Verification

5.7

VERIFICATION

5.7.1

Introduction

The purpose of verifying national greenhouse gas inventories is to establish their reliability and to check theaccuracy of the reported numbers by independent means. Verification can be performed at several levels: project,national and international.
The overall goals of verification are to:•Provide inputs to improve inventories;•

Build confidence on estimates and trends;•

Help to improve scientific understanding.

These goals can be achieved through internal or external inventory checks. Internal verification is generally
performed by inventory agencies, while other bodies (e.g., other government agencies, private companies,
research consortiums, independent scientists, non- governmental organisations) will carry out externalverification.
The Glossary of IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas
Inventories (GPG2000, IPCC 2000) defines verification as shown in Box 5.7.1 (see also Glossary):
BOX 5.7.1
DEFINITION OF VERIFICATION FOR THE INVENTORY

Verification refers to the collection of activities and procedures that can be followed during theplanning and development, or after the completion of an inventory, that can help to establish its
reliability for the intended application of the inventory.
In general, verification as discussed in Annex 2, Verification, of GPG2000 is also relevant to the LULUCF
sector. There are many approaches to verification, including: comparison of the inventory estimates withindependent assessments, procedures and datasets; peer and public review; and direct measurement of emissions
and removals of greenhouse gases. Verification approaches can also include examination of specific aspects of
the inventory, such as underlying data (collection, transcription, and analysis), emission factors, activity data,assumptions and rules used for the calculations (suitability and application of methods, including models), andupscaling procedures. No matter which verification approaches are used or what aspects of the inventory areverified, it is good practice to conduct verification using data and methods that are independent from those used
to prepare the inventory.
To some extent specific approaches for verification are needed for LULUCF sector because of the uniqueness of
estimation methods. Ideally, verification of LULUCF activities would be based on complete accounting ofemissions and removals at the national scale, measured by independent methods at different levels and, possibly,complemented by top-down approaches based on atmospheric measurements. Such verification would becomplex and resource intensive, and will be possibly performed by research consortiums and/or programmes. It
is more likely that inventory agencies would apply some more limited verification approaches or seek to address
their verification needs through already ongoing research activities. The external verification approaches
described in this section may help inventory agencies to evaluate their results.
This section presents a range of verification approaches and provides practical guidance on how to apply them tothe entire national inventory, or parts of it. Section 5.7.2 describes some of the approaches available for verifying
inventory estimates and/or the data on which they are based. Section 5.7.3 provides practical recommendations
for verifying LULUCF Inventories. Section 5.7.4 considers some of the verification issues that are specific to the
Kyoto Protocol 24 . Section 5.7.5 addresses reporting and documentation issues. QA/QC is closely related toverification, and it is covered in Section 5.5 of this chapter. Finally, some details for verification approaches are
given in Section 5.7.6.

24

Verifiability is a requirement under Article 3.3 of the Kyoto Protocol and for Articles 3.3 and 3.4 under paragraph 17 of theAnnex to the draft LULUCF decision agreed in Marrakesh (see FCCC/CP/2001/13/Add.1, page 61).

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5.7.2

Verification Approaches

An inventory agency (or an external group) may decide to verify the entire inventory, a part of it or theunderlying data and models from which the inventory estimates have been calculated. This section describes
approaches that can be used to verify inventory estimates, including some techniques that allow the verificationof the overall inventory, and many that can be used to verify selected elements of an inventory. The criteria for
selecting verification approaches include: scale of interest, costs, desired level of accuracy and precision,
complexity of design and implementation of the verification approaches, and the required level of expertiseneeded to verify. For each approach, a technical description is given with reference to its applicability (e.g., for aparticular category, types of data). Guidance for the application of the approach is also provided, and Table 5.7.1
contains information to assist in identifying the most suitable approaches for particular categories or inputs.
Table 5.7.1 addresses verification approaches for land area classification, major carbon pools and non- CO2 gases,
although it is not exhaustive. The general applicability of the verification approaches for the estimations ofemissions and removals from LULUCF sector for the reporting under the Kyoto Protocol is described in Section
5.7.4.
Generally, the most significant emissions and removals related to LULUCF are of carbon dioxide (CO2).
However, the LULUCF sector also includes non-CO2 greenhouse gases (mainly emissions) from the fertilisationof forests, land clearing, soil preparation for afforestation/reforestation, grasslands and croplands management
and other practices. These non- CO2 greenhouse gases include methane (CH4), nitrous oxide (N2O), carbon
monoxide (CO), oxides of nitrogen (NOx), and non- methane volatile organic compounds (NMVOCs). Emissions
and removals of CO2 can be determined and verified directly as changes in carbon stocks in biomass or soils. For
non- CO2 gases, fluxes can be measured to verify annual emission estimates.
There are many approaches that can be used to verify emission and removal estimates for the LULUCF sector.
An overall verification exercise may include cross-checking of the results at different geographical scales, fromregional to global. Such cross-checking, however, requires considerable time and it is likely to be implemented
over multiple years rather than on single year basis. Compared to fossil fuel emissions, LULUCF activities are
more difficult to assess over short time periods, because biospheric carbon is often difficult to monitor and slow
to equilibrate. The assessment of the net anthropogenic impacts on biospheric carbon would consequently
require a long-term perspective (Nilsson et al. 2001).
Table 5.7.1 summarises the applicability of a range of verification approaches to different aspects of LULUCF
inventory estimation. More detailed descriptions of the approaches are given in the following part in this section.

APPROACH 1: COMPARISON TO OTHER I NFORMATI ON
Comparison of the LULUCF inventory to other independently compiled inventories or data sets can be a usefuland efficient means of verification. Two broad types of verification are possible under this approach:comparison with independent inventories (Approach 1a) or comparison with international programmes anddatasets (Approach 1b).

Approach 1a: Comparison w ith independent i nventories
In some countries, it may be possible to verify the national LULUCF estimates prepared by the inventory agency
with inventories put together by other organisations (i.e., other national, regional/provincial agencies, researchorganisations, etc.). Such external inventories can be used for verification if the same underlying data have notbeen used to produce the reported estimates and if the relationships between sectors and categories in the
different inventories can be assessed. In this respect, it is good practice to ensure that the same dataset has notbeen already used to calculate/estimate some of the reported LULUCF category. When comparing independent
inventories it is also important to take into account the uncertainties in the estimates.
Another effective verification approach is to compare inventory information between countries or groups ofcountries. Such comparison could be made for overall estimates of particular source/sink categories, default
assumptions and/or data used to compile the national inventory. This approach can be quite inexpensive toperform, but care must be taken to ensure that the characteristics of the selected countries are, in fact,comparable (i.e., they should have similar climatic or biome characteristics). Sometimes data based oninventories from other countries can be better related to national circumstances than those calculated withgeneral default emission factors or activity data, and can in turn be used to improve the inventory.
The comparison of inventory data or estimates with other inventories can be an inexpensive and fairly simple
verification approach. In general, it does not require skilled technicians or highly trained personnel, particularly
when compared to the requirements of approaches like remote sensing or modelling. It can be applied to allelements of an estimate, including land area classification, inventories of various carbon pools, estimates of nonCO2 gases, and activities like afforestation, reforestation and deforestation. The key determinant in its

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applicability is the availability of alternative inventories against which to compare. It is good practice to use thisapproach if such inventories are available. If such comparisons identify significant differences, the causes should
be investigated, in order to correctly interpret the results and flag possible areas for further inventory checks.

