Environmental Science & Policy 6 (2003) 441–455

Review

A review of remote sensing technology in support of the Kyoto Protocol
Åke Rosenqvist a,∗ , Anthony Milne b , Richard Lucas c , Marc Imhoff d , Craig Dobson ea Earth Observation Research Center, National Space Development Agency of Japan, Triton Square X-22F,

1-8-10 Harumi, Chuo-ku, Tokyo 104-6023, Japanb School of Biological, Environmental and Earth Sciences, University of New South Wales, Sydney 2052, Australiac Institute of Geography and Earth Sciences, University of Wales at Aberystwyth, Aberystwyth, Ceredigion SY23 3DB, UKd Biospheric Sciences Branch, NASA Goddard Space Flight Center, Mailcode 923, Greenbelt, MD 20771-0001, USAe Department of Electrical Engineering and Computer Science, University of Michigan, 3228 EECS Building, Ann Arbor, MI 48109-2122, USA

Abstract
This paper presents an overview of the role of remote sensing technology in the context of the United Nations Framework Convention
on Climate Change (UNFCCC) Kyoto Protocol and is based largely on discussions held at an international workshop in MI, USA, andthe report that followed [A. Rosenqvist, M. Imhoff, T. Milne, C. Dobson (Eds.), Remote Sensing and the Kyoto Protocol: A Review
of Available and Future Technology for Monitoring Treaty Compliance, Workshop Report, Ann Arbor, MI, USA, 20–22 October 1999,
2000a, 159 pp. Available at http://www.eecs.umich.edu/kyoto]. The implications of significant decisions pertaining to the definition of the
key terms forest and afforestation, reforestation and deforestation (ARD) activities taken at the conference of parties (COP 6:2 and COP7) meetings in Bonn and Marrakesh, respectively in 2001 are also discussed. Past, current and near-future remote sensing instrumentswith applications appropriate to Kyoto requirements are short listed; research topics that need to be advanced to support use of these are
outlined, and future actions recommended.
© 2003 Elsevier Ltd. All rights reserved.
Keywords: Kyoto Protocol; Remote sensing; Verification; Compliance; ARD

1. Introduction
The Kyoto Protocol to the United Nations Framework
Convention on Climate Change (UNFCCC) contains quantified and legally binding commitments to limit or reducegreenhouse gas emissions to 1990 levels. The protocol allows sinks associated with vegetation growth and expansion
to be included to offset carbon emissions, which in turnraises debate about the adequacy of existing methods forestablishing reliable estimates of 1990 carbon stocks/sinks
levels and for measuring and monitoring current and futurecarbon stock/sinks. The role of remote sensing is therefore
under scrutiny given its potential capacity for systematic
observations at scales ranging from local to global and forthe provision of data archives extending back over several
decades. These issues also underscore a need for the exchange of information between remote sensing scientists andorganisations (e.g. national and international policy makers,
government agencies and both legal and scientific bodies)
involved in the development and implementation of the
protocol.
∗ Corresponding author. Tel.: +81-3-6221-9074; fax: +81-3-6221-9192.

E-mail address: ake.rosenqvist@jaxa.jp
ake.rosenqvist@nasda.go.jp(Å.
(Å.Rosenqvist)
Rosenqvist).
1462-9011/$ – see front matter © 2003 Elsevier Ltd. All rights reserved.
doi:10.1016/S1462-9011(03)00070-4

In October, 1999, two working groups of the International Society for Photogrammetry and Remote Sensing(ISPRS) joined with the University of Michigan (MI, USA)to convene discussions on how remote sensing technology
could contribute to the information needs required for the
implementation of the Kyoto Protocol and compliance with
its terms. The workshop, which was attended by representatives from national government agencies, internationalorganisations and academic institutions, set out to review
the Kyoto Protocol and to identify areas where remote sensing technology could provide support; to review current andnear future remote sensing technologies that could support
the information requirements identified; and to highlightshortcomings and areas where additional research would
be necessary. In addition, legal aspects of trans-national
remote sensing in the context of the Kyoto Protocol were
investigated.

