Integrated Approach to Global Land Use and Land Cover Reference Data Harmonization

INTRODUCTION

This document outlines the creation of a global inventory of reference samples and Earth Observation (EO) / gridded datasets for the Global Pasture Watch (GPW) initiative. This inventory supports the training and validation of machine-learning models for GPW grassland mapping. This documentation outlines methodology, data sources, workflow, and results.

Keywords: Grassland, Land Use, Land Cover, Gridded Datasets, Harmonization

OBJECTIVES

Create a global inventory of existing reference samples for land use and land cover (LULC);

Compile global EO / gridded datasets that capture LULC classes and harmonize them to match the GPW classes;

Develop automated scripts for data harmonization and integration.

DATA COLLECTION 

Datasets incorporated:

Datasets

Spatial distribution

Time period
Number of individual samples

WorldCereal
Global
2016-2021
38,267,911

Global Land Cover Mapping and Estimation (GLanCE)
Global
1985-2021
31,061,694

EuroCrops
Europe
2015-2022
14,742,648

GeoWiki G-GLOPS training dataset
Global
2021
11,394,623

MapBiomas Brazil
Brazil
1985-2018
3,234,370

Land Use/Land CoverArea Frame Survey (LUCAS)
Europe
2006-2018
1,351,293

Dynamic World
Global
2019-2020
1,249,983

Land Change Monitoring,Assessment, and Projection (LCMap)
U.S. (CONUS)
1984-2018
874,836

GeoWiki 2012
Global
2011-2012
151,942

PREDICTS
Global
1984-2013
16,627

CropHarvest
Global
2018-2021
9,714

Total: 102,355,642 samples

WORKFLOW

Harmonization Process

We harmonized global reference samples and EO/gridded datasets to align with GPW classes, optimizing their integration into the GPW machine-learning workflow.

We considered reference samples derived by visual interpretation with spatial support of at least 30 m (Landsat and Sentinel), that could represent LULC classes for a point or region.

Each dataset was processed using automated Python scripts to download vector files and convert the original LULC classes into the following GPW classes:

       0. Other land cover

       1. Natural and Semi-natural grassland

       2. Cultivated grassland

       3. Crops and other related agricultural practices

We empirically assigned a weight to each sample based on the original dataset's class description, reflecting the level of mixture within the class. The weights range from 1 (Low) to 3 (High), with higher weights indicating greater mixture. Samples with low mixture levels are more accurate and effective for differentiating typologies and for validation purposes.

The harmonized dataset includes these columns:

Attribute Name
Definition

dataset_name
Original dataset name

reference_year
Reference year of samples from the original dataset

original_lulc_class
LULC class from the original dataset

gpw_lulc_class
Global Pasture Watch LULC class

sample_weight
Sample's weight based on the mixture level within the original LULC class

ACKNOWLEDGMENTS

The development of this global inventory of reference samples and EO/gridded datasets relied on valuable contributions from various sources. We would like to express our sincere gratitude to the creators and maintainers of all datasets used in this project.

REFERENCES

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Tseng, G., Zvonkov, I., Nakalembe, C. L., & Kerner, H. (2021). CropHarvest: A global dataset for crop-type classification. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.