Chapter 3: Consistent Representation of Lands

CHAPTER 3

CONSISTENT REPRESENTATION OF
LANDS2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Authors
Shanti Reddy (Australia), Luis Panichelli (Argentina), Robert Michael Waterworth (Australia), Sandro Federici(San Marino), Carly Green (Australia), Inge Jonckheere (Belgium), Serah Kahuri (Kenya), Werner Alexander
Kurz (Canada), Robert de Ligt (Australia), Jean Pierre Ometto (Brazil), Hans Petersson (Sweden), Endo Takahiro(Japan), Thomas Paul (New Zealand), Jason Tullis (USA), Zoltan Somogyi (Hungary), Mehul Pandya (India),Marcelo Theoto Rocha (Brazil), Kei Suzuki (Japan).

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Chapter 3: Consistent Representation of Lands

Contents
3

Consistent Representation of land
3.1

Introduction .......................................................................................................................................... 3.5

3.2

Land-use categories .............................................................................................................................. 3.6

3.3

Representing land-use areas ............................................................................................................... 3.11
3.3.1 Three Approaches ......................................................................................................................... 3.13
3.3.2 Data of Land Representation ........................................................................................................ 3.19
3.3.3 Methods for Land-Use and Land-Use Change Estimation ........................................................... 3.21
3.3.4 Combining Multiple Data Sources ............................................................................................... 3.24
3.3.5 Derivation of IPCC Land-Use Categories from Land Cover Information .................................... 3.25
3.3.6 Stratification of land-use data ....................................................................................................... 3.27
3.3.7 Preparing area data for emissions and removals estimation ......................................................... 3.29

3.4

Matching Land Areas With Factors For Estimating Greenhouse Gas Emissions And Removals ..... 3.29
3.4.1 Use of different approaches and methodological Tiers when estimating emissions and removals
due to land-use change ................................................................................................................. 3.30

3.5

Uncertainties Associated With The Approaches ................................................................................ 3.31

Annex 3A.1

Examples of international land cover datasets ........................................................................ 3.35

Annex 3A.2

Development of land-use databases ........................................................................................ 3.37

Annex 3A.3

Sampling ................................................................................................................................. 3.45

Annex 3A.4

Overview of potential methods for developing Approach 3 datasets...................................... 3.45

Annex 3A.5

Default climate and soil classifications ................................................................................... 3.45

Annex 3A.6

Example process for allocating lands to IPCC land-use classes using Approach 3 wall-to-wall
methods ................................................................................................................................... 3.51

References ........................................................................................................................................................... 3.53

FiguresFigure 3.1

Decision tree for preparation of land-use area data................................................................ 3.21

Figure 3A.5.1 (Updated) Delineation of major climate zones, updated from the 2006 IPCC Guidelines. ........ 3.47
Figure 3A.5.2

Classification scheme for default climate regions.................................................................. 3.48

Figure 3A.5.3

Classification scheme for mineral soil types based on USDA taxonomy .............................. 3.49

Figure 3A.5.4

Classification scheme for mineral soil types based on World Reference Base for Soil Resources(WRB) classification.............................................................................................................. 3.50

Figure 3A.6.1 (New) Decision tree for classifying land-use and land-use change through time ........ in Approach 3............................................................................................................................................... 3.52

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TablesTable 3.1

Example stratifications with supporting data for Tier 1 emissions estimation methods ........ 3.11

Table 3.2

Example of Approach 1: Available land use data with complete national coverage .......... 3.14

Table 3.3

Illustrative example of stratification of data for approach 1 .................................................. 3.15

Table 3.4

Illustrative example of tabulating all land-use conversion for approach 2 including nationallydefined Strata ......................................................................................................................... 3.17

Table 3.5

Illustrative example of approach 2 data in a land-use conversion matrix with category
stratification ........................................................................................................................... 3.18

Table 3.6

Simplified land-use conversion matrix for approach 2 example ............................................ 3.18

Table 3.6a (New) Examples of different data inputs and methods to derive IPCC land-use classes and theresulting approaches (1, 2 or 3) .............................................................................................. 3.20
Table 3.6b (New) Examples of auxiliary data and possible assumptions that can help to determine and stratify
land-use .................................................................................................................................. 3.28
Table 3.7

Summary of uncertainties under approaches 1 to 3 ............................................................... 3.32

Table 3A.1.1 (Updated) Examples of global land cover datasets in 2017 ......................................................... 3.35

Box
Box 3.1a (New) Examples of assigning IPCC land-use and land-use change categories...................................... 9

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Chapter 3: Consistent Representation of Lands

3 CONSISTENT REPRESENTATION OF LANDS
3.1

INTRODUCTION

This chapter provides guidance on using different types of data to represent land-use categories, and conversions
between land-use categories, so that they are applied as appropriately and consistently as possible in inventory
calculations.
Countries use various methods to obtain data, including annual census, periodic surveys and remote sensing. Eachof these methods of data collection will yield different types of information (e.g., maps or tabulations), at different
reporting frequencies, and with different attributes. Guidance is provided on the use of three generic approaches.
Approach 1 identifies the total area for each individual land-use category within a country, but does not provide
detailed information on the nature of conversions between land uses. Approach 2 introduces tracking of
conversions between land-use categories. Approach 3 extends Approach 2 by allowing land-use conversions to be
tracked through time on a spatially explicit basis. Countries may use a mix of Approaches for different regions
over time.
The guidance presented here is intended to assist countries in making the best use of available data and reducing,as far as practicable, possible overlaps and omissions in reporting. The guidance allows informed decisions on theappropriate use of data of different types by those preparing greenhouse gas inventories, but is not intended to beprescriptive on how data may be collected. Generally, all data should be:• adequate, i.e., capable of representing land-use categories, and conversions between land-use categories, as
needed to estimate carbon stock changes and greenhouse gas emissions and removals;• consistent, i.e., capable of representing land-use categories consistently over time, without being unduly
affected by artificial discontinuities in time-series data;• complete, which means that all land within a country should be included, with increases in some areas balanced
by decreases in others, recognizing the bio-physical stratification of land if needed (and as can be supported
by data) for estimating and reporting emissions and removals of greenhouse gases; and• transparent, i.e., data sources, definitions, methodologies and assumptions should be clearly described.
• The descriptions of land use follow the framework of:• land-use category - is the broad land use (one of the six land-use categories described below) reported as either
land remaining in a land-use category (i.e., remaining in the same use throughout the inventory time-series) orland converted to a new land-use category (representing a change in land use).
• sub-category - refers to special circumstances (e.g., areas of grazing within Forest Land) that are estimated andreported separately but do not duplicate land in the broad land-use category.
• Land-use categories and sub-categories may be further stratified on the basis of land-use practices and biophysical characteristics in order to create more homogeneous spatial units as may be used for emissions
estimation (see Table 3.1 for examples).
Using the above approaches and framework, consistent representation of lands at the national level for inventory
purposes is achieved by following the main steps outlined below:
1. provide country-specific definitions of land-use categories (see Section 3.2);
2. decide which Approaches and methods to use to develop activity data (see Sections 3.3.1 and 3.3.3),
considering the methods to be used for estimating greenhouse gas emissions and removals (see Section 3.4)
and for estimating uncertainties (see Section 3.5).;
3. stratify the entire land area of the country as appropriate (see Section 3.3.6);
4. obtain data for these categories ensuring that the data cover the total land area of the country (see Section 3.2
and 3.3);
5. where needed, develop rules to translate land cover information into IPCC land-use and land-use change
categories, using auxiliary information as appropriate (see Section 3.3.5);
6. collect additional information if required (e.g., in situ or ground reference data, sampling, land use statisticsetc.);

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7. develop area estimates for land-use and land-use change categories according to good practice ensuring that
all IPCC requirements for completeness, avoidance of double-counting, accuracy and time-series consistency(Chapter 5, Volume 1), are met;
8. develop uncertainty estimates for the area estimates (see section 3.5).

3.2

LAND-USE CATEGORIES

While the terms “land-use” and “land cover” are sometimes used interchangeably, they are not the same. Land
cover refers to the bio-physical coverage of land (e.g., bare soil, rocks, forests, buildings and roads or lakes). Landuse refers to the socioeconomic use that is made of the land (e.g. agriculture, commerce, residential use orrecreation) (UNEP/FAO 1993). The definitions of land-use categories may incorporate management options andpredominance over other land-uses when a land is subject to multiple uses.
Attribution is the process of associating observed land cover and cover changes with land-use and land use change.
Because different management and disturbance types have different impacts on carbon stocks and GHG emissions,knowledge of the cause of disturbance is needed not only to estimate areas of land-use and land-use change but
also to estimate the associated GHG emissions and removals.
The six broad land-use categories described below form the basis for estimating and reporting greenhouse gas
emissions and removals from land-use and land-use conversions. The land-uses may be considered as top-level
categories for representing all land-use areas, with sub-divisions describing specific circumstances significant toemissions estimation. The categories are broad enough to classify all land areas in most countries and toaccommodate differences in national land-use classification systems, and may be readily stratified (e.g., by climate
or ecological zones). The categories (and sub-categories) are intended to be identified through the use of
Approaches for representing land-use area data described in subsequent sections.
The land-use categories for greenhouse gas inventory reporting are listed below. These definitions are provided
for the IPCC land-use categories because they are:• robust as a basis for emissions and removals estimation;• implementable; and• complete, in that all land areas in a country may be classified by these categories without duplication.
(i) Forest Land
This category includes all land with woody vegetation consistent with thresholds used to define Forest Land in thenational greenhouse gas inventory. It also includes systems with a vegetation structure that currently fall below,
but in situ could potentially reach the threshold values used by a country to define the Forest Land category.
(ii) CroplandThis category includes cropped land, including rice fields, and agro-forestry systems where the vegetation structure
falls below the thresholds used for the Forest Land category.
(iii) Grassland
This category includes rangelands and pasture land that are not considered Cropland. It also includes systems withwoody vegetation and other non-grass vegetation such as herbs and bushes that fall below the threshold values
used in the Forest Land category. The category also includes all grassland from wild lands to recreational areas aswell as agricultural and silvi-pastural systems, consistent with national definitions.
(iv) Wetlands
This category includes areas of peat extraction and land that is covered or saturated by water for all or part of theyear (peatlands and other wetland types) and that does not fall into the Forest Land, Cropland, Grassland or
Settlements categories. It includes reservoirs as a managed sub-division and natural rivers and lakes as unmanaged
sub-divisions. Further definitions of wetlands sub-divisions are provided in the IPCC Wetland Supplement (IPCC2014).
(v) Settlements
This category includes all developed land, including transportation infrastructure and human settlements of any
size, unless they are already included under other categories. This should be consistent with national definitions.
(vi) Other Land
This category includes bare soil, rock, ice, and all land areas that do not fall into any of the other five categories.
It allows the total of identified land areas to match the national area, where data are available.

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Chapter 3: Consistent Representation of Lands

If data are available, countries are encouraged to classify unmanaged lands by the above land-use categories (e.g.,
into Unmanaged Forest Land, Unmanaged Grassland, and Unmanaged Wetlands). This will improve transparency
and enhance the ability to track land-use conversions from specific types of unmanaged lands into the categories
above.
Countries can apply other definitions within the IPCC categories, which may or may not refer to internationallyaccepted definitions, such as those proposed by FAO, Ramsar 1, SEEA 2, WCA 3 and others. However, where there
are inconsistencies between these other definitions and the IPCC land-use categories definitions, the data should
be adjusted to fit within the IPCC categories. To ensure and show consistency and completeness of the land
representation reported, it is good practice to map the relationship between IPCC land-use categories and any otherland-use and land cover classification systems 4 from which data for the land representation are derived. All
definitions and classifications of land-use categories (and sub-categories) should be specified at the national level,
described in a transparent manner, and be applied consistently over time. To avoid double-counting of land areas
or misallocation of lands, each land unit is only reported in one category (or sub-division) in each year.
When moving unmanaged land to managed land, it is good practice to describe the processes that lead to the recategorization. Managed land generally cannot become unmanaged as the legacy effects of past management cancontinue for extended periods, and such moves could result in anthropogenic emissions and removals being
unreported.
Where countries choose to develop country-specific methods for addressing issues of interannual variability (IAV),
it is good practice to describe the methods used to identify lands subject to natural disturbances (see Section 2.6,
Chapter 2, Volume 4) and to transparently report the area of these lands together with the rest of the lands in thesame land use category.

LAND-USE CONVERSIONS
Full application of the guidance requires estimation of land-use conversions that take place between data collection
intervals, particularly when different carbon stock estimates and different emission and removal factors areassociated with lands before and after a transition. Applicable land-uses and land-use conversions are shown below:
FF=

Forest land Remaining Forest land

LF=

Land Converted to Forest land

CC=

Cropland Remaining Cropland

LC=

Land Converted to Cropland

GG=

Grassland Remaining Grassland

LG=

Land Converted to Grassland

WW=

Wetlands Remaining Wetlands

LW=

Land Converted to Wetlands

SS=

Settlements Remaining Settlements

LS=

Land Converted to Settlements

OO=

Other Land Remaining Other Land

LO=

Land Converted to Other Land

Where detailed data about the origin of land converted to a category are available (which will depend on the
Approach available to a country to represent land-use areas), countries can specify the land-use conversion. For
example, LC can be sub-divided into Forest Land Converted to Cropland (FC) and Grassland Converted to
Cropland (GC). While both land areas end up in the Cropland category, the differences in their emissions andremovals of greenhouse gases due to their origin should be represented and reported wherever possible. When
applying these land-use category conversions, countries should classify land under only one (end land-use)
category to prevent double counting. The reporting category is therefore the end-use category, not the category oforigin prior to the land-use conversion.
If a country's national land-use classification system does not match categories (i) to (vi) as described above, theland-use classifications should be combined or disaggregated in order to represent the categories presented here.
(See Section 3.3.5 “Derivation of IPCC Land-Use Categories from Land Cover Information” in this Chapter).
Countries should report on the procedure adopted for the reallocation. The national definitions for all categories
used in the inventory and any threshold or parameter values used in the definitions should be specified. Where
1 Refers

to Ramsar Convention on Wetlands. The Convention on Wetlands, signed in Ramsar, Iran, in 1971, is anintergovernmental treaty which provides the framework for national action and international cooperation for the conservation
and wise use of wetlands and their resources.

2 System of Environmental Economic Accounting (SEEA) - https://seea.un.org/
3 World Programme for the Census of Agriculture (WCA) - http://www.fao.org/world-census-agriculture/en/
4 The

relationship between IPCC, SEEA, WCA and FAO land cover and land-use classifications can be found at:
http://www.fao.org/economic/ess/ess-standards

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Volume 4: Agriculture, Forestry and Other Land Usenational land classification systems are being changed or developed for the first time, compatibility with land-use
classes (i) to (vi) above should be sought.
The broad land-use categories listed above may be further stratified (as described in Section 3.3.6) by climate or
ecological zone, soil and vegetation type, etc., as necessary, to match land areas with the methods for assessing
carbon stock changes and greenhouse gas emissions and removals described in Chapters 2 and 4 to 9 of thisVolume. Default climate and soil classification schemes are provided in Annex 3A.5. Examples of stratifications
that are used for Tier 1 emissions and removals estimation are summarized in Table 3.1. Specific stratification
systems vary by land use and carbon pools and are used in the estimation methods later in this Volume. Guidanceon stratifying land-use areas to match data needs for estimating emissions and removals is provided in Section
3.3.6 of this chapter.
The method of determining areas of land-use and land-use change should be capable of representing lands
according to the definitions applied by the country, and ensure that losses or gains smaller than the minimum
mapping unit do not lead to bias in emission and removal estimates.
In some cases, the spatial resolution of existing maps or sample units may be coarser than the definitions used todescribe some of the land-use categories (e.g., if the Forest Land definition applied by a country includes aminimum area of, say, one hectare, yet the available land-use data has a minimum mapping unit of five hectares).
This may lead to a situation where:• small areas of one or more land-use categories are reported under another category; and,• areas of land-use change are either under or overestimated.
Where this occurs, it is good practice to assess the extent of under or over reporting and, where necessary,
supplement the results with further samples or auxiliary information (e.g., concession boundaries, subsidies forland use changes or land management) that reflect the chosen definitions to validate the results and/or correct forthese errors. Where data are not available, techniques provided in Chapter 5 of Volume 1: Time Series Consistency
can be used to address the data gaps.
When land cover change information is used, auxiliary data is commonly required to allocate land cover change
to the underlying cause of disturbance and to assign lands to the IPCC land-use categories through time. This
process of attribution typically requires a combination of information including, but not limited to, past and current
land cover, management practices and country-specific decisions on a series of reporting rules (see Box 3.1a).
Moreover, reporting rules can also be applied to help countries determine how land-use change is categorized (Box3.1a).

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Chapter 3: Consistent Representation of Lands

BOX 3.1A (NEW)EXAMPLES OF ASSIGNING IPCC LAND-USE AND LAND-USE CHANGE CATEGORIESIPCC landuse categories

Key elements that may
need to be considered

ExamplesForest Land

Definition of Forest
Land to be applied todetermine areas ofForest Land.

While countries can set their own definitions, Forest Land should
include all land with woody vegetation that meets country specificthresholds (e.g., a combination of minimum canopy cover, minimum
height and minimum area) used to define Forest Lands.

Reporting lands
converted to Forest
Land but where thevegetation structure
currently does notnecessarily meet thenational definition ofForest Land.

When establishing new forests (e.g., reforestation, forest restoration) it
is often the case that the vegetation will not meet the national
definition of Forest Land for some years. However, this land can beclassed as Forest Land at the point of conversion.
Determining if the land has the ‘potential’ to reach the national
definitions can consider criteria such as 1) that a woody vegetation
type exists on the land (e.g., newly planted or regrowing trees), and 2)
it will be able to reach the Forest Land definition thresholds (e.g., theforest type will be able meet the Forest Land definition on that land).
Countries typically document the assumptions used to assess if landmeets these criteria. Countries also often include the time period within
which the land should reach the Forest Land definition thresholds
following the conversion.

Reporting Forest Land
areas that in a specificinventory year or years
fall below the country
definition of Forest
Land.

