{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:45:49Z","timestamp":1760147149313,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["300102262902","300102260108","SXLK2021-0225"],"award-info":[{"award-number":["300102262902","300102260108","SXLK2021-0225"]}]},{"name":"Shaanxi Forestry Science and Technology Innovation Program","award":["300102262902","300102260108","SXLK2021-0225"],"award-info":[{"award-number":["300102262902","300102260108","SXLK2021-0225"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>There are a variety of land cover products generated from remote-sensing images. However, misclassification errors in individual products and inconsistency among them undermine their utilities for research and other applications. While it is worth developing advanced pattern classifiers and utilizing the images of finer spatial, temporal, and\/or spectral resolution for increased classification accuracy, it is also sensible to increase map classification accuracy through effective map fusion by exploiting complementarity among multi-source products over a study area. This paper presents a novel fusion method that works by weighting multiple source products based on their map-reference cover type transition probabilities, which are predicted using random forest for individual map pixels. The proposed method was tested and compared with three alternatives: consensus-based weighting, random forest, and locally modified Dempster\u2013Shafer evidential reasoning, in a case study, over Shaanxi province, China. For this case study, three types of land cover products (GlobeLand30, FROM-GLC, and GLC_FCS30) of two nominal years (2010 and 2020) were used as the base maps for fusion. Reference sample data for model training and testing were collected following a robust stratified random sampling design that allows for augmenting reference data flexibly. Accuracy assessments show that overall accuracies (OAs) of fused land cover maps have been improved (1~9% in OAs), with the proposed method outperforming other methods by 2~8% in OAs. The proposed method does not need to have the base products\u2019 classification systems harmonized beforehand, thus being robust and highly recommendable for fusing land cover products.<\/jats:p>","DOI":"10.3390\/rs15020481","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T05:09:32Z","timestamp":1673586572000},"page":"481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition Probabilities"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9769-4277","authenticated-orcid":false,"given":"Wangle","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites (BDCGS), Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Jiwen","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites (BDCGS), Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Hate","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites (BDCGS), Chang\u2019an University, Xi\u2019an 710054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6101-2070","authenticated-orcid":false,"given":"Ming","family":"Cong","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Yue","family":"Wan","sequence":"additional","affiliation":[{"name":"College of Resource and Environment, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Jingxiong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3289","DOI":"10.1080\/014311697217099","article-title":"The IGBP-DIS Global 1-Km Land Cover Data Set, DIScover: First Results","volume":"18","author":"Loveland","year":"1997","journal-title":"Int. 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