2. Remote sensing relevance to the Kyoto Protocol
Article 3.1 of the Kyoto Protocol states that “The Parties
included in Annex-I shall, individually or jointly, ensurethat their aggregate anthropogenic carbon dioxide equivalent

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Å. Rosenqvist et al. / Environmental Science & Policy 6 (2003) 441–455

emissions of the greenhouse gases listed (in Annex A) do
not exceed their assigned amounts . . . ” “. . . with a view to
reducing their overall emissions of such gases by at least
5% below 1990 levels in the five-year commitment period
2008 to 2012”. Emissions of six greenhouse gases (measured
in terms of carbon dioxide (CO2 ) equivalents as a functionof the gases global warming potential) are covered by the
Kyoto Protocol; CO2 , methane (CH4 ), nitrous oxide (N2 O),hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) andsulphur hexafluoride (SF6 ). Of these gases, remote sensing
is (at present) best equipped to quantify changes in CO2 and,to a certain extent, CH4 , particularly in relation to land use,land use change and forestry (LULUCF) activities.
A major benefit of using remote sensing data to support
calculations of emissions is that systematic observation systems are provided and historical archives of data exist, which
can be augmented through current and future data acquisitions. As such, remote sensing can (to some extent) meet therequirements of Article 10, which highlights the importanceof developing such systems and archives for monitoring andassessing status and trends in the global terrestrial carbon
budget.
Remote sensing can also play a role in the establishmentof national carbon stock baseline datasets and assessmentof change in stocks. Such datasets are implicit requirements
under Article 3, which introduces the need to quantify afforestation, reforestation and deforestation (ARD; Article
3.3), revegetation and land use management (under specified categories; Article 3.4) and to establish a baseline ofcarbon stocks for 1990 (Article 3.1; where 1990 is regarded
as the starting point for accounting) against which to assesschange. A role for remote sensing was also identified in relation to Articles 4 and 12, which outline the rules for emission
trading between Annex-I Parties (by way of joint implementation) and between Annex-I and non-Annex-I Parties(by way of the clean development mechanism (CDM)).
References to CH4 in the protocol are few and the roleof significant anthropogenic CH4 sources (e.g. through rice
irrigation, aquaculture and hydroelectric reservoirs) is not
explicitly mentioned. Directives for accounting for such CH4sources may, however, be included in the future and the roleof remote sensing is therefore considered.

the key terms referred to in the original version of the protocol. For this reason, the characterisation of “forest”, ARD
terms (under Article 3.3) and other points of uncertainty
were clarified at the conference of parties (COP) meetings
in Bonn and Marrakesh in 2001.
Significantly, the Bonn Agreements (UNFCCC, 2001a)state that forest and ARD activities shall be defined on thebasis of change in land use (Annex, VII/2) rather than land
cover, as observed through remote sensing. This implies that
remote sensing data may not be used directly to distinguishland belonging to either “forest” or the inverse “non-forest”
category and, in turn, recognition of A, R or D as changes
between these categories. As an example, a clear-felled areathat is “expected to revert to forest” will (according to thisterminology) remain within the forest class and the action
will not be accounted for as deforestation in the Kyoto
sense. The distinction of “Kyoto ARD” events from otherchanges in forest cover will not therefore be possible using remote sensing data alone, and additional land-based insitu information will be required. Likewise, as only direct
human-induced ARD events are to be accounted for, remotesensing data will have to be used synergistically with othersources of information.
3.2. Forest and ARD

Although remote sensing data can be used to support the
Kyoto Protocol, the utility of these data is dictated largely
by protocol definitions and requirements, which are clarified
in the following sections.

Forest has been defined in the Marrakesh Accords(UNFCCC, 2001b) as a minimum area of land of 0.05–1.0 hawith tree crown cover, or equivalent stocking level, of
more than 10–30% and containing trees with the potential to reach a minimum height of 2–5 m at maturity.
Young natural stands, all plantations and harvested areas“temporarily” below the thresholds applied but which are
expected to grow or revert to forest, are to be included under the forest category (UNFCCC, 2001b). The definition
to be adopted by any one country is optional within theintervals given for minimum area and crown cover, implying that the (remote sensing) observation requirements
will vary between countries. The minimum area specified(0.05–1.0 ha) implies observation at a minimum effective ground resolution requirement of magnitude 20–25
to 100 m.
Afforestation is the direct human-induced conversion ofland that has not been forested for a period of at least
50 years, while reforestation implies conversion of landthat once was forested, but that had been converted to
non-forested land. For the first commitment period, reforestation activities are limited to reforestation occurring on
those lands that did not contain forest on 31 December 1989.
Deforestation is simply defined as the direct human-induced
conversion of “forest” to “non-forest” (UNFCCC, 2001b).

