Resource Manual

Measurement of Forest Carbon Stocks for

Capacity Building of State Forest Departments

2020

Indian Council of Forestry Research and Education

(An Autonomous Body of Ministry of Environment, Forest and Climate Change, Government of India)
P. O. New Forest, Dehradun - 248006 (INDIA)
The Resource Manual on "Measurement Manual Preparation Team
of Forest Carbon Stocks" is prepared Direction and Guidance
under the World Bank funded Ecosystem Sh. Anurag Bhardwaj
Services Improvement Project being Director (International Cooperation) and Project Director, ESIP, ICFRE
implemented by the Indian Council of
Forestry Research and Education, Dehradun Edited and Finalized by
in the states of Chhattisgarh and Madhya Dr. R.S. Rawat
Pradesh as a reference manual for Capacity Scientist In-charge, BCC Division and Project Manager, ESIP, ICFRE
Building of the State Forest Departments. Dr. Sanjay Singh
Scientist 'D'& Technical Manager, ESIP, ICFRE
Dr. Shilpa Gautam
Scientist 'D' & Project Coordinator, ESIP, ICFRE

© ICFRE, 2020 Prepared by

Dr. Md. Shahid
Carbon Sequestration Consultant, ESIP
Sh. V.R.S. Rawat
Published by Policy-cum-Knowledge Management Consultant, ESIP
Biodiversity and Climate Change Division,
Indian Council of Forestry Research and Page Layout & Type Setting
Education P.O. New Forest, Sh. Umang Thapa
Dehradun - 248 006 (INDIA) Establishment-cum-Secretarial Expert, ESIP

Citation
ICFRE (2020). Resource Manual: Measurement of Forest Carbon
Stocks for Capacity Building of State Forest Departments. Indian
ISBN : 978-81-936157-8-2
Council of Forestry Research and Education, Dehradun (INDIA).
 
 

Arun Singh Rawat, IFS 
Director General
Indian Council of Forestry Research and Education
P.O. New Forest, Dehradun – 248006
(An ISO 9001:2008 Certified Organisation)

FOREWORD

Globally, burning of fossil fuel and deforestation emerged as principal anthropogenic sources of rising concentration
of atmospheric carbon dioxide (CO2). There are compelling scientific evidences that humans are altering the climate in
ways that threaten our societies and the ecosystems. Forests are both source and sink of CO2 due to which forests are
an integral part of international agreements dealing with climate change. Forests are considered to provide a large
mitigation opportunity at relatively low costs along with other significant ecosystem goods and services benefits.
India is one of the few countries where forest and tree cover are increasing, and transforming forests into a net sink of
CO2. India has submitted its Nationally Determined Contribution (NDC) to United Nations Framework Convention on
Climate Change (UNFCCC) under Paris Agreement and forestry target of NDC is to capture an additional 2.5 to 3
billion tonnes of CO2 eq through additional forest and tree cover by 2030.
ICFRE is proactive in the field of forests and climate change and contributing significantly in climate change issues
relevant in the forestry sector at national and international level. ICFRE has contributed to UN Climate Change
negotiations for simplifying modalities for CDM A/R and policy approaches for REDD+. ICFRE has prepared National
REDD+ Strategy on behalf of Ministry of Environment, Forest and Climate Change, Government of India. ICFRE also
contributed to India’s Initial and Second National Communications, BUR I, BUR II and BUR III to UNFCCC. ICFRE has
also been actively engaged in capacity building programmes on forests and climate related issues for forest officers,
scientists and technologists at national level.
The World Bank funded Ecosystem Services Improvement project (ESIP) supports the goals of the Green India Mission
by demonstrating models for adaptation-based mitigation through sustainable land and ecosystem management
and livelihood benefits. ESIP attempts to introduce new tool and technologies for better management of natural
resources, including biodiversity and carbon stocks. As a Project Implementing Agency for ESIP, ICFRE is building the
capacity of State Forest Departments and Joint Forest Management Committees of Chhattisgarh and Madhya
Pradesh on forest carbon measurement and monitoring. I hope this resource manual on Measurement of Forest
Carbon Stocks will be a guiding field manual for the foresters, scientists, researchers, trainees and students for
measurement of forest carbon stocks. I congratulate the Project Director, Project Manager and entire team of ESIP for
putting in their best efforts for conceptualizing and preparing this resource manual.

Date: 02/06/2020 (Arun Singh Rawat)


An Autonomous Body of Ministry of Environment, Forest & Climate Change, Government of India

Phone : 0135-2759382 (O) E-mail : dg@icfre.org

EPABX : 0135-2224855, 2224333 (O) Fax : 0091-135-2755353
 
 

Anurag Bhardwaj, IFS 
Director (International Cooperation)
Indian Council of Forestry Research and Education
P.O. New Forest, Dehradun – 248006
(An ISO 9001:2008 Certified Organisation)

PREFACE

In the recent years, climate change is one of the global issues that have received tremendous attention of common
man, scientists and policy planners. Global climate change is a threat having perceptible and tangible impacts upon
human kind and nature. Scientists have documented climate induced changes in a number of physical and biological
processes. The adverse effects are evident that world is experiencing more intense rainfall, floods and storms are
more severe, heat waves are becoming more extreme, trees flower earlier in spring, glaciers are melting and the
global mean sea level is rising.
The world governments are looking towards effective mitigation of climate change. Under the Paris Agreement,
world governments have agreed to limit rise in global temperature by 1.5°C by the end of this century. A report of
IPCC concludes that meeting a 1.5°C target is possible but would require "deep emissions reductions" and "rapid,
far-reaching and unprecedented changes in all aspects of society." Forests are now an integral part of international
protocols dealing with climate change mitigation. Achieving forestry target of capturing an additional 2.5 to 3 billion
tonnes of additional CO2 equivalent through additional forest and tree cover is a challenging task for India under its
Nationally Determined Contributions commitment.
India is one of the fastest growing economies of the world. Its large and fast growing population requires forests for
the valuable ecosystem goods and services they provide. ICFRE as an implementing agency of Ecosystem Services
Improvement Project (ESIP) shall be attempting to upscale various Sustainable Land and Ecosystem Management
best practices in the selected forest landscapes of Chhattisgarh and Madhya Pradesh and also building the capacities
of State Forest Departments of Chhattisgarh and Madhya Pradesh on measurement and monitoring of forest carbon
stocks.
Field foresters have limited exposure to the new tools and techniques for measurement of forest carbon stocks. The
resource manual on ‘Measurement of Forest Carbon Stocks’ has been developed in such a manner that a field
forester can easily make an assessment of forest carbon stored in forests. The manual covers all aspects of carbon
assessment right from determining the sample size, laying out sample plots, measuring various variables and finally
analysis of different carbon pools in a forest ecosystem. I am sure this manual will be useful for field foresters for
measurement of forest carbon stocks. I congratulate the Project Manager, ESIP and all the team members of ESIP for
developing this resource manual.

(Anurag Bhardwaj)
Date: 02/06/2020 Project Director, ESIP
 C O N T E N T S

1. FORESTS AND CLIMATE CHANGE 1-3

2. FOREST CARBON STOCKS MEASUREMENT 5-7

3. USE OF REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEM IN

MEASUREMENT OF FOREST CARBON STOCKS 9-12

4. SAMPLING DESIGN AND ALLOCATION OF SAMPLE PLOTS 13-16

5. LAYING OUT OF SAMPLE PLOTS IN THE FIELD AND COLLECTION OF DATA 17-23

6. FOREST CARBON STOCKS ESTIMATION 25-30

REFERENCES 31-32

GLOSSARY 33-34

ANNEXES 35-65
Acknowledgement
: Indian Council of Forestry Research and Education, Dehradun
: Ministry of Environment, Forest and Climate Change, Government of India
: The World Bank
: Forest Survey of India, Dehradun
: Sh. Arun Singh Rawat, Director General, ICFRE
: Dr. Suresh Gairola, Former Director General, ICFRE
: Sh. S.D. Sharma, Dy. Director General (Research) and Former Project Director, ESIP, ICFRE
: Mr. Andrew M Mitchell, Team Task Leader, Ecosystem Services Improvement Project, the World Bank
: Dr. Anupam Joshi, Co- Team Task Leader, Ecosystem Services Improvement Project, the World Bank
: All the consultants of ESIP-PIU, Indian Council of Forestry Research and Education
: All the researchers and staff of Biodiversity and Climate Change Division, Indian Council of Forestry Research and
Education, Dehradun
 1 FORESTS AND
CLIMATE CHANGE

Climate Change greenhouse gas emissions (2007-2016) were derived

from Agriculture, Forestry and Other Land Use
Intergovernmental Panel on Climate Change stated
(IPCC,2019). The IPCC report on the impacts of global
that “Human influence on the climate system is clear
warming of 1.5°C above pre-industrial levels (IPCC,
and recent anthropogenic emissions of greenhouse
2018) has highlighted that average global earth’s
gases are the highest in history. Recent climate changes
temperature has increased by about 1°C as compared
have had widespread impacts on human and natural
to pre-industrial level due to anthropogenic activities.
systems” (IPCC, 2014). Earth’s atmosphere is made up
In line with increasing trends witnessed in global
of various gases released by the natural processes and
surface temperature, the average yearly temperature
anthropogenic activities. The earth’s atmosphere acts
over India for the period 1901 to 2017 has shown
like a blanket of greenhouse gases viz. carbon dioxide
significant rising trend of 0.66°C over 100 years.
(CO 2 ), methane (CH 4 ), nitrous oxide (N 2 0),
Extreme events like heat waves have risen in past 30
perfluorocarbons (PFCs), hydrofluorocarbons (HFCs)
years (MoEF&CC, 2017).
and sulfur hexafluoride, which traps the long wave
terrestrial radiations released by the planet earth. This is Forests and Climate Change
a natural phenomenon and known as greenhouse
The impact of climate change has alarmed the global
effect. However, human activities have increased the
communities and attracted the interest of scientific
concentration of greenhouse gases into the
communities towards various mitigation and
atmosphere which are responsible for the trapping of
adaptation measures. Forest ecosystem plays a
the outgoing long wave terrestrial radiations into the
significant role in reducing the impact of climate
earth’s atmosphere resulting, an increase in
change. Intrinsically forests and climate change are
atmospheric temperature.
directly linked to each other. Forests are known as the
According to IPCC (2014), globally CO2 emissions from sink as well as the source of carbon dioxide. Forest
fossil fuel combustion and industrial processes ecosystem during the process of photosynthesis,
contributed about 78% of the total greenhouse gas absorbs the carbon dioxide from the atmosphere and
(GHG) emission increase from 1970 to 2010, with a releases oxygen into the atmosphere. Role of forests
similar percentage contribution for the period has been increasingly recognized as most cost-effective
2000–2010. An estimated 23% of total anthropogenic option for climate change mitigation through carbon

Indian Council of Forestry Research and Education | 1

Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

captured in biomass and soils. Forests are considered to million tonnes (FSI, 2019) while in 2017 it was 7082
provide a large climate change mitigation opportunity million tonnes (FSI, 2017) which is a net increase of
at relatively lower costs along with other significant co- 42.6 million tonnes in country’s carbon stocks within
benefits. Global forests cover around 30% of earth’s two years. Various national programmes and policies
surface, spread over about 4 billion hectares of land have converted India’s forests into net sink of CO2. The
mass. Forestry mitigation options including reduced land use, land use change and forestry (LULUCF) sector
deforestation, forest management, afforestation, and was source of CO2 in the year 1994 accounting for
agro-forestry are estimated to contribute 0.2-13.8 1.16% of CO2eq emissions when India submitted its
GtCO2 per year of economically viable abatement in first National Communication (NATCOM) to United
2030 (IPCC, 2014). Nations Framework Convention on Climate Change
(UNFCCC) in 2000 (MoEF, 2004). In its second National
The overall contribution from agriculture, forestry and
Communication, LULUCF sector was a net sink of total
other land use (AFOLU) sector is around 23% of total
national emissions (MoEF, 2012). India’s first biennial
anthropogenic greenhouse gas emissions for the
update report to UNFCCC has reported that the
period from 2007 to 2016 (IPCC, 2019). In forest
LULUCF sector was a net carbon sink offsetting 252.5
ecosystem the carbon is stored in the growing stock
million tonnes of CO2 eq (MoEF&CC, 2015). LULUCF
(standing trees, herbs, shrubs etc.) and in the soil. The
sector was a net sink of 301.19 million tonnes CO2eq
cutting down of trees and removal of vegetation from
during 2014, registering an increase in the sink activity
the forest ecosystem for fuel wood, timber, fodder etc.
of the sector. Forests were net sinks and about 12% of
releases the stored carbon in the form of CO2. Various
India’s GHG emissions were offset by the LULUCF
anthropogenic activities like burning of fossil fuels,
sector. Thus, forestry sector in India is making a positive
industrial as well as urban growth, deforestation and
contribution to climate change mitigation (MoEF&CC,
forest degradation are mainly responsible for
2018).
increasing the concentration of CO2 and other
greenhouse gases into the atmosphere. Ful Filling International Commitments on
Carbon Services of India’s Forests Climate Change

India is a vast country with a rich biological diversity. Fulfilling emission reduction commitments under the
Forest is the second-largest land use in India after United Nations Framework Convention on Climate
agriculture. Roughly, 275 million rural people in India Change (UNFCCC), Government of India launched
depend on forests for at least part of their subsistence National Action Plan on Climate Change (NAPCC) in
and livelihood (World Bank, 2006). As per the India 2008. Government of India is committed to achieve its
State of Forest Report 2019 (FSI, 2019), the forest cover Nationally Determined Contributions (NDCs) under the
of the country stood at 7,12,249 km2, while it was Paris Agreement.
7,08,273 km2 in 2017 (FSI, 2017), recording an India’s National Action Plan on Climate Change
increase of 3976 km2 within two years. The total forest
National Action Plan on Climate Change identifies
and tree cover of the country is 24.56% of its
several measures that simultaneously advance the
geographical area. The National Forest Policy of India
country’s development and climate change related
envisages 33% of its geographical area under forest
objectives of adaptation and mitigation. The
and tree cover.
implementation of the NAPCC is designed to take place
With its focus on sustainable management of forests, through eight National Missions, which form the core of
afforestation and regulating diversion of forest lands the National Action Plan on Climate Change and
for non-forest purposes, India has been successful in incorporate multi-pronged, long-term and integrated
improving carbon stocks in its forests. In 2019, strategies for achieving India’s key goals in the context
estimated total carbon stocks in forest was 7,124.6 of climate change. National Mission for a Green India

