{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T18:21:49Z","timestamp":1781720509706,"version":"3.54.5"},"reference-count":72,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T00:00:00Z","timestamp":1633132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Fund of China and  the Science &amp; Technology Department of Sichuan Province","award":["41401659&2015JY0145"],"award-info":[{"award-number":["41401659&2015JY0145"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hilly areas are important parts of the world\u2019s landscape. A marginal phenomenon can be observed in some hilly areas, leading to serious land abandonment. Extracting the spatio-temporal distribution of abandoned land in such hilly areas can protect food security, improve people\u2019s livelihoods, and serve as a tool for a rational land plan. However, mapping the distribution of abandoned land using a single type of remote sensing image is still challenging and problematic due to the fragmentation of such hilly areas and severe cloud pollution. In this study, a new approach by integrating Linear stretch (Ls), Maximum Value Composite (MVC), and Flexible Spatiotemporal DAta Fusion (FSDAF) was proposed to analyze the time-series changes and extract the spatial distribution of abandoned land. MOD09GA, MOD13Q1, and Sentinel-2 were selected as the basis of remote sensing images to fuse a monthly 10 m spatio-temporal data set. Three pieces of vegetation indices (VIs: ndvi, savi, ndwi) were utilized as the measures to identify the abandoned land. A multiple spatio-temporal scales sample database was established, and the Support Vector Machine (SVM) was used to extract abandoned land from cultivated land and woodland. The best extraction result with an overall accuracy of 88.1% was achieved by integrating Ls, MVC, and FSDAF, with the assistance of an SVM classifier. The fused VIs image set transcended the single source method (Sentinel-2) with greater accuracy by a margin of 10.8\u201323.6% for abandoned land extraction. On the other hand, VIs appeared to contribute positively to extract abandoned land from cultivated land and woodland. This study not only provides technical guidance for the quick acquirement of abandoned land distribution in hilly areas, but it also provides strong data support for the connection of targeted poverty alleviation to rural revitalization.<\/jats:p>","DOI":"10.3390\/rs13193956","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3956","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Temporal Fusion of Multi-Source Remote Sensing Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Shan","family":"He","sequence":"first","affiliation":[{"name":"Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources of China, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaiyong","family":"Shao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources of China, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Xian","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources of China, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialong","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Management Science, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8183-0297","authenticated-orcid":false,"given":"Jiaguo","family":"Qi","sequence":"additional","affiliation":[{"name":"Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48824, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.habitatint.2018.12.006","article-title":"Abandoned rural residential land: Using machine learning techniques to identify rural residential land vulnerable to be abandoned in mountainous areas","volume":"84","author":"Xu","year":"2019","journal-title":"Habitat Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.landusepol.2017.10.039","article-title":"The spatial distribution of farmland abandonment and its influential factors at the township level: A case study in the mountainous area of China","volume":"70","author":"Shi","year":"2018","journal-title":"Land Use Policy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s11442-019-1616-z","article-title":"Extraction and analysis of abandoned farmland: A case study of Qingyun and Wudi counties in Shandong Province","volume":"29","author":"Xiao","year":"2019","journal-title":"J. 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