20.500.12556/RUL-165996 Annual 30-m maps of global grassland class and extent (2000–2022) based on spatiotemporal Machine Learning The paper describes the production and evaluation of global grassland extent mapped annually for 2000–2022 at 30 m spatial resolution. The dataset showing the spatiotemporal distribution of cultivated and natural/semi-natural grassland classes was produced by using GLAD Landsat ARD-2 image archive, accompanied by climatic, landform and proximity covariates, spatiotemporal machine learning (per-class Random Forest) and over 2.3 M reference samples (visually interpreted in Very High Resolution imagery). Custom probability thresholds (based on five-fold spatial cross-validation) were used to derive dominant class maps with balanced user’s and producer’s accuracy, resulting in f1 score of 0.64 and 0.75 for cultivated and natural/semi-natural grassland, respectively. The produced maps (about 4 TB in size) are available under an open data license as Cloud-Optimized GeoTIFFs and as Google Earth Engine assets. The suggested uses of data include (1) integration with other compatible land cover products and (2) tracking the intensity and drivers of conversion of land to cultivated grasslands and from natural / semi-natural grasslands into other land use systems. grasslands global grassland class maps spatiotemporal distribution machine learning travinje zemljevidi strojno učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2024-12-16 15:17:53 2024-12-16 15:17:57 2024-12-17 04:11:08 0000-00-00 00:00:00 2024 0 0 22 str. art. 1303 Vol. 11 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 63 2052-4463 10.1038/s41597-024-04139-6 219245571 Annual_30-m_maps_of_global_grassland_class_and_extent_(2000–2022)_based_on_spatiotemporal_Machine_Learning.pdf Annual_30-m_maps_of_global_grassland_class_and_extent_(2000–2022)_based_on_spatiotemporal_Machine_Learning.pdf 1 646B7BBC728D46903C418B18713B9430 47666b5803ca701c2aa21cc8bfdc793c37c2d6b4b277c1b71bc34fb2f04b7ed2 1ba293ac-bbb9-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=197351 https://www.nature.com/articles/s41597-024-04139-6 0 3c40a6e4-bbb9-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=197352 Biotehniška fakulteta 0 0 0