{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T19:06:59Z","timestamp":1770232019025,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Alliance of International Science Organizations","award":["ANSO-CR-KP-2022-06"],"award-info":[{"award-number":["ANSO-CR-KP-2022-06"]}]},{"name":"Alliance of International Science Organizations","award":["2022FY101902"],"award-info":[{"award-number":["2022FY101902"]}]},{"name":"Alliance of International Science Organizations","award":["CKCEST-2023-1-5"],"award-info":[{"award-number":["CKCEST-2023-1-5"]}]},{"name":"Science &amp; Technology Fundamental Resource Investigation Program of China","award":["ANSO-CR-KP-2022-06"],"award-info":[{"award-number":["ANSO-CR-KP-2022-06"]}]},{"name":"Science &amp; Technology Fundamental Resource Investigation Program of China","award":["2022FY101902"],"award-info":[{"award-number":["2022FY101902"]}]},{"name":"Science &amp; Technology Fundamental Resource Investigation Program of China","award":["CKCEST-2023-1-5"],"award-info":[{"award-number":["CKCEST-2023-1-5"]}]},{"name":"Construction Project of China Knowledge Centre for Engineering Sciences and Technology","award":["ANSO-CR-KP-2022-06"],"award-info":[{"award-number":["ANSO-CR-KP-2022-06"]}]},{"name":"Construction Project of China Knowledge Centre for Engineering Sciences and Technology","award":["2022FY101902"],"award-info":[{"award-number":["2022FY101902"]}]},{"name":"Construction Project of China Knowledge Centre for Engineering Sciences and Technology","award":["CKCEST-2023-1-5"],"award-info":[{"award-number":["CKCEST-2023-1-5"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs16214048","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T09:57:36Z","timestamp":1730368656000},"page":"4048","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Cropland Disturbance Monitoring Method Based on Probabilistic Trajectories"],"prefix":"10.3390","volume":"16","author":[{"given":"Jiawei","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5641-0813","authenticated-orcid":false,"given":"Juanle","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"given":"Keming","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China"}]},{"given":"Denis","family":"Fetisov","sequence":"additional","affiliation":[{"name":"Institute for Complex Analysis of Regional Problems, Far Eastern Branch Russian Academy of Sciences, 679016 Birobidzhan, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6234-6806","authenticated-orcid":false,"given":"Kai","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Meng","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China"}]},{"given":"Weihao","family":"Zou","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.isprsjprs.2018.07.002","article-title":"Towards a polyalgorithm for land use change detection","volume":"144","author":"Saxena","year":"2018","journal-title":"ISPRS J. 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