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Variations in mangrove regeneration rates under different management plans: An analysis of Landsat time-series in the Matang Mangrove Forest Reserve, Peninsular Malaysia
Otero, V.; Martinez-Espinosa, C.; Dahdouh-Guebas, F.; Van De Kerchove, R.; Satyanarayana, B.; Lucas, R. (2017). Variations in mangrove regeneration rates under different management plans: An analysis of Landsat time-series in the Matang Mangrove Forest Reserve, Peninsular Malaysia, in: 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). pp. 3. https://hdl.handle.net/10.1109/Multi-Temp.2017.8035238
In: (2017). 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). IEEE: [s.l.]. ISBN 978-1-5386-3327-4.

Beschikbaar in  Auteurs 
Documenttype: Congresbijdrage

Trefwoorden
    Marien; Brak water; Zoet water
Author keywords
    mangroves; time-series analysis; BFAST; Landsat; silviculture; Malaysia

Auteurs  Top 
  • Otero, V.
  • Martinez-Espinosa, C.
  • Dahdouh-Guebas, F.
  • Van De Kerchove, R.
  • Satyanarayana, B.
  • Lucas, R.

Abstract
    The Matang Mangrove Forest Reserve (MMFR) has been under silvicultural management using a 30-year rotation cycle since 1902. Whereas two artificial thinnings in 15 and 20-year-old stands yield mangrove poles, the clear-felled greenwood from 30-year-old stands is used for charcoal production. Although the reserve appears to be (economically) sustainable in terms of wood production, there is insufficient information on mangrove regeneration and growth dynamics within the MMFR to confirm this. In the present study, we investigate changes in mangrove regeneration rates after clear-felling by analysing a Landsat time-series (1986-2016) using the BFAST algorithm. We created an image-stack from 1986 to 2016 and calculated different vegetation indices (e.g. NDVI) for each image. Based on the vegetation indices, we could identify the clear-felling activities and observe different trends in the mangrove regeneration rate. We will further analyse the time series using the trend and seasonal components of the BFAST algorithm.

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