International Journal of Agriculture and Forestry 2012, 2(6): 315-323
DOI: 10.5923/j.ijaf.20120206.09
Carbon Inventory Methods in Indian Forests - A Review
Akhlaq A. Wani1,2,* , P. K. Joshi3 , Ombir Singh1 , Rajiv Pandey4
1
Silviculture Division, Forest Research Institute, Dehradun 248006 India
Krishi Vigyan Kendra, Pombay, P.O. Gopal Pora, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, J&K,
192233, India
3
Department of Natural Resources, TERI University New Delhi , 110070, India
4
Department of Forestry, Post Box No: 59, HNB Garhwal University Srinagar Garhwal Uttarahand, 246174
2
Abstract Under the United Nat ions Framewo rk Convention on Climate Change (UNFCC), part icipating countries are
required to report national inventory of greenhouse gas (GHG) emissions or uptake. The current challenge is to reduce the
uncertainties in producing accurate and reliable act ivity data of Carbon (C) stock changes and emission factors essential for
reporting national inventories. Improvements in above ground biomass estimation can also help account for changes in C
stock in forest areas that may potentially participate in the Clean Develop ment Mechanism (CDM), REDD plus and other
initiat ives. The methods adopted for such estimations vary with respect to geography, objective of the study, available
expertise, data and scientific excellence adopted. However the current objectives for such estimates need a unified approach
which can be measurable, reportable, and verifiable. Th is might result to a geographically referenced bio mass density
database for tropical forests that would reduce uncertainties in estimat ing annual bio mass increment and forest
aboveground biomass. In the light of above requirements, this paper intends to present an overview of the methodologies
adopted in India fro m local to country level estimates to assess C sequestration potential in d ifferent forest co mponents.
The paper also discusses remote sensing and Geographical Informat ion System (GIS) in itiat ives taken in this field and the
possibility of adopting an integrated approach for reliable, accurate and cost effective estimates.
Keywords Carbon Inventory Methods, Forests, Bio mass, CDM, REDD Plus
1. Introduction
At the first Conference of Part ies (COP), wh ich took
place in Berlin in 1995, the parties agreed that the specific
commit ments of the convention for the Annex I parties
were not adequate because they were too vague and after
two and a half year of intense negotiations, the Kyoto
protocol was adopted at the third COP on 11 December
1997 in Japan. This protocol is the first international
implementation of a cap and trade scheme. Kyoto Protocol
in its Article 12 of Clean Develop ment Mechanism (CDM)
allo ws Annex I countries to achieve ‘additional’ emissions
and reduction in non-Annex I countries. Forests play a
critical role[1] in stabilizing CO2 concentration for it acts as
significant source of g lobal CO2 and also provides
opportunities to act as sink through soil, vegetation and
wood products.
Intergov ern mental Pan el on Cli mat e Change (IPCC)
provides us the gu idelines for estimation of Carbon (C)
inventory for land use change and forest sector[2] and for
ag ricu ltu re, fo res t and ot h er land uses [3] an d goo d
* Corresponding author:
akhlaqwani@yahoo.co.in (Akhlaq A. Wani)
Published online at http://journal.sapub.org/ijaf
Copyright © 2012 Scientific & Academic Publishing. All Rights Reserved
practice guidance for land use, land use change and forestry
(LULUCF) sector[4]. Adoption of C inventory methods and
guidelines should lead to accurate, reliable and cost
effective estimates of C stocks and changes for a given land
use system and period[5]. Imp lementation of sustainable C
forestry and C storage led forest management in India
warrants for specific research support for status monitoring
and technology generation. Considerable variat ions in terms
of assumptions and estimates on C sequestration call for
standardization of estimation of C estimation emissions for
forest and other resources and land-use changes[6].
Worldwide nu merous ecological studies have been
conducted to assess C stocks based on C density of
vegetation and soils[7,8,9]. The results of these studies are
not uniform and have wide variations and uncertainties
probably due to aggregation of spatial and temporal
heterogeneity and adaptation of different methodologies[10].
Five pools have been identified in Marrakech Accord viz.
above ground biomass, below ground biomass, soil carbon,
dead organic matter and litter. A mong these only those
pools need to be measured and monitored under CDM
which are most likely to be impacted by the pro ject
activities. Various methods are available for estimation of
carbon and flux in these pools but these methods vary on
account of accuracy, precision, cost and scale of applicat ion.
The broad categories of programmes requiring carbon
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Akhlaq A. Wani et al.: Carbon Inventory M ethods in Indian Forests - A Review
inventory[5] are for national g reen house inventory, climate
change mitigation pro jects, Clean Develop ment Mechanism
projects, projects under the global environment facility and
forest grassland and agroforestry development projects.
Hence, it becomes extremely important for a developing
country like India where there is an excellent opportunity of
having 26 million degraded land as a potential storage for
carbon to evolve and refine the methodology as per the
objectives. The present paper gives an insight into the
varied methodologies adopted by different workers as per
their objectives of study for estimations at various levels
related to carbon in India. India having diverse vegetation
coupled with variat ion in climates, the inventory experts
need to exp lore all sources of informat ion fro m all local
sources and create data bases on the basis of inventory
parameters (pools) and factors like growth rates, wood
density etc. to improve the quality of carbon inventory.
