Presented by Dr. Kakoli Banerjee, Assistant Professor & Founding Head, Department of Biodiversity & Conservation of Natural Resources, School of Biodiversity & Conservation of Natural Resources Central, University of Odisha at Mangrove Research in Indian sub-continent: Recent Advances, Knowledge Gaps and Future Perspectives on 8 - 10 December 2021

1. BLUE CARBON STOCKS IN
MANGROVE FORESTS OF
EASTERN INDIA
INTERNATIONAL WEBINAR ON WETLANDS KNOWLEDGE EXCHANGE
“MANGROVES AS NATURE-BASED SOLUTIONS TO CLIMATE CHANGE”
08th December, 2021
Dr. KAKOLI BANERJEE
Assistant Professor & Founding Head
Department of Biodiversity & Conservation of Natural Resources
School of Biodiversity & Conservation of Natural Resources
Central University of Odisha

2. 0.9 deg Centigrade rise over a period of 140 years

3. 1.55 ppm rise over a period of 60 years

4. • The challenges of climate change can be effectively overcome by storage of carbon over
long period of time. Carbon storage is a situation where degraded soil is restored
through afforestation increased biomass and reduces CO2 concentration generated due
to fossil fuel (Sheikh et al. 2014 and Panda and Panda, 2015).
• Mangroves can sequester 3 to 5 times more atmospheric CO2 than other terrestrial
forest (Mcleod et al. 2011; Donato et al. 2011 and Lee et al. 2014). The average
productivity of mangrove biomass ranges between 3.07 to 24.1 tha–1y–1 having turn over
time period < 30 year (Twilley et al. 1992; Estrada and Soares, 2017).
• Sediment is transported to mangrove and salt marsh habitat by tide, wave action and
storms, which carry inorganic and organic substances like dead plants and detritus. Salt
marshes and mangroves are highly productive source of organic matter, from which
there is a net out welling of energy to support the complex estuarine and near shore
web (Ewel et al., 1998).
FOCUS: MARINE ECOSYSTEM

5.  India is also very vulnerable to climate change, notably due to the melting
of the Himalayan glaciers and changes.
 The country has pledged a 33-35% reduction in the “emissions intensity
of its economy by 2030”, compared to 2005 levels.
 India’s population is expected to become the world’s largest by 2025, overtaking
China and peaking at 1.7 billion in 2060.
 In 2002, India hosted the eighth formal meeting of the UNFCCC (United
Nations Framework Convention on Climate Change) in New Delhi. This meeting
adopted the Delhi Ministerial Declaration which called for developed countries
to transfer the technology needed to cut emissions and adapt to climate
change to developing countries.
 India is the world’s third largest emitter of greenhouse gases (GHGs), after
China and the US.
 India published its National Action Plan on Climate Change (NAPCC) in 2008,
split into eight missions viz. Coal, Low-carbon energy, Energy efficiency,
Transport, Oil and gas, Agriculture and forests, Impacts and Adaptation and
Climate finance on diverse aspects of climate mitigation and adaptation policy.
BRIEF CARBON PROFILE OF INDIA

6. Mangroves: Utility and Diversity
• The term mangrove has originated from the
portuguese word “Mangue” which means the
community of mangrove trees and the English
word “Grove” which means trees or bushes.
• Duke (1992) defined mangrove as “…..A tree,
shrub, palm and ground fern generally
exceeding one half metre in height and which
normally grows above mean sea level in the
intertidal zone of marine coastal environments
and estuarine margins”.

7. Versatility of Mangrove Ecosystem
•Control Erosion
•Buffer against storm and cyclonic depression
•Fertilize the water with nutrients
•Biodiversity Reservoir
•Honey,Wax, Timber and Fuel
•Medicinal Storehouse
•Source of Bio-Active Substances
•Bio-Purification of the Environment
•Housing Complex of endangered species
•Alternative livelihood schemes from untapped resources
•Natural library: Scope for environmental education

8. MANGROVES IN THE INTERTIDAL DOMAIN

10. 0
200
400
600
800
1000
1200
Area
(km
2
)
States
Dense mangrvoe
Moderte mangrove
Open Mangrove
Map showing the Mangrove habitats in India State wise Mangroves in India
Indian mangrove vegetation covers about
6,749 km² along the 7516.6 km long Coastline,
including Island territories. Which is fourth
largest mangrove area in the world (Naskar &
Mandal, 1999).
 Mangroves in India account for about
3% of the global mangroves and 8% of
Asian mangroves (SFR, 2009; FAO,
2007).
 About 60% of the mangroves occur on
the east coast along the Bay of Bengal,
27% on the west coast bordering the
Arabian Sea, and 13% on Andaman &
Nicobar Islands.

