{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T17:41:10Z","timestamp":1783791670568,"version":"3.55.0"},"reference-count":76,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovative Research Team of Ministry of Education of China","award":["IRT_17R59"],"award-info":[{"award-number":["IRT_17R59"]}]},{"name":"Innovative Research Team of Ministry of Education of China","award":["ZDZX2018020"],"award-info":[{"award-number":["ZDZX2018020"]}]},{"name":"Inner Mongolia Key Project","award":["IRT_17R59"],"award-info":[{"award-number":["IRT_17R59"]}]},{"name":"Inner Mongolia Key Project","award":["ZDZX2018020"],"award-info":[{"award-number":["ZDZX2018020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Knowledge of grassland classification in a timely and accurate manner is essential for grassland resource management and utilization. Although remote sensing imagery analysis technology is widely applied for land cover classification, few studies have systematically compared the performance of commonly used methods on semi-arid native grasslands in northern China. This renders the grassland classification work in this region devoid of applicable technical references. In this study, the central Xilingol (China) was selected as the study area, and the performances of four widely used machine learning algorithms for mapping semi-arid grassland under pixel-based and object-based classification methods were compared: random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and naive Bayes (NB). The features were composed of the Landsat OLI multispectral data, spectral indices, Sentinel SAR C bands, topographic, position (coordinates), geometric, and grey-level co-occurrence matrix (GLCM) texture variables. The findings demonstrated that (1) the object-based methods depicted a more realistic land cover distribution and had greater accuracy than the pixel-based methods; (2) in the pixel-based classification, RF performed the best, with OA and Kappa values of 96.32% and 0.95, respectively. In object-based classification, RF and SVM presented no statistically different predictions, with OA and Kappa exceeding 97.5% and 0.97, respectively, and both performed significantly better than other algorithms. (3) In pixel-based classification, multispectral bands, spectral indices, and geographic features significantly distinguished grassland, whereas, in object-based classification, multispectral bands, spectral indices, elevation, and position features were more prominent. Despite the fact that Sentinel 1 SAR variables were chosen as an effective variable in object-based classification, they made no significant contribution to the grassland distinction.<\/jats:p>","DOI":"10.3390\/rs15030750","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T10:19:28Z","timestamp":1675073968000},"page":"750","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Comparing Machine Learning Algorithms for Pixel\/Object-Based Classifications of Semi-Arid Grassland in Northern China Using Multisource Medium Resolution Imageries"],"prefix":"10.3390","volume":"15","author":[{"given":"Nitu","family":"Wu","sequence":"first","affiliation":[{"name":"Key Laboratory of Grassland Resources of the Ministry of Education, College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot 010011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu\u00eds Guilherme Teixeira","family":"Crusiol","sequence":"additional","affiliation":[{"name":"Embrapa Soja (National Soybean Research Center\u2014Brazilian Agricultural Research Corporation), Londrina 86001-970, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guixiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot 010010, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5890-1758","authenticated-orcid":false,"given":"Deji","family":"Wuyun","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Grassland Resources of the Ministry of Education, College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot 010011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e02582","DOI":"10.1002\/ecs2.2582","article-title":"Grasslands\u2014More important for ecosystem services than you might think","volume":"10","author":"Bengtsson","year":"2019","journal-title":"Ecosphere"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1093\/aob\/mcs209","article-title":"The role of grasslands in food security and climate change","volume":"110","year":"2012","journal-title":"Ann. 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