DEVELOP FOREST REFERENCE EMISSION LEVELS/FOREST

REFERENCE LEVEL AND NATIONAL FOREST MONITORING SYSTEM, MEASUREMENT AND REPORTING AND VERIFICATION SYSTEM FOR

REDD+

Mid-term Report

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EXECUTIVER SUMMARY

Under the overarching objective of developing forest reference emission levels/forest reference level

and the National Forest Monitoring System for Measurement, Reporting and Verification for REDD+ in

Pakistan; a two-phase systematic multi-purpose inventory has been designed and piloted to measure

aboveground, deadwood, litter and soil organic carbon pool in all the ecological regions of the country.

So far, the inventory has been completed in Azad Jammu & Kashmir, Punjab and Sindh and is being

undertaken in Balochistan and will be continued in April in Federally Administered Tribal Areas.

Satellite Land Monitoring System based approach has been developed using Landsat imagery to map

spatially explicit time series activity data for the reference years of 1996, 2000, 2004, 2008, 2012 and

2016. The first level of assessment in AJK showed net decrease of 38,007 ha forests during 1996-2000,

net increase of 19,252 ha during 2000-2004, net increase of 14,191 ha in 2004-2008, net increase of

14,27 ha in 2008-2012 and net increase of 7,584 ha in 2012-2016. Similar assessments are being done

of other provinces to meet the required confidence levels for estimation.

Allometric based height-diameter regression model was used to estimate tree-level aboveground

biomass and carbon stock. A generic D-H models was developed for conifers and deciduous trees in

case of AJK carbon stock assessment. For other species, already available specific models developed

for GB and available global allometric models were used. The tree level biomass values are aggregated

at the plot-level and both aboveground and belowground biomass densities were converted to

represent hectare wise amounts. The same was applied for the standing deadwood densities

consisting of both aboveground and belowground parts. The belowground deadwood is calculated for

stumps through root-shoot ratio approximations in case of an approximated standing tree. The default

IPCC fraction (0.47) is applied to convert biomass and carbon, and further carbon to carbon dioxide

equivalent tons with the molecular weight of 44/12. Soil carbon samples are analysed for their organic

carbon contents in the laboratory besides soil bulk densities for cluster samples taken from up to three

depths of 0-10 cm, 10-20 cm and 20-30 cm.

The emission factor for AJK based on the carbon inventory results, for a hectare of forest land

converted to non-forest was found to be 211 t per ha. However, the contributions of litter and soil

pools are found to be very low. The EF’s for other provinces are being computed based on the pilot

inventory data collected.

Based on the mapped temporal activity data and estimated emission factor, forest carbon is modelled

using the most appropriate carbon model and growth rates in the scope of FREL/FRL development for

different forest types of Pakistan. This will serve as a business-as-usual historic baseline (1996-2016)

to project for 2016-2026.

An internationally accepted FREL/FRL template for national and sub-national reporting is developed

and consulted with the stakeholders. The next phase of reporting (draft final) will include the national

land sub-national FREL/FRL reporting based on the ongoing field inventory and activity data mapping.

As some recommendations for methodological indications for Pakistan to move from Tier 2 to Tier 3,

a comprehensive outline has been proposed. As such Tier 3 higher order methods rely on models and

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data from the national ground monitoring adopting a systematic carbon stock and regular disturbance

monitoring conducted at the lowest forest management level of natural and plantation forest

compartments. However, Tier 3 requirements may not be met by field observations only, especially

for large scale forest monitoring. Multi-source approach such as LiDAR based 3D remote sensing in

model-based inventories can assist in accurately collecting tree height and density information.

Alternatively, very high resolution stereo satellite imagery can be used to map and model the canopy

height with similar accuracy and more cost efficiency. To meet the Tier 3 targets, specific allometric

models can be improved significantly without destructive method using the terrestrial LiDAR

measurement of the volume of individual branches and stems of the representative standing trees.

Simulation models such as CO2FIX can be used as an option to model the carbon stocks and fluxes in

the forest biomass, soil organic content, and wood product chain.

A National Forest Monitoring System, integrating the National Forest Inventory, Satellite Land

Monitoring System and the Multiple Benefits, Impacts, Governance and Safeguards is designed and

developed under the cope of REDD+ for Pakistan. A web-based application interface is developed for

data management, sharing, national/sub-national reporting and decision making. This web-based

application is developed using open source tools and application components for the long-term

sustainability of the system. Along with the development of the software system, institutional

framework, processes and methodological protocols are formulated and prescribed based on the

consultation with stakeholders at the national land provincial levels.

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ABBREVIATIONS

AD Activity data AJK Azad Jammu & Kashmir (autonomous territory) BAU Business as Usual BEF Biomass Expansion Factor BEF Biomass Expansion Factor BGB Below Ground Biomass BGC Below Ground Carbon BN Balochistan (province) CBO Community Based Organization cm Centimeter CO2 Carbon dioxide CoP Conference of Parties DBH Diameter at breast height (1.3 m) DGPS Differential GPS EF Emission Factor ESA European Space Agency FAO Food and Agriculture Organization of the United Nations FATA Federally Administered Tribal Areas FCPF Forest Carbon Partnership Facility FD Forest Department (provincial) FOSS Free and Open Source Software FREL Forest Reference Emissions Levels FRL FSMP

Forest Reference Levels Forestry Sector Master Plan

G-B Gilgit-Baltistan (autonomous territory) GCISC Global Change Impact Studies Centre GHG-I Greenhouse Gas Inventory GIS Geographic Information System GoP Government of Pakistan GPS Global Positioning System GUI Graphical Users’ Interface ha Hectare (1 ha = 10,000 m2) HR High Resolution ICIMOD International Centre for Integrated Mountain Development ICT Islamabad Capital Territory (federal capital territory) IPCC Intergovernmental Panel on Climate Change IT Information Technology IUCN International Union for Conservation of Nature km2 Square kilometer (I km2 = 1,000,000 m2)

KP Khyber Pakhtunkhwa (province) LAMP Lidar-Assisted Multisource Program LCCS FAO’s Land Cover Classification System LiDAR Light Detection and Ranging LULC Land Use Land Cover LULUCF Land Use, Land Use Change and Forestry MBIGS Multiple benefits, impacts, governance, safeguards MMU Minimum mapping unit MOCC MOE

Ministry of Climate Change Ministry of Environment

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MRV Measurement, Reporting and Verification NARC Pakistan Agricultural Research Council NASA The National Aeronautics and Space Administration NESPAK National Engineering Services Pakistan NFI National Forest Inventory NFMS National Forest Monitoring System NGO Non-governmental Organization NTFP Non-Timber Forest Product NUST National University of Sciences and Technology (NUST) OIGF OBIA

Office of Inspector General of Forests Object Based Image Analysis

PB Punjab (province) PEPA Pakistan Environmental Protection Agency PFI Pakistan Forest Institute QGIS Quantum GIS REDD Reducing Emissions from Deforestation and Forest Degradation REDD+ Reducing emissions from deforestation and forest degradation and the

role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries

RF Removal Factor R-PP Readiness Preparation Proposal RS Remote Sensing SAR Synthetic Aperture Radar SD Sindh (province) SIS Safeguard Information System SLMS Satellite Land Monitoring System SOP Survey of Pakistan SSL Secure Sockets Layer SUPARCO Pakistan Space and Upper Atmosphere Research Commission THBS Timber Harvesting Ban Study UNDP United Nations Development Programme UNEP United Nations Environment Programme UNFCCC The United Nations Framework Convention on Climate Change

UoP University of Peshawar

VHR Very High Resolution

WWF-P World Wide Fund-Pakistan

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CONTENTS

Executive Summary

1. INTRODUCTION 1

2. MODELLING/ESTIMATION AND STATISTICAL PROJECTIONS OF EMISSIONS/REMOVALS 2

2.1. Overview of the modelling and estimation process 2

2.2. Time-series analysis with Activity Data 3

2.3. Emission and removal factor modelling 5 2.3.1. Above-ground and below-ground biomass modelling 5 2.3.2. Emission and removal factor development considering all the relevant carbon pools 7 2.3.3. Literature review for forest carbon modelling references 9

2.4. Statistical Projections 15

3. NATIONAL AND SUB-NATIONAL FREL/FRL 16

3.1. National FREL/FRL 16

3.2. Sub-national FREL/FRL 16

4. METHODOLOGICAL INDICATIONS FOR PAKISTAN HOW TO EVENTUALLY MOVE FROM TIER 2 TO TIER 3 17

5. NATIONAL FOREST MONITORING SYSTEM DESIGN AND INSTITUTIONAL FRAMEWORK 20

5.1. Design of measurement, reporting and verification 20

5.2. Design of multiple benefits, impacts, governance and safeguards 23 5.2.1. Multiple Benefits 23 5.2.2. Benefit-sharing 24 5.2.3. NFMS and MBIGS/SIS linkage 25

5.3. Web Portal 27 5.3.1. User interface 27 5.3.2. Information contents 27 5.3.3. System Architecture 28 5.3.4. User management (permission) 28 5.3.5. Uploading/updating interface 29

5.4. GHG-I reporting 29

5.5. Institutional framework 30

References

List of Tables

Table 1 Illustration of the activity data consistency check logic to avoid false detection due to optical

remote sensing data properties. 4

Table 2 Allometric models for biomass estimation (ρ = basic wood density, D = Diameter at Breast

Height in cm, H = Height in meters). 7

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Table 3 Forest carbon stock densities by the main land use and cover classes in AJK. 8

Table 4 The emission factor for AJK based on the carbon inventory results when a hectare of forest

land is converted to non-forest. The contribution of litter and soil pools are found to be very low. 9

Table 5 The above-ground carbon density summary by forest types in case of KP and GB inventories

and comparison with the draft AJK results. 10

Table 6 Indicated anthropogenic and non-anthropogenic drivers of deforestation (DEF) and forest

degradation (DEG) during the provincial consultations. 22

Table 7 NFMS functions and recommended institutional roles 32

List of Figures

Figure 1 The modelling process for estimating the emissions and removals by the forestry sector are

covered in the workflow steps 4 and 5. 2

Figure 2 AJK – Error-adjusted forest and non-forest cover with 95 % confidence intervals estimated

based on visually interpreted plots and Olofsson et al. (2013) methodology. 3

Figure 3 Deforestation and forest restoration statistics for AJK during the 5 epochs from 1996 to 2016.

