2BASIS FOR CONSISTENTREPRESENTATION OF LAND AREAS

IPCC Good Practice Guidance for LULUCF

Chapter 2: Basis for Consistent Representation of Land Areas

AUTHORS AND REVIEW EDITORS
Co-ordinating Lead Authors
Ronnie Milne (UK) and Bubu Pateh Jallow (The Gambia)

Lead Authors
Dominique Arrouays (France), Peter Beets (New Zealand), Paul Drichi (Uganda), Ismail Bin Harun (Malaysia),
James Hrubovcak (USA), Ted Huffman (Canada), William Irving (USA), Michael Koehl (Germany), Erda Lin(China), Lennart Olsson (Sweden), Jim Penman (UK), Ryosuke Shibasaki (Japan), Brian Turner (Australia),
Julio C.Vargas (Ecuador) and Ernesto F. Viglizzo (Argentina).

Contributing Author
Ralph Alig (USA)

Review Editors
Mike Apps (Canada) and Jose Domingo Miguez (Brazil)

2.2

IPCC Good Practice Guidance for LULUCF

Contents

Contents
2.1

INTRODUCTION

2.5

2.2

LAND-USE CATEGORIES

2.5

2.3

REPRESENTING LAND AREAS

2.7

2.3.1

Introduction ..................................................................................................................................... 2.7

2.3.2

Three Approaches ........................................................................................................................... 2.7

2.3.2.1 Approach 1: Basic land-use data ............................................................................................... 2.7
2.3.2.2 Approach 2: Survey of land use and land-use change .............................................................. 2.9
2.3.2.3 Approach 3: Geographically explicit land use data ................................................................. 2.12
2.3.3

Using the Approaches ................................................................................................................... 2.14

2.3.4

Uncertainties Associated with the Approaches ............................................................................. 2.17

2.4

DEVELOPMENT OF LAND-USE DATABASES

2.18

2.4.1

Use of Data Prepared for Other Purposes ..................................................................................... 2.18

2.4.2

Collection of New Data by Sampling Methods............................................................................. 2.19

2.4.3

Collection of New Data in Complete Inventories ......................................................................... 2.19

2.4.4

Tools for Data Collection.............................................................................................................. 2.19

2.4.4.1 Remote sensing (RS) techniques ............................................................................................. 2.19
2.4.4.2 Ground-based surveys ............................................................................................................. 2.21
Annex 2A.1

Examples of Approaches in Individual Countries

2.23

Annex 2A.2

Examples of International Land-Cover Datasets

2.27

References

2.29

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Chapter 2: Basis for Consistent Representation of Land Areas

FiguresFigure 2.3.1

Overview of Approach 3: Direct and repeated assessments of land use from full
spatial coverage. ............................................................................................................ 2.12

Figure 2.3.2

Decision tree for use of existing data in the land area approaches ................................ 2.15

Figure 2.3.3

Decision tree for choosing land area approach for countries with no existing data…….2.16

Figure 2A.1.1 Stages in preparation of New Zealand Land-Cover Databases ..................................... 2.25

TablesTable 2.3.1

Example of Approach 1: Available Land-Use data with complete territorial coverage .... 2.8

Table 2.3.2

Illustrative example of sub-division of data for Approach 1 .......................................... 2.9

Table 2.3.3

Illustrative example of tabulating all transitions for Approach 2 including
nationally defined sub-categories ..............................................................................

2.10

Table 2.3.4

Illustrative example of Approach 2 data in a LUC matrix with category subdivisions. 2.11

Table 2.3.5

Simplified land-use change matrix for example Approach 2 ........................................ 2.11

Table 2.3.6

Summary of uncertainties under Approaches 1 to 3...................................................... 2.17

Table 2A.1.1 Land Use and Land-Use change matrix for USA .......................................................... 2.23
Table 2A.1.2 Land-Use change matrix for Scotland between 1984 and 1990 .................................... 2.24

2.4

IPCC Good Practice Guidance for LULUCF

Land-Use Categories

2.1

INTRODUCTION

Information about land area is needed to estimate carbon stocks and emissions and removals of greenhouse gases
associated with Land Use, Land-Use Change and Forestry (LULUCF) activities. This chapter seeks to provide
guidance on the selection of suitable methods for identifying and representing land areas as consistently as
possible in inventory calculations.
In practice, countries use methods including annual census, periodic surveys and remote sensing to obtain area
data. Starting from this position, Chapter 2 provides good practice guidance on three approaches for representing
land area. The approaches are intended to provide the area data specified in Chapters 3 and 4 for estimating andreporting greenhouse gas inventories for different categories of land. The approaches are also intended to makethe best use of available data and models, and to reduce, as far as practicable, possible overlaps and omissions inreporting land areas. The approaches described here should minimize the chance that some areas of land appear
under more than one activity whilst others are overlooked. The approaches and guidance presented here allow
informed decisions on these matters to be made by those preparing greenhouse gas inventories but are notintended to be definitive or exhaustive. Good practice approaches for representing areas should have thefollowing general characteristics:•

Firstly, the approaches should be adequate, i.e., capable of representing carbon stock changes andgreenhouse gas emissions and removals and the relations between these and land use and land-use changes.

•

Secondly, they should be consistent, i.e., capable of representing management and land-use change
consistently over time, without being unduly affected either by artificial discontinuities in time series data or
by effects due to interference of sampling data with rotational or cyclical patterns of land use (e.g., theharvest-regrowth cycle in forestry, or managed cycles of tillage intensity in cropland).

•Thirdly, the approaches should be complete, which means that all land area within a country should be
included, with increases in some areas balanced by decreases in others where this occurs in reality, andshould recognise subsets of land used for estimation and reporting according to definitions agreed in the
Marrakesh Accords for Parties to the Kyoto Protocol.

•

Finally, the approaches should be transparent, i.e., data sources, definitions, methodologies and assumptions
should be clearly described.

2.2

LAND-USE CATEGORIES

Six broad categories1 of land are described in this section. These may be considered as top-level categories forrepresenting land areas within a country. The categories are consistent with the IPCC Guidelines and therequirements of Articles 3.3 and 3.4 of the Kyoto Protocol, and may be further subdivided as described in
Chapters 3 and 4 of this report. The categories are broad enough to classify all land areas in most countries andto accommodate differences in national classification systems. These national classification systems should be
used consistently over time. The categories are intended for use in conjunction with the approaches described insubsequent sections of this chapter to facilitate consistent estimation of land use over time. This does not imply
that carbon stock changes or greenhouse gas emissions and removals need be estimated or reported for areas
where this is not required by the IPCC Guidelines or for some countries, the Marrakesh Accords2.
It is recognized that the names of these land categories are a mixture of land cover (e.g., Forest land, Grassland,
Wetlands) and land use (e.g., Cropland, Settlements) classes. For convenience, they are here referred to as landuse categories. These particular categories have been selected because they are:•Reasonably consistent with the IPCC Guidelines;•

Robust as a basis for carbon estimation;•

Reasonably mappable by remote sensing methods; and1

The basic categories are generally consistent with on-going work on harmonizing forest-related definitions by Food andAgriculture Organisation (FAO), IPCC, International Union of Forestry Research Organizations (IUFRO) and Centre forInternational Forestry Research (CIFOR) (FAO 2002), with definitions for forestry and other land use types by the UnitedStates Geological Survey (USGS (2001)), FAO (1986, 1995) described by IPCC (2000), and with the definitions adopted
for land use under the Kyoto Protocol and Marrakesh Accords (FCCC/CP/2001/13/Add.1, p58).

2 Carbon stock changes and greenhouse gas emissions on unmanaged land are not reported under the IPCC Guidelines,
although reporting is required when unmanaged land is subject to land use conversion.

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Chapter 2: Basis for Consistent Representation of Land Areas•

Complete in that all land areas should be represented in one or another category.

