ArticleScientific data2024
Annual 30-m maps of global grassland class and extent (2000-2022) based on spatiotemporal Machine Learning.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Increasing forest disturbance enhances habitat suitability for Europe's large herbivores.Nature ecology & evolution · 2026Article
- Evaluating model robustness in landslide susceptibility mapping using a unified data-consistent framework in northern Thailand.Scientific reports · 2026Article
- National 10-m soybean maps for South Africa from 2018 to 2025.Scientific data · 2026Article
- Overlooked and overexploited: Extensive conversion of grasslands and wetlands driven by global food, feed, and bioenergy demand.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Global patterns of commodity-driven deforestation and associated carbon emissions.Nature food · 2026Article
- Projected human land-use pressures and natural habitat conversion risk within global terrestrial protected areas.Nature ecology & evolution · 2026Article
- Spatial patterns of cattle densities across the Brazilian Amazon revealed by very high-resolution satellite imagery.Communications sustainability · 2026Article
- Article
- Identifying global hotspots of agricultural expansion into non-forest ecosystems.Nature communications · 2025Article
- Consistent global dataset on biodiversity intactness footprint of agricultural production from 2000 to 2020.Scientific data · 2025Article
- Global 30-m annual median vegetation height maps (2000-2022) based on ICESat-2 data and Machine Learning.Scientific data · 2025Article
- Addressing critiques refines global estimates of reforestation potential for climate change mitigation.Nature communications · 2025Article
- Article
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.