2. Biomass Carbon
Bio mass is defined as the total quantity of live and inert
or dead organic matter, above and below the ground,
expressed in tones of dry matter per unit area, such as
hectare. (Bio mass carbon = above ground bio mass carbon +
below ground biomass carbon + dead organic matter).
Above ground biomass is the most important visible and
dominant C pool in forests and plantations, although not in
grasslands and croplands5.
2.1. National Level Es timates
A study was conducted[10] to estimate contribution of
India’s forests from 1995 to 2005 towards C sink using
secondary data of growing stock fro m various sources.
Suitable bio mass increment values (expansion and
conversion for calculating total tree above ground biomass)
and the ratio of below and above ground biomass (for
calculating total tree biomass above and below ground) as
available in different studies covering a range of forest
types of the country were used with a conservative value of
C 40 % and 20 % of mo isture content on dry basis (mcdb)
for realistic estimat ions[11,12].
𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑠𝑠𝑠𝑠𝐺𝐺𝑠𝑠𝑠𝑠 𝑠𝑠𝐺𝐺𝑠𝑠𝑡𝑡𝑡𝑡 =
𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑠𝑠𝑠𝑠𝐺𝐺𝑠𝑠𝑠𝑠 𝑠𝑠𝐺𝐺𝑡𝑡𝑡𝑡 + 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑠𝑠𝑠𝑠𝐺𝐺𝑠𝑠𝑠𝑠 𝐺𝐺𝑠𝑠ℎ𝑡𝑡𝐺𝐺 𝑣𝑣𝑡𝑡𝐺𝐺𝑡𝑡𝑠𝑠𝑡𝑡𝑠𝑠𝐺𝐺𝐺𝐺𝐺𝐺
𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑠𝑠𝑠𝑠𝐺𝐺𝑠𝑠𝑠𝑠 𝑠𝑠𝐺𝐺𝑡𝑡𝑡𝑡 =
𝑣𝑣𝐺𝐺𝑡𝑡𝑣𝑣𝑣𝑣𝑡𝑡 𝑡𝑡𝑎𝑎𝐺𝐺𝑣𝑣𝑡𝑡 𝐺𝐺𝐺𝐺𝐺𝐺𝑣𝑣𝐺𝐺𝑔𝑔 + 𝑣𝑣𝐺𝐺𝑡𝑡𝑣𝑣𝑣𝑣𝑡𝑡 𝑎𝑎𝑡𝑡𝑡𝑡𝐺𝐺𝐺𝐺 𝐺𝐺𝐺𝐺𝐺𝐺𝑣𝑣𝐺𝐺𝑔𝑔
𝑉𝑉𝐺𝐺𝑡𝑡𝑣𝑣𝑣𝑣𝑡𝑡 𝑡𝑡𝑎𝑎𝐺𝐺𝑣𝑣𝑡𝑡 𝐺𝐺𝐺𝐺𝐺𝐺𝑣𝑣𝐺𝐺𝑔𝑔 =
𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑠𝑠𝑠𝑠𝐺𝐺𝑠𝑠𝑠𝑠 𝑠𝑠𝐺𝐺𝑣𝑣𝑣𝑣𝑡𝑡𝐺𝐺𝑠𝑠𝐺𝐺𝑡𝑡𝑡𝑡 ( 𝑎𝑎𝐺𝐺𝑡𝑡𝑡𝑡 𝑣𝑣𝑢𝑢𝑠𝑠𝐺𝐺 10 𝑠𝑠𝑣𝑣) × 𝑡𝑡𝑒𝑒𝑢𝑢𝑡𝑡𝐺𝐺𝑠𝑠𝐺𝐺𝐺𝐺𝐺𝐺 𝑓𝑓𝑡𝑡𝑠𝑠𝑠𝑠𝐺𝐺𝐺𝐺
𝑉𝑉𝐺𝐺𝑡𝑡𝑣𝑣𝑣𝑣𝑡𝑡 𝑎𝑎𝑡𝑡𝑡𝑡𝐺𝐺𝐺𝐺 𝐺𝐺𝐺𝐺𝐺𝐺𝑣𝑣𝐺𝐺𝑔𝑔 =
𝑣𝑣𝐺𝐺𝑡𝑡𝑣𝑣𝑣𝑣𝑡𝑡 𝑡𝑡𝑎𝑎𝐺𝐺𝑣𝑣𝑡𝑡 𝐺𝐺𝐺𝐺𝐺𝐺𝑣𝑣𝐺𝐺𝑔𝑔 × 𝑅𝑅 (𝐺𝐺𝐺𝐺𝐺𝐺𝑠𝑠 𝑠𝑠ℎ𝐺𝐺𝐺𝐺𝑠𝑠 𝐺𝐺𝑡𝑡𝑠𝑠𝐺𝐺𝐺𝐺)
𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑠𝑠𝑠𝑠𝐺𝐺𝑠𝑠𝑠𝑠 𝐺𝐺𝑠𝑠ℎ𝑡𝑡𝐺𝐺 𝑣𝑣𝑡𝑡𝐺𝐺𝑡𝑡𝑠𝑠𝑡𝑡𝑠𝑠𝐺𝐺𝐺𝐺𝐺𝐺 = 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑠𝑠𝑠𝑠𝐺𝐺𝑠𝑠𝑠𝑠 𝑠𝑠𝐺𝐺𝑡𝑡𝑡𝑡 +
𝐺𝐺𝑡𝑡𝑠𝑠𝐺𝐺𝐺𝐺 𝐺𝐺𝑓𝑓 𝐺𝐺𝑠𝑠ℎ𝑡𝑡𝐺𝐺 𝑓𝑓𝐺𝐺𝐺𝐺𝑡𝑡𝑠𝑠𝑠𝑠 𝑓𝑓𝑡𝑡𝐺𝐺𝐺𝐺𝐺𝐺 𝑎𝑎𝐺𝐺𝐺𝐺𝑣𝑣𝑡𝑡𝑠𝑠𝑠𝑠 𝑠𝑠𝐺𝐺 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑠𝑠𝑠𝑠𝐺𝐺𝑠𝑠𝑠𝑠 𝐺𝐺𝑓𝑓 𝑠𝑠𝐺𝐺𝑡𝑡𝑡𝑡
𝐵𝐵𝐺𝐺𝐺𝐺𝑣𝑣𝑡𝑡𝑠𝑠𝑠𝑠 ( 𝑣𝑣𝑠𝑠) = 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑠𝑠𝑠𝑠𝐺𝐺𝑠𝑠𝑠𝑠 𝑠𝑠𝐺𝐺𝑠𝑠𝑡𝑡𝑡𝑡 ( 𝑣𝑣)3 × 𝑣𝑣𝑡𝑡𝑡𝑡𝐺𝐺 𝐺𝐺𝐺𝐺𝐺𝐺𝑔𝑔 𝑔𝑔𝑡𝑡𝐺𝐺𝑠𝑠𝐺𝐺𝑠𝑠𝑑𝑑
𝐶𝐶𝑡𝑡𝐺𝐺𝑎𝑎𝐺𝐺𝐺𝐺 𝑎𝑎𝐺𝐺𝐺𝐺𝑣𝑣𝑡𝑡𝑠𝑠𝑠𝑠 =
𝑎𝑎𝐺𝐺𝐺𝐺𝑣𝑣𝑡𝑡𝑠𝑠𝑠𝑠 × ( 1 − 𝑣𝑣𝑠𝑠𝑔𝑔𝑎𝑎) × 𝑢𝑢𝐺𝐺𝐺𝐺𝑢𝑢𝐺𝐺𝐺𝐺𝑠𝑠𝐺𝐺𝐺𝐺𝐺𝐺 𝐺𝐺𝑓𝑓 𝑠𝑠𝑡𝑡𝐺𝐺𝑎𝑎𝐺𝐺𝐺𝐺 𝑠𝑠𝐺𝐺𝐺𝐺𝑠𝑠𝑡𝑡𝐺𝐺𝑠𝑠
Spatial data bases of climat ic, edaphic, and geomorpholo