Care will be needed in inferring land use from these categories. For example, in some countries significant areasof the forest land category may be grazed, and firewood may be collected from scattered trees in the grasslandcategory lands. These areas with different use may be significant enough for countries to consider themseparately in which case it is good practice to make these additional classes subcategories of the suggested highlevel categories and to ensure that all land is accounted for.
Countries will use their own definitions of these categories, which may, of course, refer to internationallyaccepted definitions, such as those by FAO, Ramsar, etc. For that reason no definitions are given here beyond
broad descriptions. Managed land may be distinguished from that unmanaged by fulfilling not only the
production but also ecological and social functions. The detailed definitions and the national approach todistinguishing between unmanaged and managed land should be described in a transparent manner.
The top-level land categories for greenhouse gas (GHG) inventory reporting are:(i) Forest landThis category includes all land with woody vegetation consistent with thresholds used to define forest land in thenational GHG inventory, sub-divided into managed and unmanaged, and also by ecosystem type as specified in
the IPCC Guidelines3. It also includes systems with vegetation that currently fall below, but are expected to
exceed, the threshold of the forest land category.
(ii) CroplandThis category includes arable and tillage land, and agro-forestry systems where vegetation falls below thethresholds used for the forest land category, consistent with the selection of national definitions.
(iii) Grassland
This category includes rangelands and pasture land that is not considered as cropland. It also includes systems
with vegetation that fall below the threshold used in the forest land category and are not expected to exceed,
without human intervention, the threshold used in the forest land category. The category also includes all
grassland from wild lands to recreational areas as well as agricultural and silvi-pastural systems, subdivided into
managed and unmanaged consistent with national definitions.
(iv) Wetlands
This category includes land that is covered or saturated by water for all or part of the year (e.g., peatland) andthat does not fall into the forest land, cropland, grassland or settlements categories. The category can be
subdivided into managed and unmanaged according to national definitions. It includes reservoirs as a managed
sub-division and natural rivers and lakes as unmanaged sub-divisions.
(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 the selection ofnational definitions.
(vi) Other land 4
This category includes bare soil, rock, ice, and all unmanaged land areas that do not fall into any of the other fivecategories. It allows the total of identified land areas to match the national area, where data are available.
When applying these categories, inventory agencies should classify land under only one category to prevent
double counting. If a country's land classification system does not match categories (i) to (vi) as described above,it is good practice to combine or disaggregate the existing land classes of this system of land-use classification inorder to use the categories presented here, and to report on the procedure adopted. It is also good practice tospecify national definitions for all categories used in the inventory and report any threshold or parameter values
used in the definitions. Where national land classification systems are being changed or developed for the firsttime, it is good practice to ensure their compatibility with land-use classes (i) to (vi).
The broad categories listed above provide the framework for the further sub-division by activity, management
regime, climatic zone and ecosystem type as necessary to meet the needs of the methods for assessing carbon
stock changes and greenhouse gas emissions and removals described in Chapter 3 (LUCF Sector Good Practice3

Forest management has particular meaning under the Marrakesh Accords, which may require subdivision of the managed
forest as described in Chapter 4.

4Carbon pools would not need to be assessed for this category, but it is included for checking overall consistency of land area.

2.6

IPCC Good Practice Guidance for LULUCF

Representing Land Areas

Guidance) and Chapter 4 (Supplementary Methods and Good Practice Guidance arising from the Kyoto Protocol)
and allows comparison with IPCC Guidelines categories 5A to 5E. Section 3.1.2 and Table 3.1.1 (Mapping
between the sections of Chapter 5 of the IPCC Guidelines and the sections of Chapter 3 of this report) describe
how to relate the structure of methods described in this report to those of the IPCC Guidelines.

2.3

REPRESENTING LAND AREAS

2.3.1

Introduction

This section describes three approaches for representing land areas using the broad categories defined in theprevious section. They are presented below in order of increasing information content. Approach 1 identifies thetotal area for each individual land-use category, but does not provide detailed information on changes of area
between categories and is not spatially explicit other than at the national or regional level. Approach 2 introduces
tracking of land-use changes between categories. Approach 3 extends Approach 2 by allowing land-use changes
to be tracked on a spatial basis.
The approaches are not presented as hierarchical tiers; they are not mutually exclusive, and the mix ofapproaches selected by an inventory agency should reflect calculation needs and national circumstances. One
approach may be applied uniformly to all areas and land-use categories within a country, or different approaches
may be applied to different regions or categories or in different time intervals. In all cases, it is good practice tocharacterise and account for all relevant land areas in a country. Using good practice in the application of any of
the approaches will increase accuracy and precision in area estimation for inventory purposes. Decision trees toassist in selecting an appropriate approach or mix of approaches are given in Section 2.3.3 (Using the Approaches).
All approaches require collection of data for estimating the historical trends in land use, which are needed for theinventory methods described in the IPCC Guidelines and Chapters 3 and 4 of this report. The amount ofhistorical data required will be based on the amount of time needed for stored carbon to reach equilibrium (often
20 years in the IPCC default methods, but longer for temperate and boreal systems). Where independent data are
available, it is good practice to verify estimates based on interpolation or extrapolation using the methods set out
in Chapter 5, Section 5.7 of this report. All approaches are capable of producing input to uncertainty calculations
discussed in Chapter 5 (Cross-cutting Issues).
A hypothetical example of each approach is provided below along with the description, and real-world examples
are provided in Annex 2A.1.

2.3.2
2.3.2.1

Three Approaches
A PPROACH 1: B ASIC LAND - USE DATA

Approach 1 is probably the most common approach used at present for preparing estimates of emissions andremovals under IPCC Guidelines categories 5A-5E. It uses area datasets likely to have been prepared for otherpurposes such as forestry or agricultural statistics. Frequently, several datasets will be combined to cover all land
classifications and regions of a country. The absence of a unified data system can lead to double counting oromission, since the agencies involved may use different definitions of specific land use for assembling their
databases. This report suggests ways to deal with this. Coverage must obviously be complete enough to include
all land areas affected by the activities set out in Chapter 5 of the IPCC Guidelines, but might not extend tocategories such as unmanaged ecosystems, wetlands or settlements.
When implementing Approach 1, it is good practice to:•Harmonise definitions between the existing independent databases and also with the broad land-use
categories of Section 2.2 (Land-Use Categories) to minimise gaps and overlaps. For example, if woodlandon farms were included both in forestry and agricultural datasets, overlaps might occur. In order toharmonise data, the woodland should be counted only once for greenhouse gas inventory purposes, taking
into account the forest definitions adopted nationally. Information on possible overlaps for the purposes ofharmonisation should be available from agencies responsible for surveys. Harmonisation of definitions does
not mean that agencies should abandon definitions that are of use to them. It is consistent with good practiceto establish the relationship between definitions in use with the aim of eliminating double counting andomissions. This should be done throughout the dataset to maintain time series consistency.

IPCC Good Practice Guidance for LULUCF

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Chapter 2: Basis for Consistent Representation of Land Areas•

Ensure that the land-use categories used can identify all relevant activities. For example, if a country needs
to track a land-use activity such as forest management, then the classification system should be able todistinguish managed from unmanaged forest areas.

•

Ensure that data acquisition methods are reliable, well documented methodologically, timely, at an
appropriate scale, and from reputable sources. Reliability can be achieved by using surveys that can be
related to the harmonised definitions. Ground surveys can be cross-checked where independent data sources
are available and will be needed for checking the accuracy of remote sensing data, where used (See Chapter
5.7-Verification). International datasets are also available for cross-checking (see Annex 2A.2).

•

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 canopy cover and otherthresholds. If changes are identified, it is good practice to correct the data using the back-casting methods
described in Chapter 5 of this report to ensure consistency throughout the time series, and report on actions
taken.

•

Construct uncertainty estimates for those land category areas and changes in area that will be used in theestimation of carbon stock changes, emissions and removals (see Chapter 5 Section 5.3.4.1).

•

Assess whether the sum of the areas in the land classification databases is consistent with the total territorialarea, given the level of data uncertainty. If coverage is complete, then the net sum of all the changes
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 territorial area should, in general, be stable orvary 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, it is good practice to investigate, explain,
and make any corrections necessary. These checks on the total area should take into account the expected
uncertainties in the annual or periodic surveys or censuses involved. Information on expected uncertainties
should be obtained from the agencies responsible for the surveys. Usually there will be remaining
differences between the sum of areas accounted for by the available data and the national area. It is goodpractice to keep track of these differences and to provide an explanation for the likely causes. Carbon stockchanges and emissions and removals of greenhouse gases implied by variation through time of thesedifferences may be due to land-use change and may therefore need to be accounted for in the GHG
inventory as required by the methods set out in Chapters 3 and 4.

Tables 2.3.1 and 2.3.2 show summary land area data for a hypothetical country (total area 140 Mha) using
locally relevant land classifications. Table 2.3.1 is prepared at the level of categories (i) to (vi) and Table 2.3.2
depicts the same information with example subdivisions to estimate the effect of various activities using themethods in Chapter 3. Table 2.3.2 also indicates where in Chapter 3 the inventory methods can be found. It isgood practice to prepare tables similar to Table 2.3.1 or 2.3.2 as part of the quality assurance and quality control(QA/QC) procedures as set out in Chapter 5.

TABLE 2.3.1
EXAMPLE OF APPROACH 1:
AVAILABLE LAND -USE DATA WITH COMPLETE TERRITORIAL COVERAGE
Time 1Land-Use Change
between Time 1 and Time 2

Time 2

F

=

18

F

=

19

Forest

=

+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).

2.8

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Representing Land Areas

TABLE 2.3.2
ILLUSTRATIVE EXAMPLE OF SUB-DIVISION OF DATA FOR APPROACH 1

Land-Use Category
Land-Use Subcategory

Initial
land
area
Mha

Forest land total
Forest land
(Unmanaged)

Final
Net Good practice Guidance
Methods Section
Comment on subdivision by activity
land Change(illustrative only)
area in area Number in Chapter 3of this Report
Mha Mha

18

19

1

5

5

0

Forest land zone A(with deforestation)

7

4

-3

3.2.1/3.4.2/3.6

Forest land zone B

6

6

0

3.2.1

No LUC. Could require subdivision fordifferent management regimes etc.

Afforestation

0

4

4

3.2.2

Could require subdivision e.g. byecosystem type

84

82

-2

Unimproved grassland

65

63

-2

3.4.1/3.2.2/3.6

Fall in area indicates LUC. Could
require subdivision for different
management regimes etc.