TABLE 5.7.1
APPLICABILITY OF VERIFICATION APPROACHES FOR LAND AREA IDENTIFICATION
AND FOR CARBON POOLS AND NON- CO2 GREENHOUSE GASES
Approach 1

Approach 2

Approach 3

Approach 4

Approach 5

Comparison
with otherinventories andotherindependent
datasets

Applying
higher tiermethods

Directmeasurement

Remotesensing

Modelling

Suitable, if data
are available

Suitable, if data
are available

Not applicable

Suitable

Not applicable

Abovegroundbiomass

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Suitable(ground dataneeded)Suitable (regression,
ecosystem andgrowth models)

Belowgroundbiomass

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Suitable, (regression,
ecosystem andgrowth models)Dead wood

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Applicable(ecosystem andinventory- basedmodels)LitterSuitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Applicable(ecosystem andinventory- basedmodels)Soil organic
matterSuitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Suitable (ecosystem
and inventory- basedmodels)Non- CO2
greenhousegases

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Suitable (ecosystemmodels)Emissionfactors

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Suitable (ecosystemmodels)Land area
Carbon pools

Activity/land-based report
Forest,grassland,
cropland, otherland uses

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Suitable,particularly toidentify land
cover/land use
and theirchanges

Suitable,
Data-intensive,
Can be an alternative
approach whenestimates from direct
measurements andremote sensing are
not available

Afforestation,
Reforestation,
Deforestation,
projects

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Suitable,particularly toidentify land
cover/land use
and theirchanges

Not practical

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Approach 1b: Comparisons w ith International Programmes and Datasets
A number of research and monitoring initiatives are currently under way at the international level, both atregional/continental scale (research projects, monitoring networks, etc.) and at global scale (remote sensing of
the biosphere, global data archiving centre, networks of similar research initiatives between regions, etc).
For the LULUCF sector, most of this research is linked to the quantification of the role of terrestrial ecosystems,
particularly forests, in the carbon cycle, from ecosystem to global scale. In this respect, many of the results
gathered by research and monitoring networks could be relevant for verification of LULUCF reporting, as well
as for other cross-cutting issues such as those linked to QA/QC and uncertainties.
The scale and aggregation level (national, regional, etc.) of the data and information that can be gathered fromsuch programmes and datasets may be useful in different phases and levels of the verification process (internaland external auditing, comparison with data collected by other agencies, etc.).
As with Approach 1a, the comparison of inventory data or estimates with independent datasets can be an
inexpensive and straightforward verification approach. It can be applied to any element of an inventory forwhich there is an alternative source of data. Generally, it is most applicable for land area classification, although
it can also be used to verify selected elements of estimates of carbon pools, non- CO2 greenhouse gases, andactivities, while data coming from research networks can be used to verify country-specific data (emission
factors). As mentioned for the previous approach, when using an international dataset for verification purposes,
it is good practice to ensure that the same dataset has not been already used to calculate or estimate someelements of the reported LULUCF category. This situation can occur particularly when the internationallyavailable programmes and datasets are compiled from national statistics or include the results of specific studies
performed in the territory of the country that is planning to use the data for verification. The analysis of theeventual differences emerging from the comparison with internationally available data- sets and inventories
should be devoted particularly to the identification of the possible reasons for such differences, with the final
objective of overall inventory improvement. Links to some international programmes and datasets that could be
useful for verification purposes can be found in Box 5.7.6, Links and Networks Relevant to LULUCF, in Section
5.7.6. Other useful links to open sources for land-use/land- cover data may be found in Chapter 2, Annex 2.A.2,
Examples of International Land Cover Datasets.

APPROACH 2: APPLYING HIGHER TIER METHODS
A country may not have sufficient data or resources to use higher tier methods for its total inventory of emissions
and removals from all of the various categories of the LULUCF sector. In some cases, however, the country may
have access to more comprehensive datasets for specific areas (e.g., a region or subcategory). In this case, thecountry could conduct verification of part of its estimate using a higher tier method. As an example, if
greenhouse gas emissions and removals in managed forests have been estimated using the Tier 1 methods, aninventory agency may consider performing verification by applying, over a portion of the forested area, countryspecific data (Tier 2 or Tier 3). In this case, biomass and growth equations would have to be available ordeveloped in selected areas at least for homogeneous growth conditions (biome, climatic regions), forest age
classes and management regimes.
The application of higher tier methods for parts of an inventory can be an effective verification technique if thenecessary data, derived from the more detailed method are available. This approach can be applied at a variety of
scales, from plot to national level. Costs will vary depending upon the scope of the verification. In general,development of higher tier estimates for verification can be fairly simple and may use the already available
inventory expertise. A key issue with this approach is whether to use the partial higher tier estimates as a part ofthe inventory itself or as a verification approach.

APPROACH 3: DIRECT MEASUREMENTS OF EMISSI ONS AND REMOVALS
OF GREENHOUSE GASES
Direct measurements are a verification approach for various carbon pools, as well as non-CO2 greenhouse gas
emissions and LULUCF activities. However, this approach is not generally applicable for verifying land areaclassification. The scale of the approach can vary from plot to national level. At limited scale, direct
measurements can provide country-specific default factors and activity data, while larger scale approaches canbe used for verification of sectoral estimates and specific activities. Costs can vary substantially, depending uponsample size and the desired accuracy. With a large sample size, accuracy can be quite high. When applying thisapproach, the most significant challenges are generally designing the sampling strategy and measurement
protocols. Once the infrastructure is in place, measurements collections are generally not technically difficult,although they can be labour intensive.
When performing direct measurements of emissions and removals of greenhouse gases in the LULUCF sector,
temporal and spatial variability needs to be properly considered, because emissions/removals in a given year are

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not necessarily indicative of long-term trends. This is due to the fact that most of the emissions and removals inthe sector are linked to biological processes and subject to climate variability. The problem can be partly
addressed by using average, cumulative measurements or smoothing over several years to get representative
results. Furthermore, the effect of inter-annual variability of data tends to decrease as larger areas are considered.
Thus, direct measurements over larger areas or with longer measurement intervals are more likely to reflect theeffect of management practices (see Chapter 4, Section 4.2.3.7, Interannual Variability). While recognising theseissues in using direct measurements as a verification tool, they can still be useful in several ways to verify
LULUCF sector estimates and background data, as described below.

