Land-cover changes and deforestation drivers in the forest landscape of Banmauk township in the Sagaing Region of upper Myanmar

ABSTRACT

This study focused on the extent of land-cover changes and prediction of probable factors in deforestation based on changes observed from 2000 to 2021 in the forest landscape of Banmauk Township in Myanmar’s Sagaing Region. Landsat 7 ETM+ and Landsat 8 OLI satellite imagery were used to identify seven land-cover classes via supervised random tree classification, and binary logistic regression analysis was used to predict the potential for biophysical and locational factors to affect deforestation. A stratified random sampling method was used to assess the accuracy of the classified maps and to estimate the areas. The study revealed that dense forest coverage decreased from 45.65% in 2000 to 29.01% in 2021, while open forest areas increased from 49.33% to 54.51%. Mining areas exhibited a considerable increase from 0.37% to 5.35%, while settlement and barren/scrub land areas increased from 0.16% to 0.51% and 1.71% to 7.70%, respectively. Agricultural areas slightly increased from 2.11% to 2.33%, while water areas remained almost the same at around 0.60%. Post-classification change detection analysis showed that deforestation occurred mainly through converting forest land to mining and barren/scrub land. The study indicated that lower altitudes and road accessibility are significantly associated with the potential for deforestation.

KEYWORDS:

• Forest cover change
• gold mining
• Landsat imagery
• Myanmar
• spatial-logistic regression

Acknowledgements

This research constitutes one portion of the data generated during PhD study of the corresponding author at the Graduate School of Agriculture, Kyoto University in Japan. The PhD program of the corresponding author is being funded by the Japanese Government MEXT scholarship program. We would like to thank MEXT as a financial supporter to conduct this research. We also thank the two editors and three anonymous reviewers for valuable comments to improve this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/13416979.2023.2185185