Improved grassland

19

19

0

3.4.1

31

29

-2

31

29

-2

Wetlands total

0

0

0

Settlements total

Grassland total

Cropland total
All CroplandNot included in the inventory estimates

3.3.1/3.2.2/3.6

5

8

3

Existing Settlements

5

5

0

3.6

New Settlements

0

3

3

3.6

2

2

0

3.7.1

Other land total
Balancing term

0

0

0

TOTAL

140

140

0

No LUC. Could require subdivision fordifferent management regimes etc.
Fall in area indicates LUC. Could
require subdivision for different
management regimes etc.

Unmanaged - not in inventory
estimates

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 ofassessment. Activities for which location data are not available should be identified by further sub-division of an appropriate Land
Category.

Determination of the area of land-use change in each category is based on the difference in area at two points intime, either with partial or full land area coverage. No specification of inter-category changes is possible under
Approach 1 unless supplementary data are available (which would of course introduce a mix with Approach 2).
The land-use distribution data may come originally from sample survey data, maps or censuses (such as
landowner surveys), but will probably not be spatially explicit 5 in the form used. The sum of all land-use
categories may not equal the total area of the country or region under consideration, and the net result of landuse changes may not equal zero. The final result of this approach is a table of land use at given points in time.

2.3.2.2

A PPROACH 2: S URVEY OF LAND USE AND LAND - USE
CHANGEThe essential feature of Approach 2 is that it provides a national or regional-scale assessment of not only thelosses or gains in the area of specific land categories but what these changes represent (i.e., changes from and toa category). Thus, Approach 2 includes more information on changes between categories. Tracking land-use
changes in this explicit manner will normally require estimation of initial and final land-use categories, as well5

When considering the possibility of adopting Approach 2 or 3, it is useful to investigate with the data collection agencies
whether the original data sources contain spatially explicit data. For example, forest inventories are usually derived fromspatially explicit data sources.

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Chapter 2: Basis for Consistent Representation of Land Areas

as of total area of unchanged land by category. The final result of this approach can be presented as a nonspatially explicit land-use change matrix. The matrix form is a compact format for representing the areas thathave come under different transitions between all possible land-use categories. Existing land-use databases may
have sufficient detail for this approach, or it may be necessary to obtain data through sampling. The input data
may or may not have originally been spatially explicit (i.e., mapped or otherwise geographically referenced).
Sample data will be extrapolated using the ratio to the total relevant area or the total relevant population. Data
will require periodic re-survey of a statistically and spatially valid sample of sites chosen according to theprinciples set out in Section 5.3 (Sampling) of Chapter 5.
Although Approach 2 is more data intensive than Approach 1, it can account for all land-use transitions. Thismeans that emission and removal factors or parameters for rate of change of carbon can be chosen to reflect
differences in the rate of changes in carbon in the opposing directions of transitions between any two categories,
and differences in initial carbon stocks associated with different land uses can be taken into account. For
example, the rate of soil organic carbon loss will commonly be much higher through ploughing than the rate ofre-accumulation if cultivation is subsequently abandoned, and initial carbon stocks may be lower for transitions
from cropland than from pasture.
Good practice points described for Approach 1 also apply to Approach 2, although at a greater level of detail,since the pattern of land-use change is available, not just the net change into or out of each land category or
subcategory.
Approach 2 is illustrated in Table 2.3.3 using the data from the Approach 1 example (Table 2.3.2) by adding
information on all the transitions taking place. Such data can be written in the more compact form of a matrix
and this is presented in Table 2.3.4. To illustrate the added value of Approach 2 and this land-use change matrix
format, the data of Table 2.3.4 is given in Table 2.3.5 without the subdivision of the land-use categories and thiscan be compared with the more limited information from Approach 1 in Table 2.3.1. In Table 2.3.5, the changes
into and out of land categories can be tracked, whereas in Table 2.3.1 only the net changes in a broad category
are detectable. When using Approach 2, it is good practice to prepare a table like Table 2.3.4 or 2.3.5 as part of
QA/QC procedures as set out in Chapter 5.
TABLE 2.3.3
ILLUSTRATIVE EXAMPLE OF TABULATING ALL TRANSITIONS FOR APPROACH 2
INCLUDING NATIONALLY DEFINED SUB-CATEGORIES
Initial Land Use

Final Land Use

Forest land (Unmanaged)

Forest land (Unmanaged)

5Good Practice Guidance Methods
Section No. in Chapter 3 of thisReportExcluded from GHG inventory
3.2.1

Forest land (Managed)

Land Area Mha

Forest land(Managed)

10(Forest zone A Table 2.3.2)

4(Forest zone B Table 2.3.2)

6

Forest land (Managed)

Grassland (Rough grazing)

2

3.4.2

Forest land (Managed)

Settlements

1

3.6

Grassland (Rough grazing)

Grassland (Rough grazing)

56

3.4.1

Grassland (Rough grazing)

Grassland (Improved)

2

3.4.1

Grassland (Rough grazing)

Forest land (Managed)

1

3.2.2

Grassland (Rough grazing)

Settlements

1

3.6

Grassland (Improved)

Grassland (Improved)

22

3.4.1

Grassland (Improved)

Forest land (Managed)

2

3.2.2

CroplandCropland

29

3.3.1

Cropland

Forest land (Managed)

1

3.2.2

CroplandSettlements

1

3.6

Wetlands

Wetlands

0

Settlements

Settlements

5

3.6

Other land

Other land

2

Excluded from GHG inventory

TOTAL

140

Note: Data are subdivided version of those in Table 2.3.2. 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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Representing Land Areas

TABLE 2.3.4
ILLUSTRATIVE EXAMPLE OF APPROACH 2 DATA IN A LUC MATRIX WITH CATEGORY SUBDIVISIONS
Final

Initial Forest land Forest land
(Unmanaged) (Managed)

Forest land
(Unmanaged)
Forest land
(Managed)

Grassland
Grassland
Other(RoughCropland Wetlands Settlements(Improved)
land
grazing)

5

Final area
5

Grassland(Rough grazing)

10

1

2

56

Grassland(Improved)
Cropland

2

1

14
58

2

22

24
29

29
0

Wetlands
1

Settlements

1

0

1

5

Other land

822

Initial area

5

13

60

24

31

0

5

2

140

NET change

0

+1

-2

0

-2

0

+3

0

0

Note: Column and row totals show net changes in land use as presented in Table 2.3.2 but subdivided into national subcategories as in Table 2.3.3.
“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. Netchanges (bottom row) are the final area minus the initial area for each of the (sub) categories shown at the head of the corresponding column. Blank
entry indicates no land-use change for this transition.

TABLE 2.3.5
SIMPLIFIED LAND-USE CHANGE MATRIX FOR EXAMPLE APPROACH 2
Land-Use Change Matrix
Initial
Final

F

G

C
1

F

15

3

G

2

80

C

W

S

O

Final sum
19
82

29

29

W
S

1

1

1

5

18

84

31

5

O
Initial sum

8
2

2

2

140

Note:
F = Forest land, G = Grassland, C = Cropland,
W = Wetlands,
S = Settlements, O = Other land
Numbers represent area units (Mha in this example).
There is no Wetlands in this example. Blank entry indicates no land use change.

Further subcategorisations, for example by forest species or combinations of species and soil type, are likely tobe required by many countries when they implement this Approach, in order to provide data on the land areas
needed for estimating carbon stock changes taking account of the guidance in Chapter 3. Table 2.3.3 illustrates
possible subdivisions, and indicates where in Chapter 3 to find methodological guidance on particular land usesor transitions.

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Chapter 2: Basis for Consistent Representation of Land Areas

2.3.2.3

APPROACH 3: GEOGRAPHICALLY EXPLICIT LAND USE DATA

Approach 3 (summarised in Figure 2.3.1) requires spatially explicit observations of land use and land-use change.
The data may be obtained either by sampling of geographically located points, a complete tally (wall-to-wall
mapping), or a combination of the two.
Approach 3 is comprehensive and relatively simple conceptually but data intensive to implement. The target area
is subdivided into spatial units such as grid cells or polygons appropriate to the scale of land-use variation andthe unit size required for sampling or complete enumeration. The spatial units must be used consistently overtime or bias will be introduced into the sampling. The spatial units should be sampled using pre-existing map
data (usually within a Geographic Information System (GIS)) and/or in the field and the land uses should beobserved or inferred and recorded at the time intervals required by Chapter 3 or 4 methods. If wall-to-wall
mapping is used, a polygon based approach can be used equivalently to a grid approach, see Figure 2.3.1.
Observations may be from remote sensing, site visits, oral interviews, or questionnaires. Sampling units may bepoints, or areas from 0.1 ha to a square kilometre or more, depending on the sample design. Units can be
sampled statistically on a sparser interval than would be used for the complete coverage, chosen at regular or
irregular intervals, and can be concentrated in areas where land-use change is expected. Recorded data could beof land use at a point or within a sampling unit on each occasion but could also include land-use change data
within a sampling unit between the sampling years.
For effective implementation of Approach 3, the sampling needs to be sufficient to allow spatial interpolationand thus production of a map of land use. Sampling methods and associated uncertainties are discussed in thesampling section of Chapter 5 (Section 5.3). All LULUCF activities in each spatial unit or collection of the units
are then tracked over time (periodically but not necessarily annually) and recorded individually, usually within a
GIS. Because Approach 3 is similar to Approach 2, summary Table 2.3.4 or 2.3.5 as described under Approach 2
should be prepared for this approach as part of QA/QC procedures as set out in Chapter 5.