gic indices, and vegetation were used to estimate the
potential carbon densities (without human impacts) in
above and below ground biomass of forests in 1980. All
data were p rocessed in GIS environ ment[13]. Land use data
and carbon estimates for South and Southeast Asia were
collected and analysed to help reduce the uncertainty
associated with the release of C in the atmosphere caused
by land use change. The database was developed in Lotus
1-2-3 TM using a sequential bookkeeping model. The
source data were obtained fro m historical and geographical
documents (Fig. 1)[14]. The total amount of C sequestered
in live vegetation of each ecological zone for 1880, 1920,
1950, 1970 and 1980 was calcu lated using the equation as
TC𝐺𝐺 = ∑𝐺𝐺𝑠𝑠 =1 L𝑗𝑗𝐺𝐺 A𝑗𝑗𝐺𝐺 , Where total C stock of vegetation at
time i (TCi) is calculated based on Lji which is the total C
(above and below ground) in vegetation type j at time i. Aji
is the area in vegetation of type j at time i and n is the total
number of land use categories within the zone.
SPREADSHEET A
Input: Land use data from all sources
Output: T ime series (1880, 1920, 1950, 1980) of areas in
official land use categories for single district or division
Land
Use
Model
Carbon
Model
⇓
SPREADSHEET A
Input: sum of spreadsheet A output for all districts or
division in single ecological zones + all information
pertaining to evaluation of land use statistics in
ecological terms.
Output: T ime series of areas translated into ecological
land use categories for single ecological zone.
⇓
SPREADSHEET A
Input: Output of Spreadsheet B for single ecological zone
+ all information needed to estimate maximum C stock
per category and value of multipliers.
Output: T ime series of C stocks for each category in
single zone, estimated C release from live vegetation for
each interval and total period for that zone.
Figure 1. Flow sheet illustrating spreadsheet methodology used in
analysis of changes in land use changes and C (Source: Richards and
Flint)[14]
Accordingly[14], the actual C stock of a given vegetation
class is calculated as the product of its potential maximu m
C stock (M) and two fractional mult ipliers which quantify
the estimated reduction of M by environmental limitat ions
(E) and degradation (D). CPH = M × E × D.
Similarly[15,16] a book keeping model was developed that
tracks the C content of each hectare d isturbed by human
activity.
