Mongolia’s
Forest Reference Levelsubmission to the UNFCCC
Modified Submission: June 2018Date:

6/22/2018

1

Citation
Government of Mongolia. 2018. Mongolia’s Forest Reference Level submission to the United Nations
Framework Convention on Climate Change. UN-REDD Mongolia National Programme, Ministry ofEnvironment and Tourism, Ulaanbaatar.
List of contributing authors:
CCPIU
Sanaa Enkhtaivan, Dorjzodov Nyamsuren and Zolbayar Purevjav
FRDC
Khosbayar Battuvshin, Altangadas Janchivdorj and Michid Khaltar
UN-REDD Mongolia National Programme
Khongor Tsogt, Bat-Ulzii Chultem and Yeseul Byun
Technical Experts:
Abu Mahmood, Remote Sensing and Land Cover Assessment Expert, FAO Bangkok
Mathieu Van Rijin, Forestry Officer, FAO, Bangkok
Marieke Sandker, Forestry Officer, FAO Rome
Ben Vickers, Regional REDD+ Programme Officer, FAO Bangkok
Acknowledgement
The Government of Mongolia would like to thank the REDD+ Mongolia technical partners for provision of
support for the development of this submission. In particular, we would like to acknowledge technicalsupport from the UN-REDD National Program, and the three partner agencies, FAO, UNDP and UN
Environment; the Mongolia GIZ Program, especially for support to the Multi-purpose National Forest
Inventory. The Government of Mongolia also would like to thank the active support from Mongolia’s
University and Research Institutions, especially those members of the Technical Working Group on Forest
Reference Level and National Forest Monitoring Systems, including Department of Forest Policy and
Coordination, Forest Research Development Centre, and Climate Change Project Implementing Unit of
MET.
Should readers wish for more information, they are encouraged to get in touch via:
The UN-REDD Mongolia National Programme Management Unit
Tel: +976-71117750
E-mail: info@unredd.mn
Web site: www.reddplus.mn

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Contents
1.

INTRODUCTION ................................................................................................................................. 10
1.1.

Objective of FRL development ................................................................................................... 11

1.2.

Underlying causes of forest change ........................................................................................... 11

1.2.1.

Direct drivers of deforestation/forest degradation ........................................................... 12

1.2.2.

Indirect drivers of deforestation ........................................................................................ 14

1.3.

1.3.1.

Scale ................................................................................................................................... 16

1.3.2.

Scope: activities, pools and gases ...................................................................................... 16

1.3.3.

Forest definition ................................................................................................................. 17

1.3.4.

Historical reference period ................................................................................................ 18

1.4.
2.

Consistency with greenhouse gas inventory reporting ............................................................. 18

MATERIALS AND METHODS .............................................................................................................. 19
2.1.

Activity data ............................................................................................................................... 19

2.1.1.

Rationale for data selection ............................................................................................... 19

2.1.2.

Sample Density................................................................................................................... 20

2.1.3.

Stratification....................................................................................................................... 22

2.1.4.

Sample point generation.................................................................................................... 23

2.1.5.

Survey design ..................................................................................................................... 24

2.1.6.

Land-use types and use of hierarchical rules ..................................................................... 25

2.1.7.

Response design................................................................................................................. 25

2.1.8.

Analysis design ................................................................................................................... 26

2.2.

Emission and removal factors .................................................................................................... 26

2.2.1.

National Forest Inventory .................................................................................................. 26

2.2.2.

Estimating biomass/carbon ............................................................................................... 29

2.2.3.

Emission and removal factors ............................................................................................ 31

2.2.4.

Removal factor - estimating annual increase in biomass carbon stocks ........................... 32

2.2.5.

Estimating emission and removal ...................................................................................... 33

2.3.
3.

Extent of FRL components ......................................................................................................... 16

Combined uncertainty ............................................................................................................... 34

RESULTS ............................................................................................................................................. 35
3.1.

Activity data ............................................................................................................................... 35

3.1.1.

Area estimate ..................................................................................................................... 35

3.1.2.

Rate of change ................................................................................................................... 36

3.2.

Emission and removal factors .................................................................................................... 38

3.3.

Removals from annual forest growth ........................................................................................ 40

3.4.

The Forest Reference Level ........................................................................................................ 40
3

3.5.

Uncertainty analysis for activity data and emission factors ...................................................... 42

4.

NATIONAL CIRCUMSTANCES ............................................................................................................. 44

5.

FUTURE IMPROVEMENT ................................................................................................................... 45

6.

5.1.

Improvement in quality of the Saxaul NFI ................................................................................. 45

5.2.

Diversifying carbon pools ........................................................................................................... 45

5.3.

Uncertainty analysis ................................................................................................................... 45

5.4.

Fraction of biomass loss from disturbance ................................................................................ 45

5.5.

Inclusion of non-CO2 GHGs ........................................................................................................ 45

REFERENCES ...................................................................................................................................... 46

APPENDIXES ............................................................................................................................................... 49
Appendix 1 ............................................................................................................................................. 49
Appendix 2 ............................................................................................................................................. 50
Appendix 3 ............................................................................................................................................. 51
Appendix 4 ............................................................................................................................................. 52
Appendix 5 ............................................................................................................................................. 56
Appendix 6 ............................................................................................................................................. 57
Appendix 7 ............................................................................................................................................. 59
Appendix 8 ............................................................................................................................................. 60
Appendix 9 ............................................................................................................................................. 614

Figures
FIGURE 1.1. FOREST HABITAT TYPES IN MONGOLIA. CLOCKWISE FROM TOP LEFT TO RIGHT: DECIDUOUS FOREST –BIRCH (BETULA PLATYPHYLLA); CONIFEROUS FOREST – LARCH (LARIX SIBIRICA); MIXED DECIDUOUS AND
CONIFER FOREST AND SAXAUL FOREST. ............................................................................................................ 11
FIGURE 2.1. FOREST COVER MAP OF THE YEAR 2005 FROM ERISC ............................................................................ 20
FIGURE 2.2. ANALYSIS FOR OPTIMAL GRID SPACING DESIGN. RELATIONSHIP BETWEEN GRID SPACING AND SIZE OF
AREA CHANGE WHEN COMPARED TO GLOBAL FOREST WATCH DATA AS WALL-TO-WALL INFORMATION FORMONGOLIAN FOREST COVER CHANGE. ............................................................................................................. 22
FIGURE 2.3. FOREST (F) AND NON-FOREST (NF) STRATA HAVE BEEN USED TO DETERMINE THE OPTIMUM DENSITYRANGE OF A SYSTEMATIC GRID. THE BOUNDARY OF THE FOREST (F) STRATUM (DIAGONAL STRIPPED) WAS
CHOSEN TO INCLUDE ALL NFI POINTS AS WELL AS THE FOREST MASK 2013. THE HIGHLIGHTED AREA IN GRAY
IS NON-FOREST (NF)........................................................................................................................................... 23
FIGURE 2.4 COARSE GRIDS FOR FOREST 4.5 KM X 4.5KM AND NON-FOREST 18 KM X 18 KM SAMPLES (LEFT) AND
CURRENT DENSER GRID FOR FOREST 2.25 KM X 2.25 KM AND NON-FOREST 9 KM X 9 KM (RIGHT). ............... 23
FIGURE 2.5. COLLECT EARTH LAND ASSESSMENT FORM FOR LAND USE AND LAND-USE CHANGE INFORMATIONCOLLECTION. ...................................................................................................................................................... 24
FIGURE 2.6. NFI PLOTS LOCATIONS (GREEN DOTS) OVER BOREAL FOREST DISTRIBUTIONAL AREA. VERTICAL LINESREPRESENTS 6 UTM ZONES FROM 45 TO 50 OVER MONGOLIAN TERRITORY. .................................................. 27
FIGURE 2.7. DISTRIBUTIONS OF ADDITIONAL FIELD INVENTORY PLOTS THAT ARE DEGRADED BY FIRE, PEST AND
LOGGING ACTIVITIES AND SPARSE TREES. ......................................................................................................... 28
FIGURE 2.8. BURNT FOREST AREA (TOP), PEST AFFECTED FOREST (LEFT) AND LOGGED FOREST (RIGHT). DURING THE
VEGETATION PERIOD IN 2017, MONGOLIA CONDUCTED FIELD INVENTORY ON ADDITIONAL 156 PLOTSLOCATED IN DEGRADED FORESTS WHICH REPRESENTS AREA OF 1.58 MILLION HA TEMPORALLY UN-STOCKED
OR LOW-STOCKED FOREST AREA. ...................................................................................................................... 28
FIGURE 3.1. FOREST LAND IN 2008 CONVERTED INTO GRASSLAND (SET17, ID: 80832) IN 2009. HIGH SPATIALRESOLUTION GOOGLE EARTH IMAGERY (TOP-LEFT), BURNT FOREST PHOTO (TOP-RIGHT) ILLUSTRATES THE
TYPE OF LAND COVER AS A VALIDATION DATA FOR LANDSAT. ANNUAL GREENEST-PIXEL TOP OF ATMOSPHERIC(TOA) REFLECTANCE COMPOSITES FOR YEARS 2008 (BOTTOM-LEFT) AND 2009 (BOTTOM-RIGHT) ARE
DISPLAYED IN FALSE COLOR COMPOSITE (NIR_SWIR1_RED IN RGB CHANNELS). THE 1HA SAMPLE PLOT IN THE
2008 LANDSAT 30-M IMAGE SHOWS FOREST COVER (DARK BROWN) WHILE THE 2009 IMAGE INDICATESGRASSLAND (GREEN) COVER. ............................................................................................................................ 37
FIGURE 3.2. NON FOREST CONVERTED TO FOREST LAND (SET42, ID: 106298, G>F 100 M * 100 M OR 1-HA PLOT)IDENTIFIED USING HIGH-SPATIAL RESOLUTION GOOGLE EARTH IMAGERY IN 2001 (LEFT), IN 2005 (MIDDLE)
AND IN 2012 (RIGHT). THE HIGH-SPATIAL RESOLUTION IMAGERY IN GOOGLE EARTH (LEFT), SHOWSAFFORESTATION WHICH PROBABLY OCCURRED PRIOR TO 2001, IDENTIFIABLE THROUGH SOME SCATTEREDCROWNS. THEREFORE, AN OPERATOR ASSESSED THE PLOT AS NON-FOREST (GRASSLAND) IN 2005,
ACCORDING TO THE DOMINANT LAND COVER TYPE, BUT THEN AS FOREST LAND IN 2012, DUE TO ≥ 10 % TREECANOPY COVER. ................................................................................................................................................. 37
FIGURE 3.3. ANNUALIZED SAMPLE-BASED FOREST LOSS (DEFORESTATION) AND FOREST GAIN (ENHANCEMENT)
ESTIMATES OVER THE REFERENCE PERIOD: 2005-2015. THE ERROR-BAR SHOWING LOWER AND UPPER LIMITOF FOREST CHANGE ESTIMATES FOR CORRESPONDING YEAR AT 95 % CONFIDENCE INTERVAL. .................... 38
FIGURE 3.4. MONGOLIA’S ANNUAL CO2 REMOVALS FROM FOREST GROWTH DURING THE REFERENCE PERIOD 20052015. .................................................................................................................................................................. 40
FIGURE 3.5. MONGOLIA’S ANNUAL CO2 EMISSIONS FROM DEFORESTATION AND FOREST DEGRADATION ANDREMOVALS THROUGH AFFORESTATION ACTIVITIES DURING 2005-2015. ........................................................ 41
FIGURE 3.6. MONGOLIA’S ANNUAL CO2 EMISSIONS FROM VARIOUS CHANGE AREAS AND REMOVALS THROUGH
NATURAL GROWTH DURING 2005-2015. .......................................................................................................... 42

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Tables
TABLE 1-1. DIRECT AND INDIRECT DRIVERS OF DEFORESTATION AND FOREST DEGRADATION IN MONGOLIA (UNREDD, 2016A) ..................................................................................................................................................... 12
TABLE 2-1. EXPANSION FACTOR FOR EACH UTM ZONE ............................................................................................. 24
TABLE 2-2. LAND USE ASSESSMENT HIERARCHY AND LAND COVER THRESHOLDS .................................................... 25
TABLE 2-3. VALIDATION DATASETS USED FOR SAMPLE ASSESSMENT ....................................................................... 26
TABLE 2-4. PLOT SIZE AND CORRESPONDING MEASUREMENTS AND OBSERVATIONS CARRIED OUT IN THE NFI FIELDINVENTORY ........................................................................................................................................................ 27
TABLE 2-5. NATIONAL SPECIES-SPECIFIC COEFFICIENTS FOR BIOMASS MODELS ...................................................... 29
TABLE 2-6. BELOW-GROUND LIVING TREE BIOMASS AS PROPORTION OF ABOVE-GROUND LIVING TREE BIOMASS 30
TABLE 2-7. COLLECT EARTH DISTURBANCE STUDY AND NUMBER OF PLOTS WHICH REACHED FOREST THRESHOLD........................................................................................................................................................................... 32
TABLE 3-1. SAMPLE-BASED AREA ESTIMATES (HA) IN MONGOLIA UNDER THE SIX IPCC LAND COVER CLASSES IN 2005,
2010, AND 2015. ................................................................................................................................................ 35
TABLE 3-2. CHANGE IN AREA (HA) BETWEEN FOREST AND NON-FOREST COVER TYPES OVER THE REFERENCE PERIOD
2005-2015. ......................................................................................................................................................... 36
TABLE 3-3. BIOMASS ESTIMATES (TONS PER HA) FOR ABOVE-GROUND, BELOW-GROUND AND DEADWOOD CARBONPOOLS IN THE INTACT AND DEGRADED BOREAL PLOTS, RESPECTIVELY. .......................................................... 39
TABLE 3-4. ESTIMATION OF ORGANIC CARBON CONTENT (TC PER HA) IN FOUR DIFFERENT CARBON POOLS FORINTACT AND DEGRADED BOREAL FORESTS PLOTS, RESPECTIVELY. ................................................................... 39
TABLE 3-5. REDD+ EMISSION AND REMOVAL FACTORS (TC PER HA) FROM FOUR DIFFERENT CONVERSIONS DURING
THE REFERENCE PERIOD 2005-2015 IN MONGOLIA. ......................................................................................... 40
TABLE 3-6. CARBON EMISSIONS AND REMOVALS IN MONGOLIA DURING THE REFERENCE PERIOD: 2005-2015 FROMVARIOUS CHANGE AREAS AS A RESULT OF VARIOUS DRIVERS OF CHANGE CLASSES. ...................................... 41
TABLE 3-7. COMBINED UNCERTAINTY ESTIMATES FOR ACTIVITY DATA AND EMISSION FACTORS ........................... 426