Figure 2.3.1

Overview of Approach 3: Direct and repeated assessments of land use fromfull spatial coverage

Description
Under Approach 3 the country is subdivided into spatial units such as grid cells or small polygons. In thisexample grid cells are used for subdivision of the area. The grid cells are sampled by remote sensing andground survey, in order to establish the areas of the land use whose estimated extent is shown by the greylines below the grid. Remote sensing enables complete coverage of all grid cells (Figure 2.3.1A) in theinterpretation of land use. Ground surveys will be carried out in a sample of grid cells and can be used toestablish land use directly as well as to help interpret remote sensed data. The sample of grid cells can be
distributed regularly (Figure 2.3.1B) or irregularly (Figure 2.3.1C), for example, to give greater coverage
where LUC is more likely. Maps can be prepared using the grid cells, which can also be aggregated into
polygons (Figure 2.3.1D). The final result of the approach is a spatially explicit land-use change matrix.

Time 1Time 2

Figure 2.3.1A

G
G
G
G

2.12

G
G
G
G
G
F

G
G
G
G
F
F
F

G
F
F
F
F
F

F
F
F
F
C
F

F
C
C
C
C

C
C
C
C
C

C
S

C
S

C
C

G
G
G
G

G
G
G
G
G
G

F
F
G
G
G
G
G

F
F
F
F
G
G

F
F
F
F
C
C

C
C
C
C
C

C
C
C
C
C

S
S

S
S

C
C

IPCC Good Practice Guidance for LULUCF

Representing Land Areas

Figure 2.3.1.B

G
G

G
G

G
G

G
F

C

F
G

G

C
F

G
C

C

F
F

C
S

F

F

G
G

C
F

F
F
G

F

G
C

C
C

G
G

S
S

C

C
C

C

C

Figure 2.3.1C

G
G
G

G

G

G
F

G
G
F

G
F
F

F
F

C
F

F
C
C
C
C

C
S

C
S

C

C

G
G

G

G

G
G

F
F
F

G
G
G

F
F

C
G

C
C
C
C

S
S

S
S

C

C

Figure 2.3.1D

F

G
F

C

S
G

F

S S
C

G

Note: F = Forest land, G = Grassland, C = Cropland, W = Wetlands, S = Settlements, O = Other land.

Data, using either a grid or polygons, at a fine scale could directly account for units of land on which
afforestation, reforestation or deforestation has occurred under Article 3.3. Gridded data may be available fromremote sensing and will normally be combined with ancillary mapped data (such as forest inventories or soilmaps) to improve the accuracy of land-use classification. The building of models to relate remote sensing toground truth data is a highly skilled process, and hence is discussed in more detail in Section 2.4.4.1 (Remotesensing techniques).

IPCC Good Practice Guidance for LULUCF

2.13

Chapter 2: Basis for Consistent Representation of Land Areas

When using Approach 3, it is good practice to:•

Use a sampling strategy consistent with the approaches and advice provided in Section 2.4.2 and Section 5.3
of Chapter 5. This strategy should ensure that the data are unbiased and can be scaled up where necessary.
The number and location of the sampling units may need to change over time in order to remain
representative. Advice on time evolution is given in Section 5.3.3 (Sampling design) in Chapter 5.

•

Where remote sensing data are used, develop a method for its interpretation into land categories using
ground reference data as set out in Section 2.4.4.1 (Remote sensing techniques). Conventional forest
inventories or other survey data can be used for this. It is necessary to avoid possible misclassification ofland types – e.g., wetlands may be difficult to distinguish from forest land using remote sensing data alone
thereby requiring ancillary data such as soil type or topography. Hence map accuracy can be established bymeans of ground reference data as outlined in the same section. The conventional technique is to establish amatrix6 showing, for any given classification of land, the probability of misclassification as one of the othercandidate classifications.

•

Construct confidence intervals for those land category areas and changes in area that will be used in theestimation of carbon stock changes, emissions and removals (see Chapter 5 Section 5.3.4.1).

•

Derive summary tables of the national areas under different land-use change (similar to those described forApproach 2 for QA/QC purposes.

2.3.3

Using the Approaches

Figures 2.3.2 and 2.3.3 are decision trees to assist in choosing an appropriate approach or mix of approaches for
identifying land-use areas. All three approaches can, if implemented consistently with the requirements in
Chapters 3 to 5, be used to produce greenhouse gas emission and removal estimates that are consistent with goodpractice. In general, Approach 3 will allow for the spatial representation required as an input to spatially basedcarbon models (described in Chapter 3).
The use of one or more approaches in a country will depend on, amongst other factors, spatial variability, thesize and accessibility of remote areas, the history of biogeographical data collection, the availability of remotesensing staff and resources (outsourced, if necessary) and the availability of spatially explicit carbon data and/or
models. Most countries will have some existing land-use data and the decision tree in Figure 2.3.2 is provided toassist in using this data in ways that meet the guidance in this Chapter. There are three key decisions to be taken:
is spatially explicit data required for Kyoto Protocol reporting, do the data cover the whole country and do they
provide an adequate time series.
For the few countries with no existing data, the decision tree in Figure 2.3.3 is provided to assist in choosing anappropriate approach or mix of approaches. Broadly speaking, good accessibility to all land area and/or limited
remote sensing resources are indicators for greater emphasis on field survey methods to develop land-use
databases. Countries with more difficult access to some locations but with access to good remote sensing data,
should consider Approach 3 with an emphasis on remote sensing. Approach 2 may be more appropriate incountries where the land area is large but resources to handle the extensive high resolution data required byApproach 3 are not available. Countries with poor accessibility and limited remote sensing resources are unlikely
to be able to develop databases suitable for Approach 2 or 3 but should be able to use Approach 1, either from
FAO data (database on land use and land cover) or other internationally available databases (e.g., see Annex
2A.2).
Different Approaches may be more effective over different time periods, or may be required for different
reporting purposes. Chapter 5 provides methods to carry out matching of the time series between the different
periods or uses that are likely to be necessary.

6

Sometimes called the confusion matrix.

2.14

IPCC Good Practice Guidance for LULUCF

Representing Land Areas

Figure 2.3.2

Decision tree for use of existing data in the land area approaches

Identify a primaryexisting land-use
dataset for the country

Is theNo
primary
data set spatially
explicit for alllandareas?

Is
spatially
explicit dataneeded for someland areas?
(cf. Chp.4)

Yes

Yes

No

Primary dataset
acceptable forspatial explicitness

Can ageographically
mixed set ofApproaches(1/2 & 3)
be used?
Yes

Modify primarydataset to use mixed
Approaches (1/2 & 3)
or Approach 3

Does
the (modified)primary dataset cover
the wholecountry?

NoCan
the gaps in thearea coverage be filledusing other existingdata sets?

Yes

Does
the (modified)No
primary dataset provide acomplete time series forthe country?

(Modified) Primary dataset will
be usable for Approach 3
Document the choiceof methods(Approaches)

Obtain and use theadditional spatial
information using
guidance in thischapterCollect new data forthe gaps in area
coverage using
guidance in thischapterCan data
No
for missing years
be supplemented withexisting secondary
data sets?
Yes
Use a mix ofapproaches to build acomplete time series(see Chapter 5 ontime seriesconsistency)

IPCC Good Practice Guidance for LULUCF

Yes

No

Collect spatial
information
where required
using guidancein this chapter

No

Yes

Combine primaryand secondary
datasets using
guidance in thischapterYes

Does
the dataset
have any underlying spatial
information?

Can
new data becollected to fill thegaps in area
coverage?

No

Yes(Modified) Primarydataset acceptablefor coverageNo

Use international
datasets tominimize gaps incoverage anddocument results

CanNo
new data becollected to complete
the time
series?
Yes

Collect new data
for the gaps in
the time series
using guidance inthis chapter

Use techniques
outlined in
Chapter 5 forinterpolation andextrapolation toestimate fluxes
for missing years

2.15

Chapter 2: Basis for Consistent Representation of Land AreasIs
KP reporting
required?
Yes
Implement
Approach 3 forcomplete landarea using
guidance inthis chapter

Willdata be collectedannually?
Yes
Approach 3

No

No

Yes

Are
No
resources available
for Approach 3?

Useinterpolation
methods from
Chapter 5 toestimate fluxes

Approach 2

Yes

Will
data be collectedannually?

Use sampled
images inApproach 2No

Useinterpolation
methods from
Chapter 5 toestimate fluxes

Yes

Use international
datasets

No

Are
resources available
for landownerquestionnaires?

Can
No
Yes
remote sensing data
be obtained?

NoAre
all locations easily
accessible?

