An Integrated Approach for Mapping Multi-Category Abandoned Cropland Based on Swin-Unet, Land-Use Trajectories and Phenological Features in a Double-Cropping Rice Region of Southern China.
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| Title: | An Integrated Approach for Mapping Multi-Category Abandoned Cropland Based on Swin-Unet, Land-Use Trajectories and Phenological Features in a Double-Cropping Rice Region of Southern China. |
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| Authors: | Jin, Yuhao1 (AUTHOR), Zhuang, Falin1,2 (AUTHOR), Zhu, Hengkang2,3 (AUTHOR) zhuhk7@mail2.sysu.edu.cn, Qiu, Xiaozhen1 (AUTHOR), Mao, Xing2,3 (AUTHOR) |
| Source: | Remote Sensing. Sep2026, Vol. 18 Issue 18, p3090. 33p. |
| Subjects: | Phenology, Normalized difference vegetation index, Reclamation of land, Land use, Cropping systems |
| Geographic Terms: | China |
| Abstract: | Highlights: What are the main findings? A hierarchical framework integrating Swin-UNet, trajectory analysis, and phenological validation distinguishes three types of abandoned cropland. Natural abandonment dominates but declines; construction-occupied abandonment increases; fallow land remains stable. What are the implications of the main findings? Multi-category mapping advances abandonment monitoring from area statistics to type-specific process identification. Recultivating natural abandonment could contribute 12.88–15.14% of regional grain output, informing targeted policy. Cropland abandonment directly undermines the regional food supply, requiring accurate monitoring to inform protection and recultivation policies. However, multi-category abandoned-cropland mapping remains challenging in southern China's double-cropping rice regions because fragmented fields, frequent cloud/rain conditions, and complex phenology can obscure abandonment signals. Focusing on a typical double-cropping rice region in southern China, this study first used Swin-UNet to generate annual land-use maps from 2018 to 2022 and derive interannual trajectories. It then identified non-cultivated cropland within each year using Normalized Difference Vegetation Index (NDVI) data from key phenological periods and block-adaptive Otsu thresholding. Overlaying cultivation status with land-use trajectories further subdivided non-cultivated cropland into construction-occupied abandoned, naturally abandoned cropland, and fallow cropland. Finally, the spatial differentiation of each type and the potential grain-production gains from recultivating naturally abandoned cropland were analyzed. Results showed that annual classification accuracies from 2018 to 2022 ranged from 94.7 to 95.5%, supporting reliable cropland change trajectory extraction. From 2019 to 2022, total abandoned area decreased from 546.60 km2 to 486.73 km2. Naturally abandoned cropland remained dominant but declined mainly because 18.5–21.7% of it was converted back to stable cropland annually, while construction-occupied abandonment increased and fallow cropland remained relatively stable. Natural abandonment concentrated on moderate slopes near settlements and roads, with a theoretical grain-production potential equivalent to 12.88–15.14% of Maoming's annual grain output. This study advances abandoned-cropland monitoring from area estimation to type-specific process interpretation and recultivation-priority assessment in southern double-cropping rice regions. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 197381213 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Integrated Approach for Mapping Multi-Category Abandoned Cropland Based on Swin-Unet, Land-Use Trajectories and Phenological Features in a Double-Cropping Rice Region of Southern China. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jin%2C+Yuhao%22">Jin, Yuhao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhuang%2C+Falin%22">Zhuang, Falin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Hengkang%22">Zhu, Hengkang</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> zhuhk7@mail2.sysu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Qiu%2C+Xiaozhen%22">Qiu, Xiaozhen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mao%2C+Xing%22">Mao, Xing</searchLink><relatesTo>2,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Sep2026, Vol. 18 Issue 18, p3090. 33p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Phenology%22">Phenology</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Reclamation+of+land%22">Reclamation of land</searchLink><br /><searchLink fieldCode="DE" term="%22Land+use%22">Land use</searchLink><br /><searchLink fieldCode="DE" term="%22Cropping+systems%22">Cropping systems</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A hierarchical framework integrating Swin-UNet, trajectory analysis, and phenological validation distinguishes three types of abandoned cropland. Natural abandonment dominates but declines; construction-occupied abandonment increases; fallow land remains stable. What are the implications of the main findings? Multi-category mapping advances abandonment monitoring from area statistics to type-specific process identification. Recultivating natural abandonment could contribute 12.88–15.14% of regional grain output, informing targeted policy. Cropland abandonment directly undermines the regional food supply, requiring accurate monitoring to inform protection and recultivation policies. However, multi-category abandoned-cropland mapping remains challenging in southern China's double-cropping rice regions because fragmented fields, frequent cloud/rain conditions, and complex phenology can obscure abandonment signals. Focusing on a typical double-cropping rice region in southern China, this study first used Swin-UNet to generate annual land-use maps from 2018 to 2022 and derive interannual trajectories. It then identified non-cultivated cropland within each year using Normalized Difference Vegetation Index (NDVI) data from key phenological periods and block-adaptive Otsu thresholding. Overlaying cultivation status with land-use trajectories further subdivided non-cultivated cropland into construction-occupied abandoned, naturally abandoned cropland, and fallow cropland. Finally, the spatial differentiation of each type and the potential grain-production gains from recultivating naturally abandoned cropland were analyzed. Results showed that annual classification accuracies from 2018 to 2022 ranged from 94.7 to 95.5%, supporting reliable cropland change trajectory extraction. From 2019 to 2022, total abandoned area decreased from 546.60 km2 to 486.73 km2. Naturally abandoned cropland remained dominant but declined mainly because 18.5–21.7% of it was converted back to stable cropland annually, while construction-occupied abandonment increased and fallow cropland remained relatively stable. Natural abandonment concentrated on moderate slopes near settlements and roads, with a theoretical grain-production potential equivalent to 12.88–15.14% of Maoming's annual grain output. This study advances abandoned-cropland monitoring from area estimation to type-specific process interpretation and recultivation-priority assessment in southern double-cropping rice regions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=197381213 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18183090 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 33 StartPage: 3090 Subjects: – SubjectFull: Phenology Type: general – SubjectFull: Normalized difference vegetation index Type: general – SubjectFull: Reclamation of land Type: general – SubjectFull: Land use Type: general – SubjectFull: Cropping systems Type: general – SubjectFull: China Type: general Titles: – TitleFull: An Integrated Approach for Mapping Multi-Category Abandoned Cropland Based on Swin-Unet, Land-Use Trajectories and Phenological Features in a Double-Cropping Rice Region of Southern China. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jin, Yuhao – PersonEntity: Name: NameFull: Zhuang, Falin – PersonEntity: Name: NameFull: Zhu, Hengkang – PersonEntity: Name: NameFull: Qiu, Xiaozhen – PersonEntity: Name: NameFull: Mao, Xing IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 18 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