Living Biom ass (aboveground and belowground biom ass)
Reported carbon stock changes in biomass can be verified by direct measurements of stock changes. Currently
available techniques allow reasonably accurate measurement of changes in aboveground biomass at periodic
intervals, although, in mature forests, the annual changes in stocks can be small for the size of the pool. Methods
for estimating belowground biomass are also available, although the sampling is more difficult than foraboveground biomass. This approach can be used particularly in forests, but it is also suitable for the changes inliving biomass in other land-uses which contain woody biomass while not matching the definition for forest land(e.g., agro- forestry systems, revegetated grasslands, etc.).
There is a variety of ways in which direct measurements can be used to verify biomass estimates. For example, acountry may decide to collect forest inventory data by direct measurements more frequently than they typicallydo, e.g., on a 5-10 year interval, for a selected subsample of plots or for a region. An inventory agency may alsouse direct measurements to derive local allometric relationships including belowground biomass that could be
used to verify stock changes for the entire living biomass component. Direct measurements could also be used asa verification tool for young forest stands or lands which are undergoing biomass regrowth, as the available
allometric equations and biomass expansion factors are normally not applicable for these pools. Available
ecosystem studies could be used to derive species-specific biomass expansion factors, which could be comparedagainst the default factors used for reporting and also to check growth rate of specific forest types.

Dead Organic Matter (dead wood and litter)
As for aboveground and belowground biomass, stocks of dead organic matter (litter and dead wood) can also be
estimated from direct measurements. However, in forests, litter and dead wood pools are highly variable both inspace and in time (e.g., seasonal changes in litter, sudden changes due to natural or human disturbances) and aproper sampling scheme would be needed to accurately assess stocks of dead organic matter. It is expected thatlitter pools are not changing significantly in mature forests and verification should be preferably directed toafforestation/reforestation areas and to forest stands that are undergoing major management operations such as
harvesting, site preparation, thinning, etc.
Generally, ecosystem studies are measuring aboveground litter input using netted traps (foliage and twigs) andlitter stocks through collection of litter in several plots (also for coarse dead wood). Such studies, if available,
could serve to check Tier 1 default factors eventually used for reporting.

Soils (soil organic m atter)
Verification of emissions and removals from soils could also be undertaken. As for aboveground biomass,
sensitive methods for estimating soil carbon stocks are available. Repeated soil sampling over a certain area,
region or at national scale can be a relevant approach for detecting changes in soil carbon in different land uses
(forests, grasslands, croplands). However, for ecosystems that are not undergoing land- use changes or are notsubject to significant management operation (e.g., harvesting of a mature forest, improvement of a grassland,ploughing of croplands, etc), changes in soil carbon stocks could be small and difficult to assess accurately overshort periods.
Greenhouse gas emissions and removals from soils can be measured at several sampling points in a plot using
portable or transportable gas- sampling systems (cuvettes and gas analyser). Measurements at the sampling
points would then need to be upscaled to plot/ecosystem levels, taking into account the significant spatial
variability typical of soil-related gas emissions and removals. Both CO2 and other greenhouse gases (N2O, CH4)
have been measured with this approach (Butterbach-Bahl et al., 2002; Janssens et al, 2001). Direct
measurements of greenhouse gas fluxes obtained in this way can be useful also in comparing emissions before
and after the application of a specific management practice (Steinkamp et al., 2001; Butterbach-Bahl and Papen,
2002). Directly measured values can be used to verify the default emission factors eventually used at lower tiers.
Verification of changes in soil carbon in land that is undergoing transition in use can be performed by comparing
measured carbon stocks in the land that has undergone the transition against carbon stocks of lands where theformer land use is still present. In such case, care should be taken to ensure paired sites are well matched interms of factors that may influence soil carbon turnover rates (e.g., soil type, native vegetation, drainage,
topography, etc.).

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Measurem ents of Greenhouse Gas Fluxes at Ecosystem Scale
Direct measurements of ecosystem fluxes of greenhouse gases can be used to verify, at a local scale, reportedchanges in carbon stocks. These flux observations are usually conducted by micrometeorological techniques,
such as eddy covariance, using canopy towers placed inside forests or other ecosystems, mainly for CO2
exchanges measurements (Aubinet et al., 2000). Generally, they provide data on the Net Ecosystem Exchange(NEE, see footnote 26). This approach is suitable for the comprehensive estimation of carbon emissions andremovals at plot/ecosystem scale, providing data that can be compared with activity data/emission factors anddefault values used in deriving the emissions/removals for a particular LULUCF category. However, there arelimitations in the upscaling of these results to regional and national level, as temporal and spatial variability,
long-term trends and disturbances needs to be properly considered (Körner, 2003). Direct measurements ofecosystem net fluxes require significant investments in equipment and have limitation for possible locations(depending on topography, vegetation and canopy structure). Once implemented, such measurements can be
performed on a continuous basis, providing an estimation of the interannual variability of the balance of CO2
emissions and removals of a certain ecosystem. Due to its complexity, it is likely that ecosystem fluxes will bemeasured by research institutes/networks. If such experiments are available within a country, the inventory
agency may consider using these results for verification.

APPROACH 4: REMOTE SENSING
Remote sensing is an effective approach for verifying land-cover/land- use attribution, detection of land-coverchange and estimations of land areas under conversion and abandonment. In addition, remote sensing can beused to estimate changes in aboveground biomass. Both of these uses of remote sensing for verification are
described below. Remote sensing is not applicable to the verification of belowground biomass, litter, dead woodor soil organic matter.
Remote sensing can be employed at scales ranging from plot to continental level. However, extracting accurate
and repeatable information from remotely sensed imagery can be a demanding task, and is likely to requireconsiderable technical expertise. The cost will depend upon the scope and scale of the programme. Costs can be
relatively low if archived data are available. If frequent measurement and extensive data interpretation are
required, however, both costs and the need for skilled expertise can increase substantially. Among other factors,
the accuracy of remote sensing will depend upon the scale at which it is used and the source of the images.
Generally, it can be quite accurate, but ground truthing is needed to improve result accuracy.

Approach 4a: Remote Sensing to Verif y Land Use and Land-use Changes
Remote sensing is the most direct tool that can be used for verification of the area involved in conversion offorests and grassland to other land-use types (cropland, settlements, etc), the abandonment of managed land, andfor fire detection (which is one of the main factors causing conversions in the tropics). However, if a country has
used remote sensing techniques for the consistent representation of land areas (see Chapter 2, Section 2.4.4.1), orfor the attribution of land- use and activities related to specific aspects of the Kyoto Protocol (see Chapter 4,
Section 4.2.2), care must be taken to ensure that the remote sensing data used for verification are independent of
those used for inventory development. From a technical point of view, remote sensing can be considered averification ex-post, comparing consecutive surveys taken in different years.
It is also important to bear in mind that although remote sensing will in many cases readily detect changes inland cover (e.g., from a vegetation cover to bare ground), it may not always provide adequate and accurate
information on changes in land use or vegetation types (e.g., from Crop A to Crop B)25. For example, detecting
clear-cuts in forests based on remotely- sensed data alone is relatively easy, but it is more difficult to distinguishwhether these are part of on- going forest management or represent deforestation (see also Chapter 4, Section
4.2.6.2.1). Similarly, separation of unmanaged pine forest from managed coniferous plantation forest has beenreported to be difficult, with accuracies of only about 50% (Okuda and Nakane, 1988). Distinguishing between
different crop types is a further area where remote sensing can have difficulty. The combination of frequent
observation by moderate spatial resolution sensors and detailed observation by high-resolution sensors cansometimes solve this problem.
Due to interactions with the atmosphere, clouds in particular, the use of optical remote sensing data may have
limits in certain regions of the globe (e.g., boreal and tropical zones) or periods of the year. In this respect,
Synthetic Aperture Radar (SAR) sensors are better suited to this purpose, as data acquisition can be performed
regardless of sunlight and cloud cover. Even using new sensors such as SAR, it would be challenging to estimate
or verify land-use and land-cover changes on a yearly basis. In part, the challenges result from the resources(personnel and funding) that are needed for such efforts. Nonetheless, as the temporal and spatial resolution of25

In some cases land cover might change, but not the land use, and vice versa.

