Monitoring Deforestation in Rainforests Using Satellite Data: A Pilot Study from Kalimantan, Indonesia

15356 15 84571 2 237624 document 84571 forests-09-00389.pdf application/pdf 835bb02c69bf89bbbbe87b1eddd5151a MD5 11973215 2018-07-02 10:42:45 https://pure.iiasa.ac.at/id/eprint/15356/1/forests-09-00389.pdf 15356 1 1 application/pdf text en public cc_by_4

forests-09-00389.pdf

published 84572 2 237632 document 84572 lightbox.jpg image/png d30a6f4a1924dc84d34649f74352b1d6 MD5 48924 2018-07-02 10:52:49 https://pure.iiasa.ac.at/id/eprint/15356/2/lightbox.jpg 15356 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/84571 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/84571 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/84571 archive 353 disk0/00/01/53/56 2018-07-02 10:47:22 2021-08-27 17:30:31 2018-07-02 10:47:22 article show Hadi Krasovskii A. 8266 0000-0003-0940-9366 Maus V. 8780 0000-0002-7385-4723 Yowargana P. 2069 Pietsch S. 2054 0000-0001-6431-2212 Rautiainen M. Monitoring Deforestation in Rainforests Using Satellite Data: A Pilot Study from Kalimantan, Indonesia pub prog_esm tropical; deforestation; monitoring; South East Asia; Landsat; REDD+ 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. 2018-07 published MDPI 10.3390/f9070389 164 3138 341 232 public Forests 9 7 e389 TRUE 1999-4907 RESTORE+ FALSE no info:eu-repo/semantics/article