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Table of Contents

PREFACE iv 1. INTRODUCTION 1 1.1 Background and Concept 1 1.2 Objectives of the Manual 3

Module 1 Inventory Planning 3

1.1 Introduction to the Training Module 5

1.2 Team formation and arrangement of instruments and logistics 5

1.3 Setting of Project Area Boundaries 7

1.4 Stratification of Forest Area 9

1.5 Sample Size Calculation 10

1.6 Estimation of variance through pilot inventory 12

1.7 Allocation, Size and Shape of Sample Plot 13

1.7.1 Locating Plot Centre in the Field 14

1.7.2 Permanent Sample Plots 15

1.8 Which Carbon Pools to measure in a forest area? 16

1.9 Sampling Techniques for forest carbon inventory 19

1.10 Field Session 23

Module 2 Field Measurement 24

2.1 Information at Plot level 25

2.1.1 Slope correction 26

2.2 Measurement of Aboveground Biomass 27

2.2.1 Measurement of Diameter 27

2.2.2 Measurement of Tree Height 30

2.2.3 Measurement of Increment 32

2.2.4 Measurement of shrubs, saplings and regeneration 33

2.3 Measurement of litter and dead wood 35

2.3.1 Measurement of litter 35

2.3.2 Measurement of dead wood 36

2.3.3 Measurement of Stumps 37

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2.4 Measurement of soil carbon 38

Module 3 Measurement of Carbon Stocks in Mangroves Forests 40

3.1 Sampling Design 41

3.1.1 Plot Shape and Size 41

3.1.2 Navigation to Sample Plots in the Field 42

3.1.3 Information at Plot Level 43

3.2 Measurement of carbon stocks in different carbon pools 43

3.2.1 Measurement of Aboveground Biomass 43

3.2.2 Belowground Biomass 44

3.2.3 Shrubs and Dwarf Mangroves 45

3.2.4 Litter 45

3.2.5 Pneumatophores 45

3.2.6 Soil Carbon 46

Module 4 Data Processing and Analysis 47

4.1 Aboveground Tree Biomass 47

4.2 Aboveground shrub/sapling biomass 48

4.3 Belowground Biomass 49

4.4 Standing and downed dead trees and stumps 49

4.5 Leaf, litter and grass biomass 50

4.6 Soil organic carbon 50

Module 5 Quality Assurance and Quality Control 51

5.1 Verification of Field Measurement 51

5.2 Verification of Laboratory Measurement 52

5.3 Verification of Data entry and analysis 53

5.4 Group Work on maintaining data quality 54

GLOSARY 55

REFERENCES 58

ANNEXURE-1 INVENTORY FORM 60

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LIST OF ACRONYMS

AGC Aboveground Carbon

AGTB Aboveground Tree Biomass

AGSB Aboveground Sapling Biomass

AGM Aboveground Biomass

BGM Belowground Biomass

cm Centimeter

C Carbon

CO2 Carbon dioxide

CV Coefficient of Variation

DBH Diameter at Breast height

D Diameter

FAO Food and Agriculture Organization

FCPF Forest Carbon Partnership Facility

FREL Forest Reference Emission Level

FRL Forest Reference Level

g Gram

GHG Green House Gases

GIS Geographic Information System

GPS Global Positioning System

GT General topographic

H Height

ha hectare

HWPs Harvested Wood Products

IPCC Inter-governmental Panel on Climate Change

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m Meter

m2 square meter

mm millimeter

NGO Non-governmental Organization

MRV Monitoring, Reporting and Verification

PES Payment for Environmental Services

PFI Pakistan Forest Institute

PSU Primary Sampling Unit

REDD+ Reducing Emissions from Deforestation, Forest Degradation, sustainable forest

management, conservation and enhancement of forest carbon

QA Quality Assurance

QC Quality Control

RPP Readiness Preparation Proposal

RS Remote Sensing

SD Standard Deviation

SE Standard Error

SOC Soil Organic Carbon

SOP Standing Operating Procedure

SSU Secondary Sampling Unit

SUPARCO Space and Upper Atmosphere Research Commission

t Tonne

UNFCCC United Nations Framework Convention on Climate Change

WWF World Wide Fund

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PREFACE

After the adoption of Cancun Agreement in 2010, the Government of Pakistan took several measures to

implement this agreement at the national, provincial and district levels. Reducing Emissions form

Deforestation and Forest Degradation (REDD+) process was initiated by Ministry of Climate Change,

Government of Pakistan in 2010 with consultative workshops and awareness raising.

In 2013, Pakistan was selected as a REDD+ Country Participant in the Forest Carbon Partnership Facility

(FCPF) of the World Bank. The participant Committee of the FCPF through its Resolution PC/16/2013/8

decided to allocate grant funding to Pakistan to enable it to move ahead with preparation for readiness.

Pakistan received a grant of US $ 3.8 million during June 2015 under REDD+ Readiness Preparation

Proposal (RPP). Under R-PP Pakistan is working on four main components i.e. (i) REDD+ Policy Analysis,

(ii) REDD+ Technical Preparation, (iii) Readiness Management Arrangements and (iv) Designing and

Testing REDD+ Payments for Environmental Services.

Pakistan Forest Institute, being the prime public sector organization in forestry research and education,

was awarded a study by the National REDD+ Office, Ministry of Climate Change for “Designing REDD+

Payment for Environmental Services” in the two selected ecosystems of the country in 2018. One of the

main components of this consultancy assignment was to prepare awareness raising material on the

following topics in English & Urdu.

What is REDD+. A Guide for Local Communities

Risks and Benefits of REDD+

Climate Change and the Role of Forests – A Community Guide

A community guide for REDD+ PES monitoring

A Manual to measure forest carbon stock.

This Manual has been prepared by Pakistan Forest Institute under the study “Designing REDD+ Payment

for Environmental Services”. This document is designed to enable forest managers, community

members, practitioners, researchers, students and other stakeholders to measure carbon stocks in their

forests and participate in REDD+ and other carbon trading schemes.

Syed Mahmood Nasir National Project Director (REDD+ ) Ministry of Climate Change,

A MANUAL TO MEASURE FOREST CARBON STOCK

1. Introduction

1.1 Background and Concept

Forests play an important role in climate change as they act as sinks of, as well as sources for,

carbon emissions. Forests sequester CO2 from the atmosphere through photosynthesis and

store this carbon in the form of biomass in stems, branches, leaves, flowers, seeds and roots.

When these parts particularly roots are died these are converted into soil organic matter by the

microbial action in the forest ecosystem. The world’s forest ecosystems hold more than half of

all terrestrial carbon. According to FAO (2006) this carbon is more than one trillion tonnes ─

twice the amount found in the atmosphere. On the other hand, when forests are cleared or

degraded, large amounts of CO2 is released into the atmosphere which is present in high levels

in trees, forests, animals and nature. In an undisturbed forest, CO2 is constantly being absorbed

and released, and a balance is maintained. However when deforestation or large scale

degradation occurs, large amounts of CO2 are released without enough being absorbed again.

It has been estimated that 17% of the global Greenhouse Gases emissions are contributed by

deforestation and forest degradation, more than the entire global transportation sector and

second only to the energy sector. About 75% of forest based emissions occur in developing

countries where forests are under tremendous pressure due to a variety of social and economic

factors. It is now clear that in order to constrain the impacts of climate change within limits that

society will reasonably be able to tolerate, global average temperatures must be stabilized

within two degrees Celsius (Baker et al., 2010).. This will be practically impossible to achieve

without reducing emissions from the forest sector, in addition to other mitigation actions.

Forests are an important part of our ecosystems and landscapes and essential for sustaining life

support processes. Continued protection of the earth’s forests will enable the earth, the

ecosystems, animals and plants, and humans to adapt and respond to climate change. Large

intact forests especially in mountainous areas help plants and animals adapt to rising

temperatures and changing precipitation patterns. Conservation and development of forests

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means that the pace of climate change will be slowed as large amounts of CO 2 emissions can be

avoided by ensuring that forests remain standing.

Because of the crucial role of forests in mitigating the effects of climate change, and their

importance for sustaining so many ecosystem services it is clear that we need to slow

deforestation and forest degradation and maintain healthy forest ecosystems. This has led to

the inclusion of forests in the international negotiations and agreements aimed at climate

change mitigation.

Reducing emissions from deforestation, degradation, sustainable forest management and

conserving and enhancing carbon stocks in the forests known as REDD+ has emerged as a

promising option for climate change mitigation in developing countries. The REDD+ idea simply

means trying to stop forests being cut down or degraded and thereby reducing the amount of

CO2 emissions into the atmosphere and increasing forest cover to sequester more CO 2 from the

atmosphere. REDD+ creates a financial value for the carbon stored in forests by offering

incentives for developing countries to reduce emissions by controlling deforestation and forest

degradation and increasing forest area. Developing countries would receive results-based

payments for results-based actions.

United Nations Framework Convention on Climate Change (UNFCCC) has provided

comprehensive guidelines for REDD+ activities through its Cancun Agreement and Warsaw

Framework. UNFCCC decisions on REDD+ encourage developing countries to contribute to

mitigation actions by voluntarily undertaking activities that reduce greenhouse gas (GHG)

emissions and that enhance carbon sinks in the forest sector. The five activities that comprise

REDD+ are:

1. Reducing emissions from deforestation

2. Reducing emissions from forest degradation

3. Conservation of forest carbon stocks

4. Sustainable management of forests

5. Enhancement of forest carbon stocks

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Pakistan has joined REDD+ Programme in an attempt to conserve its limited forest resources

which cover 4.55 million ha (only 5.1% of the total country area). At Federal level a REDD+

Project titled as "REDD Readiness Preparation Proposal (RPP)" is being implemented by Ministry

of Climate Change with Financial Support of World Bank. At provincial level, different provinces

have started REDD+ Projects from their ADPs to provide enabling environment for REDD+ in the

provinces. A key step in REDD+ mechanism is estimating the amount of carbon stock in the

forests and the rate at which carbon is emitted or sequestered by these forests at different

spatial and temporal scales.

1.2 Objectives of the Manual

The current manual is aimed at building the capacity of forestry professionals, researchers,

students and community members to estimate carbon stocks in their respective forest areas.

The manual is intended to be a reference guide for conducting training workshops on

measuring and monitoring forest carbon stocks in the context of REDD+. The manual comprises

five modules to cover the main components of forest carbon measurement:

1. Inventory planning

2. Field measurement

3. Measurement of carbon stocks in mangroves forests

4. Data processing and analysis; and

5. Quality Assurance and Quality Control in field measurement, laboratory work and data

analysis.

Each module has different sections which include objectives, outcomes, training methods,

lectures, exercises and field sessions.