11. Global Distribution of mangroves
Mangrove diversity of different countries
across the globe
Mangroves lie between latitude 32°20' in northern hemisphere in Bermuda to
38°59' in southern hemisphere in New Zealand (Spalding et al., 1997).
Spreading a total area between 137760 to 152000 km2 globally 118 to 124
numbers of countries (FAO, 2007; Alongi, 2008; Spalding et al., 2010; Giri et al.,
2011).
Mangroves of the World have been divided into two groups:
Eastern group i.e. East Africa, India, Southeast Asia, Australia and the Western
Pacific and
Western group comprises of West Africa, South and North America and the
Caribbean Countries.

12. MARINE ECOSYSTEM: SINK OF CARBON

13. Mechanism of Carbon sequestration by Mangroves

14. As per IUCN, 2017

15. Mangrove environments, are composed of all the three types of carbon sink i.e. Biomass,
soil & ocean. So all together it shows highest carbon sequestration potential.
Carbon Sequestration by Mangroves
Mean carbon storage above and belowground in coastal ecosystems versus terrestrial Forest
(Source: Fourqurean et al. 2012; Pan et al. 2011; P endieton et al .2012)

16. EFFECTIVENESS OF BLUE CARBON STORAGE
Recent studies estimate carbon storage
 in the top meter of soil to be approximately 280 ton C
per ha for mangroves,
 250 ton C per ha for tidal marshes, and
 140 ton C per ha for seagrass meadows,
 equivalent to 1,030 tons of carbon dioxide
equivalence per hectare for estuarine mangroves,
920 tons CO2 eq per ha for tidal marshes, and 520
tons CO2 eq per ha for seagrass meadows.

17. The Integrated Carbon Observation
System (ICOS)
ICOS is the European
pillar of a global GHG observation
system.
It promotes technological
developments and
demonstrations related to GHGs by
the linking
of research, education and
innovation.
ICOS-based knowledge supports
policy- and decision-making to
combat climate change and its
impacts. It is a distributed research
infrastructure operating
standardized, high-precision, and
long-term observations, facilitating
research to understand the carbon
cycle, providing necessary
information
on greenhouse gases.

18. a. Bhitarkanika mangrove ecosystem
• Bhitarkanika with an area of 672 km2 was
declared as Wildlife Sanctuary in 1975.
Geographically the Bhitarkanika mangrove
ecosystem is located between the coordinates 20o
40' to 20o 48' N latitude and 86o 45' to 87o 50' E
longitude bordered and surrounded by river
Hansua on the West, Dhamra Port on the North
and Bay of Bengal in the eastern and southern
side.
• Five stations namely Stn.1 Dangmal, Stn.2
Bhitarkanika, Stn.3 Gupti, Stn.4 Habalikhati and
Stn.5 Ekakula were selected in Bhitarkanika
mangrove ecosystem.
b. Mahanadi mangrove ecosystem
• Out of the total mangrove area of the Odisha
state, Mahanadi delta covers an area of 120 sq.
km. The Mahanadi mangrove ecosystem (20015′
to 20070′ N latitude and 87°00 ′ to 87° 40′E
longitude) extends from south eastern boundary
of Mahanadi river to river mouth of Hansua (a
tributary of Brahmani) in the north, from the
north eastern end of Mahanadi river up to
Jamboo river in east.
• Five sites were selected for carrying out
vegetation survey, i.e. Jambu, Kansaridia,
Kandarapatia, Kantilo and Bhitar Kharinasi.
Study area