2 | Indian Council of Forestry Research and Education

 >> Forests and Climate Change

also known as Green India Mission (GIM) is one of the Agreement. Under Paris Agreement India has
key missions under NAPCC dealing with mitigation and committed to meet its Nationally Determined
adaptation of climate change in the forestry sector Contribution (NDC). The forestry sector goal of NDC is
(MoEF&CC, 2014). to create an additional carbon sink of 2.5 to 3 billion
tonnes of CO2 equivalent through additional forest and
National Mission for a Green India: The National
tree cover by 2030. To achieve this, India is determined
Mission for a Green India (GIM) recognizes that climate
to continue with its on-going interventions, enhance
change phenomena will seriously affect and alter the
the existing policies and launch new initiatives in the
distribution, type and quality of forests of the country
priority areas inter alia full implementation of Green
and the associated livelihoods of the people.
India Mission and other programmes of afforestation.
GIM puts the “greening” in the context of climate Planned afforestation has been seen as a major
change adaptation and mitigation, meant to enhance mitigation strategy in the forestry sector. India is one of
ecosystem services like carbon sequestration and the few countries where forest and tree cover has
storage (in forests and other ecosystems), hydrological increased in recent years transforming the country’s
services and biodiversity; along with provisioning forests into a net sink owing to national policies aimed
services like fuelwood, fodder, small timber and non- at conservation and sustainable management of
timber forest produce. GIM aims at responding to forests.
climate change by a combination of adaptation and
India’s efforts to increase the forest and tree cover have
mitigation measures, which would help in: (i)
been further augmented by policies like National
enhancing carbon sinks in sustainably managed forests
REDD-plus Strategy (2018), National Agroforestry
and other ecosystems; (ii) adaptation of vulnerable
Policy (2014), Joint Forest Management Guidelines
species/ecosystems to the changing climate; and (iii)
1990; National Afforestation Programme, Namami
adaptation of forest dependent local communities in
Gange programme and afforestation along the river
the face of climatic variability. The objectives of the GIM
sides, Green Highways Mission and afforestation under
are as follows:
Compensatory Afforestation Fund Management and
: Increased forest/tree cover on 5 million ha of Planning Authority Act.
forest/non-forest lands and improved quality of
forest cover on another 5 million ha (a total of 10
million ha).
: Improved ecos ystem services including
biodiversity, hydrological services and carbon
sequestration as a result of treatment of 10 million
ha.
: Increased forest-based livelihood income for 3
million forest dependent households.
: Enhanced annual CO2 sequestration of 50-60
million tonnes.
India’s Nationally Determined Contribution for
Forestry Sector
India is a signatory to the UNFCCC and its Paris

Indian Council of Forestry Research and Education | 3

 2 FOREST CARBON
STOCKS MEASUREMENT

Purposes of Carbon Measurement in Forest at project, regional or country level

Ecosystem (ii) To estimate future forest carbon stocks and
Forests are both source and sink of carbon dioxide. A emissions under a wide range of forest
growing forest captures atmospheric carbon and this management and land use scenarios, allowing for a
carbon is released into the atmosphere through comparison of the emissions, or carbon storage,
activities like deforestation and forest degradation. The (iii) To assess potential to monetize carbon sequestration
climate change mitigation benefit of forests is one of under various domestic and international carbon
the ecosystem services rendered by forests which are trading mechanisms.
fully measurable, reportable and verifiable.
Measurement of forest carbon stocks is a vital part of
Ecosystem Services Improvement Project (ESIP)
implementation or even other forestry projects because
CO 2 emission reductions and remova l s b y
implementing various forestry activities are estimated
by measuring changes in the amount of forest carbon
and credits are also issued on the basis of carbon
accrued through these actions. Measurement can be
defined as the continuous measurement and collection
of data on anthropogenic forest-related greenhouse
gas emissions by sources and removals by sinks. The
measurement system must be transparent, consistent
and accurate, and uncertainty should be minimized. The
purpose of carbon measurement in forests is given
below in nutshell:
(i) To estimate plot level forest carbon stocks at above Fig. 1 : Various carbon pools in a forest ecosystem
ground and belowground carbon pools and
develop a comprehensive picture of carbon stocks

Indian Council of Forestry Research and Education | 5

Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

Table 1: Definitions of terrestrial carbon pools

Pool Description
Living Above All living biomass above the soil including stem, stump, branches, bark, seeds
Biomass Ground and foliage.
Biomass
Below All living biomass of live roots. Fine roots of less than 2 mm diameter
Ground (suggested) are often excluded because these often cannot be distinguished
Biomass empirically from soil organic matter or litter.

Dead Dead wood Includes all non-living woody biomass not contained in the litter, either
Organic standing, lying on the ground, or in the soil. Dead wood includes wood lying
matter on the surface, dead roots, and stumps larger than or equal to 10 cm in
diameter or any other diameter used by the country.

Litter Includes all non-living biomass with a diameter less than a minimum
diameter chosen by the country [Forest Survey of India (FSI) has selected 5 cm
diameter], lying dead, in various states of decomposition above the mineral
or organic soil. This includes the litter, fulvic, and humic layers. Live fine roots
(of less than the suggested diameter limit for below-ground biomass) are
included in litter where they cannot be distinguished from it empirically.

Soil Soil Organic Includes organic carbon in mineral and organic soils (including peat) to a
Matter specified depth chosen by the country (FSI as selected : 30 cm) and applied
consistently through the time series. Live fine roots (of less than the
suggested diameter limit for below-ground biomass) are included with soil
organic matter where they cannot be distinguished from it empirically.

(Source: IPCC, 2003 and FSI, nd)

Carbon Pools increasing level of data requirement and analytical

complexity.
Carbon pool may be defined as a system that has the
capacity to store or release carbon. For the estimation Tier 1 methods are designed to be the simplest to use,
of carbon pools in a forested stand, IPCC (2003) for which equations and default parameter values (e.g.,
identified five carbon pools. The detailed description of emission and stock change factors) are used. For Tier 1
these carbon pools is given in Table 1 and also depicted there are often globally available sources of activity
in Figure 1. data estimates (e.g., deforestation rates, agricultural
production statistics, global land cover maps, fertilizer
Different Tiers of Estimation of Forest Carbon use, livestock population data, etc.) though these data
Stocks are usually spatially coarse.
The IPCC (2006) provides three general approaches for Tier 2 can use the same methodological approach as
estimating emissions/removal of greenhouse gases, Tier 1 but applies emission and stock change factors
called “Tiers” ranging from 1 to 3, representing an that are based on country or region-specific data, for

6 | Indian Council of Forestry Research and Education

 >> Forest Carbon Stocks Measurement

the most important land-use or livestock categories. accuracy of the estimation. This manual has been
Higher temporal and spatial resolution and more designed to follow the Tier 3 approach using GIS for
disaggregated activity data are typically used in Tier 2 to estimation of forest carbon stocks.
correspond with country-defined coefficients for
specific regions and specialized land-use or livestock
Forest Cover Mapping in India
categories. India is among the few countries which are regularly
using satellite based remote sensing technology in
Tier 3 higher order methods are used, including
detecting forest cover changes. The application of
models and inventory measurement systems tailored to
satellite remote sensing technology to assess the forest
address national circumstances, repeated over time,
cover of the entire country in India began in early
and driven by high-resolution activity data and
1980s. The Forest Survey of India (FSI) is assessing the
disaggregated at sub-national level. These higher order
forest cover of the country on a two-year cycle. Over the
methods provide estimates of greater certainty than
years, there have been improvements both in the
lower tiers. Such systems may include comprehensive
quality of remote sensing data and the accuracy of
field sampling repeated at regular time intervals and/or th
interpretation techniques. The 16 biennial cycle of
GIS-based systems of age, class/ production data, soil
forest inventory has been completed by using 23.5 m
data, and land-use and management activity data,
resolution images. The minimum mappable unit in
integrating several types of monitoring.
respect of forest cover assessment is an area of 1 ha in
Despite differences in approach among the three Tiers, extent and having tree canopy of 10% (FSI, 2019).
all tiers have common adherence to IPCC good practice Forest cover is classified by FSI in terms of canopy
concepts of transparency, completeness, consistency, density as given in Table 2.
comparability and accuracy, thereby, increasing

Table 2: Forest cover classified in terms of canopy density classes

S. No. Class Description

1 Very Dense Forest All lands with tree canopy density of 70% and above
2 Moderately Dense Forest All lands with tree canopy density of 40% and more but less than 70%
3 Open Forest All lands with tree canopy density of 10% and more but less than 40%
4 Scrub Degraded forest lands with canopy density of less than 10%
5 Non Forest Lands not included in any of the above classes (including water)
(Source: FSI, 2017)

Indian Council of Forestry Research and Education | 7

 3 USE OF REMOTE SENSING
AND GEOGRAPHICAL
INFORMATION SYSTEM
IN MEASUREMENT OF
FOREST CARBON STOCKS

Forests are essential natural resources which have huge contact with the object. The data collected could be
contribution in regulating global carbon cycle. about various aspects of the object or phenomenon. It
Therefore measurements of forest cover and change relies upon technical instruments to collect data over
are important to understand the status of forest. It has large areas which reduce the manual works, allows
potential to mitigate climate change and stabilize the retrieval of data of inaccessible areas and collect large
atmospheric carbon dioxide into various carbon pools amounts of data over a large area in a relatively short
via carbon sequestration. Quantification of various period. GIS is a computer based tool for mapping and
attributes of forest stands such as volume, analyzing features and events on earth. GIS technology
aboveground biomass, soil carbon, etc. are important integrates common database operations such as query
to assess at different intervals. There are conventional and statistical analysis and interprets the huge data
and unconventional methods to quantify carbon stocks sets on natural resources into more meaningful
from forests. Conventional methods include intensive information in various forms that can guide in decision
field inventory which are costly, time and labour making process. This requires software to handle the
consuming. Unconventional method includes whole geographical and spatial data for further
application of remote sensing and geographical analysis. The function of the remote sensing and GIS
information system (GIS) in forest carbon stocks based software is to collect, process, analyse, and
measurement. It is an efficient and economical way for understand raw geospatial data/ images and extract
the quantification and regular monitoring of the forest meaningful information. The software belongs to two
carbon stocks having wide coverage. categories based upon its purchasing value.
Remote sensing technique is designed to collect and Paid remote sensing and GIS softwares : These
retrieve large amount of data without any physical are commercial software and the companies provide

Indian Council of Forestry Research and Education | 9

Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

licensed versions after requisite payment for example

ERDAS IMAGINE, ENVI, Arc-GIS, Manifold GIS,
ECognition, TerrSet etc.
Open source remote sensing and GIS softwares :
Open source remote sensing softwares are freely
available in public domain for public use. Some of the
open source softwares such as Quantum GIS (QGIS)
which is one of the most powerful open source GIS
software where satellite imagery can be directly
downloaded in the plug-in and also provides tools for
pre and post-processing of imagery. Others are MODIS
Sentinel Toolbox (s-1 tbx), SAGA GIS: System for
Automated Geoscientific Analyses (ideal for most
remote sensing needs because of its rich library grid,
imagery and terrain processing modules), ORFEO
(Optical and Radar Federated Earth Observation), Southern Dry Mixed Moderately Dense Forest

GRASS GIS (Geographic Resources Analysis Support Southern Dry Mixed Deciduous Open Forest
Dry Teak Very Dense Forest

System-GIS), PolSARPro, Whitebox GAT (Geospatial Dry Teak Open Forest

Dry Teak Moderatley Dense Forest

Analysis Tool), ILWIS (Integrated Land and Water

Information System), E-fot, OSSIM (Open Source
Software Image Map) etc.
Fig. 2 : Forest type and density map of Budhni Forest
Stratification of Forest for Carbon Stocks Range, MP
Measurement
For accuracy, the prior requirement for carbon stock
Sample Size Determination
measurement is to achieve homogeneity conditions of
forest which can resonate with the satellite imagery To reach to a good precision and acceptable
and further statistics can be applied on it. This brings accuracy for estimation of forest carbon stocks,
the importance of forest canopy density and forest type there is a strong necessity to have population
together for stratification especially in natural forest. representativeness in the samples during sampling.
For plantations, the type of species planted and its age Number of sample plots and size of plots plays a crucial
becomes the factor for homogenization. Currently, IRS- role for representing the area and for extrapolation of
Resouresat-2 and LISS III sensors are used for forest the data. Knowledge of local conditions which
cover mapping. Latest tools and geo informatics influence the variability in forest area is the key element
techniques provide technical acuity for analysis of forest that should be focused upon for achieving the accuracy.
carbons stocks by intersecting both the forest type and To remove the bias and avoid a sampling error, random
forest canopy density as layers. This brings an increment points are useful for the sample size determination. In
in accuracy when applied with the field inventory Arc-GIS system, an important tool such as ‘create
attributed as sample plots in different homogenous random tool’ helps in generating random points within
blocks of forests. One such example is given for ESIP the study area. Further, the latitude and longitude of the
areas of Budhni Forest Range in Madhya Pradesh in sample points can be generated in a separate file
Figure 2 showing forest type and density map. through following steps:

10 | Indian Council of Forestry Research and Education

 >> Use of Remote Sensing and Geographical Information System
in Measurement of Forest Carbon Stocks

Open attribute table of feature class

For latitude, add field within the attribute

table with type double and then click ok.
Perform these steps for longitude too

Highlight the field, right click on the

header and calculate geometry

Ensure property lists Y coordinate of Point

and click ok then list X coordinate of Point
for latitude and longitude field

To illustrate this, sample plots laid down in ESIP areas of

Budhni Forest Range (Figure 3). Attributes of sample
plots marked as red dots is fiven in Figure 4.