Acronyms and Abbreviations
AD
AFOLU
AGB
ALAMGC
BAU
BCEF

Activity Data
Agriculture Forests and Other Land Use
Above-ground Biomass
Agency for Land Administration and Management, Geodesy and Cartography
Business as usual
Biomass conversion and expension factor

BGB
IBUR
C
CE
CBD
CCPIU
CI
COP
CSO
DBH
DW
EF
EIC

Below Ground Biomass
Initial Biennial Update Report
Carbon stock in forest biomass
Collect Earth
Convention on Biodiversity
Climate Change Project Implementing Unit
Confidence Interval
Conference of Parties
Civil Society Organization
Diameter at breast height
Deadwood
Emission Factor
Environmental Information Centre

ERISC
FAO
FRA
FRDC
FRL
FREL
FUG
GASI
GCF
GCM
GDP
GFW
GHG
GHGI
GIZ
GPG
IGEB
IPCC
LULUCF
MET

Environmental Research and Information Study Center
Food and Agriculture Organization of the United Nations
Forest Resource Assessment
Forest Research and Development Centre
Forest Reference Level
Forest Reference Level
Forest user group
General Agency for Specialized Inspection
Green Climate Fund
Global Climate Model
Gross domestic product
Global Forest Watch
Greenhouse gas
Greenhouse Gas inventory
Technical Cooperation Agency of the German Government
Good practice guidanceInstitute of General and Experimental Biology, Mongolian Academy of Sciences
Intergovernmental Panel on Climate Change
Land Use, Land Use Change and Forestry
Ministry of Environment and Tourism

MPNFI
MRV
NDC
NFI

Multi-purpose National Forest Inventory
Measurement Reporting and Verification
Nationally Determined Contribution
National forest inventory
7

NFMS
PAM’s
QGIS
RBP
RCP
REDD+

SDG
SIS
SLMS
SoI
Stdev
tCO2e
TCC
TFI
TWG
UNDP
UNFCCC
UN-REDD

National Forest Monitoring System
Policies and Measures
Quantum GIS
Results-based payments
Representative GHG concentration pathway
Reduced emission from deforestation and forest degradation, Conservation of forest
carbon stocks, sustainable management of forests, and enhancement of forest carbon
stocksSustainable Development Goal
Safeguard Information System
Satellite Land Monitoring System
Summary of Information
Standard deviation
Tonnes of carbon dioxide equivalent
Tree crown cover
Taxation Forest Inventory
Technical Working Group
United Nations Development Programme
United Nations Framework Convention on Climate Change
United Nations REDD Programme

8

SUMMARYThis report is Mongolia’s first Forest Reference Level (FRL), one of the four main elements of REDD+,
which enables the measurement of performance of REDD+ measures associated with theimplementation of the national REDD+ strategy for Mongolia. It is based on the United Nations
Framework Convention on Climate Change (UNFCCC) decisions, technical documentations, and areview of available tools and methodologies, through national Technical Working Group meetings.
Mongolia’s FRL is at national scale reflecting the historical reference period 2005-2015. Greenhouse
Gas (GHG) emissions from deforestation and forest degradation are estimated, from 4 different
drivers1 - fire/pest, grazing, soil erosion, logging, as well as GHG removals from enhancement throughafforestation and/or reforestation activities.
The TWG made recommendations of the elements needed to construct a FRL and convened a FRL coregroup which has revisited all interim technical decisions to reach national consensus and developed
nationally-available data and information necessary to construct a national FRL. Activity data were
derived from the assessment of systematic samples to cover the entire country using FAO’s Open Foris
Tool Kit, Collect Earth and through visual interpretation of high-spatial resolution reference data
available from various sources. Emission factors are mostly derived from the data of the National
Forest Inventory executed in 2014-2016 and biomass models developed by the Mongolian academyof science (IGEB). A total of 52,660 ha, 1,394,810 ha and 3,038 ha were estimated to have undergone
forest loss (deforestation and forest degradation) and forest gain respectively between 2005 and2015.
Mongolia’s annual GHG emissions and removals from the forestry sector were estimated as 3,551,439
tCO2e at 95 % confidence interval (2,928,271 tCO2e, 4,174,606 tCO2e) and -74,055 tCO2e at 95%
confidence interval (-133,303 tCO2e, -14,806 tCO2e), respectively, during the reference period 200515.

1 Fire and Pest were not separately considered in Activity Data9

1. INTRODUCTION
Mongolia is a landlocked country geographically located between China and Russia. The climate ischaracterized by high fluctuations and extremes in temperature and precipitation. The annual mean
temperature ranges from -8oC to 6oC across regions and the annual precipitation varies from 50 mm
in the Gobi Desert to 400 mm in the northern mountainous area. It is an immense area of 156 million
ha, largely consisting of grasslands which have traditionally supported nomadic herding lifestyles forthousands of years.
The country supports two major forest biomes, boreal forests in the north accounting for 14.2 million
ha (87 %), dominated by larch and birch; and 2.0 million ha of saxaul forests (13%), a dryland woodland
ecosystem in the southern arid regions of Mongolia that is considered under national definitions as‘forest’ (CCPIU, 2018) (Figure 1.1). The boreal forest comprises deciduous and coniferous forests
growing in the forest steppe, boreal forest and mountain zones. Boreal forest is dominated by six
main conifer species: larch (Larix sibirica), birch (Betula platyphylla), Siberian pine (Pinus sibirica),
Scots pine (Pinus sylvestris), aspen (Populus tremula) and spruce (Picea obovata), with much of theforests being dominated by larch (FRDC, 2016). The broad-leaved trees found here are mainly birch(Betula platyphylla), aspen (Populus tremula) or poplar (Populus diversifolia). Northern boreal forests
are part of the transitional zone between the Siberian taiga forest to the north and the grasslands tothe south. They typically grow on mountain slopes between 800 m and 2500 m above mean sea level.
According to the forest taxation inventories conducted by the Forest Research and Development
Center (FRDC), larch, birch and saxaul trees account for more than 60%, 10% and 15% of forest areas,
respectively. In terms of growing stock, larch contributes close to 80%, while all other trees are below
10%. The boreal forest average growing stock is estimated 114 m3/ha, excluding saxaul forest (MET,2016).
The reduced emissions from deforestation and forest degradation, conservation of forest carbon
stocks, sustainable management of forests, and enhancement of forest carbon stocks (REDD+)
readiness process commenced in June 2011 when Mongolia became a partner country of the UNREDD programme and the implementation plan of its REDD+ Readiness Roadmap was developed
afterwards. Mongolia officially adopted its national REDD+ Readiness Roadmap in 2014, followed by
the UN-REDD National Programme being launched in 2016 to support Mongolia’s REDD+ readinessprocess. A comprehensive process of engagement with stakeholders has been undertaken and thereadiness process is expected to be completed in December 2018 with completion of the “Warsaw
Framework” elements for REDD+.
A Forest Reference Level (FRL) or a Forest Reference Emission Level (FREL) is one of the four elementsof REDD+ according to the Cancun Agreement: Decision 1/CP.16 of the United Nations Framework
Convention on Climate Change (UNFCCC) at the 16th Conference of Parties (COP) in 2010. The Cancun
Agreement defined a FRL/FREL as a benchmark for assessing the performance of each country in
implementing a REDD+ strategy. In addition to the Cancun Agreement, decisions relating to the rules
and modalities for FRL/FREL development can be found from COP 15 in Copenhagen (Decision 4), COP
17 in Durban (Decision 12) and COP 19 in Warsaw (Decision 13, of the ‘Warsaw Framework for
REDD+’). The methodological guidance for FREL/FRLs contained in these decisions included, forexample, the importance of consistency and tranparency of data; the recommendation for a step-wise
approach to development; and the process for Technical Assessment (TA) by UNFCCC experts in thecontext of result-based payments (RBPs) for REDD+. Since Mongolia included Greenhouse Gas (GHG)
removal estimates from carbon stock enhancement activities, as well as GHG emission reduction
estimates from deforestation and forest degradation, this submission is for a FRL instead of a FREL,
which is understood to deal with emission reduction estimates only.

10Figure 1.1. Forest habitat types in Mongolia. Clockwise from top left to right: Deciduous forest – birch(Betula platyphylla); Coniferous forest – larch (Larix sibirica); Mixed deciduous and conifer forest andsaxaul forest.

1.1. Objective of FRL development
Mongolia’s national stakeholders have come to an agreement on the objectives of FRL development
as follows:
1) to access RBPs under the REDD+ initiative,2) to assess the contributions that the forest sector makes toward the Nationally Determined
Contributions (NDC) to the Paris Agreement of 2015, and
3) to assess the impacts of REDD+ policies and measures taken to mitigate and adapt to climatechange in the Agriculture, Forest and Land Use (AFOLU) sector.

1.2. Underlying causes of forest change
Deforestation is defined as the permanent conversion of forest cover to another type of land cover,while forest degradation is the reduction of biomass in an area of forest which, while still remaining
as forest, reduces the functionality of the forest ecosystem. Drivers of deforestation and forest
degradation are often a complex set of interactions between direct and indirect factors, often
attributing the cause to one clear driver is not possible. Table 1.1 shows the main direct and indirectdrivers of deforestation and degradation in Mongolia (UN-REDD, 2016a).

11

Table 1-1. Direct and indirect drivers of deforestation and forest degradation in Mongolia (UN-REDD,
2016a)
Deforestation drivers
Permanent land use change from forests
to other land use
Direct drivers
Mining and land use change
Deforestation through continued
degradation

Indirect drivers and underlying causes
Demographic
Socio-Economic
Factors
Factors

Degradation drivers
Persistent reduction in forest ecosystem function, or in
the case of REDD+, in carbon stock and canopy cover,
but the predominant land use remains as forest.
Forest Fire
Unsustainable logging and subsequent degradation
Minor damage from pest outbreaks
Grazing
Fuelwood collection

Institutional &GovernanceFactors

Environmental
Factors

Policy and Legal
Issues

1.2.1. Direct drivers of deforestation/forest degradation
Mongolian forests are slow in growth, and are vulnerable to disturbance from drought, fire and pests.
Therefore forests could easily lose their ecological balance following a disturbance and they have arelatively less natural regrowth afterwards, partly due to the biophysical environment in the northernhemisphere’s harsh continental climate, which significantly limits vegetative growth and soil moisture
content.
The long-term compounded effect from several drivers, such as forest fire, followed by pest
infestation and grazing, often exacerbated by loss of moisture content in soil, which may lead topermanent deforestation. Once disturbed, forests increasingly degrade and ultimately, they could
turn into steppe with few trees or shrubs. Since several factors are compounded in deforestation, it isdifficult to identify a single factor of the major cause, therefore they should be treated as a group offactors. It is due to the long-term compounded effect of forest fires, pest damage, often triggered by
improper unsustainable logging practice, and exacerbated by uncontrolled grazing and recurrent
burning inhibiting regeneration (Tsogtbaatar, 2013). Once disturbed, the resilience of a forest
ecosystem declines, and a combination of pressures from unsustainable logging practice, livestock
grazing, fires and pests occur together. Any of these drivers can provide an entry point to a process ofincreasing forest degradation, both in spatial extent and in severity. Independent to the specific driverthat triggered degradation, forests increasingly degrade and ultimately turn into steppe with few trees
or shrubs.

MiningMining can also cause deforestation when mining operations remove the top soil and open pits. To
date, 2,736 mining licenses have been issued covering 11 million ha (7.0 % of the total national
territory). These include 1.1 million ha of exploitation licenses and 9.9 million ha of exploration
licenses (MET, 2015). In 2009, the total area under exploration and mining licenses is estimated to be
between 2 % and 14 % of forest areas within the forest provinces. Further collection of detailed
datasets from the Ministry of Mining needs to be undertaken. Mine-site restoration is insufficient onmuch of the mining areas. Technical reclamation refers to closing the mining pit; whereas biologicalrestoration refers to re-establishing vegetation cover by sowing of perennial plants and planting trees.
12

The absence of grown soil makes biological reclamation difficult and often establishment of vegetation
cover is unsatisfactory. A mining moratorium on new areas, declared in 2010, has drastically reduced
the forest land areas affected by mining activities.

Forest Fire
Forest fires affect large areas in Mongolia, generally about 95 % of these are regarded as caused byhuman activities, only 5 % are due to natural factors, mainly lightning (MET, 2017b). Fires most likely
occur during the spring and autumn period, the highest fire risk season, when activities such as timber
harvesting, use of non-timber forest products, hunting may cause forest fires.2 Forest fires burn large
amounts of herbaceous plants, and parts of the soil, that can contain humus layer, moss, peat, tree
crowns, shrubs, and trees on the surface of the forest soil. There can be surface, ground and crown
fires depending on environmental conditions and the amount of fuel in the forest ecosystem, withcrown fires resulting in effects that are more deleterious and may lead to many tree deaths. Once
burnt, forests are more susceptible to damage from pests, and are more accessible for logging,specifically for deadwood collection and grazing, as the resulting opening of crown cover will benefit
grass and therefore attract grazing animals, and may lead to the deforesting through continued
degradation that is described above. The effects of fire also result in increased oxidization of organic
matter in soils leading to emissions several years after the forest fire incident. Recurrent burning often
compounded with grazing suppresses natural regeneration that would occur in natural forests. The
potential of drier summers and increased temperature, as observed in other northern hemispherecountries, may increase the risk of forest fire.

Forest Pests
Insect damage is caused by a complex interaction of factors that are only partly human induced.
Beneficial and detrimental insects in any forests live in harmony with the ecosystem, with outbreaks
often being part of the natural ecological cycles. However, the ecological balance can be lost andprovide suitable conditions for pest outbreak due to weakened forest tree health because of forest
fire, logging, competition for water. After fire, there is high risk of pest infestation and logged-overforests tend to attract pests. Damage can be severe, but most commonly the forest will become
degraded rather than undergo a land use change. Increasing aridity because of climate change has
also been said to favor mass proliferation of harmful insects (Dorjsuren, 2014); climate change has
also been reported to lower the abundance of insect parasites which may ultimately cause increased
abundance of insect pests. Minor pest outbreaks will only slow growth for one season and these tend
to lead to forest degradation, herewith classified as minor pest damage. If there is no further pest
outbreak, or other cause of degradation, it is expected that in following years the forests will gradually
recover. Pest damage is also one of the contributing factors of forest degradation (combined with fire,
environmental change, logging) which eventually leads to deforestation through continued forest
degradation.

2 Nyamjav, Goldammer, and Uibrig 200713

Unsustainable logging and fuelwood collection

Many logging practices in Mongolia are unsustainable and consequently lead to long-term forest
degradation. Logging companies do not follow best logging practices and do not follow reduced impact
logging practices. Though Codes of Practice are available in Mongolia, logging safeguards are usually
not implemented to reduce the subsequent effects of increased fire, pest damage, and grazing
following timber extraction. Unsustainable logging compromises the forests’ capacity forregeneration, by increasing the risk of soil compaction, forest fire, and grazing damage.
However, it should be noted that large areas of Mongolia’s forests need to be placed under asustainable forest harvesting regime to increase ecosystem health and resilience to pests and fire, andto provide economic incentives and benefits for communities and enterprises.

