Monitoring Cropland Abandonment in Southern China from 1992 to 2020 Based on the Combination of Phenological and Time-Series Algorithm Using Landsat Imagery and Google Earth Engine
Cropland abandonment is one of the most widespread types of land-use change in Southern China. Quickly and accurately monitoring spatial-temporal patterns of cropland abandonment is crucial for food security and a good ecological balance. There are still enormous challenges in the long-term monitori...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/3/669 |
| _version_ | 1827759559391838208 |
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| author | Yingyue Su Shikun Wu Shanggui Kang Han Xu Guangsheng Liu Zhi Qiao Luo Liu |
| author_facet | Yingyue Su Shikun Wu Shanggui Kang Han Xu Guangsheng Liu Zhi Qiao Luo Liu |
| author_sort | Yingyue Su |
| collection | DOAJ |
| description | Cropland abandonment is one of the most widespread types of land-use change in Southern China. Quickly and accurately monitoring spatial-temporal patterns of cropland abandonment is crucial for food security and a good ecological balance. There are still enormous challenges in the long-term monitoring of abandoned cropland in cloud and rain-prone and cropland-fragmented regions. In this study, we developed an approach to automatically obtain Landsat imagery for two key phenological periods, rather than as a time series, and mapped annual land cover from 1989 to 2021 based on the random forest classifier. We also proposed an algorithm for pixel-based, long-term annual land cover correction based on prior knowledge and natural laws, and generated cropland abandonment maps for Guangdong Province over the past 30 years. This work was implemented in Google Earth Engine. Accuracy assessment of the annual cropland abandonment maps for every five years during study period revealed an overall accuracy of 92–95%, producer (user) accuracy of 90–96% (73–87%), and Kappa coefficients of 0.81–0.88. In recent decades, the cropland abandonment area was relatively stable, at around 50 × 10<sup>4</sup> ha, while the abandonment rate gradually increased with a decrease in the cultivated area after 2000. The Landsat-based cropland abandonment monitoring method can be implemented in regions such as southern China, and will support food security and strategies for maintaining ecological balance. |
| first_indexed | 2024-03-11T09:27:14Z |
| format | Article |
| id | doaj.art-6a79d1e0bdc54deb91f01975584d20d8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T09:27:14Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6a79d1e0bdc54deb91f01975584d20d82023-11-16T17:52:32ZengMDPI AGRemote Sensing2072-42922023-01-0115366910.3390/rs15030669Monitoring Cropland Abandonment in Southern China from 1992 to 2020 Based on the Combination of Phenological and Time-Series Algorithm Using Landsat Imagery and Google Earth EngineYingyue Su0Shikun Wu1Shanggui Kang2Han Xu3Guangsheng Liu4Zhi Qiao5Luo Liu6Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, ChinaGuangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, ChinaGuangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, ChinaGuangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, ChinaGuangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, ChinaKey Laboratory of Indoor Air Environment Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, ChinaGuangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, ChinaCropland abandonment is one of the most widespread types of land-use change in Southern China. Quickly and accurately monitoring spatial-temporal patterns of cropland abandonment is crucial for food security and a good ecological balance. There are still enormous challenges in the long-term monitoring of abandoned cropland in cloud and rain-prone and cropland-fragmented regions. In this study, we developed an approach to automatically obtain Landsat imagery for two key phenological periods, rather than as a time series, and mapped annual land cover from 1989 to 2021 based on the random forest classifier. We also proposed an algorithm for pixel-based, long-term annual land cover correction based on prior knowledge and natural laws, and generated cropland abandonment maps for Guangdong Province over the past 30 years. This work was implemented in Google Earth Engine. Accuracy assessment of the annual cropland abandonment maps for every five years during study period revealed an overall accuracy of 92–95%, producer (user) accuracy of 90–96% (73–87%), and Kappa coefficients of 0.81–0.88. In recent decades, the cropland abandonment area was relatively stable, at around 50 × 10<sup>4</sup> ha, while the abandonment rate gradually increased with a decrease in the cultivated area after 2000. The Landsat-based cropland abandonment monitoring method can be implemented in regions such as southern China, and will support food security and strategies for maintaining ecological balance.https://www.mdpi.com/2072-4292/15/3/669cropland abandonmentkey phenological periodsprior knowledgelong-termsouthern China |
| spellingShingle | Yingyue Su Shikun Wu Shanggui Kang Han Xu Guangsheng Liu Zhi Qiao Luo Liu Monitoring Cropland Abandonment in Southern China from 1992 to 2020 Based on the Combination of Phenological and Time-Series Algorithm Using Landsat Imagery and Google Earth Engine Remote Sensing cropland abandonment key phenological periods prior knowledge long-term southern China |
| title | Monitoring Cropland Abandonment in Southern China from 1992 to 2020 Based on the Combination of Phenological and Time-Series Algorithm Using Landsat Imagery and Google Earth Engine |
| title_full | Monitoring Cropland Abandonment in Southern China from 1992 to 2020 Based on the Combination of Phenological and Time-Series Algorithm Using Landsat Imagery and Google Earth Engine |
| title_fullStr | Monitoring Cropland Abandonment in Southern China from 1992 to 2020 Based on the Combination of Phenological and Time-Series Algorithm Using Landsat Imagery and Google Earth Engine |
| title_full_unstemmed | Monitoring Cropland Abandonment in Southern China from 1992 to 2020 Based on the Combination of Phenological and Time-Series Algorithm Using Landsat Imagery and Google Earth Engine |
| title_short | Monitoring Cropland Abandonment in Southern China from 1992 to 2020 Based on the Combination of Phenological and Time-Series Algorithm Using Landsat Imagery and Google Earth Engine |
| title_sort | monitoring cropland abandonment in southern china from 1992 to 2020 based on the combination of phenological and time series algorithm using landsat imagery and google earth engine |
| topic | cropland abandonment key phenological periods prior knowledge long-term southern China |
| url | https://www.mdpi.com/2072-4292/15/3/669 |
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