Development of Higher-Order Model for Nonlinear Interactions in Hyperspectral Data of Mangrove Forests

Abstract

The present article analyses the accuracy of application of higher-order nonlinear interaction models on hyperspectral data to identify mangrove mixtures present in the Sunderbans Delta -a World Heritage Site in West Bengal, India. It is observed that intra-species interaction between similar mangrove species (interaction between the same type of end-members) in a homogeneous mangrove stand is more accurately modelled by the linear-quadratic model and hence results in more accurate fractional abundance estimations after unmixing when compared with linear-unmixing models. Specifically, we observe that quadratic models provide more accurate estimates than linear and bilinear models for the study area (Henry Island of Sunderbans), which is mostly dominated by pure and mixed mangrove species of Avicennia marina, Excoecaria agallocha, Avicennia alba, Phoenix paludosa, Avicennia officinalis, Ceriops decandra, Bruguiera cylindrica and Aegialitis. In this study, the quadratic nonlinear model successfully characterizes the interaction of endmember mixtures comprising E. agallocha, A. officinalis, B. cylindrica and A. alba in the study area.Keywords: Higher-order interaction models, hyperspectral data, mangrove species, nonlinear interactions.HYPERSPECTRAL remote sensing (HSRS) is a powerful tool for detailed spatio-temporal mapping and sustainable management of large forested lands. The wide spectral range of hyperspectral data and their high spectral resolution allows for accurate detection and classification of surface canopies and ground features through the application of hyperspectral image processing algorithms 1,2 . For example, the characteristic bio-optical properties of different mangrove species in a dense mangrove forest can be integrated into spectral libraries for improved discrimination and mapping of mangrove eco-types.Outside India, researchers have made a detailed study of mangroves at species level using hyperspectral data 3-5 . Studies in India have also established the capability of hyperspectral data for species-level discrimination of mangroves in distantly located islands of the Sunderban Delta. The Delta is well-known for its homogeneous and heterogeneous patches of mangrove species that include a particular form or various forms of mangrove species within the ecosystem. Application of remote sensing technologies for accurate identification and discrimination of mangrove species is being considerably encouraged in recent times. Homogeneous mangrove areas could be identified through linear spectral unmixing, which follows the principle of singular reflection with negligible multiple scattering 6 . However, in natural forests such as the Sunderban Biosphere Reserve, intra-and inter-species scattering occurs, thus making the reflections nonlinear in nature 7 . Nonlinear spectral unmixing is expected to provide a more accurate estimate of fractional abundance estimation of mangrove mixtures along with their identification.Nonlinear models have been developed at microscopic scale for materials which a...