Figure 2.3.3 Decision tree for choosing land area approach for countries with no existing data

2.16

Yes

Use landownerquestionnaires tocollect Approach
1 or 2 data

Will
data be collectedannually?

Yes

Approach 1 or 2

No

Use groundsurvey tocollect Approach
1 or 2 data

Useinterpolation
methods from
Chapter 5 toestimate fluxes

IPCC Good Practice Guidance for LULUCF

Representing Land Areas

2.3.4

Uncertainties Associated with the Approaches

Good practice requires uncertainties to be reduced as far as practicable and Chapter 5.2 (Identifying andquantifying uncertainties) sets out methods to quantify them. These methods require area uncertainty estimatesas an input. Although the uncertainty associated with the Approaches 1 to 3 obviously depends on how they are
implemented and on the quality of the data available, it is possible to give an indication of what can be achievedin practice. Table 2.3.6 sets out the sources of uncertainty involved, the basis for reducing uncertainties andindicative levels of uncertainty under conditions that might be encountered in practice.
The sources of uncertainty of area 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 reduction in
uncertainties due to cancellation of errors familiar in statistics. The main difference between Approach 1, andApproaches 2 and 3 is that percentage uncertainties on changes in land area are likely to be greater in Approach
1. This is because in Approach 1 changes in land use are derived from differences in total areas. Under Approach
1, the uncertainty in the difference will be between 1 and 1.4 times the uncertainty in areas being compared,
depending on the degree of correlation between the surveys. Approach 3 produces detailed spatially explicit
information; which may be required e.g., for some modelling approaches, or for reporting Kyoto Protocol
activities. In these cases additional spatial information would be needed if Approach 1 or 2 is being used for landarea identification. Kyoto Protocol requirements are identified in Chapter 4, Section 4.2.2.

TABLE 2.3.6
SUMMARY OF UNCERTAINTIES UNDER APPROACHES 1 TO 3

Approach 1

Sources of uncertainty

Ways to reduce uncertainty

Indicative uncertainty
following checks

Sources of uncertainty may include
some or all of the following,
depending on the nature of thesource of data:•

Check for consistent
relationship with national
area

Order of a few % to order of
10% for total land area in eachcategory.

•

Correct for differences indefinitions•

Consult statisticalagencies on likely
uncertainties involved•

Compare withinternational datasets•

Error in census returns•

Differences in definition
between agencies•

Sampling design•

Interpretation of samples

In addition:
Cross-checks on area changes
between categories cannot be
conducted under Approach 1 andthis will tend to increase
uncertainties.

Greater % uncertainty forchanges in area derived fromsuccessive surveys.

Systematic errors may besignificant when data preparedfor other purposes is used.

Approach 2

As Approach 1 with ability to carryout cross-checks

As above plus consistency
checks between inter-categorychanges within the matrix

Order of a few % to order of
10% for total land area in eachcategory, and greater forchanges in area, since these arederived directly

Approach 3

As Approach 2 plus uncertainties
linked to interpretation of remotesensing data, where used

As Approach 2 plus formal
analysis of uncertainties using
principles set out in Chapter 5

As Approach 2, but areas
involved can be identified
geographically. However, using
Approach 3 the amount of
uncertainty can be determined
more accurately, than forApproach 2.

IPCC Good Practice Guidance for LULUCF

2.17

Chapter 2: Basis for Consistent Representation of Land Areas

2.4 DEVELOPMENT OF LAND-USE DATABASES
There are three broad ways to develop the land-use databases needed for greenhouse gas inventories:•

Use of existing databases prepared for other purposes;•

Use of sampling, and•Use of complete land inventories.

The following subsections provide general good practice advice on the use of these types of data forconsideration by inventory agencies in consultation with other agencies responsible for provision of statistical
data at the national level. Inventory preparers might not be involved in the detailed collection of remote sensing
data or ground survey data, but can use the guidance provided here to help plan inventory improvements andcommunicate with experts in these areas.

2.4.1

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 agencies may use international datasets. Both types of
databases are described below.
Natio nal da ta ba se s
Approaches 1 and 2 will usually be based on existing data, updated annually or periodically. Typical sources ofdata include forest inventories, agricultural census and other surveys, censuses for urban and natural land, land
registry data and maps. Use of this information is illustrated by the examples in Annex 2A.1: Examples ofApproaches in individual countries. Good practice in using data of this type is set out in Section 2.3.2.1.
In t er na t io na l da t a ba se s
Several projects have been undertaken to develop international land-use and land-cover datasets at regional to
global scales (Annex 2A.2 lists some of these datasets). Almost all of these datasets are stored as raster data7
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. Conventional inventories usually provide only the total sum ofland-use area by classes. Spatial distribution can be reconstructed using international land-use and landcover 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
in national data and/or improve the usability of the international data if required for purposes such as
extrapolation.

When using an international dataset, it is good practice to consider the following:•

The classification scheme (e.g., definition of land-use classes and their relations) may differ from that in thenational system. The equivalence between the classification systems used by the country and the systems
described in Section 2.2 (Land-Use Categories) therefore needs to be established by contacting theinternational agency and comparing their definitions with those used nationally.

•

Spatial resolution (typically 1km nominally but sometimes an order of magnitude more in practice) may becoarse, so national data may need aggregating to improve comparability.

•

Classification accuracy and errors in geo-referencing may exist, though several accuracy tests are usuallyconducted at sample sites. The agencies responsible should have details on classification issues and tests
undertaken.

•

As with national data, interpolation or extrapolation will probably be needed to develop estimates for thetime periods to match the dates required for reporting to the UNFCCC or under the Kyoto Protocol.

7

Raster data means information stored on a regular grid of points, as opposed to polygon data, which is information stored as
the coordinates of an outline area sharing a common attribute.

2.18

IPCC Good Practice Guidance for LULUCF

Development of Land-Use Databases

2.4.2

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 give inaccurate
results. It is good practice to apply sampling concepts that are based on sampling set out in Section 3 of Chapter 5, andthus allow for estimation procedures that are consistent and unbiased and result in estimates that are precise.
As discussed in Section 3 of Chapter 5, good practice on sampling usually involves a set of sampling units thatare 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 land-use categories within the inventory area.
Multiplying the proportions by the total area provides estimates of the area of each land-use category. Where thetotal 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 change matrices.
Applying a sample-based approach for area assessment enables the calculation of sampling errors andconfidence intervals that quantify the reliability of the area estimates in each category. It is good practice to use
the confidence interval to verify if observed category area changes are statistically significant and reflect
meaningful changes.

2.4.3

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 (Section 2.3.2.3), thedata will be most easily used in a GIS based on a set of grid cells or polygons supported by ground truth dataneeded to achieve unbiased interpretation. If the resolution of these data is sufficiently fine then they may allow
direct use for Kyoto Protocol reporting of relevant activities. Coarser scale data could be used to build Approach
1 or 2 data for the whole country or appropriate regions.
A complete inventory could also be achieved by surveying all landowners and each would need to provide
suitable data 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 coveragewith no overlaps.

2.4.4
2.4.4.1

Tools for Data Collection
R EMOTE SENSI NG (RS) TECHNIQUES

Remotely sensed data, as discussed here, are those acquired by sensors (optical or radar) on board satellites, orby cameras equipped with optical or infrared films, installed in aircraft. These data are usually classified toprovide estimates of the land cover and its corresponding area, and usually require ground survey data to provide
an estimate of the classification accuracy. Classification can be done either by visual analysis of the imagery or
photographs, or by digital (computer-based) methods. The strengths of remote sensing come from its ability toprovide spatially explicit information and repeated coverage including the possibility of covering large areas aswell as remote areas that are difficult to access otherwise. Archives of past remote sensing data also span several
decades and can therefore be used to reconstruct past time series of land cover and land use. The challenge of
remote sensing is related to the problem of interpretation: the images need to be translated into meaningful
information on land use and land management. Depending on the satellite sensor, the acquisition of data may be
impaired by the presence of atmospheric clouds and haze. Another concern, particularly when comparing dataover long time periods, is that remote sensing systems may change. Remote sensing is mainly useful for
obtaining area estimates of land-cover/use categories and for assisting in the identification of relatively
homogeneous areas that can guide the selection of sampling schemes and the number of samples to be collected.
For additional information on remote sensing and spatial statistics, see Cressie (1993) and Lillesand et al (1999).

IPCC Good Practice Guidance for LULUCF

2.19

Chapter 2: Basis for Consistent Representation of Land Areas

Types of remote sensi ng data
The most important types of RS data are 1) aerial photographs, 2) satellite imagery using visible and/or nearinfrared bands, and 3) satellite or airborne radar imagery (see Table 5.7.2 for features of main remote sensing
platforms). Combinations of different 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 change could include many sensor and data typecombinations at a variety of resolutions.
Important criteria for selecting remote sensing data and products are:•

Adequate land-use classification scheme;•

Appropriate spatial resolution (The smallest spatial unit for assessing land-use changes under the Kyoto
Protocol is 0.05 ha);•Appropriate temporal resolution for estimating of land-use and carbon stock changes;•

Availability of accuracy assessment;•

Transparent methods applied in data acquisition and processing; and•Consistency and availability over time.