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Module 1: Inventory Planning

Forest carbon inventory is a highly technical job and therefore requires proper planning. A

sound planning will not only result in generation of reliable data but will also enhance efficiency

of field work. Two types of information are important for inventory planning i.e. general

information regarding logistics and technical guidelines regarding planning steps.

Objectives

Outline the crucial steps for planning of forest carbon inventory

Enable the participants to learn basic principles of carbon inventory

Enable the forest managers to identify the key tools, equipment and material for

undertaking forest carbon inventory

Delineate boundaries of accounting area

Stratify forest area into homogenous units

Determine sample size, plot size and plot shape

Select significant forest carbon pools for

Determine an efficient sampling design for field inventory

Outcomes

Prerequisites identified and arranged for forest carbon inventory

Project area map showing different forest strata

An efficient sample size

An efficient sampling design

Material required

Multimedia projector

Laptops or computers for participants

Charts, paper, pens, markers and notebooks

Project area maps

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Training Method

This module will have 5 lectures (one hour each) on planning and logistics, determination of

sample size, plot shape and size, forest carbon pools and sampling designs, 4 exercises (2 hour

each) on sample size calculation, boundary setting and stratification. There will also be one field

session (3 hour) on orientation of the participants with different instruments and data

collection tools. This module will require two full days.

1.1 Lecture 1: Introduction to the Training Module

The first session of the training workshop will be a power point presentation by the resource

person which will cover the following points:

Introduction of the resource persons, training facilitators and participants

Objectives of the training workshop

Expectations of the training participants

Introduction to climate change and the emergence of REDD+

Key international requirements for accessing result based payments under REDD+

Setting of rules for the training workshop

1.2 Lecture 2: Team formation and arrangement of instruments and logistics

Team formation is an essential step in inventory planning. The following guidance should be

followed for team formation:

Before going to the field to start forest inventory, form a sufficient number of field

teams.

Each team should have a Team Leader preferably a forestry graduate or a Forest Ranger

with basic knowledge of the areas, field measurement and use of equipment. He will be

responsible for data entry and coordinating the team’s activities and assigns

responsibilities to the other team members.

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The team should have two enumerators one for measuring DBH and tree heights and

the third for measuring, shrubs, dead wood, litter and soil.

There should be one helper for carrying the instruments and samples in the field and

one driver for driving the vehicle.

The team members should be well-connected to the team leader and each other.

Arrangement of the materials and equipment must be ensured before moving to the

field.

All instruments should be prepared, checked and calibrated in advance.

The team leader has to ensure that every instrument is functioning, so that field work

can take place without any disturbance.

A complete checklist should be prepared so that no materials are left behind: this

checklist will also be useful during field work as the team moves from one location to

the other. A simple checklist is given in the following table 1.

Table 1: List of Instruments and tools for Field Inventory

S.No Item Purpose

1 Base Map (GT Sheet or GIS Map)

For locating sample plots positions in the field

2 Diary/Writing Pads For recording daily events and detail of field work

3 Pens/Pencils For recording data on inventory forms 4 Rope For plot boundary delineation

5 Chalk For marking of trees during measurement in sample plot 6 Measuring tape For laying out of sample plots

7 Nails For placing tags

8 Spray paint For marking of trees 9 Iron rods For permanent marking of sample plots

10 Plastic bags For soil samples 11 Gunny bags or cloth bags For collecting samples of shrubs, litter and grass

12 Sickle For cutting herbs, shrubs and grass

13 Scissor For cutting herbs, shrubs and grass

14 Digital weighing machine For recording fresh weight of litter, herbs, shrubs and soil samples

15 Metal scale For measuring soil depth

16 Soil corer For collecting soil samples 17 Hammer For fixing metal tags on trees

18 Permanent marker For marking of trees and writing information on chits put

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in bags

19 Stapler For binding of soil sample bags 20 GPS Plot navigation, recording position of plots

21 Cells For use in GPS and Camera 22 Haglof Vertex For measuring tree height and establishing circular plots

23 Suunto Clinometer For measuring tree height and slope of plots

24 Compass For determining direction 25 Caliper For measuring diameter of trees

26 Diameter tape For measuring diameter of trees especially for large size trees

27 Densitometer For measuring canopy cover 28 Pressler borer For extracting cores from trees for measurement of

growth rate

29 Core covers For string cores 30 Magnifying glass For measuring length of annual rings

31 Camera For taking photos of stands, sample plots, sample trees etc.

32 Mobile Phones For communication between field parties and base camp

All logistic arrangements should be properly in place before commencement of field work.

Proper vehicles are required for transportation of the crew members and field

equipment in the rugged terrain. Only 4x4 vehicles e.g. double cabin, jeep or other

proper vehicles must be arranged for the field work.

It is also essential to identify places for accommodation in the field.

Sometime it is also advisable to carry proper tents for camping in the forest where

accommodation is not available.

Proper field gear such as clothes and shoes are also required.

First aid box containing emergency medicines is also essential for field work.

Cooking utensils, water bottles, umbrellas, torches etc. are also required for field work.

1.3 Exercise 1: Setting of Project Area Boundaries

Setting of well-defined boundaries of the proposed REDD+ area on the ground as well as on

map is the first step in forest carbon measurement. The final estimates of carbon stocks, carbon

emissions and emissions reduction depends on the two factors: the forest area and carbon

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density. The basic objective of this SOP is to enable the practitioners to set spatial boundaries

of the intended REDD+ area and calculate forest area for carbon stock assessment. Clearly

defined, mapped and geo-referenced boundaries will facilitate measurement, monitoring and

verification and avoid double counting in field measurements . Use the following steps to

determine area and boundaries of the forest area.

1. Collect maps, satellite imageries, aerial photographs, topographic maps, land record and

other documents containing spatial information about the area of interest. These may

be available with Forest Department, Wildlife Department, Agriculture Department,

Survey of Pakistan, Soil Survey Department, Revenue Department, SUPARCO, PFI,

universities, other Government Agencies and NGOs working in the area.

2. Review the collected documents and extract the relevant spatial information. Check

when was the prepared. If it is older than 10 years it may limited utility for forest carbon

measurement.

3. Check if any good quality map of the area is available and whether it is of appropriate

resolution, ground validated and geo-referenced. If the available map is not geo-

referenced, the first step would be to geo-reference it with the help of General

Topographic sheets and GPS coordinate of atleast 3-4 prominent points on the ground.

4. Define area on the basis of forest compartment or watershed which might be a natural

entity in the given area.

5. Identify permanent boundary markers on the ground, e.g., rivers, streams, creeks,

mountain ridges, roads etc.

6. Larger areas across the landscape can be defined by specific boundary descriptions

using GPS-based coordinates on topographic maps or by using satellite images. Medium

or high resolution satellite images (e.g., spot-5, spot-6, Geo eye, Quick bird) are

extremely useful if available. If not available then Landsat or Sentinel-II data can also be

used which is freely available. Google Earth images can also be used for delineating

boundaries of the REDD+ area.

7. Calculate total area of the target area in hectares.

8. Show major settlements, villages, roads and other facilities on the map.

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9. Prepare maps on A-0 size paper for field inventory.

10. Save digital copy of the map in a computer, laptop or mobile phone.

1.4 Exercise 2: Stratification of Forest Area

Stratification is the division of forests area into homogenous units. Stratification is an important

step that needs to be done before starting field inventories. Stratification is conducted with the

objective to have more homogenous units for sampling and to have small and efficient sample

size for field inventory.

Use the following steps for stratification:

1. Once the project area has been delineated, collect basic information about the land

cover as well as vegetation and topography.

2. Identify sources of available data (e.g. working plans, maps, project reports) and collect

the relevant reports.

3. Prepare a geo-referenced base map using high or medium resolution satellite imageries.

4. Classify the area into six landuses as per IPCC Guidelines and national definitions i.e.

forestland, cropland, grassland, wetland, settlement and other land.

5. Take up forest area for further stratification based on forest type, stocking/density,

canopy cover, stand composition, age, elevation, soil type and level of disturbance.

6. Classify forest areas into separate strata with more than 20% variation in carbon stock.

7. Use Remote-sensing software (e.g. ERDAS Imagine, Arc GIS, QGIS or ILWIS) for land-

cover classification and forest stratification.

8. If virtually no data on forest types or strata is available then stratify the country initially by

ecological zone or ecoregion using global datasets e.g. Holdridge life zones

(http://geodata.grid.unep.ch/). WWF ecoregions

(http://www.worldwildlife.org/science/data/terreco.cfm). FAO ecological zones

(http://www.fao.org/geonetwork/srv/en/main.home, type ‘ecological zones’ in search box)

9. Once stratification is completed, calculate total area of different strata.

10. Take colour print outs of the maps showing different strata.

11. Conduct a preliminary field visit is to recognize different features on the ground and

identify those features on the map.

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12. Strata and sub-strata can also be identified using the expert knowledge of local

foresters.

1.5 Exercise 3: Sample Size Calculation

Sample size is the number of samples that are required for obtaining the results about the

population with the desired precision level. Choosing the optimum sample size is an important

parameter, which should be thoroughly examined before starting inventory. A reasonable

sample size is a balance between the accuracy of the results and the costs of the field work.

Sample size can be calculated on the basis of existing data on biomass, growing stock or basal

area. However if these information are not available, then there is a need to collect these

BASES FOR STRATIFICATION OF FOREST Forest type: Forest types are based on ecological zonation and species composition of the area. As different forest types occur on different elevation zones, it is possible to stratify different forest types on the basis of DEM. Forest types include subtropical broad-leaved forests, subtropical pine forest, moist temperate forest, dry temperate forest, sub-alpine forests. Dominant tree species: Sites containing a dominant tree species are regarded as different strata. For example oak forest within dry temperate forest can be classified as different strata because these have different carbon stocks than the dry temperate conifers. Stocking density of trees: Stocking density refers to the number of trees per unit area. Resultant strata could be open forest, medium forest, dense forest. Remote-sensing analysis is used to identify forest areas which differ in tree density. Age of trees: Sites with distinct age classes are stratified further, as carbon sequestration differs markedly with the age of the stand. Aspect: Within a dominant forest type, sites differing in aspect in a hilly areas slope may have different rates of carbon sequestration. Therefore, sites with different aspects can be categorized as different strata. Physical boundary: Strata can also be based on physical boundary which can be easily visualized on the ground such as rivers, roads, ridges etc. Site quality: The productivity of forest land is defined in terms of the maximum amount of volume that the land can produce over a given amount of time. Site quality is measured as an index related to this timber productivity. Stratification of forest area can also be based on site quality.