19. Phytoplankton as sink of carbon
1. Phytoplankon are the major sink of atmospheric CO2. Phyto
biomass in the world’s ocean amount to only approximately 1-
2% of the global plant carbon, yet the community fixes between
30 – 50 billion metric tones of carbon annually, which is about
40% of the total (Paul G, Falkowski, 1994).
2. Phytoplankton constitute the foundation of marine and
estuarine food webs; hence any change in this community may
likely to be reflected on the members of higher trophic level.
3. Phytoplankton may be an important component of CDM
because of their efficiency in absorption of nutrients.
4. Phytoplankton standing stock and biomass variation can be
easily detected through satellite imageries (owing to the presence
of chlorophyll a); hence impact of climate change can be scanned

20. PHYTO
ALBUM

21. In the Phytoplankton community
appearance of stenohaline species
is a major change as confirmed by
Duncan’s test
1985.00
1980.00
1990.00
1995.00
2000.00
2005.00
Sig.
VAR00004
N
Subset for alpha = .05 1
Subset for alpha = .05 2
Statistics
0.0000
25.0000
50.0000
75.0000
100.0000
Values
VAR00005
Test : Duncan
Carbon content per cell volume
log CDiatoms = 0.758logV-0.422
Log C Other species = 0.866logV-
0.460
(After Strathmann, 1967)

22. Associate pics -- seaweeds
SEAWEED CARBON
Enteromorpha intestinalis
Enteromorpha intestinalis
Ulva lactuca
Catenella repens

23. We estimated stored carbon in these three species in three different
seasons (pre-monsoon, monsoon and post-monsoon) in the deltaic
ecosystem of Indian Sundarbans during 2014.
The average stored carbon content varied from 1134.43 g/sq m (during
monsoon) to 1291.00 g/sq m (during pre-monsoon) in Enteromorpha
intestinalis.
In Ulva lactuca, the stored carbon ranged from 49.88 g/sq m (during
post-monsoon) to 155.66 g/sq m (during pre-monsoon).
In case of Catenella repens, the range of stored carbon is 14.13 g/sq m
(during monsoon) to 55.66 g/sq m(during pre-monsoon).

24. Salt Marsh Grass
(Porteresia coarctata)
Carbon varied from 0.8 to 1.7 Mg/ha
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1,8
2
Stn.1 Stn.2 Stn.3 Stn.4 Stn.5
AGC
(Mg
ha
-1
)
Stations
Monsoon
Post monsoon
Premonsoon

25. Estimation of biomass:
The DBH was measured at breast height, which is 1.3 m from the ground level. It was
measured by using tree calliper and measuring tape. Stem height was recorded by
using laser based height measuring instrument (BOSCH DLE 70 Professional model).
The methodology and procedures to estimate the stem biomass of the selected true
mangrove tree species were carried out step by step as per the VACCIN project
manual of CSIR considering and measuring parameters like DBH, DBR (Diameter of
basal region), height of the stem, density of the stem wood and form factor. The
population density of each species was also documented to express the value of
stem biomass in tha-1. Similarly branches and leaves were done as per simple
mathematical unitary method.

26. Estimation of carbon:
Direct estimation of percent carbon in the
species was done by Vario MACRO elementar
CHN analyzer, after grinding and random
mixing the oven dried stems from 15 different
sampling plots. The estimation was done
separately for each species and mean values
were expressed as t ha-1.
Biomass of salt marsh grass:
The quadrant of 1m × 1m size was placed
randomly in the selected stations for estimation
of biomass and carbon. Porteresia coarctata
(salt marsh grass) samples were collected as
per standard procedure (Van Wagner 1968).

27. i. Soil and water
temperature
ii. Soil and water pH
iii. Soil and Water
salinity
iv. Soil organic
carbon
v. Soil texture
vi. Soil bulk density
ANALYSIS OF PHYSICO-CHEMICAL PARAMETERS
OF THE AMBIENT
MEDIA

28. Mangrove ecosystems thrive along coastlines
throughout most of the tropics and subtropics.
These intertidal forests play important ecological
and socioeconomic roles by acting as a nutrient
filter between land and sea (Robertson and Phillips
1995), contributing to coastline protection (Vermatt
and Thampanya 2006), providing commercial
fisheries resources (Constanza et al 1997) and
nursery grounds for coastal fishes and
crustaceans.
SOIL ORGANIC CARBON