There are typically two reasons that Forest Land temporarily falls
below the country definition: 1) forest harvesting 2) other disturbances(e.g., fire, pest attack). When cover loss is only temporary countries
generally continue to report these areas under Forest Land. Countries
may use tenure or forest type maps to determine if a loss of cover isdue to harvest or clearing. For other disturbances data on the type of
disturbance can be obtained from maps or statistical information.
It is possible that some areas of temporarily destocked Forest Land will
not recover to meet the definition of Forest Land. Countries can decide
how long an area of Forest Land can remain temporarily destocked
before it should be moved to a conversion category. The time chosen
typically depends on expected recovery rates and may vary by, forexample, forest type, land conditions and management practices andtenure.

Reporting lands that areunder opportunistic or
rotational
cropping/grazing/fallow
practices.

Management of agricultural lands often moves opportunisticallybetween cropping-pasture/grazing systems or fallow depending on
climate, soils and market conditions. Where this occurs countries may
choose to either 1) keep reporting these lands under the predominantLand use, if any, or 2) transfer the lands between land use categories
each reporting year. Countries using option 1 still apply the methods
and emissions factors relevant for the actual land use and management
system for estimating emissions and removals.
Countries using option 1 typically document the land management
practices and how they are grouped into a land use. They also may
define the number of years after which if the land has not been cropped
the land is moved to Grassland.

Reporting of orchards,
agroforestry or otherwoody crops.

Depending on the definition of Forest Land used, some areas oforchards, agroforestry and woody crops can meet the definition ofForest Land. Countries typically document which woody crops meet
the Forest Land definition and may also create sub-divisions under
Cropland or Forest Land to separate these lands.

Reporting of wooded
areas and other nongrass vegetation such as
herbs and brushes thatfall below the thresholdvalues used in theForest Land category.

Where areas of wooded grasslands meet the national definition ofForest Land, they are reported under Forest Land. There may also besome areas of wooded grassland that are considered woody crops, suchas naturally occurring areas of fruit or nut trees.

Cropland

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BOX 3.2A. (NEW) (CONTINUED)
EXAMPLES OF ASSIGNING IPCC LAND-USE AND LAND-USE CHANGE CATEGORIESIPCC land-use
categories

Key elements that may
need to be considered

ExamplesWetlands

Separating different
types of Wetlands andwater bodies.

Wetlands include a range of different lands and waterways that
occur within a national boundary.
Countries typically adopt national definitions of Wetlands. Somealso use globally available products such as maps of wetlands
reported under the Ramsar [1] convention to assist with subcategorisation.

Determining theboundary between land
and marine systems.

In many areas there is an indistinct boundary between land andmarine ecosystems (e.g., mangroves).
To remain consistent with other areas of the inventory, countries
typically use the agreed national border to separate land from marine
systems. Emissions occurring in the marine ecosystem outside of thenational borders are not captured under the AFOLU sector.

Reporting of areas that
could also be classified
as other land-uses.

Settlements may also contain lands with a cover that could be
included in other land uses, such as urban parks, lawns and small
semi-urban farms.
Where an area of land meets the national definition of Forest Land
then the land is reported as Forest Land. Other areas, such as lawns,
may be included under Settlements unless they meet the definition
applied for the other land uses, such as Grassland or Cropland. For
example, urban areas with a land cover of scattered trees and grass
are often classed as Settlements as they do not meet the definition ofForest Land and are not managed in line with the national definitions
for other land use categories.

Settlements

[1]

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Chapter 3: Consistent Representation of Lands

TABLE 3.1
EXAMPLE STRATIFICATIONS WITH SUPPORTING DATA FOR TIER 1 EMISSIONS ESTIMATION METHODS
FactorStrata

CLIMATE(see Annex 3A.5)

BorealCold temperate dry
Cold temperate wet
Warm temperate dry
Warm temperate moist
Tropical dry
Tropical moist
Tropical wet

SOIL(see Annex 3A.5)

High activity clay
Low activity clay
Sandy
SpodicVolcanic
Wetland
Organic

BIOMASS (ECOLOGICAL ZONE)(see Figure 4.1, in Chapter 4 Forest
Land)

Tropical rainforest
Tropical moist deciduous forest
Tropical dry forest
Tropical shrubland
Tropical desert
Tropical mountain systems
Subtropical humid forest
Subtropical dry forest
Subtropical steppe
Subtropical desert
Subtropical mountain systems
Temperate oceanic forest
Temperate continental forest
Temperate steppe
Temperate desert
Temperate mountain systems
Boreal coniferous forest
Boreal tundra woodland
Boreal mountain systems
Polar

MANAGEMENT PRACTICES (morethan one may be applied to any landarea)

Intensive tillage/Reduced till/No-till
Long term cultivated
Perennial tree crop
LimingHigh/Low/Medium Input Cropping Systems
Improved Grassland
Unimproved Grassland

3.3

REPRESENTING LAND-USE AREAS

This section describes three Approaches that may be used to represent areas of land-use using the categories
defined in the previous section. The Approaches are presented below in order of increasing information content.
Approach 1 identifies the total change in area for each individual land-use category within a country but does notprovide information on the nature and area of conversions between land-uses. Approach 2 introduces tracking ofland-use conversions between categories, but it does not allow land-use conversions to be tracked through time.
Approach 3 extends Approach 2 by allowing land-use conversions to be tracked through time on a spatially explicit
basis.
The Approaches are not presented as a hierarchical system. When considering which Approach to adopt countries
should consider their national circumstances, including data availability and quality, patterns of land use and landuse change, land management, ecosystem characteristics and the emissions estimation methods to be used. Using
activity data that are not consistent with the emissions estimation methods can decrease accuracy of carbon stockchanges and the associated emissions and removals estimates.

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The Approaches are not mutually exclusive, and a country can use a mix of Approaches for different regions of
the country and/or land uses based on national circumstances. In all cases, it is good practice to describe how theapproaches are used together and demonstrate how approaches applied cover all the land uses and land use changes,
provide consistent time-series and prevent misallocation of lands within and between land use categories.
All data should reflect the historical trends in land-use area, as needed for the inventory methods described in
Chapters 2 and 4 to 9 of this Volume. The commencement time for the historical data required is based on theamount of time needed for dead organic matter and soil carbon stocks to reach equilibrium following land-use
conversion (20 years is recommended as a default, but can e.g. be longer, e.g., for temperate and boreal systems).
After the period to reach equilibrium has passed, land that was added to a land-use conversion category needs tobe transferred to “land remaining in a land-use category”. The time-series data on land-use conversion is therefore
also used to determine the annual transfer of area from the category “land converted to category” to “landremaining in a land-use category”.

TIME-SERIES
Inventories require data on land-use area for at least two points in time relevant to the inventory year. For
Approach 1 (identifying only the net national change in area of each land-use category, but not the transfers
between them), the historical land-use may still not be known. In such circumstances countries should either infer
the previous land-use (see Section 3.3.7 below) or assume that the land has remained in the land-use category for
all time prior to the land-use conversion. This assumption may underestimate removals where conversions to landuses with higher carbon contents predominate, or underestimate emissions in the opposite case.
It is important that there is a consistent time-series in the preparation of land-use category and conversion data sothat artefact from method change is not included as an actual land-use conversion. Care should also be taken to
ensure that the areas of managed and unmanaged land are both defined and estimated consistently. The following
section details how to deal with changes in managed land areas (and consequent changes in carbon stock) whenusing stock change methods for emissions estimation.

CONSISTENT USE OF LAND AREA IN CARBON STOCK ESTIMATESOver the time-series of a national inventory, it is likely that the total area of managed lands will increase as
unmanaged lands are converted to managed land. In this case, where the land area is used to estimate the carbon
stock (when using a stock-difference method of emissions estimation), it is possible that the entry of additional
land into the inventory (by changing from an unmanaged to managed status) will incorrectly appear as a carbon
stock increase. This could wrongly be inferred as a removal from the atmosphere, whereas in reality it is only an
increase due to the expanded land-use area over the inventory time-series. To separate carbon stock increases
arising from changes in area from true carbon stock changes, carbon stock estimates should be recalculated for thecomplete inventory time-series area whenever the total area of managed land changes in an annual inventory.
The maximum area of land (and associated carbon stock) at any point in the time-series should be used as the basis
for emissions and removals estimation throughout the inventory time-series. Carbon stocks on unmanaged lands
can be assumed to remain constant (thus, carbon stock changes would be zero) until the year in which land isclassified as a managed use. The recalculation will therefore change the initial carbon stock estimate in the year
the land entered the inventory but will not affect the estimation of carbon stock change over the inventory timeseries until the relevant land becomes managed.

DATA AVAILABILITY
For many countries, implementing these inventory guidelines may require new data collection. Annex 3A.2.4
provides guidance on remote sensing techniques, Annex 3A.3 provides general guidance on sampling techniquesand Annex 3A.4 on spatially explicit (Approach 3) datasets. Where the data needed to apply these inventory
guidelines on land-use are not available nationally, data on land categories may be derived from global datasets.
For instance, FAO has such datasets, however, care should be taken as these are compiled with national data,(primary data), or secondary data gathered by a third party. More examples are provided in Annex 3A.1, but
generally report on the basis of land cover only, and not land-use (See Section 3.3.5). It is preferable that data usedshould be capable of producing input to uncertainty calculations (See Section 3.5).
When using land-use data, inventory compilers should:• Harmonize definitions between the existing independent databases as well as with the land-use categories tominimize gaps and overlaps. For example, overlaps might occur if woodland on farms were included both inforestry and agricultural datasets. In order to harmonize data, the woodland should be counted only once forgreenhouse gas inventory purposes, taking into account the forest definition adopted nationally (See Section“Multiple land-uses in a single unit of land”). Information on possible overlaps for the purposes ofharmonization should be available from agencies responsible for surveys. Harmonization of definitions does
not mean that agencies should abandon definitions that are of use to them but should establish the relationship3.12

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Chapter 3: Consistent Representation of Lands

between definitions in use with the aim of eliminating double counting and omissions. This should be done
throughout the dataset to maintain time-series consistency.
• Ensure that the land-use categories used can identify all relevant activities. For example, if a country needs totrack a managed land-use category such as Forest Land, then the classification system must distinguishmanaged from unmanaged Forest Land.
• Ensure that data acquisition methods are reliable, well documented methodologically, timely, at an appropriate
scale, and from reliable sources.
• Ensure the consistent application of category definitions between time periods. For example, countries should
check whether the definition of forest has changed over time in terms of tree crown cover and other parameters.
If changes are identified, use the corrected data for recalculation consistently throughout the time-series, andreport on actions taken. Guidance on recalculation can be found in Volume 1 Chapter 5.
• Prepare uncertainty estimates for those land-use areas and conversions in area that will be used in the estimationof carbon stock changes, greenhouse gas emissions and removals.
• Ensure that the national land area is consistent across the inventory time-series; otherwise stock changes willreflect false C increases or decreases due to a change in total land area accounted for when using a stock change
emissions estimation method.
• Assess whether the sum of the areas in the land classification databases is consistent with the total nationalarea, given the level of data uncertainty. If coverage is complete, then the net sum of all the changes in landarea between two time periods should be zero to within the uncertainties involved. In cases where coverage isincomplete, the difference between the area covered and the national area should, in general, be stable or vary
slowly with time, again to within the uncertainties expected in the data. If the balancing term varies rapidly, or(in the case of complete coverage) sums are not equal, inventory compilers should investigate, explain, andmake any corrections necessary. These checks on the total area should take into account the uncertainties in
the annual or periodic surveys or censuses involved. Information on uncertainties should be obtained from theagencies responsible for the surveys. Remaining differences between the sum of areas accounted for by theavailable data and the national area should be within the expected uncertainty for area estimation.
For some activities reported, such as the application of nitrogen fertilizer, liming and harvested wood products,
only national aggregate data may be available. Where emissions and removals estimation methods are applied ata national level, it is appropriate to use such data without categorization by land-use

3.3.1

Three Approaches

A PPROACH 1:

T OTAL L AND -U SE A REA , N O D ATA ON
C ONVERSIONS B ETWEEN L AND -U SESApproach 1 represents land-use area totals within a defined spatial unit, which is often defined by politicalboundaries, such as a country, province or municipality. Another characteristic of Approach 1 data is that only thenet changes in land-use area can be tracked through time. Consequently, the exact location or pattern of the landuses is not known within the spatial unit, and moreover the exact changes in land-use categories cannot be
ascertained. Datasets are likely to have been prepared for other purposes, such as forestry or agricultural statistics.
Frequently, several datasets will be combined to cover all national land classifications and regions of a country. In
this case the absence of a unified data system can potentially lead to double counting or omission, since theagencies involved may use different definitions of specific land-use for assembling their databases. Ways to deal
with this are suggested below.
Tables 3.2 and 3.3 show summary land-use area data for a hypothetical country (with a national land area of 140
million ha) using locally relevant land classifications. Table 3.2. is prepared at the level of the broad land-use
categories. Table 3.3 depicts the same information with example stratifications to estimate the effect of various
activities using the emissions estimation methods described elsewhere in this Volume.
Determination of the area of land-use conversion in each category is based on the difference in area at two points
in time, either with partial or full land area coverage. No specification of inter-category conversions (i.e., ‘land
remaining in a land-use category’ and ‘land converted to a new land-use category’) is possible under Approach 1
unless supplementary data are available (which would then introduce a mix with Approach 2).
The land-use area data may come originally from periodic sample survey data, maps or censuses (such as
landowner surveys), but will probably not be spatially explicit. The sum of all land-use category areas may or may

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not equal the total area of the country or region under consideration, and the net result of land-use conversions
may or may not equal zero, depending on the consistency in data collection and application in the inventories foreach land-use category. The final result of this Approach is a table of land-use at given points in time. Because
the total land base that is reported each year for all land-use categories should remain constant, a table similar to
Table 3.3 should be generated as a QA/QC measure. If inconsistencies are found, it is good practice to identify
and correct the problem(s) for future inventories. This may require closer coordination among inventory teams forseparate land-use categories (if analysed separately) or possibly new surveys or other types of data collection.
Other parts of this Volume require information on land area in each land-use category presented in Table 3.3 to bebroken down into the categories “land remaining in the same land-use category” and “land converted to a newland-use category”. This is dependent on methodological requirements in other chapters of this Volume. If landuse data are not sufficient to support Approach 2 (see below), where the total (gross) land conversion areas can bequantified, the emissions and removals may be reported in the “land remaining in the same land-use category” (asspecified in Table 3.2). This is because the data may only be sufficient to identify the net change in area of eachland-use category, and not the total effect of all land conversions. However, in general the methods for both soils
and biomass related emissions estimation require land area data categorized by “lands remaining” and “converted
to” categories and thus it is desirable to do this if possible, even if this is done using expert judgment.
Note that by reporting only in the “land remaining” category, emissions and removals will include, but not
explicitly reflect a changing land base within a land-use category (different areas, e.g., by the net transition in areas
to and from the Forest Land category) over time. This may overestimate or underestimate emissions for thatparticular “land remaining” category. However, a complete inventory will tend to counter-balance this withemissions and removals from another “land remaining” category in the inventory.
It is acceptable to report non-CO2 emission by source category without attribution to land-uses if emissions are
estimated based on national statistics, without reference to individual land-uses (e.g., N2O emissions from soils).
Methods outlined in this Volume frequently estimate emissions using national statistics in this manner.
TABLE 3.2
EXAMPLE OF APPROACH 1: AVAILABLE LAND USE DATA WITH COMPLETE NATIONAL COVERAGE
Time 1

Net land-use conversion
between Time 1 and Time 2

Time 2

F

=

18

F

=

19

Forest Land

=

+1

G

=

84

G

=

82

Grassland

=

-2

C

=

31

C

=

29

Cropland

=

-2

W

=

0

W

=

0

Wetlands

=

0

S

=

5

S

=

8

Settlements

=

+3

O

=

2

O

=

2

Other Land

=

0

Sum

=

140

Sum

=

140

Sum

=

0

Note: F = Forest Land, G = Grassland, C = Cropland, W = Wetlands, S = Settlements, O = Other Land. Numbers represent
area units (Mha in this example).

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Chapter 3: Consistent Representation of Lands

TABLE 3.3
ILLUSTRATIVE EXAMPLE OF STRATIFICATION OF DATA FOR APPROACH 1
Land-use category/strataInitial land area
(million ha)

Final land area
(million ha)

Net Change in area
(million ha)

Forest Land total

18

19

1

Forest Land
(Unmanaged)

5

5

0

Not included in theinventory estimates

Forest Land
(temperatecontinental forest;
converted toanother land-use

7

8

1

Estimates should be
prepared on the 8
million ha
No land-use
conversion. Could
require stratification
for different
management
regimes etc.

Forest Land (boreal
coniferous)

6

6

0

Grassland total

84

82

-2

Grassland(Unimproved)

Grassland(Improved)

65

19

63

19

Status-2

Fall in area
indicates land-use
conversion. Could
require stratification
for different
management
regimes etc.

0

No land-use
conversion. Could
require stratification
for different
management
regimes etc.
Fall in area
indicates land-use
conversion. Could
require stratification
for different
management
regimes etc.

Cropland total

31

29

-2

Wetlands total

0

0

0

Settlements total

5

8

3

Other Land total

2

2

0

Unmanaged - not ininventory estimates

TOTAL

140

140

0

Note: areas should
reconcile

Note: “Initial” is the category at a time previous to the date for which the assessment is made and “Final” is the category
at the date of assessment. Activities for which location data are not available should be identified by further subcategorisation of an appropriate land category.

