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Climate Variability and Mangrove Cover Dynamics at Species Level in the Sundarbans, Bangladesh
Mangrove ecosystems are complex in nature. For monitoring the impact of climate variability in this ecosystem, a multidisciplinary approach is a prerequisite. Changes in temperature and rainfall pattern have been suggested as an influential factor responsible for the change in mangrove species composition and spatial distribution. The main aim of this study was to assess the relationship between temperature, rainfall pattern and dynamics of mangrove species in the Sundarbans, Bangladesh, over a 38 year time period from 1977 to 2015. To assess the relationship, a three stage analytical process was employed. Primarily, the trend of temperature and rainfall over the study period were identified using a linear trend model; then, the supervised maximum likelihood classifier technique was employed to classify images recorded by Landsat series and post-classification comparison techniques were used to detect changes at species level. The rate of change of different mangrove species was also estimated in the second stage. Finally, the relationship between temperature, rainfall and the dynamics of mangroves at species level was determined using a simple linear regression model. The results show a significant statistical relationship between temperature, rainfall and the dynamics of mangrove species. The trends of change for Heritiera fomes and Sonneratia apelatala show a strong relationship with temperature and rainfall, while Ceriops decandra shows a weak relationship. In contrast, Excoecaria agallocha and Xylocarpus mekongensis do not show any significant relationship with temperature and rainfall. On the basis of our results, it can be concluded that temperature and rainfall are important climatic factors influencing the dynamics of three major mangrove species viz. H. fomes, S. apelatala and C. decandra in the Sundarbans.
Climate Variability and Mangrove Cover Dynamics at Species Level in the Sundarbans, Bangladesh
Mangrove ecosystems are complex in nature. For monitoring the impact of climate variability in this ecosystem, a multidisciplinary approach is a prerequisite. Changes in temperature and rainfall pattern have been suggested as an influential factor responsible for the change in mangrove species composition and spatial distribution. The main aim of this study was to assess the relationship between temperature, rainfall pattern and dynamics of mangrove species in the Sundarbans, Bangladesh, over a 38 year time period from 1977 to 2015. To assess the relationship, a three stage analytical process was employed. Primarily, the trend of temperature and rainfall over the study period were identified using a linear trend model; then, the supervised maximum likelihood classifier technique was employed to classify images recorded by Landsat series and post-classification comparison techniques were used to detect changes at species level. The rate of change of different mangrove species was also estimated in the second stage. Finally, the relationship between temperature, rainfall and the dynamics of mangroves at species level was determined using a simple linear regression model. The results show a significant statistical relationship between temperature, rainfall and the dynamics of mangrove species. The trends of change for Heritiera fomes and Sonneratia apelatala show a strong relationship with temperature and rainfall, while Ceriops decandra shows a weak relationship. In contrast, Excoecaria agallocha and Xylocarpus mekongensis do not show any significant relationship with temperature and rainfall. On the basis of our results, it can be concluded that temperature and rainfall are important climatic factors influencing the dynamics of three major mangrove species viz. H. fomes, S. apelatala and C. decandra in the Sundarbans.
Climate Variability and Mangrove Cover Dynamics at Species Level in the Sundarbans, Bangladesh
Manoj Kumer Ghosh (author) / Lalit Kumar (author) / Chandan Roy (author)
2017
Article (Journal)
Electronic Resource
Unknown
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