A Global Dataset of Location Data Integrity-Assessed Reforestation Efforts

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 information, which we summarize in one easy-to-communicate key indicator: LDIS -- the Location Data Integrity Score. We find that approximately 79\% of the georeferenced planting sites monitored fail on at least 1 out of 10 LDIS indicators, while 15\% of the monitored projects lack machine-readable georeferenced data in the first place. In addition to enhancing accountability in the voluntary carbon market, the presented dataset also holds value as training data for e.g. computer vision-related tasks with millions of linked Sentinel-2 and Planetscope satellite images.