29. Sundarbans
Mangrove Ecosystem:
the only mangrove
chunk of the planet
serving as the home of
Royal Bengal Tiger
(Panthera tigris tigris)

30. Station wise variation plotted for five selected species showed highest value of AGB at station Stn.2 for
A.officinalis at Bhitarakanika mangrove ecosystem and Stn.4 for R. mucronata at Mahanadi mangrove ecosystem.
Comparing all the species and all the stations the growth of E. agallocha was the highest owing to its high
adaptability in all stations.
AGB values 6.25±1.52 tha-1 for X. granatum < 70.09±6.68 tha-1 for A. marina < 98.66±5.24 tha-1 for E. agallocha
< 224.41±21.20 tha-1 for R. mucronata, 616.94±50.15 tha-1 for A. officinalis
AGC varied from 3.25±0.31 tha-1 for X. granatum to 280.83±21.29 tha-1 for A. offficinalis

31. y = -0,0015x + 10,51
R² = 0,7475
7,5
7,6
7,7
7,8
7,9
8
8,1
8,2
8,3
1500 1550 1600 1650 1700 1750 1800 1850 1900
water
pH
DIC (µ mol/l)
y = 0,0024x - 2,7849
R² = 0,1935
0,0
0,5
1,0
1,5
2,0
2,5
1500 1550 1600 1650 1700 1750 1800 1850 1900
SOC
(%)
DIC (µ mol/l)

32. CARBON STORAGE POTENTIAL (tha-1)
Bhitarkanika
Mahanadi
Stations AGC (tha–1
) SOC (tha–1
)
Total carbon (tha–1
)
(AGC+SOC)
CO2 eqivalent (t)
Stn.1 71.17±18.57 6.68±0.47 77.84±19.04 285.69±69.87
Stn.2 322.69±111.52 7.71±0.45 330.41±111.97 1212.59±410.92
Stn.3 121.38±23.69 5.83±0.24 127.20±23.94 466.84±87.85
Stn.4 140.05±23.13 3.52±0.12 143.57±23.25 526.91±85.33
Stn.5 62.77±13.28 3.55±0.11 66.31±13.39 243.37±49.14
Mean±SD 143.61±38.04 5.46±1.68 149.07±38.32 547.08±140.62
Stations AGC (tha–1
) SOC (tha–1
)
Total carbon (tha–1
)
(AGC+SOC)
CO2 eqivalent (t)
Stn.1 90.67±17.24 3.61±0.30 94.28±17.53 346.00±64.35
Stn.2 69.64±17.92 7.55±0.56 77.19±18.48 283.28±67.83
Stn.3 73.44±21.49 7.71±0.45 81.15±21.94 297.83±80.53
Stn.4 182.53±43.51 5.36±0.29 187.89±43.81 689.55±160.77
Stn.5 49.82±7.66 5.39±0.25 55.20±7.80 202.60±29.00
Mean±SD 93.22±21.56 5.92±1.54 99.14±21.93 363.85±80.50

33. 17,656,013 tonnes of C (IN AGB)
64795648 = 64.80 TgC tonnes of CO2
Odisha =142,261 Km2

