A Global Dataset of Location Data Integrity-Assessed Reforestation Efforts
Angela John, Selvyn Allotey, Till Koebe, Alexandra Tyukavina, Ingmar Weber

TL;DR
This paper introduces a comprehensive global dataset of reforestation projects with a novel location data integrity score, revealing significant data quality issues and providing a valuable resource for verification and machine learning applications.
Contribution
It provides the first large-scale dataset with standardized location data integrity assessment for reforestation efforts, enhancing transparency and enabling new validation methods.
Findings
79% of sites fail at least 1 LDIS indicator
15% of projects lack machine-readable georeferenced data
Dataset includes over 1.2 million sites across 33 years
Abstract
Afforestation and reforestation are popular strategies for mitigating climate change by enhancing carbon sequestration. However, the effectiveness of these efforts is often self-reported by project developers, or certified through processes with limited external validation. This leads to concerns about data reliability and project integrity. In response to increasing scrutiny of voluntary carbon markets, this study presents a dataset on global afforestation and reforestation efforts compiled from primary (meta-)information and augmented with time-series satellite imagery and other secondary data. Our dataset covers 1,289,068 planting sites from 45,628 projects spanning 33 years. Since any remote sensing-based validation effort relies on the integrity of a planting site's geographic boundary, this dataset introduces a standardized assessment of the provided site-level location…
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