In another study[17] estimated forest cover, growing
stock and bio mass for the year 1984. This was done at state
level for the entire country using information available fro m
the vegetation maps, thematic maps and ground forest
inventory collected by Forest Survey of India (FSI). For this
purpose all the states and union territories were divided into
grids of 2.50 × 2.50 . Data was collected for parameters
related to growing stock fro m 170000 g rids. The growing
stock of each state was estimated by calculating the nu mber
of grids for each co mbination o f density and forest
composition. The volume per ha (termed as wood volume
factor) for a particu lar co mbination of density and forest
composition was generated using data of forest inventory
International Journal of Agriculture and Forestry 2012, 2(6): 315-323
surveys. Three wood volume factors were calculated for
each stratum and density class for each map sheet for each
state. The estimated volume (or growing stock) was
converted into biomass by using specific gravity[18,19] of
dominant tree species in each grid and C stock was
computer emp loying the formulae, 𝐵𝐵𝐺𝐺𝐺𝐺𝑣𝑣𝑡𝑡𝑠𝑠𝑠𝑠 (𝑠𝑠) = 𝑉𝑉𝐺𝐺𝑡𝑡𝑣𝑣𝑣𝑣𝑡𝑡 𝑣𝑣3 ×
𝑆𝑆𝑢𝑢𝑡𝑡𝑠𝑠𝐺𝐺𝑓𝑓𝐺𝐺𝑠𝑠 𝐺𝐺𝐺𝐺𝑡𝑡𝑣𝑣𝐺𝐺𝑠𝑠𝑑𝑑 and 𝐶𝐶𝑡𝑡𝐺𝐺𝑎𝑎𝐺𝐺𝐺𝐺 ( 𝑠𝑠) = 𝐵𝐵𝐺𝐺𝐺𝐺𝑣𝑣𝑡𝑡𝑠𝑠𝑠𝑠( 𝑠𝑠) × 𝐶𝐶𝑡𝑡𝐺𝐺𝑎𝑎𝐺𝐺𝐺𝐺(%) .
A study was conducted[20] to estimate C flu x through
litter fall in forest plantations in India. Data on 24 species
fro m 82 stands was tabulated so as to cover the entire
country. Mean litter fall (total and alone) fro m the
plantation was computed. A C fraction of 0.45 was used for
converting litter fall to C flu x. Above ground biomass was
recorded at the site for shrubs and grasses whereas standard
relationship was used to record tree biomass at the site in
arid and semi arid areas of Rajasthan and 0.48 part of C was
assumed in vegetation on dry weight basis[21].In another
study CO2 FIX a stand level simu lation model[22] was used
to quantify the carbon storage and sequestration potential of
selected tree species in India using published data on
growth rate and biomass with a carbon factor of 50%.
Allo metric equations[23] (models) have been suggested
for national level studies in estimating Above ground tree
biomass (A GTB) developed[24] on the basis of climate and
forest stand types. Bio mass stock densities are converted to
carbon stock densities using the default carbon fraction[2]
of 0.47. Furthermore root-to-shoot ratio value[25] of 1:5
was suggested to estimate below-ground biomass as 20% of
above-ground tree biomass. Carbon sequestration projected
26] upto year 2050 has been calculated for forestry options
under different land use scenarios in India fro m standing
biomass, wood products and fossil-fuel use and the equation
used is carbon = carbon in standing biomass + carbon in
wood products + carbon in fossil fuel. Carbon in standing
biomass is determined by mult iplying the area of each land
use category by its average biomass and then mu ltiply ing
the sum by the carbon content of bio mass, wh ich is
assumed to be 0.5. If there is an insufficient amount of fuel
wood in the project region, the model auto matically begins
to burn fossil fuel wh ich results in increasing carbon
emissions[26]. The model estimates the amount of carbon
sequestered by approximating land use and relative b io mass
changes in the landscape over time.
317
Survey of India publication[28]. Below ground bio mass
was has been calculated using IPCC default value which is
above ground biomass x 0.27 and carbon[2] sequestered
was obtained after mult iplying the bio mass with 0.45). For
soil organic carbon three samples were taken fro m each
quadrant and samples were collected at the depths of 15 cm,
30 cm and 45 cm and Walkley’s method was used for
estimation of So il Organic Carbon (SOC). A similar
study[29] was carried out to estimate elig ible carbon pools
under CDM fo r med icinal trees of Haryana in which
observations on growth (height, girth and crown cover) of
selected plantation interventions was taken as per the
structured data sheets. For calculation of Mean Annual
Increment (MAI) of plantation intervention on private lands
restricting to bund plantations, it was assumed that the farm
size will be 0.25 ha (50 x 50 m) and 32 trees would be
planted at a spacing of 7 m as co mmonly practiced in that
area. Bio mass expansion factor and wood density have been
used as per good practice guidelines by IPCC. Default value
of 0.27 has been for below ground carbon and SOC has
been calculated as per Walkley’s method. Spreadsheet
model PRO-COMAP was used for data analysis.
2.3. Local Level Es timates
A study[30] was conducted to evaluate C sequestration
through commun ity based forest management in Sambalpur
Forest Division Orissa. Two villages with total area of 200
ha were selected on the basis of number of years for which
the allotted peripheral reserved forests have been protected.