1 . Ae r ia l p ho t o g r a p h s
Analysis of aerial photographs 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, RS can show crop species, crop stress, and tree cover in agro-forestry
systems. The smallest spatial unit possible to assess depends on the type of aerial photos used, but for standard
products it is often as small as 1 square metre.
2 . Sa t e ll it e i ma g e s in v i si bl e a nd nea r i nf ra r e d w a v e le ng t h sComplete land use or land cover of large areas (national or regional), if not available otherwise, may befacilitated by the use of satellite images. The possibility exists of obtaining long time series of data from thedesired 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/use categories commonly requires groundreference data from maps or field surveys. The smallest unit to be identified depends on the spatial resolution of
the sensor and the scale of work. The most common sensor systems have a spatial resolution of 20 – 30 metres.
At a spatial resolution of 30 metres, for example, units as small as 1ha can be identified. Data from higher
resolution satellites is also available.
3. Ra dar i mag ery
The most common type of radar data are 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, and acquire
data during night-time. They may therefore be the only reliable source of remote sensing data in many areas of theworld with quasi-permanent cloud cover. By using different parts of the spectrum and different polarisations, SAR
systems may be able to distinguish land-cover categories (e.g., forest/non-forest), or the biomass content of vegetation,although there are at present some limitations at high biomass due to signal saturation.

Ground ref erence dat a
In order to make use of remote sensing data for inventories, and in particular to relate land cover to land use it isgood practice to complement the remotely sensed data with ground reference data (often called ground truthdata). Ground reference data can either be collected independently, or be obtained from forest or agricultural
inventories. Land uses that are rapidly changing over the estimation period or that have vegetation cover known
to be easily misclassified should be more intensively ground-truthed than other areas. This can only be done byusing ground reference data, preferably from actual ground surveys collected independently but high-resolution
photographs may also be useful.

Integration of remote sensing and GIS
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 beaffected by subjective interpretations by different operators.

2.20

IPCC Good Practice Guidance for LULUCF

Development of Land-Use Databases

Full use of remote sensing generally requires integration of the extensive coverage that remote sensing can provide
with ground-based point measurements or map data to represent areas associated with particular land uses in space andtime. This is generally achieved most cost effectively using a geographical information system (GIS).

Land-cover classif ication using remotely sensed data
Classification of land cover using remotely sensed data may be done by visual or digital (computer based)
analysis. Each one presents advantages and disadvantages. Visual analysis of imagery allows for humaninference through the evaluation of overall characteristics of the scene (analysis of the contextual aspects in theimage). 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 as tree diameter, height, basal area, biomass) using the remotely sensed data. In addition, digital analysis
allows for the immediate computation of areas associated with the different land categories. It has developed
rapidly over the past decade, along with the associated technical computer development, making hardware,
software and also the satellite data readily available at low cost in most countries, although capacity to use thesedata and facilities may have to be outsourced, particularly in mapping at national level.

Detection of land use change using RS
Remote sensing can be used to detect locations of change related to LULUCF. Methods for land-use 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 very sensitive to inconsistencies in interpretationand classification of the land 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 bystatistical methods and these differences are used to provide information on land-cover/use changes. Thisapproach 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 remote sensing data.

Evaluation of mapping accuracy
Whenever a map of land cover/use is being used, it is good practice to acquire information about the reliabilityof the map. When such maps are generated from classification of remote sensing data, it should be recognisedthat the reliability of the map is likely to vary between the different land categories. Some categories may beuniquely distinguished while others may easily 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 conventional to conservation tillage on a specific land area.
It is therefore good practice to estimate the accuracy of land-use/land-cover maps on a category-by-categorybasis. A number of sample points on the map and their corresponding real world categories are used to create a
confusion matrix (See Approach 3; Footnote 6) with the diagonal showing the probability of correct
identification and the off-diagonal elements showing the relative probability of misclassification of a land
category into one of the other possible categories. The confusion matrix expresses not only the accuracy of themap but it is also possible to determine which categories are easily confounded with each other. Based on the
confusion matrix, a number of accuracy indices can be derived (Congalton, 1991). It is good practice to present
an estimate of the accuracy of the land-use/cover map category-by-category and a confusion matrix may be
employed for this purpose where remote sensing is used. 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.

2.4.4.2

G ROUND - 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 is essentially one of visiting the area under study and recording visible and/or other physical attributes ofthe landscape for mapping purposes. Digitisation of boundaries and symbolising attributes are used to make hard
copy field notes and historical maps useful in Geographic Information Systems (GIS). This is done via protocols

IPCC Good Practice Guidance for LULUCF

2.21

Chapter 2: Basis for Consistent Representation of Land Areason minimum land area delineation and attribute categorisation 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 Global
Positioning Systems (GPS) 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 andcoordination with other 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 change. With this census approach, the data collection
agency depends on the knowledge and records of landowners (or users) to provide reliable data. Typically, theresident is visited and interviewed by a representative of the collection agency and data are recorded in apredetermined format, or a questionnaire is issued to the land-user for completion. The respondent is usuallyencouraged to use 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
categorical differences 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 requiredof 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-site verification surveys. Finally, presentation of results must follow the initialstratification parameters.

2.22

IPCC Good Practice Guidance for LULUCF

Annex 2A.1

Annex 2A.1 Examples of Approaches in individual countries
2A.1.1

Use of Existing Resource Inventories by USA(Approaches 1, 2 and 3)

In the United States, the National Resources Inventory (NRI) is designed to assess soil, water, and related
environmental resources on non-Federal lands (Nusser and Goebel, 1997; Fuller, 1999)8. The NRI uses datafrom several sources to verify estimates. A Geographic Information System (GIS) for the United States is used tohold the inventory and includes the total surface area, water area, and Federal land. Data from other sources e.g.,soils databases and other inventories such as the Forest Inventory and Analysis (FIA), can be linked to the NRI9.
While sampling techniques for the NRI and FIA are similar, differing objectives require different sampling grids
and make the estimates from the two inventory systems statistically independent. The raw sampled data could,
however, be used as a basis for Approach 3.
The data (See Table 2A.1.1) are sufficient to provide a land-use change matrix (Approach 2) that illustrates
several important land use and land-use change characteristics for the United States. First, comparing the 1997
total to the 1992 total for each of the broad land-use categories depicts the net change in land use. For example,
the amount of cropland declined by 2.1 million hectares from 1992 to1997, falling from 154.7 million hectares to152.6 million hectares, while the amount of non-Federally owned range and forests remained relatively stable.
These aspects of land use could also have been seen from an Approach 1 database. In addition, the total area of
the United States remains fixed from 1992 to 1997 at almost 800 million hectares, and thus any area increases ina one land-use category must be offset by area declines in other categories as could have been provided in an
Approach 2 structure.
However, the data can also describe land-use change dynamics using its Approach 2 structure. The diagonal andoff-diagonal elements in Table 2A.1.1 show how much land has remained in a land category and how much landhas changed use respectively. Comprehensive measures of changes in land use (the off-diagonal elements) canbe extremely important for carbon estimation and reporting. For example, the total amount of non-Federal forest
land remained relatively stable from 1992 to1997, increasing by about 400 000 hectares. However, the land-use
change elements show that 1.9 million hectares of non-Federal forest land were converted to settlements while
2.5 million hectares of pastureland were converted to forest land. Therefore, inferring small changes in carbon
stock based on small changes in overall land use could be incorrect if the individual land-use dynamics (e.g.,
Forest land to settlements and pastureland to forests) are relatively large.
TABLE 2A.1.1
LAND USE AND LAND-USE CHANGE MATRIX FOR USA
Final

Crop

CRP

Pasture

Range
(NF)

Forest
(NF)

Other
Rural

Settlements

Water andFederal

1997
Total

146.8

0.9

3.5

0.8

0.3

0.3

--

--

152.6

CRP

0.8

12.3

--

--

--

--

--

--

13.2

Pasture

3.7

0.3

43.2

0.3

0.8

0.3

--

--

48.6

Range (NF)

0.6

0.1

0.6

162.3

0.5

0.2

--

--

164.4

Forest (NF)

0.8

--

2.5

0.6

160.1

0.6

--

--

164.5

Other Rural

0.7

--

0.4

0.3

0.4

18.9

--

--

20.7

Settlements

1.2

--

0.8

0.5

1.9

0.2

35.2

--

39.8

Water and Federal Land

0.1

--

--

0.1

0.2

--

--

182.6

183.1

1992 Total

154.7

13.8

51.0

165

164.1

20.5

35.2

182.8

787.4

InitialCrop

Note: (i) Data from the 1997 NRI and excludes Alaska. (ii) NF is Non-Federal. Areas are millions of hectares. (iii) CRP represents land
enrolled in the Conservation Reserve Program. (iv) Some row and column totals do not add up due to rounding errors.

8

The NRI is conducted by the U.S. Department of Agriculture’s (USDA) Natural Resources Conservation Service, in
cooperation with the Iowa State University Statistical Laboratory. More information on the NRI is found at:
http://www.nhq.nrcs.usda.gov/technical/NRI/1997/.

