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.

Geo-informatics technology has dynamic role in mapping, monitoring and management of forest resources. The transformation of forest cover and its analysis on the earth’s surface are essential for understanding the associations and interactions between natural phenomena and living organism, especially human. Also deforestation is a major reason for global warming and one of the origin keys for the enhancement of greenhouse effect and climate change. The present study is grounded on the 3S technology in assessment of LU/LC changes within the forest cover area in Dalma Wildlife Sanctuary (DWLS) Jamshedpur-Jharkhand, India. The movement of forest-cover variation over the years 2009, 2011, 2013, 2015 and 2016 is precisely studied using high resolution Satellite Data (SD). It is noted that, due to shifting cultivation, forest fire, and conversion of forest cover into crop land/bare land and settlement encroachments in forest region by villagers are rapidly increasing; as a result deforestation is taking place. It is predicted that the study would demonstrate the effectiveness of 3S technology in forest renovation, planning and management.