{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T07:16:41Z","timestamp":1773818201217,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T00:00:00Z","timestamp":1610496000000},"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":["41631179, 4207010881, 61806022,"],"award-info":[{"award-number":["41631179, 4207010881, 61806022,"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["300102120201, 300102269205, 300102320202, 300102269103"],"award-info":[{"award-number":["300102120201, 300102269205, 300102320202, 300102269103"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2017YFB0503600"],"award-info":[{"award-number":["2017YFB0503600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ningxia Academy of Agricultural and Forestry Sciences Foreign Science and Technology Cooperation Project","award":["07030002"],"award-info":[{"award-number":["07030002"]}]},{"DOI":"10.13039\/501100011354","name":"State Key Laboratory of Geo-Information Engineering","doi-asserted-by":"publisher","award":["SKLGIE2018-M-3-4"],"award-info":[{"award-number":["SKLGIE2018-M-3-4"]}],"id":[{"id":"10.13039\/501100011354","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Projects of Key Laboratory of Spatial Data Mining &amp; Information Sharing of Ministry of Education, Fuzhou University","award":["2018LSDMIS03"],"award-info":[{"award-number":["2018LSDMIS03"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2018JQ1038"],"award-info":[{"award-number":["2018JQ1038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precise vegetation maps of mountainous areas are of great significance to grasp the situation of an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, to realize effective vegetation mapping in mountainous areas when samples are difficult to collect due to their perilous terrain and inaccessible deep forest, we propose a novel and intelligent method of sample collection for machine-learning (ML)-based vegetation mapping. First, we employ geo-objects (i.e., polygons) from topographic partitioning and constrained segmentation as basic mapping units and formalize the problem as a supervised classification process using ML algorithms. Second, a previously available vegetation map with rough-scale label information is overlaid on the geo-object-level polygons, and candidate geo-object-based samples can be identified when all the grids\u2019 labels of vegetation types within the geo-objects are the same. Third, various kinds of geo-object-level features are extracted according to high-spatial-resolution remote sensing (HSR-RS) images and multi-source geospatial data. Some unreliable geo-object-based samples are rejected in the candidate set by comparing their features and the rules based on local expert knowledge. Finally, based on these automatically collected samples, we train the model using a random forest (RF)-based algorithm and classify all the geo-objects with labels of vegetation types. A case experiment of Taibai Mountain in China shows that the methodology has the ability to achieve good vegetation mapping results with the rapid and convenient sample collection scheme. The map with a finer geographic distribution pattern of vegetation could clearly promote the vegetation resources investigation and monitoring of the study area; thus, the methodological framework is worth popularizing in the mapping areas such as mountainous regions where the field survey sampling is difficult to implement.<\/jats:p>","DOI":"10.3390\/rs13020249","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T21:50:54Z","timestamp":1610574654000},"page":"249","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Geo-Object-Based Vegetation Mapping via Machine Learning Methods with an Intelligent Sample Collection Scheme: A Case Study of Taibai Mountain, China"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0178-2342","authenticated-orcid":false,"given":"Tianjun","family":"Wu","sequence":"first","affiliation":[{"name":"School of Science, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Jiancheng","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Lijing","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yingwei","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Wen","family":"Dong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, 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\/0000-0002-4880-6439","authenticated-orcid":false,"given":"Ya\u2019nan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, 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\/0000-0001-8323-2728","authenticated-orcid":false,"given":"Xiaodong","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, 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\/0000-0003-2258-0993","authenticated-orcid":false,"given":"Jiangbo","family":"Xi","sequence":"additional","affiliation":[{"name":"School of Geology Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Changpeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Science, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Yun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geology Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710064, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1080\/10106040508542343","article-title":"Land cover classification using IRS LISS III Image and DEM in a rugged terrain: A case study in Himalayas","volume":"20","author":"Saha","year":"2005","journal-title":"Geocarto Int."},{"key":"ref_2","first-page":"1","article-title":"World forest resource assessment 1990: An overview","volume":"44","author":"Janz","year":"1993","journal-title":"Unasylva"},{"key":"ref_3","unstructured":"Burley, J., Evans, J., and Youngquist, J.A. 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