3.1. Definitions of land use

3.3. Revegetation and land management

A point of uncertainty impeding the assessment of emissions was the lack of specific definitions relating to some of

As an option, revegetation, forest management, cropland
management and grazing land management may, according

3. Implications of the Bonn Agreements andMarrakesh Accords

Å. Rosenqvist et al. / Environmental Science & Policy 6 (2003) 441–455

to the Bonn Agreements, also be accounted for under Article 3.4 and during the first commitment period (UNFCCC,
2001a).
Revegetation is an activity which does not meet the definition of either afforestation or reforestation, but whichaims to increase carbon stocks (on sites larger than 0.05 ha)
through the establishment of vegetation (UNFCCC, 2001b).
While the carbon stock levels presumably remain within the“non-forest” category, the detection and monitoring of suchareas may be undertaken by remote sensing.
Forest management is defined as a system of practices
for stewardship and use of forested lands aimed at fulfilling relevant ecological, economic and social functionsof the forest in a sustainable manner. Cropland management is the system of practices on agricultural land andon land that is set aside or temporarily not being used forcrop production; grazing land management is the systemof practices on land used for livestock production aimed
at manipulating the amount and type of vegetation andlivestock produced (UNFCCC, 2001b). While the threemanagement categories and the “systems of practices” referred to do not rule out the potential use of remote sensing,the definitions are presently too general to establish clear
strategies.
3.4. The clean development mechanism
The CDM is intended to encourage, through transfer oftechnology and capacity building, sustainable developmentof Annex-I countries and to enable these countries to meetpart of the Kyoto commitments through abatement projects
in non-Annex-I countries. At COP 6:2 in Bonn, it was determined that afforestation and reforestation projects may optionally be accounted for under the CDM (Article 12). The
decision concerns the first commitment period only and theregulations for CDM in subsequent commitment periods areto be decided as a part of future negotiations between theparties (UNFCCC, 2001a).
The inclusion of LULUCF projects under CDM is, fromthe remote sensing perspective, a significant decision which,amongst others, implies an extension of eligible lands in the
Kyoto context, from Annex-I territory only, to encompass
areas in all ratifying countries. A key and notable issue
is that eligible LULUCF activities comprise afforestation
and reforestation projects whilst deforestation activities are
excluded. This omission raises the possibility that forest
accumulation through the CDM may result in deforestation elsewhere, or that deforestation may precede a CDM
project. There is thus a need for a surveillance system todocument the past and future land use history—a task inwhich remote sensing can be expected to play a significant role. As information about CDM projects, including
start-dates, areal coverage and geographic locations are
not publicly available—at least not at present—repetitive
observations with extensive global coverage will be
required.

443

4. The contribution of remote sensing
Over the past half century, a range of airborne andspace-borne sensors has acquired remote sensing data, with
the number of sensors and their diversity of capability
increasing over time. Today a large number of satellite
sensors observe the Earth at wavelengths ranging fromvisible to microwave, at spatial resolutions ranging fromsub-metre to kilometres and temporal frequencies ranging
from 30 min to weeks or months. In addition, archives ofremotely sensed data are increasing and provide a unique,but not complete, chronology of the Earth during this time
period. New sensors are continually being launched andexisting sensors are often replaced to ensure continuity inthe data record. Given this enormous resource, there seems
potential for remote sensing to assist countries meet their
obligations under the Kyoto Protocol.
Five major areas have been identified where remote sensing technology could be applied to support implementationof the treaty:• provision of systematic observations of relevant land
cover (Articles 5 and 10);• support to the establishment of a 1990 carbon stock baseline (Article 3);• detection and spatial quantification of change in land
cover (Articles 3 and 12);• quantification of above-ground vegetation biomass stocks
and associated changes therein (Articles 3 and 12);• mapping and monitoring of certain sources of anthropogenic CH4 (Articles 3, 5 and 10);• the application of remote sensing to each of these areas
is outlined in the following sections.
4.1. Provision of systematic, repetitive and consistentobservations
Remote sensing instruments are capable of providing
systematic observations of land cover, repeatedly and consistently, as implied under Articles 5 and 10. Optical sensors
providing systematic observations at the regional/global
level and at coarse (≥1 km) spatial resolution include the
NOAA advanced very high resolution radiometer (AVHRR)
and SPOT VEGETATION. At finer (10–30 m) spatial resolution, Landsat sensor (currently the Enhanced Thematic
Mapper Plus or ETM+) and SPOT sensor (currently thehigh resolution visible infrared or HRVIR) data can be
combined to provide regional and even continental level observations. The Terra-1 Moderate Resolution Imaging Spectroradiometer (MODIS) and ADEOS II Global Land Imager(GLI) represent a new generation of medium (250–500 m)
spatial resolution sensors and an important bridge between
those observing at fine and coarse spatial resolutions.
The frequency of observation by optical sensors ranges
from several times daily (e.g. NOAA AVHRR) to every
16–18 days (e.g. Landsat ETM+) although cloud cover,444