Fig. 4 : X and Y Coordinates of the sample plots in

an attribute table presented in ARCGIS
software

Variety of data sources and statistical methods

can be used to improve the accuracy and predictive
Southern Dry Mixed Moderately Dense Forest
Southern Dry Mixed Deciduous Open Forest quality. Schematic representation of forest carbon
Dry Teak Very Dense Forest
Dry Teak Open Forest measurement methodology is given in following flow
Dry Teak Moderatley Dense Forest
chart :

Fig. 3 : Allocation of Sample Plots in Budhni

Forest Range

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Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

Forest type Forest canopy parametric algorithms which include regression

map density map models, weighted regression and machine learning
techniques can be incorporated for extrapolation of
forest biomass for the larger area. Examples of machine
learning techniques are random forest, support vector
Stratification to get homogenous blocks of
machine, random learning, artificial neural network etc.
forest stand to get forest type
which increases the complexity to produce the desired
results.
Using command from ARCGIS software ‘Generate Advanced Techniques in Remote Sensing for
random points’ in each stratum inside the forest Forest Carbon Stocks Measurement
boundary and create latitude and longitude of
sample plots The necessity for accuracy in forest carbon stocks
measurement in forests in varied climate especially in
cloud prone areas demands use of advanced
Locate the sample plots on field using the help of techniques in remote sensing and GIS. This help in
latitude and longitude generated in the software bringing the judicious use of resources for
and laid down the sample plot in each stratum strengthening of the data and reducing the error.
Advanced techniques like LiDAR, RADAR and
geostatistics can be useful for forest carbon stocks
Measurements of five carbon pools on the sample measurement. Geostatistics is an evolutionary method
plots for forest carbon stock assessment that helps in analyzing and further predicting values at
unsampled places that vary in space from both more
and less sparse sample data. Geostatistics is helpful at
Extrapolation of Forest Biomass at Higher regional level which helps in constructing the biomass
Geographical Area database. LIDAR and RADAR, provide high quality 3D
With the help of remote sensing, the field data can be image and can be useful for mapping forest canopy
presented for the larger area with efficient cost and height, biomass and biomass change, forest cover
time induced. It also helps in various future projections change and monitoring in the time effective manner.
based on the extrapolated data without direct Application of integrated approach of remote sensing
observation. Quality assurance/ quality control depends and field inventory will be useful for measurement of
on many factors which includes sincerity during plotting forest carbon stocks.
for the biomass estimation which helps in reducing the (Contributed by Dr. Gurveen Arora, Research Associate, BCC Division, ICFRE)
error. Many methods like parametric and non

12 | Indian Council of Forestry Research and Education

 4 SAMPLING DESIGN
AND ALLOCATION OF
SAMPLE PLOTS

Sampling Design forest has the same probability of being sampled.

Sample plots are laid out randomly to avoid bias in
Sampling is the most important step for the estimation
locating the plots. Random sampling ensures that each
of forest carbon stocks. Appropriate sampling for the
sample plot in the area has an equal probability of
carbon stocks estimation can provide reliable estimates
being chosen. A simple random sample is meant to be
at a reasonable cost with limited man power. Sampling
an unbiased representation of population. Sample plot
includes the number, size and shape of the plots to be
location has no impact on the position of other sample
required to measure the forest carbon stocks. Sampling
plots. Generally, simple random sampling is not
a small portion of the entire population enables
adopted for the assessment of forest carbon stock as it
conclusions to be drawn about an entire population.
considers the population as homogenous. The project
Sampling theory provides the means for collecting
area is considered as one unit and the heterogeneity of
information from the sample plots to the whole project
forest type, forest cover soil, topography is not
area or even to a regional and national level (IPCC,
considered.
2003). Thus, measurements of forest carbon stocks of
sample plots can be extrapolated to per hectare or for Systematic Sampling: Systematic sampling involves
the whole project area. Sampling methods include measuring plots at fixed spaced intervals in the project
simple random sampling, stratified random sampling area. After the sample size calculation, systematic
and systematic sampling. Standard sampling theory sampling may be followed depending on the time and
relies on random selection of a sample from the resources. The systematic sampling may be based on
population so that each unit of the population has an the system of grids of latitude and longitude or
equal probability of being included in the sample. distance, say 1 km × 1 km. The size of grid can be
ascertained using area of the concerned division and
Simple Random Sampling: Simple random
optimum sample size and sample grids be selected. It
sampling involves measurement of sampling units
may be all grids of 25” × 25” (approximately one plot
(such as a tree or plot) allocated randomly across the
per 0.56 km2, assuming that at the centre of country on
forest area in such a way that every sampling unit in the

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Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

an average 2.5’ × 2.5’ covers an area of 20 km2) or : Select a random number smaller than k (smaller
alternate grids of 25” × 25” size (approximately one than 40 in this example), say 25.
plot per 1.11 km2) or all grids of 50” × 50” size : Select and mark the first grid based on the
2
(approximately one plot per 2.22 km ) or alternate grids random number.
of 50” × 50” size (approximately one plot per 4.44 : The first sampling grid number is 25.
2
km ), all grids of 1.25’ × 1.25’ size (approximately one
2
: The second sampling grid = sampling interval k
plot per 5 km ) and so on (MoEF&CC, 2014). This
(40) + first sampling grid (25) = 65.
method is simple, regular spacing and systematic layout
: The third sampling grid = sampling interval k
provides easy pattern for travels and fieldwork. In other
(40) + second sampling grid (65) = 105.
method, the project area converted into grids of
appropriate size. The grids size could be of 100 m × 100 : The procedure repeated for the remaining
m, 200 m × 200 m, 300 m × 300 m, 400 m × 400 m, number of sample plots.
500 m × 500 m. Grid size of any dimension may be Stratified Random Sampling: Stratified sampling
selected depending on the resource’s availability. For involves defining a forest stratification system, and then
example, 200 grids with a total project area of 40 ha. establishing a target number of plots within each
Plot numbers may be marked on the grid map of project defined stratum (allocated on either a random or
area. systematic basis).‘Stratification’ is the process of
: Calculate the sampling interval “k” by using the grouping a forest into areas with similar characteristics.
following equation : This process is intended to improve the efficiency of the
k = N/n sampling program, as variation within a stratum is
where, minimized, making it more likely that the measurements
taken in the sub-sample are representative of the entire
k = sampling interval of grids or
stratum.
plots = 200/5 =40,
N = total number of grids representing a This provides a better (more precise) estimate of the
given strata (200) and average forest carbon stocks for the stratum generated
n = number of sample plots to be selected. with the least amount of effort and cost.

S1 = Strata 1
S2 = Strata 2

Random Sampling Systematic Sampling Stratified Sampling

Fig. 5 : Layout for Random Sampling and Systematic Sampling Fig. 6 : Layout for Stratified Sampling

14 | Indian Council of Forestry Research and Education

 >> Sampling Design and Allocation of Sample Plots

Literature review should be conducted for the quality Determination of Number of Permanent
assurance and sampling design. Sources that may be Sample Plot
consulted include peer-reviewed research articles/
The level of precision required for a forest carbon
reports on the project area or similar area. National
inventory has a direct effect on inventory for forest
level reports having similar kind of forest may also be
carbon stock assessment. Once the level of precision
consulted before deciding the sampling design.
has been decided upon, sample size can be determined
Information regarding the range, standard deviations, for each stratum in the project area. Volume or the
standard errors and coefficient of variation of carbon aboveground biomass can be used to estimate the
stocks in project area or area similar to project area is variance and further to estimate the sample size. Study
useful to determine the number of sampling plots. of 10-15 sample plots in the project area are usually
Sample size depends on the required precision and the enough to evaluate variance. For preparation of forest
inventory for carbon estimation normally sampling can
anticipated variance in the specific forest strata. At least
be done at an intensity of 90/10 (90% confidence level
10% extra plots may be laid for more reliable estimate
and 10% precision). Confidence level amounts to
of carbon density. uncertainty one can tolerate. Precision level is the
Stratification margin of error one can tolerate.

It is useful to stratify the project area into strata that For example, for the estimation of the variance,
form relatively homogenous units. The IPCC (2006) mean (M) is assumed as 60.41 t/ha and standard
recommends stratifying by climate, soil, ecological zone deviation as ± 24.81 and further can be worked out as
follows:
and management practices. In general, stratification
also decreases the costs of measurement because it Coefficient of
typically diminishes the sampling efforts while Variation (CV) = Standard deviation/Mean × 100
maintaining the same level of confidence. Potential
Mean (M) = 60.41 t/ha
stratification options include:
Standard
: Land use (for example forest, plantation, etc) Deviation (SD) = ± 24.81
: Type of vegetation or species (if several)
CV = 24.81/60.41 × 100
: Slope (for example: steep, flat) = 41.06
: Drainage (for example: flooded, dry)
Use the value of variance in the following equation
: Age of vegetation (MoEF&CC, 2014) :
: Proximity to settlement 2
Sample Size (N) = (1.64 × CV/AE)
: Management Practice: Natural or plantation (at 90% confidence interval with
In India, Forest Survey of India (FSI) has prepared forest 10% allowable error)
density map and forest types map of India. These forest Where,
density map and forest types map could be used to CV = Coefficient of Variation as calculated above
stratify the project area into forest type density stratum. AE = Allowable error (e.g. 10%, 5%)
Intersect tool in ArcGIS or other GIS softwares can be 1.64 = Student’s t-value at 90 % confidence interval
2
used to produce the forest type and density maps of the N = (1.64 × 41.06/10)
sampling area.
Where Sample Size (N) is the number of sample plot

Indian Council of Forestry Research and Education | 15

Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

required to estimate the forest carbon stocks in given sample size to different strata is required. This is done
area. using following formula:
N= 45 A = Area of Strata/Total Area × Sample Size
This means 45 plots are required to be laid in the where,
project area. A = number of sample plots allocated to stratum

Allocation of Sample Plot in Each Stratum For example : In a Forest Range, total forest area is
9296.30 ha and having six forest types. Forest types
Once the sample size is calculated, the allocation of
wise area is given in the following table:

Stratum Forest Strata Area (ha)

S1 Dry Teak Open Forest 3091.90
S2 Dry Teak Moderately Dense Forest 537.46
S3 Dry Teak Very Dense Forest 253.08
S4 Southern Dry Mixed Deciduous Moderately Dense Forest 3874.30
S5 Southern Dry Mixed Deciduous Open Forest 1469.00
S6 Southern Dry Mixed Deciduous Very Dense Forest 70.67
Total 9296.30

Allocation of sample plots to be laid in each stratum is Forest type/strata wise allocation of sample plots will
calculated by using the above mentioned formula. be as follows:

Stratum Forest Strata Calculation No. of Sample Plots

S1 Dry Teak Open Forest A1 = 3091.90/9296.30x45 15
S2 Dry Teak Moderately Dense Forest A2 = 537.46/9296.30x45 3
S3 Dry Teak Very Dense Forest A3 = 253.08/9293.30x45 1
S4 Southern Dry Mixed Deciduous A4 = 3874.30/9296.30x45 19
Moderately Dense Forest
S5 Southern Dry Mixed Deciduous A5 = 1469/9296.30x45 7
Open Forest
S6 Southern Dry Mixed Deciduous A6 = 70.67/9296.30x45 0
Very Dense Forest

It is further advised that to increase 10% sample size to sample plot respectively. It is advisable to allocate at
give adequate coverage to the under represented class. least 03 sample plots in each stratum for sound
Here A3 and A6 allocation of sample size shows 1 and 0 statistical analysis.