34. Regression models
• A total of 240 linear regression equations taking AGB and AGC as dependent variables were computed along with 120
non-linear equations with DBH, total height and first forking as independent variables and 144 non-linear regression
equations was computed using water and soil parameters as independent variables and the following equations were
selected for the best fit models.
YAm (AGB)= AD2
BH+BT2
H+CFF2
H+D (DBH Х TH Х FFH) +E ----------------------------(i)
YAm (AGC)= AD2
BH+BT2
H+CFF2
H+D (DBH Х TH Х FFH) +E ---------------------------(ii)
YAo (AGB)= AD2
BH+BT2
H+CFF2
H+D (DBH Х T2
H Х FFH) +E --------------------------(iii)
YAo (AGC)= AD2
BH+BT2
H+CFF2
H+D (DBH Х T2
H Х FFH) +E --------------------------(iv)
YEa (AGB)= AD2
BH+BTH+CFF2
H+D (DBH Х T2
H Х FFH) +E --------------------------(v)
YEa (AGC)= AD2
BH+BTH+CFF2
H+D (DBH Х T2
H Х FFH) +E -------------------------(vi)
YRm (AGB)= ADBH+BT2
H+CFF2
H+D B2
SR+E (DBH Х T2
H Х FFHX BSR) +F ---------(vii)
YRm (AGC)= ADBH+BT2
H+CFF2
H+D B2
SR+E (DBH Х T2
H Х FFHX BSR) +F --------(viii)
YXg (AGB)= AD2
BH+BT2
H+CFF2
H+D (D2
BH Х T2
H Х FF2
H) +E -----------------------(ix)
YXg (AGC)= AD2
BH+BT2
H+CFF2
H+D (D2
BH Х T2
H Х FF2
H) +E ------------------------(x)
YAm (AGB)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW
2
×SW
2
×pHS
2
×SS
2
×BDS×OCS
2
×Sa×Si×Cl)+L ----------(i)
YAm (AGC)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW
2×SW
2×pHS
2×SS
2×BDS×OCS
2×Sa×Si×Cl)+L ----------(ii)
YAo (AGB)= ATW+BpHW
2
+CSW
2
+DpHS
2
+ESS
2
+FBDS+ GOCS
2
+HSa+ISi+JCl+K (TW×pHW
2
×SW
2
×pHS×SS×BDS×OCS
2
×Sa×Si×Cl)+L --------(iv)
YAo (AGC)= ATW+BpHW
2
+CSW
2
+DpHS
2
+ESS
2
+FBDS+ GOCS
2
+HSa+ISi+JCl+K (TW×pHW
2
×SW
2
×pHS×SS×BDS×OCS
2
×Sa×Si×Cl)+L ---------(v)
YEa (AGB)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L ----------(vi)
YEa (AGC)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L ---------(vii)
YRm (AGB)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L --------(viii)
YRm (AGC)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L ----------(ix)
YXg (AGB)= ATW+BpHW
2
+CSW
2
+DpHS
2
+ESS
2
+FBDS+ GOCS
2
+HSa+ISi+JCl+K (TW×pHW
2
×SW
2
×pHS×SS×BDS×OCS×Sa×Si×Cl)+L ---------(x)
YXg (AGC)= ATW+BpHW
2
+CSW
2
+DpHS
2
+ESS
2
+FBDS+ GOCS
2
+HSa+ISi+JCl+K (TW×pHW
2
×SW
2
×pHS×SS×BDS×OCS×Sa×Si×Cl)+L --------(xi)
YPc (AGB)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW
2
×SW
2
×pHS
2
×SS
2
×BDS×OCS
2
×Sa×Si×Cl)+L --------(xi)
YPc (AGC)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW
2
×SW
2
×pHS
2
×SS
2
×BDS×OCS
2
×Sa×Si×Cl)+L --------(xii)
For Bhitarkanika mangrove ecosystem