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haze and smoke often limit the number of usable scenes.
These limitations can be overcome using fine (10–20 m)
space-borne synthetic aperture radar (SAR) sensors thatobserve under all weather conditions and also during day
and night. Key sensors include the European Resources
Satellite (ERS-1/2), RADARSAT-1/2, the Japanese Earth
Resources Satellite (JERS-1) SAR and the ENVISAT Advanced SAR (ASAR). These sensors have the capacity toprovide more consistent coverage at continental scales andover short timeframes—if systematic acquisition strategies
are implemented–and their use for regional-scale vegetation
mapping has been demonstrated both for the tropics andthe boreal zone (Rosenqvist et al., 2000b; Schmullius et al.,2001).
Data from both optical and SAR sensors are available
to support the requirements of the Kyoto Protocol on along-term basis. As time progresses, archives of these data
are augmented allowing long-term continuity of observationand chronicling of land cover change. As examples, datafrom the NOAA, Landsat and SPOT sensors are available
from the 1960s, early 1970s and mid 1980s, respectively
and continuation of these archives is anticipated given theplanned launch of Landsat-8 and NOAA AVHRR successors. New sensors are often designed such that data areinter-comparable (in terms of their spectral, temporal andspatial resolutions) with their predecessors, and successful
deployment and provision of data by several (e.g. Terra-1
ASTER and MODIS) is likely to render these as key complementary or supplementary land observing systems infuture years.
Although space-borne sensors can provide ample data,
the systematic acquisition of data is essential to optimise
support for the Kyoto Protocol and climate change research (as addressed by the UNFCCC). For fine spatial
resolution optical and SAR sensors, which operate at local to regional rather than continental scales, observations
at similar times (i.e. seasons) within and between years is
critical and “random” observations should be discouraged.
Although most satellite operators have performed dedicated acquisition campaigns for certain regions, repetitive
regional-scale monitoring over longer time periods is essential (Rosenqvist et al., 2003). However, to achieve this, afederated multi-national approach with common goals andthematic definitions is required, and schemes such as thoseundertaken within the framework of the global observationsof forest cover (GOFC) pilot project (now under the auspicesof FAO GTOS; Ahern et al., 1998) should be encouraged.
4.2. Support to the establishment of a 1990 carbon stockbaseline
Article 3:4 of the Kyoto Protocol states that each Annex-I
country shall “provide data to establish its level of carbon
stocks in 1990 and to enable an estimate to be made of itschanges in carbon stocks in subsequent years”. However,
Article 3:5 of the protocol also states that Annex-I countries“undergoing the process of transition to a market economy”
may, under certain circumstances, “use a historical baseyear or period other than 1990 for the implementation of
its commitments”. Hence, baselines formulated after 1990
may (for certain countries) be considered. Nevertheless, as
1990 is expected to be the dominant base year, the selectionof sensors used to support the establishment of this baseline dataset will be limited to those in operation during orin proximity to that year.
Among the fine spatial resolution optical sensors providing local to regional observations, only Landsat TM and
SPOT High Resolution Visible were in operation in 1990.
The use of these data for compiling a regional 1990 land
cover map to support the establishment of the carbon stockbaseline is realistic, particular at a national level and forsmaller countries or regions. Furthermore, the cost of historical data from these sensors has generally been loweredin recent years. An assessment of land (vegetation) cover in1990 can be made using these data, although carbon stocks
are less easily quantified for several reasons. First, relationships established between vegetation biomass (carbon
stocks) and data from optical sensors are generally weak.
Second, in many countries, biomass data are often sparseand inaccurate and generally insufficient to quantify the carbon stocks of the diverse range of vegetation types/covers
mapped using such remote sensing data, particularly wherea wide range of regeneration and degradation stages exist.
An alternative approach is to use coarser resolution remote sensing data acquired during or in proximity to 1990
to generate the baseline datasets. As an example, a Global
Land Cover map (for 1992) was generated from 1 km NOAAAVHRR data within IGBP DIS. Archives of NOAA AVHRR
data are also available to assist establishment of a 1990
baseline (Belward et al., 1999; Townshend, 1994). However,to be useful, land cover classes need to be re-defined andadapted to classes relevant to the Kyoto Protocol. An more
involved approach would be to integrate data from both fineand coarse spatial resolution systems to provide greater detail in areas of change and less in areas of no-change.
No orbital active microwave systems were in operation in 1990 and the use of SAR data to support a 1990
baseline is generally not feasible. Even so, established relationships between JERS-1 SAR data and above groundbiomass (Luckman et al., 1998; Lucas et al., 2000) could
be used to generate baseline datasets of regenerating forest biomass for any period between 1992 and 1998 (i.e.
the active operation period) which could subsequently bebackdated to 1990 using knowledge of vegetation biomass
accumulation rates (obtained either through field observations or productivity models) and information on land usechange over the intervening period. For non-1990 baselinecountries in the tropical and boreal zones of the Earth,continental scale (100 m resolution) JERS-1 SAR mosaics
from 1995–1996, generated within the Global Forest Mapping projects (Rosenqvist et al., 2000b), could support theestablishment of a mid-1990s carbon baseline.