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satellite sensors improve, detection of sudden and/or recent changes in land use or cover may become possible
on an annual or even more frequent basis.

Approach 4b: Remote sensing to verif y changes in living biomass
Satellite remote sensing and its image products may also be appropriate for assessing biomass and biomasschanges at the major ecosystem level (e.g., grassland vs. forest). Carbon stocks in forests can be estimated using
correlations between spectral image data and biomass, provided that adequate data (not used for inventory
estimates) are available to represent the range in forest biomes and management regimes for which estimates are
required (Trotter et al. 1997). Correlation equations, may be affected by several parameters (canopy andunderstorey type, season, illumination, satellite- viewing geometry) (Okuda et al., 2003), and must in general be
developed for each forest type. In addition, vegetation indices (e.g., the Normalised Difference Vegetation Index,
NDVI) have also been used for the estimation of above ground biomass (see Section 5.7.6 for an overview onsuch indices).
Another approach is to employ Synthetic Aperture Radar (SAR) data that provide structural, rather than spectral,
information about the monitored land cover. For some forest types, wood biomass can be estimated with acertain level of accuracy, using the relationships between biomass and radar intensity (amplitude, backscattering)(Rauste et al., 1994; Foody et al., 1997; Luckman et al., 1998; Saatchi et al., 2000; Terhikki Manninen andUlander, 2001) or indirectly, for instance by linking SAR derived tree heights with in situ derived allometric
relationships. SAR data are suitable for assessing relative incremental changes in aboveground biomass stocks
between two or more points in time, particularly when changes are relevant. Time sequences rather than singledate imagery allows characterisation of change trends and minimisation of errors in the estimations.
Both optical and SAR sensors have limitations in rough topographic terrain and in areas with heterogeneous
canopy cover. The accuracy level of remotely sensed data varies with the geometric and radiometriccharacteristics of the sensors, including change in sensor calibration over time. The imaging data used should bechosen according to the geographical scale of the target area and the desired degree of resolution. Specifications(sensor type, spatial resolution, availability, etc.) of various satellite sensors are listed in Table 5.7.2, in Section
5.7.6.
Other approaches for area and biomass verification using imagery data may include:•

Airborne photography (for the vertical canopy structure of forest, labour-intensive);•

Laser profiler (LIDAR canopy height and structure, accuracy still to be examined, experimental, expensive);•

Comparison with maps/data produced by independent agencies using remote sensing.

APPROACH 5: VERI FICATION USI NG MODELS
Models can be used to verify estimates of carbon pools, activity data and also the overall inventory. Generally,
they are not used in verification of land area classification. For specific land-use categories under the UNFCCC
and activities selected under the Kyoto Protocol, models can be an attractive option when direct measurements
combined with remote sensing are not feasible. Modelling costs can vary significantly, depending on the specificapplications, availability of appropriate tools, and the degree of resolution desired. Starting costs associated withmodel design and calibration are generally much higher than ongoing running costs. Verification using models isquite complex and requires a high level of technical expertise.
There are two very different types of modelling approaches for verification purposes: bottom-up models andtop-down models. Bottom- up models scale up from lower scale processes to higher aggregation levels, whereas
top-down models follow the other direction and try to infer smaller scale processes from larger scalemeasurements. Although in principle both approaches may be used for verification purposes at the national level,
the top-down models are more suited for continental scale verification. Bottom- up models can be used fromsite/plot level scale to regional and national and even continental level, provided the input data are available.
Models that are used for verification purposes, as models used in inventory preparation, need to be well
documented and should have undergone peer-review. Input parameters, data, functions and assumptions shouldhave been subjected to scrutiny, which is typically referred to as validation. The term validation is used in thegenerally accepted meaning of testing adequately the performance of a model, which is not equal to say that themodel is the only true representation of reality (Oreskes et al, 1994).
As with other approaches, it should be noted that models have their advantages and drawbacks, and so far there
is no such thing as a “best model”. For avoiding some of the possible biases associated with model choice, anensemble of identically calibrated models could be used (Alexandrov et al., 2002). Expert advice is often
required to use models as verification tools.

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Approach 5a: Bottom-up Modelling
There are several types of bottom-up models that can be used for verification:
Ecosystem and growth models can simulate growth of vegetation and the fate of carbon at sufficiently longtime scales, which can be used for verification. They compute biomass growth and fluxes of carbon, water, andnitrogen, and are able to provide estimates of gross primary production (GPP)26 and net primary production(NPP)26 of carbon per unit area in forests (Kramer et al., 2002) and other vegetation types. They can be used toverify Tier 1 and Tier 2 component estimates of biomass and fluxes, and also to derive “emission factors” and/or
country- specific parameters relevant to Tier 2 calculations (see Table 5.7.1). In the case of forests, there arebasically two classes of ecosystem models that can be applied: those that focus on physiology andbiogeochemistry of the ecosystem, and those that are based on forest inventories. Well- known examples of thesetwo classes are FOREST-BGC (Waring and Running 1998), Biome-BGC (Running and Coughlan, 1988;
Running and Hunt, 1993; Running, 1994) and inventory based models (Kauppi et al., 1992; Nabuurs et al., 1997;
Birdsey, 1996; Kurz and Apps, 1999)
Recently, a new generation of terrestrial carbon cycle models have been developed to integrate the effects ofchanges in climate, atmospheric chemistry, disturbance rates on NPP, NEP26 and NBP26 (e.g., Landsberg andWaring, 1997; Chen et al., 2000a; Chen et al., 2000b; McGuire et al., 2001). Using spatial data from remotesensing (e.g., land cover, burnt area, and leaf area index) and georeferenced datasets of climate, atmosphericchemistry and soil inventory, these process-based models can scale up site-level data (e.g., ecosystem flux
measurements) to regional and national scales. Without direct dependence on a forest inventory, the data
estimated using these models could be used to compare to forest inventory-based carbon accounting. However,
the ability of models in which land representation is based on remote sensing to quantify the carbon stockchanges resulting from land use changes at small scale (e.g., afforestation, reforestation and deforestation) is
limited by the spatial resolution of the remote sensing information.
If models are used to aggregate results and to provide data on biomass changes at national scale, model
parameterisation needs to be adequately performed, taking into account the different land use and land coverexisting in a country. As an example, to use model results as verification for forest inventory data, theparameterisation should be performed at least for the main tree species.
Regression models have been used to calculate NPP from basic meteorological data (e.g., Chikugo models,
Uchijima and Seino, 1985). NPP values derived from regression and process-based models can be used forcross-checking of Tier 1 and Tier 2 data at large scale (see Table 5.7.1).
Modelling approaches using Geographical Information Systems (GIS) that incorporate ground truth data
provide more accurate values than remote sensing approaches. GIS based data, such as topography and canopy
cover and structural features such as climate can also be used to drive ecosystem and growth models in order toretrieve spatially explicit results. Accordingly, at continental and global scales, GIS modelling can be used toverify national land survey methodologies (Mollicone et al., 2003).

