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

Gong, Peng; Li, Xuecao; Wang, Jie; Bai, Yuqi; Чэн, Бин; Hu, Tengyun; Li, Xiaoping; Xu, Bing; Yang, Jun; Zhang, Wei; Zhou, Yuyu

doi:10.1016/j.rse.2019.111510

Cited by 683 publications

(444 citation statements)

References 55 publications

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“…This is because, only with urban land use maps produced with similar criteria, consistent environmental policies can be made, and action efforts can be compared and assessed for large scale environmental administration. However, despite of the fact that a number of urban-extent maps exist at global scales [3,4], more detailed urban land use maps do not exist at the same scale. Even at big country or regional levels such as for the United States, China and European Union, consistent land use mapping efforts are rare [5,6] (e.g., https://sdi4apps.eu/open_land_use/).…”

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confidence: 99%

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Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

et al. 2020

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“…During the past 40 years, China's urban land has expanded so rapidly that it took over the US in 2015 as the nation with the greatest area in artificial impervious surfaces [3]. However, we do not have a complete knowledge about the distribution, pattern and composition of more detailed land use types in urban China.…”

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Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

et al. 2020

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“…To derive QF, we implement buffering around cloud and shadow pixels, calculate the distance to clouds (along cloud shadow projection), and calculate areas affected by topographic shadows using the DEM and sun position. The list of criteria for output QFs is presented in Table 3 (values [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Observation Quality Assessmentmentioning

confidence: 99%

“…Given the "time machine" capabilities of the Landsat archive, it is extensively used for land cover and land use change assessment [4,5]. In recent decades, development of high-performance computing and machine learning algorithms has allowed scaling up image characterization and change detection approaches to global extent [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping

et al. 2020

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The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.

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“…As an important economic center, industrial base, and foreign trade port in East China, Ningbo has experienced rapid urbanization during the past four decades. According to Gong et al [16], the artificial impervious areas (including roofs, paved surfaces, hardened grounds, and major road surfaces mainly found in human settlements) reached 1441.27 km 2 in Ningbo in 2018, accounting for 14.68% of the city's total area.…”

Section: Study Areamentioning

confidence: 99%

Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach

Чэн

Zhang

et al. 2020

Remote Sensing

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Understanding distributions of urban land use is of great importance for urban planning, decision support, and resource allocation. The first mapping results of essential urban land use categories (EULUC) in China for 2018 have been recently released. However, such kind of national maps may not sufficiently meet the growing demand for regional analysis. To address this shortcoming, here we proposed a segmentation-based framework named EULUC-seg to improve the mapping results of EULUC at the city scale. An object-based segmentation approach was first applied to generate the basic mapping units within urban parcels. Multiple features derived from high-resolution remotely sensed and social sensing data were updated and then recalculated within each unit. Random forest was adopted as the machine learning algorithm for classifying urban land use into five Level I classes and twelve Level II classes. Finally, an accuracy assessment was carried out based on a collection of manually interpreted samples. Results showed that our derived map achieved an overall accuracy of 87.58% for Level I, and 73.53% for Level II. The accurate and refined map of EULUC-seg is expected to better support various applications in the future.

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

Cited by 683 publications

References 55 publications

Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping

Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach

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