Grazing

Mongolia has a long tradition of raising livestock, with pastoral nomadism the prevailing form of land
use and an important way of life for many communities living in forested areas. Currently around one
third of Mongolia’s population lives as nomads from livestock husbandry. Grazing is not a driver offorest change, but works together with several other factors contributing to deforestation throughcontinued degradation and directly affecting areas that are assigned for carbon stock enhancement.
The complexity of factors is described in the section on deforestation through continued degradation.
Grazing within forests is widespread in degraded or edge forests and an important factor inhibiting
regeneration; animals eat young trees and it suppresses regeneration. Well-stocked forests’ with low
intensity grazing have been observed to occur high seedling density compared with forests’ having
low-stocking along with high intensity grazing (MET, 2017). Grazing is therefore a contributing factor
to the degradation of already disturbed forest; it also is an inhibiting factor for any naturalregeneration or plantations on degraded areas.
1.2.2. Indirect drivers of deforestation
Underlying causes of forest change include demographic, economic, technological, policy &institutional, cultural & socio-political, and environmental factors (Chapter 6 of UN-REDD, 2016). In
Mongolia, technical capacity development, regulation and policy strengthening, awareness raising,and direct funding of activities for maintaining and enhancing forest carbon stocks, and sustainable
forest management have promise for addressing the drivers of forest change. These broad types ofpolicies and measures will emerge from examining the interactions between drivers’ underlying thecauses. All drivers provide entry points for designing useful interventions to address them.

Demographic Factors
Growing population has increased the general activity levels in forest areas; and small-scale rural
activities result in fires and pervasive pressure placed upon forest resources. Population growth iscompounded by urbanization and urban expansion creates a need for wood products and construction
areas.

14

Socio-economic factors
Fast economic growth in recent years resulted in more government funds available for activities
enhancing forest carbon stocks, such as tree planting and pest control; though this funding has beensubsequently reduced because of Mongolia’s economic downturn caused by a downturn in the mining
industry. Prior to the economic expansion, there was higher demand for wood products leading to anincrease in unsustainable logging and subsequent degradation. Lingering rural poverty continues todrive unsustainable forest management, overgrazing, forest fires (or grassland fires which impact
nearby forests), illegal logging and ultimately deforestation by continued degradation.

Institutional factors
Low technical capacity for forest management exacerbates forest fires and unsustainable logging andsubsequent degradation; it compromises the effectiveness of pest control and tree planting. Forest
skills and capacity would benefit from updating and experience from other countries facing similar
issues in North America and Scandinavia. The lack of firefighting equipment is widespread, but more
importantly, forest fire management techniques are outdated. Weak governance and poortransparency around the allocation of mining permits may result in deforestation. Poor compliancewith applicable environmental regulations in mining can increase deforestation, reduce restorationand cause other environmental and social impacts, including possibility of communities being
relocated. The lack of transparency in public procurement for services such as tree planting, may resultin the selection of ineffective service providers and lack of tree care and maintenance, whichsignificantly compromises success rates.

Environmental Factors
Climate change has led to increasing occurrence of pests and compromised forests’ regenerative
capacity. Reduced rainfall and the increased occurrence of droughts have created unfavorable
conditions for tree plantings, led to more forest fires and increased pest occurrence. The potentialchange in climate in Mongolia with predicted rainfall change, permafrost thaws, seasonal temperaturechange, including extreme winters, has the potential to increase the vulnerability of the forests tosnow and ice damage, forest fire risk, pest infestation, and places inordinate ecological pressure,especially through competition for water in dense forest stands, through reduced soil moisture fromreduced rainfall and lost permafrost. Both thawing peatland permafrost as well as drying anddegrading peatlands result in increased greenhouse gas emission from peat soils. Mongolia isestimated to host one of the largest peatland areas in the world, potentially covering more than 1.7
percent of the country’s surface (Minayeva et al. 2016). It is important to note that due to lack ofdata, especially soil sampling for reliable peatland mapping, the emissions from soil organic carbon
are not considered herewith.

Policy and Legal Factors

The regulatory environment and law enforcement capabilities are insufficient for fire prevention, illegal
logging, grazing and mining. The insufficient regulatory environment is compounded by a low capacity
15

for law enforcement and fire prevention, which promotes unsustainable illegal logging, fails to curtailgrazing activities and leads to environmentally damaging mining practices in forest areas. The politicalenvironment has overemphasized forest protection at the expense of development of a sustainable
wood industry, this can be restrictive and counter-productive preventing the legalized sustainable
harvesting and resource use which would ensure better management of the forest resources. The low
harvest rates prevent an effective forest sector from developing, yet wood processing factories still
demand timber, this is either imported or comes from illegal sources within Mongolia. Most
government funding is allocated to tree planting and to pest control rather than being directed toinvestment in more effective forest management. Forest planting has not been successful despite
receiving large budgets and should be reserved for selected areas with natural regeneration processes
being the focus of forest establishment.

1.3. Extent of FRL components
1.3.1. Scale
Boreal forests in the northern part of the steppe and taiga zone take up most of the forested area in
Mongolia. Saxaul (Haloxylon ammodendron) trees in the southern part of the desert zone form
wooded land (Enkhsaikhan, 2017). It is also considered as forest land in Mongolia (FRDC, 2017).
Mongolia’s FRL is at national-scale, that takes into account for changes in all types of forest.
1.3.2. Scope: activities, pools and gases

REDD+ activities in the FRL
The FRL includes the REDD+ activities: deforestation, forest degradation and enhancement of forest
carbon stocks (afforestation/reforestation).
Deforestation and forest degradation are the significant contributors of GHG emissions from forest
land and therefore have been included in the emission calculation. Fire, pest outbreaks, logging andmining are the key drivers of deforestation and forest degradation in Mongolia.
Forest areas where tree canopy cover has been reduced to below 10 % by the drivers are defined as
deforestation. The forest areas converted for settlement and agricultural purposes are also considered
as deforestation even if the minimum threshold of 10 % canopy cover is reached.
Forest areas with a canopy cover equal to, or above, 10 % but in which canopy cover has been reduced
due to fire, pest or logging activities were considered as degraded forest.
Removals from reforestation/afforestation are included as enhancement of forest carbon stocks in the
FRL calculation.
Furthermore, removals in forest land remaining forest land are also observed as enhancement offorest carbon stocks. Mongolia included these removals in order to be complete and for consistency
with the GHG inventory. Growth in stable forest land, however, is not expected to be very different in
the results reporting period so even though these concern large amount of removals, they are notexpected to contribute to the results reporting. Even more, considering that under the current
calculation the growth rate is considered stable in the stable forest area, where growth is happening,

16

decreases slightly, the inclusion of removals in forest land remaining forest land has an expected minor
negative impact on REDD+ results.
No separate calculation has been considered for GHG emissions or removals due to conservation offorest carbon stocks and sustainable forest management. However, the impacts of these the two‘REDD+ activities’ are considered to be covered by the measurement of emissions due to deforestationand forest degradation and by the measurement of removals due to forest carbon stock enhancement.

Carbon pools in the FRL
Above-ground biomass (AGB), below-ground biomass (BGB), deadwood (DW), and litter pools were
included in the FRL, excluding Soil Organic Carbon (SOC). For AGB and DW, data from the NFI wasused, and for BGB default estimate (root-shoot ratio) from IPCC guidance was used.
In its initial FRL submission, Mongolia had included the soil carbon pool but assessed the emissions
from soil at zero following a Tier 1 calculation suggested by IPCC (2006). This assumption is correct
considering the default stock change factors for land-use systems (FLU), management regimes (FMG)
and input of organic matter (FI) in equation 2.25 if all deforestation would happen on mineral soils.
However, a map-based inventory demonstrated that as much as 272 000 km2 (approximately 1.7
percent of Mongolia’s land area) might consist of peatlands (Minayeva et al., 2016), bound to foothills
and mountains within forest steppe, taiga, and mountain tundra. Joosten (2010) suggests Mongolia isamong the 10 world’s largest peatland emitters though the study does not specify whether theseemissions are from forest lands or outside. Other sources suggest the peatland areas in Mongolia to
be much smaller (e.g. Xu et al 2018) based on an analysis using the Harmonized World Soil Database
yet the accuracy of this database for peatland (or ‘histosols’) is disputed.
In absence of reliable information on spatial distribution, EFs or RFs from soil organic carbon pool
(SOC) in forested peatland areas, this FRL does not include SOC pool in the emissions and removals
calculations.

Gases in the FRL
CO2 released from all four carbon pools was included in the FRL. Other GHGs believed to be significant
in terms of emissions from the forest sector include N2O, from forest fire, and CH4 from permafrost
melt. Work has begun on the development of emissions of these two gases, but is currently
incomplete at the time of this modified submission.
1.3.3. Forest definition
Since 1958 Mongolia has implemented a Taxation Forest Inventory (TFI) which was designed for
management planning purposes. The TFI uses a minimum relative stocking density threshold value of
0.3 within an area of 1 ha to qualify land as forest. The estimate based on the TFI has been used toreport to the FRA. In 2014, the first National Forest Inventory (NFI) was initiated and data collection
has been made over the period of 2014-2016 based on a forest definition designed to fit in Mongolian
boreal forest context. To ensure consistency between the FRL and the recently completed NFI, thisFRL has adopted the same forest definition for boreal forest, as used in the NFI (2016, page 17).
Based on the review of TFI, FRA and NFI, the decision to adopt the forest definition for boreal forest
was made by the Technical Working Group (TWG) for National Forest Monitoring System (NFMS) and17

FRL development (UN-REDD, 2016b). Revisions have been made by the TWG for the minimum height,
from 5 m to 2 m, and for the minimum area, from 0.5 ha to 1 ha, thresholds compared with the FRA
definition. These revisions were taken to include some forest tree species such as birch (Betula exilis,
B. Humilis) and dwarf pine (Pinus pumila), which do not reach 5 m in height.
The definition of ‘forest’ for Mongolia’s boreal areas can be summarized as follows: “all land spanningof at least 1 ha covered by trees with a height of at least 2 m and with a canopy cover of at least 10
percent”. This definition will be used in future national and international communication.
Forest definition for saxaul forest was adopted from the publication of Jalbaa and Enkhsaikhan (1991).
A standard table in the study provides that minimum height class is 1 m and minimum crown cover is
4.65 %. For this FRL, a 4% area threshold was used for Saxaul forest.
1.3.4. Historical reference period
For the national GHG inventory in the IBUR, Mongolia compiled a LULUCF assessment for 30 years,
covering the time period from 1986 to 2016. The proposed reference period for the FRL is the 10years period from 2005 to 2015.

1.4. Consistency with greenhouse gas inventory reporting
Both IBUR and FRL reports followed the six IPCC land use categories (IPCC, 2006). Mongolia reported
emissions and removals from both boreal and saxaul forests in the IBUR (MET, 2017a). In the IBUR, it
was estimated that of the total forest area, about 75 % is under boreal forest cover while remaining
25 % is saxaul type, based on data from the Agency for Land Administration and Management,
Geodesy and Cartography (ALAMGC).
ALAMGC data does not contain spatially explicit information on forest change, therefore the estimatesof changes in the FRL cannot be compared directly with the IBUR. Forest area change estimates are
based on the systematic dot-grid sampling which provides uncertainty estimates for forest/non-forest
areas as well as change areas. The IBUR indicated the use sample-based approach using openforis
collect earth (CE) as the main source of Activity Data (AD) for the LULUCF sector for subsequent
submissions of BUR in terms of LULUCF area estimates and changes (MET, 2017c, Chapter 6.1.7). The
carbon fraction (0.51) from biomass and root-shoot ratios are taken from the table 4.4 of IPCC GL(2006), similar to the IBUR.

18

2. MATERIALS AND METHODS
2.1. Activity data
2.1.1. Rationale for data selection
Mongolia has both spatially explicit wall-to-wall maps and dot-grid systematic sample-based data
available for estimating forest cover changes. Those datasets were reviewed for their reliability,
suitability, and accuracy in the context of construction of the FRL to ensure transparency andconsistency.
1. The Collect and Collect Earth system of FAO’s openforis were used to assess 123,577
systematically gridded sample points covering the entire country. Ten percent randomly
chosen sample points were independently assessed to determine the operators assessment
uncertainties (CCPIU, 2017). By using dot-grid sampling to assess forest area and its change
from satellite imagery, the need for time consuming and vast territorial area coverage ofMongolia’s image processing workload was substantially reduced. Dot-grid sampling isconceptually simple, but appropriate sample design and dot intensity are essential to ensure
estimations that are both accurate and precise.
2. The Environmental Research Information and Study Center (ERISC) has been producing
Mongolian forest cover maps using a Moderate Resolution Imaging Spectroradiometer(MODIS) -derived NDVI value of 0.65 threshold since the year 2000 based on 30 m-Landsat
imagery. To date ERISC has produced sixteen annualized forest/non-forest maps from 2000 to2015 (Ariunzul et al., 2017). Figure 2.1 shows the 2005 forest map from ERISC. Theseannualized forest/non-forest maps could potentially be a useful database to generate activity
data. The ERISC mapping however do not comply with the forest definition adopted in the FRL.
The wall-to-wall ERISC maps do not provide information on degraded forest and also on forest
gain. Moreover, the maps have not been tested for a robust accuracy assessment to generate
uncertainty estimates. ERISC maps are therefore of limited use as a source of AD for the FRL.
To develop historical data on forest cover change at the national-scale, which had not previously been
done, the TWG decided to follow sample-based approach and to use FAO’s openforis Collect Earth(CE) system for the assessment of samples. The analysis was conducted using high to medium spatial
resolution Earth Observation data accessible through Google Earth, Google Earth Engine (GEE), andBing Maps. A sample plot was determined to be forest land when ≥10% tree cover was estimatedusing the dot grid within a sample.