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A PPROACH 2:

T OTAL L AND -U SE A REA , I NCLUDING C HANGESB ETWEEN C ATEGORIESThe essential feature of Approach 2 is that it provides an assessment of both total losses and gains in the area ofspecific land-use categories and what these conversions represent (i.e., changes both from and to a category). Thus,
Approach 2 differs from Approach 1 in that it includes information on conversions between categories, but is still
only tracking those changes across two points in time. Tracking land-use conversions in this manner will normallyrequire estimation of initial and final land-use categories for all conversion types, as well as of total area of
unchanged land by category. The final result of this Approach can be presented as a non-spatially-explicit landuse conversion matrix. The matrix form is a compact format for representing the areas that have come under
different conversions between all possible land-use categories. Existing land-use databases may have sufficientdetail for this Approach, or it may be necessary to obtain data through sampling or other methods. The input data
may or may not have originally been spatially-explicit (i.e., mapped or otherwise geographically referenced).
For Approach 2, emission and removal factors can be chosen to reflect differences in the rate of changes in carbon
according to the conversions between any two categories, and differences in initial carbon stocks associated withdifferent land-uses can be taken into account. For example, the rate of soil organic carbon loss will commonly bemuch higher from cropping than from pasture.
Approach 2 is illustrated in Table 3.4 using the data from the Approach 1 example (Table 3.3) by adding
information on all the conversions taking place. Such data can be written in the more compact form of a matrix
and this is presented in Table 3.5. To illustrate the added value of Approach 2 and this land-use conversion matrix
format, the data of Table 3.5 is given in Table 3.6 without the stratification of the land-use categories. This can becompared with the more limited information from Approach 1 in Table 3.2. In Table 3.6, the conversions into andout of land categories can be tracked, whereas in Table 3.2 only the net changes in a broad land-use category aredetectable.
In Tables 3.5 and 3.6, the area in the diagonal cells represents the area in each land-use category that was notaffected by land-use conversion in this inventory year. In preparation for the greenhouse gas emission and removal
estimations described elsewhere in this Volume, this area should be further sub-divided into the area that hasremained in the land-use category and area that has been affected by a land-use conversion (i.e., the land converted
to a different land-use category) in the previous Y years (where Y is the time period during which C pools are
expected to reach equilibrium (the IPCC default is 20 years, based on soil C pools typical time to equilibrium afterland-use conversion).
Therefore, under the default assumption in every inventory year, the area converted to a land-use category should
be added to the category “land converted to” and the same area removed from the land remaining in the land-use
category. The area of land that entered that “land converted to” category, 21 years ago (if using the default 20 year
period), should be removed and added to the category “land remaining land”. For example, in Table 3.5 if data
indicated that four of the 56 Mha in the Grassland category had been converted from Forest Land 21 years ago,then four Mha of land should be moved from the category Land Converted to Grassland to the category Grassland
Remaining Grassland in this annual inventory.

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Chapter 3: Consistent Representation of Lands

TABLE 3.4
ILLUSTRATIVE EXAMPLE OF TABULATING ALL LAND-USE CONVERSION FOR APPROACH 2 INCLUDING NATIONALLY
DEFINED STRATA
Initial land-use

Final land-use

Land area, Mha

Inclusions/Exclusions

Forest Land (Unmanaged)

Forest Land (Unmanaged)

5

Excluded from GHG inventory

Forest Land (Managed,
temperate continental)

Forest Land (Managed,
temperate continental)

4

Included in GHG inventory

Forest Land (Managed,
temperate continental)

Grassland (Unimproved)

2

Included in GHG inventory

Forest Land (Managed,
temperate continental)

Settlements

1

Included in GHG inventory

Forest Land (Managed,
boreal coniferous)

Forest Land (Managed,
boreal coniferous)

6

Included in GHG inventory

Grassland (Unimproved)

Grassland (Unimproved)

61

Included in GHG inventory

Grassland (Unimproved)

Grassland (Improved)

2

Included in GHG inventory

Grassland (Unimproved)

Forest Land (Managed,
temperate continental)

1

Included in GHG inventory

Grassland (Unimproved)

Settlements

1

Included in GHG inventory

Grassland (Improved)

Grassland (Improved)

17

Included in GHG inventory

Grassland (Improved)

Forest Land (Managed,
temperate continental)

2

Included in GHG inventory

CroplandCropland29

Included in GHG inventory

Cropland

Forest Land (Managed,
temperate continental)

1

Included in GHG inventory

CroplandSettlements

1

Included in GHG inventory

Wetlands

Wetlands0

Included in GHG inventory

Settlements

Settlements

5

Included in GHG inventory

Other Land

Other Land

2

Excluded from GHG inventory

TOTAL

140

Note: Data are a stratified version of those in Table 3.3. Sub-categories are nationally defined and are illustrative only.
“Initial” indicates the category at a time previous to the date for which the assessment is made and “Final” the category
at the date of assessment.

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 3.5
ILLUSTRATIVE EXAMPLE OF APPROACH 2 DATA IN A LAND-USE CONVERSION MATRIX WITH CATEGORY STRATIFICATION
ForestInitial Land
(unmanaged)
Final
Forest Land
(unman5aged)

Forest Land Forest Land Grasslan Grass- Croplan Wetland Settles
ments(managed, (managed, d (unim- land (im- d
temperateborealproved) proved)
continental) coniferous)

Other Final
Land area

5

Forest Land(managed,temperatecontinental)

41

Forest Land(managed,borealconiferous)

2

8

1

6

6

Grassland(unimproved)

2

63

61

Grassland(improved)

2

19

17

Cropland29

29

Wetlands

00

Settlements

1

1

1

8

5

Other Land2

2

Initial area

5

7

6

65

19

31

0

5

2

140

Net change

0

1

0

-2

0

-2

0

+3

0

0

Note: Column and row totals show net conversion of land-use as presented in Table 3.3. “Initial” indicates the category at atime previous to the date for which the assessment is made and “Final” the category at the date of assessment. Net changes(bottom row) are the final area minus the initial area for each of the (conversion) categories shown at the head of thecorresponding column. Blank entry indicates no land-use conversion for this transition.

TABLE 3.6
SIMPLIFIED LAND-USE CONVERSION MATRIX FOR APPROACH 2 EXAMPLE
Gross and Net land-use conversion matrix
Initial
Final

F

G

C
1

F

15

3

G

2

80

O

19
29
0

1

1

Final sum

82

W
1

0
5

8
2

2

18
84
31
0
5
2
Initial sum
Note:
F = Forest Land,
G = Grassland,
C = Cropland,
W = Wetlands,
S = Settlements, O = Other Land
Numbers represent area units (Mha in this example).

140

O

3.18

S

29

C
S

W

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Chapter 3: Consistent Representation of Lands

A PPROACH 3:

S PATIALLY -E XPLICIT L AND -U SE C ONVERSIOND ATA

The key defining characteristic of Approach 3 is that it is both spatially and temporally consistent and explicit.
Sample-based, survey-based and wall-to-wall methods can be considered Approach 3 depending on the design ofthe sampling/mapping program and the way the data is processed and analysed (Table 3.6A). The decision to use
sample based, survey based or wall-to-wall methods, and how to process them, depends on national circumstances
and the method applied to estimate carbon stock changes and the associated emissions and removals.
Approach 3 data can be summarized in tables similar to Tables 3.5 and 3.6. The main advantage of spatiallyexplicit data is that analysis tools such as Geographic Information Systems can be used to link multiple spatiallyexplicit data sets (such as those used for stratification) and describe in detail the conditions on a particular pieceof land prior to and after a land-use conversion. This analytical capacity can improve emissions estimates by betteraligning land-use categories (and conversions) with strata mapped for classification of carbon stocks and emission
factors by soil type, vegetation type. This may be particularly applicable for Tier 3 emission estimation
methodologies. However, issues of compatible and comparable spatial resolutions need to be taken into account.
An overview of potential methods for developing Approach 3 datasets is provided in Annex 3A.4.

3.3.2

Data of Land Representation

Figure 3.1 is a decision tree to assist in describing and/or obtaining the data on land-use areas. It provides guidanceon which Approach and method a country can use for representing lands depending on the availability of primaryand secondary datasets. Approach 3 method, for example, can be applied if spatially explicit land-use data isavailable for the whole country including complete time series coverage. Geographically mixed Approach (1, 2 &3) can be used where limited spatial data is available. As shown in this figure, where data is missing new data canbe collected or international datasets can be used to minimise gaps in geographical coverage. Similarly,
interpolation or extrapolation techniques can be used where complete time series is not available and new datacannot be collected. This will ensure all lands are represented consistently using one of the three genericapproaches. Lastly, it is important to document the choice of methods applied for land representation.
All three Approaches can, if implemented appropriately and consistently, be used to produce robust greenhousegas emission and removal estimates. However, it should be noted that Approach 1 will probably not detect changes
in biomass, such as those due to the full extent of deforestation and reforestation on separate areas of land, but
only those due to the net conversion of land-use area from a forest to a non-forest use. In general, only Approach
3 will allow for the spatial representation required as an input to spatially-based carbon models.
Different Approaches may be more effective over different time periods or may be required for different reporting
purposes. Methods to carry out matching of the time-series between the different periods or uses should be applied.
There are numerous sources of data and methods to process data that can be used to derive activity data. It is notnecessarily the data itself that determines of the approach. For example, depending on how the data is used, a timeseries of data could be used to generate information at Approaches 1, 2 or 3. Other data, such as single surveys or
sample processes used in isolation can only generate activity data at Approach 1. Where the data available allow
for the application of approach higher than approach 1 it is good practice to do so to ensure that uncertainties areminimized as far as practicable. Table 3.6A provides some examples of different data and methods and theresulting Approach.

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 3.6A (NEW)EXAMPLES OF DIFFERENT DATA INPUTS AND METHODS TO DERIVE IPCC LAND-USE CLASSES AND THE RESULTING
APPROACHES (1, 2 OR 3)1
MethodApproach 1

Samplebased
methods••

Single sample
Temporary sample
units

•

Surveybased
methods•

Single census at one
point in time.
Repeat census but
without reference toprevious censuses.

•

Wall-toWall
methods•••

Single map
Inconsistent maps
developedatdifferent times.

Approach 2

•

•

•

Approach 3

Samplescollected
frompermanent units but changes
only tracked across twoconsecutive sample periods.

•

General surveys between two
periods.
National census data that canrefer a past period.

•

Specific survey designs that
identify activities throughtime for each land unit withina known region.

Inconsistent maps through time
combined with Approach 2-typesamples (e.g. using maps as
stratifications).
Maps developed using consistentmethods changes tracked across
two consecutive maps only not
tracked through a time-series of
maps.

•

Tracking pixels / land units
using time-series consistent
data.

•

Permanent and consistentgeoreferenced ground plots.
Continuous and consistentsamplesusing
remotesensing data.

1
These examples assume that only one type of data and process is used. In many cases the data inputs and processes can be combined
resulting in a higher quality of the land representation than can be achieved with any one single data source.

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Chapter 3: Consistent Representation of Lands

Figure 3.1

Decision tree for preparation of land-use area data

Identify a primaryexisting land-use
dataset for the country(e.g., FAO data).

Are
spatially
explicit data
available, if
needed?

Arespatially
explicit dataneeded forsome or any
land
areas?

No

Yes

Are
spatialdata fora geographically
mixed Approach(1, 2, & 3)
available?

Yes

Yes
Primary dataset
acceptablefor use.

Does
the (modified)primary dataset
cover the wholecountry?

No

No

No

Yes

Modify primarydataset to use
mixed Approaches(1, 2, & 3), if needed.

Canthe gaps in thearea coverage befilled using otherexisting data
sets?

Does
the dataset
have any
underlying
spatial information?
No

No

Obtain and use theadditional spatial
information.

Can
new data becollected to fill thegaps in area
coverage?

Yes

Yes

Yes

(Modified) Primary dataset
acceptable for coverage.

Combine primaryand secondary
datasets.

Collect new data forthe gaps in area coverage.

Does
the (modified)primary dataset provide
a complete timeseries
for thecountry?

Candata for missing
years be supplemented
with existing
secondary data
sets?

Can
new data becollected tocomplete the timeseries?

No

No

Yes

Yes

Yes

Document the choiceof methods(Approaches).

Use a mix ofApproaches to build
a complete timeseries.

Collect new data forthe gaps in the timeseries using guidancein this chapter.

3.3.3

Collectspatialinformation
where
required.

No
Useinternational
datasets tominimize
gaps incoverageand document
results.

No
Use
techniques forinterpolation
andextrapolation
to estimate
fluxes formissing years.

Methods for Land-Use and Land-Use Change
Estimation

The three main methods for estimating areas of land-use and land-use change are sample-based, survey-based andwall-to-wall. These methods are not mutually exclusive; for example, wall-to-wall methods typically require
samples for calibration, validation and uncertainty analysis, and some sample methods require wall-to-wall maps
for scaling as well as for dimensioning the sample size and designing the sample grid. The method itself does not

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determine the Approach and all these methods can be used to develop land-use information at Approaches 1, 2 or
3 (see Table 3.6A).

Wall-to-wall methods
The continually increasing volume and improving quality of data available from remote sensing allows countries
to develop wall-to-wall maps of land cover and land cover change that, when combined with other data, can beused to generate land-use and land-use change information. There are numerous potential applications for remotesensing products to derive consistent land use and land use change estimates:•

identifying land cover and land cover change (e.g., forest cover change and multiple land cover change types);•

attribution of land cover change to specific disturbances (e.g., harvesting, clearing, fire) and processes (e.g.,biomass growth) to determine land use; and,•stratification of land-use categories into logical units that facilitate the estimation of emissions and removals,
such as forest condition, growth stage, time since disturbance and forest type.

Although there is an ever-increasing focus on and availability of remote sensing data for wall-to-wall mapping, it
is also possible to generate wall-to-wall methods using traditional mapping processes. For example, some countries
have access to detailed maps of forest stands or agricultural areas with associated records of human interventions(such as harvesting) and other disturbances, such as fire. Combining these maps and records can produce timeseries consistent activity data. Where maps are not available, the record data can still be used in a survey typeapproach.
There are two broad wall-to-wall methods:
1. a consistent time-series of data using the same or similar sensors, common analysis methods and time-series
processing methods; and,2. one or more maps developed using different sensors and methods, and not applying time-series consistentprocesses.
When using Approach 3, wall-to-wall methods it is good practice to:• minimize the influence of misalignment of images or artefacts in data (e.g., cloud cover);• ensure the data will be consistent with the methods for estimating emissions and removals• ensure the time-series is dense enough to identify activities that drive emissions and removals (e.g., if the period
between two points in time (i.e. the change detection period) is 5 years, but forest cover following clearing or
harvesting recovers in 2 years, then management events affecting emissions and removals may be missed,
depending on the method applied);• demonstrate that, in cases where the time between maps differ (e.g., a 5-year gap, followed by a 2-year gap),
this does not bias results by changing detection rates;• use that the sensor data used in the maps does not cross over the mapping time period. For example, whencreating composite products (e.g., to remove cloud or sensor errors) ensure that the images selected for one
year are not the same or cross over image dates in the previous or following years (cross over occurs when e.g.,
a 2005 map uses data from 2002-2008 and a 2010 map uses data from 2007-2013);• demonstrate that the changes tracked through time are consistent and to report on any corrected biases andknown uncertainties of the analysis.
• ensure that any improvements made to any single map in the time-series are consistently applied to the othermaps in the time-series and the results are recalculated, in particular when new maps are added to the timeseries; and• evaluate the final products to ensure consistent representation of land-use with no double counting or omissionof lands.
An example of an Approach 3 wall-to-wall approach can be found in Australia’s national inventory report(Department of the Environment and Energy 2018).
It is challenging to maintain a spatially consistent time series where different land cover maps have been developed
using different data (e.g., different sensors) or methods (different algorithms or operators using visualinterpretation). In such cases it may not be possible to use this data in an Approach 3 context, since it is difficult
to ensure that the land-uses will be spatially consistent through time in the time series. However such data may be
used to stratify samples used in the application of Approach 2 (GFOI 2016).
When using wall-to-wall Approach 2 methods it is good practice to:

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Chapter 3: Consistent Representation of Lands• describe the difference between the land cover data in the time series;• apply sample-based methods to determine uncertainties and correct for bias; and• describe how areas with potential multiple changes in land-use through time are addressed in estimating
emissions and removals using the data.

Sample based methods
Sample based methods directly estimate land-use and land-use change from repeated samples. Samples may beobtained from ground surveys (such as a national forest inventory or national land survey) or remote sensing (e.g.,satellite imagery, aerial photography or lidar or a combination of both). Well-designed sample-based methods
provide an accurate statistical representation of land-use and land-use change but do not provide information on
every specific area of the land territory (i.e. is not wall-to-wall spatially explicit).
The two most common sampling methods applied are:• permanent sampling methods, where the same sample area is measured or analysed through time using
consistent methods and processes; and,• temporary sampling methods, where data is collected for only one point in time or, if repeated measurements
are taken through time, these are not taken for the same locations.
Within these two broad methods there are a range of options countries can apply, including combining permanentand temporary sampling methods.
Where permanent sample methods have been applied it is possible to use these data in an Approach 3 system bytracking each sample unit through time and determining the history and scaling appropriately. These units could
also be used in an Approach 2 method by only determining land use and land use change between two consecutive
periods. An example of Approach 3 sample based method for estimating land-use and land-use change can befound in Sweden’s national inventory report (Swedish Environmental Protection Agency 2016).
Where only temporary sample units are used without repeat measurements, it is not possible to apply Approach 2
or 3 methods unless temporary sample data is combined with other data (auxiliary data or permanent plots).
A key issue when selecting a sampling design is that the sampling methods must be able to be applied over thewhole area of interest and the sample size must be large enough to produce sufficiently accurate estimates of landuse and land-use change categories and sub-divisions, given the policy requirement and the costs involved. No
matter what type of sample method applied (ground or remote sensing), it is good practice to ensure:• a sufficient number of samples are used with repeat measurements over time to identify both land-use and landuse changes with a desired level of uncertainty;• where samples are used to determine land cover, that these data are used with other information, if necessary,
to identify the land-use category;• samples are collected or re-measured with sufficient temporal frequency to ensure land-use changes andmanagement events affecting emissions and removals are identified;• samples are collected with sufficient temporal consistency that detection rates of change do not alter due todifferences in sampling frequency;• where sampling methods have changed through time, these changes do not lead to inconsistencies in thereporting of areas of land-use and land-use change; and• the sample assessment protocols are well documented.

Survey based methods
Statistical survey methods involve obtaining information on land-use and land-use change and land management
practices either through national programs or through targeted requests to land holders, land management agencies
and companies.
There are two broad methods for statistical surveys:• surveys that collect information on land management practices through time for a specific area or land use;and,• surveys that aim to collect information on land use and management practices in a specific period only, or only
on land use without information on land management.