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information through a pilot inventory. Sample Size can be calculated by the following formula

separately for each stratum.

N= (CV)2xt2

E2

Where

N= Number of required sample plots CV= Coefficient of Variation t= Student t-test value E= Allowable Error

Use the following steps to calculate sample size for forest carbon inventory:

Review the available inventory reports, working plans document or other literature to

find out information about variance in the forest population. CV is a measure of the

variation in the forest crop. If the forest vegetation is more homogenous, then CV will

be small and if there is more variation in the forest, then the CV will be higher.

If estimate of CV is available, put this in the above mentioned formula using MS Excel.

Put the value of t as 1.96 or 2 at 95% Confidence Level.

Put the value of E as 10% which is generally accepted for forest carbon measurement.

Sometimes Standard Deviation (SD) is reported in forest inventories instead of CV. In

this convert SD to CV using the following formula in MS Excel.

CV= Standard Deviation x 100

Mean

Calculate N separately for different strata repeating the above mentioned steps.

Add 10-20% additional plots to the N obtained from the above formula. Because it is

possible that the actual variation in the forest may be more than the estimate obtained

from the existing data or pilot inventory. There is also a possibility that some of the

sample plots are inaccessible in the field. Therefore, it is essential to take more plots

than the calculated number.

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Alternatively use Winrock International Sample Plot Calculator available at

http://www.winrock.org/resources/winrock-sample-plot-calculator to calculate the

number of sample plots for different strata. This calculator needs input data on area of

forest stratum, mean biomass (ton per ha) and Standard Deviation of each stratum

which can be derived from the pilot inventory or available literature. Sampling Error is

set at 10% and Confidence level at 95% by default. However, this can be changed

according to the situation or precision required for the inventory.

Figure 1: Sample Size Calculator

1.6 Exercise 4: Estimation of variance through pilot inventory

As mentioned above variance of the forest vegetation is an important variable on which the

sample depends. More variance in the forest will require high number of sample plots for

inventory and vice versa. Variance can be estimated from the available inventory data.

However, if inventory data is not available, then it is advisable to conduct a pilot inventory in

each forest stratum to provide a basis for calculating the number of sample plots required for

the inventory. Use the following method to conduct pilot inventory in the forest:

Establish 10 to 15 circular plots in each forest strata using simple random sampling

technique.

Randomization of sample plots is crucial to cover the natural variability present within

the different forest strata.

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Though carbon inventory is more complex to carry out than a traditional forest

inventory because there are several carbon pools in a forest ecosystem, but the main

source of variation is generally due to aboveground tree biomass, therefore, it is

reasonable to calculate number of sample plots on the basis of variance in the

aboveground tree biomass.

Measure diameter and heights of all trees in the sample plots as per standard inventory

procedure to calculate aboveground biomass of trees.

Use plot size of 0.1 ha (17.84 m radius) which will be used in the full inventory.

The data recorded during pilot inventory can be included later on in the full inventory.

1.7 Lecture 4: Allocation, Size and Shape of Sample Plot

After calculating the sample size for inventory, allocate sample plots to different strata on the

basis of proportionate allocation. A base map is used to mark locations of sample plots.

Distribute sample plots randomly within each stratum using standard sampling methods or Arc

GIS software. Upload the plots’ coordinates are onto the GPS and save them. Navigate to the

sampling location in the field using GPS.

Forest carbon measurement can be carried out in circular, square and rectangular plots.

However, circular samples are recommended for field inventory because they are relatively

easy to establish and have less edge effect. As there are several pools of biomass and carbon in

a forest ecosystem, nested circular plot approach is the best option to collect data on these

pools.

The following guidance should be followed for establishment of sample plots in the forest:

The radius of circular plot is dependent on the stocking density of the forest, the default

being a 17.84 m radius for open to moderately dense forest. However, it can be reduced

to 8.92 m in case of pole dense crop.

As illustrated in Figure 2, several sub plots are established within each plot for specific

purposes: inside of the 17.84 m radius plot (~1000 m2), a sub plot with a 5.64 m radius

(~100 m2) is established for shrubs and saplings; and a sub plot with a 0.56 m radius (~1

m2) is established for sampling leaf litter, herbs, grass, and soil.

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The wider circular plot can be easily and accurately established with the help of Haglöf

Vertex. Two options are available in Haglöf Vertex VL5 for measurement of distance

i.e. laser and ultrasonic. However, laser is not available in Haglöf Vertex VL4.

Calibrate the Haglöfs Vertex after moving from one forest area to the other as it is

affected by temperature and humidity especially if the ultrasonic option is used.

If Haglöfs Vertex is not available, then ordinary measuring tape can be used to lay out

the sample plot.

In hilly areas it is essential that slope corrector factors are applied to get accurate radius

of the plot when using ordinary tape. However, for Haglöfs Vertex there is no need of

slope correction as it is automatically taken care of by the device.

Figure 2. Nested Circular Plot

1.7.1 Locating Plot Centre in the Field

When reach the sample plot site, the plot centre is located as below:

17.84 m

5.64 m

0.56 m

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Locate the exact centre of the plot by inserting a 30 cm long iron rod or putting a stone

in the middle of the plot. The exact location of the plot can be found later with the help

of this iron rod.

Use GPS to locate the accurate centre point of the plot by recording latitude, longitude

and altitude.

Wait for 10-15 minutes before recording the GPS coordinates so that GPS device has

enough time to catch full signals and stabilize.

Record GPS accuracy.

Record GPS data on inventory form and also save it in the device for downloading later

on into the computer system.

If the centre point of the plot is occupied by a tree, shift the rod to the north side of the

tree.

Use Differential GPS if available. Differential GPS will give the exact location of the plot

centre with sub-meter accuracy.

After establishing the plot center, mark the boundary of the plot by encircling the plot

with a rope and marking on the trees on the border.

1.7.2 Permanent Sample Plots

Permanent sample plots are very helpful in future monitoring of changes in the forest carbon

stocks. Therefore it is a good practice to establish permanent sample plots in the forests. These

will also provide data on tree growth and carbon sequestration. If the locations of sample plots

are sufficiently documented, then the same plots can be re-measured over time to obtain more

precise estimates of forest change, such as tree growth, mortality, harvesting, regeneration,

and changes in the areas of forest conditions and land-use categories. Re-measurement of plots

increases estimation efficiency and contributes to better understanding of the components of

change. However, if permanent plots are used, their locations must be very accurately

documented.

Use the following steps to establish Permanent Sample Plots:

Establish Permanent Sample Plots by recording the GPS coordinates of the sample plots.

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Insert iron rods in the centres of the plots to accurately access the centre points in the

future.

These rods should not be visible to tress passers, graziers and loggers so that the sample

plots receive the same treatment as other forest area. This will help in accurately

measuring forest degradation or enrichment over time. Inserting iron rods are

important because GPS always have errors of few meters and it may not be possible to

accurately locate plot centre only with ordinary GPS.

Produce sketch or map of sample plot showing the position of different trees with serial

numbers starting from the North and moving in clock-wise direction to complete the

circle.

Also show the position of a prominent feature inside or close to sample plot such as a

big stone, stream, ridge or road.

Take four photographs from the four corners of the plot.

1.8 Lecture 4: Which Carbon Pools to measure in a forest area?

Carbon pools are components of the forest ecosystem that can either store or release carbon.

Carbon is present in five pools in a forest ecosystem which include aboveground biomass, dead

wood, litter, belowground biomass and soil organic carbon (Figure 3). Harvested wood products

(HWPs) are now increasingly recognized as a carbon pool outside the traditional forest

boundaries. However, the current rules of IPCC state that reporting of carbon in HWPs is

separate and optional.

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Figure Error! No text of specified style in document.. Diagrammatic representation of Carbon pools Source: Watson, 2009

i. Above-Ground Biomass (AGB)

Above-ground biomass (AGB) consists of all living vegetation, both woody and herbaceous,

above the soil surface including stems, stumps, bark, twigs, seeds and foliage (IPCC, 2006). AGB

is divided into two categories, upper story i.e. trees and under story i.e. shrubs.

ii. Below-Ground Biomass (BGB)

This pool consists of living roots. Fine roots of diameter less than 2mm are excluded due to the

difficulty of separating them from soil organic matter (IPCC, 2006). This is the most difficult pool

of forest ecosystem to measure accurately. BGB is estimated through root-shoot ratios of trees

or allometric equations based on the relationship between root biomass and an easily

measurable variable such as stem diameter at breast height. These ratios vary with forest types

and the amount of aboveground biomass in the area. Based on a review of 160 research studies

covering tropical, temperate and boreal forests, Cairns et al. (1997) found that the root-shoot

ratio ranged between 0.18-0.30 with an average of 0.26. Root-shoot ratios for different forest

types have been provided by IPCC Guidelines, 2006.

iii. Dead Wood

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This pool consists of all dead wood including standing and fallen dead trees, stumps and roots

with diameter more than 10 cm. This pool usually contains carbon in the range of 10-20% of the

above-ground pool in mature stands (Delaney et al., 1998). However, in young forests and

plantations this pool is usually insignificant and therefore ignored in field measurements.

Different methods are applied to estimate carbon in the dead wood pool. Dead standing trees

are measured like living trees but their biomass is reduced by 20% for loss of branches and by 2-

3% for loss of foliage (McDicken, 1997), that is, their biomass is considered to be 77-78% of that

for living trees.

iv. Litter

The litter layer is defined as the recently fallen non-woody, dead, organic material on the soil

surface. Typically, it consists of dead leaves, flowers, fruits, seeds and bark fragments.

This pool includes dead wood lying on the ground with diameter 2 mm-10 cm. This pool is

readily decayed and transformed to soil organic matter depending on climatic conditions. The

carbon in this pool can be directly measured by collecting all litter from a sample plot and

weighing after oven-drying. Default values are also provided by IPCC guidelines, ranging from 2

t/ha in tropical forests to 39t/ha in moist boreal broad-leaved forests (IPCC, 2006).

v. Soil Organic Matter (SOM)

SOM is an important pool of forest carbon, comprising both organic and mineral carbon in the

soil. Only organic carbon is measured due to its significance in the forest ecosystem. This pool is

also affected by landuse change and management activities. Both soil depth of soil sample

extraction and time required for reaching equilibrium after a landuse change are important for

estimating soil carbon. For measuring soil carbon, samples are extracted from 30 cm depth and

carbon is estimated in the laboratory. The amount of organic carbon in forest soils varies

spatially, even within a relatively homogeneous forest and depends on site conditions,

vegetation type and soil type (Watson, 2009). Typically, 20 years is considered as the minimum

period required for a soil pool to reach equilibrium in a forest ecosystem (Watson, 2009).

