Quantifying oil palm expansion in Southeast Asia from 2000 to 2015: A data fusion approach

The fusion of optical imagery with radar data can provide more accurate land cover change analysis of deforestation and tree-based agriculture. Radar data is limited temporally with most geographic areas not covered prior to 2007. This paper presents a new methodology to classify land cover change r...

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Bibliographic Details
Published in:Journal of land use science 2022-01, Vol.17 (1), p.26-46
Main Authors: Wagner, Melissa, Wentz, Elizabeth A., Stuhlmacher, Michelle
Format: Article
Language:English
Subjects:
accuracy assessment
Agricultural land
Classification
Data fusion
Data integration
Deforestation
Information processing
Land cover
land cover change
Land management
Land use
land use change
oil palm
Radar
Radar data
Radar imaging
remote sensing
Sustainability management
Vegetable oils
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Summary:The fusion of optical imagery with radar data can provide more accurate land cover change analysis of deforestation and tree-based agriculture. Radar data is limited temporally with most geographic areas not covered prior to 2007. This paper presents a new methodology to classify land cover change related to oil palm expansion that takes historic data limitations into account. Our approach utilizes Hansen's Global Forest Cover data, optical imagery, and texture information, to extract land cover information in Sumatra and Western Malaysia, where historical data is absent. Our method demonstrates how to accurately classify oil palm without radar data with overall accuracies for optical only experiments within 4.4% of optical plus radar classifications. Our results show agricultural land use was the primary driver of land cover change with the largest increase due to oil palm expansion (6.1%). Better estimations of oil palm expansion could be used in sustainable land management policies.
ISSN:1747-423X
1747-4248
DOI:10.1080/1747423X.2021.2020918