19

Figure 2.1. Forest cover map of the year 2005 from ERISC
2.1.2. Sample Density
The CE study required the selection of an optimum density of sample points that captures maximum
changes of the activities within the forest, that are statistically sound, and that ensures unbiased forestchange estimates. This study was used to develop forest and land use change estimates for Mongolia,
not only for the FRL.
The study first examined the annual spatial changes in forests that are reported in the Global Forest
Watch3 (GFW) datasets and then investigated how accurately the annual area changes in GFW are
captured using systematic grids at different sampling densities. The theory is that there should be an
optimum sample density that serves three purposes. First, the optimum sampling density should
minimise the errors in estimates. Second, the time required for analysis should be minimised. Third,
it should result in an acceptable level of accuracy in estimates.
Figure 2.2 shows how the annual changes in the GFW data were actually estimated in relation todifferent grid point densities. It can be seen that, as might be expected, the error as expressed by thevariance (the deviation from the value of 1, the ratio of estimated area to observed area) reduces as
the number of points in the design increases. In the case of a country like Mongolia, with relatively
small annual changes in land cover, the point at which the error is largely eliminated was shown to bea systematic grid with spacing between 1 km and 2 km. Analysis of this range suggested that,theoretically, about 99% of the changes could be captured at a systematic grid spacing between 1 km
and 2 km (without consideration of operator’s error) (Figure 2.2). It was concluded that a target
sample grid of 2 km by 2 km would be sufficient to capture annual forest changes in Mongolia.
The NFI conducted between 2014 and 2016 used a national sampling grid of 9 km by 9 km (MET, 2016).
In order to enable consistency of the CE analysis with current and future NFI exercises, it was decided
that the grid design for CE should be compatible with this national sampling density, hence a distanceof 2.25 km between sample points was selected, as both compatible with the results of the analysis
described above, and with existing national datasets.

3 http://www.globalforestwatch.org/

20

Level of uncertainty at various systematic grid spacing
year_2001
65

year_2002
year_2003
year_2004

Estimated/Observed

4
3
2

year_2005
year_2006
year_2007
year_2008
year_2009

1
0
64

32

16

8

4

2

year_2011
year_20121-1-2

Grid spacing (km) in the sampling design

year_2010

year_2013
year_2014
total

Figure 2.2. Error associated with grid density and the ability of the sample grid to estimate the area ofannual forest changes as recorded in the Global Forest Watch dataset for Mongolia (one sample every
km means 4096 times more samples than one sample every 64 km).
Mongolia’s total land area is 156,411,556 hectares (ALAMGC, 2016). In order to assess land use andland-use change, over 123,577 regularly-distributed sample points were created and assessed acrossthe territory of the entire country. The Climate Change Project Implementation Unit (CCPIU), under
the Ministry of Environment and Tourism (MET) led the AD collection and analysis for the FRL between
the years of 2005 and 2015 (Bey et al., 2015; CCPIU, 2018).
Considering the national GHGi reporting has been conducted on a two-year cycle, it was further
concluded that a grid of 2 km by 2 km (one quarter of the point density) would capture the cumulativechanges taking place over a two year period with the same accuracy (Figure 2.2).

21

100

Estimated total area ('000 ha)

80

60

40

20

1x1 kmR² = 0.9971

2x2 kmR² = 0.9928

5x5 kmR² = 0.8719

10x10 kmR² = 0.9011

-

20

40

60

80

100

Observed total area ('000 ha)

Figure 2.2. Analysis for optimal grid spacing design. Relationship between grid spacing and size of areachange when compared to Global Forest Watch data as wall-to-wall information for Mongolian forest
cover change.
2.1.3. Stratification
The sample design was stratified into boreal forest zone and the rest of the country (including Saxaul
forest as well as all non-forest zones). Most of the observable changes between forest and non-forestover time occur in the boreal forest zone, so the sampling density for non-boreal areas does not need
to be as high.
For boreal forest areas, therefore, a 2.25 km by 2.25 km grid (as described above) was used to locate
sample points and for other areas a 9 km by 9 km grid was used, with the intention that this will beconsistent for all subsequent reporting of FRL and IBUR. This results in a total of 123,669 sample points
across the entire country. The grids have been chosen on a nested basis so that if the country decided
to change density of the assessment points, the different density grids will always be coincident for
any amended spacing between points. (Figure 2.3 and Figure 2.4), i.e. if the forest mask is adjusted infuture, no new sample points will need to be created for non-forest areas, and new sample points forforest areas will be consistent with the existing grid.

22

Figure 2.3. Forest (F) and non-forest (NF) strata have been used to determine the optimum density
range of a systematic grid. The boundary of the forest (F) stratum (diagonal stripped) was chosen to
include all NFI points as well as the forest mask 2013. The highlighted area in Gray is non-forest (NF).

Figure 2.4 Coarse grids for forest 4.5 km x 4.5km and non-forest 18 km x 18 km samples (left) andcurrent denser grid for forest 2.25 km x 2.25 km and non-forest 9 km x 9 km (right).
2.1.4. Sample point generation
It was decided that an equal distance sample grid would be constructed based upon the six Universal
Transverse Mercator (UTM) projection zones that cover Mongolia. The alternative would be to use agrid based upon equal angular units in a geographic coordinate system (latitude/longitude), for whicha single grid could be constructed for each stratum. However, in northern latitudes as in Mongolia,
this results in a variable grid size and the need for a unique expansion factor for each grid point. It also
creates a potential bias, in terms of the grid density (more dense in the north) and directionality of
the grid (greater density in the east/west direction than north/south) which is considered undesirable.
A systematic sample design was chosen for the study. QGIS 2.14 was used to automatically generatea rectangular array of points over each zone (using a grid generator), then spatial query was used to
select points from the array that falls within the stratum and UTM zone. Expansion factor for eachzone is multiplied by total number of the plots to estimate total area of the polygon area (see Table
2-1.). Detailed process of creating the sampling grids is provided in annex Collect Earth user manual(Daniel et al., 2015).
23

Table 2-1. Expansion factor for each UTM zone
StrataUTM zones

No. of points

Area (ha)Expansion factor (ha)Forest (boreal
zone)

45
46
47
48
49
50
sub total
45
46
47
48
49

3,660
14,752
43,231
33,251
14,646
1,714
111,254
68
2,362
2,555
3,151
2,771

1,850,126
7,471,554
21,881,587
16,833,385
7,417,686
868,568
56,322,906
579,301
19,170,923
20,747,694
25,613,817

505.50506.44506.16506.25506.47506.7550
Sub total
Total

1,416
12,323
123,577

Non-forest(including Saxaul)

22,473,093
11,454,767
100,039,595
156,362,501

8,519.13
8,119.83
8,120.43
8,128.79
8,110.10

Square grid
spacing2.25 km by 2.25km

9 km by 9 km

8,089.53

2.1.5. Survey design
The Survey Form was designed using openforis-Collect tool to gather information in a mannerconsistent with the IPCC guidelines, thus enabling Mongolia to use the resulting data to address someof its data needs for reporting to the UNFCCC. Figure 2.5 shows an example of a data collection form
used in this Collect Earth Land Assessment (CELA) project for collecting data on LULUCF. As shown in
the figures, the CELA project used square-shaped plots of one hectare which again contains 49 subplots or grid-points. Each of the 49 sub-plots or grid-points, therefore, represents to approximately 2% of a 1-ha square sample plot. Data has been analyzed at the plot level while the sampling points
have been used to quantify and to characterize a land cover type.

Figure 2.5. Collect Earth land assessment form for land use and land-use change information collection.

24

2.1.6. Land-use types and use of hierarchical rules
Land use assessment follows certain rules in terms of reducing subjective effects on the result, as
described in Table 2-2. For the historical assessment, the level of disturbance is associated with thedifferent land use categories as a result of human impact, i.e., fire/pest, logging, grazing, soil and soil
erosion. The degree of disturbance impact is determined by the operators’s own interpretation. As forthe land use change, the operator has to use Google Earth Engine with different time series of Landsat
imagery to determine the actual year of change from one land use conversion to another.
Table 2-2. Land use assessment hierarchy and land cover thresholds
1
2
3

4
5
6

Land use categories
Settlement
Cropland
ForestBorealSaxaulGrassland
Wetland
Otherland
No dataClassification rule, %
14
14
10
4
14
14
14
Omitted2.1.7. Response design
The desired goal of this validation was to derive a statistically robust, quantitative assessment of theuncertainties associated with the forest area change estimates. Several factors potentially impact on
the quality of forest mapping (GOFC GOLD, 2015), namely
The spatial, spectral and temporal resolution of the imagery
The radiometric and geometric pre-processing of the imagery
The automated and manual procedures used to interpret the forest map category
Thematic standards (i.e. minimum mapping unit and land use definitions)
The availability of field reference data for evaluation of the results.
Approaches were used in the case of Mongolia’s FRL to minimize these sources of error following IPCC
and GOFC-GOLD good practice guidelines, as appropriate. However, the representativeness ofreference data and sample selection of the accuracy assessment of change area were slightly
compromised by the restricted availability of high spatial resolution imagery in Google Earth, in GEE
and in Bing Maps across Mongolia.
Through a collect survey design form (see Figure 2.5), using Open Foris Collect, the six Land cover types
were assessed within each sample through an expert image interpretation of medium (15-m pansharpened Landsat) to very high (sub-meter) spatial resolution aerial imagery and satellite data. The
reference datasets were used in the accuracy assessment are listed in Table 2-3 and have sufficient
temporal representation consistent with the reference period: 2005-2015. The Collect survey designform has been designed to allow a confidence-level specified by an interpreter.

25

Table 2-3. Validation datasets used for sample assessment
Data types
High-resolution RGB imagery from various
satellite sensors, such as SPOT, GeoEye-1,
WorldView-1/2/3, Digital Globe, IKONOS,
etc.
Landsat time series archive: 2005-2015

Spatial resolution
10-m to 30-cm

SourceGoogle Earth Pro/ Bing Maps

30-m
(15-m pan-sharpened)

Google Earth Engine
Landsat/Sentinel MSI 2 Archive

2.1.8. Analysis design
Appendix 1 shows the analysis design for six IPCC land cover/use categories and also for forest/nonforest categories, respectively. Appendix 2 illustrates the calculation of estimators for a systematic
random sampling design, following GFOI (2016) guidelines. The samples were analyzed and thesample-based estimates were derived through using the survey package (Lumley, 2004 and 2014)
available within the statistical package R Core Team (2014). The survey package uses the standard
formulae for estimation of means and variances.

2.2. Emission and removal factors
2.2.1. National Forest Inventory
The NFI (2014-2016) also followed a pre-stratified systematic design, with different sampling intensity
in different regions, to survey a representative fraction of the forests in order to derive information
on quantitative and qualitative conditions of forest. A national-level sampling grid was prepared withthe objective of generating NFI results with satisfactory precision at national-level, i.e. statistics
reflecting the average boreal forest in Mongolia. This national sampling grid covers the entire boreal
forest area (with a few exceptions) with a grid spacing of 9 km North and 9 km East. In order togenerate NFI statistics with satisfactory precision also at the regional level, further intensified
sampling grids were added in the main forest inventory regions. In the Khangai, Khuvsgul and Khentii
regions grids with spacing 4 km north and 4 km East were added. In the Altai region a denser grid withspacing 1.5 km North and 1.5 km East was added, to better capture the relatively small forest area.
The NFI field inventory sampling design was developed to generate per-hectare based forest statistics.
A total of 4,284 sampling units were inventoried (see Figure 2.6) of which 1,007 sample units fornational square-grid (9 km by 9 km) and 3,277 units for intensified grid. Each sampling unit is a clusterof 3 sample plots, in order to cover the variety in the forest characteristics in the tract (forest site).
Therefore, the total of 12,216 sample plots were measured during the NFI inventory. The centers ofsample plots are distanced 100-112 m of each other in a triangle. A sample plot is composed by nested
circles with radii of 20 m, 12 m, 6 m, 2 m; on which different measurements and assessments werecarried out as specified in Table 2-4. Appendix 3 illustrates the sampling unit design and design of thenested plots within a sampling unit.

26

Table 2-4. Plot size and corresponding measurements and observations carried out in the NFI fieldinventory
Plot radius
2 meter
6 meter

12 meter
20 meter

Measurements and observations
Regeneration
Standing trees with diameter at breast height (dbh) – 6 cm -14.99 cm
Deadwood
Soil assessment
Standing trees with dbh of 15 cm – 29.9 cm
Standing trees with dbh ≥ 30 cm
Stand structure, fire, grazing, erosion, protection status, etc.

Figure 2.6. NFI plots locations (green dots) over boreal forest distributional area. Vertical lines
represents 6 UTM zones from 45 to 50 over Mongolian territory.
EFs and/or RFs for ABG, DW, and Littler were derived from NFI (MET, 2016) inventory plots data. The
NFI sample locations were selected using a Forest Mask 2013/2015 covering only the dominant wellstocked forest areas in the Boreal forest zone, with approximately 66 % canopy cover or more. Therewas, therefore, a lack of information regarding EFs from low-stocked boreal forests. To be consistent
with the national forest definition, where the canopy cover threshold is 10 %, additional NFI plots
were needed to be surveyed to cover low-stocked forest areas.
According to the drivers study (UN-REDD, 2016a), direct and indirect impacts of wildfire, pest outbreakand logging leads to deforestation and degradation. In order to estimate EFs for the change classes
within forest area, additional data collection for low-stocked forest inventory was organized by the
Mongolia UN-REDD National Programme, with 156 new NFI clusters measured in 2017 following same
methodology of NFI (MET, 2016), representing 1,583,492 ha low-stocked Boreal forest area (Figure
2.7 and Figure 2.8).

27

Figure 2.7. Distributions of additional field inventory plots that are degraded by fire, pest and logging
activities and sparse trees.

Figure 2.8. Burnt forest area (top), Pest affected forest (left) and logged forest (right). During thevegetation period in 2017, Mongolia conducted field inventory on additional 156 plots located indegraded forests which represents area of 1.58 million ha temporally un-stocked or low-stocked forest
area.

