![projects/global-pasture-watch/assets/ggc-30m/v1/nat-semi-grassland_p](https://developers.google.com/earth-engine/datasets/images/global-pasture-watch/projects_global-pasture-watch_assets_ggc-30m_v1_nat-semi-grassland_p_sample.png) info


This dataset is part of a Publisher Catalog, and not managed by Google Earth Engine.

Contact [Land \& Carbon Lab](https://landcarbonlab.org/subscribe)

for bugs or [view more datasets](https://developers.google.com/earth-engine/datasets/publisher/global-pasture-watch.md.txt)
from the Global Pasture Watch Catalog. [Learn more about Publisher datasets](https://developers.google.com/earth-engine/datasets/publisher).
[![Catalog Owner Logo](https://developers.google.com/static/earth-engine/datasets/logos/global-pasture-watch_logo.png)](https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring)

Catalog Owner
:   Global Pasture Watch

Dataset Availability
:   2000-01-01T00:00:00Z--2023-01-01T00:00:00Z

Dataset Producer
:


    [Land and Carbon Lab Global Pasture Watch](https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring)

Contact
:   [Land \& Carbon Lab](https://landcarbonlab.org/subscribe)

Cadence
:   1 Year

Tags
:
    [global](https://developers.google.com/earth-engine/datasets/tags/global.md.txt) [global-pasture-watch](https://developers.google.com/earth-engine/datasets/tags/global-pasture-watch.md.txt) [land](https://developers.google.com/earth-engine/datasets/tags/land.md.txt) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover.md.txt) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse.md.txt) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover.md.txt) [pasture](https://developers.google.com/earth-engine/datasets/tags/pasture.md.txt) [publisher-dataset](https://developers.google.com/earth-engine/datasets/tags/publisher-dataset.md.txt) [rangeland](https://developers.google.com/earth-engine/datasets/tags/rangeland.md.txt) [vegetation](https://developers.google.com/earth-engine/datasets/tags/vegetation.md.txt)

#### Description

This dataset provides global annual probability maps of natural/semi-natural
grassland from 2000 to 2022 at 30-m spatial resolution.
Produced by the Land \&
Carbon Lab Global Pasture Watch initiative, the mapped grassland extent
includes any land cover type, which contains at least 30% of dry or wet
low vegetation, dominated by grasses and forbs (less than 3 meters)
and a:

- maximum of 50% tree canopy cover (greater than 5 meters),
- maximum of 70% of other woody vegetation (scrubs and open shrubland), and
- maximum of 50% active cropland cover in mosaic landscapes of cropland \& other vegetation.

The grassland extent is classified into two classes:
- **Cultivated grassland** : Areas where grasses and other forage plants have
been intentionally planted and managed, as well as areas of native
grassland-type vegetation where they clearly exhibit active and
heavy management for specific human-directed uses, such as directed
grazing of livestock.
- **Natural/Semi-natural grassland**: Relatively undisturbed native
grasslands/short-height vegetation, such as steppes and tundra,
as well as areas that have experienced varying degrees of human
activity in the past, which may contain a mix of native and
introduced species due to historical land use and natural processes.
In general, they exhibit natural-looking patterns of varied vegetation
and clearly ordered hydrological relationships throughout the landscape.

The implemented methodology considered [GLAD Landsat ARD-2 images](https://glad.umd.edu/ard) (processed into cloud-free bi-monthly
aggregates, see [Consoli et al, 2024](https://doi.org/10.7717/peerj.18585)
), accompanied by climatic, landform and proximity covariates,
spatiotemporal machine learning (per-class Random Forest) and over
2.3 million reference samples (visually interpreted in Very High
Resolution imagery). Custom probability thresholds (based on five-fold
spatial cross-validation and balanced precision and recall values)
were used to derive dominant class maps, 0.32 and 0.42 for
cultivated and natural/semi-natural grassland probability thresholds, respectively.

