# A Global Dataset of Location Data Integrity-Assessed Reforestation Efforts

## TL;DR

This paper introduces a global dataset of reforestation efforts with a new score to assess the reliability of location data, addressing concerns about project integrity in carbon markets.

## Contribution

The paper introduces the Location Data Integrity Score (LDIS) to evaluate the reliability of georeferenced planting site data in reforestation projects.

## Key findings

- Approximately 79% of georeferenced planting sites fail at least one of the ten LDIS indicators.
- 15% of monitored projects lack machine-readable georeferenced data.
- The dataset includes 1,289,068 planting sites from 45,628 projects over 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 satellite images.