35. YAm (AGB)= AD2
BH+BT2
H+CFF2
H+D (DBH Х T2
H Х FFH) +E --------------------------(i)
YAm (AGC)= AD2
BH+BT2
H+CFF2
H+D (DBH Х T2
H Х FFH) +E -------------------------(ii)
YAo (AGB)= AD2
BH+BT2
H+CFF2
H+D (D2
BH Х T2
H Х FF2
H) +E ------------------------(iii)
YAo (AGC)= AD2
BH+BT2
H+CFF2
H+D (D2
BH Х T2
H Х FF2
H) +E ------------------------(iv)
YEa (AGB)= AD2
BH+BT2
H+CFF2
H+D (DBH Х TH Х FFH) +E -------------------------(v)
YEa (AGC)= AD2
BH+BT2
H+CFF2
H+D (DBH Х TH Х FFH) +E -----------------------(vi)
YRm (AGB)= AD2
BH+BTH+CFF2
H+D BSR+E (DBH Х TH Х FFHX BSR) +F --------------(vii)
YRm (AGC)= AD2
BH+BTH+CFF2
H+D BSR+E (DBH Х TH Х FFHX BSR) +F ------------(viii)
YXg (AGB)= AD2
BH+BT2
H+CFF2
H+D (DBH Х T2
H Х FFH) +E ---------------------(ix)
YXg (AGC)= AD2
BH+BT2
H+CFF2
H+D (DBH Х T2
H Х FFH) +E ---------------------(x)
YAm (AGB)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW
2×SW
2
×pHS
2×SS
2×BDS×OCS
2×Sa×Si×Cl)+L--------(i)
YAm (AGC)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW
2
×SW
2×pHS
2
×SS
2
×BDS×OCS
2
×Sa×Si×Cl)+L--------(ii)
YAo (AGB)= ATW+BpHW
2
+CSW
2
+DpHS
2
+ESS
2
+FBDS+ GOCS
2
+HSa+ISi+JCl+K(TW×pHW
2
×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L------(iii)
YAo (AGC)= ATW+BpHW
2+CSW
2+DpHS
2+ESS
2+FBDS+ GOCS
2+HSa+ISi+JCl+K(TW×pHW
2×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L------(iv)
YEa(AGB)=ATW+BpHW
2+CSW
2+DpHS
2+ESS
2+FBDS+GOCS
2+HSa+ISi+JCl+K(TW×pHW
2×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L--------- (v)
YEa (AGC)= ATW+BpHW
2
+CSW
2
+DpHS
2
+ESS
2
+FBDS+GOCS
2
+HSa+ISi+JCl+K(TW×pHW×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L---------(vi)
YEa (AGC)= ATW+BpHW
2
+CSW
2
+DpHS
2
+ESS
2
+FBDS+ GOCS
2
+HSa+ISi+JCl+K(TW×pHW
2
×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L-------(vii)
YRm (AGB)= ATW+BpHW
2+CSW
2+DpHS
2+ESS
2+FBDS+GOCS
2+HSa+ISi+JCl+K(TW×pHW×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L-------(viii)
YRm (AGC)= ATW+BpHW
2+CSW
2+DpHS
2+ESS
2+FBDS+ GOCS
2+HSa+ISi+JCl+K(TW×pHW
2×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L-------(ix)
YXg (AGB)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L --------------(x)
YXg(AGB)=ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW
2
×SW
2
×pHS
2
×SS
2
×BDS×OCS
2
×Sa×Si×Cl)+L----------(xi)
YXg (AGC)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L -------------(xii)
YXg (AGC)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW
2×SW
2×pHS
2×SS
2×BDS×OCS
2×Sa×Si×Cl)+L--------(xiii)
YPc (AGB)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L------------- (xiv)
YPc (AGC)= ATW+BpHW+CSW+DpHS+ESS+FBDS+GOCS+HSa+ISi+JCl+K(TW×pHW×SW×pHS×SS×BDS×OCS×Sa×Si×Cl)+L--------------(xv)
For Mahanadi mangrove ecosystem
• It seems from the allometric equations of the two blocks, Bhitarkanika and Mahanadi mangrove ecosystems are not uniform in
nature. This may be due to variation in edaphic factors and other environmental parameters preferably salinity, whose weightage
can be determined from the coefficients in the multiple linear regression model and five (05) each (AGB and AGC) best allometric
equations are suggested for every part of the estuary as the physico-chemical variables have significant variations in every sector of
the estuary.

36. MANGROVE LOSS
Current rates of loss of these ecosystems may result in 0.15–1.02
billion tons of CO2 released annually.
Although the combined global area of mangroves, tidal marshes,
and seagrass meadows equates to only 2–6% of the total area of
tropical forest, degradation of these systems account for 3–19% of
carbon emissions from global deforestation.
Note that previous estimates of the greenhouse gas impact of
coastal ecosystem conversion only accounted for lost sequestration
and not the release of carbon, and hence were significant
underestimates.
Recent analysis suggests that the annual loss of the three blue
carbon ecosystems is resulting in emissions (0.45 Pg CO2 per yr).

