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

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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.

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Keywords

Data Quality, Data Cleaning, Forest Monitoring, Remote Sensing, GEDI

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