Monitoring large forest areas is presently feasible with satellite remote sensing as opposed
to time-consuming and expensive ground surveys as alternative. This study evaluated, for the first
time, the potential of using freely available medium resolution (30 m) Landsat time series data for
deforestation monitoring in tropical rainforests of Kalimantan, Indonesia, at sub-annual time scales.
A simple, generic, data-driven algorithm for deforestation detection based on a consecutive anomalies
criterion was proposed. An accuracy assessment in the spatial and the temporal domain was carried
out using high-confidence reference sample pixels interpreted with the aid of multi-temporal very
high spatial resolution image series. Results showed a promising spatial accuracy, when three
consecutive anomalies were required to confirm a deforestation event. Recommendations in tuning
the algorithm for different operational use cases were provided within the context of satisfying REDD+
requirements, depending on whether spatial accuracy or temporal accuracy need to be optimized.
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:6478 |
| Date | January 2018 |
| Creators | Hadi, Krasovskii, Andrey, Maus, Victor, Yowargana, Ping, Pietsch, Stephan, Rautiainen, Miina |
| Publisher | MDPI |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf |
| Rights | Creative Commons: Attribution 4.0 International (CC BY 4.0) |
| Relation | http://dx.doi.org/10.3390/f9070389, http://www.mdpi.com/, http://orcid.org/0000-0002-7385-4723, http://www.mdpi.com/journal/forests/special_issues/IUFRO_2017_future_forests, http://epub.wu.ac.at/6478/ |
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