4

Figure 4 Net forest area change statistics for AJK during the 5 epochs from 1996 to 2016. 5

Figure 5 The D-H model for Abies pindrow developed with help of the height sample trees measured

in AJK. Other species-specific models have been developed in case of Cedrus deodara, Pinus

wallichiana and Pinus roxburgii. 6

Figure 6 The planned clustered sample plot distribution over the ecological zones 8

Figure 7 Forest canopy height and density profile measured with airborne lidar. Yellow points show

ground level and white points are returns from vegetation. 18

Figure 8 The modules of CO2FIX (Schelhaas et al. 2004). 19

Figure 9 NFMS system design 21

Figure 10 NFMS System architecture 28

Figure 13 Institutional framework for NFMS in scope of REDD+. 31

Glossary of Relevant Terms

Annexes Annex 1 Survey and Mapping License from SOP for mapping works Annex 2 Forest cover maps for AJK Annex 3 Screenshots of NFMS Graphical Users’ Interface

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1. INTRODUCTION

As an obligation step for the REDD+ readiness, Joint Venture Arbonaut and WWF-Pakistan is in process

of developing national and sub-nation FREL/FRL, expressed in tons of CO2 (equivalent) per ha, as the

benchmark for assessing Pakistan’s performance in implementing the provisions of REDD+.

Since the interim report revision and final submission in November 2017 the field inventory work has

been completed in Azad Jammu & Kashmir (AJK) (October-November 2017), Punjab (PB) (end of

December 2017), Sindh (SD) (end of January 2018), while it continues in Balochistan (BN) in March and

Federally Administered Tribal Areas (FATA) in April. The field inventory campaign aims to pilot the

multi-purpose forest inventory protocol and field survey measurements to meet the objectives in

scope of the FREL/FRL/NFMS project in Pakistan. The purpose of this pilot inventory is to collect

necessary reference data for

▪ an informed decision to include the most relevant pools in the national FREL/FRL, ▪ integrating existing provincial forest inventory data and newly collected data for developing

national emission factors (Tier 2), ▪ collecting ground-truthing reference data for land use and cover map validation, ▪ validating the forest boundary demarcation produced by WWF, ▪ assessing and enhancing national and provincial capacities for forest inventories, and ▪ designing the National Forest Inventory as a component of the National Forest Monitoring System. The two-phase sampling design for measuring planned 87 clusters (PSUs) has been described in detail

in the Interim Report. The pilot inventory includes aboveground (living trees, shrubs, saplings),

deadwood (standing, downed and downed deadwood), litter and soil organic carbon pool

measurements. Soil bulk density samples are collected to support soil carbon estimation. In parallel

with the inventory campaign completion, the boundary demarcation pilot in 10 districts is planned to

be conducted PB, AJK and KP in April-May 2018.

During the period between interim and mid-term reporting a series of visits were conducted to various

provincial capitals, for discussions to understand the viewpoints of the Provincial Management

Committees’ members representing forestry, agriculture, environment, livestock, and research and

academic institutions. After presentations about the progress of work done by JV Arbonaut and WWF-

Pakistan, open house discussions were held about the drivers of deforestation and forest degradation

and the strategic options to deal with the threats.

WWF has been granted with a mapping and surveying license extension in January 4th, 2018 by the

Survey of Pakistan (SOP) and a copy of the extension confirmation letter is found as Annex 1. This has

allowed proceeding effectively with the land use and cover mapping process. Landsat imagery for

mapping activity data between the years 1996 and 2016 are screened, selected, acquired and pre-

processed as photo mosaics. The maps containing the national and provincial boundary data has been

accessed and the SOP boundaries are being used as the reference for further mapping works and

allowing to produce the official statistics.

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2. MODELLING/ESTIMATION AND STATISTICAL PROJECTIONS OF

EMISSIONS/REMOVALS

2.1. Overview of the modelling and estimation process

Modelling and estimating statistical projections for the future emissions and removals relies on the

activity data produced with the SLMS methodology and emission factors developed with the forest

inventory data of the terrestrial carbon stocks. When each forest inventory mission is completed the

measurement, data is entered into a digital database, cleansed for any missing data or errors, compiles

and exported as pre-processing steps before starting the forest inventory calculation process. A forest

inventory calculation process produces carbon density value (tons per hectare) at sample plot-level by

aggregating tree bio-physical tree measurements and sample data collected for lab analysis.

The field inventory planning (1), data collection (2) and calculation steps (4) are presented in Figure 1.

The forest carbon emissions and removals are accounted and compiled in step 5.

Figure 1 The modelling process for estimating the emissions and removals by the forestry sector are

covered in the workflow steps 4 and 5.

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2.2. Time-series analysis with Activity Data

The past trends in gross deforestation, forest degradation and carbon enhancement stock are acquired

through activity data mapping. The activity data mapping is based on land use and land cover

classification using satellite imagery for each of the reference years (Figure 2). The SLMS training

manual provides the detailed process how the one-time maps can be produced for a point of time

under interest. Once the SLMS process is completed for two points of time, the changes over the time

can be analysed and activity data generated accordingly.

For the FRL development, the activity data statistics should consider the spatial events of 1) no change

(i.e. no land use or cover change), 2) deforestation (i.e. forest conversion to other land uses), 3) forest

degradation (i.e. forest canopy cover / carbon loss, dense forest to sparse forest), 4) forest

regeneration (i.e. afforestation/reforestation), and 5) carbon stock enhancement (i.e. regrowth due to

conservation). The FREL process can consider deforestation and degradation processes. Each land unit

needs to be classified as forest and non-forest. In general, forest canopy cover and height changes can

be applied as proxy variables associated to changes in forest biomass and carbon density. In practice

separating the forest cover classes accurately when using 30-meter Landsat imagery is very

challenging. The forest height information cannot be derived without applying mapping techniques and

somewhat costly 3D datasets.

Both the deforestation and forest degradation events can be declared when the changes can be

considered to remain permanent according to the regeneration and regrowth succession time

thresholds. Applying the minimum time elapse thresholds and consistency checks eliminates the false

declarations when forest clear-felling or selective logging temporary effects are accounted as

deforestation and forest degradation. The applied approach for activity consistency checks is

illustrated in Table 1.

Hec

tare

s

Figure 2 AJK – Error-adjusted forest and non-forest cover with 95 % confidence intervals estimated

based on visually interpreted plots and Olofsson et al. (2013) methodology.

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Table 1 Illustration of the activity data consistency check logic to avoid false detection due to optical

remote sensing data properties.

SN Land cover in 2004

Mapped activity in 2008-2012

Land cover in 2016

Result

1 Forest Deforestation Non-forest Real deforestation

2 Non-forest Afforestation Forest Real afforestation

3 Non-forest Deforestation Forest No change

4 Forest Afforestation Non-forest No change

The activity data based on the SLMS mapping approach regarding to deforestation and forest

restoration in AJK for the period 1996-2016 is illustrated in Figure 1. It provides accounts for the

changes considered permanent in the forest and non-forest categories for the consequent reference

years.

Figure 3 Deforestation and forest restoration statistics for AJK during the 5 epochs from 1996

to 2016.

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2.3. Emission and removal factor modelling

2.3.1. Above-ground and below-ground biomass modelling

Tree-level aboveground biomass and carbon stock are normally estimated using either allometric

models or biomass expansion factors (BEF). BEF helps to convert stem volume to above-ground

biomass when applied when tree or forest stem volumes are known. Similarly, belowground biomass

is estimated with allometric models or root shoot ratios. An allometric models predicts a dry weight of

tree aboveground and below-ground biomass. Allometric models for volume and biomass can be used

for deriving BEF. Destructive sampling is conducted to collect the reference data samples to produce

an allometric equation through regression modelling using the independent variables that are easily

measurable during forest inventories. In destructive sampling a representative count of uprooted trees

is dissected in pieces as 1) leaves, 2) branches, 3) stems, and 4) stump and roots. A destructive sampling

procedure is very laborious as each dissected tree piece is weighted and disc samples are taken for

wood density assessment to a lab.

Tree height has often a very high prediction power in allometric equations but measuring heights for

every sample tree may be time-consuming to measure accurately in field. For that reason, a limited

sample of enumerated trees (every 5th tree) is measured for its height during the pilot field campaign.

Allometry is utilized to develop a height-diameter regression model and estimate missing tree heights

(Figure 5). There are many non-linear regression model options for D-H model development, but to

name a few such as Näslund, Power, Curtis and Korf functions being some of the most prominent ones

(Mehtätalo et al 2015). These functions may help to enhance species-specific D-H modelling if a

representative number of trees is available from different site conditions. The generic D-H models have

been developed for conifers and deciduous trees in case of AJK carbon stock assessment.

Figure 4 Net forest area change statistics for AJK during the 5 epochs from 1996 to 2016.

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Tree allometry depends on environmental and genetic factors that vary from country to country and

eco-region to eco-region. Consequently, theoretical models that include too few ecological explicative

variables or empirical generic models that have been calibrated at sites, are unlikely to yield accurate

tree biomass estimates at other sites. The absence of species-specific or mixed-species allometric

equations may lead to broad use of general equations to estimate above-ground and below-ground

biomass. This may raise concerns on their accuracy, since equations were derived from biomass

collected outside the application context Chave et al. 2014. Allometric modelling is an important

source of uncertainty to be quantified in the carbon estimation process.

Some valuable allometric biomass model has been developed in GB by (Ali 2015). The discovered

candidate models with references are provided in Table 2. The behaviour of each model is to be tested

for their validation before their full extent application for national and sub-national reference level

development. The global allometric models developed by Chave et al. 2014 will serve as an option

when the suitable forest type specific model is not available from the local research projects.

Figure 5 The D-H model for Abies pindrow developed with help of the height sample trees measured

in AJK. Other species-specific models have been developed in case of Cedrus deodara, Pinus

wallichiana and Pinus roxburgii.

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Chave et. al. 2014 have studied general allometric models based on their research on tree allometry

and improved estimation of carbon stocks and balance in tropical forests. These regression models

have been tested for secondary and old-growth forests, for dry, moist and wet forests, for lowland and

montane forests, and for mangrove forests. The most important predictors of AGB have been found -

in decreasing order of importance – DBH, wood specific gravity, height and forest type (i.e. dry, moist,

or wet). However, these regression models can be used reliably to predict aboveground tree biomass

across a broad range of tropical forests, if local general or species-specific models are not available.

