{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T22:08:33Z","timestamp":1772489313071,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,26]],"date-time":"2022-03-26T00:00:00Z","timestamp":1648252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42130508"],"award-info":[{"award-number":["42130508"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41977421"],"award-info":[{"award-number":["41977421"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2021YFD1300501"],"award-info":[{"award-number":["2021YFD1300501"]}]},{"name":"National Key Research and Development Program of China","award":["2016YFB0501502"],"award-info":[{"award-number":["2016YFB0501502"]}]},{"name":"Strategic Priori-ty Research Program of the Chinese Academy of Sciences","award":["XDA19040301"],"award-info":[{"award-number":["XDA19040301"]}]},{"name":"Strategic Priori-ty Research Program of the Chinese Academy of Sciences","award":["XDA20010202"],"award-info":[{"award-number":["XDA20010202"]}]},{"name":"Strategic Priori-ty Research Program of the Chinese Academy of Sciences","award":["XDA23100200"],"award-info":[{"award-number":["XDA23100200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-accuracy, long-time-series and large-scale land classification mapping are essential for assessing the evolutionary patterns of land systems and developing sustainability studies. In this paper, using Google Earth Engine (GEE) and Landsat satellite remote sensing images, based on the Random Forest (RF) algorithm, we carried out remote sensing classification to obtain a year-by-year land use\/cover data set in Vietnam over the past 21 years (2000\u20132020). Further applying principal component analysis and multiple linear regression methods, we examined the spatio-temporal characteristics, dynamic changes and driving mechanisms of land use change. The results show the following: (1) The RF classification algorithm supported by the GEE can quickly and accurately obtain a land use\/cover data set. The overall classification accuracy is 0.91 \u00b1 0.01. (2) The land cover types in Vietnam are dominated by woodland and cropland, with an area share of 54.62% and 37.90%, respectively. In the past 20 years, the area of built-up land has increased the most (+93.49%), followed by the area of water bodies (+54.19%), while the area of woodland has remained almost unchanged. (3) The expansion of built-up land is driven by regional economic development; the area changes in cropland, water bodies and woodland are influenced by both national economic development and climate change. The results of the study provide a basis for assessing land use policies in Vietnam and a reference methodological framework for rapid land mapping and analysis in other countries in the China\u2013Indochina Peninsula.<\/jats:p>","DOI":"10.3390\/rs14071600","type":"journal-article","created":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T21:29:36Z","timestamp":1648416576000},"page":"1600","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Analysis of Land Use Change and Driving Mechanisms in Vietnam during the Period 2000\u20132020"],"prefix":"10.3390","volume":"14","author":[{"given":"Xuan","family":"Guo","sequence":"first","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9187-7056","authenticated-orcid":false,"given":"Junzhi","family":"Ye","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6219-6251","authenticated-orcid":false,"given":"Yunfeng","family":"Hu","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"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7492","DOI":"10.1073\/pnas.1405557111","article-title":"Projected land-use change impacts on ecosystem services in the United States","volume":"111","author":"Lawler","year":"2014","journal-title":"Proc. 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