Abstract
Mangrove forests possess one of the most affluent biodiversity in the coastal region around the globe. Due to their rich floral and faunal diversity, they hold higher ecological and economic importance. Identification of species composition in mangrove ecosystem is very crucial for better decision-making in conservation and planning restoration practices. Hyperspectral Remote Sensing has made remarkable contribution in mangrove species identification in contrast to other remote sensing techniques. Nowadays, integrated approach of traditional classification techniques with machine learning algorithms is prevalent in achieving highly accurate species identification in mangrove forests. This chapter gives a brief account on the role of HRS in mangrove species identification, methodologies involved in producing robust discrimination results, and a few case studies to assess the real-time application of HRS in mangrove species identification.