16 | Indian Council of Forestry Research and Education

 5 LAYING OUT OF SAMPLE
PLOTS IN THE FIELD
AND COLLECTION OF DATA

Location of Sample Plots monitoring requires both size and number of sample
plots to be decided. Plot size has the impact on the cost
Randomization of sample plots in a stratum:“ArcGIS”
of carbon inventory and monitoring. Larger the plots,
Software or other GIS softwares may be used to locate
lower the variability between two samples. National
the sample plot randomly in random manner in each
Working Plan Code-2014 may be followed for the
stratum. ArcGIS is a Geographic Information System
sample plot design and layout methods. National
(GIS) for working with maps and geographic
Working Plan Code-2014 is following the square
information. It is used for creating and using maps,
sample plot design and the same is adopted for
compiling geographic data, analyzing mapped
collection of the data on forest carbon stocks.
information, sharing and discovering geographic
information, using maps and geographic information in After reaching the predetermined sampling plot
a range of applications, and managing geographic location, a square plot of 0.1 ha (31.62 m × 31.62 m)
information in a database. Service “Create Random would be laid out by measuring 22.36 m horizontal
Points” function of ArcGIS randomly places a specified distance i.e. half of the diagonal in all the four directions
number of points within an extent window or inside the at 45°in north-east, at 135°in south-east, at 225° in
features of a polygon, line, or point feature class. the south west, and at 315°in north-west corners of the
plot from true north. Care should be taken for laying out
Sample Plot Layout the proper dimensions of the plot. Then subplots of size
Permanent sample plots are generally considered as 3 m × 3 m and 1 m × 1 m would be laid out at 30 m from
statistically more efficient in estimating changes in the center of the main plot of 0.1 ha in all the four
forest carbon stocks compared to temporary sample directions for the collection of samples for shrubs,
plots because typically there is high covariance climber and regeneration and herbs/grasses
between observations taken at successive sampling respectively (Figure 7). Along with, the quadrats of size
events in temporary plots. Permanent sample plots 1 m × 1 m, 3 m × 3 m and 5 m × 5 m would be laid at
should be established for the assessment and North East (NE) and South West (SW) direction. In 5 m ×
monitoring of carbon stocks in the forest. Carbon 5 m plot, all the dead wood above 5 cm diameter would

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Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

be collected, weighed and recorded. In 3 m × 3 m, all square plot to cluster of circular plots for national forest
woody litter that is branches below 5 cm diameter, leaf inventory as mentioned in India State of Forest Report
litter, dried fruits litter would be collected weighed and 2019. A brief about circular cluster sample plot design
recorded. All shrubs and climbers in 3 m × 3 m plot is also described in this section for the knowledge of
should be cut at ground level, weighed and recorded. users. However, development of this manual is based
In 1 m ×1 m plot, all the herbs/grasses would be on square plot sample design as prescribed in the
collected, weighed and recorded. For estimation of soil Working Plan Code – 2014.
organic carbon, forest floor should be swept and a pit of
Circular sample plot design is the cluster of four circular
30 cm × 30 cm × 30 cm would be dug at the center of 1
sub-plots each with radius of 8 m in a fixed pattern. The
m x 1 m plot at NE and SW corner of the main 0.1 ha
central sub-plot 1 is laid out at the point with assigned
plot. A composite sample of soil (mixture of soil from
latitude and longitude. Other three sub-plots 2, 3 and 4
various depths 0-10 cm, 10-20 cm and 20-30 cm)
weighing 200 gm should be collected for soil organic are to be located at a distance 40 m in north, 120
carbon analysis. The soil sample should be kept in a degree and 240 degree from north respectively. Sample
polythene bag and tightly closed and properly labeled plot design given in Figure 8 can be laid out as per
for further laboratory analysis. following steps:

Circular Cluster Sample Plot Design : Cluster of four sub-plots of 8 metre radius each
from the centre of sub-plot 1 at azimuth at 360
Now, Forest Survey of India has shifted from single degree, 120 degree and 240 degree at a

(Source: MoEF&CC, 2014)

Fig. 7 : Sample Plot Layout : Configuration of main plot and attached sub-plots

18 | Indian Council of Forestry Research and Education

 >> Laying out of Sample Plots in the Field and Collection of Data

Equipment Required
Field sampling kit should be prepared well in
advance before proceeding to the field for sampling.
The following equipments and items are required for
laying the sample plots in the forest for carbon
measurement:

Equipment/Items Purpose
Nylon Rope (04) of the Marking of the plot
length 31.62 m (or
more) for all the four
side of the plot.
Clinometer/Hagaalti- Tree height measurement
meter/ Ravi multimeter
Compass Direction checking
Global Positioning Locating the sample plot
System (GPS)
Camera Clicking photograph of
the sample plot/
prominent features
Aluminium Tags Marking on the trees
Hammer Marking
Source: FSI, 2019
Field Maps (Stratified Locating the sample plot
Fig. 8 : New National Forest Inventory Sample Forest Maps)
Plot Layout Electronic Weighing Weigh the sample
Balance collected
Measuring Tape (50 m) Laying sample plot
distance of 40 metre.
Iron Pole/Wooden stick Marking boundary
: Three 1 m x 1m plot for soil and forest floor with
centre at a distance of 20 metre from the centre of Polybags/Paper bags Collection of samples
sub-plot -1 in the direction of sub-plot 2, 3 and 4. Rubber Bands Packing the samples
: Two circular plots for herbs (0.6 m radius), shrub, Nails Tagging
climber and regeneration and woody litter (1.7 m Permanent Marker Marking
radius) in all four sub-plots at a distance of 5 metre Secateurs Cutting
from the centre towards east. Khurpi/Kudal Digging
: A circular plot of 2.8 metre for stump & dead wood Core Sampler Collection of soil sample
collection will be laid out in all four sub-plots at a for bulk density estimation
distance of 5 metre from the centre towards east. Batteries for GPS Power backup
: Set of herbs, shrubs-climbers-regeneration-woody Paint and Brush Marking on the trees
litter and stump, dead wood circular plots is Spade/Shovel Digging soil
concentric.

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Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

Constitution of Field Team Tree Circumference at Breast Height (CBH)

Measurements : For aboveground biomass all trees
Field team for data collection from sample plots require
having diameter of 10 cm and above or circumference
at least one experienced/ trained person and four
at breast height of 30 cm and above are enumerated.
assistants to layout the plots and collect required data
Species and diameter class wise information obtained
and samples. Human resource should be properly
from the sample plot of 0.1 ha is recorded carefully in
trained before field sampling, if required a hands on
the data collection form (Annex-V). Borderline trees i.e.
training to the staff/ field team can be provided on the
the stem of the trees touching the north and west
first day. Proper division of work helps in the efficient
borderline of the plot should be enumerated. However,
coordination between the team members and reduces
the cost and time of carbon measurement. the stem of the trees touching the east and south
borderline of the plot should be treated as “out trees”
Field Background Information and information about out trees should not be recorded
in the Data Collection Form. Trees below 10 cm
Before going for field sampling, information regarding
diameter at breast height over bark will be enumerated
the field location should be collected that will help in
as sapling. One should be clear that the enumeration in
locating sample plots and other locality features. Field
the plot should be started from the North-east corner
maps should be prepared with the help of Geographic
Information System. Project area, project boundary, and should proceed in clockwise direction. The same
nearby settlements, roads, river, forest types, forest procedure should be followed for all the sample plots.
cover, and other land use features should be properly The height of the trees in all the sample plots should be
marked on the field maps. Base camp should be measured.
established in the project area. Interaction with the Circumference of the tree (circumference at breast
local communities can help in collecting information height, CBH) is measured at 1.37 m or 4.5 feet from the
about the project area and ground truthing. Local ground. The circumference may be measured by
labour can also be hired for easy movement in the forest wrapping measuring tape firmly around the stem,
and nearby locations. perpendicular to axis. The point must be marked for
Tree Measurements repeated measurements for assessing the growth rate
to ensure that the same position will be measured on
Measurement from individual tree to forest stand for each occasion.
estimation of tree volume and biomass is important for
assessment of biomass carbon. In tree and forest Following precautions are to be observed while
measurement some variables are not measured directly measuring tree CBH:
like volume of wood, biomass etc. It is difficult to (i) On sloping ground measurements should be
measure some parameters or it cannot be measured taken from the uphill side of the stem.
directly at all, indirect methods/models are applied to (ii) For leaning trees (on level ground), the point will
approximate or estimate the parameter of interest. be on the under-side of the tree parallel to the
These methods often involve measuring parts of the axis of the stem.
body (e.g., tree trunk), or parts which can be measured
(iii) Trees forked below breast height should be
with desired accuracy. Then mathematical models/
treated as a double stem i.e. two separate trees.
procedures are used to convert the known
measurements of the parts to estimate the parameter (iv) Trees forked above breast height should be
of interest (e.g. tree biomass carbon in the present treated as a single stem and measured according
case). to the position of the tree on ground or hills.
(v) Trees forking at breast height or slightly above

20 | Indian Council of Forestry Research and Education

 >> Laying out of Sample Plots in the Field and Collection of Data

are measured at the point of minimum diameter standardization and comparability of records.
below the fork. Normally, measurement is made above the
(vi) Coppice crops should be measured from ground buttress/fluting. Where this extends well up the
level, not from the stool level. bole, an arbitrary height is specified, e.g. 3 m
above ground (Figure 9).
Besides above, following precautions should also be
taken for proper and accurate measurements. Diameter at breast height (DBH) of tree can be
(i) The loose mounds of soil and litter should be calculated from CBH by using following formula:
displaced and cleared. DBH = CBH/
(ii) The vines, moss, loose bark and other loose
material at breast height should be removed. Where,
(iii) The breast height should be fixed by using a fixed DBH = Diameter at breast height
height stick of 1.37 m. CBH = Circumferance at breast height
(iv) Measure at right angles to the stem axis. Keep  = pie (value of  = 3.14)
tapes taut. Tree Height Measurement: Height of a tree is an
(v) Special attention should be placed for important characteristic for measuring the total
buttressing and fluting situations to ensure amount of wood contained in tree. It is the vertical

Fig. 9 : Tree CBH measurement under different situations at 1.37 m

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Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

distance from ground level to the highest given point Procedure

on the tree known as tree top. Identifying actual tree
(i) Cut all the shrubs from the plot from ground level
top and the fact that the tree top may not be directly
over the base of the tree are main sources of error for (ii) Fresh weight of the harvested shrubs from the
tree height measurements. Therefore, the concept of shrub plot should be measured using a portable
merchantable tree heights is adopted with the view of weighing machine
utilization perspective. It is the height of the tree (or the (iii) Collect the 200 gm fresh sample and pack in the
length of trunk) up to which a particular product may be poly bags to be carried to the laboratory for
obtained. The height can be measured by specially further analysis of dry weight
designed instruments specifically for tree-height Tree Regeneration/Sapling
measurements such as clinometers, altimeters or
hypsometers. Height can also be measured through In 3 m × 3 m plot, CBH all the saplings having CBH <10
ocular estimate, non-instrumental, (shadow method, cm should be measured. Biomass equations developed
single pole method). by FSI to estimate the biomass of the tree having a
diameter <10 cm should be used to calculate the
Measuring tree parameters carbon stocks in saplings.
(i) Walk around the tree and find the best location
Herb Sampling
to view the top of the tree
(ii) Stand far enough from the tree so that the top of Sampling of the herbs is done by laying out of 1m ×1 m
the tree is less than 90 degrees above the line of plot using destructive sampling.
sight Procedure
(iii) Always stand up-slope of the tree. Standing
down-slope of the tree should only take place Species name and number of each herb should be
when no other option exists recorded in the format.
(iv) Measure height of all the trees (i) Harvest all the herbs in the plot of 1m ×1m
(v) Follow the instructions provided by the (ii) Fresh weight of the harvested herbs/grasses
manufacturer of the instruments should be recorded through portable electronic
weighing machine
(vi) Place chalk mark on the tree to indicate that the
tree has been measured (iii) A small sample of known quantity should be
properly packed and brought to the laboratory
(vii) All trees should be tagged with the placement of
for further dry weight estimation
an aluminum numbered tag and nail
(viii) Record species name with the local name and Litter Sampling
the associated CBH and height into the format
Litter sample should be collected from 3 m × 3 m plot
(ix) When all of the trees in the plot have been from all four corners.
measured, there should be a check to see that all
of the trees have been measured Procedure

Shrub Sampling (i) Collect all the litter in the sample plot after
enumeration of regeneration, shrubs, herbs etc.
Four quadrats of 3 m × 3 m should be laid at 30 m from Litter contains all dead plant material that
the center of the main plot (0.1 ha). The sample of every includes fallen leaves (fresh, dry, semi or partially
shrub should be collected and data from all the quadrats decomposed leaves), fruit, flower, twigs, bark
should be recorded in the data collection form. etc.

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 >> Laying out of Sample Plots in the Field and Collection of Data

(ii) Record the fresh weight of the total litter soil sample will be kept in a polythene bag and tightly
collected. closed and properly labeled.
(iii) Take the 200 gm sample of the litter. Concept of Bulk Density of Soil: Bulk density of the
(iv) Sample should be properly marked and packed soil is defined as the dry weight of soil per unit volume
for laboratory analysis for determination of dry of the soil. It is required to convert between volume and
weight. weight of the soil. Information on bulk density is
Dead wood sampling required for determining soil organic carbon content
per unit area. Collection of soil sample for bulk density
All dead wood above 5 cm diameter should be recorded estimation is done in 1 m × 1 m plot. A core sampler of
in 5 m × 5 m plot. known volume (bulk density core sampler) is inserted in
soil between 0-10 cm depth with the help of hammer,
Soil Sampling
up to the top of the core. Remove the core carefully so
For collecting data on soil organic carbon, forest floor that soil inside the core may not drop down. Collect the
should be swept and a pit of 30 cm × 30 cm × 30 cm entire soil in a polythene bag and proper label should
should be dug at the center of 1 m × 1 m plot at NE and be fixed on the sample. Repeat this exercise again in the
SW corner of the main 0.1 ha plot. A composite sample soil 10-20 cm and 20-30 cm depth and samples should
of soil weighing 200 gm should be kept for soil organic be kept in polythene bags with proper labelling for
carbon using Walkley and Black (1934) method. The further laboratory analysis.

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 6 FOREST CARBON
STOCKS ESTIMATION

Estimation of Tree Biomass leaves). Biomass can be measured directly or through

estimation functions.
Biomass is defined as the total amount of living organic
matter (above ground and below ground) in trees. It is Biomass by Direct Measurement: Direct
generally expressed on oven-dry weight basis. Carbon measurement of biomass involves felling, dissecting
is one of the most abundant chemical elements on and weighing different components of tree. Stratified
earth and is present in all living beings. It is also a tree technique method is normally used for biomass
naturally occurring component of earth’s atmosphere. estimation by harvesting the sample trees, for which
Denoted by the symbol C, carbon is found in large temporary sample plots of different sizes are laid out
quantities in the leaves, branches, stems and roots of according to the size of the area in the forest. The
trees. In addition to about 50% of water, the biomass of diameter at breast height (DBH) and height of all the
a live tree contains approximately 25% carbon and standing trees in the sample plots covering the entire
remaining 25% is made up of varying amounts of other diameter range of each plot are recorded and
elements including nitrogen (N), phosphorous (P), correlation (diameter & height) is established by having
2
potassium (K), calcium (Ca), magnesium (Mg) and regression coefficient (R ) values. The whole diameter
other trace elements. However, on oven dry weight range is divided into three or four diameter classes. One
basis, the approximate amount of carbon will be 50% mean tree from each diameter class is harvested.
of biomass. All the tree components (leaves, twigs, branches, bark,
More recently there has been increasing interest in bole) including roots are separated immediately after
measurement of the weight that is the biomass of tree. felling and their fresh weights are recorded in the field.
Role of forest has been increasingly recognized as The representative samples of each tree component
most cost-effective option for climate change (100 or 200 gm each of leaves, twigs, branches, bark,
mitigation through carbon captured in biomass and fruits) are taken for oven dry weight estimation in
soils. Furthermore, it is not just stem which are of laboratory.
interest but the whole living biomass components of The bole portion of the sample trees is cut into 2 m long
the tree (bole, bark, branches, twigs, roots and sections (billets) for convenience of weighing.

