Plant Diversity Assessment in Indian Sundarban Mangroves: A Geoinformatics Approach

Mangroves are biologically diverse and productive ecosystems on planet Earth providing large number of goods and services. Yet, they are one of the most threatened ecosystems in the world. Sundarbans, the world’s largest mangrove forest, is a world heritage as well as Ramsar site known for its rich biodiversity. Over time, remote sensing (RS) has become a vital tool for monitoring of natural resources including mangroves for effective management and conservation. Several studies have been carried out on mapping/monitoring of mangroves using remotely sensed imagery and parametric classifiers. During recent decades, non-parametric classifiers, viz., random forest, support vector machine, k-nearest neighbour, decision tree, neural network, etc., have been found to be highly useful. Biological richness or the plant diversity status which is a function of ecosystem uniqueness, species richness, biodiversity value, and disturbance index was determined by integrating RS and field data. Around 75% mangroves of Indian Sundarbans were found to be highly rich in diversity, with the highest richness being observed in mixed mangroves. The availability of higher spectral, spatial, temporal and radiometric resolution RS data can play a significant role not only in mapping and monitoring of mangroves but also in rapid assessment of their biodiversity.