2.3.2. Emission and removal factor development considering all the relevant carbon pools

The tree level biomass values are aggregated at the plot-level and both aboveground and belowground

biomass densities are converted to represent hectare wise amounts. The same applies to the standing

deadwood densities consisted of both aboveground and belowground parts, while a decomposition

factor classified for each tree are used to discount carbon density. The belowground deadwood is

calculated for stumps through root-shoot ratio approximations in case of an approximated standing

tree. The default IPCC fraction (0.47) is applied to convert biomass and carbon, and further carbon to

carbon dioxide equivalent tons with the molecular weight of 44/12.

Soil carbon samples are analyzed for their organic carbon contents in the laboratory besides soil bulk

densities for cluster samples taken from up to three depths of 0-10 cm, 10-20 cm and 20-30 cm. The

soil carbon contents for each depth is calculated with the following formula (Yigini and Panagos 2016):

Table 2 Allometric models for biomass estimation (ρ = basic wood density, D = Diameter at Breast

Height in cm, H = Height in meters).

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SOCstock = SOC × ρb ×d Eq.1

Where, SOCstock represents soil organic carbon stock (tons/ha), SOC the soil organic carbon content

(%), ρb the bulk density (g/cm3) and d the sampling depth (cm).

Table 3 Forest carbon stock densities by the main land use and cover classes in AJK.

Land use/cover AGC BGC DWC LC SOC (0-30 cm)

Forest

Temperate (Dry) 102 33 0.9 86

Temperate (Moist) 62 20 0.8 89

Subtropical Broadleaved 32 6 0.8 86

Subtropical Pine 27 9 0.9 61

Tropical thorn 1 0 0.3 90

Non-forest

Cropland 13 6 0.6 88

Grassland 3 1 1.4 80

Mixed 19 8 0.4 96

Carbon densities for all the

carbon pools (aboveground

(AGC), belowground (BGC),

deadwood (DWC), litter and soil

organic carbon) are calculated for

forest and non-forest plots as

averages by ecological zones

(Figure 6). The pilot inventory

sampling has been designed to

cover proportionally all the major

ecological zones found in AJK,

Punjab, Sindh, Balochistan and

FATA. The newly collected data

and existing carbon data from GB

and KP will be used to develop

unbiased carbon density

estimates and emission/removal

factors.

When dense forests are

converted to sparse forest and

non-forests, net CO2 emissions are normally released into the atmosphere (Table 4). The total emission

impact is consisted of the changes in different pools. When non-forest areas are converted to forests,

CO2 is sequestrated in forest biomass and soil over time. Sustainable forestry practices are maintaining

the forest carbon stock level, even though temporary changes can happen due to harvesting activities

and wood recovered for construction purposes can be accounted separately in scope of GHG-I.

Figure 6 The planned clustered sample plot distribution over the

ecological zones

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Table 4 The emission factor for AJK based on the carbon inventory results when a hectare of forest land

is converted to non-forest. The contribution of litter and soil pools are found to be very low.

Carbon density by land use Aboveground Belowground Deadwood Litter Soil Total

Forest (t/ha) 57 18 - 1 86 163

Non-forest (t/ha) 12 5 0 1 87 105

Forest C - Non-Forest C (t/ha) 45 13 0 0 -1 57

Emission factor (CO2-e t per ha)

166 47 0 0 -2 211

2.3.3. Literature review for forest carbon modelling references

A literature review has been conducted for the publications with relevance to quantifying carbon

sequestered in forest carbon pools. The review results may support making decisions for adopting the

most appropriate pools and associating the best available carbon models and growth rates in scope of

the FREL/FRL development with the ecological forest types of Pakistan.

Terrestrial Carbon Stocks

The carbon stock assessment of Khyber Pakhtunkhwa (Ali 2017) consisted of 1263 clustered plots

indicates the highest amount of carbon (78 %) is sequestered in temperate forests followed by sub-

tropical chirpine forests (10 %). Oak and other sub-alpine forests have a share of 4 % each, sub-tropical

broad-leaved forests 3 %, while dry tropical thorn forests contribute 1 % to the total carbon stock.

About 46 % of the total carbon stock is stored in aboveground biomass; 14 % belowground; 1 % in leaf

litter and 39 % in soil. In the forest carbon inventory of Gilgit-Baltistan carbon stocks in different forest

areas and strata were determined through terrestrial carbon inventory (Ali et al. 2017). For this

purpose, data was collected from 537 sample plots (0.1 ha each) selected through stratified random

sampling. Deodar had a share of 23 % followed by blue pine (22 %), spruce (20 %), oak (15 %), fir (7 %),

birch (7 %) and followed by others. The same field plot design is used in the current pilot inventory.

The above-ground carbon densities of the KP and GB inventories are summarized by forest types in

Table 5.

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Table 5 The above-ground carbon density summary by forest types in case of KP and GB inventories and comparison with the draft AJK results.

Forest Type

KP Dense KP Sparse KP Average GB Dense GB Sparse AJK Average

(CC > 50%)

(50 >CC >10%)

(CC > 10%) CC > 35% CC < 35% CC > 10%

No. of

Plots

No. of

Plots

AGC (ton/ha)

No. of Plots

AGC (ton/ha)

No. of Plots

AGC (ton/ha)

No. of Plots

AGC (ton/ha)

Sub-alpine - - 34.27 - - - - -

Dry temperate (conifer) 129 103 99.41 240 91.65 177 8 102

Dry temperate (mixed) - - 29 68.97 40 25.77 - -

Dry temperate (broadleaved) 66 96 34.58 18 60.68 33 18.74 -

Moist Temperate 220 264 85.04 - - - - 21 62

Subtropical (pine) 119 162 24.77 - - - - 5 27

Subtropical (broadleaved evergreen) 3 83 4.52 - - - -

5 32

Dry Tropical Thorn 0 18 4.48 - - - - 3 1

Total 537 726 - 287 - 250 - 42 -

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For both of the carbon stock assessments IPCC (2006) default root-shoot ratios have been applied in

both cases of KP (0.29) and GB to calculate belowground carbon contents, as well. The IPCC default

factors have been used to generate the AJK belowground carbon stocks.

Carbon stock assessment of moist temperate forest Yakhtangay district Shangla and its relation to

insect infestations by Iqbal and Hussain 2016. The study was conducted at Yakhtangay located at an

elevation of about 1830-meter above sea level. It was found that mean trunk density of Pinus

wallichiana was 404 (± 4.7) trees per ha, of Abies pindrow, 160 (± 4.32) trees per ha. In case of Pinus

wallichiana the average stem biomass 78.51912 ± 2.03 (t per ha) in the basal area of 18.17 ± 0.8 m2

per ha. The average total biomass in the most extreme Pinus wallichiana 159.26 ± 1.78 t per ha and

there was less biomass 0.76 ± 0.86 tons per hectares in Abies pindrow. The BEF value of 1.59 was

adopted to convert stem biomass to above-ground biomass and carbon fraction 0.5 to convert to

biomass to carbon.

Variation of biomass and carbon pools with forest type in temperate forests of Kashmir Himalaya, India

(Dar and Sundarapandian 2015). That study aimed at an accurate characterization of tree, understory,

deadwood, floor litter, and soil organic carbon pools in temperate forest ecosystems. In this regard, a

research was conducted to measure the carbon stocks for Populus deltoides (PD), Juglans regia (JR),

Cedrus deodara (CD), Pinus wallichiana (PW), mixed coniferous (MC), Abies pindrow (AP), and Betula

utilis (BU). The results showed that tree biomass ranged from 100.8 t per ha in BU forests to 294.8 tons

per ha for the AP forest. The understory biomass ranged from 0.16 t per ha in PD forest to 2.36 t per

ha in PW forest. Deadwood biomass ranged from 1.5 t per ha in PD forest to 14.9 t per ha for the AP

forest, whereas forest floor litter ranged from 2.5 t per ha in BU and JR forests to 3.1 t per ha in MC

forests.

The BEFs for hardwoods, spruce, and pine were calculated using equations:

Hardwood: BEF = exp {1.91–0.34 x ln(GSVD)} (for GSVD ≤ 200 m3 ha–1), Eq. 2

BEF = 1.0 (for GSVD > 200 m3 ha–1). Eq. 3

Spruce–fir: BEF = exp {1.77–0.34 x ln(GSVD)} Eq. 4

(for GSVD ≤ 60 m3 ha–1),

BEF = 1.0 (for GSVD > 160 m3 ha–1). Eq. 5

Pine: BEF = 1.68 (for GSVD < 10 m3 ha–1), Eq. 6

BEF = 0.95 (for GSVD = 10–100 m3 ha–1); Eq. 7

BEF = 0.81 (for GSVD > 100 m3 ha–1). Eq. 8

and belowground biomass using the regression equation of Cairns et al. 1997:

BGB = exp {-1.059 + 0.884 x ln AGB + 0.284} Eq. 9

Results showed that a higher percentage (63 %) of C was stored in biomass and less in soil in temperate

forests except at the higher elevation broad-leaved BU forest. The mean soil organic C up to 0–30 cm

depth of soil ranged from 39.1 to 91.4 t C per ha with the mean value of 60.7 t C per ha. The SOC pool

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varied significantly among the forest types. Of the total carbon stock in 0–30-cm soil, about 55 % was

stored in the upper 10 cm.

Variation in carbon stocks on different slope aspects in seven major forest types of temperate region

of Garhwal Himalaya, India by Sharma et al 2011. The study was undertaken in seven major forest types of

temperate zone (1500 to 3100 m asl) of Garhwal Himalaya to understand the effect of slope aspects on

carbon density. In this regard, tree density, biomass and soil organic carbon (SOC) on four aspects

(north/east, north/west, south-east, and south-west) were assessed in forest stands dominated by Abies

pindrow, Cedrus deodara, Pinus roxburghii, Cupressus torulosa, Quercus floribunda, Quercus semecarpifolia

and Quercus leucotrichophora. Tree carbon density ranged between 77.3 t per ha on SE aspect (Quercus

leucotrichophora) and 291.6 t per ha on NE aspect (moist Cedrus deodara). SOC varied between 40.3 t per

ha on SW aspect (Himalayan Pinus roxburghii) and 177.5 t per ha on NE aspect (moist Cedrus deodara

forest). Total tree and soil carbon density ranged between 118.1 t per ha on SW aspect (Pinus roxburghii

forest) and 469.1 t per ha on NE aspect (moist Cedrus deodara forest). The total densities were significantly

higher on northern aspects compared to southern aspects. The same BEF and belowground models were

used as by Dar and Sundarapandian 2015.

