Consistent, accurate, high resolution, long time-series mapping of built-up land in the North China Plain
Accurate, long time-series, high-resolution mapping of built-up land dynamics is essential for understanding urbanization and its environmental impacts. Despite advances in remote sensing and classification algorithms, built-up land mapping which only uses spectral data and derived indices remains p...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-10-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2021.1948275 |
| _version_ | 1827811740315811840 |
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| author | Jinzhu Wang Michalis Hadjikakou Brett A. Bryan |
| author_facet | Jinzhu Wang Michalis Hadjikakou Brett A. Bryan |
| author_sort | Jinzhu Wang |
| collection | DOAJ |
| description | Accurate, long time-series, high-resolution mapping of built-up land dynamics is essential for understanding urbanization and its environmental impacts. Despite advances in remote sensing and classification algorithms, built-up land mapping which only uses spectral data and derived indices remains prone to uncertainty. We mapped the extent of built-up land in the North China Plain, one of China’s most important agricultural regions, from 1990 to 2019 at three-yearly intervals and 30 m spatial resolution. We applied Discrete Fourier Transformation to dense time-stack Landsat data to create Fourier predictors to reduce mapping uncertainty. As a result, we improved the overall accuracy of built-up land mapping by 8% compared to using spectral data and derived indices. In addition, a temporal correction algorithm applied to remove misclassified pixels further improved mapping accuracy to a consistently high level (>94%) over the time periods. A cross-product comparison showed that our maps achieved the highest accuracies across all years. The built-up land area in the North China Plain increased from 37,941 km2 in 1990–1992 to 131,578 km2 in 2017–2019. Consistent, high-accuracy, long time-series built-up land mapping provides a reliable basis for formulating policy and planning in one of the most rapidly urbanizing regions on this planet. |
| first_indexed | 2024-03-11T23:08:52Z |
| format | Article |
| id | doaj.art-e495ec1a63524ff4a34f1d58c99a7b6d |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:08:52Z |
| publishDate | 2021-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-e495ec1a63524ff4a34f1d58c99a7b6d2023-09-21T12:34:17ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262021-10-0158798299810.1080/15481603.2021.19482751948275Consistent, accurate, high resolution, long time-series mapping of built-up land in the North China PlainJinzhu Wang0Michalis Hadjikakou1Brett A. Bryan2Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin UniversityCentre for Integrative Ecology, School of Life and Environmental Sciences, Deakin UniversityCentre for Integrative Ecology, School of Life and Environmental Sciences, Deakin UniversityAccurate, long time-series, high-resolution mapping of built-up land dynamics is essential for understanding urbanization and its environmental impacts. Despite advances in remote sensing and classification algorithms, built-up land mapping which only uses spectral data and derived indices remains prone to uncertainty. We mapped the extent of built-up land in the North China Plain, one of China’s most important agricultural regions, from 1990 to 2019 at three-yearly intervals and 30 m spatial resolution. We applied Discrete Fourier Transformation to dense time-stack Landsat data to create Fourier predictors to reduce mapping uncertainty. As a result, we improved the overall accuracy of built-up land mapping by 8% compared to using spectral data and derived indices. In addition, a temporal correction algorithm applied to remove misclassified pixels further improved mapping accuracy to a consistently high level (>94%) over the time periods. A cross-product comparison showed that our maps achieved the highest accuracies across all years. The built-up land area in the North China Plain increased from 37,941 km2 in 1990–1992 to 131,578 km2 in 2017–2019. Consistent, high-accuracy, long time-series built-up land mapping provides a reliable basis for formulating policy and planning in one of the most rapidly urbanizing regions on this planet.http://dx.doi.org/10.1080/15481603.2021.1948275built-up landurbanizationfourier transformationmappingremote sensingtime-series |
| spellingShingle | Jinzhu Wang Michalis Hadjikakou Brett A. Bryan Consistent, accurate, high resolution, long time-series mapping of built-up land in the North China Plain GIScience & Remote Sensing built-up land urbanization fourier transformation mapping remote sensing time-series |
| title | Consistent, accurate, high resolution, long time-series mapping of built-up land in the North China Plain |
| title_full | Consistent, accurate, high resolution, long time-series mapping of built-up land in the North China Plain |
| title_fullStr | Consistent, accurate, high resolution, long time-series mapping of built-up land in the North China Plain |
| title_full_unstemmed | Consistent, accurate, high resolution, long time-series mapping of built-up land in the North China Plain |
| title_short | Consistent, accurate, high resolution, long time-series mapping of built-up land in the North China Plain |
| title_sort | consistent accurate high resolution long time series mapping of built up land in the north china plain |
| topic | built-up land urbanization fourier transformation mapping remote sensing time-series |
| url | http://dx.doi.org/10.1080/15481603.2021.1948275 |
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