On the Data Quality of Remotely Sensed Forest Maps
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Date
2023-08-10
Authors
Ghasemitaheri, Shadi
Advisor
Golab, Lukasz
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
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.
Description
Keywords
Data Quality, Data Cleaning, Forest Monitoring, Remote Sensing, GEDI