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The selection of carbon pools to measure in the field depends on: magnitude of the pool; rate

of change of pools in response to human disturbance; expected direction of change; costs and

time to measure; and desired accuracy and precision.

The following guidance will help in choosing a carbon pool for field measurement:

All C pools with 5% or more of total carbon stock should be included in the carbon stock

assessment

The aboveground biomass is the most important pool of carbon in a forest ecosystem. It

must be measured in every forest carbon inventory.

Below ground biomass is important when landuse changes are involved or likely in

future.

Standing and lying dead wood, because it can represent up to 10% of the total carbon

pool. But it can be excluded if dead wood is collected as a fuelwood in a community

forest.

Understory in a mature forest is often negligible i.e. less than 2–3% of the total; in a

secondary forest this can be a larger proportion up to 5% of the total.

The soil carbon pool should be included if the forest conversion is to agriculture or roads

or other land uses with high soil disturbance (e.g., mining); if converted to pasture soil, C

pool can be ignored.

The soil C pool should be included if deforestation occurs on peat swamp forests or

mangroves.

1.9 Lecture 5: Sampling Techniques for forest carbon inventory

Forests often cover large tracts and, therefore, full enumeration of forest trees is practically not

possible as it requires lots of time, cost and labour. Consequently, sampling has to be resorted

to measure carbon stock at landscape level. Sampling is a process through which representative

subsets are derived from the population to make inferences about the population. Estimates

based on data collected from the measured samples are then extrapolated to the entire population, the

majority of which has not been measured.

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If the sample is representative of the entire population, then the estimate will be accurate and less likely

to deviate from the true population value. Otherwise, estimates will be inaccurate and misleading – a

situation that may not be known because the true condition of the whole population will remain

unknown. The best possible approach is to increase the chances of measuring a representative sample.

This can be done by using scientifically rigorous rules to select the sample, maximizing the number of

sample units observed or measured, and minimizing the errors in measuring each sample.

Sampling must be objective in order to avoid bias in the sampling estimates. A forest inventory

using subjectively selected plots cannot give valid estimates it is impossible to determine the

sampling error of them. Theoretically the most efficient sampling design is the one which

provides the most precise estimates for a given cost, or which costs the least for a given

precision of the estimate.

Several sampling techniques can be used for forest inventory in different forest types. Following

are the most commonly used sampling techniques in forest inventory.

Simple Random Sampling

This method is commonly referred to as unrestricted random sampling. Selection is purely by

chance i.e. not influenced by the personal judgment of the selector. The procedure involves

selecting 'n' units out of a total of 'N' units in such a manner that each unit has an equal chance

of selection. Commonly, each unit (plot) is identified by a number or rectangular coordinate. To

locate plots randomly within a stand, the x, y co-ordinates of the plot's center or one corner are

selected from a table of random numbers or generated by computer.

Simple Random sampling has several disadvantages. Locating the position of sample plots in

the field and travelling between plots is time consuming and costly. Uneven distribution of the

sampling units is not uncommon, i.e. some sections of the area may, by chance, are sampled

too heavily and others too lightly.

Systematic Random Sampling

A systematic sample uses a fixed grid to assign plots in a regular pattern . The advantage of

systematic sampling is that it maximizes the average distance between plots and therefore

minimizes spatial correlation among observations and increases statistical efficiency. In

addition, a systematic sample, which is clearly seen to be representative in some sense, can be

21

very convincing to decision-makers who lack experience with sampling. Systematic samples are

normally based on rectangular grids. For example, a sample plot could be established at the

intersections of a 2 km x 2 km grid. A random number is used to select the starting point and

orientation for this grid, but no other random numbers are required. This sampling design is

common in forestry. This technique reduces bias and gives precise results. However, this

technique is difficult when extensive area is to be covered and it is costly when there is

fragmentation in the forest.

Stratified Random Sampling

In this sampling design, the forest population is separated into strata (relatively homogeneous

units) so that the variation within each stratum is minimized at the expense of variation

between the strata. Individuals within one stratum are more likely to be similar to their

neighbours than to individuals in another stratum. Stratification can be done on the basis of

forest type, elevation, age, density etc. Samples can be allocated to different strata on the basis

of area.

Stratified approaches to sampling are used for multiple reasons, but primarily to vary sampling

intensities to accommodate selected criteria. For example, for an MRV that emphasizes

geographic regions subject to human-induced carbon emissions, lesser sampling intensities may

be acceptable for remote, inaccessible regions less likely to be developed or harvested. In

addition, the cost associated with greater sampling intensities in remote regions may be

prohibitive. Nevertheless, sampling, though with lesser intensities, must be conducted in these

regions to achieve spatial balance.

Multiple principles also guide stratified approaches to sampling. First, strata with stable

boundaries are generally preferable. Otherwise, changes to boundaries of strata with different

sampling intensities lead to different sampling inclusion probabilities and complicate

estimation. In addition, stratified estimation requires that a plot be assigned to one and only

one stratum. If the stratum to which a plot is assigned changes between measurements, then

difficulties arise as to the stratum to which a plot change observation should be assigned. Thus,

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strata defined by topography, climatic zones, biomes or political boundaries may be preferable

to strata defined by forest attributes such as density or perhaps forest type.

Stratified sampling is most often implemented using one of three plot allocation schemes. With

equal allocation, the same number of plots is allocated to all strata, regardless of strata sizes.

This scheme is preferred if the objective is estimates for individual strata. With optimal

allocation, sampling intensities selected for strata are based on optimization criteria, such as

measurement costs, and/or within-stratum variation of observations of variables of interest,

such as volume or biomass or their likely changes. Greater sampling intensities are selected for

strata with greater variation and/or lesser measurement costs. With proportional allocation,

sampling intensities selected for strata are proportional to strata sizes.

Cluster Sampling

Cluster Sampling is a type of multistage sampling that consists of selecting primary sample

points (PSUs) and establishing a number of closely located secondary sampling units (SSUs) as a

group or cluster. Cluster sampling has been used with success in tropical forests in remote

areas involving difficult access. This type of sampling technique has been used in Khyber

Pakhtunkhwa for forest carbon inventory during 2013-2017. This technique was also used in

Pakistan by Arbonaut and WWF for developing Forest Reference Emission Level for the country.

This type of sampling is very efficient in hilly areas where most of the inventory time is spent in

travelling. Cluster sampling was also found very efficient in Mangroves forests in coastal areas

where movement is also very difficult. Each Cluster is uniquely numbered with pre-indexed

number/code or ID.

Plots (PSU and SSUs) are numbered 1-5 in a cluster. The Primary Sampling Unit (PSU) is numbered 5 for

every PSU. The bottom-left SSU is numbered 1 and serially 2,3 and 4 in clockwise direction as shown in

Figure 4. The Plot ID is a combination of <ClusterNo>-<PlotNo> and is automatically generated in

GIS/database. In the given example, for Cluster No 344, the PSU plot is numbered 5, hence the Plot ID is

344-5. Likewise, other plots have IDs 344-1, 344-2, 344-3 and 344-4 serially in clockwise direction.

Therefore, each PSU and SSUs are uniquely identified as a composite of Cluster No and Plot No.

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1.10 Field Session

This session will be dedicated to enable the participants to use different instruments mentioned

in table 1 and to fill the inventory form given at Annex-1. The participants will be divided into

groups of 5 persons. Each group will be provided with a set of instrument and inventory forms.

The resource person will first describe the procedure for using different instruments and will

demonstrate the use of different instruments. Every participant will have to get full command

on the use of instruments and how to record field information on the given inventory sheet.

Figure 4. Cluster No, Plot No and Plot ID

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Module 2: Field Measurement

This module provides different methods used for data collection in the field. This is the core

module for field staff and inventory teams who are practically involved in data collection. When

the inventory teams reach the plot location, they establish the centre and boundary of the

sample plot and start field measurement. Field measurements include measurement of trees,

shrubs, litter, dead wood, soil carbon and other important parameters needed to describe

forest composition and structure.

Objectives

Identify and collect crucial information for forest carbon stocks assessment.

Enable the participants to learn different methods used for measurement of carbon

stocks in different pools in a forest ecosystem.

Accurately measure diameter, height, increment, tree form, crown diameter of sample

trees in the forest.

Measure shrubs, litter and dead wood in the forest.

Accurately collect samples for determination of soil organic carbon.

Outcomes

Data collection protocols

Data on plot level variables

Data on different forest carbon pools

Material required

Multimedia projector

Instruments and tools used for field measurements

Charts, paper, pens, markers and notebooks

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Training Method

This module will have two sessions: first session will have 3 lectures (one hour each) on

collection of information at plot level, measurement of aboveground biomass, leaf/litter, dead

wood and soil organic carbon. The second session will be field exercise for demonstrating

measurement of data collection in the field. This session will be conducted inside forest or a

woody area to enable the participants to learn field measurement. This module will require one

full day.

2.1 Lecture 1: Information at Plot level

The sample plots are navigated in the field with the help of GPS and map. It is a good practice

to identify a prominent feature on the map close to the sample plot and locate this feature on

the ground as well when reach at site. After identify the exact location of the plot, the first step

is to establish the center of the plot by marking on a tree or a big stone and inserting iron rod in

the plot centre. It is advisable that distances and bearing between the plot center and at least

3-4 permanent features around the center should be recorded and sketched on the inventory

form. After establishing the plot center, the boundary of the plot is marked by encircling the

plot with a rope or ribbon and marking on the trees on the border. Two-three photographs of

the sample plot should be taken from different angles.

The following general information should be recorded for every sample plot.

Plot Number : In case of cluster sampling also note PSU Number and SSU Number

Date

Recorder Name

Location: Name of District, Forest Division, Forest Subdivision/Forest Range, Forest

Block, Forest Compartment and Forest Area

Landuse Class: Forest Land, Grass Land, Crop Land, Wetland, Settlement, Other Land

Forest Tenure: Guzara Forest, Reserved Forest, Protected Forest, Communal Forest,

Private Forest

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Forest Type: Natural Forest, Plantation

Stand Composition: Pure Conifer, Pure Broad-Leaved, Mixed

GPS Co-ordinates: Latitude, Longitude of the plot centre

Elevation: Altitude of the plot centre

Slope: Average slope of the plot recorded in percent with clinometer

Aspect: Aspect is recorded with the help of compass e.g. North, South, East, West, North

East, North West, South East, South West

Topographic Position: Position of the plot in the landscape such as top of ridge, middle,

bottom

Crown cover: crown cover is determined with the help of densitometer and counting

the number of grids having shade. Crown cover should be taken at 3-4 points inside the

plot to take average. Crown cover also called canopy cover can be visually interpreted

by the crew members that how much open sky is visible through the crown of the trees.