Live tree biomass and carbon variation along an altitudinal gradient has been studied in moist

temperate valley slopes of the Garhwal Himalaya, India (Gairola et al 2011). The relationships between

diversity, biomass and carbon stocks at varied altitudes can have crucial implications for the

management and conservation of carbon sinks. In this regard, the research was conducted in moist

temperate forests of Chamoli District, Garhwal to assess live tree biomass and carbon stocks along an

altitudinal gradient and to determine the relationship of live tree carbon density with altitude, species

richness, diversity and density. It was also intended to compare values of live tree biomass and carbon

density with the earlier reported values in other parts of Garhwal. Results showed that total live tree

biomass density varied from 215.5 to 468.2 t per hectare while tree carbon density varied from 107.8

to 234.1 t per ha. The average values of tree biomass and carbon density for the study area were 356.8

(± 83.0) t per ha and 178.4 (± 41.5) t per ha respectively. The same BEF and belowground models were

used as by Dar and Sundarapandian 2015.

Carbon stocks assessment in subtropical forest types of Kashmir Himalayas by Shaheen et al 2016.

Total carbon stock was computed as 186.27 t per ha with the highest quantity of 326 t per ha at Pinus

roxburghii forest and the lowest of 75.86 t per ha at mixed forests. Values for tree density were 492

stems per ha while the average DBH was 87.27 cm; tree height, 13.3 m; and regeneration of 83

seedlings per ha. Forest soil carbon stock was reported 34.89 t per ha and for agricultural soil, it was

27.18 t per ha. Above ground biomass was calculated by using allometric equations developed on the

basis of forest types, species and ecological conditions. The literature survey was carried out for the

selection of species specific as well as general allometric models were used:

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Acacia spp. 0.071+0.0818D2H (Nizami et al. 2009) Eq. 10

Broussonetia papyrifera 0.776(ρD2H) 0.940 (Chave et al. 2005) Eq. 11

Dalbergia sissoo 0.667D1.832 (Chave et al. 2005) Eq. 12

Ficus spp. 0.0421(D2*H)0.9440 (Gibbs et al. 2007) Eq. 13

Mallotus philippensis 0.0547*D2.1148*H0.6131 (Gibbs et al., 2007) Eq. 14

Pinus roxburghii 0.0509* ρ D2 H (Chave et al., 2003) Eq. 15

Biomass and carbon stocks estimation in Chichawatni irrigated plantation in Pakistan by Arif et al.

2017. Carbon density was estimated through sample plots on a five-year interval age class basis. The

studied plantations area extent was total 3823 ha with Dalbergia sissoo (71.6 %), Eucalyptus

camaldulensis (19 %), Prosopis juliflora and Prosopis glandulosa (7.70 %) and another 1.7 % occupied

by other species. Results revealed that Diverse biomass had different carbon stock per hectare:

Dalbergia sissoo, Eucalyptus camaldulensis and Prosopis spp. and other mix species had carbon stock

of 95.45, 139.47, 34.54 and 97.06 t per ha respectively. Carbon stocks available in the upper storey

biomass was 120.61 (± 17.85) t per ha and in the lower storey, it was 2.45 (± 0.83) t per ha while carbon

stock in the soil was 29.26 (± 14.54) t per ha. The overall proportion of carbon density in upper storey,

under storey and the soil thus came to 74.44 %, 1.96 % and 23.60 % respectively. It was concluded that

upper storey biomass produced maximum carbon density while Eucalyptus camaldulensis was the

leading tree species producing the highest carbon density. On the other hand, mixed tree species

stands produced the highest carbon density both in under storey and soil. Stem biomass was computed

from volume and wood density (PARC 1994, Haripriya, 2000) of relevant tree species by using the

following relationship (Ahmad et al. 2014; Saeed et al. 2016);

Biomass (kg) = Volume (m3) × Basic wood density (kg m-3) Eq. 16

Volume of leaves, twigs, branches and stem was calculated by using Biomass Expansion Factor (BEF)

(Lehtonen et al., 2004; Somogyi et al., 2007; Ahmad et al., 2014; Saeed et al., 2016). The BEF values of

different tree species were obtained from the source literature (Haripriya, 2000).

Soil Organic Carbon Stock and Sink Potential in High Mountain Temperate Himalayan Forests of India

Bhat et al. 2012. Estimation of soil organic carbon stock along altitudinal gradient was done in

Kedarnath Wildlife Sanctuary of Garhwal. Moisture content in the soil increased with increasing

altitude and the highest moisture was recorded in Betula utilis forest at an elevation of 3,550 m amsl.

This was followed by Grassland (3,050 m); Rhododendron arboreum forest, (2,550 m); mixed forests

(Quercus and Rhododendron species, 2050 m); and Pinus roxburghii forest (1,550 m). The bulk density

also followed similar trend as moisture, which increased with increasing altitude. The soil carbon stock

also increased with altitude and the highest carbon stock (35.4±1.8 C ton per ha) was found in Betula

forest soils and the lowest (19.2±2.7 ton C per ha) in Pinus roxburghii forest soils. Thus, moisture

showed positive correlation with carbon stocks - higher the moisture, higher was the carbon stock.

Organic carbon pool in the soils under different forest covers and land uses in Garhwal Himalayan

Region of India by Gupta and Sharma 2011. The soil organic carbon pool was estimated in Shorea

robusta, Cedrus deodara, Quercus leucotrichophora, Pinus roxburghii, Picea smithiana and Abies

pindrow, Pinus wallichiana and plantations of Eucalypts, Tectona grandis, Dalbergia sissoo and Pinus

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roxburghii. It was found that SOC pool was the maximum in the forest lands followed by grass lands

and plantation areas. P. smithiana and A. pindrow forests had higher mitigation potential as they can

store more than double the SOC pool compared to S. robusta forests.

Soil Carbon Stocks along an Altitudinal Gradient in Different Land-Use Categories in Lesser Himalayan

Foothills of Kashmir by Shaheen et al. 2017. Carbon stocks were estimated in different land use

categories: closed canopy forests, open forests, disturbed forests, and agricultural lands within the

altitudinal range from 900 to 2,500 m. Average soil carbon stock was 2.59 kg per m2 however the

average soil carbon stocks in closed canopy forests, open forests, and disturbed forests were 3.39,

2.06, and 2.86 kg per m2 respectively. As against this, average soil carbon stock in the agricultural soils

was 2.03 kg per m2. Carbon stocks however showed a decreasing trend with the altitudinal gradient

with maximum of 4.13 kg per m2 at 900–1,200 m and a minimum of 1.55 kg per m2 at 2,100–2,400 m.

Agricultural soil showed the least carbon content indicating negative impacts of soil leading to soil

degradation. Lower carbon values at higher altitudes attest to the immature character of forest stands,

as well as to degradation due to immense wood extraction besides harsh climatic conditions.

Carbon sequestration in tree biomass growth

Biomass production of some salt tolerant plantation tree species grown in different ecological zones

of Pakistan by Khalid Mahmood et al 2016. The study was conducted in 5 sites near Badin, Gawadar,

Lahore, Faisalabad and Peshawar in different ecological zones of Pakistan. The study included

plantations of 7 tree species common to all the sites including Eucalyptus camaldulensis, Phoenix

dactylifera, Acacia nilotica, Acacia ampliceps, Prosopis juliflora, Casurina obesa and Tamarix aphylla.

Biomass of whole tree and its components (stem, branches, twigs, leaves and fruits) were determined.

Results indicated that E. camaldulensis produced maximum average biomass of 329 kg in 8.5-year

plantation while T. aphylla produced 188 kg in 9.5 years. A. nilotica produced biomass of 187 kg in 10

years at Faisalabad while at Lahore, 369 kg in 18 years under lower soil salinity level biomass was

produced. P. juliflora produced minimum biomass of 123 kg in 8 years at Lahore and 278 kg at lower

salinity in 16 years at Faisalabad. Both soil and water quality were comparatively better at Gawadar

and Faisalabad than other sites. Overall, it is concluded that studied tree species were good performers

on salt-affected soils.

A biomass and yield model for Acacia nilotica plantations in Sindh by Maguire et al. 1990. The yield

model for Acacia nilotica was constructed from data collected from 65 plots, distributed across age

and stand density. Plantation development was simulated by projecting changes in quadratic mean

diameter and the number of stems per ha. Stands were assumed to approach a size/density limit by a

self-thinning process. Model output included biomass of four tree fractions, defined by diameter

outside bark; fodder (leaves and succulent twigs); fine woody material (<2.0 cm Dob); fuelwood (2.0–

6.5 cm Dob); and pit props (6.5–17.0 cm Dob). Pit-prop-grade distribution was estimated from stem

diameter distribution and a rule defining the grades of pit props expected from a tree of given

diameter. Simulation results and plot data indicate that Acacia plantations can produce up to 40 t dry-

weight of total above-ground biomass ha per year, rivalling some of the most productive forest

systems in the World.

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Conclusions

There are numerous studies available to provide references to provide reference variation of the

terrestrial carbon pools, especially from the temperate forests of Pakistan and India. The Abies pindrow

and Cedrus deodara forests have been reported with the largest values for tree biomass representing

up to 138 tons and 291.6 t per ha on NE aspect of carbon per hectare. Some lowest carbon density

values have been attributed as 47.4 tons of carbon per hectare for Betula utilis forests, because the

diameter and height in maturity remain in average lower than in the conifer forests. The moisture

correlates positively with carbon stocks - the higher the moisture, the higher is normally the carbon

stock, especially on the NE aspects. Forest soil carbon stock has been somewhat higher values than for

agricultural soil. In the temperate forest zone, the mean soil organic C up to 0–30 cm depth of soil has

been reported to range from 39.1 to 177.5 t per ha (moist Cedrus deodara forest). The significantly lower

values of the reported carbon stocks may have applied different soil depth range. Average total carbon

stocks of the subtropical forest types of Kashmir Himalayas has been reported as 186.27 t per ha with

the highest quantity of 326 t per ha at Pinus roxburghii forest and the lowest of 75.86 t per ha at mixed

forests. The sub-tropical plantations have been reported 34.54-139.47 t per ha carbon stocks and

Acacia species can produce up to 40 t dry-weight of total above-ground biomass ha per year. However,

there was no national growth data available for the naturally regenerated forests.

