Previous Article in event Previous Article in session
Next Article in event Next Article in session
Damage and Post-cyclone Regeneration Assessment of the Sundarbans Botanic Biodiversity caused by the Cyclone Sidr
Published: 02 November 2011 by MDPI in The 1st World Sustainability Forum session Remote Sensing for Sustainable Management of Land and Biodiversity
Abstract: The Sundarbans is the largest single block of tidal halophytic mangrove forest in the World. The versatile biodiversity of this forest, situated at the southwest of Bangladesh, plays a vital role in maintaining environmental sustainability of the country. This study identifies and quantifies the damage caused by the tropical cyclone Sidr in 15 November 2007 and the post-cyclone regeneration of the botanic biodiversity of the Sundarbans. Unsupervised classification and the normalised difference vegetation index (NDVI) were carried out over a temporal series of four Landsat 7 Enhanced Thematic Mapper Plus (ETM +) images for the month of February in the period between years 2007 and 2010. The obtained overall accuracies for the classification of the images of years 2007 and 2010 (the ones\' for which there were ground-truth data available) were, respectively, 76% and 88%. Classification results and land change analysis show that three important botanic species - Heritiera fomes (Sundari), Excoecaria agallocha (Gewa) and Sonneratia mangrove (Kewra) have been significantly affected by the cyclone. On the other hand NDVI analysis indicated that 45% area of the Sundarbans (approximately 2500 sq.km) has been damaged due to the cyclone action. Results further indicate that the rate of post-cyclone regeneration in 2009-2010 is four times higher than the regeneration rate of 2008-2009. Although cyclone Sidr has done significant damage to the diversity of the mangrove forest, it has regenerated to a satisfactory condition despite the effects of climate change and man-made encroachment.
Keywords: environmental sustainability, botanic biodiversity, cyclone, remote sensing, unsupervised classification, NDVI