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| A Cropland Disturbance Monitoring Method Based on Probabilistic Trajectories | |
Jiang, Jiawei1; Wang, Juanle2,3,4 ; Yang, Keming1; Fetisov, Denis5; Li, Kai2,3; Liu, Meng1,6; Zou, Weihao1,2
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| 2024-11-01 | |
| Source Publication | REMOTE SENSING (IF:4.118[JCR-2018],4.74[5-Year]) |
| Volume | 16Issue:21Pages:4048 |
| Abstract | 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. |
| Keyword | cropland disturbance machine learning LandTrendr agriculture Heilongjiang River basin Amur state |
| WOS Keyword | AGRICULTURAL LAND ABANDONMENT ; FOREST DISTURBANCE ; CLASSIFICATION ; LANDTRENDR ; ENSEMBLE ; IMAGERY ; TM |
| DOI | 10.3390/rs16214048 |
| Indexed By | Science Citation Index Expanded (SCI-EXPANDED) |
| WOS Research Area | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS Subject | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:001352055100001 |
| Subtype | Article |
| Contribution Rank | 2 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.igsnrr.ac.cn/handle/311030/209518 |
| Collection | 资源与环境信息系统国家重点实验室_外文论文 |
| Corresponding Author | Wang, Juanle |
| Affiliation | 1.China Univ Min & Technol Beijing, Coll Geosci & Surveying Engn, Beijing 100083, 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.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 5.Russian Acad Sci, Inst Complex Anal Reg Problems, Far Eastern Branch, Birobidzhan 679016, Russia 6.Jiangsu Ocean Univ, Sch Marine Technol & Geomat, Lianyungang 222005, Peoples R China |
| Recommended Citation 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. |
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| 043-jiangjw.pdf（3952KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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