In midday it can also be estimated from the shade.

Photos: In addition to descriptive data, it is advisable e to take four photos with a good

camera in all four directions at the plot center. Photos should be stored in computer

with other data. It is also important to keep backup of all data in atleast two different

computer systems and external hard drives.

2.1.1 Slope correction

Slope correction is essential in hilly areas where the ground is generally not flat and ordinary

tape is used for laying out the plot. However, if Haglof Vertex is used for measuring the plot

radius then there is no need for slope correction as it automatically measures horizontal

distance.

Use the following SOP for slope correction:

Measure the slope angles with a Clinometer.

The slope angle on uphill and downhill sides can be measured by placing a ranging rod

marked at eye level height and reading the slopes angles with clinometer either in

degree or percent.

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If ranging rod is not available, mark eye level on trees on uphill side and downhill side

and take angles with clinometer.

Add the two angles and divide it by two to get the average slope of the site.

Use a slope correction factor to get the desired radius of the plot for the sloping ground.

2.2 Lecture 2: Measurement of Aboveground Biomass

Aboveground biomass is the most important pool of carbon in a forest ecosystem as it is

subjected to disturbances and changes. Careful measurement of aboveground biomass is

essential for development of emission factors for carbon accounting. Aboveground biomass

includes trees and shrubs.

2.2.1 Measurement of Diameter

Use the following methods for measurement of trees in the sample plot.

Measure all trees with diameter more than 5 cm and occurring inside the 17.84 m radius

sample plots.

Mark the trees at 1.37 m above ground with chalk or paint.

Number the trees in sequence starting from the north and moving clockwise until the

circle is completed.

Record the name of the tree species on the inventory form either in the form of species

code, local name or botanical name.

Record the tree form as healthy, diseased, dead, dry, burnt, leaning, forked, top broken,

wolf tree etc.

Measure Diameter at Breast Height (DBH) on uphill side at 1.37 m from the ground with

the help of caliper or diameter tape.

The tree is marked by putting a cross with chalk to avoid double counting.

If the tree bole is not circular then take two readings with caliper at right angle.

For large size trees which cannot be measured with caliper, use diameter tape.

Put diameter tape around the stem exactly at the indicated point of measurement.

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The person measuring the diameter should loudly speak the name of the species and

the diameter and the person recording the data should repeat the species name and

diameter so that there is no error between the measurer and the recorder.

Include trees on the border of the sample plot if more than 50% of their basal area falls

within the plot and excluded if less than 50% of their basal area falls outside the plot.

Exclude trees having trunks outside and overhanging into the plot.

Include trees with trunks inside the plot and branches overhanging outside the plot.

For trees with unusual or abnormal shape the following principles should be applied:

- If the tree stem is abnormal at DBH due to branching, buttressing, aerial roots or burning,

then the diameter should be measured at a point above or below that abnormal point.

- If a tree is forked and the forking point is at breast height, the tree is regarded as one tree

and the measurement is taken at a point below the forking point. If the forking point is

below breast height, each stem is regarded as a separate tree.

- If the tree is leaning, then DBH should be measured along the stem.

- If the tree has been broken below 1.3 metres, the tree is classified as a stump, and not

measured. It may also be marked as living or dead.

- A tree is regarded as dead, if it does not have any living branches. Trees that are alive but so

badly damaged that they cannot grow until the next growing season are regarded as dead

trees.

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Figure 5: Measurement of Tree DBH (Source: Subedi et al., 2010)

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A view of measurement of DBH in the field

2.2.2 Measurement of Tree Height

Tree height refers to the vertical distance from the ground level to the top of the leading shoot

of the tree. Data on tree height is required for volume and biomass estimation. Tree height

measurement is more difficult and time consuming than DBH measurement. It is usually not

feasible to measure height of each and every tree in the sample plot. It is therefore advisable to

measure height of every fifth tree in the sample plot starting from the north. Later on,

regression equation is developed between DBH and height to estimate height for trees whose

heights have not been recorded in the field. However, if a tree is broken, dry, dead or having

abnormal shape then its height must be measured. Following SOP should be followed for

measurement of tree height in the field.

Measure tree height with Haglöfs Vertex which gives the most accurate estimate for

tree height.

Press ON to activate the instrument.

Set Eye Height -EYE.HGT – in the setting menu.

For measuring tree height on flat ground use 1PL Option. Target top of the tree and

press laser. After pressing the laser a beef sound will be heard indicating that

measurement has been taken.

For measuring tree height on slope, use 2PL Option and aim at of the top of the tree and

press laser, then aim at bottom of the tree and press laser.

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Tree height measurement is displayed on the screen.

For measuring tree height on flat ground, target top of the tree and press laser

If Haglof Vertex is not available, then use Haga Altimeter or Clinometer for

measurement of tree height.

Figure 6: Tree height

For measurement of height of leaning tree, measure the vertical line from the ground to

the top of the tree using Haglof Vertex, Haga Altimeter or Clinometer

Measure the base length i.e. horizontal distance from the bole to the vertical line as

shown in the figure.

Tree height is then determined by applying Pythagorean theorem as follows.

Height of Leaning Tree = Square Root (Square of Vertical Axis + Square of Base Length)

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Figure 7: Measurement of Height of Leaning Tree

2.2.3 Measurement of Increment

Increment is an important variable used to estimate growth rate of forest which is used to

determine carbon sequestration rate. Increment can be determined through periodic

measurement of sample trees in permanent sample plots. However in case permanent sample

plots are not available then diameter increment of coniferous species can be determined from

the measurement of annual rings. It may be noted here that marked and defined annual rings

are only available in coniferous species whereas broad-leaved species do not have distinct

annual rings and hence cannot be counted through naked eye.

Use the following methods to determine increment from annual rings:

Extract cores from the stems at 1.37 m above the ground with the help of Pressler

Borer.

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Count 10 rings in the core starting from the bark side and mark the 10th annual ring with

a pen.

If rings are not fully visible through naked eye use a magnifying glass in the field to

visualize the rings.

Measure the length of 10 annual rings with a ruler which gives radial growth of the tree.

For achieving accurate results measure the cores with the help of Digital Positiometer or

other electronic devices in the laboratory.

2.2.4 Measurement of shrubs, saplings and regeneration

Non-tree woody vegetation includes shrubs, saplings and regeneration. Shrubs, saplings and

regeneration are measured in sub plot having a 5.64 m radius . Shrubs are multi-stem broad-

leaved plants usually forming understory in a forest. Trees with diameter 1-5 cm are considered

as saplings. Plants belonging to the major tree species of the forest and having diameter less

than 1 cm are termed as regeneration. Biomass of shrubs, sapling and regeneration is measured

as follows:

Saplings:

Record name of the species of the saplings.

Measure diameter of saplings at 1.37 m aboveground with the help of caliper or

diameter tape.

Destructively sample 4-5 saplings in the sample plot. Measure their DBH and take fresh

weight of the plants with the help of weighing machine.

Collect representative sample from the felled saplings including stem, leaves and

branches.

Put the samples in the bags and label the bags with plot number and fresh weight of

sample.

Transport samples bags to the base camp.

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Regeneration:

Count regenerations in 4.56 m radius plots.

Record species of regeneration.

Destructively sample 4-5 regenerations in the sample plot. Measure their fresh weight

with the help of weighing machine.

Collect representative sample from the felled regeneration.

Put the samples in the bags and label the bags with plot number and fresh weight of

sample.

Transport samples bags to the base camp.

Shrubs:

Record names of the shrubs. If scientific names are not known then local names are

recorded.

Destructively sample shrubs in subplot with radius 5.64 m (100 m2 area).

Record shrubs cover percentage in the subplot.

Cut all shrubs within the plot flush to the ground and weigh on the spot.

If the shrub cover is dense (i.e. more than 50%) and uniformly distributed then cut 1/4th

of the shrub cover in the plot. If the shrub cover is medium (i.e. 25-50%) then cut ½ of

the plot is cut and if the shrub cover is sparse (i.e. less than 25%) then it is advisable to

cut all shrubs in the subplot.

Take a sub-sample of 1 kg from the cut shrubs. This should be representative of the

total sample and shall be made up of a mix of species and vegetation found within the

total sample.

Place the sample in a sample bag.

Weigh the subsample bag empty and record its weight.

Take fresh weight of the bag with sample using digital balance.

Label the bag with the plot number and fresh weight of the sample.

Bring the sample bags to base station and then to the Laboratory for drying in the oven.

35

Dry the samples in the oven at 105oC till constant weight using a digital balance.

Moisture content is determined by the following formula:

MC% = (Fresh Weight of sample – Dry Weight of sample)/Fresh weight of sample x100

Moisture percentage will be used to create a wet-to-dry ratio. This ratio will then be

used to estimate the total dry weight of shrubs in the sample plot.

If there is any delay between field data collection and laboratory analysis, then place the

sample bags in a location that allows air drying to occur.

2.3 Lecture 2: Measurement of litter and dead wood

Litter and dead wood are two main pools of carbon stock in a forest ecosystem. Different

methods are used to measure carbon stocks in these pools which are elaborated in the

following sections.

2.3.1 Measurement of litter

Litter is defined as the recently fallen non-woody, dead, organic material on the soil surface.

Typically, it consists of dead leaves, flowers, fruits, seeds , dead grass and bark fragments. Dead

wood with a diameter of less than 10 cm is included in the litter layer. Though herbs and grass

are not part of the litter but these may also be measured along with litter.

Circular plot with radius 0.56 m (1 m2 area) is used for measuring litter as follows:

Collect all leaf, litter and grass within 1 m2 plot and put in the bag.

Use knife or scissor or sickle for cutting grass.

Weigh the bag on the spot and record its weight.

Take a well-mixed subsample of 100 g for drying in the oven to determine the ratio of

ovendry to fresh biomass.

Label the bag with the plot number and fresh weight of the sample.

Bring the sample bags to base station and then to the Laboratory for drying in the oven.

Dry the samples in the oven at 105oC till constant weight using a digital balance.

Moisture content is determined by the following formula:

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MC% = (Fresh Weight of sample – Dry Weight of sample)/Fresh weight of sample x100

Moisture percentage will be used to create a wet-to-dry ratio. This ratio will then be

used to estimate the total dry weight of the litter in the sample plot.

If there is any delay between field data collection and laboratory analysis, then place

the sample bags in a location that allows air drying to occur.

2.3.2 Measurement of Dead Wood

Dead wood may be standing or fallen and should be measured within the full 17.84 m radius

plot. Use the following guidance for measurement of dead wood in the forest.

