Extraction of Abandoned Cropland Using Multisource Remote Sensing Images in Suburban Regions: A Case Study of Zengcheng, Guangdong Province
The accurate extraction and temporal monitoring of abandoned croplands are essential for the effective scientific management of such abandonments. Currently, analyzing time-series normalized difference vegetation index (NDVI) variations serves as a widely used method for abandoned cropland extractio...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10762842/ |
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| author | Shanshan Feng Shun Jiang Xu Liu Lei Zhang Yangying Gan Ning Xia Wenbin Wu Canfang Zhou |
| author_facet | Shanshan Feng Shun Jiang Xu Liu Lei Zhang Yangying Gan Ning Xia Wenbin Wu Canfang Zhou |
| author_sort | Shanshan Feng |
| collection | DOAJ |
| description | The accurate extraction and temporal monitoring of abandoned croplands are essential for the effective scientific management of such abandonments. Currently, analyzing time-series normalized difference vegetation index (NDVI) variations serves as a widely used method for abandoned cropland extraction. However, acquiring complete cloud-free images to establish time-series NDVI data across the entire crop growth cycle is usually challenging. To enhance the accuracy of abandoned cropland extraction, the method of annual maximum of NDVI value was proposed to extract abandoned cropland using Gaofen-2 (GF-2) and Sentinel-2 imagery in Zengcheng District, Guangdong Province, China. The method involves the following steps. First, the GF-2 images were used to generate land use map by object-based image analysis method. Subsequently, the images of annual maximum of NDVI value from 2018 to 2022 of Zengcheng District were calculated based on Sentinel-2 data on the Google Earth Engine platform. Crucially, the optimal threshold distinguishing planted cropland and unplanted cropland was determined. After conducting repeated comparisons from Google Earth images and field survey data, it was determined that an object with an annual maximum NDVI value below 0.4 should be regarded as an unplanted object. With this threshold (NDVI = 0.4), the spatial distribution of unplanted cropland within year was identified. Finally, croplands that remained unplanted for two or more consecutive years were extracted as abandonment. The extraction results and accuracy assessment showed that our method achieved an overall accuracy ranging from 0.80 to 0.85. In summary, this study presents a novel approach for accurate abandonment extraction based on available NDVI data. |
| format | Article |
| id | doaj-art-b50b6996435a4115a5f422788f976b5d |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-b50b6996435a4115a5f422788f976b5d2025-08-20T03:58:40ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118180551806710.1109/JSTARS.2024.350451810762842Extraction of Abandoned Cropland Using Multisource Remote Sensing Images in Suburban Regions: A Case Study of Zengcheng, Guangdong ProvinceShanshan Feng0https://orcid.org/0000-0001-6283-9707Shun Jiang1Xu Liu2Lei Zhang3Yangying Gan4Ning Xia5Wenbin Wu6https://orcid.org/0009-0006-7273-0741Canfang Zhou7https://orcid.org/0009-0002-1319-6658Institute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences, Guangzhou, ChinaInstitute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences, Guangzhou, ChinaInstitute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences, Guangzhou, ChinaInstitute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences, Guangzhou, ChinaInstitute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences, Guangzhou, ChinaInstitute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou, ChinaState Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Guangzhou, ChinaInstitute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences, Guangzhou, ChinaThe accurate extraction and temporal monitoring of abandoned croplands are essential for the effective scientific management of such abandonments. Currently, analyzing time-series normalized difference vegetation index (NDVI) variations serves as a widely used method for abandoned cropland extraction. However, acquiring complete cloud-free images to establish time-series NDVI data across the entire crop growth cycle is usually challenging. To enhance the accuracy of abandoned cropland extraction, the method of annual maximum of NDVI value was proposed to extract abandoned cropland using Gaofen-2 (GF-2) and Sentinel-2 imagery in Zengcheng District, Guangdong Province, China. The method involves the following steps. First, the GF-2 images were used to generate land use map by object-based image analysis method. Subsequently, the images of annual maximum of NDVI value from 2018 to 2022 of Zengcheng District were calculated based on Sentinel-2 data on the Google Earth Engine platform. Crucially, the optimal threshold distinguishing planted cropland and unplanted cropland was determined. After conducting repeated comparisons from Google Earth images and field survey data, it was determined that an object with an annual maximum NDVI value below 0.4 should be regarded as an unplanted object. With this threshold (NDVI = 0.4), the spatial distribution of unplanted cropland within year was identified. Finally, croplands that remained unplanted for two or more consecutive years were extracted as abandonment. The extraction results and accuracy assessment showed that our method achieved an overall accuracy ranging from 0.80 to 0.85. In summary, this study presents a novel approach for accurate abandonment extraction based on available NDVI data.https://ieeexplore.ieee.org/document/10762842/Abandoned croplandland use classificationnormalized difference vegetation index (NDVI) maximum valueunplanted croplandZengcheng |
| spellingShingle | Shanshan Feng Shun Jiang Xu Liu Lei Zhang Yangying Gan Ning Xia Wenbin Wu Canfang Zhou Extraction of Abandoned Cropland Using Multisource Remote Sensing Images in Suburban Regions: A Case Study of Zengcheng, Guangdong Province IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Abandoned cropland land use classification normalized difference vegetation index (NDVI) maximum value unplanted cropland Zengcheng |
| title | Extraction of Abandoned Cropland Using Multisource Remote Sensing Images in Suburban Regions: A Case Study of Zengcheng, Guangdong Province |
| title_full | Extraction of Abandoned Cropland Using Multisource Remote Sensing Images in Suburban Regions: A Case Study of Zengcheng, Guangdong Province |
| title_fullStr | Extraction of Abandoned Cropland Using Multisource Remote Sensing Images in Suburban Regions: A Case Study of Zengcheng, Guangdong Province |
| title_full_unstemmed | Extraction of Abandoned Cropland Using Multisource Remote Sensing Images in Suburban Regions: A Case Study of Zengcheng, Guangdong Province |
| title_short | Extraction of Abandoned Cropland Using Multisource Remote Sensing Images in Suburban Regions: A Case Study of Zengcheng, Guangdong Province |
| title_sort | extraction of abandoned cropland using multisource remote sensing images in suburban regions a case study of zengcheng guangdong province |
| topic | Abandoned cropland land use classification normalized difference vegetation index (NDVI) maximum value unplanted cropland Zengcheng |
| url | https://ieeexplore.ieee.org/document/10762842/ |
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