https://orcid.org/0000-0002-5398-8441
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ABSTRACT
The most practical method for monitoring forest change over large areas is using remotely sensed data. However, given that current techniques are somewhat weak for monitoring small-scale forest disturbances, achieving accurate monitoring remains challenging, especially in tropical areas where selective and illegal logging occurs frequently. To further improve the ability to monitor forest changes, we estimated tree canopy cover (TCC) using Sentinel-1 and Sentinel-2 data. We developed an approach to monitor forest change on the obtained TCC time series. This approach was applied to monitor forest change in the Bago Mountains of Myanmar from 2017 to 2021. We then completed accuracy assessments and area estimation using reference data obtained from stratified random sampling and unbiased estimators. The final results indicated that: (1) in TCC estimation, Sentinel-1 played a limited role; the red-edge bands of Sentinel-2 achieved slightly different results to the other bands, and superior results were obtained by using all bands; (2) our method successfully mapped forest change with the overall accuracy of 93%. Furthermore, compared with the most widely used and the most recent approaches, our method was better at capturing forest disturbances.
Acknowledgments
We thank Leonie Seabrook, PhD, from Edanz (https://jp.edanz.com/ac) for English editing.
We thank anonymous reviewers for their constructive comments.
Data availability
The data supporting this study will be provided by the corresponding author upon reasonable request.
Disclosure statement
No potential conflict of interest was reported by the author(s).