**Limitations:** Grassland extent is partly under-predicted in southeastern
Africa (Zimbabwe and Mozambique) and in eastern Australia (shrublands and
woodlands of the Mulga ecoregion). Cropland is misclassified as grassland
in parts of northern Africa, the Arabian Peninsula, Western Australia,
New Zealand, the center of Bolivia, and Mato Grosso state (Brazil). Due
to the Landsat 7 SLC failure, regular stripes of grassland probabilities
are visible at parcel-level, particularly in the year 2012. The usage of
coarser resolution layers (accessibility maps and MODIS products)
introduced curvilinear macroscopic errors (due to the downscaling
strategy based on cubicspline) in Uruguay, Southwest Argentina, South
of Angola and in the Sahel region in Africa. Users need to be aware
of the limitations and known issues; whilst considering them
carefully to ensure appropriate use of maps at this initial prediction
stage. GPW is working actively to collect systematic feedback via the [Geo-Wiki
platform](https://www.geo-wiki.org), validate the current version
and improve future versions of the dataset.

**For more information see [Parente et. al, 2024](http://doi.org/10.1038/s41597-024-04139-6),
[Zenodo](https://zenodo.org/records/13890401) and
[Global Pasture Watch GitHub site](https://github.com/wri/global-pasture-watch)**

### Bands

**Bands**

Pixel size: 27.83 meters (all bands)

| Name | Min | Max | Pixel Size | Description |
|---|---|---|---|---|
| `probability` | 0 | 100 | 27.83 meters | Natural/Semi-natural grassland probability value derived through Random Forest. |

### Image Properties

**Image Properties**

| Name | Type | Description |
|---|---|---|
| version | INT | Product version |

### Terms of Use

**Terms of Use**

[CC-BY-4.0](https://spdx.org/licenses/CC-BY-4.0.html)

### Citations

Citations:

- Parente, L., Sloat, L., Mesquita, V., et al. (2024)
  Global Pasture Watch - Annual grassland class and extent
  maps at 30-m spatial resolution (2000---2022) (Version v1)
  \[Data set\]. Zenodo
  [doi:https://doi.org/10.5281/zenodo.13890401](https://doi.org/10.5281/zenodo.13890401)
- Parente, L., Sloat, L., Mesquita, V., et al. (2024).
  Annual 30-m maps of global grassland class and extent (2000--2022)
  based on spatiotemporal Machine Learning, Scientific Data.
  [doi: http://doi.org/10.1038/s41597-024-04139-6](http://doi.org/10.1038/s41597-024-04139-6)

### DOIs

- <https://doi.org/10.1038/s41597-024-04139-6>
- <https://doi.org/10.5281/zenodo.13890401>

### Explore with Earth Engine

> [!NOTE]
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### Code Editor (JavaScript)

```javascript
Map.setCenter(63.72366666, 38.30182604, 4);

var nat_semi_grassland = ee.ImageCollection(
  "projects/global-pasture-watch/assets/ggc-30m/v1/nat-semi-grassland_p"
)
var min_prob = 42 // Probability threshold
var visParams = {min: 15, max: 85, palette: 'f7f1e5,af8260,803d3b,322c2b'}

var nat_semi_grassland_2022 = nat_semi_grassland.filterDate('2022-01-01', '2023-01-01').first();
Map.addLayer(
    nat_semi_grassland_2022.mask(nat_semi_grassland_2022.gte(min_prob)), 
    visParams, 'Natural/Semi-natural grassland prob. (2022)'
);

var nat_semi_grassland_2000 = nat_semi_grassland.filterDate('2000-01-01', '2001-01-01').first();
Map.addLayer(
    nat_semi_grassland_2000.mask(nat_semi_grassland_2000.gte(min_prob)), 
    visParams, 'Natural/Semi-natural grassland prob. (2000)'
);
```
[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/global-pasture-watch/projects_global-pasture-watch_assets_ggc-30m_v1_nat-semi-grassland_p) [GPW Annual Probabilities of Natural/Semi-natural Grasslands v1](https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_nat-semi-grassland_p.md.txt) This dataset provides global annual probability maps of natural/semi-natural grassland from 2000 to 2022 at 30-m spatial resolution. Produced by the Land \& Carbon Lab Global Pasture Watch initiative, the mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated ... projects/global-pasture-watch/assets/ggc-30m/v1/nat-semi-grassland_p, global,global-pasture-watch,land,landcover,landuse,landuse-landcover,pasture,publisher-dataset,rangeland,vegetation 2000-01-01T00:00:00Z/2023-01-01T00:00:00Z -90 -180 90 180 Google Earth Engine https://developers.google.com/earth-engine/datasets

- [https://doi.org/10.5281/zenodo.13890401](https://doi.org/https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring)
- [https://doi.org/10.5281/zenodo.13890401](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_nat-semi-grassland_p)