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Surveys can provide inventory compilers with access to lists of stands or land areas subject to different land-use
and activities. These lists can provide detailed information on land areas and their management but may or may
not include information on the exact location of the land unit. For example, within a region, information on thearea, species, type and management of all forest areas (stands) may be available to the inventory compiler as atable, but the exact location of the stand is unavailable (e.g., due to privacy, commercial or political reasons). Thisdata can be particularly accurate for land-uses with high-commercial value as detailed data is collected on these.
However, these types of survey data do have temporal consistency and known geographic boundaries and can beconsidered Approach 2 or 3 depending on whether the land use changes are tracked across time or not. When using
this method, it is good practice to:• ensure that the area of the land units surveyed is consistent with the area of the entire land use category andother land uses, in particular where the land units do not cover all the land-use categories (i.e., where a mix ofApproaches are applied); and• where possible, compare the area estimates obtained from other methods, such as sample-based methods.
Surveys that provide an estimate of the area of land use for a single point in time or where land use and activities
cannot be assigned to any land unit only can be used to develop Approach 1 land representation. This data is often
used in combination with other data to develop a complete land use estimate. An example of an Approach 3 survey
based approach for estimating land-use and land-use change can be found in Canada’s national inventory report(Environment and Climate Change Canada 2018).

3.3.4

Combining Multiple Data Sources

Remote sensing products are increasingly being used by countries as a source of information to estimate land-use
and land-use change (GFOI 2016). The most common use of these products is to detect land cover and coverchange. There are few cases where one single data source or method are used to develop area estimates for landuse and land-use change for all strata, sub-strata and reporting categories. For instance, while remote sensing data
is useful for identifying land cover and where a change in cover has occurred, the resulting products often do notprovide information on the drivers that occurred to cause the change, the actual land uses and the likely associatedemissions and removals. Combining remote sensing data products with other data sources is often required toobtain all the required information for estimating emissions and removals and to correctly allocate lands to the
IPCC land-use categories over time.
Typically, countries will combine a variety of different data sources and approaches to estimate areas of land-use.
This could include multiple remote sensing products (including wall-to-wall and sampling approaches), census,
survey, farmer interviews, field observations, expert knowledge, or some combination of these sources (Ogle etal. 2013; GFOI 2016). Combinations of data sources may also occur within a type of data. (e.g., national andregional or local statistics may be combined when national data is incomplete). These may occur for several
reasons, including that the time-series is incomplete (i.e. some years are missing and are supplemented with otherstatistics), a land-use class or stratum is missing (e.g. sugarcane area is missing in the national cropland area
statistics), more accurate statistics are available (e.g. from a different data provider).
When combining different data types and sources it is good practice to:• report the spatial and temporal scales of the data sources;• ensure consistency between different temporal or spatial scales in the data sources;• verify spatial datasets conform to national mapping standards (e.g., appropriate equal area projections) toensure accurate area calculations, and that raster and/or vector layers align and are within official nationalboundaries;• ensure that land conversion areas are consistent with each other across the entire time-series. For example,
losses in the area of Forest Land categories are consistent with gains in the areas of Forest Land converted to
Cropland, Grassland, Settlements, Wetlands, and Other Land;• ensure that the land conversion period is applied consistently across all land-use categories (i.e., that the same
number of years is used before lands in a ‘converted to’ sub-category move to the ‘remaining’ sub-category);• establish a hierarchy among various data sources and proceed to their integration accordingly (i.e., higher
quality data prevail to other data when an inconsistency appears among them);• fill data gaps to derive consistent time-series of land-use and land-use change (See Section 5.3, Chapter 5
Volume 1); and,• report uncertainties of land-use and land-use change estimates.

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Chapter 3: Consistent Representation of Lands

Spatially explicit approaches are commonly combined with other spatial data (e.g., forest and/or soil types, climate
data) to produce emissions estimates. When using multiple spatial data layers, especially when combining vector
and raster data sources of different spatial and temporal resolutions (Merchant & Narumalani 2009) it is goodpractice to ensure that:• all data layers are registered to a common projection, and that the layers align as far as possible, to prevent
errors due to misalignment such as slivers or areas of false change along the edges of boundaries between
different land-use categories;• reprojection of spatial data do not cause errors if applied correctly using appropriate type of projection for agiven location (Seong 2003);• when combining data of different pixel sizes (e.g., climate data at 1km, with satellite land cover data at 25m)that the pixels align with ground coordinates; and,• if pixels are resampled (e.g., resampling of Landsat pixels from nominal 30 m to 25 m) this is done prior toclassification.

3.3.5

Derivation of IPCC Land-Use Categories from Land
Cover Information

Inferring land use from land cover at a specific point in time can lead to misclassification of the predominant landuse. It is good practice to clearly document the country-specific rules applied in the inventory to consistently
derive land-use from land cover, both spatially and temporally, including predominance among land use categories.
When deriving IPCC land-use and land-use change categories from land cover data, the following generic steps
should be considered:• translate remote sensing data to land cover types using decision rules and image classification;• develop rules to translate land cover and cover change types to land-use and land-use change categories (i.e.,
attributing land cover information to land-use) using well-defined specific supplementary information• collect any required supplementary information and apply the developed rules.

Existing national data
Existing national data can be used for estimating land areas, alone or in combination with other data to derive
IPCC land-use categories. Defining the equivalence between national land-use categories and IPCC land-use
categories may not be straightforward, as national datasets are often developed for other purposes and do notnecessarily match the IPCC definitions. For example, the definition of forest cover in some existing remote sensing
products may differ from the nationally adopted definition for Forest Land. Even where the definitions are thesame, existing forest type maps generally cannot compare to new remote sensing products due to differences inspectral and geometrical resolutions and the methods applied for land-use classification. This is particularly thecase for older forest type maps derived from visual interpretation compared to semi-automated and automatedmethods.
In developing IPCC land-use information, it is good practice to:• define the national land-use categories and develop rules to track them in the inventory, where needed;• describe how multiple data sources are combined to classify land-use and how the methods ensure consistent
representation of lands;• demonstrate that the land-use categories definitions cover the entire variability of land-uses of the country
territory, and do not overlap;• report an equivalence table between the categories used in the national land-use classification scheme and the
IPCC land-use categories defined in Section 3.2, and• report which land cover elements and classification rules are used to identify land-use categories andattributions, including predominance among land uses. The applied classification rules need to be explainedby reporting additional information used and any assumptions made to match land-use categories for thenational classification system and the IPCC Guidelines discussed in this Chapter.

Global datasets for land-use classification
Accuracy of global products (Table 3.A.1.1) varies regionally due to factors including differential sensitivity ofdetection at biome and eco-regional scales, limited availability of regional data to calibrate algorithms and limited

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validation of outputs. Furthermore, many global products only produce estimates of land cover not land-use, withdefinitions that may not match national country definitions. Because of these issues, using global maps forinventory reporting can lead to inconsistencies in data and tend to produce activity data estimates with lower
accuracy and higher uncertainty than are attainable by national mapping (GFOI 2016). Conversely, national
products can be tuned to national circumstances and land-use definitions using knowledge and auxiliary data
available at the national/international level. Therefore, when using global data sets, it is good practice to:• assess the consistency of the global dataset with national definitions of land-use and suitability for reporting(e.g., time-series consistency, spatial scales, update processes);• assess the accuracy of the products for the mapped land-use categories and correct for bias by using ground orother reference data; and,• ensure that the accuracy assessment processes represent not just the IPCC land-use categories, but also thestrata (e.g., by forest types, areas impacted by disturbances, soil classes) used to estimate emissions andremovals.
National assessment of the relative advantages of global and national maps to generate national level estimates ofland-use and change are also related to: 1) preferences for national ownership of the process; 2) whether national
mapping capacity already exists and 3) national needs for a land cover map (e.g. related to forest definition andland cover classifications, for integration with domestic planning).
The relationship between global data and the national land-use definitions is important and in comparing national
estimates and global products, it is good practice to:• ensure that products are applied to the same geographic extent and time period;• ensure that the land-use area and changes derived from the global data correspond as nearly as possible to thenational definitions and legend;• use reference observations consistent with the national definition. If the reference data are stratified, e.g. byaccessibility or biomass quantity, strata should be applied consistently over time irrespective of whether
national or global map products are being used; and,• reduce common inconsistencies between global data and national definitions which are related to e.g. theminimum canopy cover thresholds, detailed consideration of land-use, the minimum size of land-use areas, andthe minimum tree height.

Addressing gaps in remote sensing data
National inventories require annual estimates of emissions and removals and ideally, annual data would enable thegeneration of annual estimates of change for all land-uses. In practice, such data is not always available for allland-uses for every year and the cost of obtaining and processing the data may be too high. Consequently, inventory
compilers will likely need to decide which data to collect, how frequently and to apply methods, such as splicing
techniques, to cover these gaps.
When covering data gaps from unavailable land-use and land cover data, it is good practice to:• define, document and report the years where remote sensing data are missing. When the number of years
between data availability varies, demonstrate that the land-use change detected across the time series is
consistent and not influenced by the change in frequency of observations;• justify the choice of the methods used to fill the data gap, and describe the method used for interpolation or
extrapolation consistent with the guidance provided in Chapter 5, Volume 1. When using interpolation methods,
if the land-use category on a sample unit or on a land use changes between consecutive inventories the year of
conversion should be identified. If this is not possible a random year for the conversion should be selected.
When extrapolating missing data based on trends and proxies, justify the length of the time-series used todevelop the trend. Whenever possible use functional proxies (i.e. driver of changes) for extrapolation orinterpolation; and• report the limitations and consequences of filling land cover data gaps with the chosen method. Wheneverpossible, estimate, document and report the uncertainty linked to the remote sensing annual data available andthe uncertainty linked to the periods where this data is not available.
Further, in the case of remote sensing data, some areas of land may not be covered with data in every period. This
often occurs due to persistent cloud or haze, errors in the satellite or due to limited acquisitions in some areas.
These areas are often removed from the analysis and classed as ‘no data’. Where wall-to-wall approaches are used,
these gaps may lead to errors in the estimates of land-use and land-use change. This problem increases with

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Chapter 3: Consistent Representation of Lands

increasing temporal density of the data. As such it is good practice to apply methods that can accurately fill thesedata gaps in a time-series consistent manner (See Annex 3A.2.4 for examples).

3.3.6

Stratification of land-use data

Once land-use and land-use conversion areas have been established, it is necessary to consider the capacity andneed for further stratification.
Stratification is the process of disaggregating a land-use category (e.g. Forest Land, Cropland, Grassland) into
logical, typically homogenous, sub-divisions (e.g. tropical/dry forest, crop types, improved or unimproved
pastures). This process is commonly applied to reduce the uncertainty of emissions and removals estimates as it isuseful to:• estimate emissions and removals for key land-use sub-categories;• enable tailoring of specific methods or data collection processes in different strata. For example, due to weather
conditions and cloud effects, it is much more difficult to measure Forest Land converted to other land uses
using multispectral remote sensing data in fragmented dryland forests than contiguous moist tropical forests;• track areas under conversion across time-series, especially to deal with subsequent changes;• assist in the management of uncertainties and plan continuous improvement of the inventory;• increase the flexibility in reporting of monitored data, such as the effectiveness of policies tailored to specificstrata (e.g. forest types, risk types).
Stratification may be needed to locate relevant data from subsequent chapters for emissions factors, carbon stocks,
etc. Table 3.1 shows the typical stratifications for which data are available for the application of Tier 1 emissions
and removals estimation. Throughout the default tables used to populate equations to calculate a Tier 1 inventory,
specific data cells are highlighted that represented the pre-defined stratifications applied to Tier 1 inventories. That
is, Tier 1 default data (tables) conform to a consistent stratification so that there is no further calculation or
ambiguity in the appropriate selection of default data to populate equations. Where countries are preparing Tiers
2 and 3 inventories, it is likely that stratification schemes may differ based on country-specific information andselection, manipulation or supplementation of default data may be required.
Common strata include layers such as soils, site class, topography, aspect, dominant tree species or species clusters
are commonly used for stratification. However, unless all land-use area and stratification data are spatially-explicit(Approach 3), the development of rules for allocations to strata may be required. Table 3.6B provides someexamples of possible data types and assumptions to stratify land-use and land cover.

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 3.6B (NEW)EXAMPLES OF AUXILIARY DATA AND POSSIBLE ASSUMPTIONS THAT CAN HELP TO DETERMINE AND STRATIFY LAND-USE.
Issue

Data

Possible assumptions1

Separate forest cover change due tomanagement activities from land
use changes

Maps of forest management
areas
Data on forest management
practices and harvestingplans

Areas of cover change in Forest Land are due
to harvesting (i.e., not land use change)

Separate cover changes between
those associated with natural
disturbances (these are only coverchanges)l and those due to humanintervention (e.g. land use changes
or harvesting)Maps of disturbances, suchas fire or pest extent maps
Maps of National parks andprotected areas

Changes in cover that occur at the same time
as fire or pest attack may be considered dueto these causes unless otherwise noted.
In certain circumstances, cover changes undercertain tenures (such as national parks) may
be due to natural processes, but these stillneed to be assessed.

Determine if the forest type isnatural or plantation

Maps of plantation
management areas, private
plantation areas.
Knowledge of new planting
areas and policies
Soils and climate

Forest areas within the plantation areas can beconsidered plantations.
Areas of newly established forest classes
depending on known planting types
Commercial plantations only occur on
specific soils or in climatic ranges

Separate crop types andmanagement practices

Climate (rainfall,temperature etc), soil
characteristics or soils types
Known crop products byregion (agricultural stats)

Certain crops and management practices canoccur in certain regions (e.g. no crops in adesert, no-tillage cultivation in low organic
matter soils)
Use product offtake to determine the types ofcrops being grown

Separate pasture from rangelands

Livestock statistics
Agricultural census data

Land with a certain concentration of animals
are pastures
Producers in a certain region use pastures(e.g. in cropland rotation).

Source: based on (GFOI 2016).
1 the validity of these assumptions will vary by country, so all assumptions should be clearly justified

To establish and report consistent land-use stratification scheme it is good practice to:• assess the availability of reliable data to classify land-use categories into sub-divisions that are available overtime;
• ensure that strata can be sufficiently distinct to be identifiable and establish clear definitions for land-use strata;• ensure that strata area cover the total land area of the category being stratified; as the boundaries of strata canchange over time e.g. if the frontier of disturbance moves into areas of previously undisturbed forest.
• ensure that the strata have the attributes required to develop estimates of emissions and removals (e.g.,
emissions factors or model parameters); and,• review the effect of the stratification to determine if further stratification would improve the estimates ofemissions and removals.
For example, Approach 1 land-use data are stratified by climate and soil type to estimate soil C stock changes.
Optimally, the land-use data can be down-scaled to capture the proportion of land-uses in each climate or soil type,with auxiliary information and expert knowledge. If re-scaling is not possible, inventory estimation can stillproceed, but the emissions and removals estimates should reflect uncertainties in the assignment of emission/stockchange factors (and associated parameters) that vary by climate and/or soil.
Management data may only be available in an Approach 1 format (e.g., expert knowledge or periodic surveys ofdifferent sets of land owners) even if Approach 2 or 3 data are available for land-use categories. In this case,management can be summarized as a proportion of the management practice (e.g., % no till, intensive tillage andreduced tillage) in each “lands remaining” and “lands converted” land-use category. This will be a limiting

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Chapter 3: Consistent Representation of Landsassumption if the management classes are not evenly distributed as the impact of management on the emission or
removal depends on land-use category.
Tiers 2 and 3 methods may also evaluate interactions between management practices that affect emission/stockchange factors. Determining the appropriate combinations of management is another issue that needs carefulconsideration. Tier 1 methods typically do not address the temporal trends in emissions/stock change factors(assuming a linear change) or capture interactions among management practices on a specific land-use, but rather
represent an average effect. Consequently, assignment of emission/stock change factors may become more
complicated with higher Tier methods and require careful explanation of the scaling processes that were used todelineate the appropriate combinations of the climate, soil, ecological zones, and/or management systems.
In some cases, management data may not cover the entire territory, being available only for specific regions, andso up-scaling of the data may be required to obtain national average coverage. A typical example is using project
and activities data (e.g. mitigation actions/activities at the sub-national/corporate/project: see Box 2.0A, Chapter
2, Volume 4) to derive extrapolation methods to transform local data into consistent national level data and report
description of these methods. In other cases, statistical/auxiliary information may be available at the aggregatednational level, so down-scaling of attributes may occur to assign management practices to particular land units.

3.3.7

Preparing area data for emissions and removals
estimation

Preparing a greenhouse gas inventory for AFOLU requires the integration of land-use area with data of land
management and biomass, dead organic matter and soil carbon stock pools, in order to estimate carbon stockchanges and CO2 and non-CO2 emissions and removals associated with land-use. Depending on the type of data
available (Approach 1, 2 or 3), there are implications for the subsequent use of the data in the preparation of
estimates of emissions and removals according to the land-use conversion framework represented in the reportingtables.
Countries that only have access to Approach 1 data have two options for reporting land-use category conversions.
Total areas for categories of “land remaining in a land-use” may include some portion of land that was converted
to that land-use since the last inventory. Countries should wherever possible apportion change in land-use areas
over time to inferred land-use conversion categories for the purposes of determining appropriate carbon stock andemission factor estimates. For example, a country with 1 Mha of forest, 1,000 ha deforestation and 1,000 haafforestation has a zero net change in Forest Land area (presuming these changes occurred on managed land), butwill have a reduction in forest biomass C stocks, at least until sufficient regrowth occurs. Subsequent decisions
will be needed to relate these inferred area conversions between land-use categories to appropriate land
management, biomass and soil C stocks and emission factors. Where this is done, countries should report the basis
for these decisions, and any methods of verification or cross-checking of estimates that have been applied, and theeffects on inventory uncertainty. If this apportioning is not done, then countries should state this, and report theeffect on uncertainties associated with doing so.
For countries with Approach 2 data, where information on the areas of each land-use conversion is known but is
not spatially-explicit, these area estimates still need to be linked to appropriate initial carbon stocks, emissions
factors, etc. In some cases, this may require the assignment of the land-use conversion data to climate, and/or
vegetation type, soil and management strata. Again, this can be done by some form of sampling, scaling or expert
judgement. Countries should report the basis for these decisions, and any methods of verification or cross-checkingof estimates that have been applied.
For countries using Approach 3 data, it is possible to apportion areas of land-use conversion by spatiallyintersecting the data with other spatial datasets, such as those on climate, and/or vegetation type, soil andmanagement strata. However, it is likely that inference, for example, based on survey data and expert judgement,
will be needed to apportion the land-use conversion and biophysical data by management practices as data on
management practices are rarely available in spatially explicit formats.

