DOI: 10.5281/zenodo.11246629
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.



 

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





Brown, C.F., Brumby, S.P., Guzder-Williams, B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Sci Data 9, 251 (2022). https://doi.org/10.1038/s41597-022-01307-4Van Tricht, K. et al. Worldcereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping. Earth Syst. Sci. Data 15, 5491–5515, 10.5194/essd-15-5491-2023 (2023)




Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963




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).



Tsendbazar, N. et al. Product validation report (d12-pvr) v 1.1 (2021).


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.