{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T19:02:15Z","timestamp":1772823735312,"version":"3.50.1"},"reference-count":101,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mathematics of Information Technology and Complex Systems (Mitacs)","award":["IT29553"],"award-info":[{"award-number":["IT29553"]}]},{"name":"Mathematics of Information Technology and Complex Systems (Mitacs)","award":["56965"],"award-info":[{"award-number":["56965"]}]},{"name":"Canadian Forage &amp; Grassland Association (CFGA)","award":["IT29553"],"award-info":[{"award-number":["IT29553"]}]},{"name":"Canadian Forage &amp; Grassland Association (CFGA)","award":["56965"],"award-info":[{"award-number":["56965"]}]},{"name":"Environment and Climate Change Canada (ECCC)","award":["IT29553"],"award-info":[{"award-number":["IT29553"]}]},{"name":"Environment and Climate Change Canada (ECCC)","award":["56965"],"award-info":[{"award-number":["56965"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and reliable knowledge about grassland distribution is essential for farmers, stakeholders, and government to effectively manage grassland resources from agro-economical and ecological perspectives. This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed in the province of Manitoba, Canada. The grassland classification process involved three stages: (1) to distinguish between vegetation and non-vegetation covers, (2) to differentiate grassland from non-grassland landscapes, and (3) to identify three specific grassland classes (tame, native, and mixed grasses). Initially, this study investigated different satellite data, such as Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 and 9, individually and combined, using the random forest (RF) method, with the best performance at the first two steps achieved using a combination of S1 and S2. The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). In step 3, after filtering out non-grassland pixels, the performance of RF, SVM, and GTB classifiers was evaluated with combined S1 and S2 data to distinguish different grassland types. Eighty-nine multitemporal raster-based variables, including spectral bands, SAR backscatters, and digital elevation models (DEM), were input for ML models. RF had the highest classification accuracy at 69.96% overall accuracy (OA) and a Kappa value of 0.55. After feature selection, the variables were reduced to 61, increasing OA to 72.62% with a Kappa value of 0.58. GTB ranked second, with its OA and Kappa values improving from 67.69% and 0.50 to 72.18% and 0.58 after feature selection. The impact of raster data quality on grassland classification accuracy was assessed through multisensor image fusion. Grassland classification using the Hue, Saturation, and Value (HSV) fused images showed higher OA (59.18%) and Kappa values (0.36) than the Brovey Transform (BT) and non-fused images. Finally, a web map was created to show grassland results within the Soil Landscapes of Canada (SLC) polygons, relating soil landscapes to grassland distribution and providing valuable information for decision-makers and researchers. Future work may include extending the current methodology by considering other influential variables, like meteorological parameters or soil properties, to create a comprehensive grassland inventory across the whole Prairie ecozone of Canada.<\/jats:p>","DOI":"10.3390\/rs16244730","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T09:43:03Z","timestamp":1734514983000},"page":"4730","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Hierarchical Machine Learning-Based Strategy for Mapping Grassland in Manitoba\u2019s Diverse Ecoregions"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8946-044X","authenticated-orcid":false,"given":"Mirmajid","family":"Mousavi","sequence":"first","affiliation":[{"name":"Digital AgroEcosystems Lab, Department of Soil Science, University of Manitoba, 13 Freedman Crescent, Winnipeg, MB R3T 2N2, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4735-9868","authenticated-orcid":false,"given":"James Kobina Mensah","family":"Biney","sequence":"additional","affiliation":[{"name":"Digital AgroEcosystems Lab, Department of Soil Science, University of Manitoba, 13 Freedman Crescent, Winnipeg, MB R3T 2N2, Canada"}]},{"given":"Barbara","family":"Kishchuk","sequence":"additional","affiliation":[{"name":"Digital AgroEcosystems Lab, Department of Soil Science, University of Manitoba, 13 Freedman Crescent, Winnipeg, MB R3T 2N2, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9986-5324","authenticated-orcid":false,"given":"Ali","family":"Youssef","sequence":"additional","affiliation":[{"name":"Adaptation Physiology Group, Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1535-5096","authenticated-orcid":false,"given":"Marcos R. C.","family":"Cordeiro","sequence":"additional","affiliation":[{"name":"Department of Animal Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada"}]},{"given":"Glenn","family":"Friesen","sequence":"additional","affiliation":[{"name":"Manitoba Agriculture, 545 University Crescent, Winnipeg, MB R3T 5S6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8444-0190","authenticated-orcid":false,"given":"Douglas","family":"Cattani","sequence":"additional","affiliation":[{"name":"Department of Plant Science, University of Manitoba, 66 Dafoe Road, Winnipeg, MB R3T 2N2, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3462-8643","authenticated-orcid":false,"given":"Mustapha","family":"Namous","sequence":"additional","affiliation":[{"name":"Data Science for Sustainable Earth Laboratory (Data4Earth), Sultan Moulay Slimane University, Beni-Mellal 23000, Morocco"},{"name":"Facult\u00e9 des Arts et des Sciences (FAFS), Universit\u00e9 de Saint-Boniface, 200, Avenue de la Cath\u00e9drale, Winnipeg, MB R2H 0H7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2539-6972","authenticated-orcid":false,"given":"Nasem","family":"Badreldin","sequence":"additional","affiliation":[{"name":"Digital AgroEcosystems Lab, Department of Soil Science, University of Manitoba, 13 Freedman Crescent, Winnipeg, MB R3T 2N2, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"162994","DOI":"10.1016\/j.scitotenv.2023.162994","article-title":"Grassland Biodiversity Response to Livestock Grazing, Productivity, and Climate Varies across Biome Components and Diversity Measurements","volume":"878","author":"Wu","year":"2023","journal-title":"Sci. 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