Use of Hyperion for mangrove forest carbon stock assessment in Bhitarkanika forest reserve: a contribution towards blue carbon initiativeUse of Hyperion for mangrove forest carbon stock assessment in Bhitarkanika forest reserve: a contribution towards blue carbon initiative Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2021-09-202020enGeorge Petropoulos's contribution was supported by the FP7- People project ENViSIoN-EO (project reference number 752094).Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy.http://creativecommons.org/licenses/by/4.0/Remote Sensing124Anand_et_al_Remote Sens._12(4)_2020.pdfChania [Greece]Library of TUC2021-09-17application/pdf7.5 MBfree Anand Akash Pandey Prem Petropoulos Georgios Πετροπουλος Γεωργιος Pavlidis Andreas Παυλιδης Ανδρεας Srivastava Prashant K. Sharma Jyoti K. Malhi Ramandeep Kaur MDPI Blue carbon Hyperspectral data Mangrove forest Carbon stock Bhitarkanika Forest Reserve Regression models Machine learning