Change prediction and modeling of dynamic mangrove ecosystem using remotely sensed hyperspectral image data

Natural ecosystems are highly dynamic and exhibit a highly nonlinear nature. We attempt to detect and analyze changes in a coastal mangrove ecosystem to predict the dynamics of the system and its biodiversity. Multitemporal hyperspectral data have been used to analyze the competition among mangrove and saline blank endmembers and their dominance with time. We aim to predict the ecodynamics of the area through subpixel analysis of multitemporal hyperspectral imagery. The biodiverse coastal zone of Sunderban Biosphere Reserve, West Bengal (a world heritage site), is considered as a case study for predicting the mangrove ecodynamics of the area through Markov chain analysis. The mangrove species of Sunderban vary in their abundance with time due to dynamic weather conditions. The model is applied to hyperspectral data from 2011 and 2014, collected over Henry Island in the Sunderban to predict species dynamics in 2017 and 2020. An endmember transition matrix is framed to determine the endmember dynamics in the area in terms of degradation and regeneration of mangroves and saline blank cover in the area. Based on the transition probability matrix, the abundance values have been predicted for 2017 and 2020. The predicted abundance values have been validated by ground truth values extracted during field visits in 2017. It is observed that, in certain locations, the increase in saline blanks has led to an overall decrease in the proportion of Phoenix paludosa and Ceriops decandra (a salt-intolerant mangrove species) over the years. However, there is an increase in Avicennia marina and Avicennia officinalis, which are salt tolerant and can sustain in extreme saline conditions.