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Approximately 5 cm broad discs are removed from the coefficient"), and a is the allometric exponent.
base of each billet for estimation of fresh and dry
These equations should be avoided outside the
weights of bark and wood (under bark) and also for the
specified diameter range, otherwise the estimates may
estimation of volume (over bark and under bark) of the
tend to be over estimated.
main bole (upto a diameter limit of 5 cm over bark). The
average diameter of the two successive discs are taken If local allometric equations are available, the biomass
to calculate the volume (over bark and under bark) of can be assessed easily by using them. If such equations
each section and finally the volume of each section is are not available, then it is better to develop site-
added up to get the volume of main bole (over bark and specific allometric equations by collecting data from
under bark). individual trees. Allometric equations for estimation of
biomass have been developed for most Indian tree
The root systems of all the sample trees are completely
species and are available in literature.
excavated excluding their fine rootlets. All possible care
is to be taken to remove the soil particles sticking to the Estimation of Carbon in Different Pools
roots and fresh weight taken immediately to prevent
1) Above ground Biomass
the weight loss. Representative root samples are also
taken for its dry weight estimation. The oven dry weight Aboveground biomass includes live tree biomass and
of each component thus obtained is summed up which non-tree biomass comprising of herbs and shrubs.
is the oven dry weight of the sample tree. The stand
a) Live Tree Biomass: The biomass of tree is usually
biomass (t ha-1) was obtained by multiplying the dry
estimated using volumetric equation. For most of the
weights of the sample trees by the number of trees in
tree species Forest Survey of India has given volumetric
respective diameter classes followed by summation of
equation (FSI, 1996). Volumetric equations relate
biomass in each diameter class.
biomass with the tree height and/or diameter at breast
Biomass Estimation Functions height (DBH) measured 1.37 m above the ground.

Given the difficulty associated with direct measurement Tree Carbon Stock Estimation
of tree biomass, allometric functions allow tree biomass C = [V x D x BEF] x (1+R) x CF
estimation from simply measured characteristics
standing trees. Allometry is the relation between the Where,
size of an organism and the size of any of its parts. V
3 -1
= merchantable volume, m ha , tree volume of a
Allometric equation is usually expressed in power-law stand are normally available in forest inventory
form or logarithmic form and is widely used in many and growing stock data
biological disciplines to describe systematic changes in -3
morphogenesis, physiology, adaptation and evolution. D = basic wood density, tonnes dry matter m ,
Once an allometric equation has been developed, the merchantable volume (Species wise
biomass can be estimated in a forest stand using just information on basic wood density is available
the simple measurements of diameter. The general form in literature)
of allometric equations is usually written as, BEF = biomass expansion factor for conversion of
y=bxa merchantable volume to above ground tree
biomass, dimension less. Biomass expansion
or, in natural logarithmic (ln) terms, factor is defined as: the ratio of total
ln y = ln b + a ln x aboveground oven-dry biomass density of trees
with a minimum dbh of 10 cm or more to the
Where, b is a constant (called the "allometric

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 >> Forest Carbon Stocks Estimation

oven-dry biomass density of the inventoried are oven dried until reaching constant weight. Biomass
volume of the shrubs is extrapolated per hectare basis after
calculation as follows:
R = root-to-shoot ratio (dimension less)
CF = carbon fraction of dry matter (default = 0.47) Dry weight of sample
Shrub Biomass = x Actual fresh weight
Fresh weight of sample
Biomass expansion factors are not available for
majority of Indian tree species. FSI has developed c) Herb Biomass: Samples brought to the laboratory
equation to estimate the biomass of small wood and are oven dried until reaching constant weight. Biomass
foliage of trees having DBH 10 cm or more as well as for of the herbs is extrapolated per hectare basis after
DBH less than 10 cm. FSI method of calculating calculation as follows:
biomass of small wood and foliage having DBH 10 cm
or more and also for the sapling having DBH less than Dry weight of sample
Herb Biomass = x Actual fresh weight
10 cm can be used. Fresh weight of sample

Biomass Estimation 2) Belowground Biomass

i) Estimate the volume of each tree in the sample Belowground biomass (BGB), commonly known as root
plot using volumetric equation (FSI, 1996, see biomass estimated by using a default root-to-shoot
Annex I) ratio value of 0.28 given by IPCC, 2006. This means that
belowground biomass is 28% of the aboveground
ii) Obtain basic wood density/specific gravity for all
biomass.
the tree species encountered in the sampling plot
from the literature (Annex III) 3) Litter Biomass
iii) Multiply the volume of each tree with the Samples brought to the laboratory are oven dried until
respective wood density to obtain the dry weight reaching constant weight. Biomass of the litter is
of each tree extrapolated per hectare basis after calculation as
follows:
iv) Use the Biomass equation for estimation of
biomass of small wood and foliage of trees Dry weight of sample
Litter Biomass = x Actual fresh weight
having DBH 10 cm or more and also for DBH less Fresh weight of sample
than 10 cm (Annex II)
4) Dead Wood Biomass
v) Sum the weight of all the trees of all tree species
for all the sample plots Samples brought to the laboratory are oven dried at 70-
85°C until reaching constant weight. Biomass of the
vi) Extrapolate the weight of each species from the
dead wood is extrapolated per hectare basis after
total sample area (sum of all the plots) to per
calculation as follows:
hectare value (tonnes of biomass per hectare for
each species). Dry weight of sample
Dead Wood Biomass = x Actual fresh weight
Fresh weight of sample
vii) Sum the biomass of each species to obtain the
total biomass of all the trees in tonnes per
Soil Organic Carbon
hectare.
IPCC (2006) recommends soil organic carbon in the
viii) Carbon is 47% of the biomass (IPCC, 2006).
upper 30 cm of soil. This zone is intended to cover the
b) Shrub Biomass: Samples brought to the laboratory actively changing soil carbon pools. Analysis of soil

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samples can be done by utilizing the services of state (ii) 0.5N (approx.) ferrous ammonium sulphate
forest research institutes, research institutes of ICFRE, (196 g of the hydrated crystalline salt per litre
state or central agricultural universities and ICAR containing 20 ml of concentrated sulphuric
institutes and NABL accredited soil testing laboratories acid). This solution is relatively more stable and
etc. convenient to work than that of ferrous
sulphate.
Bulk Density of Soil: The bulk density of the soil
sample can be determined by Core Sampler Method (iii) Diphenylamine indicator: 0.5 g diphenylamine
described by Wilde et al.,1964. Soil samples would be dissolved in a mixture of 20 ml of water and 100
dried in oven at 105°C and measure the weight of ml of concentrated sulphuric acid
sample. Bulk density of soil is calculated as:
(iv) Concentrated sulphuric acid (sp. gr 1.84)
Weight of soil (gm) containing 1.25 percent silver sulphate (in case
Bulk Density = of soils free from chloride use of silver sulphate
Volume of core (cylinder) in cm3
can be avoided)
Estimating Percent Course Fragment in the Soil:
(v) Ortho-phosphoric acid (~5%) and sodium
Percent coarse fragment (>2 mm size) in soils is
fluoride (chemically pure).
estimated to work out the correct soil weight. After
taking the weight of the sample dried for bulk density, Procedure: The soil is ground completely and passed
the same sample is put in the 2 mm sieve, and run the through 2 mm sieve and 1.00 g is placed at the bottom
water over it. Soil particles less than 2 mm will go away of a dry 500 ml conical flask (Corning Pyrex). 10 ml of IN
with water. Take out the fraction from the sieve and dry K2Cr207 is pipetted in and swirled a little. The flask is kept
it and weigh it. Calculate the percentage of the coarse on asbestos sheet. Then 20 ml of sulphuric acid (H2S04)
fragment. (containing 1.25 % Ag2SO4) is run in and swirled again
two or three times. The flask is allowed to stand for 30
Preparation of the Sample for Soil Organic
minutes and thereafter 200 ml of distilled water is
Carbon Estimation: Open the polythene bag and
added. Add 10 ml of ortho-phosphoric acid (H3P04), 0.5
spread the samples on a brown paper sheet in the
g sodium fluoride and 1 ml of diphenylamine indicator.
laboratory. Let the sample air dry at room temperature
The contents are titrated with ferrous ammonium
in the laboratory. Avoid direct sun drying or oven drying.
sulphate solution till the colour flashes from blue-violet
After drying the samples, grind it and sieve it through 2
to green. A combination of H3P04 and sodium fluoride
mm sieve. This sieved sample is used for soil organic
(NaF) is found to give a sharper end point.
carbon estimation.
Simultaneously a blank is run without soil. If more than
Laboratory Analysis of Soil Samples: Soil organic 7 ml of the dichromate solution is consumed the
carbon percentage is estimated by standard Walkley determination must be repeated with a smaller
and Black (1934) method. The organic matter (humus) quantity (0.25-0.5g) of soil.
in the soil gets oxidized by chromic acid (potassium
Calculation: Organic carbon calculated as per
dichromate plus concentrated sulphuric acid) utilizing
following formula:
the heat of dilution of sulphuric acid. The untreated
chromate is determined by back titration with ferrous B–T 100
ammonium sulphate (redox titration). Following Organic carbon (%) = 10 x 0.003 x
B Weight of soil
reagents are required for laboratory analysis:
Where,
(i) 1N potassium dichromate (49.04g of AR grade,
K2Cr207 per liter of solution) B = Volume (in ml) of ferrous ammonium sulphate

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 >> Forest Carbon Stocks Estimation

solution required for blank titration LTC = Litter carbon

T = Volume of ferrous ammonium sulphate DWC = Deadwood carbon
needed for soil sample SOC = Soil organic carbon

Soil Carbon Stock Calculations: Soil stoniness and Quality Assurance/ Quality Control (QA/QC)
land use should be recorded. Soil samples should be
Following points should be taken in consideration for
analyzed for required parameters viz bulk density and
maintaining the QA/QC plan for measurement of forest
organic carbon. Soil organic carbon stock Qi (Mg m-2) in
carbon stocks:
a soil layer or sampling level i with a depth of Ei (m)
depends on the carbon content Ci (g C g-1), bulk density : During all data collection in the field, the field
-3
Di (Mg m ) and on the volume fraction of coarse members responsible for recording must check all
elements Gi, given by the formula (Batjes, 1996): measurements. This is to ensure that the proper
number should be recorded on the data sheet.
Qi=CiDiEi (1-Gi)
: After data is collected at each plot and before
Total Forest Carbon Stocks the field members leave the plot, the team
(i) Carbon contents for trees, shrubs, herbs, dead leader should double check to make sure that all
wood, litter and soil are calculated at plot level. data are to correctly filled.

(ii) The carbon contents for the different : At the end of each day all data sheets must be
components (trees, shrubs, herbs, dead wood, checked by team leader to ensure that all the
litter and soil) within plots are summed up to get relevant information are collected. If for some
carbon stock per plot in tonne C/ha. reason there is some information that seems
odd or is missing, mistakes can be corrected the
(iii) The plot level results are then extrapolated on following day.
per hectare basis. This is carbon density or
carbon stock per unit area, or tonnes of carbon : Samples should be properly labelled so that
per hectare). there must not be any confusion during handling
and testing of the samples.
(iv) The carbon stocks per unit area are then
multiplied by the area of the stratum (e.g. forest : Soil samples should be collected from surface
type/density) to produce an estimate of the total layers (0-30 cm), and should be mixed
carbon stock of the stratum. thoroughly to prepare homogenous soil, and
200 gm of soil sample should be taken for
Carbon contents of different strata are summed to analysis.
produce the total carbon stock of the project area
(Annex - IV). The following equation was used to : Sampling should be done before monsoon (rainy
calculate the total forest carbon stock: season) and early fall period of the year. The
samples should be representative of the
Total Forest landscape.
Carbon Stocks = ABGC+BGBC+LTC+DW+SOC
: Plant residue, roots, debris, etc. from soil sample
Where, should be removed.
ABGC = Aboveground biomass carbon (composed
of aboveground tree biomass, sapling : Collected soil sample (approximately 200 gm)
biomass, herb biomass and shrub biomass) should be kept in labeled bags in cool and dark
places.
BGBC = Belowground biomass carbon

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: Soil samples should be air dried in dark/ shadow laboratory to assure its accuracy 2 to 5%
place and not to be exposed direct sun). Later, variability.
samples should be crushed with wooden
: To ensure that data is entered correctly, the
hammer, and passed through 10-mesh (2 mm)
person entering data (whether during fieldwork
sieve for analysis.
or after a return to the office) should recheck all
: Analytical Grade (AR) regent should be used for the data entered and compare it with the
organic carbon estimation to maintain the original hard copy data sheet before entering
quality. another sheet.
: 30 percent soil samples should be planned to be : After data entry into computer, a random check
repeated in the same laboratory to confirm the should be conducted. Sheets should be selected
standardization and procedure. randomly for re-checks and compared with data
entered. Ten percent of all data sheets should be
: 10 percent of soil samples should be cross
checked for consistency and accuracy in data
checked for their soil organic carbon in other soil
entry.

