A Cropland Disturbance Monitoring Method Based on Probabilistic Trajectories
文献类型：期刊论文
| 作者 | Jiang, Jiawei3; Wang, Juanle1,2,6; Yang, Keming3; Fetisov, Denis5; Li, Kai1,2; Liu, Meng3,4; Zou, Weihao2,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 16期号:21页码:4048 |
| 关键词 | cropland disturbance machine learning LandTrendr agriculture Heilongjiang River basin Amur state |
| DOI | 10.3390/rs16214048 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Acquiring the spatiotemporal patterns of cropland disturbance is of great significance for regional sustainable agricultural development and environmental protection. However, effective monitoring of cropland disturbances remains a challenge owing to the complexity of the terrain landscape and the reliability of the training samples. This study integrated automatic training sample generation, random forest classification, and the LandTrendr time-series segmentation algorithm to propose an efficient and reliable medium-resolution cropland disturbance monitoring scheme. Taking the Amur state of Russia in the Amur river basin, a transboundary region between Russia and China in east Asia with rich agriculture resources as research area, this approach was conducted on the Google Earth Engine cloud-computing platform using extensive remote-sensing image data. A high-confidence sample dataset was then created and a random forest classification algorithm was applied to generate the cropland classification probabilities. LandTrendr time-series segmentation was performed on the interannual cropland classification probabilities. Finally, the identification, spatial mapping, and analysis of cropland disturbances in Amur state were completed. Further cross-validation comparisons of the accuracy assessment and spatiotemporal distribution details demonstrated the high accuracy of the dataset, and the results indicated the applicability of the method. The study revealed that 2815.52 km2 of cropland was disturbed between 1990 and 2021, primarily focusing on the southern edge of the Amur state. The most significant disturbance occurred in 1991, affecting 1431.48 km2 and accounting for 50.84% of the total disturbed area. On average, 87.98 km2 of croplands are disturbed annually. Additionally, 2495.4 km2 of cropland was identified as having been disturbed at least once during the past 32 years, representing 83% of the total disturbed area. This study introduced a novel approach for identifying cropland disturbance information from long time-series probabilistic images. This methodology can also be extended to monitor the spatial and temporal dynamics of other land disturbances caused by natural and human activities. |
| WOS关键词 | AGRICULTURAL LAND ABANDONMENT ; FOREST DISTURBANCE ; CLASSIFICATION ; LANDTRENDR ; ENSEMBLE ; IMAGERY ; TM |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001352055100001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/209518]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Juanle |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.China Univ Min & Technol Beijing, Coll Geosci & Surveying Engn, Beijing 100083, Peoples R China 4.Jiangsu Ocean Univ, Sch Marine Technol & Geomat, Lianyungang 222005, Peoples R China 5.Russian Acad Sci, Inst Complex Anal Reg Problems, Far Eastern Branch, Birobidzhan 679016, Russia 6.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Jiawei,Wang, Juanle,Yang, Keming,et al. A Cropland Disturbance Monitoring Method Based on Probabilistic Trajectories[J]. REMOTE SENSING,2024,16(21):4048. |
| APA | Jiang, Jiawei.,Wang, Juanle.,Yang, Keming.,Fetisov, Denis.,Li, Kai.,...&Zou, Weihao.(2024).A Cropland Disturbance Monitoring Method Based on Probabilistic Trajectories.REMOTE SENSING,16(21),4048. |
| MLA | Jiang, Jiawei,et al."A Cropland Disturbance Monitoring Method Based on Probabilistic Trajectories".REMOTE SENSING 16.21(2024):4048. |
入库方式： OAI收割
来源：地理科学与资源研究所
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