Quadrates were laid and observations were recorded for
girth and height for each species of trees, shrubs and herbs.
The data collected during 1997-98 was used for the
estimation of gro wing stock and other indices. The growing
stock was calculated using the regression equation[31] as
(Standing Woody Bio mass (tonnes/ha) = -1.689 + 8.32 ×
BA).
Sequestration potential of natural forests in seven village
forests of Ch indwara Forest Division o f Madhya Pradesh
was estimated for different density classes using harvested
method of stratified t ree technique. Quadrates were laid and
sample t rees were felled and roots excavated for
determination of above and below ground bio mass. The
whole tree b io mass without foliage was recorded for
different co mponents viz. t wigs, branches, bole and roots
2.2. Regional Level Esti mates
and presented on oven dry weight basis[32]. In a study
Carbon mit igation potential[27] and cost effectiveness of carried out to find[33] C content of some forest tree species
different tree of med icinal importance of Haryana have the plant samples of various parts were subjected to oven
been estimated for a period of 30 years (2008-2038) using drying. Calciu m was estimated by flame photometer and C
spreadsheet model (PRO-COMAP), acrony m, Pro ject was carried out using Walkley and Black’s rap id titrat ion
Based Co mprehensive Mitigation Assessment Process. The method and regression equation method developed between
model uses data on selected carbon pools as collected from Calciu m and C of various tree components. Ash content
the field, viz. above ground biomass, below ground bio mass, method was also used to estimate C. In another study[34] to
soil carbon and woody litter along with data on costs and access carbon sequestration potential under agroforestry in
benefits. Above ground biomass was calculated by laying Rupnagar district o f Punjab PRO-COMAP (Project based
quadrates and Mean Annual Increment (MAI) was Co mprehensive Mitigation Analysis Process) model was
calculated using volume equations as given in Forest used for the period (2005-2030) as also suggested by
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Akhlaq A. Wani et al.: Carbon Inventory M ethods in Indian Forests - A Review
Ravindranath[35] and five sample plots of 0.1 ha each were
selected for measurements. Below ground bio mass was
calculated as AGB × 0.26. Sequestered carbon was
calculated in the model by mult iplying the dry b io mass with
a default value of 0.45. In a study[36] to assess comparison
between different methods for estimation of bio mass in a
forest ecosystem it was concluded that stratified tree
technique is the best but urged to develop estimat ing
equations of wide applicability to obtain reliab le estimates
of stand biomass without destructive sampling.
Carbon allocation in different parts of three year old
agroforestry species was studied[37] adopting destructive
method of sampling. Field measurements taken were fitted
into regression equation with a general form factor of 0.5
regardless of the actual form or taper[38]. The carbon and
nitrogen content percent in each plant component was
estimated on CHNS analyser. Similarly destructive
sampling[39] was adopted to assess carbon sequestration
potential of selected bamboo species of Northeast India.
Total dry bio mass of samp le co mponent was calculated by
mu ltip lying weight of oven dry sample with total fresh
weight of p lant component and divided it by fresh weight of
plant sample co mponent taken. The total oven dry weight of
each component was then multiplied by the total number of
plants in that category. Carbon content was estimated by
indirect method[40] using a factor of 0.48.
3. Remote Sensing and GIS Based
Estimates
A study was conducted on biomass distribution of natural
and plantation forests of humid tropics in northeast India
using GIS and different types of forests were mapped using
IRS LISS III imageries through supervised classification
and a forest type map within the study area was prepared.
Sampling of vegetation in the two forests was carried out by
belt transect method. Because of high species richness in
tropical forests, it is difficu lt to use species-specific
regression models, as used in the temperate zone[41,42,43].
Therefore, mixed species tree biomass regression models
(Table: 1) were used for A GB estimation of natural and
plantation forests[44].
Geospatial technology[50] was used to estimate C stock
in natural forests of Eastern Ghats Tamil Nadu. IRS 1D
LISS III d igital data and Survey o f India topo sheets were
used to prepare the fo rest cover density map and p lot
sampling technique was followed to estimate the stand
density. Volu me of about 1000 trees was estimated using
Smalian’s formu la by Chaturvedi and bi-variate equations
were derived using calculated volu me, girth at breast height
(gbh) and height for different girth class. Vo lu me was
mu ltip lied with wood density to obtain biomass. C was
obtained following standard methodology[51] by with 49.1
as the conversion factor.
On the basis of thematic maps prepared by FSI and
survey done by FSI for forest inventory, growing stock,
ground biomass and C stock was determined[52] for the
assessment years 1979-1981 and 1994-1995 for a particu lar
combination of density and forest composition in Ranchi
district. Estimated volu me of growing stock was converted
to biomass based on specific grav ity[18,19] of dominant
tree species in each grid and dry b io mass was multip lied by
the factor 0.48 for estimating Carbon[40]. Similarly satellite
data was used in a study[53] to estimate carbon pool in
Gov ind Wildlife Sanctuary and National Park to generate
forest type and density maps by visual and digital
interpretation methods. Field measurements of height and
girth were taken to calculate volu me of sample plots of 0.1
ha using the site specific volu me equations provided by FSI.