28

2.2.2. Estimating biomass/carbon

Above-ground biomass (AGB)
The Institute of General and Experimental Biology (IGEB) of the Mongolian Academy of Science (MAS)
has carried out a field survey of biomass of main tree species of Mongolia (Dorjsuren, 2017). The fieldsurveys were carried out in most typical forest stands. A total of 23 circular samples of 20 m radius
were established. All the plots were inventoried following the NFI (MET, 2016) plot measurement
methods.
A total of 192 selected trees were harvested and measured for tree volume and biomass (Table 2-5)
following methods of Picard et al. (2012). Tree biomass measurement was organized with sevendifferent operations: site preparation and felling; measurement of felled trees – stem profile; crosscutting into logs and disks; weighting of logs and brushwood; sampling of branches; and sample
weighing. Each model tree was divided into three different sections: trunk wood, crown wood (top,middle, bottom section), and branches and foliage. Standard oven dry methods were used to derive
dry biomass of the samples.
The allometric models (Eq. 1) were derived based on the relationship between AGB and diameter at
breast height (DBH) and total height of tree (Htot) measurements.
𝑐
𝐴𝐺𝐵 = 𝑎 ∗ 𝐷𝐵𝐻 𝑏 × 𝐻𝑡𝑜𝑡

Eq.1

Where:
𝐴𝐺𝐵 = Above-ground live Biomass (tonnes), (= dry biomass of stem incl. bark, branch and leaves)
𝐷𝐵𝐻 = Tree stem diameter (m) at breast height (1,3m)
𝐻𝑡𝑜𝑡 = Tree total height/length (m)𝑎 = Species specific factor
𝑏 = Species specific DBH exponential factor𝑐 = Species specific Htot exponential factor
The species specific coefficients are provided in Table 2-5.
Above ground carbon calculated by following equation:
𝐴𝑏𝑜𝑣𝑒 − 𝑔𝑟𝑜𝑢𝑛𝑑 𝑐𝑎𝑟𝑏𝑜𝑛 = 𝐴𝐺𝐵 × 0.51

Eq. 2

Table 2-5. National species-specific coefficients for biomass models

1
2
3
4
5
6
7
8
9
10

Scientific nameCommon name

Larix sibirica
Pinus sylvestris
Pinus sibirica
Picea obovata
Abies sibirica
Betula platyphylla
Betula humilis
Betula rotundifolia / B. nana
Populus laurifolia
Populus balsamifera var. Suaveolens

Siberian Larch
Scotch Pine
Siberian Pine
Siberian Spruce
Siberian Fir
Asian White Birch
Shrubby Birch
Dwarf Birch
Laurel poplar
Mongolian poplar (Siberian
poplar)Species specific factorsabc0.05342.03321 0.5996
0.037
2.28750.44180.06771.99440.57740.0313
1.53391.34350.12120.4343
1.97440.0735
2.19502 0.4053
0.0735
2.19502 0.4053
0.0735
2.19502 0.4053
0.13962.5168-0.38620.13962.5168-0.3862

29

Scientific name
11
12
13
14
15
16
17
18
19
100

Populus tremula
Ulmus pumila
Haloxylon ammodendron
Populus diversifolia / P. Euphratica
Salix berberifolia
Salix glauca
Salix reticulata
Padus asiatica
Sorbus sibirica
not specified

Common name
Aspen
Siberian Elm
SaxaulDesert poplar
Willow
Gray willow
Net-leaved willow
Black Cherry
Service Treenot specified

Species specific factorsabc0.05792.01676 0.5845
0.0735
2.19502 0.4053
0.0735
2.19502 0.4053
0.0735
2.19502 0.4053
0.0735
2.19502 0.4053
0.0735
2.19502 0.4053
0.0735
2.19502 0.4053
0.0735
2.19502 0.4053
0.0735
2.19502 0.4053
0.05342.03321 0.5996

Below-ground biomass (BGB)
BGB was estimated from IPCC GL (2006) default value of root-shoot ratio for boreal forest (Eq. 3)
with a criteria of equal to or above 75 tonnes of dry AGB per ha (Table 2-6).
𝐵𝐺𝐵 = 𝐴𝐺𝐵 ∗ 𝑅𝑆𝑅

Eq. 3

Where:
𝑅𝑆𝑅 = Root-Shoot ratio for Boreal forest from IPCC GL (2006)
Table 2-6. Below-ground living tree biomass as proportion of above-ground living tree biomass
BGB/AGB Root-Shoot ratio for Boreal forest
AGB<75
AGB≥750.39
0.24
Below-ground carbon calculated by following equation:
𝐵𝑒𝑙𝑜𝑤 − 𝑔𝑟𝑜𝑢𝑛𝑑 𝑐𝑎𝑟𝑏𝑜𝑛 = 𝐵𝐺𝐵 × 0.51

Eq. 4

Litter (L)Baatarbileg et al. (2016) conducted the first study in Mongolia on forest litter C stock estimation, basedon an analysis of litter samples collected from 210 quality control sampling units in the NFI that
includes all forest regions in boreal forests (MET, 2016).
The IPCC guidelines define litter an organic horizon (all leaves, twigs, small branches, fruits, flowers,
roots, and bark) on the soil surface (IPCC 2006), although the term litter differs in soil science, where
its meaning is restricted to fallen dead leaves. However, in this study, litter was defined as an organic
horizon (O horizon) that is divided into four litter types: fine woody debris, which includes fallen boles,
branches, twigs (<5 cm maximum diameter), strobilus, and bark; fresh leaves; the fermentation layer
and the humus layer. We defined the humus layer as continuously stocked humus ≥5 cm in depth.
All samples were collected by NFI quality control team according to the manual developed during theassessment of MPNFI in 2014. At each point, a sample of litter was taken from each plot centre withcylindrical core with surface area of 117.81cm2.
All samples were kept in the cold storage until the laboratory procedures were started. Process of
estimating carbon stock of litter samples were completed step by step in order to estimate C stock of30

litter samples based on loss on ignition methods developed by Walkley-Black (Craft et al., 1991;
Takahashi et al., 2010)

Deadwood (DW)
In the NFI (MET, 2016) deadwood organic matter was calculated as the sum of three types of organic
matter: standing above-ground deadwood, on-the-ground deadwood (branch ≥ 5cm DBH), and belowground deadwood (Eq. 5). The three types of deadwood were calculated separately (Eq. 6-8).
𝐷𝑊𝑡𝑜𝑡𝑎𝑙 = 𝐷𝑊𝑔𝑟𝑜𝑢𝑛𝑑 + 𝐷𝑊𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 + 𝐷𝑊𝐵𝐺

Eq. 5

Where:
𝐷𝑊𝑔𝑟𝑜𝑢𝑛𝑑 = Deadwood on-the-ground and stump (based on mid-log diameter, log length and decay)
𝐷𝑊𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 = Standing deadwood (only stem)𝐷𝑊𝐵𝐺 = Below-ground deadwood (based on stump/tree DBH and decay)𝜋

𝐷𝑊𝑔𝑟𝑜𝑢𝑛𝑑 = 𝐷𝑚 2 ∗ 4 ∗ 𝐿 ∗ (100 − 𝐵𝑑)

Eq. 6

Where:
𝐷𝑚 = Diameter at middle length of a log
𝐿 = Log length
𝐵𝑑 = decay as percentage
𝐷𝑊𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 = 𝑎 ∗ 𝐷𝐵𝐻 𝑏 ∗ 𝐻𝑡𝑜𝑡 𝑐 ∗ (100 − 𝐵𝑑)/100

Eq. 7

𝐷𝑊𝐵𝐺 = 𝐴𝐺𝐵 ∗ 𝑅𝑆𝑅

Eq. 8

2.2.3. Emission and removal factors

Land use change – Deforestation and Re/Afforestation
Forest converted to other land was considered to lose 100 % of four carbon pools: AGB, BGB, DW and
Litter. Non-forest land converted to forest land was calculated as 100 % gain of four carbon pools:
AGB, BGB, DW and Litter.
None of the NFI plots were located in Saxaul forest and Shrub and country-specific biomass or EF data
are lacking for those two forest types. Therefore, this FRL has not considered Saxaul forest and Shrubin emission factor calculations and this is an area for future improvement. The Saxaul forest and Shrub
cover has been stable since 2005 (see Table 3-2.), hence the lack of EF data for those two forest types
has no impact on the Forest Reference Level emission and removal estimates.

31

Forest remaining forest – changes of carbon stock due to disturbances
Mongolia’s forests are major sinks, capturing carbon from the atmosphere as they grow. All forest
land remaining forest land removes carbon through growth. Mongolia has included natural growth as
gain in the calculation of removals in the FRL for both intact and degraded forests to increase
consistency with the GHG reporting. Forest land remaining forest land where forests are disturbed byfire, pests or logging activities are included in the FRL under the REDD+ activity “forest degradation”.
To correlate activity data with emission factors we have studied forest disturbance years over the NFIplots using the CE tool to determine whether the plots have been affected by fire/pest and logging
disturbances. This resulted in two different biomass estimates for intact and degraded forest areas
respectively (Table 2-7). In total, 4276 first sample plots out of 4284 sampling units were assessedusing the CE tool for fire/pest and logging disturbances, which are the key drivers of deforestation anddegradation. 8 samples were excluded from the LULUCF assessment due to missing and/or incorrect
geospatial information.
Table 2-7. Collect Earth disturbance study and number of plots which reached forest threshold
Crown cover < 10 % Crown cover ≥ 10 %Disturbance recorded
3843
No disturbance recorded
193
3237
Total
196
4080

Total
846
3430
4276

4080 sample plots with crown cover of more than or equal to 10 % were used for emission factor
development out of 4276 sample plots. It was difficult to separately identify fire distributed and pest
affected area by using a visual assessment on Collect Earth, so all plots that were fire and pest
disturbed were classified into the same disturbance category.
Due to the limited number of samples for broadleaved and mixed forests during the CE study, it was
not possible to develop specific EFs for different forest categories. Therefore, the EF estimates forboth intact and degraded forests are assumed to be similar for all Boreal forest types.
EFs for forest degradation due to forest fire/pest and logging disturbances were assessed by thedifference in mean carbon values for both intact and degraded forests. Associated confidence interval
ranges around the mean carbon values were also calculated. Estimates for AGB, BGB and DW carbon
pools were calculated for intact forest and degraded forest seperately. For litter, a single carbon stock
estimate was used for both intact and degraded forests.
The estimation of carbon in living biomass and in DW is estimated by converting the corresponding
biomass pools to carbon with IPCC 2006 recommended carbon fraction for boreal coniferous trees,
0.51 tonnes carbon per tonne dry biomass. The carbon content per hectare values for litter is adopted
from the NFI (MET, 2016).
2.2.4. Removal factor - estimating annual increase in biomass carbon stocks
Annual gain in biomass (ΔCG) is a product of mean annual biomass increment (GTOTAL), area of land(A) and carbon fraction of dry matter (CF); Equation 9 (IPCC, 2006).
∆𝐶𝐺 = 𝛴(𝐴 ∗ 𝐺𝑇𝑂𝑇𝐴𝐿 ∗ 𝐶𝐹)

Eq. 9

Where:∆𝐶𝐺 = annual increase in biomass carbon stocks due to biomass growth in forest (boreal) landremaining in the same land-use category, tonnes C yr-1
32

𝐴 = area of forest (boreal) land remaining in the same land-use category, ha
𝐺𝑇𝑂𝑇𝐴𝐿 = mean annual biomass growth of boreal forest, tonnes d. m. ha-1 yr-1
𝐶𝐹 = carbon fraction of dry matter, tonne C (tonne d.m.)-1
𝐺𝑇𝑂𝑇𝐴𝐿 is calculated using Equation 2.10 of IPCC (2006) for given values of annual AGB growth (GW),BGB to AGB ratio (R), and default data tables in Section 4.5 of IPCC (2006).
For the Mongolia,
𝐺𝑊 = 1.0 tonnes d.m. ha-1 yr-1 (Table 4.9 of IPCC, 2006);
𝑅 = 0.3 tonne d.m. (tonne d.m.)-1 (Table 4.4 of IPCC, 2006);
𝐺𝑇𝑂𝑇𝐴𝐿 = 1.0 tonnes dm.ha-1yr-1 × (1 + 0.3) = 1.3 tonnes d.m. ha-1 yr-1 (see Equation 2.10 IPCC, 2006);
𝐶𝐹 = 0.51 tonne C (tonne d.m.)-1 (Table 4.3 of IPCC, 2006).
A removal factor of forest growth determined Tier 1 of IPCC (2006) guideline and were used for
calculating annual increase of AGB growth rates as 1 tonnes d.m. ha-1 yr-1 and ratio of BGB were 0.3
for both intact and degraded forests, based on IBUR Annex in MET (2017). No annual growth
calculated for Saxaul and Shrub forest types. Annual forest change matrices were produced in orderto calculate annual forest growth related emission (Appendix 4).
2.2.5. Estimating emission and removal
Carbon stocks were calculated following the general guidelines of IPCC (2006) on the gain-loss method(Eq. 10).
∆𝑪 = ∆𝑪𝑮 − ∆𝑪𝑳
Eq. 10
Where:∆𝐶 = annual carbon stock change in the pool, tonnes C per yr ∆𝐶𝐺 = annual gain of carbon, tonnes
Cper yr∆𝐶𝐿 = annual loss of carbon, tonnes C per yr
Overall, carbon stock changes within a forest stratum are estimated by adding up the changes in all
carbon pools as in Eq. 11.
∆𝑪𝑳𝑼 𝒊 = ∆𝑪𝑨𝑮𝑩 + ∆𝑪𝑩𝑮𝑩 + ∆𝑪𝑫𝑾 + ∆𝑪𝑳𝑰

Eq. 11

Where:∆𝐶𝐿𝑈 𝑖 = carbon stock changes for a stratum of a forest land-use category𝑖 = denotes a specific stratum or subdivision within the land-use category
AGB – above-ground biomass, BGB – below-ground biomass, DW – deadwood, LI – litter
The following equation (12) is used to calculate the FRL in tonnes of carbon dioxide equivalent (tCO2e):
𝑭𝑹𝑳𝑫𝒆𝒇&𝑨𝒇 =𝜮𝒕 ∆𝑪𝑩 𝒕,𝑫𝒆𝒇 +𝜮𝒕 ∆𝑪𝑩 𝒕,𝑨𝒇
𝒑

𝟒𝟒

∗ 𝟏𝟐

Eq. 12

Where:
𝐹𝑅𝐿𝐷𝑒𝑓&𝐴𝑓 = annual mean losses of carbon stocks from forest land during the reference period, in
tonnes of CO2e year-1∆𝐶𝐵 𝑡,𝐷𝑒𝑓 = change in carbon stocks in forest land converted to non-forest land, and in forest land
which has undergone degradation, in year t of the reference period, in tonnes of C. Reference ismade to the reservoirs included below.
∆𝐶𝐵 𝑡,𝐴𝑓 = change in carbon stocks in non-forest land converted to forest land in year t of thereference period, in tonnes of C.
𝑝 = years in the reference period, 10 years (2005-2015)33

44= factor for converting carbon to CO2 equivalent, t CO2e
12

2.3.

Combined uncertainty

The definition of good practice requires that inventories should be accurate in the sense that they areneither over- nor underestimated as far as can be judged, and that uncertainties are reduced as far aspracticable.
In the GPG2000, the percentage uncertainty is defined as:% 𝑢𝑛𝑐𝑒𝑟𝑡𝑎𝑖𝑛𝑡𝑦 =1⁄ (95% 𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝐼𝑛𝑡𝑒𝑟𝑣𝑎𝑙 𝑤𝑖𝑑𝑡ℎ)
2
× 100
µ

Eq. 13

Where:µ = the mean of the distribution
The uncertainty of the overall estimates for activity data and emission/removal factors was calculated
by error propagation with the following equation,
𝑈 𝑡𝑜𝑡𝑎𝑙 4 =√(𝑈1 ∗ 𝑥1 ) 2 + (𝑈2 ∗ 𝑥2 ) 2 +⋯+(𝑈𝑛 ∗ 𝑥𝑛 ) 2
𝑥1 + 𝑥2 +⋯+ 𝑥𝑛

Eq. 14

Where:
𝑈 𝑡𝑜𝑡𝑎𝑙 = the percentage uncertainty in the sum of the quantities
𝑥𝑛 𝑎𝑛𝑑 𝑈𝑛 are the uncertain quantities and the percentage uncertainties associated with them,
respectively.

