Blue Carbon Stock of the Bangladesh Sundarban Mangroves: What could Be the Scenario after a Century?

Potential CO2 Emission Due to Loss of Above Ground Biomass from the Indian Sundarban Mangroves During the Last Four Decades

(2016) Anirban Akhand et al.   Journal of the Indian Society of Remote Sensing

Carbon stock in the Sundarbans mangrove forest: spatial variations in vegetation types and salinity zones

(2014) Md. Mizanur Rahman et al.   WETLANDS ECOLOGY AND MANAGEMENT

A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique

(2014) Sandip Giri et al.   JOURNAL OF COASTAL CONSERVATION

Carbon stocks of intact mangroves and carbon emissions arising from their conversion in the Dominican Republic

(2013) J. Boone Kauffman et al.   ECOLOGICAL APPLICATIONS

Carbon Stocks of Tropical Coastal Wetlands within the Karstic Landscape of the Mexican Caribbean

(2013) Maria Fernanda Adame et al.   PLoS One

Predicting Global Patterns in Mangrove Forest Biomass

(2013) James Hutchison et al.   Conservation Letters

Global-change research to understand, handle and solve problems of a Planet under Pressure

(2012) Rik Leemans   Current Opinion in Environmental Sustainability

Carbon sequestration and annual increase of carbon stock in a mangrove forest

(2011) R. Ray et al.   ATMOSPHERIC ENVIRONMENT

Standing biomass and carbon storage of above-ground structures in dominant mangrove trees in the Sundarbans

(2011) Abhijit Mitra et al.   FOREST ECOLOGY AND MANAGEMENT

A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2

(2011) Elizabeth Mcleod et al.   FRONTIERS IN ECOLOGY AND THE ENVIRONMENT

Mangroves among the most carbon-rich forests in the tropics

(2011) Daniel C. Donato et al.   Nature Geoscience

Carbon pool and biomass dynamics associated with deforestation, land use, and agricultural abandonment in the neotropics

(2009) J. Boone Kauffman et al.   ECOLOGICAL APPLICATIONS

CO2 emissions from forest loss

(2009) G. R. van der Werf et al.   Nature Geoscience

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

(2008) M.J. Aitkenhead et al.   JOURNAL OF ENVIRONMENTAL MANAGEMENT