Approach 5b: Top-dow n Modelling and Large-Scale Approaches
Top-down models could be used for the verification of carbon stocks and stock changes from regional to global
scales. These approaches are not easily applicable to country level estimates, but can be used for aggregatedcountries, large regions or continents. For countries with very large land area or with features that allow toseparation of in-country from external air- mass movements (e.g., North America, Boreal Zone- Siberia,
Australia, United Kingdom, etc.), regional/continental scale approaches can be useful also at national scale.
While top-down modelling can provide overall constraints on regional carbon budgets, they are not suitable forverification of sectoral carbon budgets, because they cannot separate the contribution of emissions and removals
from different land-use categories or management activities -as required for the reporting under the UNFCCC
and the Kyoto Protocol. Moreover, top-down modelling approaches include emissions and removals from landuse categories that are not subject to reporting under either the UNFCCC or the Kyoto Protocol (e.g., nonmanaged lands). Nevertheless, at larger scales, atmospheric measurements of greenhouse gas concentrations andisotopic composition should in principle be able to prove if the aggregate actions taken under UNFCCC and the
Kyoto Protocol will be effective with respect to the trend in atmospheric greenhouse gas concentrations (Schulze
et al., 2002).
26

GPP: Gross Primary Production, given by the gross photosynthesis; NPP: Net Primary Production, net photosynthesis or
GPP minus autotrophic respiration (from above- and belowground living plant biomass); NEP: Net Ecosystem Production,
the net emissions or removals of carbon (CO2), or NPP minus heterotrophic respiration (soil organic matter and soilorganic carbon decomposition, animals), when NEP is measured using flux techniques in correctly defined as NEE, Net
Ecosystem Exchange; NBP: Net Biome Production, the net emissions or removals of carbon at large scale (biome), whichtakes into account also natural and human-induced disturbances (fire, windtrows, harvest, NBP=NEP-disturbances). NBP
is the term that finally is reflected in the global carbon budget (i.e., the atmosphere).

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Verification

Inverse models calculate fluxes from concentration measurements and atmospheric transport models. They canbe used to determine overall carbon dynamics at continental to global scales, but have limited ability to separate
the contribution of different land-use categories or management activities to the total budget. By measuring the
spatial and temporal distribution of CO2 concentrations, it is possible to detect terrestrial and oceanic carbon
fluxes. Inverse models are also used to calculate fluxes of methane and other greenhouse gases.
Incorporating airborne observations and using regional- level transport models in the inverse analysis canimprove the estimates, as can the consideration of spatially distributed emissions/removals data. The
implementation of inverse modelling approaches is under continuous development, requiring scientificcollaboration and a networked system among nations. It is probable that such estimations will be independent
from country data and will be valuable for overall verification at regional to continental level (see Gurney et al.,2002 for a comparison of several inverse modelling results at continental scale).
At national level, another large scale approach that can be used for overall verification is the use of tall towers,
which are generally available within a country (e.g., TV towers, transmission towers), to measure the CO2
gradients (Bakwin et al., 1995). This approach can be combined with the use of inverse modelling to derive
regional/national balances of emissions and removals. Once in place, the system can be automated and is notvery expensive.

5.7.3

Guidance for Verification of LULUCF Inventories

Several components of an inventory can be identified by inventory agencies (or external groups) for verification
including emissions/removals estimates, input data, and assumptions. The questions in Box 5.7.2 can be used byan inventory agency as guidance for the development of a verification plan.

BOX 5.7.2
GUIDANCE FOR SELECTING INVENTORY COMPONENTS FOR VERIFICATION AND VERIFICATION APPROACHES

Which criteria can be used to choose the inventory elements for verification?
If any source/sink category is “key”, it should be given priority for verification. However,
emissions and removals that are not “key” can also be selected for verification, especially if theseare of relevance to mitigation policies or their uncertainty is high. If a pool is expected to change
significantly over the inventory reporting period, particular attention should also be devoted to it.
How will the inventory elements be verified?
Selection of the verification approach will depend largely on the suitability/availability of theapproach for the inventory agency or the country-specific conditions. Additional criteria are: thetype of data to be verified, the spatial scale of the inventory coverage, the quantity and quality ofthe data to be verified, and the accuracy, precision and cost of the approach itself. The approaches
and criteria for choosing them are elaborated in Table 5.7.1 and described in detail in Section
5.7.2.

If a country undertakes internal verification of its inventory, it is good practice to ensure that:•

Sufficient independent expertise is available;•

Documentation of the verification is included in the national inventory report;•

Uncertainty estimates and QA/QC documentation is included in the report;•

Other available national verification activities are described;•

Applied verification methods are transparent, rigorous and scientifically sound;•

Verification results are reasonable and well-explained;•

Final calculations can be reasonably linked to underlying data and assumption.

The checklist in the Box 5.7.3 summarises some of the tools that can be used for internal verification of an
inventory, with particular emphasis on the LULUCF sector. A specific box is provided also for Kyoto Protocol
aspects (see Section 5.7.4, Box 5.7.5).

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Chapter 5: Cross-Cutting Issues

BOX 5.7.3
VERIFICATION OF INVENTORY OF LULUCF SECTOR IN A NATIONAL INVENTORY

A. Checks:
Does the inventory of the LULUCF sector document the data and assumptions used for estimating
emissions and removals for all IPCC source/sink categories?
Have all important carbon pools been included in the inventory?
If some LULUCF emission/removal categories have been omitted, does the report explain why?
Are emissions and removals reported as positive and negative terms, respectively?
For the total area of the inventory of the LULUCF sector, are the overall changes in land- use forthe inventory year equal to zero within the confidence limit?
Are any discontinuities in trends from base year to end year evaluated and explained?
B. Comparisons of emissions and removals from LULUCF:
Compare the inventory of the LULUCF sector with independently prepared national inventories
for the same country or compare regional sub- sets of the national inventory with independently
prepared inventories for those regions. (Table 5.7.1, Approach 1).
Compare the inventory of the LULUCF sector with national inventories for a different, but similarcountry (Table 5.7.1, Approach 1).
Compare activity data and/or emission factors of the inventory of the LULUCF sector withindependent international databases and/or other countries. For example, compare Biomass
Expansion Factors of similar species with data from countries with similar forest conditions(Table 5.7.1, Approach 1).
Compare the inventory of the LULUCF sector with results calculated using another tiermethodology, including defaults (Table 5.7.1, Approach 2).
Compare the inventory of the LULUCF sector with available high- intensity studies andexperiments (Table 5.7.1, Approach 1-3).
Compare land areas and biomass stocks used in the inventory with remote sensing(Table 5.7.1, Approach 4).
Compare the inventory of the LULUCF sector with models (Table 5.7.1, Approach 5).
C. Comparisons of uncertainties:
Compare uncertainty estimates with uncertainty reported in the literature.
Compare uncertainty estimates with those from other countries and the IPCC default values.
D. Direct measurements:
Carry out direct measurements (such as local forest inventory, detailed growth measurements
and/or ecosystem fluxes of greenhouse gases, Table 5.7.1, Approach 3).