4 Table 6.1: Tier 1 Uncertainty Calculation and Reporting under IPCC Good Practice Guidance and Uncertainty Management

in National Greenhouse Gas Inventory34

3. RESULTS
3.1. Activity data
3.1.1. Area estimate
Table 3-1 shows the total area estimates along with 95% confidence interval (CI) under the six IPCC
land cover classes during 2005-2015, derived from the 123,4725 systematic random samples. The area
estimates (156,309,341 ha) are the sum of the expansion factors of the samples identified under each
IPCC land cover class. During the reference period, the most dominant land use category is grassland,
which covers approximately 80 % of the country. Forest land is the second major land use in Mongolia,
which covers approximately 10 % of the total land area. The forest area was 15.5 million ha in the year
2005, with standard error (SE) of 0.1 million ha; at 95 % CI the forest cover was between 15.2 and 15.8
million ha. In 2015, the forest cover declined to 15.4 million ha, with SE of 0.1 million ha, and between
15.1 - 15.8 million ha at 95 % CI (see Table 3-1). Contrary to the forest cover trend, land area under
settlements has expanded by approximately 53,700 ha during 2005-2015. The remaining land coverclasses have remained relatively stable during the reference period.
Table 3-1. Sample-based area estimates (ha) in Mongolia under the six IPCC land cover classes in 2005,
2010, and 2015.
IPCC categories

Total area

Standard error(SE)

Year 2005

95% Confidence Interval
Lower-2.5%
Hectare (ha)Upper-97.5%

Cropland

1,392,327.0

51,543.6

1,291,304.0

1,493,350.0

Forest15,513,267.0

154,972.7

15,209,526.0

15,817,008.0

Grassland

124,336,515.0

764450.3

122,838,220.0

125.834,810.0

Other land

12,187,411.0

297,604.0

11,604,117.0

12,770,705.0

Settlement

1,333,698.0

83,456.0

1,170,128.0

1,497,269.0

Wetland1,546,122.0

71,710.0

1,405,571.0

1,686,673.0

1,383,711.0

50,901.0

1,283,947.0

1,483,475.0

Forest15,469,215.0

154,936.5

15,165,545.0

15,772,885.0

Grassland

124,357,252.0

764389.3

122,859,076.0

125,855,427.0

Other land

12,187,411.0

297,604.5

11,604,117.0

12,770,705.0

Settlement

1,364,112.0

84,280.2

1,198,926.0

1,529,298.0

Wetland

1,547,640.0

71,716.0

1,407,080.0

1,688,201.0

Cropland

1,389,283.0

50,927.5

1,289,466.0

1,489,099.0

Forest15,463,645.0

154,932.0

15,159,984.0

15,767,306.0

Grassland

124,341,548.0

764,404.7

122,843,343.0

125,839,754.0

Other land

12,187,411.0

297,605.0

11,604,117.0

12,770,705.0

Settlement

1,387,428.0

84,713.3

1,221,392.0

1,553,463.0

Wetland

1,540,027.0

71,258.3

1,400,363.0

1,679,690.0

Year 2010
CroplandYear 2015

5Of the 123,577 samples, 105 samples were omitted due to cloud cover. Assessment area sum is less than the country’s total territory by 53,160 ha.

35

3.1.2. Rate of change
Three land cover change matrices were produced from the samples for 2005-2010, 2010-2015 and2005-2015 for IPCC land cover classes (Appendix 5) and annual forest/non-forest classes (Appendix 4).
About 51,647 ha, (at SE 5,109 ha, 95% CI (41,632 ha, 61,662 ha)) of forest has been converted into
grassland during 2005-2015, and this conversion into grassland was a major cause of forest loss during
the reference period (see Appendix 6). A similar amount of grassland - 52,717 ha, (at SE 14,566 ha,
95% CI (24,173 ha, 81,261 ha)) - has been converted to settlements during the same period, reflecting
a period of accelerated urban expansion.
Between forest and non-forest land cover types, the analysis shows that deforestation occurred in 104
samples (out of 123,472 total), while only 6 samples showed forest gain during 2005-2015 (seeAppendix 7). Therefore, loss outstripped forest gain during the reference period. The area under
Saxaul forest cover has been relatively stable since 2005, but further analysis is required to obtainaccurate AD for Saxaul.
Table 3-2 shows sample-based estimates of forest changes in Mongolia from 2005 to 2015. 11.9
million ha of forest has remained intact during 2005-2015. 1.4 million ha of intact forest has been
degraded during this period due to fire, repeated burning, logging and other activities. 52,660 ha ofintact forest has been completely lost during the reference period. Examples of forest loss
(deforestation) detected in the Collect Earth-based sample assessment are illustrated in Figure 3.1,
while Figure 3.2 shows an example of forest gain, identified using high-spatial resolution Google Earthimagery.
Table 3-2. Change in area (ha) between forest and non-forest cover types over the reference period
2005-2015.
Year of assessments

Mean

Std. Eror.

11,902,880.0

85,783.6

2005
Stable classes

2015

Intact forest

Intact forest

Degraded forest

Degraded forest

110,867.7

Saxaul forest

Saxaul forest

2,048,003.0

Shrub

Shrub

Non-forest

Non-forest

95% Confidence Interval

2.50%
Area estimates in ha

97.50%±(%)

11,734,740.0

12,071,010.0

1.4

7,482.2

96,202.9

125,532.4

13.2

128,498.3

1,796,151.0

2,299,855.0

12.3

766,739.7

38,646.9

690,993.1

842,486.3

9.8

140,030,300.0

808,637.4

138,445,400.0

141,615,200.0

1.1

1,394,810.0

28,504.0

1,338,943.0

1,450,676.0

4.0

52,659.7

5,159.5

42,547.3

62,772.2

19.2

506.5

506.3

-485.8

1,498.7

200.0

2,531.2

1,131.5

313.5

4,748.9

87.6

Change classes
Intact forest

Degraded forest

Intact forest

Non-forest

Non-forest

Degraded forest

Non-forest

Intact forest

36

Figure 3.1. Forest land in 2008 converted into grassland (Set17, id: 80832) in 2009. High spatial
resolution Google earth imagery (top-left), burnt forest photo (top-right) illustrates the type of land
cover as a validation data for Landsat. Annual Greenest-Pixel top of atmospheric (TOA) ReflectanceComposites for years 2008 (bottom-left) and 2009 (bottom-right) are displayed in false color
composite (NIR_SWIR1_Red in RGB channels). The 1ha sample plot in the 2008 Landsat 30-m image
shows forest cover (dark brown) while the 2009 image indicates grassland (green) cover.

2001

2005

2012

Figure 3.2. Non forest converted to forest land (Set42, id: 106298, G>F 100 m * 100 m or 1-ha plot)
identified using high-spatial resolution Google Earth imagery in 2001 (left), in 2005 (middle) and in2012 (right). The high-spatial resolution imagery in Google Earth (left), shows afforestation which
probably occurred prior to 2001, identifiable through some scattered crowns. Therefore, an operator
assessed the plot as non-forest (Grassland) in 2005, according to the dominant land cover type, butthen as Forest land in 2012, due to ≥ 10 % tree canopy cover.

37

Figure 3.3 shows the amount of sample-based forest loss and/or gain estimated per year from 2006
to 2015 where year 1 means forest change (loss/gain) estimated during 2005-2006 and so on. Mostof the forest loss has been reported during 2005-2010 with a very insignificant amount of forest
enhancement. The greatest amount of deforestation was recorded in year 3 (2007-2008) during thereference period, with 18,700 ha converted due to numerous incidence of severe forest fire.
Compared with deforestation, gain in forest area was relatively scarce, as it was detected only in 3rd,
5th and 6th years of the reference period.
Deforestation and enhancement ('000 ha)

25
18.7

20

Loss

Gain

15
9.6

10
5.6

6.1

5.1

2.0 4.1

5
0.5

0.5

1.5

1.5

0.5

0
-5
1

2

3

4
5
6
7
Years of the reference period

8

9

10

Figure 3.3. Annualized sample-based forest loss (deforestation) and forest gain (enhancement)
estimates over the reference period: 2005-2015. The error-bar showing lower and upper limit of forestchange estimates for corresponding year at 95 % confidence interval.
Over the period 2005-2015, forests have been converted to non-forest land at an average annual rateof 5,266 ha. Conversely, 304 ha of forest per year have been gained, based on the assessment of
123,457 samples during the reference period: 2005-2015. These figures are not consistent with the
FRA report on forest change during 1990-2015 (FAO, 2015), which used forest cover data derived fromthe TFI. The TFI estimates forest cover over a 10 year period rather than an annualized forest data,
through visiting the same area at 10-year intervals. In addition, the TFI estimates forest cover basedon ocular methods and defines forest based on relative stem density at equal to or higher than 0.3.
Therefore, the change estimates from TFI are less reliable compared with the CE sample-based
assessment used for this FRL submission, which was also aligned with data from the 2014-2016 NFI
and is therefore considered more accurate.

3.2. Emission and removal factors
Table 3-3 shows the biomass values for above-ground, below-ground and deadwood carbon pools
estimated from the 4,080 NFI plots which were aligned with the CE study. Detailed deadwood biomass
estimates for standing, below-ground and on-the-ground deadwood are provided in Appendix 8. The
dry carbon content (tC per ha) of all four carbon pools are presented in Table 3-4. Boreal forests both38

intact and degraded classes contain 67.2 tC per ha and 62.8 tC per ha, respectively. The difference
between the intact forest and degraded forest categories was because of the differences in AGB, BGB
and DW pools.
Table 3-3. Biomass estimates (tons per ha) for above-ground, below-ground and deadwood carbon
pools in the intact and degraded Boreal plots, respectively.
Variables
Degraded forest plots
Above-ground
Below-groundDeadwood
Intact forest plots
Above-ground
Below-groundDeadwood

843
843
843

52.6
15.9
23.5

[95% Confidence Interval]
Lower-2.5% Upper- 97.5%
Biomass (t. ha-1)
1.5
49.6
55.6
0.4
15.2
16.7
1.0
21.5
25.4

3,237
3,237
3,237

67.7
19.6
13.3

0.8
0.2
0.4

Observations

Mean

Number

Std. Err.

66.1
19.3
12.6

69.3
20.0
14.0

CI range (%)
5.7
4.7
8.1
2.4
1.9
5.5

Table 3-4. Estimation of organic carbon content (tC per ha) in four different carbon pools for intact
and degraded Boreal forests plots, respectively.
Variables

Observations

Degraded forest plots
Above-ground
Below-groundDeadwood
Litter
Total
Intact forest plots
Above-ground
Below-groundDeadwood
Litter
Total

Number
843
843
843
196

3,237
3,237
3,237
196

26.8
8.1
4.7
15.9
62.8

[95% Confidence Interval]
Lower-2.5%
Upper- 97.5%
Carbon (tC. ha-1)
0.8
25.3
28.4
0.2
7.7
8.5
0.3
4.1
5.3
1.1
13.7
18.1
1.0
60.9
64.8

34.5
10.0
6.8
15.9
67.2

0.4
0.1
0.2
1.1
1.1

Mean

Std. Err.

33.7
9.8
6.4
13.7
66.1

35.4
10.2
7.2
18.1
68.3

CI range (%)
5.7
4.7
8.1
13.8
3.1
2.4
1.9
5.5
13.8
1.6

Table 3-5 shows EFs from four change classes: from intact forest to degraded forest (4.4 tC per ha,±25.0 % at 95 % CI), intact forest to non-forest (67.2 tC per ha, ±1.6 % at 95 % CI), non-forest to
degraded forest -62.8 tC per ha, ±3.1 % at 95 % CI) and non-forest to intact forest (-67.2 tC per ha,±1.6 % at 95 % CI). Although the EF from deforestation (intact forest > non-forest) is about eight times
higher than that for degraded forest, a significantly larger area of intact forest (1.4 million ha) has
been converted to degraded forest during the reference period than has been subject to deforestation.
63 % of the emissions are originated from degraded forest. Compared with degraded forest,
deforestation resulted in approximately 37 % of annual CO2 emissions from forests.

39

Table 3-5. REDD+ emission and removal factors (tC per ha) from four different conversions during
the reference period 2005-2015 in Mongolia.
95% Confidence Interval
Lower 2.5% Upper 97.5%
CI range (%)
Boreal forest cover types
tC.ha-1
Intact forest Non-forest
67.2
0.6
66.1
68.3
1.6
Intact forest Degraded forest
4.4
0.6
3.3
5.5
25.0
Non-forest
Intact forest
-67.2
0.6
-68.3
-66.1
1.6
Non-forest
Degraded forest
-62.8
1.0
-64.8
-60.9
3.1
Note: (-) indicates removals and no sign indicates emissions.
Year 2005

Year 2015

Mean

Std. Err.

3.3. Removals from annual forest growth
Total CO2 removals from intact forest, forest degradation and carbon stock enhancement during thehistorical reference period from 2005 to 2015 are calculated as -301,246,610.0 tCO2e, -25,101,861.1
tCO2e and -7,384.5 tCO2e, respectively. Figure 3.4 shows annual forest growth related removals fromthe three different activities.
2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15-32.54

Removals (million tCO2e)-32.56-32.58-32.6-32.62-32.64-32.66-32.68-32.7-32.72Year

Figure 3.4. Mongolia’s annual CO2 removals from forest growth during the reference period 20052015.

3.4. The Forest Reference Level
Total CO2 emissions from deforestation and forest degradation, and removals from carbon
enhancement, from areas within forests during the historical reference period from 2005 to 2015 arepresented in Table 3-6. During the reference period, CO2 emissions and removals of the three REDD+
activities do not show any clear trend (see Figure 3.5). Mongolia’s annual CO2 emissions and removals
from the forestry sector were estimated as 3,551,438.6 tCO2e and -74,054.5 tCO2e, respectively,
during the reference period 2005-2015 (see Table 3-7).

40

Table 3-6. Carbon emissions and removals in Mongolia during the reference period: 2005-2015 fromvarious change areas as a result of various drivers of change classes.
Reference period:
2005-2015

Area (ha)ChangeAD

Carbonemissions(tC.ha-1)
EF/RF

C emissions/removals (tC)AD*EF

Intact forest > Non52,659.7
67.2
3,539,859.6
forestIntact forest >1,394,810.0
4.4
6,145,882.1
Degraded forest
Non-forest > Intact
2,531.2-67.2-170,150.8
forestNon-forest >
506.5
-62.8
-31,815.9
Degraded forest
Note: (-) indicates removals and no sign indicates emissions.