2.4. Statistical Projections

The business-as-usual baseline serving is constructed as a rolling average based on the past 10-year

emission and removal records. FREL/FRL will be updated every 4 years, so the statistical projections

are to be made over the coming 4 years after the reference period end. This implies that in case of the

initial reference period 1996-2016 and the projections are made for the monitoring period 2016-2026.

In case there are development plans that are likely to increase rates of deforestation or degradation

compared to the historical average, the projected forest emissions over the monitoring period that

may justify upward adjustment of the historical FREL/FRL projections if reliable and conservative

evidence are available. For example, planned road-building, investment, and development programs,

especially in the context of China-Pakistan Economic Corridor (CPEC) may have significant direct

impacts on the forest resources.

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3. NATIONAL AND SUB-NATIONAL FREL/FRL

3.1. National FREL/FRL

The national FREL/FRL will be compiled based on the sub-national FREL/FRL information. The FREL/FRL

document table of contents is planned to be as following:

Executive Summary

1.Introduction 1.1 Forest sector and REDD+ context in Pakistan

2. Reference to UNFCCC Modalities 2.1. UNFCCC modalities 2.2. Consistency with National GHG reporting

3. Information used for development of FREL/FRL 3.1 Scale 3.2 Scope of activities 3.3 Forest definition and stratification 3.4 Pools and gases 3.5 Historic time period 3.6 Adjustment needs based on national circumstances during the historical period 3.7 Approach for FREL/FRL establishment 3.8 Methodology 3.9 Activity Data 3.10 Emission and Removal Factors

4. Historic emissions and removals (1996-2016) 5. Proposed FREL/FRL (2016-2026) and uncertainty assessment 6. Transparency, completeness, consistency and uncertainty of information 6.1 Transparency 6.2 Completeness 6.3 Consistency 6.4 Accuracy 7. Plan for stepwise FREL/FRL improvement

Annexes

3.2. Sub-national FREL/FRL

The sub-national FREL/FRL are developed and reported with separate statistics for AJK, PB, SD, BN,

FATA and GB. KP is expected to provide its final FRL/FREL to be integrated as a part of the national

FREL/FRL.

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4. METHODOLOGICAL INDICATIONS FOR PAKISTAN HOW TO

EVENTUALLY MOVE FROM TIER 2 TO TIER 3

The IPCC classifies the methodological approaches in three different Tiers, according to the quantity

of information required, and the degree of analytical complexity (IPCC 2003, 2006). Tier 1 referred as

default method employs using country specific activity data and the default emission/removal factors

and other parameters provided by the IPCC. Tier 1 methodology simplifying assumptions about some

of the five IPCC terrestrial carbon pools. They can be combined with spatially explicit activity data

produced with remote sensing.

Tier 2 methodology applies country-specific emission and removal factors and parameters are more

appropriate to the forests, climatic regions and land use systems in that country in case of Tier 1. All

the five pools are covered explicitly. More detailed stratified activity data may be needed in Tier 2 to

correspond with country-specific emission and removal factors and parameters for specific regions

and specialized land-use categories.

Tier 3 higher-order methods rely on models and data from national ground monitoring programs. Tier 3 can

more easily accommodate a wide range of different disturbance events compared to Tier 1 and 2

methodologies. When properly implemented, these methods can provide estimates of lower uncertainty

than other tiers and model the link between biomass and soil carbon dynamics. Such modelling may

combine forest type and age class production systems with connections to soil modules, integration several

types and sources of data. Tier 3 methodology provide accurate estimates of carbon stock changes and

associated emissions and removals for changes in land use or management over time. These systems

may take into account climate dependency and provide estimates with inter-annual variability.

Harmonized methodologies to meet Tier 2 requirements are implemented in scope of the current

FREL/FRL and NFMS/MRV development process in Pakistan. Progressing from Tier 2 to Tier 3 generally

represents a reduction in the uncertainty of GHG estimates, though at a cost of an increase in the

complexity of measurement processes and analyses. Lower Tier (1 and 2) methods may be combined

with higher Tiers for pools which are less significant and costly to monitor.

Tier 3 methodologies require adopting a systematic carbon stock and regular disturbance monitoring

conducted at the lowest forest management level of the natural and plantation forest compartments.

At the first place this requires that there is an operational forest management system in place and field

personnel (i.e. forest guards) posted to carry out forest management inventories, working plans,

monitoring harvesting of timber and fuel wood, and natural disturbances. Forest management

inventories are normally easily adapted to track changes in above-ground and below-ground carbon

stocks to promote the efficiency of Tier 3 monitoring. However, the inclusion of deadwood, litter and

soil pool records may require some additional efforts and capacities in terms of the currently practiced

routines. In order to improve monitoring and reporting efficiency it is recommended to put in place

GIS-enabled provincial forest information management systems to facilitate data flows, security and

quality.

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Figure 7 Forest canopy height and density profile measured with airborne lidar. Yellow points show

ground level and white points are returns from vegetation.

As forests are complex ecosystems Tier 3 requirements may not be met by field observations only,

especially at larger scale forest monitoring. For example, monitoring above-ground carbon stocks and

their changes can be monitored by replacing a significant amount of field plots when forest height and

canopy gaps can be measured accurately with 3D remote sensing in model-based inventories. The

traditional inventories are based on sample points distributed according to probabilistic rules across

the forest landscape, while in a model-based inventory non-probabilistic sampling can be used to

establish model parameters. The field reference data should cover the range of forest types and

variables to be estimated with the developed model.

For example, lidar-based inventory for above-ground biomass estimation requires about 50 sample

plots per forest stratum to be collected in field. With help of lidar data the forest compartments can

be segmented into smaller homogeneous management and harvesting units based on the vegetation

height and density information derived from lidar (Figure 3). Converting 3D point clouds into carbon

stock information requires statistical tools and skills. Wall-to-wall carbon maps can be produced

implementing the model using a desktop GIS software with a lidar data processing extension. Very

high-resolution stereo-imagery acquired from satellites can be used for mapping the canopy heights

with similar accuracy more-cost efficiently for the targeted areas, if there is an accurate terrain model

available.

The unit costs of airborne 3D data collection remain reasonable and even bring some cost savings with

increased data precision, when wall-to-wall data collection is conducted in scale of 10,000 – 100,000

hectares at minimum when operating with fixed wing aircrafts or helicopters with mounted lidar

sensors. Survey of Pakistan (SOP) has indicated its intentions to procure a lidar sensor and data

collected over forest areas could be also potentially used for forest reference data collection purposes.

Even though some valuable work has been completed for developing local allometric models through

destructive sampling, meeting the Tier 3 targets benefit from developing accurate and un-biased

species-specific allometric AGB models. One option for assembling whole-tree AGB measurements

without destructive harvesting is to collect data with terrestrial LiDAR to measure the volume of the

individual branches and stems of the representative standing trees (Hildebrandt & Iost, 2012).

Simulation models, such as CO2FIX (Schelhaas et al. 2004), can be considered as an option to model

the carbon stocks and fluxes in the forest biomass, the soil organic matter and the wood products

chain (Figure 4). Besides the biomass and soil modules it has 4 other modules such as biomass, soil,

products, bioenergy, financial and carbon accounting modules. The model simulates stocks and fluxes

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of carbon in trees, soil, and the wood products, as well as the financial costs and revenues and the

carbon credits that can be earned under different accounting systems. Stocks, fluxes, costs, revenues

and carbon credits can be simulated at the hectare scale with time steps of one year. Once there is

sufficient data available from different forest conditions as inputs for local parametrization of the

biomass and soil models, this kind of modelling approach could be considered to improve the cost-

benefits when adopting the Tier 3 approach. The forest product model relies on the harvesting activity

data to be collected systematically by the forest departments. Transparency is ensured by sharing the

reports and the model parameters so that in principle anyone can run the model using the same

settings and parameters.

Figure 8 The modules of CO2FIX (Schelhaas et al. 2004).

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5. NATIONAL FOREST MONITORING SYSTEM DESIGN AND

INSTITUTIONAL FRAMEWORK

5.1. Design of measurement, reporting and verification

REDD+ Measuring, Reporting and Verification (MRV) forms a core function of the National Forest

Monitoring System. The robust national forest monitoring systems should provide data and

information that are transparent, consistent over time, and are suitable for measuring, reporting and

verifying anthropogenic forest-related emissions by sources and removals by sinks, forest carbon

stocks, and forest carbon stock and forest-area changes resulting from the implementation of the

activities. The effectiveness of the REDD+ strategies and interventions can be measured and prioritized

among the most significant emission sources reported and verified independently. The NFMS/MRV

systems are to be consistent with guidance on measuring, reporting and verifying nationally

appropriate mitigation actions by developing country Parties agreed by the Conference of the Parties,

taking into account methodological guidance in accordance with decision 4/CP.15.

Decision 4/CP.15 outlines methodological guidance for activities relating to reducing emissions from

deforestation and forest degradation and the role of conservation, sustainable management of forests

and enhancement of forest carbon stocks in developing countries. It requests developing country

Parties.

a) to identify drivers of deforestation and forest degradation resulting in emissions and also the means to address these;

b) to identify activities within the country that result in reduced emissions and increased removals, and stabilization of forest carbon stocks;

c) to use the most recent Intergovernmental Panel on Climate Change guidance and guidelines, as adopted or encouraged by the Conference of the Parties, as appropriate, as a basis for estimating anthropogenic forest-related greenhouse gas emissions by sources and removals by sinks, forest carbon stocks and forest area changes;

d) to establish, according to national circumstances and capabilities, robust and transparent national (and sub-national) forest monitoring systems that

i. use a combination of remote sensing and ground-based forest carbon inventory approaches for estimating, as appropriate, anthropogenic forest-related greenhouse gas emissions by sources and removals by sinks, forest carbon stocks and forest area changes;

ii. provide estimates that are transparent, consistent, as far as possible accurate, and that reduce uncertainties, taking into account national capabilities and capacities; and

iii. are transparent and their results are available and suitable for review as agreed by the Conference of the Parties.

