Hyperspectral data finds wide applicability in species level mapping of forest cover in pure and mixed stands. The Sunderban Biosphere Reserve of West Bengal is an ideal locale where hyperspectral image data may be successfully utilized for accurate mapping of nearly 94 mangrove species that exist here. The present study is the first attempt in the Sunderban eco-geographic province to make species level discrimination of mangroves in a mixed stand. However, prior to data classification, several corrections are required to be made for meaningful interpretation of data. Atmospheric correction is one such crucial correction and pre-processing step which is done to minimize the effect of atmospheric agents that alters the actual radiance data that the sensor should represent. It therefore becomes essential to properly analyse, process and correct hyperspectral data by applying atmospheric correction techniques to reduce or remove the influence of atmospheric agents on the sensor captured data.
MODTRAN based FLAASH algorithm and scene based QUAC algorithm, both available in ENVI have been found to be effective in for atmospheric correction of data captured by the Hyperion sensor onboard the EO-1 satellite launched by NASA. In this paper the FLAASH and QUAC models have been applied on the Hyperion data and a comparative analysis carried out. The application of hyperspectral data is a unique attempt in the unexplored field of research for Indian mangroves in general and Sunderban mangroves in particular which is the world's largest single patch mangrove forest. This paper analyses the data processing steps for atmospheric correction of Hyperion data taken over the dense mangrove forest cover of the Henry and Lothian Islands of the Sunderban Delta of West Bengal. This data has been interpreted to understand the properties of mangroveforest cover and how they relate to the measurements actually made by the hyperspectral sensor.
Keywords: Hyperspectral data, FLAASH, QUAC, Sunderban |