Standing dead tree may be measured in the same way as live tree except that their

decomposition class must be noted.

It is also essential to measure the height of all dead trees in the sample plot.

The specific decomposition classes for standing dead trees are as follows:

1) Tree with branches and twigs and resembles a live tree (except for leaves); 2) Tree with no twig, but with persistent small and large branches; 3) Tree with large branches only; 4) Bole (trunk) only, no branches.

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Fallen dead wood lying on the ground is measured in the 5.64 m radius plot.

There are two methods for measurement of downed dead wood.

Directly weigh the pieces of dead wood and take samples for drying in the oven.

Alternately measure the volume of the dead wood by measuring the diameter at

midpoint and length of the piece of the wood.

Fallen branches and stems should be divided into sections of 2 meters and the exact

length and diameter at the middle of each section should be measured.

2.3.3 Measurement of Stumps

Stumps are the portions of tree stem left after logging. Use the following procedure for

measurement of stumps in the forest.

Measure stumps in 5.64 m radius plot.

If a stump is taller than 1.37 m, it should be measured in the same way as standing dead

trees.

If it is less than 1.37 m tall, the diameter should be measured as close as possible to the

top in two diagonals.

The height of the stump and the state of decay in percent should be recorded.

Figure 8: Measurement of Dead wood and stupms

Source: Subedi et al., 2010

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2.4 Lecture 3: Measurement of soil carbon

Soil organic carbon is an important pool of carbon in the forest ecosystem. This pool is also

affected by landuse change and management activities. Soil carbon is estimated by collecting

soil to a certain depth and then analyzing it in a laboratory for carbon content. This information

is then combined with bulk density measurement to estimate the amount of soil carbon to a

certain depth usually 30 cm (Subedi et al., 2010). For measuring soil carbon, samples are

collected from 0-30 cm depth as prescribed by IPCC (2006). Soil sample is collected from the

center of the subplot with radius 0.56 m (1 m2 area) or other place which best represents forest

types in terms of slope, aspect, vegetation, density, and cover. The exact location of soil sample

is decided on the basis of foresters’ knowledge of the area.

One of the important variables for estimating soil carbon is to determine soil bulk density. For

determining bulk density, three individual soil samples are taken from three depths (0-10 cm,

10-20 cm, and 20-30 cm) with the help of a standardized metal soil sampling corer. One

composite soil sample is made from these three samples to determine concentration of organic

carbon in the soil sample.

The following procedure should be followed for measurement of soil carbon:

Select site for collection of soil sample.

Remove vegetation, leaf/litter from the soil surface.

Collect three soil samples from the same pit 0-10 cm, 10-20 cm and 20-30 cm depths with the

help of soil corer.

Record fresh weight of the samples with the help of digital balance.

If depth of soil at sampling point is less than standard depth measured, then the actual depth of

the sample must be recorded.

Put these samples in separate bags properly labeled with plot number, soil depth, soil weight

etc.

Each sampling plot will have four soil samples: 1 composite sample for soil carbon

determination, and 3 samples for bulk density estimation.

Bring all samples to the base station and then to the laboratory.

39

In laboratory dry the samples in open air.

Put the samples in the oven at 105oC till constant weight.

Record weights of the samples after drying in the oven for determining bulk density as

below:

Bulk Density = Oven Dry Weight of Sample (g) Volume of Soil Corer (cm3)

Take 50 g of oven dry soil in a china dish.

Measure the weight of the empty dish.

Measure the weight of the dish+soil.

Put the dish in the Muffle Furnace at 440oC for 8 hours. The soil organic matter will be burnt on

this high temperature. This method is known as Loss on Ignition (LoI) method

(Schumacher, 2002; Rehman et al., 2011).

After burning of the soil sample, record weight of ash with the help of digital balance.

Organic matter is determined as below:

Organic Matter = Weight of Oven Dry Sample (g) – Weight of Ash (g)

Soil organic carbon is determined by multiplying the amount of organic matter with 0.58.

40

Module 3: Measurement of Carbon Stocks in Mangroves Forests

Mangroves are unique type of forests mostly in the form of small trees or shrubs found in the

estuaries in the coastal areas. They have specialized roots adapted to the coastal ecosystem. In

Pakistan mangroves forests are found in Indus delta of Sindh and coastal areas of Balochistan.

Avicennia marina, Rhizophora mucronata and Ceriops tagal are the common tree species in the

mangroves forests. Due to different plant structure and environment, different measurement

protocol should be followed for measurement of carbon stocks in Mangroves forests.

Objectives

Identify pre-requisites for carbon stocks assessment in mangroves.

Enable the participants to learn different methods used for measurement of carbon

stocks in different pools in mangrove forests.

Accurately collect samples for determination of soil organic carbon in mangroves.

Outcomes

Data collection protocols.

Plot level variables identified.

Tree level variables identified for field measurements.

Material required

Multimedia projector

Instruments and tools used for field measurements

Charts, paper, pens, markers and notebooks

This session comprises 2 lectures and one filed session. First lecture is on basic requirements for

measurement of carbon in mangroves and second on methods used for measurement of

carbon in the field. Field session will be conducted if mangroves forests are available in nearby

areas. This module can be completed in 4 hours.

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3.1 Lecture 1: Sampling Design

Cluster sampling is the most efficient inventory design for carbon stock assessment in

mangroves forest. A cluster sample consists of one plot in the center which is called the primary

sampling unit and four secondary plots on the corners called secondary sampling units 25 m

apart from each other. Thus one cluster covers 0.25 ha area. As there are multiple sample units

in the cluster sampling, they cover a larger portion of the sampled site than a single plot.

Cluster sampling design captures more variation in the forests thereby reducing inter-plot

variation and increasing overall precision. Cluster sampling is also more suited for mangroves

forests because movement in mangroves is difficult to the presence pneumatophores, mud,

deep channels, standing water and extremely dense thickets composed of stilt roots and main

stems.

3.1.1 Plot Shape and Size

Nested circular plot approach is a preferred design for data collection on different carbon pools

in Mangroves Forest. This approach consists of a wider circular plot with radius 8.92 m for

measuring trees with DBH more than 5 cm, and subplots with radius 5.64 m for shrubs and

regeneration and 0.56 m radius plot for measuring pneumatophores and litter. A soil sample is

also extracted from 1 m depth in the center of the plot to determine soil organic carbon in the

ecosystem.

42

Figure 9: A view of cluster sample laid out in Mangroves Forests

3.1.2 Navigation to Sample Plots in the Field

Follow the following steps for navigation to sample plots in the field.

Upload GPS coordinates of the sample plots from the Map.

Navigate to the plot locations with the help of base maps and GPS.

After reaching the sample plot position, identify the exact location of the plot.

Establish the plot center by marking on a tree or another prominent feature.

The sample plots are navigated in the field with the help of GPS and map.

After identifying the exact location of the plot, establish the plot center by marking on a

tree or a stone.

After establishing the plot center, mark the boundary of the plot by encircling the plot

with a rope and marking on the trees on the border.

Take photographs of the sample plot from different angles.

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3.1.3 Information at Plot Level

The following general information should be recorded for every sample plot.

Plot Number : In case of cluster sampling also PSU Number and SSU Number

Date

Recorder Name

Location: Name of Forest Area, District, Forest Division, Forest Range

Landuse Class: Mangroves, Mudflats, water

GPS Co-ordinates: Latitude, Longitude of the plot center and GPS precision

Direction to the plot location

Crown cover: crown cover was determined through visual interpretation by atleast

three crew members and the average was recorded on the inventory form.

Site category: forest, dwarf or scrub (<5 m height), or other (if other, describe)

Ecological condition and land use: intact, degraded or deforested;

Topography (microrelief ): flat, depression, levee or hummock, etc.

Geomorphic setting: river delta, coastal fringe, interior or basin, etc.

Soil description: organic or mineral soil – sand, clay, silts, etc.

Disturbance evidence: - Cyclone damage: not evident, light, moderate or severe; - Timber harvest: not evident, low (<30% basal area), medium (30–70%), or high

(>70%).

- Other disease or disturbance: not evident, light, moderate or severe. Also describe the other disease or disturbance.

3.2 Lecture 2: Measurement of carbon stocks in different carbon pools

This lecture will cover different methods used for measurement f carbon stock stocks in

different pools of mangrove forest ecosystem.

3.2.1 : Aboveground Biomass

Measure all trees falling within 8.92 m radius plots in the field.

44

Record species name, diameter at base, diameter at breast height (DBH) i.e.1.37 m and

tree height on the inventory form.

Measure diameter with the help of caliper or diameter tape.

Record height with the help of Haglof Vertex.

Trees on the border of the sample plot were included if more than 50% of their basal

area was within the plot and excluded if less than 50% of their basal area was outside

the plot.

Trees having trunks outside and overhanging into the plot were excluded, but trees with

trunks inside the plot and branches outside were included.

Determine aboveground tree biomass through the following allometric equations:

Species Allometric Equation Basic Wood Density Source

Avicennia marina B = 0.1848 * D2.3524 0.65 Dharmawan and

Siregar, 2008

Ceriops tagal B = 0.251 * p * D2.46 0.78 Komiyama et al., 2005 Rhizophora mucronata B = 0.043 * D2.63 0.74 Amira, 2008

A view of field measurement in mangroves forest in Karachi

3.2.2 Belowground Biomass

Belowground biomass refers to the biomass present in the roots of plants in the ecosystem

below the soil surface. For belowground biomass, use the following general equation .

45

BGM= 0.199*p^0.899*D2.22………………. (Komiyama et al., 2008) Where

BGM is belowground Biomass in Kg,

p is basic wood density, and D is diameter at breast height

3.2.3 Shrubs and Dwarf Mangroves

A great percentage of the mangroves have an aboveground structure of small trees less than

few meters in height, often referred to as dwarf mangrove or scrub. The best predictors of

biomass for these smaller individuals are stem diameter at 30 cm aboveground level, crown

area, height and/or crown volume.

Shrubs and dwarf mangroves are destructively sampled in 5.64 m radius plot and weighed on

the spot. Samples are collected for drying in the oven. The oven-dried biomass is converted into

carbon stock by multiplying with 0.47 as per IPCC Guidelines.

3.2.4 Litter

In most mangroves, the amount of litter is usually negligible due to the high efficiency of

detritus-consuming crabs, as well as export through tides and seasonal river flooding.

Litter is measured through collecting litter in 0.56 m radius plot. The material is weighed on the

spot and a sample is taken in bags for drying in the oven. The bags are labeled with the

location, date, plot and sample number. Given the wet nature of mangroves, pre-labeled plastic

bags should be used for collection of litter samples.