If REDD+ activities can be implemented at national, sub-national and project scales, the MRV system

should allow monitoring the potential emissions leakage from one area to the other one. There are

two types of leakages to be accounted, primary and secondary leakage. Primary leakage refers to direct

leakage effects caused by displacement of baseline activities or agents from one area to the next

(activity shifting). Secondary leakage occurs when forest conservation in one place indirectly creates

incentives to deforest in other places as the market effect. For example, the latter situation can be

caused by increased prices for commercial products like timber after reduction in supply.

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Figure 9 NFMS system design

The NFMS system components are shown in Figure 5. For the first few years the NFMS/MRV system

in Pakistan will be limited to the gain-loss method applied for producing national and sub-national

FREL/FRL in scope of the REDD+ readiness project. This will be based on regular activity data

monitoring with the SLMS methodology on the basis of medium or high-resolution satellite data.

The NFI data collection helps to produce carbon stocks and emission/removal factors with

temporary sample plot measurements. The regeneration, mortality and growth rate modelling

require inputs from permanent sample plot designs and research arrangements. The stock

difference methodology based on the 3D remote sensing assisted inventories and other Tier 3

modelling approaches, may help to overcome the inhibitive costs of conducting large scale carbon

monitoring with regular intervals. The capacity of national stakeholders needs to be built and

enhanced to undertake the methodology used for Tier 3 reporting requirements.

In scope of the MRV system it is also necessary to discover evidence that can be linked to the direct

and underlying causes altering the state of forests by accelerating or halting deforestation, forest

degradation as well as afforestation and reforestation processes. Forest sector logging, transportation,

export and planting records are useful to support accounting for the carbon emission removals due to

sustainable management of forests (selective logging) and carbon stock enhancement

(afforestation/reforestation). Table 5 summarizes the direct drivers, indirect and non-anthropogenic

causes of deforestation and forest degradation as indicated during the provincial management

committee consultations. The detailed information about drivers and causes can also come from the

related sectoral agencies or communities before deforestation or degradation is observed and verified

by the forest departments and their field staffs.

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Table 6 Indicated anthropogenic and non-anthropogenic drivers of deforestation (DEF) and forest

degradation (DEG) during the provincial consultations.

Direct Driver AJK BN FATA GB KP PB SD

Infrastructure DEF/DEG DEG DEF DEF/DEG X DEF/DEG DEF

Development

Habitation / Settlements / DEF DEF DEF DEF DEF DEF DEF

Urban expansion / Shanty towns

Mining (especially surface DEF/DEG DEG DEF DEF/DEG X DEF/DEG X

mining, also indirectly wood for

heating)

Forest fires (natural /intentional DEF DEF X DEF DEF/DEG DEF/DEG X

or due to negligence)

Agricultural expansion DEF/DEG DEF/DEG DEF DEF DEF DEF DEF

(subsistence vs. commercial)

Forest clearing for security X X DEF X X X X

Purposes

Unsustainable timber and DEF/DEG DEG DEF DEF/DEG DEF DEG X

fuelwood extraction

Fish ponds X X x X X X DEF

Water-logging X X x X X X DEF/DEG

Free / uncontrolled livestock DEF/DEG DEF/DEG DEF DEG DEG DEG DEG

grazing / overgrazing and

Browsing

Land lease/ hand over X X X X X X DEF

Tourism and hoteling industry X X X DEF X X X

Unscientific forestry operations X X X X X DEG X

and management

Forest fragmentation X X X X DEG X X

Indirect anthropogenic / Natural Cause AJK BN FATA GB KP PB SD

Floods DEF DEF X X X X DEF/DEG

Diseases, and pest attacks DEF X X X DEG X X

Landslides, heavy snowfall, DEF X X X X X X

Earthquakes

Run off; erosion X DEF X X DEG X X

Droughts / reduced rainfall DEF DEF X X DEG X DEG

Oceanic intrusion and tsunamis X DEF X X X X X

Freshwater pollution X DEF X X X X X

threatening marine ecosystem

Atmospheric pollution X X X X DEG X X

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5.2. Design of multiple benefits, impacts, governance and safeguards

5.2.1. Multiple Benefits

REDD+ implementations can potentially generate substantial benefits and other impacts (intended or

unintended). The multiple benefits of REDD+ may include, but not be limited to, the improvement of

local livelihoods, building of transparent and effective forest governance structures, making progress

on securing land tenure, and enhancing or maintaining biodiversity and/or other ecosystem services.

The benefits generated through REDD+ depend on forest type, socio-economic conditions and forest

resource utilization at provincial and national level. Some potential monetary and non-monetary

benefits are found in Table 6.

Monetary Non-Monetary

Direct Indirect

▪ Increased livelihoods and ▪ Capacity building, training, ▪ Lower risk of flood, drought

▪ income (salaries and ▪ Extension ▪ and landslides of floods and ▪ allowances) from

▪ Enhanced participation in ▪ Droughts ▪ employment opportunities

▪ decision-making and better ▪ Resilience to ▪ Carbon benefits

▪ Governance ▪ seasonal variations ▪ Tax incentives

▪ Community infrastructure ▪ Health benefits due to less ▪ Access to loans and

▪ Development ▪ environmental ▪ Credits

▪ Legal access to forest ▪ contamination ▪ etc.

▪ products; larger supply of ▪ Better water quality and

▪ timber and non-timber forest ▪ Quantity

▪ Products ▪ Reduced human/wildlife

▪ Land use planning and ▪ Conflict

▪ allocations for the REDD+ ▪ Soil conservation

▪ Activities ▪ (benefiting agriculture)

▪ Alternative livelihood ▪ Larger numbers of rare and

▪ Opportunities ▪ threatened plant and

animal

▪ Clarified land / forest tenure, ▪ Species

▪ land titles ▪ Improved intra- and

▪ Community nurseries ▪ intersectoral working

▪ Enhanced market access ▪ relationships and conditions

▪ and business networks Travel opportunities to

▪ Social benefits (e.g. sense of share knowledge and

▪ ownership reduced conflicts) Experiences

▪ etc. Pride, prestige, social status

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Various factors affect how the benefits are finally captured, including the type, location and condition

of the forest involved, how the REDD+ activity is undertaken and implemented keeping in view the

dependence of local people on forest resources. Multiple benefit outcomes will also depend on where

these policies and measures are implemented, capacity of the implementing agencies and how they

are implemented, in practice.

Policy coherence between the national REDD+ programs and other broader socio-economic strategies,

such as sustainable development, climate change or green economic growth policies, can greatly

enhance the potential of REDD+ to achieve multiple benefits. To realize the national REDD+ strategy’s

multiple benefit objectives, environmental and social considerations also need to be mainstreamed

into REDD+ actions at the operational level of subnational planning. This means adopting a landscape-

level approach to facilitating land use choices and negotiating trade-offs in terms economic,

environmental and social returns from productive landscapes through low-emission development

planning. Analytical tools for mainstreaming multiple benefits, into either national strategies or

subnational plans, include participatory social and environmental impact assessments, spatial analysis,

and economic valuation of the benefits.

5.2.2. Benefit-sharing

The success of REDD+ is expected to depend on the design and implementation of benefit-sharing

mechanisms and arrangements, which are operational at multiple levels of governance (Thuy et al

2013). They can allow affected communities to become partners in REDD+ activities, governments to

achieve greater social inclusiveness, and investors to reduce risks associated with a project. If benefits

are fairly shared with local stakeholders, it will also reduce the likelihood of reversals of emission

reductions, which could be caused by local populations that lack economic alternatives. A benefit-

sharing plan should contain the following information details (FCPF 2016):

a) The Beneficiary categories, describing their eligibility to receive potential benefits (Monetary

and Non-Monetary) and the types and scale of such potential benefits that may be received.

b) Criteria, processes, and timelines for the distribution of benefits.

c) Monitoring provisions for the implementation of the Benefit-Sharing Plan, including an opportunity

for participation in the monitoring and/or validation process by the Beneficiaries themselves.

The beneficiary categories and type of benefits depend highly on the activities that are based on

prioritizing the strategic options to formulate the REDD+ strategy.

Equitable benefit-sharing will be meaningful if it can address the needs of local communities which

should be empowered to make and influence decisions in forest resource management. Revenue

accruing from REDD+ activities should contribute to the development of rural health centers, schools,

access roads, and be invested in value-added activities, such as small-scale forest enterprise

development. Moreover, community conceived and managed schemes may make payments to

community members, or the monetary gains derived from investments may be put into a trust fund to

be used to fund community-conceived and operated projects.

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Benefit-sharing would require clarification of property rights over carbon rights and land tenure. The

promotion of multiple benefits requires effective participation of stakeholders, clearly-defined

benefit-sharing mechanism, dispute resolution mechanisms, land tenure arrangements and

continuous quantitative assessment of carbon valuation of forests for appropriate compensation

under REDD+. In this regard, transparency and accountability should be the cornerstone leading to the

achievement of multiple benefits. To facilitate transparent monitoring a national REDD+ information

system or registry should be in place to provide public access to geo-referenced information on the

location, ownership, carbon accounting and financial flows for sub-national and national REDD+

programs and projects (FCPF/UN-REDD 2015).

Economic incentives can also play an important role in the delivery of multiple benefits. Measures

aimed at incentivizing the delivery of multiple benefits, within a national REDD+ program, may include

removing barriers to multiple benefits, providing additional financial incentives or simply providing the

right market linkages for which benefits beyond carbon can be provided.

A wide range of economic incentive models to incentivize multiple benefits are available. Selecting the

most appropriate instrument will depend on the existing legal and market structures, as well as the

selected policies and measures comprising the national REDD+ strategy and subnational plans.

Incentive models will be most effective when coupled with disincentives, such as laws and their

enforcement, stipulating fines or other penalties for non-compliance.

5.2.3. NFMS and MBIGS/SIS linkage

The national forest monitoring systems (NFMS) may integrate data and information that is relevant for

other components of the REDD+ information system, such as the Safeguards Information System (SIS)

(UNFCCC Decision 1/CP.16, Para 71 d). SIS provides a systematic approach for collecting and providing

information on how REDD+ safeguards are being addressed and respected throughout REDD+

implementation, which are to be submitted periodically in national communications to the UNFCCC.