3.4

MATCHING LAND AREAS WITH FACTORSFOR ESTIMATING GREENHOUSE GASEMISSIONS AND REMOVALSThis section provides brief guidance on matching the land-use area data with carbon stocks, emissions factors andother relevant data (e.g., forest biomass stocks, average annual net increment) to estimate greenhouse gas emissions
and removals. An initial step in preparing national inventory estimates is to assemble the required activity data

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Volume 4: Agriculture, Forestry and Other Land Use(i.e., land-use areas) and match them with appropriate carbon stock, emissions and removal factors, Tier 3 models
and other relevant data.
This Volume provides default data (specifically marked) needed to make Tier 1 estimates for all AFOLU
categories according to specified climate and ecological zone stratifications. In addition, countries may develop
country-specific carbon stock, emission and removal factors and other relevant data (Tiers 2 and 3 inventory
methods). The following summarizes the principles to be followed when matching activity data with carbon stock,emission and removal factors and other relevant data:• match national land-use area classifications to as many land-use categories as possible;• when national land-use classifications do not conform to the land-use categories of these guidelines, document
the relationship between classification systems;• use classifications consistently through time and, when necessary, document any modifications made toclassification system;• document definitions of land categories, land-use area estimates, and how they correspond to emission andremoval factors; and,• match each land-use category or sub-category to the most suitable carbon stock estimates, emission andremoval factors and other relevant data.
Following are the recommended steps for matching land areas with emission and removal factors:
1. Start with the most disaggregated land-use area stratification as well as the most detailed available emission
and removal factors needed to make an estimate. For example, the Forest Land methodologies, described in
Chapter 4 of this Volume, provide a default factor for above-ground biomass stocks in forest plantations thatis disaggregated at the most detailed stratification, relative to other factors (i.e., forest type, region, species
group, age class, and climate). These strata would be used as an initial base stratification.
2. Include only those strata applicable in your country and use this as a base stratification.
3. Match land-use area estimates to the base stratification at the most disaggregated level possible. Countries
may need to use expert judgment to align the best available land-use area estimates with the base stratification.
4. Map emission and removal factors onto the base stratification by matching them as closely as possible to thestratification categories. Note that many of the default stock change and emissions factors and other parametersin Tier 1 (default) equations were statistically derived for specifically defined strata (e.g., climate type, soiltype) so that countries wishing to use Tier 1 methods for these emissions and removals should stratify land-use
categories using the definitions as specified for Tier 1 change factors and parameters.
If a national land-use classification is fitted to the land-use categories (and sub-categories) this facilitates matchingof emission and removal factors that follow the same classification. For example, default soil carbon factors for
Forest Land, Cropland, and Grassland are disaggregated by the same climate regions (see Annex 3A.5). Therefore,
the same land area classification can be used to estimate soil carbon changes in each of the land-use categories,
enabling consistent tracking of lands and carbon fluxes on lands resulting from land-use category conversions.
Countries may find that national land classifications change over time as national circumstances change and moredetailed activity data and emission/removal factors become available. In some cases, the stratification will beelaborated with the addition of more detailed emission and removal factors. In other cases, new stratifications
systems will be established when countries implement new forest inventories or remote sensing sampling designs.
When changes to the stratification system occur, countries should recalculate the entire time-series of estimatesusing the new stratification if possible.

3.4.1

Use of different approaches and methodological Tiers
when estimating emissions and removals due to landuse change

Emissions and removals of CO2 for the AFOLU sector are calculated from estimates of the total changes in carbon
stocks for each land-use category. The overarching calculation process is described in Chapter 2, Volume 4.
The change in carbon stocks can be estimated using emissions factors (Tier 1 and 2), models (Tier 3 gain-loss
methods) or direct measurements (Tier 3 stock difference) or any logical and consistent combination of all three.
As the different Approaches provide different levels of detail, the methods for estimating emissions and removals
need to be tailored to the available land-use data. When considering how to apply methods for estimating GHG
emissions and removals using activity data from different Approaches, it is important to differentiate between:

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Chapter 3: Consistent Representation of Lands• emissions and removals that occur in the year of the activity, such as fire or biomass loss from harvesting or
clearing of land and emissions from drainage of organic soils and removals from forest growth; and,• lagged emissions/removals that may occur for years after an activity or change in land-use occurs, such as
forest regrowth, decay/accumulation of soil organic matter or decay of carbon stock in forest products.
As Approach 1 does not produce estimates for changes in land use, estimates for lagged emissions from carbon
pools following transitions might produce emission and removals estimates that are different from those that canbe calculated using Approach 2 or 3 (see Boxes 2.1 and 2.2). This limitation needs to be considered where
Approach 1 data are being used in countries where land use change is occurring.
Approach 2 data allow for the use of estimation methods that account for emissions and removals both in the yearof the activity and also lagged emissions and removals from past activities. Approach 2 data can be used with any
combination of Tier 1 and 2 emissions factors or Tier 3 models. Approach 2 does not allow for the tracking ofmultiple changes (>2) in land use on a single land unit through time. As such, when using Approach 2 methods it
is good practice to stratify land into appropriate age or condition classes that can address these issues. For example,
when using Tier 1 methods in forest land, stratifying into young forest land (less than 20 years) and mature forests(older than 20 years) can enhance the estimate of a land use change occurring in forest land. Similarly, astratification into forest types or condition classes can enhance the accuracy of GHG estimates since the conversion
of a mature forest typically results in higher C stock losses and associated GHG emissions than the conversion ofa young, heavily disturbed or plantation forest. The same considerations apply to Approach 1 land representation.
Approach 3 uses the time-series of data for land units to capture multiple changes in land-use increases thecomplexity of Tier 3 modelling systems for estimating emissions and removals. While it is possible to use different
emissions estimation methods in spatially explicit approaches, it is important to ensure that all the estimation
methods are applied consistently. For some carbon pools, such as biomass, using different methods and models
for different land uses or sub-divisions of land use (e.g., forest type) will not create any inconsistencies even whenland-use changes. However, other pools, in particular soil carbon, require that the estimation methods be consistent.
For example, if two or more methods are used for estimating soil carbon changes for different land-uses, then thestocks and estimated stock changes need to be handled consistently when the land-use changes. Where multiple
methods are applied for estimating changes in carbon stocks within and between land-uses it is good practice todescribe how these models work consistently across land-uses. These issues are addressed in more depth in Chapter
2.5, Volume 4.
For Approach 3 gain-loss methods, the quantity of information on land-use and change through time often makes
it difficult to use spreadsheets to calculate emissions and removals. Advanced methods using integrating tools(Brack et al. 2006; Kurz & Apps 2006) are typically used is such circumstances. These tools estimate emissions
and removals for each uniquely identified land unit, assign the land unit to an IPCC land-use category then sum
the results for reporting.

Use of biomass maps with approach 3 data
There is active research ongoing on methods to estimate biomass in tropical forests using remote sensing
techniques, including analysis of spectral indices and use of SAR and lidar data. Information on the current stateof biomass maps is provided in Chapter 2 Volume 4.
The use of biomass maps needs to be considered in the context of the national inventory system to ensure thatreporting of carbon stock changes for all pools and across land-uses is consistent. If biomass maps are used thenit is good practice to demonstrate how the maps are consistent with national land-use classification system, inparticular how they are integrated with the land-use data chosen by the country.

3.5

UNCERTAINTIES ASSOCIATED WITH THE
APPROACHES

Uncertainties should be quantified and reduced as far as practicable. Land-use area uncertainty estimates are
required as an input to overall uncertainty analysis. Although the uncertainty associated with the Approaches (1 to
3) obviously depends on how well they are implemented, it is possible to give an indication of what can be achievedin practice. Table 3.7 sets out the sources of uncertainty (not the significance) for different Approaches. This
provides a guide to sources of uncertainties, indicative levels of uncertainty under certain conditions that might beencountered, and a basis for reducing uncertainties.
The number of potential sources of uncertainty in area estimates will tend to increase from Approach 1 to Approach
3, because successively more data are brought into the assessment. This does not imply that uncertainty increases,
however, because of the additional cross-checks that are made possible by the new data, and because of the general
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2 and 3 is that percentage uncertainties on conversion between land-uses are likely to be greater in Approach 1 (ifknown at all). This is because in Approach 1 land-use conversions are derived from differences (net change) in
total areas. The effect of this Approach 1 uncertainty on emissions and removals from conversions will depend on
the relative amount of land conversion in the country as a fraction of total land area. Approach 3 produces detailed
spatially-explicit information; which may be required e.g., for some spatial modelling approaches to emissions
estimation.
TABLE 3.7
SUMMARY OF UNCERTAINTIES UNDER APPROACHES 1 TO 3
Indicative uncertainty
following checks

Sources of uncertainty

Ways to reduce uncertainty

Sources of uncertainty may
include some or all of thefollowing, depending on thenature of the source of data:
Error in census returns
Differences in definition
between agencies
Sampling design
Sampling error variability
Interpretation of samples
Only net change in area isknown
In addition:
Cross-checks on area changes
between categories cannot be
conducted under Approach 1 andthis will tend to increase
uncertainties.

Check for consistent
relationship with national
area
Correct for differences indefinitions
Consult statistical agencies on
likely uncertainties involved
Compare with international
datasets

Order of a few % to order of
10% for total land area ineach category.

Approach 2

As Approach 1, but grosschanges in area are known, andwith ability to carry out crosschecks

As above, plus consistency
checks between intercategory changes within thematrixOrder of a few % to order of
10% for total land area ineach category, and greater forchanges in area, since theseare derived directly

Approach 3

As Approach 2 plus uncertainties
linked to interpretation of remotesensing data where used, andminus any sampling uncertainty

As Approach 2 plus formal
analysis of uncertainties using
principles set out in Volume 1
Chapter 3

As Approach 2, but areas
involved can be identified
geographically. However, for
Approach 3, the amount of
uncertainty can be estimatedmore accurately than forApproach 2 because errors are
mapped and can be tested
against independent data/field
checked.

Approach 1

Greater % uncertainty forchanges in area derived fromsuccessive surveys.
Systematic errors may besignificant when dataprepared for other purposes isused.

Evaluation of land-use and land-use change information generated from remotesensing techniques and estimation of uncertainties
Accuracy assessments on the land cover inputs can be useful in understanding the influence these inputs have on
overall uncertainty, but alone such assessments are unlikely to be representative of the total uncertainty of the data
used in estimating emissions and removals.
When using remote sensing data to generate estimates of land use and land use change, it is good practice to ensurethat:• uncertainty estimates are specific for the relevant land-use and land-use change categories, not for interim
products;• uncertainty estimates include consideration of all sources of potential error• uncertainty assessment methods can be applied through the entire time-series, either as a single value or for set
periods;

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Chapter 3: Consistent Representation of Lands• evaluation and uncertainty estimation methods are relevant to the Approach;• when using remote sensing data to assess accuracy, validation data of higher quality (e.g., greater spatial
resolution or spectral range) are used;• analysis should be consistent with the discussion in Chapter 3 of Volume 1: Uncertainties.

Collection of validation data
Validation data (also called reference or accuracy assessment data) used in accuracy assessments can be collectedusing direct observations of ground conditions by field crews or from other remote sensing sources, such as highresolution satellite data or aerial imagery including drone surveys.
Many biophysical features of interest can be collected on the ground to support the development and evaluationof land area estimates. However, ground measurements can be time consuming and expensive. Additionally,certain features are difficult to measure accurately from the ground but can be achieved relatively easily using
high-resolution satellite data or aerial imagery. Also, it is important to consider spatial variability and plot size
when ground information is used for validating pixel level data.
Remote sensing data are typically available at lower cost, allowing for more samples to be collected rapidly andoften are available for the entire time series to create a suitable validation dataset. Use of high-resolution remotesensing data can be cost effective to validate medium resolution remote sensing outputs. As such, many countries
will use a combination of ground and remotely sensed reference data to make best use and advantage of each data
source (GFOI 2016).
It is good practice to ensure that validation data is:• of at least the same quality as the calibration data;• collected close to the time of the images used in the maps; and• of sufficient size and positional accuracy compared to the spatial resolution of the maps.
When designing the validation sampling strategy countries may also consider assessing other spatial input data
used to estimate emissions (e.g., underlying strata used in emissions estimation, such as soil type maps).

Evaluation of sample-based method
Remote sensing data can be used in a sample-based method. In these cases, the remote sensing data can often betreated in a similar manner to point based ground samples and uncertainties estimated using standard methods
outlined in Chapter 3 of Volume 1: Uncertainties. However, unlike ground measurements, additional steps areoften required to create land-use data as the remote sensing samples will represent land cover. As such, some of
the methods used to develop wall-to-wall methods will be applicable for sample approaches as well.
When using sample-based methods where the sample units are large (e.g., greater than 1km2) but the spatialassessment unit is small (e.g., a 30 m pixel), it may be appropriate to apply the same methods used to evaluate
wall-to-wall methods to the sample unit to assess accuracy of the sample units themselves.

Evaluation of wall-to-wall methods
Wall-to-wall maps of land-use and land-use change data can be derived from remote sensing and other data.
Multiple steps are required to develop time-series consistent maps of land-use and land-use change data; including
but not limited to developing time-series consistent maps of land cover, attributing cover and cover changes tospecific activities then applying country specific policy rules of assigning lands to an IPCC land-use category
through time.
Wall-to-wall mapping products are a form of census. Census approaches are subject to two types of error withineach IPCC category: errors of inclusion (commission errors) and errors of exclusion (omission errors). Wall-towall methods typically do not apply a sample-based estimator and therefore there is no estimate of bias. However,it cannot be assumed that wall-to-wall methods are free of bias, as errors will occur through all the processes ofdeveloping the land-use maps.
Classification accuracy refers to the percentage of sample units correctly classified and can be calculated ascommission and omission errors for each mapped category as well as an overall accuracy for all categories.
Confusion or error matrices and map accuracy indices, can inform issues of systematic errors and precision in themaps, but do not produce the information necessary to construct confidence intervals (GFOI 2016).
A statistical estimator corresponding to the sampling design (see Chapter 3 of Volume 1: Uncertainties) can beused to assess (and adjust for) bias and construct confidence intervals.

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To assess map accuracy and create information that can be used for estimating the uncertainty of emissions andremovals estimates it is good practice to collect and use validation data relevant to the estimation of emissions andremovals, noting that:• the method and Tier adopted for generating emissions and removals estimates may influence how and whenbias in activity data is addressed; and,• activity data accuracy needs to be assessed at the scale and for the strata used to develop the emissions andremovals estimates otherwise the resulting emissions and removals estimates may still be biased.
For transparency purposes it is good practice to clearly document the sampling methods (including sample sizes),
how the samples relate to the classification system, and the QA/QC processes applied in sampling.

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Chapter 3: Consistent Representation of Lands

Annex 3A.1

Examples of international land cover datasets

In recent decades, satellite remote sensing has become the primary source of data for developing for global
estimates of land cover. Several global products are currently available (Table 3A.1.1.) and more are underdevelopment. Countries considering the use global products should refer to the issues raised in Annex 3A.2.1.
TABLE 3A.1.1 (UPDATED)EXAMPLES OF GLOBAL LAND COVER DATASETS IN 2017
(A)

(B)

(C)

(D)

Dataset name

ESA Climate Change
Initiative – Global
Land Cover Products(CCI – LC)

Global Forest Change
Global Forest Watch

MODIS Land Cover
Type Product(MCD12Q1)

Global PALSAR2/PALSAR/JERS1 Forest/NonForest Map

AuthorEuropean Space
Agency (ESA)

University of Maryland(UMD)
World Resources
Institute (WRI)

NASA / US
Geological Survey

Japan Aerospace
Exploration
Agency (JAXA)

Brief
description ofcontents

Consistent global
land cover maps at
300 m spatial
resolution on anannual basis from1992 to 2015.

Global forest extent,
forest cover loss andgain based on land
cover information from2000 to 2017 using
Landsat.

Time-series analysis
of MODIS data at
500 m spatial
resolution tocharacterize global
land cover from2001-2013.

The global
forest/non-forest
map (FNF)
generated byclassifying thebackscattering
intensity values at
25 m spatial
resolution using
PALSAR2/PALSAR
mosaicClassification
schemeThe system uses ahierarchicalclassification, whichallows adjusting thethematic detail of thelegend to the amountof information
available to describe
each land coverclass, whilst
following astandardized
classification
approach.

This dataset captures
vegetation taller than 5m in height and tree
canopy cover (0 to100%) for year 2000,
global forest cover gain(2000-2012), year ofgross forest cover loss
event defined as standreplacement
disturbance, data mask
and cloud free Landsat
mosaics for 2000 and2017.

Contains fiveclassification
schemes derived
from yearly Terra
and Aqua MODIS
data. The primaryland cover scheme
identifies 17 land
cover classes definedby the IGBP. This
includes 11 naturalvegetation classes, 3
developed andmosaicked land
classes and 3 nonvegetated classes.

Forest is definedwith an area larger
than 0.5 ha andforest canopy
cover over 10%(FAO definition).

Remote sensing
data type

Optical

Optical

OpticalRadar

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TABLE 3A.1.1 (CONTINUED)
EXAMPLES OF GLOBAL LAND COVER DATASETS IN 2017
(A)

(B)

(C)

(D)

Data acquisition
year

Annual from 1992 to2015

Annual from 2000 to
2017

Annual from 2001 to
2013

2007, 2008, 2009,2010, 2015, 2016

Spatialresolution or
grid size

300 m(1100m for 19921999 years using
AVHRR)

30 m

500 m

25 m, 100 m, 1000m and 0.25 degree

Revisioninterval (fortime-series
datasets)

Annual (1992-2015)– baseline 10-year
global land covermap

Annual time-series
from 2000 to 2017

Annual time-series
from 2001 to 2013

PALSAR - 2007,2008, 2009, 2010,2015 and 2016
JERS-1 1993,
1994, 1995, 1996,
1997 & 1998 (fortropics only);
Global-1996

Qualitydescription

The land cover maps
are delivered alongwith four quality
flags whichdocument thereliability of theclassification andchange detection.

Data mask shows areasof no data, mapped land
surface and permanent
water bodies.

Contains quality
control flags for eachpixel. Use latest
collection of MODIS
data processing.

The overall
agreement with
forest/non-forest
assessments fromPALSAR datausing the Degree
Confluence
Project, the Forest
Resource
Assessment and
Google Earthimages was 85%,
91% and 95%
respectively.