3.2.5 Pneumatophores

Pneumatophores of Avicennia marina have of significant biomass in the pilot site.

Pneumatophore density is determined by counting their numbers and taking their fresh weight

in the 1 m2 plots established for the litter layer.

46

A view of measurement of pneumatophores in mangrove forest in Karachi Dead and downed wood in mangroves ecosystems should be measured in the same way as in other forest types. 3.2.6 Soil Carbon

Soil carbon is the most important pool of carbon in mangroves ecosystem. Use the same

procedure for measurement of soil carbon in mangroves forests except that samples should be

collected from 0-100 cm with the help of soil corer. As erosion and deposition are regular

features of coastal areas, it is advised by IPCC (2013) to take soil samples from 100 cm depth for

measurement of soil carbon in mangroves forests.

A view of soil sampling in Mangroves forests

47

Module 4: Data Processing and Analysis

Data processing and analysis is an important step in carbon accounting. After completing data

collection in the field, the data is entered in Excel sheets in computer. For this purpose

database is created in Excel. The database should contain sufficient numbers of columns with

proper headings to facilitate data entry and analysis.

Objectives

Enable the participants to learn basic methods for analysis of data collected from the

field.

Learn the application of different allometric equations for biomass estimation.

Learn the analysis of field data for estimation of biomass and carbon stocks.

Learn the development of regression models in MS Excel.

Outcomes

Statistical methods for data analysis

Estimates of biomass and carbon stocks from the field data.

Estimates of soil organic carbon from the field data.

Material required

Multimedia projector

Computers and laptops

Field data samples

This module comprises 6 practical exercises on data analysis which will be performed in the

training hall. This module can be completed in 8 hours.

48

4.1 Exercise 1: Aboveground Tree Biomass

Use the following steps for calculation and analysis of tree biomass:

First of all calculate heights for trees whose heights have not been measured in the

field. For this purpose develop a regression model between DBH and height using the

inventory data collected in the field.

Calculate AGTB by select appropriate allometric equation from the list of equations

given in Table 2.

Directly copy the equations for the correct species. Add an equals-sign, “=”, and copy the

equation into the cell.

Replace the p, D, and H letters with the correct references from the table. p is wood gravity

(density), D is diameters and H is total height.

Drag down the formula to calculate biomass of all trees.

Sum biomass of all individual trees in the plot and multiply it with 10 and divide by 1000 to get

estimates of biomass per ha

Convert tree biomass to carbon by multiplying biomass with Carbon fraction which is 0.47 for all

species.

Table 2: Allometric Equations

Species Regression Model Allometric equation Basic Wood Density

Biomass Expansion Factor

General (Coniferous species)

M = a(pD2H)b M= 0.1645(pD2H)0.8586

0.45 1.30

Cedrus deodara (Deodar)

M = a(D2H)b M= 0.1779(D2H)0.8103

0.46 1.37

Pinus wallichiana (Kail)

M = a(D2H)b M= 0.0631(D2H)0.8798

0.43 1.24

Pinus roxberghii (Chirpine)

M = a(D2H)b M=0.0224(D2H)0.9767

0.45 1.3

Pinus gerardiana (Chilghoza)

M = aDb M = 0.0253D2.6077

0.50 1.30

Abies pindrow (Fir)

M = a(D2H)b M= 0.0954(D2H)0.8114

0.42 1.30

Picea smithiana (Spruce)

M = a(D2H)b M= 0.0843(D2H)0.8472

0.43 1.19

Quercus ilex M = a(D2H)b M= 0.8277(D2H)0.6655 0.89 1.67

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(Oak) Eucalyptus camaldulensis

M = a(D2H)b M=0.023(D2H)0.9985

0.60 --

Acacia nilotica (Kikar)

M = a(D2H)b M=0.0569*( D2H)0.9745

0.70 --

Other species M = a(pD2H)b M= 0.112*(0.6* D2H)0.916 -- --

Source: Ali, 2015, Ali, 2017

4.2 Exercise 2: Aboveground shrub/sapling biomass

Biomass of shrubs, and saplings below DBH of 5 cm were destructively sampled from a 5.64

m radius sample plot. Fresh weight of all shrubs and saplings has been recorded in the field.

A representative sample of this has been dried in the oven to get fresh-dry weight ratio.

Apply fresh-dry weight ratio to get dry biomass of shrubs and saplings for the plot.

Multiply the plot biomass estimates with 100 to get biomass per ha.

Convert biomass to carbon by multiplying biomass with carbon fraction which is 0.47.

4.3 Exercise 3: Belowground Biomass

Calculate belowground biomass using root-shoot ratios from default values provided by IPCC

Guidelines 2006.

Generally Belowground biomass is 25 % of the aboveground biomass.

Multiply the aboveground biomass by 0.25 or the actual root:shoot ratio obtained from IPCC to

determine belowground biomass.

Convert belowground biomass to carbon by multiplying biomass with carbon fraction 0.47.

4.4 Exercise 4: Standing and downed dead trees and stumps

Use the same calculation method for standing dead trees as for standing trees with one

exception. The allometric model is multiplied with the decomposition constant for the

specific decomposition class.

For down dead trees, measure the volume of wood pieces whose diameter and lengths

have been recorded in 5.64 m subplot in the field. Use the following formula for volume

calculation:

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V = (πD2xL)/4

Note that for calculating volume in m3, both diameter and length, should be in meters.

Convert the above calculated volume into biomass by multiplying with the wood

density.

Multiply the biomass with fresh-dry ratio determined for dead wood sample in the

Laboratory to get dry biomass of the dead wood.

Calculate total dead wood biomass per ha by multiplying with 100.

Convert biomass to carbon by multiplying biomass with carbon fraction which is 0.47.

4.5 Exercise 5: Leaf, litter and grass biomass

Biomass of leaf/ litter and grass was sampled from 0.56 m radius sample plot (1m2 area)

in the field.

Fresh weight of all shrubs and saplings has been recorded in the field. A representative

sample of this has been dried in the oven to get fresh-dry weight ratio.

Apply fresh-dry weight ratio to get dry biomass of shrubs and saplings for the plot.

Multiply the plot biomass estimates with 10,000 to get biomass per ha.

Convert biomass to carbon by multiplying biomass with carbon fraction which i s 0.47.

4.6 Exercise 6: Soil organic carbon

Soil samples have been collected from 0-30 cm depth in the field. Two variables are important

for calculating the soil carbon: Soil Bulk Density and Organic Carbon concentration in the

sample.

Calculate soil bulk density through the following formula:

Bulk Density = Oven Dry Weight of Sample (g) Volume of Soil Corer (cm3)

Determine soil carbon through the following formula:

SOC= ρ*d*C*10………………………(IPCC, 2003)

Where ρ is the bulk density of the soil; d is depth of soil sample; and C is carbon

content in the sample.

51

Module 5: Quality Assurance and Quality Control

Quality assurance (QA) and quality control (QC) are must to ensure reliability and accuracy of the carbon

stocks and to meet minimum measurement and analysis standards. The QA/QC provisions are an

integral part of Forest Carbon Measurement SOPs and include procedures for QA/QC related to : (1)

field measurements; (2) laboratory procedures; and (3) data entry and analysis techniques.

Objectives

Enable the participants to learn basic concepts of data quality and data assurance.

Identify crucial steps for ensuring quality of data collection in the field.

Learn good practice about maintaining quality of laboratory analysis.

Identify sources of errors in the data entry and analysis.

Outcomes

Data quality and assurance protocols.

Plan for ensuring quality control in field measurement.

Improved capacity of the participants regarding quality control in laboratory work and

data entry and analysis.

Material required

Multimedia projector

Instruments and tools used for field measurements

Charts, paper, pens, markers and notebooks

This module can be completed in three lectures and one group work. The whole session will be

completed in the training hall. This module can be completed in 5 hours.

5.1 Lecture 1: Verification of Field Measurement

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It is important to check the quality of measurement work in the field. The following steps

should be followed for QA/QC of field measurements.

Separate Quality Control Teams should be constituted and trained for checking the field

measurements.

During every field visit, a document should be produced mentioning that all steps from

the guidelines have been followed: similarly, all deviations from the guidelines, if any,

must be listed.

Three types of checks are recommended for Q/C: a hot check, a cold check and a blind

check.

During a hot check, checkers (technical experts) observe members of the field team

during data collection in the field and provide on-the-spot guidance for correction if any

error or deficiency is observed. Hot checks are focused on correction of errors in

measurement techniques.

Cold checks occur when field parties have completed the field measurement. The data

sheets are checked by the experts who assess the data entry and completeness of the

data.

Blind check is carried out when field work is completed. About 10% of the sample plots

are-measured by the experts/auditors independently. Field data collected during blind

checks is compared with the original data, and errors are identified and corrected. To

provide an estimate of the measurement errors, errors can be expressed as a

percentage of all plots that have been rechecked.

5.2 Lecture 2: Verification of Laboratory Measurement

Forest carbon stock assessment involves some laboratory operations which include

determination of moisture content in vegetation samples and litter; and determination of oven

dry weight and carbon contents in soil samples. The following steps should be followed for

QC/QA of Laboratory Analysis.

53

Calibrate digital balance or weighing machine periodically to check the accuracy.

Check thermometers of oven and Muffle Furnace periodically.

Re-analyze 10-20% of the vegetation samples and soil samples for moisture and carbon

contents determination.

If possible determine carbon concentration in the soil samples through alternate

methods e.g. TOC Analyzer or Walkley Black Method.

5.3 Lecture 3: Verification of Data entry and analysis

The following procedure should be used for verification of data entry and analysis.

At the end of every field campaign, data sheets should be reviewed by the team leader

for completeness, accuracy and correction if required.

Once satisfied with the quality of the data, the team leader should write his name and

the date of the review, along with any notes on issues that were noticed during the

checking, so they can be addressed in the future.

When data is entered into a computer system, each data sheet should be compared to

entered data to ensure accuracy.

Once the data checking is completed and corrections made if any, the data entry

operators should write their name and the date of data entry at the bottom of the data

sheet. If any issue is noticed it should be noted so that they can be corrected in the

future.

A subsample of data sheets e.g. 10% of the total sheets should be compared to the

computer entry by someone other than the person who entered the data. The data

checkers should also write their name and the date of the data review, along with any

notes on issues or corrections made during checking.

It is important that all issues found during data entry and checking are brought to the

notice of the field supervisor so that such mistakes are not repeated in future.

54

5.4 Exercise: Group Work on maintaining data quality

During this session the participants will be divided into groups of 5 members. They will discuss

and identify sources of errors and omissions in the field measurement, laboratory work and

data entry and analysis. Each group will prepare a plan for maintaining data quality in the field

measurement, laboratory work and data analysis. They will share their plans with other

participants in the plenary. At the end of this session a comprehensive plan will be prepared for

quality control and quality assurance in forest carbon measurement.