30 | Indian Council of Forestry Research and Education

 REFERENCES

Batjes, N.H. (1996). Total carbon and nitrogen in the IPCC (2014). Climate Change 2014: Synthesis Report.
soils of the world. European Journal of Soil Contribution of Working Groups I, II and III to
Science, 47:151-163. the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change
FSI (1996). Volume Equations for Forests of India, Nepal
[Core Writing Team, R.K. Pachauri and L.A.
and Bhutan. Forest Survey of India, Ministry of
Meyer (eds.)]. IPCC, Geneva, Switzerland, 151
Environment and Forest, Dehradun.
pp.
FSI (nd). Carbon Stock in India’s Forests. Forest Survey
IPCC (2018). Summary for Policymakers. In: Global
of India, Ministry of Environment and Forest
Warming of 1.5°C. An IPCC Special Report on
Dehradun.
the impacts of global warming of 1.5°C above
FSI (2017). State of Forest Report 2017. Forest Survey of pre-industrial levels and related global
India, Ministry of Environment, Forest and greenhouse gas emission pathways, in the
Climate Change, Dehradun. context of strengthening the global response to
FSI (2019). State of Forest Report 2019. Forest Survey of the threat of climate change, sustainable
India, Ministry of Environment, Forest and development, and efforts to eradicate poverty
Climate Change, Dehradun. [Masson-Delmotte, V., P. Zhai, H.O. Portner, D.
Roberts, J. Skea, P.R. Shukla, A. Pirani, W.
IPCC (2003). Good Practice Guidance for Land Use, Moufouma Okia, C. Pean, R. Pidcock, S.
Land- Use Change and Forestry. Institute for Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I.
Global Environmental Strategies (IGES), Japan. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T.
IPCC (2006). IPCC Guidelines for National Greenhouse Waterfield (eds.)].
Gas Inventories. Prepared by the National IPCC (2019). An IPCC Special Report on climate
Greenhouse Gas Inventories Programme. [(H.S. change, desertification, land degradation,
Engleston, L. Bundia, K. Miwa, T. Nagra and K. sustainable land management, food security,
Tanabe, (eds.)] IPCC-IGES, Japan. and greenhouse gas fluxes in terrestrial

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Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

ecosystems. Summary for Policy Makers. MoEF&CC (2017). Extreme Changes in Climate. Press
https://www.ipcc.ch/site/assets/ uploads Information Bureau, Government of India.
/2019/08/4.-SPM_Approved_ Microsite_ Download from: http://pib.nic.in/newsite/
FINAL.pdf. erelcontent. aspx?relid=159973
MoEF (2004). India's Initial Communication to the MoEF&CC (2018). India: Second Biennial Update
United Nations Framework Convention on Report to the United Nations Framework
Climate Change. Ministry of Environment, Convention on Climate Change. Ministry of
Forest and Climate Change, Government of Environment, Forest and Climate Change,
India. Government of India.
MoEF (2012). India's Second National Communication Rajput, S.S., Shukla, N.K., Gupta, V.K.and Jain,
to the United Nations Framework Convention J.D.(1996). Timber Mechanics: Strength,
on Climate Change. Ministry of Environment Classification and Grading of Timber. Indian
Forest and Climate Change, Government of Council of Forestry Research and Education,
India. Dehradun.
MoEF&CC (2014). National Working Plan Code-2014 Walkley, A. and Black, I.A. (1934). An examination of
(for Sustainable Management of Forests), Degtjareff method for determining soil organic
Published by Forest Research Institute, Dehradun, matter and a proposed modification of the
on behalf of Ministry of Environment, Forest and chromic acid titration method. Soil Science, 37:
Climate Change, Government of India. 29-37.
MoEF&CC (2015). India: First Biennial Update Report Wilde, S.A., Voigt, G.K. and lyer, J.G. (1964). Soil and Plant
to the United Nations Framework Convention Analysis for Tree Culture. Oxford Publishing
on Climate Change. Ministry of Environment, House, Calcutta, India.
Forest and Climate Change, Government of
India

32 | Indian Council of Forestry Research and Education

 GLOSSARY

ArcGIS particulate material. It's the weight of the particles of

the soil divided by the total volume.
ArcGIS is an architecture geographic information
system (GIS) for working with maps and geographic Carbon dioxide equivalent (CO2 eq)
information. It is used for creating and using maps,
To convert carbon in to CO2, the tones of carbon are
compiling geographic data, analyzing mapped
multiplied by the ratio of the molecular weight of
information, sharing and discovering geographic
carbon dioxide to the atomic weight of carbon (44/12).
information, using maps and geographic information in
a range of applications, and managing geographic Carbon pool
information in a database.
A system which has the capacity to accumulate or
Biomass density release carbon.
Amount of vegetation biomass per unit area. Therefore, Carbon sequestration
when using the term “biomass” it refers to the
The removal of carbon from the atmosphere and long-
vegetation biomass density, that is mass per unit area
2
term storage in sinks.
of live or dead plant material. Unit of measure is g/m or
t /ha or multiples. Carbon sink

Biomass It is a carbon reservoir that absorbs more carbon

than release. Forests can act as sink through the
Biomass is defined as mass of live or dead organic
process of tree growth and resultant biological
matter. It includes the total mass of living organisms in a
carbon sequestration. Activities like afforestation &
given area or volume; recently dead plant material is
reforestation (AR), sustainable forest management
often included as dead biomass. The quantity of
(SFM), conservation and enhancement of forests acts as
biomass is expressed on oven dry weight basis.
carbon sinks.
Bulk Density
Carbon source
Bulk Density is a property of soils and other masses of
It is a carbon pool from which more carbon flows out

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than flows in forests can often represent a net source of Net emission reduction
carbon due to the processes of decay, combustion and
Indicates the expected amount of emissions reductions
respiration. Activities like deforestation, forest fire and
in terms of carbon dioxide equivalent (CO2 eq) that will
forest degradation acts as sources of carbon.
be generated by the project activities for a certain
Carbon stock period.
The mass of carbon contained in a carbon pool. Sample size
Carbon Sample size is a count of the individual samples/
observations or number of sample plots.
It is the term used for the C stored in terrestrial
ecosystems, as living or dead plant biomass Sampling
(aboveground and belowground) and in the soil as soil
Sampling is a process used in statistical analysis in
organic carbon.
which a predetermined number of observations are
Coarse fragment taken from a larger population.
Coarse fragment means a rock fragment contained Standard deviation
within the soil which is greater than two millimeters in
The standard deviation (SD) is a statistic that measures
equivalent spherical diameter or which is retained on a
the dispersion of a dataset relative to its mean and is
two millimeter sieve.
calculated as the square root of the variance.
Coefficient of variation
Standard error
The coefficient of variation (CV) is a statistical measure
A measure of the statistical accuracy of an estimate,
of the dispersion of data points in a data series around
equal to the standard deviation of the theoretical
the mean. The coefficient of variation represents the
distribution of a large population of such estimates.
ratio of the standard deviation to the mean, and it is a
useful statistic for comparing the degree of variation Variance
from one data series to another, even if the means are
Variance is the expectation of the squared deviation of
drastically different from one another.
a random variable from its mean.
Composite sample
Wood density
A composite sample is made by thoroughly mixing
Wood density is the ratio of the wood mass to the wood
several grab samples. The whole composite may be
volume at a certain moisture content.
measured or random samples from the composites may
be withdrawn and measured.
Confidence level
Confidence level indicates the probability, with which
the estimation of the location of a statistical parameter
(e.g. an arithmetic mean) in a sample survey is also true
for the population.

34 | Indian Council of Forestry Research and Education

 Annex I PHYSIOGRAPHIC
ZONE WISE VOLUME
EQUATIONS
(Source: FSI. nd. Carbon Stock in India's Forests. Forest Survey of India, Dehradun)

Western Himalayas
S.No. Species Name Volume Equation
1 Abies densa √V = -0.084305 + 3.060072 D
2 Abies pindrow V = 0.293884 - 3.441808 D + 15.922114 D2
3 Picea smithiana √ = 0.20050 + 4.58840 D -1.42603 √D
4 Acacia catechu V = 0.02384 - 0.72161 D + 7.46888 D2
5 Acer sp. √V =-0.10851 + 3.04250 D
6 Lyonia ovalifolia V = 0.03468 - 0.56878 D + 4.72282 D2
7 Mallotus philippinensis V = 0.14749-2.87503 D + 19.61977 D2-19.11630 D3
8 Pinus wallichiana V/D2 = 0.213315/D2 + 12.631292 - 2.519227/D
9 Pinus roxburghii √V = 0.05131 + 3.9859 D -1.0245 √D
10 Quercus floribunda V/D2 = 0.0988/D2 -1.5547/D + 10.1631
11 Quercus incana √V = 0.240157 + 3.820069 D -1.394520 √D
12 Quercus semecarpifolia V/D2 = 0.0988/D2 -1.5547/D + 10.1631
13 Rhododendron arboreum V = 0.06007 - 0.21874 √D + 3.63428 D2
14 Shorea robusta V/D2 =0.1919/D2 - 2.7070/D + 11.7563
15 Taxus baccata V = 0.04430 - 0.84266 D + 6.36239 D2 + 2.27556 D3
16 Tectona grandis V/D = 0.00341/D-0.65623 + 7.881 D

Eastern Himalayas

S.No. Species Name Volume Equation

1 Alnus nepalensis V/D2 = 0.06674/D2 - 0.02039/D + 0.001559 (dia D is in cm)
2 Castanopsis indica √V = -0.07109 + 2.99732 D - 0.26953 √D
3 Castanopsis sp. V = 0.05331 - 0.87098 D + 6.52533 D2 + 1.74231 D3
4 Cinnamomum sp. V = 0.10970 - 0.88666 D + 6.09700 D2 -1.62672 D3
5 Ficus sp. √V = 0.03629 + 3.95389 D - 0.84421 √D
6 Macaranga sp. √V = -0.07109 + 2.99732 D - 0.26953 √D
7 Machilus sp. V/D2 = 4.84009-0.02402/D2

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.
8 Michelia sp. V = 0.23057 - 3.51494 D + 17.62619 D2
9 Quercus sp. V/D2 = 5.09470 + 0.00563/D2
10 Symplocos lucida V = -0.03754 + 0.000587 D2 (dia D is in cm)
11 Terminalia myriocarpa V = -0.096981 + 0.001065 D2 (dia D is in cm)
North-Eastern Ranges
S.No. Species Name Volume Equation
1 Albizzia sp. √V = -0.07109 + 2.99732 D - 0.26953 √D
2 Bauhinia sp. √V = -0.07109 + 2.99732 D - 0.26953 √D
3 Callicarpa arborea √V = -0.04506 + 2.33446 D
4 Castanopsis hystrix Syn. C. tribuloides √V = 0.34640 + 3.99269 D - 1.64666 √D
5 Castanopsis sp. V = 0.05331 - 0.87098 D + 6.52533 D2 + 1.74231 D3
6 Dysoxylum gotadhora Syn. D. binectariferum √V = -0.07109 + 2.99732 D - 0.26953 √D
7 Eugenia sp. V = -0.02792 + 0.92933 D - 5.56465 D2 + 25.77488 D3
8 Ficus sp. √V = 0.03629 + 3.95389 D - 0.84421 √D
9 Gmelina arborea V = 0.01156 + 0.21230 D + 5.10448 D 2
10 Macaranga sp. √V = -0.07109 + 2.99732 D - 0.26953 √D
11 Schima wallichii √V = -0.07109 + 2.99732 D - 0.26953 √D
12 Stereospermum tetragonum Syn. S. personatum √V = 0.49746 + 5.98454 D - 2.84986 √D
13 Syzygium cumini √V = -0.05923 + 2.33654 D
14 Tectona grandis √V = -0.07109 + 2.99732 D - 0.26953 √D
Equation for rest of species V = 0.15958- 1.57976 D + 8.25014 D2 - 0.48518 D3

Northern Plain
S.No. Species Name Volume Equation
1 Acacia catechu V/D2 = 0.16609/D2- 2.78851/D + 17.22127 - 11.60248 D
2 Aegle marmelos V/D2 = 0.16609/D2- 2.78851/D + 17.22127 - 11.60248 D
3 Bombax ceiba V/D2 = 0.18573/D2 - 2.85418/D + 15.03576
4 Butea monosperma √V = -0.24276 + 2.95525 D
5 Dalbergia sissoo V/D2 = 0.00331/D2 + 0.000636 (dia D is in cm)
6 Diospyros melanoxylon V = 0.024814-0.578532 D + 6.11017 D 2
7 Diospyros sp. V/D = 0.06206/D - 1.43609 + 9.778164 D
8 Ehretia laevis V/D2 = 0.16609/D2- 2.78851/D + 17.22127 - 11.60248 D
9 Eucalyptus sp. V = 0.02894 - 0.89284 D + 8.72416D2
10 Holarrhena pubescens Syn. H. antidysenterica V = 0.17994 - 2.78776 D + 14.44961 D 2
11 Lagerstroemia parviflora V = 0.10529 - 1.68829 D + 10.29573 D 2
12 Mallotus philippinensis V = 0.14749 - 2.87503 D + 19.61977 D 2- 19.11630 D3
13 Shorea robusta √V = 0.16306 + 4.8991 D - 1.57402 “D
14 Syzygium cumini V = 0.08481 - 1.81774 D + 12.63047 D 2- 6.69555 D3
15 Tectona grandis V = 0.08847 - 1.46936 D + 11.98979 D 2+ 1.970560 D3
16 Terminalia tomentosa V/D2 = 0.18149/D2 - 2.85865/D + 18.60799
17 Mallotus polycorpus Syn. Trewia nudiflora W = -0.45312 - 0.41426 D + 2.10913 √D
18 Zizyphus mauritiana V = 0.027354 + 4.663714 D 2

Eastern Plain

S.No. Species Name Volume Equation

1 Amoora sp. √V = 0.00905 + 3.7648 D - 0.64993 √D
2 Aglaia spectabilis Syn. Amoora wallichii √V = 0.00905 + 3.7648 D - 0.64993 √D

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3 Careya arborea √V = -0.07109 + 2.99732 D - 0.26953 √D

