Next Article in Journal
Quality Assessment and Practical Interpretation of the Wave Parameters Estimated by HF Radars in NW Spain
Previous Article in Journal
Assessing Terrestrial Ecosystem Resilience using Satellite Leaf Area Index
Open AccessArticle

Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative

1
Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India
2
Center for Environmental Sciences and Engineering, School of Natural Sciences, Shiv Nadar University, Greater Noida, Uttar Pradesh 201314, India
3
Department of Geography, Harokopio University of Athens, El. Venizelou St., 70, Kallithea, Athens 17671, Greece
4
School of Mineral Resources Engineering, Technical University of Crete, Crete 73100, Greece
5
DST-Mahamana Centre for Excellence in Climate Change Research Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 597; https://doi.org/10.3390/rs12040597
Received: 6 December 2019 / Revised: 4 February 2020 / Accepted: 5 February 2020 / Published: 11 February 2020
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. View Full-Text
Keywords: blue carbon; hyperspectral data; mangrove forest; carbon stock; Bhitarkanika Forest Reserve; regression models; machine learning blue carbon; hyperspectral data; mangrove forest; carbon stock; Bhitarkanika Forest Reserve; regression models; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Anand, A.; Pandey, P.C.; Petropoulos, G.P.; Pavlides, A.; Srivastava, P.K.; Sharma, J.K.; Malhi, R.K.M. Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative. Remote Sens. 2020, 12, 597.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop