DOI: 10.5281/zenodo.11271525
title: Integrated Approach to Global Land Use and Land Cover Reference Data Harmonization

description: 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.



 

REPOSITORY STRUCTURE





data/: Raw datasets and metadata;




workflow/: Methodology and reference ontology used for harmonizing datasets;




scripts/: Python scripts for downloading, processing, harmonizing datasets, and data analysis/visualization;




results/: Final harmonized datasets in Parquet format.



 

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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d’Andrimont, R. et al. Harmonised lucas in-situ land cover and use database for field surveys from 2006 to 2018 in the european union. Sci. data 7, 352, 10.1038/s41597-019-0340-y (2020)




Fritz, S. et al. Geo-Wiki: An online platform for improving global land cover, Environmental Modelling & Software, 31, https://doi.org/10.1016/j.envsoft.2011.11.015 (2012)




Fritz, S., See, L., Perger, C. et al. A global dataset of crowdsourced land cover and land use reference data. Sci Data 4, 170075 https://doi.org/10.1038/sdata.2017.75 (2017)




Schneider, M., Schelte, T., Schmitz, F. & Körner, M. Eurocrops: The largest harmonized open crop dataset across the european union. Sci. Data 10, 612, 10.1038/s41597-023-02517-0 (2023)




Souza, C. M. et al. Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote. Sens. 12, 2735, 10.3390/rs12172735 (2020)




Stanimirova, R. et al. A global land cover training dataset from 1984 to 2020. Sci. Data 10, 879 (2023) 


Stehman, S. V., Pengra, B. W., Horton, J. A. & Wellington, D. F. Validation of the us geological survey’s land change monitoring, assessment and projection (lcmap) collection 1.0 annual land cover products 1985–2017. Remot Sensing environment 265, 112646, 10.1016/j.rse.2021.112646 (2021).



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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.