Contact address
and reference
URL

http://maps.elie.ucl.ac.be/CCI/viewer/dow
nload.php

http://earthenginepartne
rs.appspot.com/science2013-global-forest
https://www.globalfore
stwatch.org/

http://glcf.umd.edu/d
ata/lc/http://www.eorc.ja
xa.jp/ALOS/en/pal
sar_fnf/fnf_index.
htm

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Chapter 3: Consistent Representation of Lands

Annex 3A.2

Development of land-use databases

There are three broad sources of data for the land-use databases needed for greenhouse gas inventories:• databases prepared for other purposes;• collection by sampling; and• complete land inventory.
The following subsections provide general advice on the use of these types of data. Greenhouse gas inventory
preparers might not be involved in the detailed collection of remote sensing data or ground survey data but canuse the guidance provided here to help plan inventory improvements and communicate with experts in these areas.

3A.2.1 Use of data prepared for other purposes
Two types of available databases may be used to classify land. In many countries, national datasets of the typediscussed below will be available. Otherwise, inventory compilers may use international datasets. Both types of
databases are described below.

NATIONAL DATABASES
These will usually be based on existing data, updated annually or periodically. Typical sources of data include
forest inventories, agricultural census and other surveys, censuses for urban and natural land, land registry data
and maps.

INTERNATIONAL DATABASES
Several projects have been undertaken to develop international land-use and land cover datasets at regional to
global scales (Annex 3A.1 lists some of these datasets). Almost all of these datasets are stored as raster data
generated using different kinds of satellite remote sensing imagery, complemented by ground reference dataobtained by field survey or comparison with existing statistics/maps. These datasets can be used for:• Estimating spatial distribution of land-use categories. Conventional inventories usually provide only the totalsum of land-use area by classes. Spatial distribution can be reconstructed using international land-use and land
cover data as auxiliary data where national data are not available.
• Reliability assessment of the existing land-use datasets. Comparison between independent national andinternational datasets can indicate apparent discrepancies and understanding these may increase confidence innational data and/or improve the usability of the international data, if required for purposes such as
extrapolation.
• When using an international dataset, inventory compilers should consider the following:(i) The classification scheme (e.g., definition of land-use classes and their relations) may differ from thatin the national system. The equivalence between the classification systems used by the country and thesystems described in Section 3.2 (Land-use categories) therefore needs to be established by contacting
the international agency and comparing their definitions with those used nationally.
(ii) Spatial resolution (typically 1km nominally but sometimes an order of magnitude more in practice) may
be coarse, so national data may need aggregating to improve comparability.
(iii) Classification accuracy and errors in geo-referencing may exist, though several accuracy tests areusually conducted at sample sites. The agencies responsible should have details on classification issues
and tests undertaken.
(iv) As with national data, interpolation or extrapolation will probably be needed to develop estimates forthe time periods to match the dates required for reporting.

3A.2.2 Collection of new data by sampling methods
Sampling techniques for estimating areas and area changes are applied in situations where total tallies by direct
measurements in the field or assessments by remote sensing techniques are not feasible or would provide
inaccurate results. Sampling concepts that allow for estimation procedures that are consistent and unbiased, andresult in estimates that are precise, should be used.

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Sampling usually involves a set of sampling units that are located on a regular grid within the inventory area. A
land-use class is then assigned to each sampling unit. Sampling units can be used to derive the proportions of landuse categories within the inventory area. Multiplying the proportions by the total area provides estimates of thearea of each land-use category. Where the total area is not known it is assumed that each sampling unit represents
a specific area. The area of the land-use category can then be estimated via the number of sampling units that fall
into this category.
Where sampling for areas is repeated at successive occasions, area changes over time can be derived to constructland-use conversion matrices.
Applying a sample-based type for area assessment enables the calculation of sampling errors and confidence
intervals that quantify the reliability of the area estimates in each category. Confidence intervals can be used toverify if observed category area changes are statistically significant and reflect meaningful changes.
Annex 3A.3 provides more information on sampling.

3A.2.3 Collection of new data in complete inventories
A complete inventory of land-use of all areas in a country will entail obtaining maps of land-use throughout thecountry at regular intervals. This can be achieved by using remote sensing techniques. As outlined under Approach
3, the data will be most easily used in a GIS based on a set of grid cells or polygons supported by ground truth
data needed to achieve unbiased interpretation. Coarser scale data can be used to build data for the whole country
or appropriate regions.
A complete inventory can also be achieved by surveying all landowners and each would need to provide suitabledata where they own many different blocks of land. Inherent problems in the method include obtaining data at
scales smaller than the size of the owner’s land as well as difficulties with ensuring complete coverage with nooverlaps.

3A.2.4 Tools for data collection
REMOTE SENSING (RS) TECHNIQUES
An increasingly remarkable array of remote sensing and other geospatial data, methods, and tools have become
available in the last decade for consistent country-specific representation of land-use and land-use change.
Advances in a) spatial and temporal higher coverage leading to increased availability of remotely sensed dataroutinely collected through earth observation satellites, b) time-series classification algorithms and related geodata
processing workflows, and c) geographic information system (GIS)-based integration of in situ, collateral, andremote sensing data can be leveraged by inventory compilers for this purpose. Increased coordination andcollaboration between the international space agencies such as NASA, JAXA, ESA, etc., have led to improved
global remote sensing data collection and free availability and open access of high and moderate resolution datasets.
Determination of fitness for use of remote sensing and other geospatial data, products, and tools is the
responsibility of the user; the producer of remote sensing data on the other hand should provide the user with
sufficient metadata to help make such a determination. The current geospatial metadata standard is based on ISO
19115 which includes workflow provenance or lineage information. Provenance is vital to understand the exact
sources, nature, and order of processing steps taken to generate a remote sensing product, and is required tounderstand how errors are expressed and propagated during the product’s creation (Tullis et al. 2015). Expertisein remote sensing systems and data processing (Jensen 2016) is necessary to interpret fitness for use in this context,
and collaboration with a national or regional geospatial laboratory in the development of seamless remote sensing
derived products is strongly encouraged. It should be noted that relevant remote sensing theory and applications
have developed over more than a century (e.g., Thenkabail (2015); Jensen (2016)), and a detailed treatment cannot
be replicated here. Instead, key aspects will be highlighted relative to the point of view of an inventory compiler.
Determination of fitness for use may change over time as new sensors, methods, and workflows are developed andbecome available. This process is punctuated as earth observation satellites are decommissioned at their end of life
and international investments are made in new launches with superior observation capacity.
There is no a priori restriction on which remote sensing products may contribute to a consistent representation oflands, and no methodological requirement to maintain historical tradition. On the contrary, increased transparency,
replicability, and accuracy in representation of land-use activity data benefits from the development of new andinnovative geospatial workflows. Ensuring that land-use (of interest due to human activity) is consistently andaccurately represented over time is more important than the specific methods that are ultimately selected. To aid
compilers or reviewers in fitness for use determinations associated with remote sensing data and products, it is

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Chapter 3: Consistent Representation of Lands

suggested that remote sensing resolutions, time-series consistency, compatibility with forest and other land-use
definitions, and attribution of land-use change all be considered.
Remotely sensed data, as discussed here, are those acquired using sensors (e.g., optical, radar or lidar) on board
satellites, or airborne. Before these data can be effectively used to generate land-use activity data, various formsof calibration and harmonization may be required. Classification can be accomplished either through expert visualinterpretation of the remotely sensed imagery, or by digital methods, or by some combination of the two. Someremote sensing approaches produce reliable sample datasets while others generate wall-to-wall maps for eachepoch in the time-series of interest. Reliable reference data samples including (where possible) in situ or groundsurvey data is utilized to both improve land-use products (e.g., to refine area estimates) as well as to estimate
accuracy of products incorporated in subsequent stages of the inventory process.
The strengths of remote sensing come from its ability to provide spatially explicit information for land
representation and repeated coverage, including the possibility of covering large and/or remote areas that are
difficult to access in situ. Archives of remote sensing data also span several decades and can therefore be used toreconstruct historical time-series of land-use information. Remote sensing is particularly useful for obtaining area
estimates of land-use categories and for assisting in the identification of relatively homogeneous strata that canguide the selection of sampling schemes and the number of samples to be collected. The challenges of remotesensing are related to interpretation: the images need to be consistently and reliably translated into meaningful
information on land-use. Depending on the satellite sensor(s) involved, the data acquisition may be impaired by
the presence of atmospheric clouds, smoke and haze. Another concern, particularly when comparing data overlong time periods, is that remote sensing quality and resolutions may change over time. Further guidance isprovided to address these challenges in the context of common remote sensing definitions, state of the art methods
and approaches, and future possibilities particularly relevant to inventory compilers.

Remote sensing resolutions
SpatialSpatial resolution refers to the approximate ground-projected dimensions of remotely sensed image pixels. Landsat
8 Operational Land Imager (OLI), for example, has a spatial resolution of 30 m, while the Sentinel 2 multispectral
instrument has higher spatial resolutions of 10 m and 20 m, depending on the band. In choosing appropriate spatial
resolution for land representation, it is critical to consider the minimum mapping unit (MMU), the smallest size
which determines whether a feature is captured from a remotely sensed image. Pixel area and detectability are two
important factors in assessing MMU suitability. A commonly accepted criterion is that the pixel area should notexceed 1/4 MMU. For example, if MMU is 0.5 ha (5,000 m2) then Landsat data at 30 m spatial resolution (900 m2
pixel area) would meet the MMU criteria as there will be at least 5 Landsat pixels within the MMU. In contrast,
using MODIS sensor data at 250m pixel (62,500 m2 pixel area) would fail the MMU criteria as the area coveredby a single pixel is greater than the MMU. Spatial resolution is generally inversely related to spatial coverage;
higher spatial resolution sensors cover smaller areas and vice versa. This relationship has direct implications forrequired processing time and expertise required and thus influences the total cost of the inventory.

SpectralSpectral resolution describes the ability of a sensor to define wavelength intervals. As spectral resolution increases,
there is a greater number of possible channels or bands, and corresponding wavelength ranges for those bands are
narrower. Often a specific sensor’s spectral resolution is fixed and thus its potential applications are limited. In
general, the higher the spectral resolution, the greater the ability of the sensor to separate different variables andto detect change. However, narrow wavelength ranges mean that less electromagnetic energy is available toimpinge upon the detectors, which can decrease signal to noise ratio (SNR). Given this principle, many of thehigher spatial resolution commercial satellites have relatively lower spectral resolutions. In general, there should
be a good balance between the amount of spectral bands and the spatial resolution depending on the application.

TemporalTemporal resolution refers to the length of time required for a satellite to revisit a land area of interest. Temporal
resolution is related to image coverage and spatial resolution; i.e., sensors that cover the Earth more frequently, on
the order of a day (e.g., MODIS) or 16 days (e.g., Landsat 8), have higher coverage and lower spatial resolution.
However, this is changing with recent and planned satellite constellations (e.g., small satellites from Planet;
RADARSAT Constellation Mission, etc.). Due to some degree of overlap in the imaging swaths of adjacent orbits
and an increase in this overlap with latitude, some areas of the Earth tend to be re-imaged more frequently. Also,
some satellite systems can point off-nadir to image the same area between different satellite passes separated byperiods from one to five days. Adequate temporal resolution is critical for the development of image time-series
that contain information relevant to human activity.

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RadiometricRadiometric resolution is related to the sensitivity of the detector elements in a sensor. In general, higher
radiometric sensitivity leads to better discrimination of land cover and ultimately land use. Due to introduction ofnoise from a variety of sources, consistent sensor radiometric resolution may be somewhat less than the bit-depth
reported in sensor specifications and may vary between bands due in part to the limitations of wavelengthdependent irradiance and atmospheric transmittance. Noticeable improvements in radiometric resolution and in its
reliability, has been observed in recent years as a function of sensor technology, such as the increase from the 8bit specification in Landsat 5 TM, 12-bits in Landsat 8 OLI, and 14-bits in Landsat 9 OLI-2 (planned for launch
in 2020).

Types of remote sensing data
Commonly used types of remote sensing data are: 1) aerial imagery, 2) satellite imagery using visible and/or
infrared bands, 3) satellite or airborne radar imagery and, 4) satellite or airborne lidar data. Combinations ofdifferent types of remote sensing data (e.g., visible/infrared and radar; different spatial or spectral resolutions)might very well be used for assessing different land-use categories or regions. A complete remote sensing system
for tracking land-use conversions can include multiple sensor and data type combinations at a variety of resolutions,with appropriate processing methods to ensure sensor system-related variables do not introduce classification
errors.
Important criteria for selecting remote sensing data and products are:• Adequate land-use categorisation scheme;• Appropriate spatial resolution and image extent;• Appropriate temporal resolution for estimating of land-use conversion;• Capability to perform accuracy assessment;• Transparent methods applied in data acquisition and processing; and• Consistency and availability over time.

1. Aerial photographs
Analysis of aerial photographs and most recently very high-resolution digital air photos can reveal forest tree
species and forest structure from which relative age distribution and tree health (e.g., needle loss in coniferous
forests, leaf loss and stress in deciduous forests) may be inferred. In agriculture, analysis can show crop species,
crop stress, and tree cover in agro-forestry systems. The smallest spatial unit possible to assess depends on thetype of aerial photos used, but for standard products it is often as small as 1 square meter.

2. Satellite images in visible and near infrared wavelengths
Complete land-use or land cover of large areas (national or regional) may be facilitated by the use of satellite
images. The possibility exists of obtaining long time-series of data from the desired area since the satellite
continuously and regularly passes over it. The images usually generate a detailed mosaic of distinct categories, but
the labelling into proper land cover and land-use categories commonly requires ground reference data from maps
or field surveys. The smallest unit to be identified depends on the spatial resolution of the sensor and the scale ofwork. The most common multispectral sensor systems used for regional to national land cover and land-use
mapping have a spatial resolution of 10 – 30 meters. At a spatial resolution of 30 meters, for example, units as
small as 1 ha can be identified. Data from higher spatial resolution satellites are now also widely available (e.g.,
ESA Sentinel-2).

3. Radar imagery
The most common type of radar data is from the so-called Synthetic Aperture Radar (SAR) systems that operate
at microwave frequencies. A major advantage of such systems is that they can penetrate clouds and haze andacquire data during night-time. They may therefore be the only reliable source of remote sensing data in many
areas of the world with quasi-permanent cloud cover. By using different wavelengths and different polarisations,
SAR systems may be able to distinguish land cover categories (e.g., forest/non-forest), or the biomass content ofvegetation, although there are at present some limitations at high biomass due to signal saturation. Reports fromJapan Aerospace Exploration Agency (2010; 2011; 2014) provide detailed examples of orbital SAR data analysis
in support of forest and wetland monitoring.

4. LidarLike SAR, light detection and ranging (lidar) is an active sensor technology (transmits and later detects its own
energy). Laser light at a specific wavelength (e.g., 532 nm, 1,064 nm) is transmitted to the surface and some portionis reflected/scattered back to the instrument. However, in contrast to SAR, lidar is used mostly to determine the

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Chapter 3: Consistent Representation of Lands

distance to and position of the reflective surface from the precise time and angles the pulse takes to return to thesensor. By using stream of pulses transmitted across the surface, the relative elevation of each reflecting target canbe derived, producing a 3-dimensional (3D) point cloud that can be analysed for surface elevation and vegetation
structure as well as composition. In addition, although currently less commonly implemented, the intensity ofreflected energy can be used to evaluate properties of the reflected surface. Lidar generally has a narrow swath
width, particularly with airborne systems which generate the most precise and detailed data. It therefore requires
significant time and expense to acquire full coverage of large areas. In dynamic landscapes where, higher temporal
resolution is needed, such data are best suited for high spatial resolution sample-based analysis.

Remote sensing data pre-processing
Imagery captured by airborne or spaceborne sensors must be corrected for radiometric, geometric and topographic
distortions prior to using this data for land cover and land-use classification. The type of pre-processing depends
on type of sensor system such as optical or radar. A detailed description of pre-processing methods can be foundin Jensen (2016) and Richards (2013). Availability of seamless radiometrically corrected data in recent years has
made it much easier to use this data for land cover and land-use change detection (Roy et al. 2010; Hansen &Loveland 2012; Hansen et al. 2013; Teillet 2015). Optical imagery might be affected by cloud cover, which canbe removed by combining data from multiple images acquired in the same season. Ubiquitous cloud cover canbenefit from recent advances (e.g., Fmask; see Zhu et al. 2015). GFOI (2016) provides detailed guidance on cloud
removal including the effects of shadows.
Development of country-specific remote sensing pre-processing capabilities may not always be practical.
Fortunately, major remote sensing data suppliers such as US Geological Survey (USGS), European Space Agency(ESA), Japan Aerospace Exploration Agency (JAXA), and others are increasingly offering analysis ready data
(ARD), which is most suitable for extraction of land-use categories required for national GHG inventories. For
example, USGS (2017) is beginning to offer Landsat ARD using harmonized collections from Landsat 4, 5, 7, and8 between 1982 and the present. When using global or country-specific georeferenced datasets, it is good practiceto ensure they meet national geodetic mapping standards.

Time-series consistency
Methodological changes and improvements in satellite data processing and calibration over time is a normal
practice and often result in improved products for change detection. It is also common to source data from multiple
sources and sensors, which, if not accounted properly, may result in inconsistent products that are unsuitable fordetecting land use change. It is therefore good practice to reprocess time-series data when new data or methods
become available such as those identified below:• Availability of improved ground control points (GCPs). For example, when using Landsat data from the USGS,
it is important to use data from the same collection and tier for the entire time series. Combining data fromdifferent tiers may result in misregistration;• Availability of improved calibration or recalibration of sensors in response to degradation of sensor
performance over time;• Availability of new data and processing methods such as Data Cube (CEOS 2016; Lewis et al. 2017); andcloud-based data processing platforms (FAO 2018);• Correction of errors.
There are many new sensors and types of remote sensing data available in recent years to assess land cover andland-use changes. Using data from multiple sensors and sources, which is increasingly common, requiresconsistent processing of time-series remote sensing data following the principles discussed in Chapter 5 of Volume
1: Time-Series Consistency. Summary of splicing techniques applicable to remote sensing data processing are:• Overlap techniques can be used when a new higher resolution sensor data becomes available in recent years,
but such data are not available in the past. In such cases, data from old and new sensors can be compared forat least one year (preferably more) to establish a consistent relationship between the two products. This
technique can be used, for example, to construct a consistent time-series using historic Landsat sensors and themore recent Sentinel-2 sensors (Zhang et al. 2018).
• Interpolation techniques can be used where availability of remote sensing data from historic archives is limited.
In such cases best available data for intermittent years in the time-series can be interpolated to fill gaps in the
missing data.
Other techniques such as merging of different spatial resolution data can be used to fill the data gaps. Pixel
compositing is also another proven technique to construct best quality cloud free composites for classification. It
is important to collect remote sensing data obtained in the same season throughout the time-series to minimise
errors due to seasonal changes.