9

The FIA is managed by the Research and Development organization within the USDA Forest Service in cooperation with
State and Private Forestry and National Forest Systems. More information on the FIA is found at: http://fia.fs.fed.us/.

IPCC Good Practice Guidance for LULUCF

2.23

Chapter 2: Basis for Consistent Representation of Land Areas

2A.1.2

Use of Agricultural Data for the Argentine Pampas(Approaches 1 and 2)

Since 1881, various national agricultural censuses involving 100% of farms in the Argentine pampas have been
undertaken. Data on land use were organized at the level of political districts in each of the 24 provinces. A
particular study on land-use change in the pampas across one century of agricultural transformation was recently
published (Viglizzo et al., 2001). Later results show that the Argentine pampas behaved as a net source ofgreenhouse gas emitter over much of the period in response to the conversion of natural grasslands into grazing
lands and croplands. However, emissions tend to decline since 1960 due to the adoption of conservation soil
management techniques, mainly reduced- and no-tillage methods - (Bernardos et al., 2001).These data can beused in the implementation of Approach 1 or 2.

2A.1.3Use of Land Registry Data in China (Approach 1)

China uses Approaches 1 and 2 for land-use change data, including forest inventories every 5 years, agricultural
censuses and other surveys. In particular, China is implementing a household contract system for returning
cultivated land to woodland. An individual contract system is being introduced whereby households are assigned
tasks, receive subsidies and own the trees and other vegetation that they plant. The programme aims at planting
about 5 million hectares with trees from year 2000 to 2010. The contracts for this scheme have been used tomake a database of specific land-use changes.

2A.1.4Land-use Matrices in the United Kingdom (Approaches
1, 2 and 3)

In the United Kingdom, land-use change matrices have been constructed from field survey data (Barr et al. 1993,
Haines-Young, 2000). Three surveys have now been completed, in 1984, 1990 and 1998. Each sample was a 1
km square area and 384 of these were used in 1984 to provide a stratified sampling of 32 eco-climatic zones.
These sample squares were revisited in 1990 and 1998 and about another 140 were added for the campaign in1990 and another 50 for 1998 to improve the coverage of the eco-climatic zones. Initially land-use /cover classes
unique to the survey were developed, but in 1998 alternative types common to other agencies in the UK wereused. The saved data for 1984 and 1990 have now been reclassified into the new classes. Each 1 km sample wasvisited by surveyors who, starting from existing 1:10 560 maps, drew outlines of different land cover/use parcels,numbered the parcels and recorded a range of information for each parcel. Subsequently, the maps were digitised
and the area of each parcel calculated from the digital data. When a square was revisited some years later, thedigitised maps, with the older parcel boundaries, became the starting point for recording of changes in theparcels. Thus data were built up, not only of the areas of land-cover/use classes in each sampling year, but of thetransitions occurring between each class. Regional and national estimates of land cover/use and change werethen made by weighted averaging of the samples against the occurrence in the different eco-climatic zones.
LUC matrices for England, Scotland and Wales between 1984 and 1990 were constructed for a simplified set ofland-use categories (Farm, Natural, Urban, Woods, Other) and have been used for estimating emissions andremovals for Category 5D (CO2 emissions and uptake by soils from LUC and management) of the UK
greenhouse gas inventory. An example is shown in Table 2A.1.2.
TABLE 2A.1.2
LAND-USE CHANGE MATRIX FOR SCOTLAND BETWEEN 1984 AND 1990
1990
1984

Farm

Natural

Urban

Woods

Other

1990 Total

Farm
Natural
Urban
Woods
Other
1984 Total

1 967
113
14
9
0
2 103

81
4 779
4
77
0
4 941

6
5
276
1
0
288

6
32
1
981
0
1 020

0
0
0
0
141
141

2 060
4 929
2 95
1 068
141
8 493

Note: Areas are thousands of hectares

The uncertainty in estimating land use and land-use change for regions using this method of sampling has been
described by Barr et al. (1993). If the variation in land use or change across a region is known or can be
estimated by an approximate value then the number of samples needed for a specified level of confidence in theregional total area for that land use or change can be estimated from statistical theory (Cochran, 1977).

2.24

IPCC Good Practice Guidance for LULUCF

Annex 2A.1

2A.1.5The New Zealand Example of Implementation of LandUse/Cover Database from Remote Sensing (Approach 3)

The first New Zealand land-use /Cover Database (NZLCDB) was completed in June 2000 from satellite images
acquired, mainly during the summer of 1996/97. For New Zealand, an appropriate period of time for detecting
significant land-cover changes is considered to be five years. Landsat Enhanced Thematic Mapper Plus (7 ETM+)
is the preferred sensor with in-fill from Système Probatoire d’Observation de la Terre (SPOT) as necessary.
Work commenced in 2001/02 on image acquisition and analyses, which will continue through until 2003/04 to
produce NZLCDB2, following stages outlined below.
The cost of Land-Cover Database 2 (NZLCDB2) is of the order of US$1 500 000 for 270 000 km2 i.e., US$5.6
per km2 and it will provide:•

A complete set of multi-spectral and ortho-corrected satellite imagery covering New Zealand sharpened to
15m spatial resolution;•

A revised NZLCDB1 digital GIS map of land-cover classes with identified classification and generalisation
errors corrected;•

A new NZLCDB2 digital GIS map of land-cover classes compatible with NZLCDB1 "parent classes";•

A digital GIS map recording changes identified in land cover for New Zealand at the 1 ha minimum
mapping unit, and•An accuracy assessment of NZLCDB2 including an error matrix to estimate data quality both spatially andby class.

A fuller description of the New Zealand Land-Cover Database project, which will be updated as the projectprogresses, can be found at http://www.mfe.govt.nz/issues/land/land-cover-dbase/index.html. The stages ofcompletion of the database are shown in Figure 2A.1.1.
Figure 2A.1.1 Stages in preparation of New Zealand Land-Cover Databases

Stage 1
Image Acquisition- Summer 2001 / 02

Stage 7
Completed LCDB
Completed
LCDB22- Compile Metadata- Produce User Manual- Distribute via Database Custodian- Institute Database MaintenanceStage 2
Image Processing- Pan sharpen ETM+ imagery to 15m- Ortho-rectify imagery- Run TopographicNormalisation
Normalisation

Stage 3Spectral Integrity check-Perform
PerformSpectral
SpectralIntegrity
IntegrityCheckCheck
ofofLCDB1LCDB1(1996)(1996)withwith2001
2001/ /02
02
imagery
imagery-Highlight
Highlightpixels
pixelsthat
thatdeviate
deviatefrom
from-assigned
assignedLCDB1
LCDB1class
class

IPCC Good Practice Guidance for LULUCF

Stage 6
Accuracy Assessment- Decide on Sampling Intensity byclass, acquire relevant aerial
photography and ancillary grounddata to complete the work
work.
- Publish Accuracy Assessment

Stage 4LCDB 2 GIS processing
Stage 4a- Correct LCDB1 classificationand generalisationerrors
errorsactualland
land-cover
cover- Confirmactualchanges between
betweenimage
imagedates
dates- Code type of land-coverland cover change--Supply
SupplyLCDB2
LCDB2classification
classificationfor
for
Field
FieldCheck
Check
Stage 4bStage 4b- Edit vectors from Field Check

Stage 5
Field Checking- Verify draft land
land-cover
cover classes- Supply recommended edits todraft classification / additional
ground data to contractors at
Stage 42.25

Chapter 2: Basis for Consistent Representation of Land Areas

2A.1.6The Australian Multi-Temporal Landsat Database for
Carbon Accounting (Approach 3)

The Australian Greenhouse Office (AGO) through its National Carbon Accounting System (NCAS) has
developed a national scale multi-temporal remote sensing programme which is an example of Approach 3, eventhough its primary purpose is to identify areas of land impacted by forest cover change rather than full land-use
mapping. Using Landsat satellite data for twelve national passes between 1972 and 2002, the forest cover statusof land units is monitored over time, at better than a one-hectare resolution. Initially a Year 2000 mosaic ofscenes was constructed for the whole continent (369 scenes) as a base dataset to which other time series wereregistered.
Consistent geographic resolution and spectral calibration of satellite data allows for objective statistical analysis
on a single land unit (pixel) through time. Remote sensing experts experienced in interpreting the Australianvegetation developed the analytical methods (Furby, 2002) that were refined over two rounds of pilot testing(Furby and Woodgate, 2002). The pilot testing was also used to train private sector providers who subsequently
competitively bid for the work.
In addition to the highly prescriptive methodology and performance standards, an independent quality assurance
programme has been implemented to ensure a consistent output standard. A Continuous Improvement andVerification Programme also monitors the quality of results and provides guidance on future improvements.
Because the methodology uses a conditional probability approach, the full time series is readily subjected to any
improvements identified.
The efficiency in processing methods developed for the programme has enabled the addition of new national
passes to the time series at a cost of approximately half a million US dollars.
The forest cover change data is integrated into a carbon/nitrogen cycle process model which is spatially operated
from within a Geographic Information System. In this way, carbon accounting of this sector is readily
accomplished.
Further information can be found in various NCAS Technical Reports available on the AGO Website: http://
www.greenhouse.gov.au/ncas.

