Journal ArticleDOI
Spatial distribution of mangrove forest species and biomass assessment using field inventory and earth observation hyperspectral data
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In this article, the authors used spectral angle mapper to identify species, provide spatial distribution of the species and estimate the biomass in the mangrove forest, Bhitarkanika India.Abstract:
The objective of this research is to identify species, provide spatial distribution of the species and estimate the biomass in the mangrove Forest, Bhitarkanika India. Mangrove ecosystems play an important role in regulating carbon cycling, thus having a significant impact on global environmental change. Extensive studies have been conducted for the estimation of mangrove species identification and biomass estimation. However, estimation at a regional level with species-wise biomass distribution has been insufficiently investigated in the past because either research focuses on the species distribution or biomass assessment. Study shows that good relationship has been achieved between stem volume (field measured data) and Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from satellite image and further these two indices are employed to estimate the biomass in the study site. Three models- linear, logarithmic and polynomial (second degree) are used to estimate biomass derived from EVI and NDVI. The hyperspectral data (spatial resolution ~ 30 m) is utilised to identify ten mangrove plant species. We have prepared the spatial distribution map of these species using spectral angle mapper. We have also generated mangrove species-wise biomass distribution map of the study site along with areal coverage of each species. The results indicate that the Sonneratia apetala Buch.-Ham. and Cynometra iripa Kostel has the highest biomass among all ten identified species, 643.12 Mg ha−1 and 652.14 Mg ha−1. Our study provided a positive relationship between NDVI, EVI, and the estimated biomass of Bhitarkanika Forest Reserve Odisha India. The study finds a similar results for both NDVI and EVI derived biomass, while linear regression has more significant results than the polynomial (second degree) and logarithmic regression derived biomass. The polynomial is found slightly better than the logarithmic when using the EVI as compared to NDVI derived biomass. The spatial distribution of species-wise biomass map obtained in this study using both, EVI and NDVI could be used to provide useful information for biodiversity assessment along with the sustainable solutions to different problems associated with the mangrove forest biodiversity. Thus, biomass assessment of larger regions can be achieved by utilization of remote sensing based indices as concluded in the present study.read more
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Journal ArticleDOI
Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art
Prem Chandra Pandey,Nikos Koutsias,George P. Petropoulos,Prashant K. Srivastava,Eyal Ben Dor +4 more
TL;DR: Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment as mentioned in this paper, and Earth observation (EO) technology provides an in...
Journal ArticleDOI
Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam
Tien Dat Pham,Naoto Yokoya,Junshi Xia,Nam Thang Ha,Nga Nhu Le,Thi Thu Trang Nguyen,Thi Huong Dao,Thuy Thi Phuong Vu,Tien Duc Pham,Wataru Takeuchi +9 more
TL;DR: Multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam and outperformed the four other ML models.
Journal ArticleDOI
Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative
Akash Anand,Prem Chandra Pandey,George P. Petropoulos,Andrew Pavlides,Prashant Srivastava,Jyoti Kumar Sharma,Ramandeep Kaur M. Malhi +6 more
TL;DR: The 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.
Journal ArticleDOI
Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - The superiority of deep learning over a semi-empirical model
TL;DR: Interestingly, a Deep Learning algorithm could translate the exact relationship between predictor variables and mangrove AGB in Bhitarkanika Wildlife Sanctuary using machine learning algorithms like Deep Learning rather than semi-empirical models.
Journal ArticleDOI
A review of carbon monitoring in wet carbon systems using remote sensing
Anthony Campbell,Temilola Fatoyinbo,Sean P. Charles,Laura L. Bourgeau-Chavez,Joaquim I. Goes,Helga do Rosario Gomes,Meghan Halabisky,James R. Holmquist,Steven E. Lohrenz,Catherine Mitchell,L. Monika Moskal,Benjamin Poulter,Han Qiu,Celio H Resende De Sousa,Michael J. Sayers,Marc Simard,A. J. Stewart,Debjani Singh,Carl C. Trettin,Jinghui Wu,Xuesong Zhang,David Lagomasino +21 more
TL;DR: In this article , the authors conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts, finding 496 references, with an additional 78 references added by experts.
References
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Journal ArticleDOI
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Journal ArticleDOI
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Estimating biomass and biomass change of tropical forests: A primer
TL;DR: In this article, the authors estimated biomass and biomass change of tropical forests and found that the biomass change was faster than the change in the number of trees in the tropical forests of the world.