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A major limitation of SAR systems with respect to vegetation mapping is their sensitivity to surface topography,
which limits their general application to flat or gently undulating terrain. Radar data are also subject to speckle, whichon one hand enables techniques such as radar interferometry,but on the other reduces the effective ground resolution—
typically by a factor of 3 to 4. As a result, effective resolutions in the order of 50–100 m are generally the best that
past, current and near future space-borne SAR systems canprovide.
4.3. Detection and spatial quantification of change
in land cover
Articles 3.3, 3.4 and 12 are indirectly concerned with theidentification and spatial quantification of areas affected by
ARD, including changes resulting from fire and revegetation.
The issue of biomass quantification is discussed in Section
4.4. While the accounting of land cover change is limited todirect human-induced changes, all disturbances and changes
initially need to be identified before the human-induced ones
can be distinguished. As mentioned previously, distinction
between natural and human-induced changes cannot be performed using remote sensing data alone and contemporary
in situ information may often be required for this task. Nevertheless, the identification of land cover changes (both natural and human-induced), and hence potential ARD, is still
considered a valid and major contribution that remote sensing can make in the context of the protocol.
To detect land cover change effectively, image acquisitions on a repetitive basis are required and data from Landsat
and SPOT sensors are well suited for this purpose. Observations should preferably occur on an annual basis and at
a similar time of year to minimise the effects of seasonality in the data. In regions where rates of vegetation change
are low or less easily detected (e.g. through low rates of regeneration), observations over 5-year periods may be sufficient. Spatial resolutions less than ∼20–25 m are generally
required to detect changes in the smallest Kyoto land parcels
at 0.05 ha, although integration within coarser spatial resolution data may be useful for detecting large disturbances orchanges in land cover.
Techniques for detecting and spatially quantifying typesof deforestation (D) activities that lead to removal of the entire forest canopy and stock (e.g. clear felling or severe fires)are reasonably well established. However, gradual degradation/decrease of vegetation through thinning or selective
logging (D) or gradual vegetation increase through growth(A, R or revegetation) is generally more difficult to discern,and longer time series (i.e. several years or even decades) of
satellite sensor data are often needed. Furthermore, as A, R
and revegetation often occur in relative small patches within
non-forest areas, extensive monitoring of these non-forest
areas is an implied requirement.
Establishing a consistent remote sensing approach for
identifying when an area passes the crown closure thresh-445

