Towards automated 10–30 m resolution land cover mapping in insular South-East Asia

J. Miettinen, C. Shi, S.C. Liew

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)


In this study, we create and critically analyse an automated decision tree classification approach for regional level land cover mapping in insular South-East Asian conditions, using a combination of 10–30 m resolution optical and radar data. The resulting map contains 11 land cover classes and reveals a great deal of contextual information due to high spatial resolution. A limited accuracy assessment indicates 59–97% class wise accuracies. The unprecedented spatial detail of closed canopy oil palm mapping (with user’s accuracy of 90%) is seen as the most promising feature of the mapping approach. The incapability of separating primary forests from other tree cover, and the large variety of different landscapes (e.g. home gardens and tea plantations) classified as shrubland, are considered the main areas for future improvement. Overall, the study demonstrates the great potential of multi-source 10–30 m resolution high data volume land cover mapping approaches in insular South-East Asian conditions.

Original languageEnglish
Pages (from-to)443-457
Number of pages15
JournalGeocarto International
Issue number4
Publication statusPublished - 2019
MoE publication typeA1 Journal article-refereed


  • decision tree classification
  • Google Earth Engine
  • Landsat
  • Sentinel


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