Identification of Areas Highly Vulnerable to Land Conversion: A Case Study From Southern Thailand
Journal article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Tantipisanuh N., Gale G.A.
Publisher: Springer
Publication year: 2022
Volume number: 69
Issue number: 2
Start page: 323
End page: 332
Number of pages: 10
ISSN: 0364-152X
eISSN: 1432-1009
Languages: English-Great Britain (EN-GB)
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Abstract
Land conversion is having major impacts on wildlife globally, and thus understanding and predicting patterns of land conversion is an important component of conservation planning. Southeast Asia is undergoing rapid habitat conversion; however, most countries in the region have very limited human resources devoted to planning, and typically land-cover trend assessments are often challenging. Here we demonstrate a rapid method for land-cover change quantification for areas of terrestrial, mangrove and peat swamp forests at high risk from land conversion that can be quickly and simply predicted using southern Thailand as an example. Land-cover maps from two time periods (1995/1996 and 2015/2016) were produced and compared to determine changes between the two time periods. Five land-cover categories (terrestrial forest, mangrove forest, peat swamp forest, human settlement, agriculture) were estimated along with land-cover changes. Hot spots of high percentage change for human settlement and agriculture were identified, and vulnerable habitats were mapped including terrestrial forest, mangrove forest and peat swamp forest. Between 1996 and 2016, 22.1% of terrestrial forests, 26.2% of mangrove forests and 55% of peat swamp forests were lost. The losses of these natural habitats were clearly associated with agricultural expansion. Approximately 10.6%, 14.3% and 33% of terrestrial, mangrove and peat swamp forest remaining were identified as highly vulnerable, of which the majority were at the boundaries between natural and human-dominated areas. The technique offers promise for rapidly identifying high priority areas for more detailed analysis and potential conservation interventions. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Keywords
Land-cover change analysis, Supervised Classification, Threat vulnerability
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