Vo lu me was mu ltip lied with specific grav ity to obtain
biomass and later the results of bio mass were ext rapolated
in the stratified fo rest type map. Carbon fro m b io mass was
calculated and the min imu m value of 48 % was adopted as
the conversion factor[51]. A ll above ground woody
components have been assumed to have 47-50 % organic
carbon[54].
Table 1. Regression models run to obtain best fit for estimation of
biomass in natural and plantation forests of northeast India
R2
(Natural
Plantations)
Plantati
on
Forests
0.87
0.84
0.8
0.83
0.87
0.84
0.87
0.65
0.88
0.67
0.82
0.76
0.93
0.91
Y= 1.276 + 0.034(D2 ×H)
0.86
0.63
Y= 38.4908 11.7883(D) + 1.1926 D2
0.88
0.85
Model
Regression Equation
FAO. 3.2.3.
(1997)[45]
FAO. 3.2.4.
(1997)[45]
FAO. 3.2.5.
(1997)[45]
Brown et al.
(1989)[46]
Brown et al.
(1989)[46]
Chave et al.
(2001)[47]
Chambers et
al.
(2001)[48]
Brown and
Iverson
(1992)[49]
Brown et
al.[46]
(1989)[50]
Y= 42.69-12.800(D) +
1.242 (D2 )
Y= exp {-2.134+2.530 ×
ln(D)}
Y= 21.297- 6.953 (D) +
0.740 (D2 )
Y= exp[-3.114+0.972 ×
ln(D2 H)]
Y= exp[-2.409+0.952 ×
ln(D2 HS)]
Y= exp (-2.00+2.42) ×
ln(D)
Y= exp[-0.37+0.33 ×
ln(D) + 0.933 ln (D)2 ×
0.122 ln (D) 3 ]
Source: Baishya[44]
Forest Bio mass and net assimilat ion of carbon of Rajaji
National Park Uttar Pradesh (Now Uttarakhand) was
mapped and assessed using IRS-1A and assessed using
IRS-1A, LISS I digital data for the year 1988. The
classified forest types were sub-classified into crown cover
levels of 20 percent interval and calibrated through field
checks. The crown cover for various forest types was
related with the stand biomass (above ground) and the
relationship was used mean bio mass was computed for each
class which when mult iplied with the respective aerial
extent gave total bio mass of the content. Belo w ground
biomass was assumed[55] to be 23 % of the above ground
International Journal of Agriculture and Forestry 2012, 2(6): 315-323
biomass[56].
In a review work[57] bio mass distribution in a forest
ecosystem was described as the function of vegetation type,
its structure and site conditions. Phenology plays an
important role in using satellite data for estimat ing
qualitative and quantitative characters especially in
deciduous vegetation as similarly reported[58] that SAR
(Synthetic Aperture Radar) being sensitive to mo isture,
temperature, branch architecture, b io mass, age classes, girth,
canopy density etc. can provide us with species based forest
stratification in areas with perpetual clouds. Spectral
response modelling[59] was applied to estimate per unit
biomass values of sample plots in homogenous vegetation
strata. The results when ext rapolated to the entire area
generated biomass map of the Madhav National Park. It
was further reported that a combination of various forest
parameters like trunk, branches, basal area, soil etc. show
better relat ionship with b io mass coupled with merged data
of optical (Landsat TM, and IRS LISS II and III) and SA RX band sensors makes way for better enhancement
techniques and mapping.
4. Soil Organic Carbon
After careful co mparison of the different international
standards to be follo wed for forest carbon estimat ion, the
carbon fraction (CF) 0.47 defau lt value[2] is proposed to
convert the biomass value of standing trees into carbon
stock[23]. A study[60] was carried out to estimate soil
organic carbon store in different forests of India for which
map sheets of all the states/UTs of the country were marked
with 2.5’ x 2.5’ (lat itude and longitude) grids. Data on the
extent of forest cover, forest stratum, density and volume
per ha for each grid were collected. The major forest
stratum in grid was marked using thematic maps prepared
by FSI (forests of India have been stratified into 24 species
strata). Grid volu me for a grid was calculated stratum wise.
Map sheet wise addition of growing stock for all the map
sheets falling in a part icular state/UT gives the total
319
estimated growing stock of that state. Soil Organic Carbon
(SOC) values under different forest species in various
locations in India were collected fro m published literature
in different journals, reports, books etc.
Six d ifferent eco zones were selected in arid and semi
arid areas of Gu jarat and Rajasthan and data presented for
only common access resources. Soil samp les were collected
in triplicate fro m each type of land upto 75 cm depth
divided into 0-25, 25-50 and 50-75 cm soil layers and
analysed for SOC[21]. A study recommended[23] the
collection of soil samples at 0-10, 10-20, and 20-30 cm
depths and calculation of carbon stock density[62] as
𝑆𝑆𝑆𝑆𝐶𝐶 = 𝜌𝜌 𝑒𝑒 d 𝑒𝑒 %C , where, SOC = soil organic carbon
stock per un it area[t ha-1 ], 𝜌𝜌 = soil bulk density[g cm-3 ], d
= the total depth at which the sample was taken[cm],
and %C = carbon concentration[%]. Different although not
too different methodologies have been adopted in various
studies[61] and methods vary in the choice of stratification,
measuring carbon pools and values or factors of estimat ion
(Table: 2).
