Annual 30-m maps of global grassland class and extent (2000–2022) based on spatiotemporal Machine Learning

Parente, Leandro (author), Sloat, Lindsey (author), Mesquita, Vinicius (author), Consoli, Davide (author), Stanimirova, Radost (author), Hengl, Tomislav (author), Bonannella, Carmelo (author), Teles, Nathália (author), Wheeler, Ichsani (author), Hunter, Maria (author), Malek, Žiga (author), Stolle, Fred (author)

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.

• Language: English
• Keywords: grasslands, global grassland class, maps, spatiotemporal distribution, machine learning
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: BF - Biotechnical Faculty
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: 22 str.
• Numbering: Vol. 11, art. 1303
• PID: 20.500.12556/RUL-165996
• UDC: 63
• ISSN on article: 2052-4463
• DOI: 10.1038/s41597-024-04139-6
• COBISS.SI-ID: 219245571
• Publication date in RUL: 16.12.2024

Record is a part of a journal

• Title: Scientific data
• Publisher: Nature Publishing Group
• ISSN: 2052-4463
• COBISS.SI-ID: 523393305

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: travinje, zemljevidi, strojno učenje