On the Data Quality of Remotely Sensed Forest Maps

Abstract

Accurate forest monitoring data are essential for understanding and conserving forest ecosystems. However, the remoteness of forests and the scarcity of ground truth make it hard to identify data quality issues. We present two state-of-the-art forest monitoring datasets, Annual Forest Change (AFC) and GEDI, and highlight their data quality problems. We then introduce a novel method that leverages GEDI to identify data quality issues in AFC. We show that our approach can identify subsets with three times more errors than a random sample, thus, prioritizing expert resources in validating AFC and allowing for more accurate deforestation detection.