4 Castanopsis sp. V = 0.05331 - 0.87098 D + 6.52533 D2 + 1.74231 D3
5 Dillenia pentagyna √V = 0.31202 + 4.75915 D -1.83940 √D
6 Lagerstroemia parviflora V = 0.11740-1.58941 D + 9.76464 D2
7 Lagerstroemia speciosa V = 0.11740-1.58941 D + 9.76464 D2
8 Schima wallichii V = 0.27609 - 3.68443 D + 15.86687 D2
9 Shorea robusta V/D2 = 0.00389/D2-0.27516/D + 6.90733
10 Tectona grandis √V = -0.07109 + 2.99732 D-0.26953 √D
11 Terminalia bellirica V = 0.26454 - 3.05249 D + 12.35740 D2
12 Terminalia tomentosa V/D2 = 0.022389/D2-0.84158/D + 9.4721
13 Mallotus polycarpa Syn.Trewia nudiflora V = 0.0549-0.0131 D +0.001 D2 (diaDincm)
14 Wrightia arborea Syn. W. tomentosa √V = 0.23229 + 4.41646 D -1.55989 √D
Western Plain
S.No. Species Name Volume Equation
1 Acacia catechu V = -0.02471 +0.16897 D + 1.12083 D 2 + 2.9328 D3
2 Acacia ferruginea √V = -0.00142 + 2.61911 D - 0.54703 √D
3 Acacia lenticularis √V = -0.00142 + 2.61911 D - 0.54703 √D
4 Acacia sp. √V = -0.00142 + 2.61911 D - 0.54703 √D
5 Anogeissus pendula V/D2 = 0.00085/D2 - 0.35165/D + 4.77386 - 0.90585 D
6 Bauhinia sp. √V = -0.07109 + 2.99732 D - 0.26953 √D
7 Boswellia serrata √V = -0.11629 + 2.4254 D
8 Butea monosperma √V = -0.24276 + 2.95525 D
9 Diospyros melanoxylon V = 0.15581 -2.2075 D + 9.17559 D2
10 Lannea coromandelica V = -0.00146 - 0.39953 D + 5.33895 D2
11 Manilkara zapota Syn. M. achras V = 0.0245 - 0.00497 D + 0.000719 D 2 (dia D is in cm)
12 Wrightia tinctoria V = 0.028917+ 7.777047 D 3
Equation for rest of species V = 0.081467 -1.063661 D + 6.452918 D 2

Central Highlands
S.No. Species Name Volume Equation
1 Acacia catechu V = -0.02471 + 0.16897 D + 1.12083 D2 + 2.9328 D3
2 Acacia lenticularis/ Leucaena leucocephala √V = -0.00142 + 2.61911 D - 0.54703 √D
3 Aegle marmelos V/D2 = 0.16609/D2- 2.78851/D + 17.22127 - 11.60248 D
4 Anogeissus latifoiia √V = -0.20236 + 3.13059 D
5 Anogeissus pendula V/D2 = 0.00085/D2 - 0.35165/D + 4.77386 - 0.90585 D
6 Boswellia serrata √V = -0.1503 + 2.79425 D
7 Buchanania cochinchinensis Syn. B.lanzan V = 0.031 - 0.64087 D + 6.04066 D2
8 Butea monosperma √V = -0.24276 + 2.95525 D
9 Chloroxylon swietenia V =-0.003156 + 2.043969 D2
10 Diospyros melanoxylon V = 0.15581 - 2.2075 D + 9.17559 D2
11 Lagerstroemia parviflora V = 0.10529 - 1.68829 D + 10.29573 D2
12 Lannea coromandelica V/D2 = 0.14004/D2- 2.35990/D + 11.90726
13 Madhuca longifolia V = 0.063632 + 5.355486 D3
14 Miliusa tomentosa √V = 0.66382 + 7.03093 D - 3.68133 √D
15 Mitragyna parviflora V/D2 = 0.099768/D2 - 1.744274/D + 10.086934
16 Tectona grandis √V = -0.405890 + 1.98158 D + 0.987373 √D
17 Terminalia crenulata / T. tomentosa √V = -0.203947 + 3.159215 D
18 Wrightia tinctoria √V = 0.050294 + 3.115497 D - 0.687813 √D
19 Ziziphus xylopyrus V = 0.027354 + 4.663714 D2
Equation for rest of species √V = -0.153973 + 2.724109 D

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North Deccan
S.No. Species Name Volume Equation
1 Acacia catechu V = 0.04235 - 0.74240 D + 7.26875 D2
2 Aegle marmelos V = 0.119- 1.768 D + 9.258 D2
3 Anogeissus latifolia V =-0.061856 + 7.952136 D 2
4 Bauhinia dicvaricata Syn. B. retusa/ B. variegata V = -0.0236 + 0.3078 D + 1.2361 D 2
5 Buchanania cochincinensis Syn. B. latifolia/ B. lanzan V = -0.00767 + 0.2654 D + 1.0383 D 2 + 7.527 D3
6 Butea monosperma Syn.Butea frondosa V = -0.032-0.0619 D + 7.208 D2
7 Chloroxylon swietenia V = 0.0242 - 0.6689 D + 5.2777 D2
8 Cleistanthus collinus √V = -0.07324 + 2.187427 D
9 Diospyros melanoxylon V/D = 0.033867/D - 0.975148 D + 8.255412 D
10 Gardenia resinifera Syn. V = 0.078- 1.188 D + 6.751 D2
G.turgida Syn.G .lucida/ G. latifolia
11 Lagerstroemia parviflora V/D2 = 0.06466/D2 - 1.371984/D + 9.629971
12 Lannea coromandelica V = 0.093318 - 1.531417 D + 9.011590 D 2
13 Madhuca latifolia V = 0.074069 - 1.230020 D + 7.726902 D 2
14 Memecylon edule V = 0.103- 1.709 D + 9.692 D2
15 Miliusa tomentosa Syn. Saccopetalum tomentosum √V = 0.66382 + 7.03093 D - 3.68133 √D
16 Syzygium cumini V = 0.2736 - 3.377 D + 12.959 D2
17 Tectona grandis √V = -0.106720 + 2.562418 D
18 Terminalia crenulata/ T. tomentosa V/D2 = 0.048532/D2 - 1.05615/D + 8.204564
19 Wrightia tinctoria V = -0.009510 + 4.149345 D 2
20 Ziziphus xylopyrus V = -0.0257 + 0.2313 D + 1.4794 D 2

East Deccan

S.No. Species Name Volume Equation

1 Anogeissus latifolia V/D2 = -0.02958/D2 + 8.05003
2 Boswellia serrata V = 0.36432 - 1.32768 √D + 9.48471 D2
3 Bridelia retusa Syn. B. Squamosa √V = 0.1162 + 4.12711 D- 1.085085 √D
4 Buchanania cochincinensis Syn. B. latifolia/
B. lanzan V = 0.031 - 0.64087 D + 6.04066 D2
5 Butea monosperma Syn. Butea frondosa W = -0.24276 + 2.95525 D
6 Chloroxylon swietenia V =-0.003156 + 2.043969 D2
7 Cleistanthus collinus V = 0.030925 - 0.567037 D + 5.709471 D2
8 Dalbergia lanceolaria Syn. D. paniculata √V = 0.76896 + 7.31777 D - 4.01953 √D
9 Diospyros melanoxylon V = 0.12401 - 2.00966 D + 10.87747 D2
10 Diospyros species V = 0.12401 - 2.00966 D + 10.87747 D2
11 Phyllanthus emblica Syn. Emblica officinalis V = -0.022635 + 4.889163 D2
12 Lagerstroemia parviflora V = 0.06913 - 1.37605 D + 11.89119 D2
13 Lannea coromandelica Syn. Lannea grandis V = 0.057424 - 1.153088 D + 8.542648 D2
14 Madhuca longifolia V = -0.00092 - 0.55547 D + 7.34460 D2
15 Pterocarpus marsupium V/D2 = -0.04659/D2 + 8.06901
16 Shorea robusta V = 0.05823 - 1.22994 D + 10.51982 D2
17 Tectona grandis V/D2 = 0.045181/D2 - 0.91863/D + 8.18261 + 1.95661 D
18 Terminalia crenulata / T. tomentosa V = 0.05061 - 1.11994 D + 8.77839 D2
19 Ziziphus xylopyrus V = 0.027354 + 4.663714 D2
Equation for rest of species V/D = 0.088074/D - 1.449236 + 8.760534 D

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 >> Annex I : Physiographic Zone Wise Volume Equations

South Deccan
S.No. Species Name Volume Equation
1 Acacia auriculiformis √V =-0.00142 + 2.61911 D-0.54703 “D
2 Albizia amara √V =-0.07109 + 2.99732 D - 0.26953 “D
3 Anogeissus latifolia V = 0.289 - 2.653 D + 11.771 D 2
4 Chloroxylon swietenia V = -0.0532 D + 3.2378 D2
5 Dalbergia lanceolaria Syn. D. paniculata V = 0.18945 - 2.46215 D + 10.54462 D 2
6 Eucalyptus species V = 0.02894 - 0.89284 D + 8.72416 D2
7 Hardwickia binata V = 0.063632 + 5.355486 D 3
8 Lagerstroemia parviflora V = 0.066188 - 1.334512 D + 9.403257 D 2
9 Lannea coromandelica Syn. Lannea grandis V = 0.091153- 1.66153 D + 10.24624 D 2
10 Tectona grandis V = -0.2414 + 2.8458 D - 5.5816 D2 + 14.816 D3
11 Terminalia crenulata / T. tomentosa V = 0.051812 - 1.076790 D + 7.991280 D 2
12 Terminalia paniculata V = 0.13100- 1.87132 D + 9.47861 D2
13 Wrightia tinctoria √V = 0.050294 + 3.115497 D - 0.687813 √D
Equation for rest of species V = 0.088183 - 1.490948 D + 8.984266 D 2

Western Ghats

S.No. Species Name Volume Equation

1 Acacia mearnsii √V = -0.143393 + 3.040067 D
2 Acacia melanoxylon √V = -0.00142 + 2.61911 D - 0.54703 √D
3 Acrocarpus fraxinifolius V/D2 = -0.0941/D2 + 0.00097 (dia D is in cm)
4 Anogeissus latifolia V = 0.030502 -1.105937 D + 12.261268 D 2
5 Aporosa cardiosperma Syn. A. lindleyana V = 0.1009 - 1.4613 D + 8.0557 D2
6 Artocarpus heterophyllus Syn. Artocarpus integrifolia V = 0.076- 1.319 D + 11.370 D2
7 Durio ceylanicus Syn. Cullenia excelsa V = 16.792 D2.7168
8 Holarrhena pubescens Syn. H. antidysenterica V = 0.17994 - 2.78776 D + 14.44961 D 2
9 Lagerstroemia lanceolata/ L. microcarpa V = 0.23839 - 2.48071 D + 10.14106 D 2
10 Macaranga peltata √V = -0.07109 + 2.99732 D - 0.26953 √D
11 Myristica malabarica V/D2 = 0.00085/D2 - 0.35165/D + 4.77386 - 0.90585 D
12 Olea dioica V = -0.03001 + 5.75523 D2
13 Palaquium ellipticum V = -0.0929 + 0.0122 D + 0.0001 D 2 + 0.00002 D3
(dia D is in cm)
14 Pinus patula √V = -0.200251 + 2.927166 D
15 Schleichera trijuga / S. oleosa V/D2 = 0.016042/D2 - 0.49647/D + 6.2214
16 Syzygium cumini Syn. S. jambolanum Syn. Eugenia √V = 0.30706 + 5.12731 D - 2.09870 √D
jambolana
17 Tectona grandis √V = -0.405890 + 1.98158 D + 0.987373 √D
18 Terminalia crenulata/ T. tomentosa √V = -0.203947 + 3.159215 D
19 Terminalia paniculata V = 0.13100- 1.87132 D + 9.47861 D 2
20 Xylia xylocarpa √V = 0.01631 + 2.20921 D

Eastern Ghats
S.No. Species Name Volume Equation
1 Albizia amara √V = -0.07109 + 2.99732 D - 0.26953 √D
2 Albizzia sp. √V = -0.07109 + 2.99732 D - 0.26953 √D
3 Anogeisus latifolia V = 0.13928 - 2.87067 D + 20.22404 D2 - 13.80572 D3
4 Bridelia retusa V/D = 0.035142/D - 0.839708 + 8.157614 D
5 Buchanania cochinchinensis Syn. B. logeV = 2.2491 + 2.5206 log eD
latifolia Syn. B. lanzan

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6 Chloroxylon swietenia V =-0.003156 + 2.043969 D 2

7 Cleistanthus collinus √V = 0.12956 + 3.7819 D- 1.04671 √D
8 Diospyros melanoxylon √V = 0.06728 + 4.06351 D - 0.99816 √D
9 Grewia tilifolia logeV = 2.2491 + 2.5206 log eD
2
10 Lannea coromandelica V = 0.057424 - 1.153088 D + 8.542648 D
11 Pterocarpus marsupium √V = -0.16276 + 2.82002 D + 0.04034 √D
12 Semecarpus anacardium √V = 0.07109 + 2.99732 D - 0.26953 √D
13 Shorea robusta √V = 0.19994 + 4.57179 D- 1.56823 √D
14 Syzygium cumini √V = 0.30706 + 5.12731 D - 2.09870 √D
15 Tectona grandis V/D2 = 0.12591/D2 - 2.45212/D + 16.52336 - 7.57135 D
16 Terminaiia crenulata/T. tomentosa V = 0.05061 - 1.11994 D + 8.77839 D2
17 Xylia xylocarpa V = 0.098- 1.52 D + 8.963 D2