In a study to assess carbon sequestration potential in
Rupnagar district of Punjab[34] samples were drawn fro m
each selected plantation and soil fro m within a depth of 30
cm and soil carbon was analysed by Walkley and Black
rapid titration method[65]. In another study C sequestration
potential in natural forests of Tamil Nadu[50] was studied
using digital data and Survey of India topo sheets and
adopted systematic samp ling technique to collect soil
samples at pre-determined sampling points. Soil samples
were co llected fro m three layers and after analysed using
the equations as:
𝑆𝑆𝑆𝑆𝐶𝐶%
× 𝑠𝑠𝐺𝐺𝐺𝐺𝐺𝐺𝑡𝑡𝑠𝑠𝑠𝑠𝑡𝑡𝑔𝑔 𝜌𝜌𝑎𝑎 ( 𝑣𝑣𝐺𝐺 𝑣𝑣−3 )
100
× 𝑡𝑡𝑡𝑡𝑑𝑑𝑡𝑡𝐺𝐺 𝑔𝑔𝑡𝑡𝑢𝑢𝑠𝑠ℎ ( 𝑣𝑣) × 104(𝑣𝑣2 ℎ𝑡𝑡−1 ) ,
𝑆𝑆𝑆𝑆𝐶𝐶 𝑔𝑔𝑡𝑡𝐺𝐺𝑠𝑠𝐺𝐺𝑠𝑠𝑑𝑑 (𝑣𝑣𝐺𝐺 ℎ𝑡𝑡−1 ) =
Where 𝜌𝜌𝑎𝑎 = bulk density
𝐶𝐶𝐺𝐺𝐺𝐺𝐺𝐺𝑡𝑡𝑠𝑠𝑠𝑠𝑡𝑡𝑔𝑔 𝑎𝑎𝑣𝑣𝑡𝑡𝑠𝑠 𝑔𝑔𝑡𝑡𝐺𝐺𝑠𝑠𝐺𝐺𝑠𝑠𝑑𝑑 (𝑣𝑣𝐺𝐺 𝑣𝑣−3) = 𝑎𝑎𝑣𝑣𝑡𝑡𝑠𝑠 𝑔𝑔𝑡𝑡𝐺𝐺𝑠𝑠𝐺𝐺𝑠𝑠𝑑𝑑 (𝑀𝑀𝐺𝐺 𝑣𝑣−3 )
×
(100−𝑢𝑢𝑡𝑡𝐺𝐺𝑠𝑠𝑡𝑡𝐺𝐺𝑠𝑠 𝑠𝑠𝐺𝐺𝑡𝑡𝐺𝐺𝑠𝑠𝑡𝑡 𝑓𝑓𝐺𝐺𝑡𝑡𝑠𝑠𝑠𝑠𝐺𝐺𝐺𝐺𝐺𝐺 )
100
𝑇𝑇𝐺𝐺𝑠𝑠𝑡𝑡𝑡𝑡 𝑆𝑆𝑆𝑆𝐶𝐶 𝑠𝑠𝑠𝑠𝐺𝐺𝐺𝐺𝑡𝑡𝐺𝐺𝑡𝑡 = 𝑆𝑆𝑆𝑆𝐶𝐶 𝑔𝑔𝑡𝑡𝐺𝐺𝑠𝑠𝐺𝐺𝑠𝑠𝑑𝑑 (𝑣𝑣𝐺𝐺 ℎ𝑡𝑡−1) × 𝑓𝑓𝐺𝐺𝐺𝐺𝑡𝑡𝑠𝑠𝑠𝑠 𝑡𝑡𝐺𝐺𝑡𝑡𝑡𝑡 (ℎ𝑡𝑡)
Table 2. Criteria, Component measured and Methods of estimation recommended
Methods
Criteria for
stratification
Carbon pools
to measure
Methods /
values for
estimation
IPCC (2006)[3]
Climate zone, ecotype, soil type,
management regime within landuse types
Above-ground biomass, belowground biomass, dead wood,
litter, and soil organic matter), as
well as emissions of non-CO2
gases
Allometric equations for trees
Ratio of BGB to AGB for tropical
dry forest 0.56 for < 20 tons
AGB/ ha 0.28 for > 20 tons AGB/
ha Carbon fraction (CF): 0.47
(default value for all parts)
Source: Gurung[61]
Pearson et al (2007)[62]
Vegetation, soil, topography
Above-ground biomass,
belowground biomass, dead
wood, litter, soil organic
carbon, and wood products
Allometric equations for trees,
destructive harvesting for
shrubs, herbs and litter Root :
Shoot ratio BGB = exp (1.0587 + 0.8836 x in AGB)
Carbon content = 0.5 (50% of
total biomass)
MacDicken (1997)[25]
Land-use, vegetation, slope,
drainage, elevation, proximity
to settlement
Above-ground
biomass/necromass, belowground biomass (tree roots),
soil carbon and standing litter
crop
Equation for moist climate,
annual rainfall (1,500 – 4,000
mm)
y = 38.4908 – 11.7883 D +
1.1926 D² Root : Shoot ratio =
0.10 or 0.15 Carbon content =
0.5 (50% of total biomass)
VCS (2007)[63] and
CCB (2008)[64]
According to the
guidance
provided by IPCC
Consider the same
pools
covered under the
IPCC
guidelines
According to the
guidance
provided by IPCC
320
Akhlaq A. Wani et al.: Carbon Inventory M ethods in Indian Forests - A Review
In a similar study[53] an integrated approach was used to
assess carbon pool. The soil samp les (0-3- cm) within each
clustered plot were collected and analysed for organic
carbon and calculated with the same formu la. In another
study[66] regarding soil organic carbon in different land use
systems in Giri catchment of Himachal Pradesh soil
samples were collected fro m all land uses by digging a pit
of 30cm, 30 cm and 45 cm width, depth and length
respectively. Bu lk density was calculated using standard
core method[67]. Soil o rganic carbon was calcu lated by
standard Walkley & Black method[68]. A ll the methods
used in this study are in accordance with[10] Ravindranath
& Ostwald (2008). The data for SOC pool was calculated
by using the follo wing equation as suggested by IPCC
Good Practice Guidance[3] for LULUCF:
𝑆𝑆𝑆𝑆𝐶𝐶 =
=
𝐻𝐻𝐺𝐺𝐺𝐺𝐺𝐺𝑜𝑜𝐺𝐺𝐺𝐺 𝐺𝐺
�
SOC Horizon
𝐻𝐻𝐺𝐺𝐺𝐺𝐺𝐺𝑜𝑜𝐺𝐺𝐺𝐺 1
𝐻𝐻𝐺𝐺𝐺𝐺𝐺𝐺𝑜𝑜𝐺𝐺𝐺𝐺 𝐺𝐺
�
⌊SOC × Bulk density × depth × (1 − C frag. )
𝐻𝐻𝐺𝐺𝐺𝐺𝐺𝐺𝑜𝑜𝐺𝐺𝐺𝐺 1
× 100 Horizon⌋
Where, SOC = Representative soil organic carbon