The SIS design covers indicators for determining whether a policy or intervention is being effectively

implemented; methodologies for information collection; and framework for provision of information

(storing and sharing). SIS is expected to be simple, accessible, inclusive, transparent, auditable,

comprehensive and according to national legislation.

MBIGS/SIS monitoring can potentially account for the following REDD+ activity related information:

Multiple benefits

▪ Sustainable extraction of NTFPs by local communities for subsistence use and small-scale local enterprises;

▪ Production of timber in natural forests; ▪ Production of timber from plantations; ▪ Natural values such as biodiversity, wildlife, potential for eco-tourism; ▪ Watershed protection, quantity and quality of streams; ▪ Desertification control, erosion control.

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Impacts

▪ Socio-economic impacts from participation in forest management; ▪ Changing forest resource utilization patterns; ▪ Availability of raw materials for processing; ▪ Resource impacts, including forest development, desertification control, erosion control,

watershed protection; ▪ Distribution of benefits on local socio-economic conditions;

Governance

▪ Forest policies including land tenure, rights to forest resources, carbon rights and policy reform; ▪ Law enforcement; ▪ Transparency and anti-corruption; ▪ Management of the national and sub-national REDD+ programs; ▪ Inclusion of stakeholders in consultation and review; ▪ Benefit distribution policies; and

▪ Conflict resolution mechanisms.

Safeguards

▪ Consistency with the national forest policy and relevant international conventions and agreements; ▪ Transparency and effectiveness of national forest governance structures; ▪ Respect for the knowledge and rights of indigenous peoples and members of local communities; ▪ The full and effective participation of relevant stakeholders, in particular indigenous peoples and

local communities; ▪ Conservation of natural forests and biological diversity, ensuring that REDD+ actions are not used for

the conversion of natural forests, but are instead used to protect and conserve natural forests and their ecosystem services, and to enhance other social and environmental benefits;

▪ Risks of reversals; ▪ Reduction of displacement of emissions.

The core feature of MBIGS/SIS monitoring function is to enable incorporation of local knowledge into

national monitoring and provide inputs to validate information in a participatory way. The design

process will be conducted under guidance of the technical working group in charge for the stakeholder

engagement and safeguards. The system should help accounting all the MBIGS matters and

encapsulating participatory monitoring mechanisms as its basic component to facilitate inputs from

the local communities.

The process of collecting MBIGS/SIS information can involve various partners from community

organizations, government and civil society organizations. Methodology to be used for the monitoring

process of indicators includes interviews, questionnaires, direct observation and public consultations

whenever necessary. Continuous dissemination programs will be part of the process to enable

stakeholders to be actively involved, making for efficient and transparent implementation of REDD+

projects and initiatives in the region

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To select and monitor social and environmental performance indicators, Pakistan can draw on existing

national and provincial monitoring programs (e.g. demographic and social surveys), and leverage both

secondary and primary datasets. Disaggregation into social groupings (i.e. along gender, age and

ethnicity lines) is needed to understand uneven social impacts and is most critical in places with greater

inequality. For social monitoring at the local level, more expensive primary data collection would

include extensive household surveys, whereas a less expensive approach would be based on

participatory methods at the community level.

5.3. Web Portal

5.3.1. User interface

User will be authenticated during the login into system. This will check the user role and his/her feature

permissions for the different views or modifications. User could update the data or just viewing some

of them according his/her permissions.

Expert or system owner can administrate the system at back-end to update data and define statistics

through a web GUI (version control management rights). User can share maps and reports.

Expert or system owner can fetch forest information by compartment level, or by any geographical

boundaries up to provincial and national level. User can aggregate statistics and generate charts using

an XML document as statistic definition. User can compare sub-national results to national. User can

compare past results to new.

User can find that each emission reduction (ER) unit is appropriately issued, serialized, transferred,

retired, cancelled and not issued, counted, or claimed by more than one entity. User can track and get

a report for the most significant emission sources.

Expert and generic user may view time series datasets: User can scroll LULUCF maps and background

satellite imagery in a timeline to produce situational picture of wanted point in time.

System is built to be open for storing LU maps for subsequent time points (underlying remote sensing

data stored externally).

5.3.2. Information contents

The following information contents is defined for the web-portal:

▪ FREL/FRL information as text

• standard methodology for FREL/ FRL development including uncertainty assessment

• forest definition

• definition for significant pools and GHGs

• sub-categories/ classes of the IPCC recommended six land cover categories

• definition of reference period

• emission factors

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Figure 10 NFMS System architecture

▪ Forest compartment boundaries ▪ Admin boundaries (national and sub-national) ▪ Biophysical boundaries (ecological zones) ▪ REDD+ project area boundaries ▪ Forest owner boundaries ▪ NFI plot-level data

▪ Land use and cover maps ▪ Activity data maps ▪ Spatial deforestation, degradation and carbon stock enhancement statistics by provinces ▪ MBIGS data for the REDD+ implementation areas ▪ Carbon registry for claimed and attributed emission reduction units

5.3.3. System Architecture

The system contains different servers parts (GeoServer, database, proxy, map server), open source

map handling applications and libraries. The servers are in a specified and enough secured place.

Server maintenance and upgrade processes need to be defined within data update process. The server

system management may be handled through VPN/SSL secured connection. Database data backups

need to be taken automatically within regular time periods into a certain store.

Open source map handling using OpenLayers library, which makes it easy to setup dynamic map in the

web page. It can display map tiles, vector data and points loaded from the dedicated database.

OpenLayers has been developed to further the use of geographic information. The reporting part may

handle the pre-defined report forms and report export format.

5.3.4. User management (permission)

User management roles:

Superuser (if needed) ▪ Giving access rights to admin (handle admin accounts)

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Expert ▪ Uploading the map layer data

▪ Editing feature attributes

▪ May modify defined features

▪ Styling features

▪ Data access handling

▪ User accounts (creating user, activate/deactivate accounts)

▪ Creating reports

Normal user ▪ Viewing, adding own features

▪ Modifying own feature attributes

▪ Seeing his/her own reports

Guest ▪ Limited content view / limited data access / no modification access

5.3.5. Uploading/updating interface

Vectors (in DB) and rasters (in Geoserver) map layer data maintaining by Admin o Loading vector data (.shp ESRI format files) through data import module

Loading measured, updated data via .CSV or .XLS data into database ▪ WMS interface (may tiled in Geoserver)

▪ WFS interface (feature attributes)

Items (like points and areas) with geometries are stored in database

Screenshots of NFMS interfaces area presented in Annex 3.

5.4. GHG-I reporting

The UNFCCC coordinates the global efforts for monitoring GHG concentrations and to encourage

climate change mitigation. Monitoring involves systematic collection of GHG emission records and

trends for their regular country submissions to the UNFCCC as per good practice guidelines of IPCC.

Pakistan submitted its first GHG emission Inventory for 1993 with its Initial National Communication

in 2003. The second one was prepared in 2009 in relation to the work of Pakistan Planning

Commission’s Task Force on Climate Change for the year 2007-08. The GHG inventory for the year

2011-12 was first developed and published officially by GCISC in 2016. The most recent draft (dated in

February 2017) report provides updated information on Pakistan’s GHG emissions for the year 2014-

15 serving as baseline for the Pakistan’s Intended Nationally Determined Contribution submitted in

November 2016 (Mir et al 2017).

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Until the latest national GHG-I report the Revised 1996 IPCC Guidelines for National Greenhouse Gas

Inventories have been applied as the methodological guidance document. There are on-going the

development efforts to use 2006 IPCC Guidelines for National Greenhouse Gas Inventories (2006GL)

for the following GHG-I reporting. The forest definition, harmonized forest classification system, forest

area changes, carbon inventory and emission factor data analyzed for developing FRL/FREL and stored

in the NFMS database will contribute as some potential Tier 2 level parameter inputs.

There is the Non-Annex I Inventory software (NAIIS) application provided by the UNFCCC and allowing

web-based GHG-I data entry interface instead of the previous Excel sheet templates. The NAIIS access

is limited to non-Annex I Parties to the Convention and can only be requested and granted through the

designated NFPs. The NFPs can grant permissions to other GHG-I team member to enter data directly

through the NAIIS application.

5.5. Institutional framework

The NFMS institutionalization involves defining institutions and their mandates, developing and

formalizing processes and methodologies in the context of the national and sub-national NFMS

activities:

1. Institutions: Defining which institutions are involved in national and sub-national NFMS activities

and what their respective roles and responsibilities are and how they should interact, how to

intervene in case of challenges and who bears overall responsibility

2. Processes: Defining the overall process of collecting, processing, reporting and verifying data. This

includes determining which role individual institutions play within this process.

3. Methodologies and tools: Identifying and developing standardized methodologies and tools

required to collect, process and store data. The methodologies and tools are needed for the

National Forest Inventory, Satellite Land Monitoring System, MBIGS monitoring and greenhouse

gas inventories.

Additionally, the resulting institutional arrangements should comply with the following criteria:

a) A solid, sustainable network of institutions with the required variety of expertise;

b) Clearly documented roles and responsibilities with a single body assigned for overall

coordination;

c) Good coordination and clear lines of communication;

d) Continuity of staff and succession planning;

e) High level of ownership by the participating stakeholders; and

f) Efficient use of existing institutions and frameworks to minimize establishment and operational

costs.

The main NFMS institutional roles can be classified as following:

a) National Focal Point with the overall responsibility for coordinating the REDD+ MRV function

and liaising with the UNFCCC (Decision 10/CP.19);

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b) Institutional Body to manage the work of institutions and organizations; and have overall

responsibility for the coordination of administrative and technical arrangements, and the overall

quality of reported estimates;

c) Mandated Institutions to perform specific tasks and provide data;

The institutional arrangements should establish frameworks for:

a) Formalizing mandates for data acquisition, processing and sharing amongst relevant institutions

to avoid duplication of efforts;

b) Maintaining documented processes for quality assurance and quality control, so as to ensure

the quality datasets (e.g. for spatial data and carbon pool measurements);

c) Continuous improvement including documentation of opportunities for improvement and

process for the inclusion of such improvements;

d) Retaining skilled staff through appropriate and ongoing training and environments to encourage

staff retention;

e) Securing adequate budgets to support the initial development of the MRV function as well as

the ongoing operation and development.

Figure 11 Institutional framework for NFMS in scope of REDD+.