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Ground reference data
To make use of remote sensing data for inventories, and in particular to relate land cover to land-use it is goodpractice to complement remote sensing data with in situ or ground reference data (often mistakenly called ground“truth” data even though it may also contain sources of error). Ground reference data can either be collectedindependently or obtained from forest or agricultural inventories. Land-uses that are rapidly changing over theestimation period or that have vegetation cover known to be easily misclassified should be more intensively groundsampled than other areas. This can typically only be done by using ground reference data, preferably from fieldsurveys collected independently. High spatial resolution imagery obtained from aerial/drone or orbiting satellites
may also be useful for reference and verification purposes.

Integration of remote sensing and geographical information systems
Visual interpretation of images is often used for identifying sampling sites for forestry inventories. The method is
simple, and reliable. However, it is labour intensive and therefore restricted to limited areas and may be affectedby subjective interpretations by different operators.
Effective use of remote sensing data generally requires integration of the extensive coverage that remote sensing
can provide with ground-based measurements or map data to represent areas associated with particular land usesin space and time. This is generally achieved most cost effectively using a geographic information system (GIS).
Use of a GIS is the most common approach to combine multiple data sources including field measurements, survey
and census data. This information is essential to train image classification or machine learning algorithms used forextracting land cover and land-use change. A number of important factors should be considered when combining
multiple data sources as discussed in Section 3.3.4.

Land-Use classification using remote sensing data
Classification of land cover using remotely sensed data may be done by visual or digital (computer based) analysis.
Each approach presents advantages and disadvantages. Visual analysis of imagery allows for human inference
through the evaluation of overall characteristics of the scene (analysis of the contextual aspects in the image).
Digital classification, on the other hand, allows several manipulations to be performed with the data, such as
merging of different spectral data, which can help to improve modelling of the biophysical ground data (such astree diameter, height, basal area, biomass) using the remotely sensed data. In addition, digital analysis allows forthe immediate computation of areas associated with the different land categories. It has developed rapidly in recent
decades, along with the associated technical computer development, making hardware, software and satellite datareadily available at low cost in most countries. Capacity to use these data and facilities may have to be outsourced(e.g., using cloud-based computing platforms), particularly in mapping at the national level.
There has also been extensive research on the best methods for image classification and as a result a wide varietyof choices are available. Common image classification and machine learning algorithms include maximum
likelihood, decision trees (e.g., random forest), support vector machines and neural networks. Many of these areavailable in standard image processing and statistical software packages (Jensen 2016).
Image classification begins with the definition of the categories or classes to be included in the map. In supervisedclassification, it is necessary to provide training samples of each of the classes to be included. These samples could
come from a variety of sources, including sample sites from a national forest inventory, or could be obtained fromhigh spatial resolution images (GFOI 2016). Often images from a single date are used for image classification.
However, multiple images from different seasons can also be used in image classification to try to capture classes
with seasonal dynamics. Multi-season satellite data is particularly useful for mapping croplands, grasslands andfallow lands. As the level of stratification increases, alternative sources of reference data to train classifiers will
be needed, such as prior vegetation maps or field plots.
Extraction of information from satellite images can also be done by visual interpretation. This is best done by asubject matter expert familiar with the area being interpreted. However, this method can be very human resource
intensive (GOFC-GOLD 2016) because the number of pixels may be very large, and the interpretations can largely
vary due to human judgement, since it is hard to maintain consistency and repeatability between interpreters.
Moreover, the minimum mapping unit for land classification is often less than 5 ha, which can be tedious to
implement using visual interpretation. Further, differencing visually interpreted maps to develop change estimatesby polygon overlay analysis typically results in gaps between polygons. It is also very difficult to make
improvements to the resulting maps, especially once the time-series includes more than 3 or 4 epochs.
This may be overcome by applying image classification algorithms to give consistent results in allocating a pixel
to a category or another, or to segment the data. Unsupervised approaches use classification algorithms to assign
image pixels into one of many unlabelled class groupings. Expert image interpreters then assign each of thegroupings of pixels a value corresponding to the desired land class. Supervised approaches use ground reference
data or expert knowledge of the region to train the classification algorithms which then identify and label areas
similar to the input training data. The approaches have different challenges which are best addressed by iterative

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Chapter 3: Consistent Representation of Lands

trials: supervised classification may wish to use more classes than are statistically separable; unsupervised methods
may generate fewer classes than are desired and a given cover type may be split between several groupings. In
both cases data analysts can check the accuracy of classification outputs.
Rarely does the first attempt at image classification result in the final product. Close examination of theclassification results often reveals issues and problems that can be resolved by changing or refining training datain the classification process. There are many ways to try to improve the results of a classification with noticeable
problems, including the addition of more or improved training data. It may also be helpful to include additionalkinds of data in the classification, such as topographic or climatic data (GFOI 2016). Any improvements in dataprocessing methods should be reflected in the entire time-series to improve the accuracy and consistency of output
data.
While two dates of satellite imagery may be useful for quickly depicting land cover change, identification of
permanent land-use changes may require more data and analysis. It is therefore good practice to ensure that allland cover changes identified by satellite data are verified using sufficient spatial and temporal resolution imagery,ground reference and other auxiliary datasets to isolate permanent land-use change from that of temporary loss offorest cover. This process, referred as attribution of satellite derived land cover change, helps to identify humaninduced land-use change. Typical data sets used in attribution include those with information relating to fires,
forest management areas, agricultural areas, road coverage and urban areas (Mascorro et al. 2015). As dataprocessing algorithms detect increasingly diverse change processes, the need to distinguish among the agents
causing the change becomes critical. Not only do different change types have different impacts on natural andanthropogenic systems, they also provide insight into the overall processes controlling landscape condition.
Reaching this goal requires overcoming two central challenges. The first is related to scale mismatch: change
detection in digital images occurs at the level of individual pixels but change processes in the real world operate
on areas larger or smaller than pixels, depending on the process. The second is related to separability: change
agents are defined by natural and anthropogenic factors that have no connection with the spectral space on which
the change is initially detected. Different change agents may have nearly identical spectral signatures of change at
the pixel and even the patch level, and must be distinguished by factors completely outside the realm of remotesensing (Kennedy et al. 2007).

Detection of land-use conversion using remote sensing
Remote sensing can be used to detect locations of change. Methods for change detection can be divided into two
categories (Singh, 1989):
Post-classification change detection: This refers to techniques where two or more predefined land cover/use
classifications exist from different points in time, and where the changes are detected, usually by subtraction of
the datasets. The techniques are straightforward but are also sensitive to inconsistencies in interpretation andclassification of the land-use categories.
Pre-classification change detection: This refers to more sophisticated and biophysical approaches to change
detection. Differences between spectral response data from two or more points in time are compared by statisticalmethods and these differences are used to provide information on land cover/use changes. This type is less sensitive
to interpretation inconsistencies and can detect much more subtle changes than the post-classification approaches
but is less straightforward and requires access to the original remotely sensed data.
There are also other viable methods. For example, one can use change enhancements and visual interpretation.
Areas of change are highlighted through display of different band combinations, band differences or derived
indices (e.g., vegetation indices). This focuses attention on potential land-use conversions sites that can then bedelineated and attributed through manual or automated techniques. These methods are subject to human interpreter
inconsistencies but are capable of detecting subtle changes and better detecting and mapping land-use conversion
where land cover, context and auxiliary information is needed to determine land-use conversion.
Change detection is one of the most common uses of remote sensing data, and many methods have been used,tested and proposed in the literature. GOFC-GOLD (2016) includes descriptions and examples of several change
detection methods and is a useful resource when considering options for combinations of methods and remotesensing data to be used for mapping change. In general, at least two dates of images (end-points) are necessary to
map change. Image classification methods are commonly used, in which case multiple images are used to makethe assignment to stable classes (places that have not changed) as well as change classes, such as Forest Land to
Grassland (Woodcock et al. 2001). Such methods use the change in a spectral bands or indices as the basis for
detecting change land cover (Lambin & Strahlers 1994; Coppin et al. 2004).

Time-series classification
Data processing methods that use many images, or a time-series of images, have been developed and tested (Chenet al. 2004; Kennedy et al. 2007; Furby et al. 2008; Zhuravleva et al. 2013). These approaches have many
advantages, as they are not so dependent on the conditions at the time the individual images were collected. Use

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Volume 4: Agriculture, Forestry and Other Land Useof a time-series of images can help avoid some kinds of errors in the monitoring of forest change (GFOI 2016).
For example, classification of time-series data can help make the distinction between permanent land-use change
and temporary loss of forest due to harvesting.
Change detection using two images has some advantages but also has some limitations (Jensen 2016). Direct
mapping of change categories has important benefits. The Australia’s Commonwealth Scientific and Industrial
Research Organization (CSIRO) National Inventory System – Land Cover Change Project (NIS-LCCP) provides
an example of how change can be confirmed from time-series information (Shimabukuro et al. 1998; Caccetta etal. 2007; Potapov et al. 2012; Hansen et al. 2013).
Emerging remote sensing-derived land surface phenology (Morisette et al. 2009) represents a future opportunity
for innovation in national inventories. Land surface phenology not only supports the extraction of land coverclasses (e.g., Zhong et al. 2012), but offers valuable information on homogeneous landscape units (e.g., Bunker et
al. 2016). Areas with unique forest and agricultural cycles characterized by both natural and anthropogenic
influence may be difficult to ascertain with only a few representative images from a time-series. For example, evenrelatively coarse spatial resolution homogenous landscape units extracted from a relatively dense time-series (e.g.,
from bi-monthly MODIS-derived vegetation index) may support adaptive land-use extraction methodologies (e.g.,based on finer spatial resolution Landsat-derived time-series) within entire countries or regions.
Analysis of dense time-series remote sensing data can help in identifying forest disturbance events such as extent,
type and year of disturbance, status of pre and post-disturbance land cover, disturbance intensity and rates ofrecovery (White et al. 2017).

Evaluation of mapping accuracy
Whenever a map of land cover or land-use is being used, inventory compilers should acquire information about
the reliability of the map. When such maps are generated from classification of remote sensing data, it should berecognised that the reliability of the map is likely to vary between the different land categories. Some categories
may be uniquely distinguished while others may be confounded with others. For example, coniferous forest isoften more accurately classified than deciduous forest because its reflectance characteristics are more distinct,
while deciduous forest may easily be confounded with, for example, Grassland or Cropland. Similarly, it is often
difficult to ascertain changes in land management practices through remote sensing. For example, it may bedifficult to detect a change from intensive to reduced tillage on a specific land area.
Inventory compilers should estimate the accuracy of land-use/land cover maps on a category-by-category basis. A
number of sample points on the map and their corresponding real-world categories are used to create a confusion
matrix (see footnote 5 in Annex 3A.4) with the diagonal showing the proportion of correct identification and theoff-diagonal elements showing the relative proportion of misclassification of a land category into one of the otherpossible categories. The confusion matrix expresses not only the accuracy of the map but it is also possible toassess which categories are easily confounded with each other. Based on the confusion matrix, a number ofaccuracy indices can be derived (Congalton, 1991). Multi-temporal analysis (analysis of images taken at different
times to determine the stability of land-use classification) can also be used to improve classification accuracy,
particularly in cases where ground truth data are limited.

GROUND-BASED SURVEYS
Ground-based surveys may be used to gather and record information on land-use, and for use as independent
ground-truth data for remote sensing classification. Prior to the advent of remote sensing techniques such as aerial
photography and satellite imagery, ground-based surveys were the only means of generating maps. The process isessentially one of visiting the area under study and recording visible and/or other physical attributes of thelandscape for mapping purposes. Digitisation of boundaries and symbolising attributes are used to make hard copy
field notes and historical maps useful in Geographical Information Systems (GIS). This is done via protocols on
minimum land area delineation and attribute categorization that are linked to the scale of the resultant map and its
intended use.
Very precise measurements of area and location can be made using a combination of survey equipment such as
theodolites, tape measures, distance wheels and electronic distance measuring devices. Development of satellite
navigation systems means that location information can be recorded in the field directly into electronic format
using portable computer devices. Data are downloaded to an office computer for registration and coordination withother layers of information for spatial analysis.
Landowner interviews and questionnaires are used to collect socio-economic and land management information
but may also provide data on land-use and land-use conversion. With this census type, the data collection agency
depends on the knowledge and records of landowners (or users) to provide reliable data. Typically, the resident isvisited and interviewed by a representative of the collection agency and data are recorded in a predetermined
format, or a questionnaire is issued to the land-user for completion. The respondent is usually encouraged to use

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Chapter 3: Consistent Representation of Lands

any relevant records or maps they may have, but questions may also be used to elicit information directly (Swanson
et al. 1997).
Census surveys are probably the oldest form of data collection methods (Darby, 1970). Land-user surveys can be
conducted on the entire population or a sample of suitable size. Modern applications employ a full range ofvalidation and accuracy assessment techniques. The survey may be undertaken through personal visits, telephoneinterviews (often with computer-assisted prompts) or mail-out questionnaires. Land-user surveys start with theformulation of data and information needs into a series of simple and clear questions soliciting concise andunequivocal responses. The questions are tested on a sample of the population in order to ensure that they are
understandable and to identify any local technical terminology variations. For sample applications, the entire study
area is spatially stratified by appropriate ecological and/or administrative land units, and by significant categoricaldifferences within the population (e.g., private versus corporate, large versus small, pulp versus lumber, etc.). For
responses dealing with land areas and management practices, some geographic location, whether precise
coordinates, cadastral description or at least ecological or administrative units should be required of the respondent.
Post-survey validation of results is conducted by searching for statistical anomalies, comparing with independent
data sources, conducting a sample of follow-up verification questionnaires or conducting a sample of on-siteverification surveys. Finally, presentation of results must follow the initial stratification parameters.

Annex 3A.3

Sampling

No refinement.

Annex 3A.4

Overview of potential methods for developing
Approach 3 datasets

No refinement.

Annex 3A.5

Default climate and soil classifications

Climate regions are classified in order to apply emission and stock change factors for estimating biomass, deadorganic matter and soil C stock changes. The default climate classification, provided in Figure 3A.5.1 (updated),
has been derived using the classification scheme shown in Figure 3A.5.2 based on the gridded Climate Research
Unit (CRU) Time Series (TS) monthly climate data for the period from 1985 to 2015 following the methodology
described by Harris et al. 2014. This classification should be used for Tier 1 methods because the default emission
and stock change factors were derived using this scheme. Note that climate regions are further subdivided into
ecological zones to apply the Tier 1 method for estimating biomass C stock changes (see Table 4.1, Chapter 4).
Inventory compilers have the option of developing a country-specific climate classification based on local climate
data, updated annually, if using Tier 2 and 3 methods, along with country-specific emission and stock change
factors. It is good practice to apply the same classification, either default or country-specific, across all land-use
types. Thus, stock change and emission factors are assigned to each pool in a national inventory using a uniform
classification of climate.
Soils are classified in order to apply reference C stocks and stock change factors for estimation of soil C stockchanges, as well as the soil N2O emissions (i.e., organic soils must be classified to estimate N2O emissions
following drainage). Organic soils are found in wetlands or have been drained and converted to other land-use
types (e.g., Forest Land, Cropland, Grassland, Settlements). Soils having organic material (Histosols) are definedas (WRB, 2015):
1. Starting at the soil surface and having a thickness of ≥ 10 cm and directly overlying:a. Ice, orb. Continuous rock or technic hard material, orc. Coarse fragments, the interstices of which are filled with organic material; or
2. Starting ≤ 40 cm from the soil surface and having within ≤ 100 cm of the soil surface a combined thicknessof either:a. ≥ 60 cm, if ≥ 75% (by volume) of the material consists of moss fibres; orb. ≥ 40 cm in other materials
All other types of soils are classified as mineral. A default mineral soil classification is provided in Figure 3A.5.3
for categorizing soil types based on the USDA taxonomy (USDA, 1999) and Figure 3A.5.4 for the World
Reference Base for Soil Resources Classification (FAO, 1998) (Note: Both classifications produce the samedefault IPCC soil types). The default mineral soil classification should be used with Tier 1 methods because default

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Volume 4: Agriculture, Forestry and Other Land Usereference C stock and stock change factors were derived according to these soil types. Inventory compilers havethe option of developing a country-specific classification for mineral and/or organic if applying Tiers 2 and 3
methods, in combination with developing country-specific reference C stocks and stock change factors (oremission factors in the case of organic soils). It is good practice to use the same classification of soils across allland-use types.

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Figure 3A.5.1 (Updated)

Delineation of major climate zones, updated from the 2006 IPCC Guidelines.

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 3A.5.2

Classification scheme for default climate regions. The classification isbased on elevation, mean annual temperature (MAT), mean annual
precipitation (MAP), mean annual precipitation to potential
evapotransporation ratio (MAP:PET), and frost occurrence.

MAT>18°C
and ≤ 7 days offrost/year

Start

MAT > 10°C?

No

Yes

TropicalMontane

Yes

Yes
Warm
Temperate
Moist

Elevation>1000m?

Yes

MAP : PET
>1?
No
No

No

Tropical Wet

Yes

Warm
Temperate
Dry

MAP>
2000mm?

No

Tropical Moist

Yes

MAT
>0°C?

MAP ≤
2000 mm and>1000mm?
No

Yes
No

MAP : PET
>1?

Tropical Dry

Yes

Cool
Temperate
Moist

No
Cool
Temperate
Dry

Polar Moist

Yes

Yes
MAP : PET
>1?

3.48

Boreal Moist

Yes

Each Mean Monthly
Temperature<10°C?

No

MAP : PET
>1?

No

No

Polar Dry

Boreal Dry

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Chapter 3: Consistent Representation of Lands

Figure 3A.5.3

Classification scheme for mineral soil types based on USDA taxonomy

Greaterthan 70% sandand less than 8%
clay?

Start

Yes

Sandy Soils

Yes

Wetland Soils

Yes

Volcanic
Soils

Yes

Spodic Soils

No

Aquic
Soil?

No

Andisols?

No

Spodisols?

