Attribution of forest disturbance types based on the Dynamic World class probability data: A case study of Myanmar

•A method is developed based on probability data from Dynamic World.•Forest disturbance types can be rapidly attributed at various spatial extents.•Overall accuracy was 93.3% and most disturbance classes achieved good accuracy.•A Google Earth Engine app of the developed method has been published. At...

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Bibliographic Details

Published in:	International journal of applied earth observation and geoinformation Vol. 134; p. 104216
Main Authors:	Li, Zhe, Ota, Tetsuji, Mizoue, Nobuya
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.11.2024 Elsevier
Subjects:	Dynamic World Forest disturbance types Google Earth Engine Myanmar Time series analysis Forest disturbance types Myanmar Time series analysis Google Earth Engine Dynamic World
ISSN:	1569-8432
Online Access:	Get full text
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