Taking into account resource limitation, the information provided in the national inventory report should beverified as far as possible, particularly for key categories. The verification approaches in Box 5.7.3 can beapplied as follows:•

The checks listed under A are essential and, ideally, these should have been conducted as part of QA/QC.

•

It is good practice to perform verification with at least one of the approaches listed in Box 5.7.3 under B(see Table 5.7.1 and Section 5.7.2 for more information on the applicable approaches).

•

If independent estimates on emissions and removals of greenhouse gases by LULUCF are not available,
then internal or external verification will most probably be limited to scrutiny of the data and methods(Smith, 2001). Under these circumstances, it is good practice for the inventory agency to carry out thesechecks and to provide sufficient documentation in its national inventory report and other supporting material
to facilitate external verification.

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Verification•

Inventory agencies, taking into account country-specific circumstances and the availability of resources, canassess the proper combination of approaches for verifying their LULUCF inventories. Approaches 1, 2 and3 are feasible for verifying several components of the inventory. Among those listed, Approaches 1 and 2
can be easily implemented by an inventory agency with low to moderate resources. Remote sensing is themost suitable method for the verification of land areas. Direct measurements (under D in Box 5.7.3) arerelevant, although this approach can be resource-intensive and, on a large scale, costs may be a constraint.
Models can be used as an alternative when direct measurements combined with remote sensing is not
feasible.

5.7.4

Specific Issues Linked to the Kyoto Protocol

In general, the same approaches discussed in Section 5.7.2 can be used for verifying both an inventory submitted
under the UNFCCC and reporting under the Kyoto Protocol. Although, the cost of measuring changes in carbon
stocks for a given area increases as both desired precision and landscape heterogeneity increase, the same
principles of good practice apply to projects and national inventories.
An inventory agency can use the questions in Box 5.7.4 to help guide the development of a verification plan for
supplementary information reported under Articles 3.3 and 3.4 of the Kyoto Protocol.
BOX 5.7.4
GUIDANCE FOR VERIFYING CARBON POOLS AND ACTIVITIESWhich carbon pools to verify?
It is good practice to focus verification on those carbon pools that are expected to be most relevantto the Kyoto Protocol but also on non-CO2 greenhouse gas emissions. The Marrakesh Accords list
the following pools: aboveground and belowground biomass, litter, dead wood and soil organiccarbon. As stated in the Marrakesh Accords, a Party may exclude particular pools from reporting,if verifiable information is provided showing that the pool has not been a source of greenhousegases for activities under Article 3.3 and elected activities under Article 3.4, or for projects.
Therefore the information required is different for selected (changes of the pools following advice
provided on Chapter 3 and 4) and non- selected pools (additional information demonstrating thatthey are not a source). As for LULUCF inventories, if a pool is expected to change significantly
over the inventory reporting period, particular attention should also be devoted to it.
Which activities to verify?
According to the Marrakesh Accords, a Party has to report activities under Article 3.3 and may
choose only certain activities under Article 3.4 of the Kyoto Protocol. For all obligatory or elected
activities, elements which are specific to the reporting under Kyoto Protocol inventories include:
the identification of the areas in which such activities have taken place, the demonstration that theactivities have occurred since 1st January 1990 and are human induced, and the establishment of
the “1990” base year (reference year for reforestation activities and base year for net- net
accounting).

Specific verification related to estimates developed under Articles 3.3 and 3.4 of the Kyoto Protocol may include:•

For lands introduced into a reporting under Kyoto Protocol, it is good practice to verify such lands using
geographical and statistical information, such as remote sensing data. Even if georeferencing was not
required, this would facilitate verification (Smith, 2001).

•

The reporting of greenhouse gas emissions and removals of most Article 3.3 and 3.4 activities requirereference to 1990 or pre-1990 data (classification of forest/non forest lands for 1990, net- net accounting forcropland management, grazing land management, revegetation, etc.). In some cases, these data may not be
available or their reliability may be limited and estimates may be used, subject to advice in Chapter 4
Section 4.2.8.1. In such cases, it is good practice to verify the estimation approach and values, as much as
possible.

Emissions and removals from project activities can be reported under Articles 6 and 12 of the Kyoto Protocol,
and Chapter 4 of this report lists different types of projects and suggests the type of information that may need tobe verified for each. While many of the approaches presented in Section 5.7.2 are useful for project verification,

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Chapter 5: Cross-Cutting Issues

additional rules are being developed under the Kyoto Protocol and the Marrakesh Accords27. This factor notwithstanding, verification of projects is generally easier than national level verification. For projects, boundaries,
carbon pools and lifetimes are all factors that can be well established, and hence verified. Generally, projects
with good monitoring and reporting plans are likely to be easier to verify.
As with inventories of LULUCF sector, inventory agencies, taking specific circumstances and the resource
availability into account, may choose the proper combination of approaches for verifying supplementary
information reported under the Kyoto Protocol. Among these approaches, remote sensing is the most suitable forthe verification of land areas. Direct measurements are relevant, although this approach can be resource intensive.
Models can be used as an alternative when direct measurements combined with remote sensing is not feasible.
Some verification steps, which are unique to the Kyoto Protocol, are presented in Box 5.7.5.
BOX 5.7.5
VERIFICATION OF LULUCF UNDER THE KYOTO PROTOCOL

Checks:If a Party reports that an activity has occurred on forest land, is the definition of ‘forest’ provided
and consistent with activities and land units reported? Is information on selected crown cover andtree height provided?
Are changes in all carbon pools reported (aboveground and belowground biomass, dead wood,
litter, soil organic carbon)? If not, is the reason and documentation for omitting a pool given?
Are geographical boundaries of land areas specified for the activities eligible under Articles 3.3
and 3.4?
Is the total land area reported under Article 3.3 and 3.4 constant or increasing throughout
subsequent or contiguous commitment periods?
Is information provided that demonstrates that the elected activities under Article 3.4 occurredsince 1990 and are human induced?
For Article 3.3, is information provided to distinguish deforestation from harvesting (clear-cut) orforest disturbance followed by re-establishment of a forest?

The checks listed in Box 5.7.5 are essential and, ideally, should have been conducted as part of QA/QC. In
addition to these specific checks, the comprehensive list presented in Box 5.7.3 under items B to D can be used
to identify additional useful verification activities.

5.7.5

Reporting and Documentation

When an inventory agency has undertaken verification activities, it is good practice to report and document thefollowing items:•

Information that has been verified;•

Criteria that were used for the selection of verification priorities;•

Verification approaches, along with relevant data that were collected;•

Any limitations in the approaches that have been identified;•

Eventual comparisons that have been performed with independent inventories, datasets, scientific literature,etc;•

Any feedback received from external reviewers, with a summary of key comments;•

Main conclusions of the verification;•

Actions taken as a result of the verification process;27

The verification to which the paragraph refers is to be considered in the context of the present chapter (as defined in
Section 5.7.1). According to the Marrakesh Accords, projects have to be subjected to specific “verification”, as defined indraft decision -/CMP.1 (Article 6), -/CMP.1 (Article 12) and their annexes (FCCC/CP/2001/13/Add.2).