No

Low Activity
Clay Soils

No

Mollisols,
Vertisols, High-basestatus, Alfisols, Aridisols,
Inceptisols?

YesHigh Activity
Clay Soils

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Figure 3A.5.4

Classification scheme for mineral soil types based on World Reference
Base for Soil Resources (WRB) classification.
Greaterthan 70% sandand less than 8%
clay?

Start

Yes

Sandy Soils

No

Gleysols?

Yes

Wetland Soils

Yes

Volcanic Soils

Yes

Spodic Soils

No

Andosols?

No

Podzols?

No

Low Activity
Clay Soils

3.50

No

Leptosols,
Vertisols, Kastanozems,
Chernozems, Phaeozems, Luvisols,
Alisols, Albeluvisols, Solonetz,
Calcisols, Gypsisols,
Umbrisols, Cambisols,
Regosols?

YesHigh Activity
Clay Soils

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Annex 3A.6

Example process for allocating lands to IPCC
land-use classes using Approach 3 wall-to-wall
methods.

Figure 3A.6.1 shows a decision tree for allocating lands to the IPCC land-use categories when using Approach 3
wall-to-wall methods (i.e., where every land unit is assumed to have information on land cover over time). Thismethod may also be applicable for some sample-based methods. The process is applied to each area of land (e.g.,
per pixel or vector unit) for each year of the inventory. The process uses three key types of information: land coverand cover change, auxiliary data and reporting rules. This approach is highly flexible and allows for numerous
iterations depending on country circumstance.
Land cover and cover change data are typically obtained from mapping such as from remote sensing (see Appendix
3Ap.2.4). Auxiliary information comprises maps or other spatial and/or non-spatial information (proxies) thatprovide context to guide assessment of land use from the land cover data. Spatial auxiliary information typically
includes maps of management or political boundaries (such as forest management areas or settlements),
geophysical conditions (e.g., soils, climate) and disturbances (e.g., fires, harvesting). Spatial auxiliary data canalso include analysis of the land cover time-series, looking forward and backward from the current years’ data to
determine, for example, if the cover change is temporary or part of another land use type. Non-spatial auxiliary
data such as management practices by region can also provide valuable context. Finally, reporting rules are used
to assign each unit to an IPCC land-use category, including the subcategories of land-use 'remaining' land-use andland-use 'converted to' land-use. These rules include the temporary land cover change period (i.e., the length oftime a new land cover type remains in place before the land is considered to have changed land-use). These periods
may change for each land use category or sub-category based on country circumstances.
The decision tree can be applied at each year of the inventory. The following clarifying text related to two key
decision points will assist in its application:
1. For the first year of data (not the first reporting year), the process assigns each land unit into an IPCC land-use
category. All lands are placed into the ‘remaining’ subcategory as there is no data on conversions prior to thefirst year of analysis.
Although not represented in this decision tree, where the first year of data is also the first reporting year it may benecessary to assign some lands to conversion categories using other auxiliary information. For example:• The cover is identified as grass, but auxiliary maps show the land is a park within a residential area. In thiscase the land may assigned to Settlements.
• The land is identified as grass, but the auxiliary map shows the land is within a forest management area andall the future cover data shows the cover as forest. In this case it is possible to assume that the cover is partof a harvest cycle and the land can be assigned to Forest Land.
2. After the initial year, the cover and auxiliary data are analysed annually (even if the auxiliary data is not updated
annually). The process is similar to the first step but includes additional analyses to ensure the lands are placedin the correct remaining or conversion sub-categories. There are two main processes for analysing land use andland use change depending on the cover change.
Land cover does not change.
• Where the cover and auxiliary data do not change, the land remains in the current remaining category.
• Where cover does not change, but auxiliary data does (for example, grass cover remains, but the urban
expansion means that the land is now classed as Settlements), the land is placed into the appropriate
converted to or remaining sub-category depending on the country specific reporting rules
Land cover does change.
• Where cover changes and the auxiliary data suggest a land use change, analyse the time-series of cover data
and apply the appropriate reporting rules to allocate the land to the appropriate converted to or remaining
sub-category.
• Where the cover data changes but the auxiliary data suggests this is not a land use change (e.g., forest
harvesting), analyse the time-series of cover data, apply the temporary destocking reporting rules andallocate the land to the appropriate converted to or remaining sub-category.
Both national data and global datasets can be used to derive IPCC land-use categories from land cover information.

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To accurately report the area of land-use change categories in the first year of the time-series of a GHG inventory
requires estimates of areas of land-use changes that occurred before the initial reporting year. Since the area to bereported in a land-use change category is the cumulative area of conversions occurred in the period Y-X, where Y
is the reporting year and X is the transition period length, in years, it is good practice to report a land-use
conversion in an appropriate conversion category. The default length of X is 20 years but may vary depending on
country circumstances.
Figure 3A.6.1 (New)

Decision tree for classifying land-use and land-use change through timein Approach 3

Start

Assign cover types to possible
land uses, identify auxiliary dataand set reporting rules

Is this the first yearof cover data?

No

Does
the auxiliary data
allocated the coverto an alternate
IPCC class?

YesClassify as landremaining land
based on theauxiliary data

Has the cover typechange since theinitial timestep?

Yes

Classify as landremaining land
based on the current
cover

No

Does
the change incover type andauxiliary data result inland usechange?

Yes

No

Yes

Does the auxiliary
data show a change
in Land use?

Determine new land
use using correctcover and auxiliary
information

Yes

No

Classify as land
converted to land forthis inventory year

No

Does
the new covertype remain for longer
than the temporarychangeperiod?

Yes

Has
the land-use
remained the samefor longer than the‘land remaining’
period?

No

YesClassify as landremaining land for thisinventory year

No

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References
REFERENCES FROM THE 2006 IPCC GUIDELINES
Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote
Sensing of Environment37(1), pp. 35-46.
Darby, H.C. (1970). Doomsday Book – The first land utilization survey. The Geographical Magazine42(6), pp.
416 – 423.
FAO (1995). Planning for Sustainable use of Land Resources: Towards a New Type. Land and Water Bulletin 2,
Food and Agriculture Organisation, Rome Italy, 60 pp.
Scott, C.T. and Kohl, M. (1994). Sampling with partial replacement and stratification. Forest Science40 (1):30-46.
Singh, A. (1989). Digital change detection techniques using remotely sensed data. Int. J. Remote Sensing10(6), pp.
989 – 1003.
Swanson, B.E., Bentz, R.,…and Sofranco, A.J. (Eds.). (1997). Improving agricultural extension. A reference
manual. Food and Agriculture Organization of the United Nations, Rome.
USGS (2001). http://edcdaac.usgs.gov/glcc/

REFERENCES NEWLY CITED IN THE 2019 REFINEMENT
Brack, C., Richards, G. & Waterworth, R. (2006) Integrated and Comprehensive Estimation of Greenhouse Gas
Emissions from Land Systems. Sustainability Science 1(1): 91-106.
Bunker, B. E., Tullis, J. A., Cothren, J. D., Casana, J. & Aly, M. H. (2016) Object-based Dimensionality Reductionin Land Surface Phenology Classification. AIMS Geosciences 2(4): 302-328.
Caccetta, P. A., Furby, S. L., O’Connell, J., Wallace, J. F. & Wu, X. (2007) Continental Monitoring: 34 Years ofLand Cover Change Using Landsat Imagery. In: 32nd International Symposium on Remote Sensing ofEnvironment, pp. 25-29. San José, Costa Rica.
CEOS. (2016) Committee on Earth Observation Satellites [WWW document]. URL http://ceos.org/.
Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B. & Eklundh, L. (2004) A Simple Method for
Reconstructing a High-quality NDVI Time-series Data Set Based on the Savitzky–Golay Filter. Remote
Sensing of Environment 91(3): 332-344.
Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B. & Lambin, E. (2004) Digital Change Detection Methods in
Ecosystem Monitoring: a Review. International Journal of Remote Sensing 25(9): 1565-1596.
Department of the Environment and Energy. (2018) National Inventory Report 2016: The Australian Government
Submission to the United Nations Framework Convention on Climate Change [WWW document]. URL
https://unfccc.int/documents/65705.
Environment and Climate Change Canada. (2018) National Inventory Report 1990-2016: Greenhouse Gas Sources
and Sinks in Canada - Canada’s Submission to the United Nations Framework Convention on Climate Change[WWW document]. URL https://unfccc.int/documents/65715.
FAO. (1995) Global and national soils and terrain digital databases (SOTER) [WWW Document]. URL
https://www.isric.org/explore/soter.
FAO. (2018) SEPAL: System for Earth Observations, Data Access, Processing & Analysis for Land Monitoring[WWW document]. URL https://sepal.io/.
Furby, S. L., Caccetta, P. A., Wu, X. & Chia, J. (2008) Continental Scale Land Cover Change Monitoring inAustralia using Landsat Imagery [WWW document]. In: International Conference on Studying, Modeling and
Sense Making of Planet Earth, p. 8. Mytilene, Lesvos, Greece.
GFOI. (2016) Integration of Remote-sensing and Ground-based Observations for Emissions and Removals ofGreenhouse Gases in Forests: Methods and Guidance from the Global Forest Observations Initiative [WWWDocument]. URL http://www.gfoi.org/reddcompass.
GOFC-GOLD. (2016) A Sourcebook of Methods and Procedures for Monitoring and Reporting Anthropogenic
Greenhouse Gas Emissions and Removals Associated with Deforestation, Gains and Losses of Carbon Stocks
in Forests Remaining Forests, and Forestation. In: GOFC-GOLD Land Cover Project Office - Report version
COP22-1, p. 268. The Netherlands: Wageningen University.

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.53

Volume 4: Agriculture, Forestry and Other Land Use

Hansen, M. C. & Loveland, T. R. (2012) A Review of Large Area Monitoring of Land Cover Change Using Landsat
Data. Remote Sensing of Environment 122(Supplement C): 66-74.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S.
V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O. & Townshend, J. R. G.
(2013) High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342(6160): 850-853.
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. (2014) Updated High-resolution Grids of Monthly Climatic
Observations – the CRU TS3.10 Dataset. International Journal of Climatology 34(3): 623-642.
IPCC. (2014) 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands.
In: ed. T. Hiraishi, Krug, T., Tanabe, K., Srivastava, N., Baasansuren, J., Fukuda, M. and Troxler, T.G. , p. 354.
Switzerland: IPCC.
JAXA. (2010) Science Team Reports Phase 1 (2006-2008). In: The ALOS Kyoto & Carbon Initiative, p. 243.
Tsukuba-shi Ibaraki, Japan: Japan Aerospace Exploration Agency.
JAXA. (2011) Science Team Reports Phase 2 (2009-2011). In: The ALOS Kyoto & Carbon Initiative, p. 253.
Tsukuba-shi Ibaraki, Japan: Japan Aerospace Exploration Agency.
JAXA. (2014) Science Team Reports Phase 3 (2011-2014). In: The ALOS Kyoto & Carbon Initiative, p. 436.
Tsukuba-shi Ibaraki, Japan: Japan Aerospace Exploration Agency.
Jensen, J. R. (2016) Introductory Digital Image Processing: A Remote Sensing Perspecitve. Pearson Education.
Kennedy, R. E., Cohen, W. B. & Schroeder, T. A. (2007) Trajectory-based Change Detection for AutomatedCharacterization of Forest Disturbance Dynamics. Remote Sensing of Environment 110(3): 370-386.
Kurz, W. A. & Apps, M. J. (2006) Developing Canada's National Forest Carbon Monitoring, Accounting andReporting System to Meet the Reporting Requirements of the Kyoto Protocol. Mitigation Adaptation Strategies
for Global Change 11(1): 33-43.
Lambin, E. F. & Strahlers, A. H. (1994) Change-vector Analysis in Multitemporal Space: A Tool to Detect andCategorize Land-cover Change Processes using High Temporal-resolution Satellite Data. Remote Sensing ofEnvironment 48(2): 231-244.
Lewis, L., Oliver, S., Lymburner, L., Evans, B., Wyborn, L., Mueller, N., Raevksi, G., Hooke, J., Woodcock, R.,
Sixsmith, J., Wu, W., Tan, P., Li, F., Killough, B., Minchin, S., Roberts, D., Ayers, D., Bala, B., Dwyer, J.,
Dekker, A., Dhu, T., Hicks, A., Ip, A., Purss, M., Richards, C., Sagar, S., Trenham, C., Wang, P. & Wang, L.
(2017) The Australian Geoscience Data Cube — Foundations and Lessons Learned. Remote Sensing ofEnvironment (202): 276-292.
Mascorro, V., Coops, N. C., Kurz, W. A. & Olguín, M. (2015) Choice of Satellite Imagery and Attribution of
Changes to Disturbance Type Strongly Affects Forest Carbon Balance Estimates. Carbon BalanceManagement: 10-30.
Merchant, J. & Narumalani, S. (2009) Integrating Remote Sensing and Geographic Information Systems. In: The
SAGE Handbook of Remote Sensing, eds. T. A. Warner, D. M. Nellis & G. M. Foody, pp. 257-268. London,
UK: SAGE Publications.
Morisette, J. T., Richardson, A. D., Knapp, A. K., Fisher, J. I., Graham, E. A., Abatzoglou, J., Wilson, B. E.,
Breshears, D. D., Henebry, G. M., Hanes, J. M. & Liang, L. (2009) Tracking the Rhythm of the Seasons in theFace of Global Change: Phenological Research in the 21st Century. Frontiers in Ecology and the Environment
7(5): 253-260.
Ogle, S. M., Buendia, L., Butterbach-Bahl, K., Breidt, F. J., Hartman, M., Yagi, K., Nayamuth, R., Spencer, S.,
Wirth, T. & Smith, P. (2013) Advancing National Greenhouse Gas Inventories for Agriculture in Developing
Countries: Improving Activity Data, Emission Factors and Software Technology. Environmental Research
Letters 8: 015030.
Potapov, P. V., Turubanova, S. A., Hansen, M. C., Adusei, B., Broich, M., Altstatt, A., Mane, L. & Justice, C. O.
(2012) Quantifying Forest Cover Loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM+
Data. Remote Sensing of Environment 122(Supplement C): 106-116.
Richards, J. A. (2013) Remote Sensing Digital Image Analysis: An Introduction. Berlin Heidelberg, Germany:
Springer-Verlag.
Roy, D. P., Ju, J., Kline, K., Scaramuzza, P. L., Kovalskyy, V., Hansen, M., Loveland, T. R., Vermote, E. & Zhang,
C. (2010) Web-enabled Landsat Data (WELD): Landsat ETM+ Composited Mosaics of the Conterminous
United States. Remote Sensing of Environment 114(1): 35-49.

3.54

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Consistent Representation of Lands

Seong, J. C. (2003) Modelling the Accuracy of Image Data Reprojection. International Journal of Remote Sensing
24(11): 2309-2321.
Shimabukuro, Y. E., Batista, G. T., Mello, E. M. K., Moreira, J. C. & Duarte, V. (1998) Using Shade Fraction
Image Segmentation to Evaluate Deforestation in Landsat Thematic Mapper Images of the Amazon Region.
International Journal of Remote Sensing 19(3): 535-541.
Swedish Environmental Protection Agency. (2016) National Inventory Report Sweden 2016: Greenhouse Gas
Emission Inventories 1990-2014 - Submitted Under the United Nations Framework Convention on Climate
Change and the Kyoto Protocol. p. 543. Stockholm, Sweden: Swedish Environmental Protection Agency.
Teillet, P. M. (2015) Overview of Satellite Image Radiometry in the Solar-Reflective Optical Domain. In: RemotelySensed Data Characterization, Classification, and Accuracies, ed. P. S. Thenkabail, pp. 87-108. Boca Raton,
United States: CRC Press.
Thenkabail, P. S. (2015) Remotely Sensed Data Characterization, Classification, and Accuracies. Boca Raton,
United States: CRC Press.
Tullis, J. A., Cothren, J. D., Lanter, D. P., Shi, X., Limp, W. F., Linck, R. F., Young, S. G. & Alsumaiti, T. (2015)
Geoprocessing, Workflows, and Provenance. In: Remotely Sensed Data Characterization, Classification, andAccuracies. Remote Sensing Handbook, ed. P. Thenkabail, pp. 401-421. Boca Raton, United States: CRC Press.
UNEP/FAO. (1993) Report of the UNEP/FAO Expert Meeting on Harmonizing Land Cover and Land-use
Classifications. In: GEMS Report Series, Geneva, Switzerland: UNEP/FAO.
USGS. (2017) Landsat Analysis Ready Data (ARD) [WWW document]. URL https://landsat.usgs.gov/ard.
White, J. C., Wulder, M. A., Hermosilla, T., Coops, N. C. & Hobart, G. W. (2017) A Nationwide Annual
Characterization of 25 Years of Forest Disturbance and Recovery for Canada using Landsat Time Series.
Remote Sensing of Environment 194: 303-321.
Woodcock, C. E., Macomber, S. A., Pax-Lenney, M. & Cohen, W. B. (2001) Monitoring Large Areas for Forest
Change using Landsat: Generalization Across Space, Time and Landsat Sensors. Remote Sensing ofEnvironment 78(1): 194-203.
Zhang, H. K., Roy, D. P., Yan, L., Li, Z., Huang, H., Vermote, E., Skakun, S. & Roger, J.-C. (2018) Characterizationof Sentinel-2A and Landsat-8 Top of Atmosphere, Surface, and Nadir BRDF Adjusted Reflectance and NDVI
Differences. Remote Sensing of Environment 215: 482-494.
Zhong, L., Gong, P. & Biging, G. (2012) Phenology-based Crop Classification Algorithm and its Implications on
Agricultural Water Use Assessments in California’s Central Valley. Photogrammetric Engineering & Remote
Sensing 78(8): 799-813.
Zhu, Z., Wang, S. & Woodcock, C. E. (2015) Improvement and Expansion of the Fmask Algorithm: Cloud, Cloud
Shadow, and Snow Detection for Landsats 4–7, 8, and Sentinel 2 Images. Remote Sensing of Environment 159:
269-277.
Zhuravleva, I., Turubanova, S., Potapov, P., Hansen, M., Tyukavina, A., Minnemeyer, S., Laporte, N., Goetz, S.,
Verbelen, F. & Thies, C. (2013) Satellite-based Primary Forest Degradation Assessment in the Democratic
Republic of the Congo, 2000–2010. Environmental Research Letters 8(2): 024034.

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.55