely. The angular effects can trigger distortion and occlusion of the land-cover maps, especially in urban scenes with complex high-rise structures, where the occlusion of buildings seriously hampers the accurate interpretation of the land-cover footprint [27]. With the available ZY-3 satellite imagery, we now have an unprecedented opportunity for mapping urban land cover.

In recent years, from the perspective of high-resolution image classification, auxiliary geospatial data has been increasingly utilized in land-use/cover classification, e.g., OpenStreetMap (OSM), open social data, and socioeconomic data [28], [29]. However, the related studies have been carried out only at the local scale or in a small area, and we do not yet have an effective and feasible mapping scheme for large-scale use. In this context, we propose an operational mapping approach by integrating ZY-3 high-resolution satellite imagery and auxiliary geospatial data (A-map, Map World, and OSM). Under this framework, we developed a high-resolution (2 m) urban land-cover map (Hi-ULCM) covering the 42 major cities of China. China is currently undergoing urbanization at an unprecedented rate, and by the end of 2017, the urban population in China accounted for 58.52% [30], which is higher than the world average level (55%) [31]. During the past few years, significant land-cover changes have occurred in China [32], and a unified high-resolution land-cover map is urgently required. The Hi-ULCM dataset has the potential to fill this gap by providing detailed land-cover information of the urban areas. In this paper, based on this new land-cover dataset, the landscape patterns of the 42 major cities of China are analyzed and compared. Moreover, the potential of the Hi-ULCM dataset is fully discussed.

Section snippets

Materials and methods

In this study, we focused on the 42 major cities in China, including four municipalities, 26 provincial capitals, and 12 large cities (Fig. 1). These cities are spread over a wide range of climatic zones [33], and represent a variety of urban sizes, landscape characteristics, and urbanization intensities. The 42 cities were stratified into three levels according to the urban population in 2016 [34]: level Ⅰ (greater than 5 million, 8 cities), level II (2 million to 5 million, 20 cities), and

Results and validation

The Hi-ULCM product for two representative cities is presented in Fig. 3. We also provide the results for an additional four representative cities in Fig. S1 (online). These maps demonstrate that the footprint of the land cover, e.g., buildings, can be well depicted by the Hi-ULCM dataset. In particular, the product can delineate compact and small buildings, such as those found in urban villages and residential areas. Some buildings with low reflectance that are difficult to distinguish from

Discussion

Based on the Hi-ULCM dataset, the land-cover composition of the 42 cities was analyzed with respect to the climatic zones (Fig. 5a) and city levels (Fig. 5b). The area percentage of OISAs is the largest among the 42 cities, with an average percentage of 27.49%, followed by buildings (16.23%), grass/shrubs (15.22%), trees (14.18%), roads (11.13%), soil (10.08%), and water (5.67%). The area percentages of artificial surfaces (i.e., buildings, roads, and OISAs) do not exhibit significant

Conclusion

In this paper, we have proposed an operational mapping framework for high-resolution urban land-cover mapping, and we have described the development of the first high-resolution (2 m) urban land-cover product—Hi-ULCM—covering the 42 major cities of China, courtesy of the high-resolution images acquired by the ZY-3 satellites. The proposed framework achieved a satisfactory performance, with an OA of 88.55%. Meanwhile, the PA and UA of each land-cover type exceeded 85%, for most of the

Conflict of interest

The authors declare that they have no conflict of interest.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (41771360 and 41971295), the National Program for Support of Top-notch Young Professionals, the Hubei Provincial Natural Science Foundation of China (2017CFA029), and the National Key Resarch & Development Program of China (2016YFB0501403). Maps in this article were reviewed by Ministry of Natural Resources of the People’s Republic of China (GS(2020)819).

Author contributions

Xin Huang and Jianya Gong conceived and supervised the work; Ying Wang, Jiayi Li, Xiaoyu Chang, and Yinxia Cao designed the method; Jiayi Li, Xiaoyu Chang and Yinxia Cao helped with the data processing; the remaining authors contributed collection of data and samples; all authors helped in writing the paper.

Xin Huang is currently a Luojia Distinguished Professor of Wuhan University, China. He is the Founder of the Institute of Remote Sensing Information Processing, School of Remote Sensing and Information Engineering, Wuhan University. His research interests include remote sensing image processing methods and applications.

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  • Cited by (0)

    Xin Huang is currently a Luojia Distinguished Professor of Wuhan University, China. He is the Founder of the Institute of Remote Sensing Information Processing, School of Remote Sensing and Information Engineering, Wuhan University. His research interests include remote sensing image processing methods and applications.

    Ying Wang is currently pursuing the master’s degree in the School of Remote Sensing and Information Engineering, Wuhan University. Her research interests include urban environment and urban land cover mapping.

    1

    These authors contributed equally to this work.

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