Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing
Impervious surfaces are important indicators of human activity, and finding ways to quantify the gain and loss of impervious surfaces is important for sustainable urban development. However, most relevant studies assume that the transformation of natural surfaces to impervious surfaces is irreversib...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224006241 |
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| author | Xiao Zhang Liangyun Liu Wenhan Zhang Linlin Guan Ming Bai Tingting Zhao Zhehua Li Xidong Chen |
| author_facet | Xiao Zhang Liangyun Liu Wenhan Zhang Linlin Guan Ming Bai Tingting Zhao Zhehua Li Xidong Chen |
| author_sort | Xiao Zhang |
| collection | DOAJ |
| description | Impervious surfaces are important indicators of human activity, and finding ways to quantify the gain and loss of impervious surfaces is important for sustainable urban development. However, most relevant studies assume that the transformation of natural surfaces to impervious surfaces is irreversible; thus, the losses of impervious surfaces are often ignored. Here, we propose a novel framework taking advantage of continuous change detection, multitemporal classification, and LandTrendr optimization to track the annual gains and losses in impervious surfaces. It may be the first study to focus on both loss and gain of impervious surfaces using time-series Landsat imagery. Specifically, we built dual continuous-change-detection models to pursue lower commission and omission errors for generating time-series training samples. Then, we adopted time-series classifications from multisource information and derived training samples to develop annual impervious-surface maps from 1985 to 2022 in Beijing. Afterwards, a novel optimization algorithm considering spatial heterogeneity and taking advantage of the LandTrendr algorithm was also proposed to optimize the spatiotemporal consistency of these impervious-surface maps. We further calculated accuracy metrics for the proposed method using time-series validation points, finding overall accuracies of 92.91 %±0.97 % and 93.17 %±1.26 % for gains and losses in impervious surfaces, respectively, using a one-year tolerance. Lastly, we revealed the gains and losses of impervious surfaces in Beijing during 1985–2022. The gained area of impervious surfaces was found to be 1996.21 km2 ± 18.58 km2, and there was a rapid increase during 2000–2010; the total lost area of impervious surfaces was 898.60 km2 ± 4.58 km2, of which 564.85 km2 ± 2.21 km2 first increased and was then lost. Therefore, the proposed method provides a new way of tracking the gain and loss of impervious surfaces, and it offers new possibilities for monitoring urban regreening. |
| format | Article |
| id | doaj-art-3c045b6b16dd432589d37f778ed48a28 |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-3c045b6b16dd432589d37f778ed48a282025-08-20T01:58:08ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-12-0113510426810.1016/j.jag.2024.104268Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in BeijingXiao Zhang0Liangyun Liu1Wenhan Zhang2Linlin Guan3Ming Bai4Tingting Zhao5 Zhehua Li6Xidong Chen7International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding author.International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, ChinaFuture Urbanity & Sustainable Environment (FUSE) Lab, the University of Hong Kong, 999007 Hong Kong, ChinaImpervious surfaces are important indicators of human activity, and finding ways to quantify the gain and loss of impervious surfaces is important for sustainable urban development. However, most relevant studies assume that the transformation of natural surfaces to impervious surfaces is irreversible; thus, the losses of impervious surfaces are often ignored. Here, we propose a novel framework taking advantage of continuous change detection, multitemporal classification, and LandTrendr optimization to track the annual gains and losses in impervious surfaces. It may be the first study to focus on both loss and gain of impervious surfaces using time-series Landsat imagery. Specifically, we built dual continuous-change-detection models to pursue lower commission and omission errors for generating time-series training samples. Then, we adopted time-series classifications from multisource information and derived training samples to develop annual impervious-surface maps from 1985 to 2022 in Beijing. Afterwards, a novel optimization algorithm considering spatial heterogeneity and taking advantage of the LandTrendr algorithm was also proposed to optimize the spatiotemporal consistency of these impervious-surface maps. We further calculated accuracy metrics for the proposed method using time-series validation points, finding overall accuracies of 92.91 %±0.97 % and 93.17 %±1.26 % for gains and losses in impervious surfaces, respectively, using a one-year tolerance. Lastly, we revealed the gains and losses of impervious surfaces in Beijing during 1985–2022. The gained area of impervious surfaces was found to be 1996.21 km2 ± 18.58 km2, and there was a rapid increase during 2000–2010; the total lost area of impervious surfaces was 898.60 km2 ± 4.58 km2, of which 564.85 km2 ± 2.21 km2 first increased and was then lost. Therefore, the proposed method provides a new way of tracking the gain and loss of impervious surfaces, and it offers new possibilities for monitoring urban regreening.http://www.sciencedirect.com/science/article/pii/S1569843224006241Impervious surfaceGain and lossContinuous change detectionLandTrendr |
| spellingShingle | Xiao Zhang Liangyun Liu Wenhan Zhang Linlin Guan Ming Bai Tingting Zhao Zhehua Li Xidong Chen Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing International Journal of Applied Earth Observations and Geoinformation Impervious surface Gain and loss Continuous change detection LandTrendr |
| title | Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing |
| title_full | Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing |
| title_fullStr | Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing |
| title_full_unstemmed | Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing |
| title_short | Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing |
| title_sort | tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in beijing |
| topic | Impervious surface Gain and loss Continuous change detection LandTrendr |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224006241 |
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