Mapping abandoned cropland using Within-Year Sentinel-2 time series
文献类型：期刊论文
| 作者 | Liu, Bo; Song, Wei |
| 刊名 | CATENA
 |
| 出版日期 | 2023-04-01 |
| 卷号 | 223页码:106924 |
| 关键词 | Food security Change detection within-year Time series Land use mapping GEOBIA Linxia County |
| ISSN号 | 1872-6887 |
| DOI | 10.1016/j.catena.2023.106924 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Against the background of the COVID-19 pandemic and various armed conflicts, the world is experiencing an unprecedented food crisis. The reclamation of abandoned cropland with food production potential may increase the global food supply in a short period of time, ensuring food security. At present, the extraction of abandoned cropland is mainly based on low-and medium-resolution remote sensing image data, making it difficult to extract fragmented areas in mountainous regions and to distinguish between abandoned cropland and transitional classes (such as fallow cropland). We developed a change-detection method based on within-year Sentinel-2 time series to extract cropland abandoned from 2018 to 2021 and defined four types of croplands, namely sponta-neously abandoned, induced abandoned, fallow, and lost cropland, using Linxia County in mountainous China as the study region. First, cropland objects were generated from multi-temporal Sentinel-2 images using the multi -resolution segmentation method, and the land use map of Linxia County from 2017 to 2021 was drawn using random forest classifier. Second, through defining and identifying different cropland types, the interannual dynamic changes in cropland from 2018 to 2021 were extracted by analyzing the annual land use change tra-jectory. Third, by analyzing the normalized difference vegetation index (NDVI) time series of cropland within -year, the active and cultivated cropland sites within-year were extracted by threshold segmentation. Finally, the changes in the four cropland types were extracted by intersecting the two result types. Our method captured the object level changes well (overall mapping accuracy = 93 +/- 5 %), and the extraction accuracy of abandoned cropland reached 81 +/- 2 %. Abandoned cropland was mostly located in areas of medium quality and with a moderate distance from rural settlements. Reclamation can potentially increase the grain production in Linxia County by at least 3.6 % and needs to be combined with the local natural geography and human activities. Our method is a robust method for extracting abandoned cropland and may be applied to other research related to land use change. |
| 学科主题 | Geology ; Agriculture ; Water Resources |
| WOS关键词 | AGRICULTURAL LAND ABANDONMENT ; USE/LAND-COVER CHANGE ; IMAGE-ANALYSIS ; FARMLAND ABANDONMENT ; BIOENERGY PRODUCTION ; MARGINAL LANDS ; MOUNTAIN AREAS ; RANDOM FORESTS ; POYANG LAKE ; CLASSIFICATION |
| WOS研究方向 | Geology ; Agriculture ; Water Resources |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/193880]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Chinese Academy of Sciences 2.Liaoning Technical University 3.Institute of Geographic Sciences & Natural Resources Research, CAS |
| 推荐引用方式 GB/T 7714 | Liu, Bo,Song, Wei. Mapping abandoned cropland using Within-Year Sentinel-2 time series[J]. CATENA,2023,223:106924. |
| APA | Liu, Bo,&Song, Wei.(2023).Mapping abandoned cropland using Within-Year Sentinel-2 time series.CATENA,223,106924. |
| MLA | Liu, Bo,et al."Mapping abandoned cropland using Within-Year Sentinel-2 time series".CATENA 223(2023):106924. |
入库方式： OAI收割
来源：地理科学与资源研究所
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