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Any recommendations for inventory improvements or research at national/international level arising fromthe findings.

Inventory agencies are also encouraged to provide information on the external verification activities by otherbodies, to the extent that they are relevant to the inventory and that any such information can be readily collected
and summarised.
If modelling has been used for verification, it is good practice to fully document the modelling process. Other
information to be reported includes: sources of input data, a discussion of model and data assumptions, anddescription of procedures and analysis. Because of the volume of input data, and the number of variables that areneeded for a typical large model, documentation may be dense, technical and lengthy. It is good practice toreport the above information comprehensively and transparently. The information to be included should allow athird party to fully understand the verification process, and to corroborate the results if needed.

5.7.6

Some Details for Verification Approaches

COMPARISONS WITH INTERNATIONAL PROGRAMS AND DATASETS
For an inventory agency that is willing to compare an inventory or part of it against datasets coming frominternational monitoring and research programmes, it can be useful to follow the links provided in Box 5.7.6.
Obviously, the Box is not exhaustive of all programmes available, but it provides information for some of thosethat are more relevant to LULUCF.
BOX 5.7.6
PROGRAMMES AND NETWORKS RELEVANT TO LULUCFFLUXNET (Ameriflux, CarboEuroflux)
Network of ecosystem flux measurements, mostly on forest stands, but also other land use type
Common database, links to ecosystem studies
http://www- eosdis.ornl.gov/FLUXNET/index.html
CarboEurope (funded by the European Commission)
Cluster of projects aimed at understanding the carbon balance of Europe with different approaches(flux measurements, ecosystem studies, regional and continental budgeting, inverse modelling,
ecosystem modelling)
http://www.bgc-jena.mpg.de/public/carboeur/International Geosphere-Biosphere Programme (IGBP)Net Primary Production data sets, coordination of international research efforts, global change andterrestrial ecosystem, etc
http://www.igbp.kva.se/cgi-bin/php/frameset.php
http://www.gcte.org/
Long Term Ecological Research (forests, grasslands)
Network of ecosystem ecological studies present in different countries
http://www.lternet.edu/
FAO
Database of terrestrial ecosystem research sites (TEM), Global Terrestrial Observing System(GTOS ), Global Climate Observing System (GCOS), Forest Resource Assessments (FRA)
http://www.fao.org/
Monitoring networks:
ICP Forests
The common European Union International Cooperative Programme on Forest (EU/ICP Forests)
works on two levels with standardised protocols and methods in 35 countries. The systematic gridnet has approximately 6000 Level I points where limited number of surveys are carried out,
whereas the intensive monitoring grid net has 860 Level II plots in the mayor forest types of theEuropean continent where large number of surveys are carried out.
http://www.icp- forests.org/
ICP/IM and EMEP
The multi-disciplinary ICP Integrated Monitoring programme (ICP/IM) and the Co-operative
Programme for Monitoring and Evaluation of the Long- Range Transmissions of Air Pollutants in
Europe (EMEP)

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Chapter 5: Cross-Cutting Issues

BOX 5.7.6 (CONTINUED)
PROGRAMMES AND NETWORKS RELEVANT TO LULUCF

A part of the monitoring strategy and evaluation of the effects under the Long-range
Transboundary Air Pollution Convention of the United Nation's Economic Commission for Europe(UNECE). The EMEP programme relies on three main elements: (1) collection of emissions data,(2) measurements of air and precipitation quality and (3) modelling of atmospheric transport anddeposition of air pollution.
http://www.vyh.fi/eng/intcoop/projects/icp_im/im.htm
http://www.emep.int/
Global Carbon Project
The Global Carbon Project is a project of the Earth System Science Partnership of the International
Geosphere Biosphere Programme (IGBP) World Climate Research Programme (WCRP) and theInternational Human Dimensions Programme (IHDP). The scientific goal of the Global Carbon
Project is to develop a complete picture of the global carbon cycle, including both its biophysicaland human dimensions together with the interactions and feedbacks between them.
http://www.globalcarbonproject.org/
The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)
A source for biogeochemical and ecological data collected on the ground, from aircraft, by satellite
or generated by computer models. The scale of data ranges from site- specific to global, andduration range from days to years. ORNL Environmental Sciences Division (ESD) operates the
ORNL-DAAC for Biogeochemical Dynamics as part of the National Aeronautics and Space
Administration's (NASA) Earth Science Enterprise (ESE) programme.
http://www- eosdis.ornl.gov/

REMOTE SENSI NG
Overview of Available Remote Sensing Sensors
Optical satellite data going from coarse to high resolution, are available worldwide from NOAA AVHRR, SPOT
Vegetation, ERS/ATSR, MODIS, Envisat MERIS, Landsat TM/ETM and several other sensors. Multifrequency/polarisation radar, that was only recently made available from NASA AIRSAR missions, is also very
useful for vegetation classification. Those sensors, being sensitive to vegetation structural characteristics,
provide an excellent complementary data source to optical remote sensing. Such radar data will begin to become
more available with Envisat ASAR and the launch of RadarSat 2. The accuracy of remotely sensed data varies
with the geometric and radiometric characteristics of the sensors. Specifications (sensor type, spatial resolution,
availability, etc.) of various satellite sensors are listed in Table 5.7.2, further information can be found at
http://idisk.mac.com/alexandreleroux/Public/agisrs/arsist.html. The imaging data used should be chosen
according to the geographical scale of the target area and the desired degree of resolution. The use of different
sensors can be a solution to circumvent the limitation of remote sensing in areas of persistent cloud cover (e.g.,
optical and radar data).

Use of Remote Sensing to Derive Vegetation Parameters
Net Primary Production (NPP) is known to be positively correlated with photosynthetically active radiation(PAR), which also can be estimated from NDVI (Normalised Difference Vegetation Index) and solar radiation.
The functional relationship between optical remote sensing data (including indices like the NDVI) and carbon
stocks is that canopy reflectance is related to leaf area index (LAI), and LAI in turn has a strong functional
relationship to woody biomass and NPP (Gholz, 1982; Waring, 1983). An alternative interpretation of therelationship is that reflectance is related to the fraction of absorbed photosynthetically active radiation (fAPAR),
which over longer time periods is linearly correlated to NPP (e.g., Monteith, 1977; Landsberg and Waring, 1997).
The NDVI has been widely used to estimate both LAI and fAPAR from remotely sensed data.
NDVI and solar radiation determined by remote sensing, coupled with meteorological measurements andgeographical information system (GIS) data, can be used to make estimates also at larger scales (regional to
global). NDVI has also been used to derive growing season duration, a parameter that has been shown to beclosely linked to the Net Ecosystem Exchange (NEE, the net carbon sink) measured by ecosystem fluxes,
particularly in deciduous forests (Baldocchi et al., 2001). However, when this approach is used, care must be
taken in considering that fine- scale differences are difficult to handle and that not all vegetation successional
phases are properly covered by NDVI (recovery processes, etc.). Furthermore, most of the ecosystem parameters

