Land Use Mapping of the Guangdong–Hong Kong Macao Greater Bay Area Based on a New Approach at 30 m Resolution for the Years 1976 to 2020

Multicategory land use data of high spatiotemporal resolution and large scale are crucial for studying regional ecological and environmental changes and urbanization impacts as well as for sustainable development planning. Currently available public data products include those of high spatial resolu...

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Main Authors: Yu Gu, Yangbo Chen, Jun Liu
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Online Access:https://ieeexplore.ieee.org/document/10827814/
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author Yu Gu
Yangbo Chen
Jun Liu
author_facet Yu Gu
Yangbo Chen
Jun Liu
author_sort Yu Gu
collection DOAJ
description Multicategory land use data of high spatiotemporal resolution and large scale are crucial for studying regional ecological and environmental changes and urbanization impacts as well as for sustainable development planning. Currently available public data products include those of high spatial resolution global land use temporally limited to a single or short period, or global annual land cover products in which only a single land use type is depicted, such that regional characteristics are overlooked. In either case, fine-scale annual variation over longer time spans may not be reflected. In this study, the Google Earth Engine platform, Landsat satellite imagery, and a substantial number of manually interpreted samples were used to develop a dataset of annual land use changes in the Guangdong–Hong Kong Macao Greater Bay Area (GBA) at a 30 m resolution for the years 1976 to 2020. This dataset, termed Annual Land Use/Cover of the Greater Bay Area (LUC-GBA), was used to analyze the annual land use variation in 11 cities within the GBA. The high level of accuracy achieved with the LUC-GBA dataset was evidenced by an overall accuracy (OA) of 93.9% in 2020. The OA of interannual classification models ranged from 83.9% to 93.9%, and the kappa coefficients from 0.805 to 0.923. These results indicate that the LUC-GBA dataset effectively reflects the surface cover distribution and interannual dynamic evolution of the land area in the GBA at a 30 m spatial resolution, thus providing reliable data support for land surface process research and related applications.
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institution Kabale University
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spelling doaj-art-50732dee41e6433f88ff6e93a407cf062025-01-31T00:00:15ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01183943395810.1109/JSTARS.2024.352370710827814Land Use Mapping of the Guangdong–Hong Kong Macao Greater Bay Area Based on a New Approach at 30 m Resolution for the Years 1976 to 2020Yu Gu0https://orcid.org/0000-0001-8772-2273Yangbo Chen1https://orcid.org/0000-0003-4445-2933Jun Liu2School of Geography and Planning, Sun Yat-Sen University, Guangzhou, ChinaSchool of Geography and Planning, Sun Yat-Sen University, Guangzhou, ChinaSchool of Geography and Planning, Sun Yat-Sen University, Guangzhou, ChinaMulticategory land use data of high spatiotemporal resolution and large scale are crucial for studying regional ecological and environmental changes and urbanization impacts as well as for sustainable development planning. Currently available public data products include those of high spatial resolution global land use temporally limited to a single or short period, or global annual land cover products in which only a single land use type is depicted, such that regional characteristics are overlooked. In either case, fine-scale annual variation over longer time spans may not be reflected. In this study, the Google Earth Engine platform, Landsat satellite imagery, and a substantial number of manually interpreted samples were used to develop a dataset of annual land use changes in the Guangdong–Hong Kong Macao Greater Bay Area (GBA) at a 30 m resolution for the years 1976 to 2020. This dataset, termed Annual Land Use/Cover of the Greater Bay Area (LUC-GBA), was used to analyze the annual land use variation in 11 cities within the GBA. The high level of accuracy achieved with the LUC-GBA dataset was evidenced by an overall accuracy (OA) of 93.9% in 2020. The OA of interannual classification models ranged from 83.9% to 93.9%, and the kappa coefficients from 0.805 to 0.923. These results indicate that the LUC-GBA dataset effectively reflects the surface cover distribution and interannual dynamic evolution of the land area in the GBA at a 30 m spatial resolution, thus providing reliable data support for land surface process research and related applications.https://ieeexplore.ieee.org/document/10827814/30-meter resolutionannual land use changeGBAlandsat satellite imageryremote sensing
spellingShingle Yu Gu
Yangbo Chen
Jun Liu
Land Use Mapping of the Guangdong–Hong Kong Macao Greater Bay Area Based on a New Approach at 30 m Resolution for the Years 1976 to 2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
30-meter resolution
annual land use change
GBA
landsat satellite imagery
remote sensing
title Land Use Mapping of the Guangdong–Hong Kong Macao Greater Bay Area Based on a New Approach at 30 m Resolution for the Years 1976 to 2020
title_full Land Use Mapping of the Guangdong–Hong Kong Macao Greater Bay Area Based on a New Approach at 30 m Resolution for the Years 1976 to 2020
title_fullStr Land Use Mapping of the Guangdong–Hong Kong Macao Greater Bay Area Based on a New Approach at 30 m Resolution for the Years 1976 to 2020
title_full_unstemmed Land Use Mapping of the Guangdong–Hong Kong Macao Greater Bay Area Based on a New Approach at 30 m Resolution for the Years 1976 to 2020
title_short Land Use Mapping of the Guangdong–Hong Kong Macao Greater Bay Area Based on a New Approach at 30 m Resolution for the Years 1976 to 2020
title_sort land use mapping of the guangdong x2013 hong kong macao greater bay area based on a new approach at 30 m resolution for the years 1976 to 2020
topic 30-meter resolution
annual land use change
GBA
landsat satellite imagery
remote sensing
url https://ieeexplore.ieee.org/document/10827814/
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