CO2e(tC02.yr-1)

U (%)

12,979,485.3

1,297,948.5

19.3

22,534,901.1

2,253,490.1

25.3-623,886.4-62,388.6

87.6-116,658.2-11,665.8

195.9

CO2e (tCO2)[AD*EF] *[44/12]

6

Emissions and removals (million tCO2e)

5

4

3

2

1

0

-1
2005-06

06-07

07-08

08-09

09-10

10-11

11-12

12-13

13-14

14-15

Year
Deforestation

Forest degradation

Carbon stock enhancemennt

Figure 3.5. Mongolia’s annual CO2 emissions from deforestation and forest degradation and removals
through afforestation activities during 2005-2015.
Therefore, the annual average emissions from deforestation and forest degradation are 3,477,384.2
tCO2e, without considering natural growth on forest land remaining forest land. And annual average
removals from enhancement of forest carbon stocks are -29,158,201.4 tCO2e when including natural
growth on forest land remaining forest (see Figure 3.6).
However, since growth in stable forest land is not expected to be very different in the results reporting
period (See Section 1.3.2 on Scope: activities, pools and gases) even though these concern very large
removals, they are not expected to contribute to the results reporting and the figures without
considering natural growth on forest land remaining forest land should be used for Mongolia’s FRL.
41

15

Emissions & Removals (million tCO2e)

10
5
0
-5
-10
-15
-20
-25
-30
-35
2005-06

2006-072007-08

2008-09

2009-10

2010-11

2011-12

2012-132013-142014-15Year
emission through change

removal through growth

Net removals

Figure 3.6. Mongolia’s annual CO2 emissions from various change areas and removals through natural
growth during 2005-2015.
Table 3-7. Combined uncertainty estimates for Activity data and Emission factors
Reference period:
2005-2015

Mean Annual
CO2e

Net emissions

Lower 2.5%

Upper 97.5%(tCO2.yr-1)

ChangeEmissionsIntact forest > Non-forest
Intact forest > Degraded forest
Removals
Non-forest > Intact forest
Non-forest > Degraded forest

95% Confidence Interval
CI range(%)

3,551,438.6

2,928,271.0

4,174,606.2

17.5-74,054.5-133,302.6-14,806.3

80.0

3,477,384.2

2,851,406.4

4,103,362.0

18.0

3.5. Uncertainty analysis for activity data and emission factors
A total of 123,577 sample plots were used to assess Mongolia’s land use and land use changes. Amongthe total number of sample plots, 105 plots were not used for uncertainty assessment due to no data.
Accuracy assessment of different operators was done using a 10 % random sample of the total sample
plots. Samples were randomly selected and independently re-assessed by the most experienced
operators among the team (CCPIU, 2018). Results show a 93 % consistency between the operatorsand the independent re-assessors of the six IPCC land use categories. A field quality control
measurement was undertaken in different types of land use and disturbance factors. Results showthat 11 % of differences occurred in forest land when counting tree elements.

42

In terms of activity data and emissions factors, the sampling error and associated uncertainties were
estimated by using systematic random sample design estimators. The uncertainty of each IPCC land
use category was assessed. Uncertainty analysis in various conversions among the forest, degraded
forest and non-forest classes occurred during the reference period were conducted and the results
were reported in the previous sections.
Uncertainties for removals due to non-forest to forest conversion were very high due to the few
numbers of non-forest to forest conversion occurrences, Appendix 5, compared to total number ofobservations, 123,472. During the reference period an area of 2,531.2 ha was converted from fromnon-forest with an assessed uncertainty of 87.6 %, and an area of 506.5 ha was converted from nonforest to degraded forest with a 195.9 % uncertainty (Table 3-6). Net emission calculations resulted ina 18 % uncertainty (see Table 3-7) when combining AD and EFs.
Mongolia used single growth increment default value (1 tonnes d.m. ha-1 yr-1 for aboveground net
biomass) for the reference period without differentiating forests by degradation status, change type,and age categories. Therefore, Mongolia did not considered growth associated uncertainty into
combined uncertainty analysis.

43

4. NATIONAL CIRCUMSTANCES
National circumstances in Mongolia are different from most other REDD+ countries as the major
drivers of deforestation and degradation are a complex mixture of anthropogenic and natural impacts,
compounded by poor forest management which has increased the forests’ vulnerability. These drivers
can aggravate forest vulnerability when combined with ongoing climate change, which is already 3
times more serious than the global average, in terms of mean temperature change (Chapter 6.6
Environmental Factors of UN-REDD, 2016b; Chapter 1.2 Natural Resources of MET, 2017b). Impacts
include increased severity of winters, prolonged dry season, modified rainfall pattern, glacier andpermafrost thawing, which may in turn lead to changes in vegetative growth pattern and forest health.
It is therefore important for the establishment of the FRL, and for the medium-term prediction of GHG
emissions over the next 20-30 years, to extrapolate what the effect of these already expected changes
will be on predictions of land cover change. Although this first FRL submission does not suggest an
adjustment to take these national circumstances into account, the extent to which future emissions
can effectively be reduced through climate change related policies and measures should be
investigated in the next cycle of FRL submission.
Mongolia has already developed and adopted several policies which are supportive of the REDD+
strategic objectives. These include the Green Development Policy (2014), State Policy on Forests(2015), National Biodiversity Program 2015-2025 (2015), Sustainable Development Vision (2016) andNational Action Plan for the State Forest Policy (2017). These provide a supportive framework for thepolicies and measures which will be proposed within Mongolia’s REDD+ strategy, conservation andsustainable forest management entrenched within these laws, and other regulations.
One of the strategies for increasing resilience of the boreal forests is to implement more effective
sustainable forest management (SFM), particularly through improved thinning and harvesting
regimes. These can ensure forests are healthier, through reduction of competition for light, water andresources, and as such generate more resilience to pest infestation and less deadwood matter which
can fuel fires.
In terms of monitoring of the results, Mongolia considers SFM and conservation of forest
implementation activities that result in reduced emissions from deforestation and degradation andthe enhancement of forest carbon stocks. Therefore, even we may define them they are notseparately measured to prevent over-complications of the FRL.
Sustainable Forest Management as part of a REDD+ strategy can also contribute to substantial socioeconomic development and poverty alleviation benefits for local communities, private sector andenterprises. Community-based forest resource management covers 3.1 million hectares of boreal
forests, about 20% of the forest area, these should be managed effectively and livelihood
opportunities enhanced to ensure resilience is developed in forest edge dependent communities.
Carbon stock enhancement strategies in Mongolia need to consider changes in climate and ecologicalconditions, and depend on maintaining genetic diversity, improved planting strategies andparticipatory land use management to eliminate grazing pressure.
The forests on peatlands are often degrading first: given their location in valleys, they are most easily
cut and grazed compared to forests in highlands. Peatland degradation in Mongolia is followed by thethawing of the permafrost layer – the largest storage of fresh water in Mongolia. When peatlands
degrade after their hydrology is altered, they lose their capacity to retain water and provide ecosystemservices, such as carbon storage. Peatland degradation causes a reduction of habitats and losses inbiodiversity. Peatlands are also called organic soils, and extremely rich in carbon content. Degraded,oxidizing peatlands therefore are significant sources of greenhouse gases.

44

5. FUTURE IMPROVEMENT
5.1.

Improvement in quality of the Saxaul NFI

Saxaul Forest data inclusion in the modification process of FRL or the next submission is now being
considered. The following actions are proposed to construct emission factors and estimate emissions
and removals from Saxaul forest areas by setting Permanent Sampling Plots and configuring Saxaul
population area:
1. Identify CE samples with saxaul forest
2. Check the distribution of the CE samples with saxaul forest in the digital elevation model
3. Check with ecoregions and soil type maps/information
4. Review various published studies/reports on saxaul forest to understand the ecology of this forest
types
5. Develop methods for mapping, EF and monitoring saxaul forest.

5.2. Diversifying carbon pools
Mongolia collected soil and litter samples which represent degraded and low stocked forests during
the additional NFI in 2017 (MET, In Prep). Therefore, soil and litter layer carbon contents will beanalyzed for the additional samples and modified calculations will be complete for these two carbon
pools.
The availability of spatial information on peatlands in Mongolia is a clear gap, which should be filled
together with the introduction of spatial information, including soil sampling based on land
management systems at the aimag and suom levels. Recognizing the urgent need to address the issueof peatland degradation and loss , and build capacity for peatland management in a systematic way,
Mongolia will include emissions or removals from SOC especially in peatland areas as one of the areas
for future improvement.

5.3. Uncertainty analysis
A comprehensive uncertainty analysis using Monte Carlo approach is expected in the future FRL
submissions.

5.4. Fraction of biomass loss from disturbance
The proportion of biomass loss from each disturbance event can be defined to reflect level of decayingor oxidization in trees.

5.5. Inclusion of non-CO2 GHGs
Initial studies have been carried out on the relative importance of non-CO2 GHGs, including CH4 fromthe impact of deforestation and degraded forest on the permafrost layer, and NO2 from degraded
forest caused by forest fire. These studies indicate that further work is required to develop data witha view to including these two gases in future FRL submissions.

45

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48

APPENDIXES
Appendix 1
Structure of change sample assessment matrix for Forest/Non-forest cover classes
Time 2Time 1

IPCC land
cover types
Cropland

Total
Cropland

ForestGrassland

Other land

Settlements

Wetland

𝑝11

𝑝12

𝑝13

𝑝14

𝑝15

𝑝16

𝑝1.

Forest

𝑝21

𝑝22

𝑝23

𝑝24

𝑝25

𝑝26

𝑝2.

Grassland

𝑝31

𝑝32

𝑝33

𝑝34

𝑝35

𝑝36

𝑝3.

Other land

𝑝41

𝑝42

𝑝43

𝑝44

𝑝45

𝑝46

𝑝1.

Settlements

𝑝51

𝑝52

𝑝53

𝑝54

𝑝55

𝑝56

𝑝2.

Wetland

𝑝61

𝑝62

𝑝63

𝑝64

𝑝65

𝑝66

𝑝3.

Total

𝑝.1

𝑝.2

𝑝.3

𝑝.4

𝑝.5

𝑝.6

123,577

Structure of change sample assessment matrix for Forest/Non-forest cover classes
Period: Time1
to Time 2
Time 1

Types ofland coverForestNon-forest

Total

Time 2
ForestNon-forest

Stable forest (𝑝11 )Loss (𝑝12 )Gain (𝑝21 )Stable non-forest (𝑝22 )(𝑝.1 )(𝑝.2 )

Total(𝑝1. )(𝑝2. )
123,57749

Appendix 2
Estimating the population mean∑𝑛 𝑦

The population means (µ) is estimated with 𝜇̂ = 𝑖=1 𝑖𝑛Where 𝑦𝑖 is the value for each unit in the sample and 𝑛 is the number of units in the sample.
The population variance 𝜎 2 is estimated with the sample variance 𝑠 2 which has an unbiased
estimator:∑𝑛𝑖=1(𝑦𝑖 − 𝑦̅)2𝑠2 =𝑛−1
𝑁−𝑛 𝑆 2
Variance of the estimate 𝜇̂ is: 𝑣𝑎̂𝑟(𝜇̂ ) = ( 𝑁 ) 𝑛
The standard error of the estimate is the square root of variance of the estimate, which as always is t
he standard deviation of the sampling distribution of the estimate. Standard error is a useful gaugeof how precisely a parameter has been estimated for a population size 𝑁.
𝑁−𝑛 𝑆 2Standard error of 𝜇̂ is 𝑆𝐸(𝜇̂ ) = √( 𝑁 ) 𝑛

Estimating the population total𝑁
The population total 𝜏 is estimated with 𝜏̂ = 𝑁𝜇̂ = ∑𝑛𝑖=1 𝑦𝑖𝑛

𝑁−𝑛 𝑆 2)( 𝑛 )
𝑁

Variance of the estimate 𝜏̂ is: (𝜏̂ ) = 𝑁 2 𝑣𝑎̂𝑟(𝜇̂ ) = 𝑁 2 (𝑁−𝑛𝑆2Standard error of 𝜏̂ is: 𝑣𝑎̂𝑟(𝜏̂ ) = √𝑁 2 ( 𝑁 )( 𝑛 )Confidence limits:𝜇̂ ± 𝑆𝐸(𝜇̂ ). 𝑡𝑑𝑓, 𝛼/250Appendix 3
Sampling unit design
Each sampling unit is a cluster of 3 sample plots, in order to cover the variety in the forest
characteristics in the tract (forest site). The centres of sample plots are distanced 100-112 m eachother in a triangle (see c). The layout of the sampling unit is presented in the following figure.