content for the forest type and soil of interest, tonnes C(ha)1
, SOC = Soil o rganic carbon content for a constituent soil
horizon, tonnes C(ha)-1 , (SOC) = Concentration of SOC in a
given soil mass obtained from analysis, g C (kg soil)-1 , Bulk
Density = Soil mass per sample volu me, tonnes soil m-3
(equivalent to Mg m-3 ), Depth = Ho rizon depth or th ickness
of soil layer, m, C frag ments = % volu me o f coarse
frag ments/100.
In another study[69] to estimate soil organic carbon pool
under different land uses in Champawat district of
Uttarakhand the same methodology and equations were
used. In an experiment to assess carbon sequestration
potential in Himalayan region of Himachal Pradesh, split
plot design[70] was adopted to assess carbon sequestration
potential in Himalayan reg ion of H.P. using six land use
systems viz. natural g rassland, Hortipastoral, Agriculture,
agri-horticu lture and agri-hort i-silviculture each system
replicat ing thrice. Agroforestry system fo rmed the main
plot and soil sampling depth as sub plot. The soil organic
pool expressed as Mega grams ha-1 for a specific depth was
computed[71] by mu ltip lying the soil organic carbon
(g kg -1 ) with bulk density (g cm-3 ) and depth (cm). A
study[9] was carried out in India’s forests for the
assessment of forest carbon stocks using primary data for
the soil carbon pool. The study covered a total of 571
samples in forest area and 101 addit ional samples in the
nearby non-forest areas collected from a p it of 30 cm wide,
30 cm deep and 50 cm in length .Soil o rganic carbon was
estimated by standard Walkley and Black method and bulk
density was estimated using standard Clod method.
5. The Way Forward
Worldwide nu merous ecological studies have been
conducted to assess carbon stocks based on carbon density
of vegetation and soils[6,7,8]. The results of these studies
are not uniform and have wide variations and uncertainties
probably due to aggregation of spatial and temporal
heterogeneity and adaptation of different methodologies[9].
A participatory approach for forest boundary delineation
should be adopted by involving GIS experts, forest
technicians, and members of co mmunity forest user groups
(CFUGs ). High- resolution satellite images printed on a
large scale can be to find the different land cover and
natural boundaries and to trace individual forest blocks
easily. For establishment of baseline scenario of the area
local socio economic situation and regional economic
trends should be taken into consideration. To create
uniformity in estimat ions the IPCC Good Practice
Gu idelines 1996, 2003 and 2006 must be adopted as per
requirement; however an integrated approach to combine
various methods is necessary for specific studies for which
guidelines are not addressed. It is further reported[10] that
IPCC guidelines till now do not provide guidance on certain
methods and parameters. The use of GIS technology offers
an approach to develop a biomass map of forests. It can be
extended to areas in which data are not availab le because
consistent patterns of biomass density frequently result
fro m similar biophysical characteristics in the study area. A
geographically referenced bio mass density database for
tropical forests would reduce uncertainties in estimat ing
annual biomass increment and forest aboveground biomass.
ACKNOWLEDGEMENTS
The authors are highly thankful to the concerned persons
to allo w reproducing the tables fro m their works in the
present study and to the anonymous reviewers for their
useful comments.
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