55

GLOSARY

Aboveground biomass All biomass of living vegetation, both woody and herbaceous, above the soil including stems, stumps, branches, bark, seeds, and foliage.

Afforestation The direct human-induced conversion of land that has not been

forested for a period of at least 50 years to forested land through planting, seeding and/or the human-induced promotion of natural seed sources.

Agroforestry A landuse system that involves deliberate retention, introduction, or

mixture of trees or other woody perennials in crop and animal production systems to take advantage of economic or ecological interactions among the components.

Basic Wood Density Ratio between oven dry mass and fresh stem-wood volume without

bark.

Biomass The total mass of living organisms in a given area or volume; recently dead plant material is often included as dead biomass.

Belowground Biomass All biomass of live roots. Fine roots of less than (suggested) 2mm

diameter are often excluded because these often cannot be distinguished empirically from soil organic matter or litter.

Carbon cycle The term used to describe the flow of carbon (in various forms

such as carbon dioxide) through the atmosphere, ocean, terrestrial biosphere, and lithosphere.

Carbon Stock The quantity of carbon in a pool. Climate Change Climate change refers to a statistically significant variation in either the

mean state of the climate or in its variability, persisting for an extended period (typically decades or longer). UNFCC defines climate change as “a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods.” (United Nations, 1992: 3)

Deforestation The direct human-induced conversion of forested land to non-forested

land. Deforestation involves a decrease in the area covered by forest.

Ecosystem A system of interacting living organisms and their physical environment. The boundaries of what can be called an ecosystem are somewhat

56

arbitrary, depending on the focus of interest or study. Thus, the extent of an ecosystem may range from very small spatial scales to ultimately the entire earth.

Emissions In the climate change context, emissions refer to the release of

greenhouse gases and/or their precursors and aerosols into the atmosphere over a specified area and period of time.

Emission Factor An emissions factor is the coefficient that relates actual emissions to

activity data as a standard rate of emission per unit of activity.

Forest Degradation Degradation does not involve a reduction of the forest area, but rather a quality decrease in its condition, this being related to one or a number of different forest ecosystem components (such as vegetation layer, fauna, soil), to the interactions between these components, and more generally to its functioning.

Forest A minimum area of land of 0.5 ha with tree crown cover of more than

10% comprising trees with the potential to reach a minimum height of 2 meters.

Forest Land This category includes all land with woody vegetation consistent with thresholds used to define Forest Land in the national greenhouse gas inventory. It also includes systems with a vegetation structure that currently fall below, but in situ could potentially reach the threshold values used by a country to define the Forest Land.

Forest Inventory System for measuring the extent, quantity, and condition of a forest.

Greenhouse Gases Greenhouse gases are those gaseous constituents of the atmosphere,

both natural and anthropogenic, that absorb and emit radiation at specific wavelengths within the spectrum of infrared radiation emitted by the earth’s surface, the atmosphere, and clouds. This property causes the greenhouse effect. Water vapour (H2O), carbon dioxide, nitrous oxide, methane and ozone (O3) are the primary greenhouse gases in the earth’s atmosphere.

IPCC The Intergovernmental Panel on Climate Change (IPCC) is a scientific

body jointly established by two United Nations organizations, the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP) in 1989. IPCC is responsible for providing scientific and technical foundations for UNFCCC.

Kyoto Protocol An international agreement covering the period 2008-2012, to slow

climate change. Under the agreement, industrialised countries are bound to reduce their collective GHG emissions by 5.2% from 1990 levels.

57

Mitigation An anthropogenic intervention to reduce the sources or enhance the

sinks of greenhouse gases.

REDD-plus Reducing emissions from deforestation, degradation, sustainable forest management, conserving and enhancing forest carbon stocks.

Reforestation Direct human-induced conversion of non-forested land to forested land

through planting, seeding and/or the human-induced promotion of natural seed sources, on land that was forested but has been converted to non-forested land.

Sink Any process or activity or mechanism that removes a greenhouse gas,

an aerosol, or a precursor of a greenhouse gas or aerosol from the atmosphere.

Source A source is any process, activity or mechanism that releases a

greenhouse gas, an aerosol, or a precursor of a greenhouse gas or aerosol into the atmosphere.

58

REFERENCES

Ali, A., 2017. Biomass and Carbon Tables for Major Tree Species of Khyber Pakhtunkhwa.

Pakistan Forest Institute, Peshawar.

Ali, A., 2015. Biomass and Carbon Tables for Major Tree Species of Gilgit Baltistan. Gilgit

Baltistan Forest Department & Pakistan Forest Institute, Peshawar.

Amira, S. 2008 An estimation of Rhizophora apiculata Bl. biomass in mangrove forest in Batu Ampar Kubu Raya Regency, West Kalimantan. Undergraduate Thesis, Bogor Agricultural

University, Indonesia. Baker, J. D., Richards, G., Grainger, A., Gonzaleza, P., Brown, S., DeFries, R., Held, A., Kellndorfer, J., Ndunda, P., Ojima, D., Skrovseth, P., Souza, C. and Stolle, F., 2010. Achieving forest carbon information with higher certainty: a five-part plan, Environmental Science and Policy, 13:249-260.

Bukhari, S.S.B., Laeeq, M.T. and Haider, A., 2012. Landcover Atlas of Pakistan. Pakistan Forest

Instittue, Peshawar.

Cairns, M. A., Brown, S., Helmer, E. H., Baumgardner, G. A., 1997. Root biomass allocation in the

world’s upland forests, Oecologia, 111:1-11.

Cairns, M.A., Olmsted, I., Granados, J. and Argaez, J., 2003. Composition and aboveground tree

biomass of a dry semi-evergreen forest on Mexico’s Yucatan Peninsula. Forest Ecology & Management 186, 125–132.

Chave, J., Andalo, C., Brown, S., Cairns, M.A., Chambers, J.Q., Eamus, D., Folster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.P., Nelson, B.W., Ogawa, H., Puig, H., Riera, B. and Yamakura, T.,

2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests, Oecologia, 145: 87–99.

Dharmawan, I.W.S. and Siregar, C.A. 2008 Soil carbon and carbon estimation of Avicennia

marina (Forsk) Vierh in Ciasem, Purwakarta. J Penelitian Hutan dan Konservasi Alam Vol V(4):317‐328.

Dixon, R. K., Brown, S. A., Houghton, R. A., Solomon, A. M., Trexler, M. C. and Wisniewski, J., 1994,

Carbon pools and flux of global forest ecosystems, Science 263, 185–190.

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FAO, 2006. Forests and Climate Change. Available from:

ftp://ftp.fao.org/newsroom/en/focus/2006/1000247/index.html

GOFC-GOLD, 2010, A sourcebook of methods and procedures for monitoring and reporting anthropogenic greenhouse gas emissions and removals caused by deforestation, gains and losses of carbon stocks in forests remaining forests, and forestation. GOFC-GOLD Report version

COP16-1, (GOFC-GOLD Project Office, Natural Resources Canada, Alberta, Canada)

IPCC , 2003. Good Practice Guidance for Land use, Land-use Change and Forestry. http://www. ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf_contents.html IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4: AFOLU. Available from: http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html .

IPCC, 2007. Summary for Policymakers: Synthesis Report. An assessment of the Intergovernmental Panel on Climate Change. Available from: http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr_spm.pdf

IPCC, 2013. Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands. Intergovernmental Panel on Climate Change.

Kauffman, J.B. and Donato, D.C., 2012. Protocols for the measurement, monitoring

and reporting of structure, biomass and carbon stocks in mangrove forests. Working Paper 86. CIFOR, Bogor, Indonesia. Komiyama, A., Poungparn, S., Kato, S. 2005. Common allometric equations for estimating the tree weight of mangroves. Journal of Tropical Ecology 21: 471-477.

Komiyama, A. O ng , J . E and Poungparn, S.2008. Allometry, biomass, and productivity of

mangrove forests: A review. Aquatic Botany 89 (2008) 128–137

Litton, M.C., 2008. Allometric Models for Predicting Aboveground Biomass in Two Widespread Woody Plants in Hawaii. Biotropica 40(3), 313–320

Rehman, M.H., Holmes, A.W. and Deurer, M. 2011. Comparison of three methods to estimate

organic carbon in allophanic soils in New Zealand. Horticulture Innovation Centre, Newnham

Park, Te Puna, Tauranga, New Zealand.

Schumacher, B.A. 2002. Methods for the determination of total organic carbon (TOC) in soils

and sediments. United States Environmental Protection Agency, Environmental Sciences

Division National Exposure Research Laboratory, Las Vegas.

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Subedi, B.P., Pandey, S.S., Pandey, A., Rana, E.B., Bhattarai, S., Banskota, T.R., Charmakar,S. and

Tamrakar, R., 2010. Forest Carbon Stock Measurement: Guidelines for Measuring Carbon Stocks

in Community-managed Forests. International Centre for Integrated Mountain Development

(ICIMOD), Kathmandu.

Walker, SM, TRH Pearson, FM Casarim, N Harris, S Petrova, A Grais, E Swails, M Netzer, KM

Goslee and S Brown. 2012. Standard Operating Procedures for Terrestrial Carbon

Measurement: Version 2012. Winrock International.

Watson, C., 2009. Forest Carbon Accounting: Overview and Principles. UNDP, UNEP, UNEP Risoe

Centre, Roskilde, Denmark.

ANNEX-1

INVENTORY FORM

Dated:___________________ Recorder Name: ____________________________

Plot No. ___________ PSU No.______________________SSU No.________________________

Location: _______________________ Compartment: _______________

GPS Coordinates: X __________________ Y ________________Elevation (m)______________

Aspect____________ Slope (%):__________ Crown Cover: ________ Forest Type:___________

Tree No.

Species DBH (cm)

Height (m)

Increment Tree form Crown Dia

1.

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Sapling/ Regeneration Biomass Plot radius:______ Leaf/litter/Grass Biomass

No. Spp. Dia. at base

Ht. (m)

Fresh Wt. (gm)

Total Wt. (Kg) Sample Wt. (Kg)

Soil Sample Weight (gm)

0-15 (Cm) 15-30 (Cm)

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Shrubs Biomass Plot radius:_________ Shrub cover (%)

Portion of plot cut Full ½ ¼

Total Wt. (Kg)

Sample Wt. (Kg)

Stumps Biomass

No. Dia. (cm)

Height (m)

Decay %

CWD/Dead Wood

No. Weight Dia. Length (m)

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