Layout of sampling unit. a) NFI inventory regions over the Mongolian boreal forest. b) Spacing of sampling units
in forest area. c) Sampling unit - cluster of sample plots. d) Sample plot design – nested circular plots

51

Appendix 4
Annualized forest cover change matrices during the reference period 2005-2015
Annual forest change area (ha) matrix during 2005-2006.
Types of land cover
Degraded forest
Intact forest
Non-forest
Saxaul forest
Shrub
Grand Total in 2005

Degraded forest
110,867.7
150,863.2

Intact forest

Non-forest

13,193,914.2

5,569.2
140,033,384.5

Saxaul forest

Shrub

2,048,003.0
261,730.9

13,193,914.2

140,038,953.7

2,048,003.0

766,739.7
766,739.7

Saxaul forest

Shrub

Grand Total in 2006
110,867.7
13,350,346.7
140,033,384.5
2,048,003.0
766,739.7
156,309,341.4

Annual forest change area (ha) matrix during 2006-2007.
Types of land cover
Degraded forest
Intact forest
Non-forest
Saxaul forest
Shrub
Grand Total in 2006

Degraded forest
261,730.9
217,713.9

Intact forest

Non-forest

12,970,124.1

6,076.2
140,038,953.7
2,048,003.0

479,444.8

12,970,124.1

140,045,029.9

2,048,003.0

766,739.7
766,739.7

Grand Total in 2007
261,730.9
13,193,914.2
140,038,953.7
2,048,003.0
766,739.7
156,309,341.4

52

Annual forest change area (ha) matrix during 2007-2008.
Types of land cover
Degraded forest
Intact forest
Non-forest
Saxaul forest
Shrub
Grand Total in 2007

Degraded forest
479,444.8
262,288.6
506.5

Intact forest

Non-forest

12,689,099.8

18,735.7
140,044,523.4

Saxaul forest

Shrub

2,048,003.0
742,239.9

12,689,099.8

140,063,259.1

2,048,003.0

766,739.7
766,739.7

Saxaul forest

Shrub

Grand Total in 2008
479,444.8
12,970,124.1
140,045,029.9
2,048,003.0
766,739.7
156,309,341.4

Annual forest change area (ha) matrix during 2008-2009.
Types of land cover
Degraded forest
Intact forest
Non-forest
Saxaul forest
Shrub
Grand Total in 2008

Degraded forest
742,239.9
243,531.3

Intact forest

Non-forest

12,435,948.2

9,620.3
140,063,259.1
2,048,003.0

985,771.1

12,435,948.2

140,072,879.4

2,048,003.0

766,739.7
766,739.7

Saxaul forest

Shrub

Grand Total in 2009
742,239.9
12,689,099.8
140,063,259.1
2,048,003.0
766,739.7
156,309,341.4

Annual forest change area (ha) matrix during 2009-2010.
Types of land cover
Degraded forest
Intact forest
Non-forest
Saxaul forest
Shrub
Grand Total in 2009

Degraded forest
985,771.1
72,394.9

Intact forest

Non-forest

12,358,490.2
506.2

5,063.1
140,072,373.3
2,048,003.0

1,058,166.0

12,358,996.3

140,077,436.4

2,048,003.0

766,739.7
766,739.7

Grand Total in 2010
985,771.1
12,435,948.2
140,072,879.4
2,048,003.0
766,739.7
156,309,341.4
53

Annual forest change area (ha) matrix during 2010-2011.
Types of land cover
Degraded forest
Intact forest
Non-forest
Saxaul forest
Shrub
Grand Total in 2010

Degraded forest
1,058,166.0
41,006.0

Intact forest

Non-forest

12,316,471.3
2,025.0

1,519.0
140,075,411.4

Saxaul forest

Shrub

2,048,003.0
1,099,172.0

12,318,496.3

140,076,930.4

2,048,003.0

766,739.7
766,739.7

Saxaul forest

Shrub

Grand Total in 2011
1,058,166.0
12,358,996.3
140,077,436.4
2,048,003.0
766,739.7
156,309,341.4

Annual forest change area (ha) matrix during 2011-2012.
Types of land cover
Degraded forest
Intact forest
Non-forest
Saxaul forest
Shrub
Grand Total in 2011

Degraded forest
1,099,172.0
209,063.3

Intact forest

Non-forest

12,105,382.9

4,050.1
140,076,930.4
2,048,003.0

1,308,235.4

12,105,382.9

140,080,980.4

2,048,003.0

766,739.7
766,739.7

Saxaul forest

Shrub

Grand Total in 2012
1,099,172.0
12,318,496.3
140,076,930.4
2,048,003.0
766,739.7
156,309,341.4

Annual forest change area (ha) matrix during 2012-2013.
Types of land cover
Degraded forest
Intact forest
Non-forest
Saxaul forest
Shrub
Grand Total in 2012

Degraded forest
1,308,235.4
119,984.9

Intact forest

Non-forest

11,983,878.4

1,519.7
140,080,980.4
2,048,003.0

1,428,220.3

11,983,878.4

140,082,500.1

2,048,003.0

766,739.7
766,739.7

Grand Total in 2013
1,308,235.4
12,105,382.9
140,080,980.4
2,048,003.0
766,739.7
156,309,341.4
54

Annual forest change area (ha) matrix during 2013-2014.
Types of land cover
Degraded forest
Intact forest
Non-forest
Saxaul forest
Shrub
Grand Total in 2013

Degraded forest
1,428,220.3
28,856.9

Intact forest

Non-forest

Saxaul forest

Shrub

11,960,085.2
140,077,436.4
2,048,003.0

1,457,077.1

140,077,436.4

2,048,003.0

766,739.7
766,739.7

Intact forest

Non-forest

Saxaul forest

Shrub

11,905,408.5

506.5
140,082,500.1

11,960,085.2

Grand Total in 2014
1,428,220.3
11,988,942.1
140,077,436.4
2,048,003.0
766,739.7
156,309,341.4

Annual forest change area (ha) matrix during 2014-2015.
Types of land cover
Degraded forest
Intact forest
Non-forest
Saxaul forest
Shrub
Grand Total in 2014

Degraded forest
1,457,077.1
49,106.5

2,048,003.0
1,506,183.7

11,905,408.5

140,083,006.6

2,048,003.0

766,739.7
766,739.7

Grand Total in 2015
1,457,077.1
11,955,021.5
140,082,500.1
2,048,003.0
766,739.7
156,309,341.4

55

Appendix 5
Sample-based IPCC land cover change matrices in three different temporal estimates: 2005, 2010
and 2015 in Mongolia.

Year 2005

Year 2010
IPCC land
cover types
Cropland
ForestGrassland
Other land
Settlements
Wetland

Total

Cropland

ForestGrassland

Other land

Settlements

Wetland

2,254
0
14
0
0
0
2,268

0
26,146200026,14816
87
88,10201
088,206

0
0
0
3,730
0
0
3,730

0
2
29
0
1,099
0
1,130

0
0
3
0
0
1,987
1,990

Total
2,270
26,23588,150
3,730
1,100
1,987
123,472

Year 2010

Year 2015
IPCC land
cover types
Cropland
ForestGrassland
Other land
Settlements
Wetland

Total

Cropland

ForestGrassland

Other land

Settlements

Wetland

2,262
0
17
0
0
0
2,279

0
26,13340
0026,1375
15
88,15400
1
88,175

0
0
0
3,730
0
0
3,730

1
0
30
0
1,130
0
1,161

0
0
1
0
0
1,989
1,990

Total
2,268
26,14888,206
3,730
1,130
1,990
123,472

Year 2005

Year 2015

Total

IPCC land
cover types
Cropland
ForestGrassland
Other land
Settlements
Wetland

Cropland

ForestGrassland

Other land

Settlements

Wetland

2,248
0
31
0
0
0
2,279

0
26,13160
0026,13721
102
88,05001
187,421

0
0
0
3,730
0
0
3,730

1
2
59
0
1,099
0
1,161

0
0
4
0
0
1,986
1,990

Total
2,270
26,23588,150
3,730
1,100
1,987
123,472

56

Appendix 6
Land cover changes during the reference period 2005-2015
Land cover change: 2005-2010

Total area

Standard error (SE)Confidence Interval
Lower- 2.5%
Upper- 97.5%
Hectare (ha)Hectare (ha)Stable classes

Hectare (ha)Hectare (ha)Stable cropland

1,376,623.0

50,867.4

1,276,925.0

1,476,321.0

Stable forest

15,468,200.0

154,935.6

15,164,530.0

15,771,870.0

Stable grassland

124,297,000.0

764,408.4

122,798,800.0

125,795,200.0

Stable settlements

1,333,192.0

83,454.5

1,169,624.0

1,496,760.0

Stable other land

12,187,410.0

297,604.5

11,604,120.0

12,770,700.0

Stable wetland

1,546,122.0

71,710.9

1,405,571.0

1,686,673.0

Forest to Non-forest (Forest Loss)

45,064.6

4,773.3

35,709.1

54,420.1

Forest > Grassland

44,052.1

4,719.4

34,802.2

53,302.0

Forest > Settlement

1,012.5

715.7-390.2

2,415.2

Non-forest to Forest (Forest Gain)

1,012.6

715.8-390.2

2,415.5

Grassland > Forest

1,012.6

715.8-390.2

2,415.5

Grassland > Cropland

7,087.9

1,893.5

3,376.7

10,799.0

Cropland > Grassland

15,704.2

8,340.5-642.832,051.2

Grassland > Settlements

29,907.3

11,775.3

6,828.1

52,986.4

Grassland > Wetland

1,518.5

876.3-199.1

3,236.1

Settlement > Grassland

506.3

506.1

-485.6

1,498.1

Change classes

Confidence Interval
Lower-2.5%
Upper-97.5%
Hectare (ha)Hectare (ha)Stable classes

Hectare (ha)Standard error(SE)
Hectare (ha)Stable cropland

1,380,673.0

50,887.0

1,280,937.0

1,480,410.0

Stable forest

15,461,620.0

154,930.2

15,157,960.0

15,765,280.0

Stable grassland

124,323,300.0

764,382.3

122,825,100.0

125,821,500.0

Stable settlements

1,364,112.0

84,280.2

1,198,926.0

1,529,298.0

Stable other land

12,187,410.0

297,604.5

11,604,120.0

12,770,700.0

Stable wetland

1,539,520.0

71,256.6

1,399,860.0

1,679,181.0

Forest to Non-forest (Forest Loss)

7,595.1

1,960.2

3,753.2

11,437.0

Forest > Grassland

7,595.1

1,960.2

3,753.2

11,437.0

Non-forest to Forest (Forest Gain)

2,025.0

1,012.1

41.3

4,008.7

Grassland > Forest

2,025.0

1,012.1

41.3

4,008.7

Grassland > Cropland

8,609.2

2,087.1

4,518.6

12,699.8

Cropland > Grassland

2,531.4

1,131.6

313.5

4,749.3

Grassland > Settlements

22,809.5

8,570.2

6,012.2

39,606.9

Grassland > Wetland

506.2

506.0

-485.5

1,497.8

Cropland > Settlements

506.2

506.0

-485.5

1,497.8

Wetland > Grassland

8,119.8

8,116.7-7,788.6

24,028.3

Land cover change: 2010-2015

Total area

Change classes

57

Land cover change: 2005-2015

Total area

Stable classes

Confidence IntervalStandard
error (SE)Lower- 2.5%
Upper- 97.5%
Hectare (ha)Stable cropland

1,373,586.0

50,853.0

1,273,916.0

1,473,255.0

Stable forest

15,460,610.0

154,929.3

15,156,950.0

15,764,260.0

Stable grassland

124,263,000.0

764,401.3

122,764,800.0

125,761,200.0

Stable settlements

1,333,192.0

83,454.5

1,169,624.0

1,496,760.0

Stable other land

12,187,410.0

297,604.5

11,604,120.0

12,770,700.0

Stable wetland

1,538,002.0

71,251.5

1,398,352.0

1,677,652.0

Forest to Non-forest (Forest Loss)

52,659.7

5,159.6

42,547.2

62,772.3

Forest > Grassland

51,647.2

5,109.8

41,632.3

61,662.2

Forest > Settlements

1,012.5

715.7-390.2

2,415.2

Non-forest to Forest (Forest Gain)

3,037.6

1,239.6

608.1

5,467.2

Grassland > Forest

3,037.6

1,239.6

608.1

5,467.2

Grassland > Cropland

15,697.0

2,817.8

10,174.2

21,219.9

Settlements > Grassland

506.3

506.1

-485.6

1,498.1

Cropland > Grassland

18,235.5

8,416.8

1,738.8

34,732.2

Grassland > Settlements

52,716.8

14,563.3

24,173.1

81,260.4

Grassland > Wetland

2,024.6

1,011.9

41.3

4,007.9

Cropland > Settlements

506.2

506.0

-485.5

1,497.8

Wetland > Grassland

8,119.8

8,116.6-7,788.5

24,028.1

Change classes

58

Appendix 7
Forest and non-forest change matrices during 2005-2010, 2010-2015, and 2005-2015, respectively.
2005-2010
Year 2005

Types of land coverForestNon-forest

Total
2010-2015
Year 2010

Types of land coverForestNon-forest

Total
2005-2015
Year 2005
Total

Types of land coverForestNon-forest

Year 2010
ForestNon-forest
26,87689
299,50526,87897,594
Year 2015
ForestNon-forest
25,86315
497,59025,867
97,605
Year 2015
ForestNon-forest
25,861104
697,501
25,86796,605

Total
25,96597,507
123,472
Total
25,87897,594
123,472
Total
25,96597,507
123,47259

Appendix 8
Deadwood biomass estimates (tons per ha) for standing, below-ground and on-the-grounddeadwoods in the intact and degraded Boreal plots, respectively.
Variables
Degraded forest plots
Standing deadwood
Below-ground deadwood
On-the-ground deadwood
Total deadwood
Intact forest plots
Standing deadwood
Below-ground deadwood
On-the-ground deadwood
Total deadwood

Observations

Mean

Number
843
843
843
843

10.4
3.8
9.2
23.5

[95% Confidence Interval]
Lower-2.5% Upper- 97.5%
Biomass (t. ha-1)
0.5
9.4
11.4
0.2
3.4
4.2
0.6
8.1
10.4
1.0
21.5
25.4

3237
3237
3237
3237

5.6
1.8
5.9
13.3

0.2
0.1
0.3
0.4

Std. Err.

5.3
1.7
5.3
12.6

5.9
2.0
6.4
14.0

CI range
(%)
9.7
10.2
12.2
8.1
8.1
8.1
8.1
5.5

60

Appendix 9
Estimate of forest and non-forest cover types during the reference period 2005-2015.
Estimate of forest and Non-forest cover types in the year 2005
Land cover
Degraded forest

Total area (ha)Standard error (SE)Confidence Interval
Lower- 2.5%
Upper- 97.5%

110,867.7

7,482.2

96,203.0

125,532.4

Intact forest

13,350,346.6

88,982.5

13,175,944.0

13,524,749.1

Non-forest

140,033,383.8

808,634.1

138,448,490.0

141,618,277.5

Saxaul forest

2,048,002.9

128,498.3

1,796,151.0

2,299,855.0

766,739.7

38,647.0

690,993.0

842,486.3

Shrub

Estimate of forest and Non-forest cover types in the year 2010Land cover

Confidence Interval
Lower- 2.5%
Upper- 97.5%

Total area (ha)Standard error (SE)

Degraded forest

1,058,166.0

25,473.0

1,008,240.0

1,108,092.2

Intact forest

12,358,996.3

86,602.6

12,189,258.4

12,528,734.1

Non-forest

140,077,435.8

808,586.1

138,492,636.1

141,662,235.4

Saxaul forest

2,048,002.9

128,498.3

1,796,151.0

2,299,855.0

766,739.7

38,647.0

690,993.1

842,486.3

Shrub

Estimage of forest and non-forest cover types in the year 2015
Land cover

Confidence Interval
Lower- 2.5%
Upper- 97.5%

Total area (ha)Standard error (SE)

Degraded forest

1,506,183.7

29,431.3

1,448,499.4

1,563,868.0

Intact forest

11,905,408.5

85,788.3

11,737,266.6

12,073,550.4

Non-forest

140,083,005.9

808,580.1

138,498,218.1

141,667,793.7

Saxaul forest

2,048,002.9

128,498.3

1,796,151.0

2,299,855.0

766,739.7

38,647.0

690,993.1

842,486.3

Shrub

61

The UN-REDD Programme / Mongolia
Address: Government Building II, United Nations Street 5/2,
Chingeltei District, Ulaanbaatar 15160, Mongolia
Tel: +976-7711-7750
Email: info@unredd.mn
Web site: www.reddplus.mn

62