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Each involved institution is recommended to have their clear roles and responsibilities, which are

expected to evolve over time. Table 7 indicates the recommended responsible, partner and co-learner

institutions for each NFMS function. The recommendations are drawn after assessing the mandates,

capacities and interests for the named institutions during the provincial consultations and technical

working group meetings held. The international and national organizations include WWF, IUCN,

ICIMOD among other international entities.

Table 7 NFMS functions and recommended institutional roles

Function Responsible institution Partner and co-learning institutions

NFMS/MRV Coordination

MOCC/OIGF/National REDD+ office Provincial (or State) Forest Departments

NFI data production Provincial (or State) Forest Departments

PFI

SLMS data production Provincial (or State) Forest Departments

PFI, SUPARCO

GHG-I GCISC EPA, National REDD+ Office (forest sector data)

MBIGS Following the SIS institutional framework (under development)

Following the SIS institutional framework (under development)

Independent verification, QA/QC

National REDD+ office PFI, SUPARCO, SOP, International and National Organizations

NFMS Database and Web Portal Hosting

GCISC

Data Ownership (primary data)

Provincial Governments Provincial (or State) Forest Departments

Data Custodianship National REDD+ Office PFI

Training and capacity- Building

PFI (NFI) SUPARCO (SLMS)

Institute of Space Technology, Universities, International and National Organizations

Methodology and System Development

REDD+ technical working group International and National Organizations

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Glossary of Relevant Terms Aboveground Biomass (AGB) Living vegetation above the soil, including stem, stump, branches, bark, seeds, and foliage. Activity Data on the extent of human activity causing emissions and removals. Allometry In forestry, generally the relationship between tree diameter, height, crown size and biomass. Belowground Biomass (BGB) The living biomass of roots greater than 2 mm diameter Biomass The total mass of living organisms in a given area or volume; dead plant material can be included as dead biomass. Business-as-Usual (BAU) Scenario The land use and emissions profile for a forest carbon project area prior to intervention, serves as a bench mark to measure the impact of REDD actions. Also referred to as “baseline”. Carbon Dioxide (CO2) A naturally occurring gas, also a by-product of burning fossil fuels from fossil carbon deposits, such as oil, gas and coal, of burning biomass and of land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth’s radiative balance. It is the reference gas against which other greenhouse gases are measured and therefore has a Global Warming Potential of 1. Conference of the Parties (COP) The supreme body of the Convention. It currently meets once a year to review the Convention's progress. The word "conference" is not used here in the sense of "meeting" but rather of "association". The "Conference" meets in sessional periods, for example, the "fourth session of the Conference of the Parties".

Crown Cover The percentage of the surface of an ecosystem that is under the tree canopy. Also referred to as ‘canopy cover’ or just ‘tree cover’. Dead Wood The term used to describe all non‐living woody biomass not contained in the litter, either standing, lying on the round, or in the soil. Dead wood includes wood lying on the surface, dead roots, and stumps larger than or equal to 10 cm in diameter or any other diameter used by the host country Deforestation Conversion of forest to non-forest. Degradation (forest degradation) The term used to describe the condition of a forest that has been reduced below its natural capacity, but not below the 10 percent crown cover threshold that qualifies as deforestation. Drivers Refers to both direct and indirect causes of deforestation and forest degradation Emission or removal factors GHG emissions or removals per unit of activity data. Forest A vegetation type dominated by trees. Many definitions of the term forest are in use throughout the world, reflecting wide differences in biogeophysical conditions, social structure, and economics. Particular criteria apply under the Kyoto Protocol. For a discussion of the term forest and related terms such as afforestation, reforestation, and deforestation see the IPCC Special Report on Land Use, Land-Use Change, and Forestry (IPCC, 2000). See also the Report on Definitions and Methodological Options to Inventory Emissions from Direct Human-induced Degradation of Forests and Devegetation of Other Vegetation Types (IPCC, 2003)

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Forest Carbon Forest carbon generally refers to the carbon stored in forests; usually in reference to climate change mitigation projects which aim to increase carbon sequestration in or decrease carbon dioxide emissions from forests. Forest Monitoring Functions of a national forest monitoring system to assist a country to meet measuring, reporting and verification requirements, or other goals. Green House Gases (GHGs) The atmospheric gases responsible for causing global warming and climate change. The major GHGs are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N20). Less prevalent - but very powerful - greenhouse gases are hydro fluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF6). Green House Gas Inventory (GHGI) Anthropogenic greenhouse gas estimates with national territorial coverage produced using IPCC methods in accordance with decisions taken at the UNFCCC Conference of the Parties (COP). Grievances Disputes with Communities and Other Stakeholders that may arise during project planning, implementation and evaluation with respect but not limited to, Free, Prior and Informed Consent, rights to lands, territories and resources, benefit sharing, and participation. Intergovernmental Panel on Climate Change (IPCC) Established in 1988 by the World Meteorological Organization and the UN Environment Programme, the IPCC surveys world-wide scientific and technical literature and publishes assessment reports that are widely recognized as the most credible existing sources of information on climate change. The IPCC also works on methodologies and responds to specific requests from the Convention's subsidiary bodies. The IPCC is independent of the Convention.

Kyoto Protocol An international agreement standing on its own, and requiring separate ratification by governments, but linked to the UNFCCC. The Kyoto Protocol, among other things, sets binding targets for the reduction of greenhouse gas emissions by industrialized countries. Land Use, Land-Use Change and Forestry (LULUCF) A greenhouse gas inventory sector that covers emissions and removals of greenhouse gases resulting from direct human-induced land use, land-use change and forestry activities. Land Use The total of arrangements, activities and inputs undertaken in a certain land-cover type (a set of human actions). The social and economic purposes for which land is managed (e.g. grazing, timber extraction, and conservation). Land-use change occurs when, e.g. forest is converted to agricultural land or to urban areas. Measurement, Reporting and Verification (MRV) Measuring is estimating the effect of the action, reporting is communication to the international community, and verifying is checking the estimation; procedures for all three are to be agreed by the UNFCCC. National Forest Inventory (NFI) A periodically updated sample-based system to provide information on the state of a country’s forest resources. National Forest Monitoring/ Management System The institutional arrangements in a country to monitor forests. NFMS will presumably include representation from responsible Ministries, indigenous peoples and local communities, forest industry representatives, and other stakeholders. In the REDD+ context, a system for monitoring and reporting on REDD+ activities, in accordance with guidance from the COP. The COP has established that a NFMS should use a combination of remote-sensing and ground- based data, provide estimates that are transparent, consistent, as far as possible accurate, and that reduce uncertainties, taking

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into account national capabilities and capacities; and their results are available and suitable for review as agreed by the COP. NFMS may provide information on safeguards. REDD+ Reducing emissions from deforestation; Reducing emissions from forest degradation; Conservation of forest carbon stocks; Sustainable management of forests; Enhancement of forest carbon stocks. Reference Emission Levels/ Reference Levels Are means to establish reference emission levels, based on historical data, taking into account, inter alia, trends, starting dates and the length of the reference period, availability and reliability of historical data, and other specific national circumstances Reforestation Replanting of forests on lands that have previously contained forests but that have been converted to some other use. Remote Sensing A method of measuring deforestation and/or forest degradation by a recording device that is not in physical contact with the forest, such as a satellite or aircraft. Removals This is the opposite of an emission of greenhouse gas and occurs when greenhouse gases are removed from the atmosphere, for example, by trees during the process of photosynthesis. Safeguards Undertakings to protect and develop social and environmental sustainability. Covers consistency with national forest programmes and relevant international conventions and agreements; transparency and effectiveness of national forest governance; respect for the knowledge and rights of indigenous peoples and members of local communities; participation of relevant stakeholders, in particular indigenous peoples and local communities.

Soil Organic Carbon (SOC) The carbon pool that includes all organic material in soil but excluding the coarse roots of the belowground biomass pool. Source Source mostly refers to any process, activity or mechanism that releases a greenhouse gas, aerosol or a precursor of a greenhouse gas or aerosol into the atmosphere. Source can also refer to, e.g. an energy source. Subnational An administrative division, administrative unit, administrative entity or country subdivision (or, sometimes, geopolitical division or subnational entity) is a portion of a country or other region delineated for the purpose of administration. Administrative divisions are each granted a certain degree of autonomy and are usually required to manage themselves through their own local governments. Transparency Mean that decisions taken and their enforcement are done in a manner that follows rules and regulations. It also means that information is freely available and directly accessible to those who will be affected by such decisions and their enforcement. It also means that enough information is provided and that it is provided in easily understandable forms and media. Uncertainty An expression of the degree to which a value (e.g., the future state of the climate system) is unknown. Uncertainty can result from lack of information or from disagreement about what is known or even knowable. It may have many types of sources, from quantifiable errors in the data to ambiguously defined concepts or terminology, or uncertain projections of human behaviour. Uncertainty can therefore be represented by quantitative measures, for example, a range of values calculated by various models, or by qualitative statements, for example, reflecting the judgment of a team of experts.

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United Nations Framework Convention on Climate Change(UNFCCC) The Convention was adopted on 9 May 1992 in New York and signed at the 1992 Earth Summit in Rio de Janeiro by more than 150 countries and the European Community. Its ultimate objective is the “stabilisation of greenhouse gas concentrations in the (UNFCCC) atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system”. It contains commitments for all Parties. Under the Convention, Parties included in Annex I (all OECD member countries in the year 1990 and countries with economies in transition) aim to return greenhouse gas emissions not controlled by the Montreal Protocol to 1990 levels by the year 2000. The Convention entered in force in March 1994

Validation A process by which an independent third-party organization, which has been certified to evaluate projects according to a specific standard, thoroughly reviews the design, methodologies, calculations and strategies employed in a project, ensuring the project follows the rules of the chosen standard. Verification The periodic independent review and ex-post determination of the monitored reductions in anthropogenic emissions by sources of greenhouse gases or increases in carbon stocks (carbon benefits) that have occurred as a result of a project activity during the verification period

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Annexes Annex 1 Survey and Mapping License from SOP for mapping works

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Annex 2 Forest cover maps for AJK

Forest cover map of AJK-2008

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Forest cover map of AJK - 2012

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Forest cover map of AJK - 2016

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Annex 3 Screenshots of NFMS Graphical Users’ Interface

Pakistan NFMS starting welcome screen

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Spatial Layers search interface

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Spatial layer metadata interface

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Web-GIS map interface

Data uploading interface

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Report contents/document download interface