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

Creators: Bernard Silva de Oliveira^{1, 2}; Nathália Monteiro Teles²; Vinícius Vieira Mesquita²; Leandro Leal Parente³; Laerte Guimarães Ferreira²

1. Instituto Mauro Borges de Estatísticas e Estudos Socioeconômicos (IMB)
2. Laboratório de Processamento de Imagens e Geoprocessamento (LAPIG)
3. OpenGeoHub Foundation

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 Cover
Area 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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