2.26

IPCC Good Practice Guidance for LULUCF

Annex 2A.2

ANNEX 2A.2 Examples of international land cover datasets
EXAMPLES OF INTERNATIONAL LAND COVER DATASETS
Dataset nameAARS Global 4-Minute Land Cover

IGBP-DIS Global 1km Land Cover Data
Set

Global Land Cover Dataset

AuthorCenter for Environmental Remote
Sensing, Chiba University

IGBP/DIS

USGS, USA

GLCF (Global Land Cover Facility)

Brief descriptionof contents

This classification is derived fromLand cover classes are identified through Advanced Very High Resolution
clustering NOAA AVHRR monthly data. Radiometer (AVHRR) 1km data andancillary data.

The data set is derived from a flexible
data base structure and seasonal landcover regions concepts

Metrics describing the temporal dynamics
of vegetation were applied to 1984 PAL
data at 8km resolution to derive a global
land-cover classification product using adecision tree classifier.

Classification
schemeOriginal classification scheme is applied.
Compatible with IGBP/DIS classification It consists of 17 classes.
scheme.

A convergence of evidence approach isused to determine the land cover type foreach seasonal land cover class.

The classification was derived by testing
several metrics that describe the temporal
dynamics of vegetation over an annual
cycle.

Data format(vector/raster)

RasterRasterRasterRasterSpatial coverage

GlobalGlobalGlobalGlobalData acquisition
year

1990

1992-1993

April 1992-March 19931987

Spatial resolution
4min x 4min.
or grid size

1km x 1km

1km x 1km

8km x 8km

Revision interval(for time-series
datasets)

Not applicableNot applicableNot applicableNot applicable

Qualitydescription

Ground truth data are compared against
the dataset.

High-resolution satellite imagery used tostatistically validate the dataset.

Sample point accuracy: 59.4% Areaweighted accuracy: 66.9% (Scepan,1999).

No description

Contact address
and referenceURLtateishi@rsirc.cr.chiba-u.ac.jp
http://ceres.cr.chiba-u.ac.jp:8080/usrdir/you/ICHP/index.html

alan.belward@jrc.it
http://www.ngdc.noaa.gov/paleo/igbpdis/frame/coreprojects/index.html

icac@usgs.govhttp://edcdaac.usgs.gov/glc
http://glcf.umiacs.umd.edu/data.html
c/globe_int.html.

IPCC Good Practice Guidance for LULUCF

Global Land Cover Dataset

2.27

Chapter 2: Basis for Consistent Representation of Land Areas

Examples of international land cover datasets (Continued)
Dataset nameAuthor1º Land Cover Map from AVHRRCORINE land cover (CLC) database

Digital Chart of the World

Global Map

Dr. Ruth DeFries University of Maryland
at College Park, USA

European Environmental Agency

ESRI Products

Produced by National Mapping
Organizations, and Compiled by ISCGM.

The data set describes the geographical
It provides a pan-European inventory of
distributions of eleven major cover types biophysical land cover. CORINE land
Brief description
based on inter-annual variations in NDVI. cover is a key database for integratedof contents
environmental assessment.

It is a worldwide base map of coastlines, Digital geographic information in 1 km
boundaries, land cover, etc. Contains more resolution covering the whole land withthan 200 attributes arranged into 17
standardized specifications and available
thematic layers with text annotations forto everyone at marginal cost.
geographical features.

Classification
schemeIt consists of the digital 13 class map

Uses a 44 class nomenclature.

8 Agriculture/ Extraction features and 7
surface cover features.

Refer to http://www.iscgm.org/gmspecifications11.pdf

Data format(vector/raster)

RasterRasterVector Polygons

Raster and Vector

GlobalAustria, Belgium, Bulgaria, Czech
Global coverage
Republic, Denmark, Finland, France,
Germany, Greece, Hungary, Ireland, Italy,
Luxembourg, Netherlands, Poland ,Portugal, Romania, Slovakia, Spain,
United Kingdom, Parts of Morocco andTunisia.

Participating countries (90 in number)

1987

Depends on the country (overall time span Based on ONCs of US Defense Mapping
is around 1985-95)
Agency. Period 1970-80. Refer to theCompilation date layer.

Depends on the participating nations.

1 x 1 degree

250m by 250m grid database which has
been aggregated from the original vector
data at 1:100,000.

1:1,000,000 scale

1km x 1km grids

CLC Update Project of 2000 for updating
it to the 1990's data

Not applicable

Approximately five-year intervals

Spatial coverage

Data acquisitionyear
Spatial resolution
or grid size

Revision interval Not applicable(for time-series
datasets)
No description
Qualitydescription
Contact address landcov@geog.umd.edu
and reference
http://www.geog.umd.edu/landcover/1dURL
map.html

2.28

No specific information available. Refer to Data quality information exists at three
http://dataservice.eea.eu.int/dataservice/oth levels within the database: feature, layer
er/land_cover/lcsource.asp for country
and source.
wise information.

Refer to http://www.iscgm.org/gmspecifications11.pdf.

dataservice@eea.eu.int

sec@iscgm.org

http://dataservice.eea.eu.int/dataservice/me
tadetails.asp?table=landcover and i=1

http://www.esri.com/data/index.html

http://www.iscgm.org/

IPCC Good Practice Guidance for LULUCF

References

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Barr C.J., Bunce R.G.H., Clarke R.T., Furse M.T., Gillespie M.K., Groom G.G., Hallam C.J., Hornung M.,
Howard D.C. and Ness M.J. (1993). Countryside Survey 1990, Main Report. Department of theEnvironment, London.
Bernardos J.N., Viglizzo E.F., Jouvet V., Lértora F.A., Pordomingo S.J., and Aid F.D. (2001). The use of EPIC
model to study the agroecological change during 93 years of farming transformation in the Argentine
pampas. Agricultural Systems, 69: pp. 215-234.
Cochran W.G. (1977). Sampling Techniques. J. Wiley and Sons, New York, 428 p 9.
Congalton R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote
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Cressie N.A.C. (1993). Statistics for Spatial Data. John Wiley and Sons, New York.
Darby H.C. (1970). Doomsday Book – The first land utilization survey. The Geographical Magazine, Vol. 42:
No.6, pp. 416 – 423.
FAO (1986). Programme for the 1990 World Census of Agriculture. FAO Statistical Development Series 2,
Food and Agriculture Organisation, Rome Italy, 90 pp.
FAO (1995). Planning for Sustainable use of Land Resources: Towards a New Approach. Land and Water
Bulletin 2, Food and Agriculture Organisation, Rome Italy, 60 pp.
FAO (2002) Proceedings of Expert Meeting on Harmonizing forest-related definitions for use by various
stakeholders.
FoodandAgriculture
Organisation,
Rome
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Availableat
http://www.fao.org/forestry/fop/fopw/Climate/doc/Y3431E.pdf.
Fuller W.A. (1999). Estimation procedures for the United States National Resources Inventory, 1999.
Proceedings of the Survey Methods Section, Statistical Society of Canada. Available at
http://www.nhq.nrcs.usda.gov/NRI/1997/stat_estimate.htm.
Furby S. (2002). Land Cover Change: Specification for Remote Sensing Analysis. National Carbon Accounting
System Technical Report No. 9, Australian Greenhouse Office, Canberra, Australia (402pp).
Furby S. and Woodgate P. (2002). Remote Sensing Analysis of Land Cover Change – Pilot Testing ofTechniques (Furby and Woodgate ed.) National Carbon Accounting System Technical Report No. 16,
Australian Greenhouse Office, Canberra, Australia (354pp).
Haines-Young R.H. and 23 others (2000). Accounting for nature: assessing habitats in the UK countryside.
Department of the Environment, Transport and the Regions, London. ISBN 1 85112 460 8.
Intergovernmental Panel on Climate Change (IPCC). (2000). Watson R., Noble I.R., Bolin B., Ravindranath
N.H., Verardo D.J. and Dokken D.J. (Eds) Land use, Land-use Change, and Forestry: A Special Report.
Cambridge University Press. Cambridge, UK.
Lillesand T.M. and Kiefer R. W., (1999). Remote Sensing and Image Interpretation, John Wiley and Sons,
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Nusser S. M., and Goebel J.J. (1997). The National Resources Inventory: A Long-Term Multi-Resource
Monitoring Programme. Environmental and Ecological Statistics. Vol. 4, pp. 181-204.
Singh A., (1989). Digital change detection techniques using remotely sensed data. Int. J. Remote Sensing, 10 no.
6: 989 – 1003.
Swanson B.E., R.P. Bentz and A.J., Sofranco (Eds.). (1997). Improving agricultural extension. A reference
manual. Food and Agriculture Organization of the United Nations, Rome.
USGS (2001) http://edcdaac.usgs.gov/glcc/globe_int.html
Viglizzo E.F., Lértora F., Pordomingo S.J., BernardosJ.N., Roberto Z.E. and Del Valle H. (2001). Ecologicallessons and applications from one century of low external-input farming in the pampas of Argentina.
Agriculture, Ecosystems and Environment, 83: 65-81.

IPCC Good Practice Guidance for LULUCF

2.29