West Coast

S.No. Species Name Volume Equation

1 Acacia catechu V = -0.048108 + 5.873169 D 2
2 Anogeissus latifolia √V = -0.357373 + 2.430449 D + 0.794626 √V
3 Bombax ceiba V/D2 = 0.136196/D2 - 2.07674/D + 10.1566
4 Boswellia serrata √V = -0.188655 + 3.021335 D
5 Bridelia retusa V/D = 0.035142/D - 0.839708 + 8.157614 D
6 Butea monosperma Syn. Butea frondosa V/D2 = 0.136196/D2 - 2.07674/D + 10.1566
7 Careya arborea √V = -0.23738 + 2.33289 D + 0.48512 √D
8 Dalbergia latifolia √V = -0.144504 + 2.943115 D
9 Garuga pinnata V/D = 0.077965/D - 1.481043 + 9.797028 D
10 Grewia tilifolia V = 0.018620 + 13.916741 D 3
11 Lagerstroemia lanceolata Syn. L. microcarpa V = 0.177-1.817 D + 9.285 D2
12 Lannea coromandelica Syn. Lannea grandis √V = 0.404153 + 5.555051 D - 2.545525 √D
13 Macaranga peltata √V = -0.07109 + 2.99732 D - 0.26953 √D
14 Schleichera trijuga/ S.oleosa V/D2 = 0.016042/D2 - 0.49647/D + 6.2214
15 Tectona grandis √V = -0.405890 + 1.98158 D + 0.987373 √D
16 Terminalia bellirica V = 10.988 D2.6676
17 Terminalia crenulata / T. tomentosa √V = -0.203947 + 3.159215 D
18 Terminalia paniculata V = 0.13100 - 1.87132 D + 9.47861 D2
19 Wrightia tinctoria √V = 0.050294 + 3.115497 D - 0.687813 √D
20 Xylia xylocarpa √V = 0.01631 + 2.20921 D

East Coast

S.No. Species Name Volume Equation

1 Albizia amara √V = -0.07109 + 2.99732 D - 0.26953 √D
2 Anogeissus latifolia V = 0.289 - 2.653 D + 11.771 D 2
3 Boswellia serrata V = 0.36432 - 1.32768 “D + 9.48471 D 2
4 Diospyros species √V = 0.06728 + 4.06351 D - 0.99816 √D
5 Grewia tilifolia logeV = 2.2491 + 2.5206 log eD
6 Lannea coromandelica Syn. Lannea grandis V = 0.057424 - 1.153088 D + 8.542648 D 2
7 Pterocarpus marsupium √V = -0.16276 + 2.82002 D + 0.04034 √D
8 Shorea robusta √V = 0.19994 + 4.57179 D - 1.56823 √D
9 Tectona grandis V = 0.023613 - 0.531006 D + 6.731036 D 2
10 Terminalia crenulata / T. tomentosa V = 0.05061 - 1.11994 D + 8.77839 D2
11 Wrightia tinctoria V = -0.009510 + 4.149345 D 2
Equation for rest of species V/D = 0.088074/D - 1.449236 + 8.760534 D

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 Annex II PHYSIOGRAPHIC
ZONE WISE BIOMASS
EQUATIONS
(Source: FSI. Nd. Carbon stock in India's Forests. Forest Survey of India, Dehradun)

BE1 : Biomass equation used to estimate biomass of small wood of trees having DBH 10 cm or more
BE2 : Biomass equation used to estimate biomass of foliage of trees having DBH 10 cm or more
BE3 : Biomass equation used to estimate biomass of small wood of trees having DBH less than 10 cm
BE4 : Biomass equation used to estimate biomass of foliage of trees having DBH less than 10 cm
D : Diameter at breast height in meter; D1: diameter at breast height in cm; unit of biomass is kg

Western Himalayas

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Eastern Himalayas

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 >> Annex II : Physiographic Zone Wise Biomass Equations

North - Eastern Ranges

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Northern Plain

Mallotus polycarpus Syn. Trewia nudiflora

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 >> Annex II : Physiographic Zone Wise Biomass Equations

Butea monosperma Syn. Butea frondosa

Eastern Plain

flos-reginae

Aglaia spectabilis Syn. Amoora wallichii

Western Plain

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 >> Annex II : Physiographic Zone Wise Biomass Equations

Central Highlands

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North Deccan

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 >> Annex II : Physiographic Zone Wise Biomass Equations

Lannea coromandelica Syn. Lannea grandis

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East Deccan

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 >> Annex II : Physiographic Zone Wise Biomass Equations

South Deccan

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Western Ghats

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 >> Annex II : Physiographic Zone Wise Biomass Equations

Aporosa cardiosperma Syn. A. lindleyana

Eastern Ghats

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West Coast

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 >> Annex II : Physiographic Zone Wise Biomass Equations

B. squamosa

East Coast

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 >> Annex II : Physiographic Zone Wise Biomass Equations

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 Annex III SPECIFIC GRAVITY
OF MAJOR SPECIES

Both density and specific gravity describe mass and may the density of water. Since (at standard temperature
be used to compare different substances. Density is a and pressure) water has a density of 1 gram/cm3, and
property of matter and can be defined as the ratio of since all of the units cancel, specific gravity is usually
mass to a unit volume of matter. It's typically expressed very close to the same value as density (but without any
in units of grams per cubic centimeter, kilograms per units). Information on specific gravity for most of the
cubic meter, or pounds per cubic inch. Indian tree species is available in literature. Therefore,
Specific gravity has been used in place of Wood
Specific gravity is the density of a substance divided by
Density

Species Specific Gravity Species Specific Gravity

(i.e. wt. oven dry/vol. green) (i.e. wt. oven dry/vol. green)

Acacia catechu 0.875 Lannea coromandelica 0.513

Acacia leucophloea 0.660 Madhuca longifolia 0.740
Aegle marmelos 0.754 Ougenia oojeinensis 0.704
Anogeissus latifolia 0.799 Phyllanthus emblica 0.800
Azadirachta indica 0.693 Pterocarpus marsupium 0.649
Bauhinia malabarica 0.670 Saccopetalum tomentosum 0.615
Bridelia retusa 0.499 Semecarpus anacardium 0.640
Buchanania cochinchinensis 0.458 Schleichera oleosa 0.841
Butea monosperma 0.465 Soymida febrifuga 0.963
Casearia tomentosa 0.620 Syzygium cumini 0.647
Cassia fistula 0.746 Tamarindus indica 0.750
Chloroxylon swietenia 0.771 Tectona grandis 0.563
Dalbergia latifolia 0.750 Terminalia bellirica 0.628
Dalbergia paniculata 0.640 Terminalia chebula 0.642
Diospyros melanoxylon 0.678 Terminalia tomentosa 0.730
Gardenia latifolia 0.635 Source: Rajput et al., 1996
Grewia tilifolia 0.679
Haldina cordifolia 0.597
Lagerstroemia parviflora 0.620

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 Annex IV EXAMPLE OF
DATA ANALYSIS
Tree Analysis

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Sapling Analysis

Herb Biomass

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 >> Annex IV : Example of Data Analysis

Shrub Biomass

Aboveground Biomass (AGB) = Tree Biomass + Sapling Biomass + Shrub Biomass + Herb Biomass
AGB (t/ha) = 68.18 +0.23+2.36+0.2810 = 71.05 (t/ha)
Below ground Biomass (BGB)
Below ground biomass = Above ground biomass x Root-Shoot ratio
= 71.05 × 0.28 = 19.89 (t/ha)

Litter Biomass (LB)

Plot No. Sample Actual Sample Sample Biomass Total Biomass Average
No. Fresh Fresh Dry (g/m2) (t/ha) Litter
Weight Weight Weight Biomass
(g) (g) (g) (t/ha)
1 L1 760 100 81.4 618.64 6.19 3.40
L2 240 100 93.2 223.68 2.24
L3 190 100 94.6 179.74 1.80
L4 340 100 99.4 337.96 3.38
Dead Wood (DWB)
Plot Sample Actual Fresh Sample Fresh Sample Dry Biomass Total Average
No. No. Weight Weight Weight (g/m2) Biomass Litter
(g) (g) (g) (t/ha) Biomass
(t/ha)
1 L21 450 100 91.4 411.30 4.11 4.23
L22 280 100 97 271.60 2.72
L23 760 100 92.6 703.76 7.04
L24 340 100 90.2 306.68 3.07
Total Biomass (TB) = AGB+BGB+LB+DWB
= 71.05+19.89+3.40+4.23 = 98.57 (t/ha)
Vegetation Carbon = TB × 0.47
= 98.57 × 0.47 = 46.33 (t/ha)
Soil Organic Carbon
The amount of organic carbon to 30 cm depth in soil with a carbon value of 1.5 % and bulk density of 1.3g/cm3 is :
15 (g C/kg soil) x 1 300 000 (kg soil/ha) = 58.5 t/ha or
1.5 x 1.3 x 30 = 58.5 t/ha
Adjusting for gravel content
If there is gravel in the soil sample, laboratory results will need to be adjusted as this is taken out before carbon
analyses. So if SOC was 1.5% but soil had 25% gravel (by volume) then: 1.5 - (1.5 x 0.25) = 1.1% SOC
Total Carbon = Vegetation Carbon + Soil Organic Carbon
= 46.33 + 58.5= 104.83 (t/ha)

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Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

Annex V FIELD DATA

COLLECTION FORM
FOR FOREST CARBON
STOCKS MEASUREMENT

General Information

Plot Number: Date:

Compartment: GPS Reading
Forest Range:
Slope: Latitude:
Aspect: Longitude:

A. Trees Plot Size: 31.62 m X 31.62 m

S.No. Species Name CBH Height Remark
(Hindi/English/Local/Scientific Name) (cm) (m)
1
2
3
4
5

B. (a) Saplings : North-West Corner Plot Size : 3 m X 3 m

S.No. Species Name CBH Height
(Hindi/English/Local/Scientific Name) (cm) (m)
1
2
3
4
5

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 >> Annex V : Field Data Collection Form for Forest Carbon Stocks Measurement

B. (b) Saplings : North-East Corner Plot Size : 3 m X 3 m

S.No. Species Name CBH Height
(Hindi/English/Local/Scientific Name) (cm) (m)
1
2
3
4
5

B. (c) Saplings : South-East Corner Plot Size : 3 m X 3 m

S.No. Species Name CBH Height
(Hindi/English/Local/Scientific Name) (cm) (m)
1
2
3
4
5

B. (d) Saplings : South-West Corner Plot Size : 3 m X 3 m

S.No. Species Name CBH Height
(Hindi/English/Local/Scientific Name) (cm) (m)
1
2
3
4
5

C. (a) Shrubs : North-West Corner Plot Size : 3 m X 3 m

S.No. Species Name Fresh Weight Sample Fresh Sample Code
(Hindi/English/Local/Scientific Name) (gm) Weight (gm)
1
2
3
4
5

C. (b) Shrubs : North-East Corner Plot Size : 3 m X 3 m

S.No. Species Name Fresh Weight Sample Fresh Sample Code
(Hindi/English/Local/Scientific Name) (gm) Weight (gm)
1
2
3
4
5

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Resource Manual
Measurement of Forest Carbon Stocks for Capacity Building of State Forest Departments

C. (c) Shrubs : South-East Corner Plot Size : 3 m X 3 m

S.No. Species Name Fresh Weight Sample Fresh Sample Code
(Hindi/English/Local/Scientific Name) (gm) Weight (gm)
1
2
3
4
5

C. (d) Shrubs : South-West Corner Plot Size : 3 m X 3 m

S.No. Species Name Fresh Weight Sample Fresh Sample Code
(Hindi/English/Local/Scientific Name) (gm) Weight (gm)
1
2
3
4
5

D. (a) Herbs : North-West Corner Plot Size : 1 m X 1 m

S.No. Species Name Fresh Weight Sample Fresh Sample Code
(Hindi/English/Local/Scientific Name) (gm) Weight (gm)
1
2
3
4
5

D. (b) Herbs : North-East Corner Plot Size : 1 m X 1 m

S.No. Species Name Fresh Weight Sample Fresh Sample Code
(Hindi/English/Local/Scientific Name) (gm) Weight (gm)
1
2
3
4
5

D. (c) Herbs : South-East Corner Plot Size : 1 m X 1 m

S.No. Species Name Fresh Weight Sample Fresh Sample Code
(Hindi/English/Local/Scientific Name) (gm) Weight (gm)
1
2
3
4
5

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 >> Annex V : Field Data Collection Form for Forest Carbon Stocks Measurement

D. (d) Herbs : South-West Corner Plot Size : 1 m X 1 m

S.No. Species Name Fresh Weight Sample Fresh Sample Code
(Hindi/English/Local/Scientific Name) (gm) Weight (gm)
1
2
3
4
5
E. Litter Plot Size : 3m X 3m
a. North West Corner
Fresh Weight (gm) = ................................................................................................................................
Sample Fresh Weight (gm) = ................................................................................................................................
b. North East Corner
Fresh Weight (gm) = ................................................................................................................................
Sample Fresh Weight (gm) = ................................................................................................................................
c. South East Corner
Fresh Weight (gm) = ................................................................................................................................
Sample Fresh Weight (gm) = ................................................................................................................................
d. South West Corner
Fresh Weight (gm) = ................................................................................................................................
Sample Fresh Weight (gm) = ................................................................................................................................

G. Soil Sample
a. Soil Sample for Bulk Density Sample: Tick below after sample collection :
0-10 cm 10-20 cm 20-30 cm

b. Soil Sample for Soil Organic Carbon :

North East Corner: Sample Code .............................................................................................................................
South West Corner: Sample Code ............................................................................................................................

H. (a) Dead Wood: North-East Corner Plot Size : 5 m X 5 m

S.No. Species Name CBH Height
(Hindi/English/Local/Scientific Name) (cm) (m)
1
2
3
4
5
H. (b) Dead Wood: South-West Corner Plot Size : 5 m X 5 m
S.No. Species Name CBH Height
(Hindi/English/Local/Scientific Name) (cm) (m)
1
2
3
4
5

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