37. Land Use Land Cover Analysis
Most area was occupied by the croplands having 353 sq. km. in it out of 661 sq. km. of total area in the
year 2007 which has decreased by 6 sq. km in the year 2017 which may be due to the increasing
demand of the alternate practices for livelihood such as aquaculture. The percentage of the croplands
decreased from 52% to 51% in this period of 10 years. The amount of land used for the construction
activities i.e. 29 sq. km. (4.4 %) in both the years which is very less as compared to the land under
agriculture. But, the degree and distribution of resource extraction shows the dependency of the local
Croplands
> Dense
mangrove
> Rivers >
Builtup
lands

38. Vegetation Cover Analysis
The dense mangrove forest which is almost unchanged during these 10 years period
showing a percentage of about 0.06% increase in the year 2017. The cause of
increase in the mangrove area from 2007 to 2017 is a clear case of conservation
measure and decrease of fuel wood cutting and increased afforestation
programmes.

39. Shoreline changes in 10 year from the extracted shorelines from the satellite imageries

40. 0
50
100
150
200
250
300
350
400
450
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Distance
in
m.
Points from South to North
Graph showing shoreline change in different
control point (total 30) from South to North
0
50
100
150
200
250
300
350
400
450
1 2 3 4 5 6 7 8 9 10 11
Change
in
m
Control Points
Southern
Central
Northern
Graph showing shoreline change in different
control points (10 in each zones)
Southern zone is the most prone zone to the recession of the shoreline as compared
to the Central and Northern zones showing and average recession of 211.17 m, which
is followed by the Central zone (74.521 m) and the Northern zone (64.040 m)
respectively. The shoreline change showed a highest value of 384.800 m in the
Southern zone and a lowest value of 3m in the Northern zone .

41. Graph showing the Azimuth with all the transects calculated along the shoreline from
North to South of the shore

42. • Carbon dynamics in plant-soil systems have been known to be extremely important in controlling the CO2 concentration of
the atmosphere. Consequently, many models have been developed to describe the accumulation and distribution of plant
litter and soil organic matter (SOM). These models have been used to simulate various soil processes involving carbon and
nitrogen cycles in agricultural lands or forests, and in some cases to predict future responses of ecosystems to climate and
environmental change.
• Isotopic fractionation of carbon in decomposing soil organic matter has been recognized in a variety of ecosystems and
can provide important insight into physical and biological processes which mediate carbon storage, nutrient availability
and trace gas emissions. It has been demonstrated that adding carbon isotopic variations into carbon dynamics models
may help constrain certain model parameters. However, there has been limited theoretical analysis of carbon isotope
systematics and dynamics in soil environments. Part of the difficulty lies in the recognition that the δ13C values of SOM
vary both as a result of isotopic fractionation during the decomposition process and due to the isotopic heterogeneity
among various components in plant materials that decay at different rates.
• Therefore, C isotopic study is needed to understand the dynamics of carbon cycle in the forest ecology and thus it will help
to quantify the amount of greenhouse gases is being added through a particular forest dominated ecosystem.
• Site specific allometric equations need to be developed, so that specific models with respect to space may be achieved.
SCOPE FOR FUTURE

43. PUBLICATIONS

46. Acknowledgements
• We would like to extend our special thanks to the organisers for inviting me to this webinar.
• We would like to extend our special thanks to Ministry of Earth Sciences, Govt. of India project
(Sanction No. MoES/36/OOIS/Extra/44/2015 dated 29th November, 2016) for providing financial
support. We would like to thank D. Jayaprasad, IFS, Director and Dr. Gillella Ravishankar Reddy,
Scientist–G, Institute of Forest Biodiversity, Dulapally, Hyderabad for analysing carbon in the soil
and plant samples. We would also like to thank Mr. GV Reddy, Soil Chemist, Soil Testing
Laboratory Semiliguda, Govt. of Odisha for providing permission to analyze soil samples in their
laboratory and also for their help and support during my research work.
• We are also grateful to the Principal Chief Conservator of Forest (Wildlife), Bhubaneswar for
providing necessary permissions to work in Bhitarkanika Wildlife Sanctuary to my Ph.D scholar and
Mahanadi mangrove ecosystem, Odisha for the project.
• The PI of the project is also grateful to University Grants Commission, New Delhi for providing
Rajiv Gandhi National Fellowship for Schedule Caste to Mr. Gobinda Bal [Award Letter No. F1–
17.1/2014–15/RGNF–2014–15–SC–ORI–67490/(SA–III/Website) dated February 2015] for carrying
out his Ph.D research work in Bhitarkanika Wildlife Sanctuary.