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Verification

derived from correlations with NDVI are likely to be species and/or biome specific. Moreover, the NDVI is
influenced by factors other than canopy LAI or fAPAR, and the relationships have a tendency to saturate at LAI
values above about 3 m2 m-2 (Moreau and Li, 1996; Carlson and Ripley, 1997; Gemmell and McDonald, 2000),
although, for conifer canopies, the saturation did not occur for LAI up to 10 m2 m-2 (Chen et al., 2002). Becauseof the saturation, NDVI derived from LANDSAT images was found to be poorly correlated with stand structure
variables or total aboveground biomass within forest stands in the tropics. In general, NDVI-based approaches toestimating LAI or fAPAR will be a function of soil reflectance, fractional cover, biome type, andillumination/viewing conditions. These factors result in a wide variation in the equations used for estimating LAI(or fAPAR) from NDVI (Moreau and Li 1996), and users should consider this if selecting or deriving equations.
If spectral indices are to be used as the basis for constructing a relationship with LAI or fAPAR, consideration
should be given to using an index that is less affected by variations in parameters such as soil reflectance(Kaufman and Tanré, 1992; Huete et al., 1997). The Enhanced Vegetation Index (EVI) is perhaps the mostpromising of these, and is both simple to implement for most sensors and linearly related to fAPAR (Huete et al.,
1997; Gobron et al., 2000). For datasets for which 1-km pixels are sufficient, users may be able to use the
MODIS or MERIS fAPAR data and MODIS LAI data. In addition, software is freely available to generate highquality fAPAR values (Gobron et al., 2000) from data acquired by the SeaWIFS, MERIS, VEGETATION, or
GLI sensors.
Above ground biomass can be estimated efficiently also by LIDAR airborne sensing that measure the canopy
surface and ground elevation height at the same time, by emitting laser pulses with wavelengths that reflect overthe canopy surface but pass through trees and reflect off the ground as well. However, because of the small
diameter beams of laser, mapping large areas requires extensive flying missions (Dubayah and Drake, 2000).
The Laser Vegetation Imaging Sensor (LVIS) by airborne or satellite instruments such as Vegetation Canopy
LIDAR with large footprints will possibly solve such problems (Blair et al., 1999; Means et al, 1999; Dubayah
and Drake, 2000). One can also estimate vegetation structure from optical satellite data using the Bi-Directional
Reflectance property based on the Sun-Target-Sensor Geometry.

Use of Remote Sensing f or Fire Detection and Burnt area
Remote sensing is also frequently applied for forest fire detection.. Examples of forest fire or fire scars detection
at different scales range from detection of 1 ha burn scars on a national basis using Landsat TM (e.g.,
ITALSCAR, 2003: Regional Burned Forest Mapping in Italy, http://www.esa.int/dup) or for European Union’s
Member States (http://natural- hazards.jrc.it/fires/) to the use of ERS SAR in Indonesia (Page et al., 2002), to
global detection of active fires (ATSR World Fire Atlas, 2003: http://earth.esa.int/ionia/FIRE/), burn scars(GLOBSCAR, 2003 Global Burned Forest Mapping, http://earth.esa.int/ionia/FIRE/; GLOBCARBON, 2003:
Global Land Products for Carbon Model Assimilation, http://www.esa.int/dup) and burnt areas (Global BruntArea 2000: http://www.gvm.sai.jrc.it/fire/gba2000_website/index.htm). As an example, a recent study using
remote sensing techniques has estimated the total area deforested due to fires in the humid tropics between 1990
and 1997, arriving at a different number to that reported by FAO statistics, that are using deforestation datareported by countries and experts (Achard et al., 2002).

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5.75

USA

AVHRR
Vegetation
GLI
MODIS
MISR
ATSR-1/2
AATSR
VIRS
MERIS
MSS
TM
ETM+
HRV/HRVIR/HRG
ASTER
PAN/LISS-3
OPS (VNIR)AVNIR-2
PRISM
Pan/Multi
Pan/Multi
Pan/Multi
ALI
Hyperion
SAR
PALSAR
AMI
ASAR
SAR
SAR

VCL

NOAA (POES)
SPOT
ADEOS-II
Terra/Aqua
Terra
ERS-1/2
Envisat
NPOESS
Envisat
Landsat
Landsat
LandsatSPOT
Terra
IRS-1C/D
JERS-1ALOS
ALOS
IKONOS
Orbview-3
QuickBird
EO-1
EO-1
JERS-1
ALOS
ERS-1/2
EnvisatRadarsat-1/2
TerraSAR
LIDAR
VCL
25

SpatialResolution(m) at nadir
1100
1150
250, 1000
250, 500, 1000
275, 550, 1000
1000
1000
400
300 (Land)80
30, 120
15, 30, 60(2.5), 10, 20
15, 30, 90
6 / 2318*24
10
2.5
0.82 / 3.3
0.82/ 3.3
0.61 / 2.5
10, 3030
18
10, 100
30
30, 100, 150(3, 8), 10, 30
1-3, 3-15
8

(km)
2700
2250
1600
2330
360
500
500
3000
1150
185
185
185
60
60
70 / 141
75
70
35/70
11
8
17
185
7.5
75
70, 250-350
100
100, 400(20), 50, 100
10, 40-60

Swath

L

R

S

-

Sensor type
Spectral informationand scaleType Scale VNIR SWIR TIR SAR
O
Co-G
M
S
M
O
Co-G
M
S
O
Co-G
M
M
M
O
Co-G
M
M
M
O
Co-G
M
O
Co-G
M
M
M
O
Co-G
M
M
M
O
Co-G
M
M
M
O
Co-G
M
M
O
R
M
O
R
M
M
S
O
R
M
M
S
O
R
M
(S)
O
R
M
M
M
O
R
M
S
O
R
M
O
R
M
O
R
S
O
R
M
O
R
M
O
R
M
O
R
M
M
O
R
H
H
S
R
L
S
R
L
S
R
C
S
R
C
S
R
C
S
R
X/L

TABLE 5.7.2 FEATURES OF SOME OF THE MAIN REMOTE SENSING PLATFORMS

5.76

PA: available for a portion of the period; MA: may be available during the period

MA

IPCC Good Practice Guidance for LULUCF

PA1980 - 1990 1990 - 1999 2000 - 2007 2008 - 2012
A
A
A
A
PA
PA
MA
PA
MA
A
PA
PA
PA
A
MA
PA
MA
A
PA
MA
A
A
PA
A
PA
A
A
PA
A
A
A
PA
PA
PA
PA
A
PA
MA
A
MA
PA
MA
PA
MA
PA
PA
PA
PA
MA
PA
PA
MA
PA
MA
PA
A
MA
PA
MA

Data availability (acquisition period)

O: optical; S: synthetic aperture radar; L: LIDAR; Co: continental; G: global; R: regional; S: single band; M: multiple band; H: hyper band. A: available for the entire period;

USA
EU
Japan
USA
USA
EuropeEuropeUSA
EuropeUSA
USA
USA
FrenchJapan/USA
India
Japan
Japan
Japan
USA
USA
USA
USA
USA
Japan
Japan
EuropeEuropeCanadaGermany

Sensor nameCountry(Operation)

Satellite

Chapter 5: Cross-Cutting Issues