old of 10–30% (and thereby changes from “forest” to“non-forest” or vice versa) is critical to the Kyoto Protocol.
The approaches adopted are, however, likely to vary depending upon the country and biota considered and also thecharacteristics of the observing sensor(s). In many cases,
it is anticipated that accuracy in the determination of thethreshold will be insufficient for Kyoto requirements andfield verification will be necessary.
For estimating canopy closure, optical systems are more
likely to be useful as data acquired in visible, near infraredand short wave infrared wavelength regions are more sensitive to the structure and closure (including the absorbed
photosynthetically active radiation (APAR)) of the vegetation canopy. Indices such as foliage projected cover (FPC),
canopy projected cover (CPC) and leaf area index (LAI) canbe quantified using optical data (Kuhnell et al., 1998) andchanges in these indices may reflect the nature of ARD activities and the impacts of, for example, fire damage.
In many cases, repetitive (annual and/or seasonal) measurements will generally be required to detect forest change
activities, particularly in rapidly changing environments.
However, regional to global observation using fine spatial
resolution data is demanding of time and resources and amore efficient approach would be to utilise coarser spatial
resolution sensors with shorter revisit cycles and larger
swaths for identifying “hot spots” of change. Once identified, the extent of the changes in these areas could be investigated further using finer spatial resolution data (Achard
et al., 1997). Sensors (e.g. AVHRR, ATSR) observing in the
mid infrared (∼3.55 ␮m wavelength) are particularly usefulfor detecting hotspots of changes as, at these wavelengths,
fires are easily detected and often signal a change in land
cover at regional (Barbosa et al., 1998, 1999) and/or global(Dwyer et al., 1998; Grégoire et al., 1998; Stroppiana et al.,
2000; Pinnock and Grégoire, 2000) scales.
Space-borne SAR data may also prove useful for detecting land cover change and quantifying canopy closure.
For example, shorter wavelength (∼5.5 cm) C-band andlonger wavelength (∼23.5 cm) L-band and SAR backscatterdata are sensitive to the amount of foliage/small branches
and woody (branch/trunk) components respectively andtime-series of these data could be used for quantifying
changes in vegetation through ARD activities. Even so, confusion with rough surfaces and herbaceous grasslands may
occur at C-band. If limited to one band, the detection andquantification of ARD areas is best addressed using longer
wavelength L-band SAR systems, which are more sensitive
to the range of vegetation structures associated with different growth stages of forests. The integration of longer andshorter wavelength SAR data also improves the capacityfor vegetation distinction as demonstrated through studies using the 1994 multi-band, polarimetric space Shuttle
Imaging Radar (SIR-C SAR) data (Ranson and Sun, 1994;
Way et al., 1994). Although no space-borne multi-band
missions are yet planned, polarimetric and dual-band data
will be available in the near future by the synergistic use446

Å. Rosenqvist et al. / Environmental Science & Policy 6 (2003) 441–455

of the Advanced Land Observing Satellite (ALOS) Phased
Array L-band SAR (PALSAR), and the C-band ENVISAT
Advanced SAR (ASAR) and RADARSAT-2/3 SAR. A
prerequisite for successful utilisation of data from different
sensors however, is that the data are acquired during thesame time periods, thus calling for joint observation campaigns and close collaboration between satellite operators,
which currently is not the case.
To the extent that short-repeat observations of C-band data
can be obtained, a technique based on interferometric coherence can be applied to significantly improve C-band sensitivity to biomass and thus changes in canopy cover and land
cover (Treuhaft et al., 1996; Wegmüller and Werner, 1997).
Coherence measurements require that SAR observations occur over a short (1 day) repeat period and such interferometric (tandem) datasets have been provided by the ERS-1
and ERS-2 SAR sensors. RADARSAT-2/3 SAR provide thenext potential opportunity for tandem operations (Lee andJames, 2001).
An advantage of SAR data for detecting land coverchange is that data takes can be timed accurately due totheir all-weather capability, thereby optimising conditions
for detecting changes in land cover. Even so, ground conditions (e.g. moisture in the soil and on vegetation following
rainfall) can impact on the SAR response and repetitive dataacquisitions therefore need to be planned (e.g. for a specificmonth or season) such that seasonal bias in the time series isavoided (Rosenqvist et al., 2003). Active fires are not possible to detect with microwave systems as the smoke plumes
are invisible to the radar. Burn scars however, may be detected in cases where the fire has caused substantial change
to the structure of the forest (Antikidis et al., 1997) and canbe detected from SAR for several years after the burn.
As long as imaging laser infrared detection and ranging(LIDAR) systems from space are unavailable, detection of
ARD activities and burn scars will be limited using this technology. As such, the feasibility of using LIDAR to address
ARD events for extensive areas is (as yet) unproven. However, LIDAR should provide the capacity to repeatedly characterise structural attributes at specific locations or collectsample sets in known ARD areas (Blair et al., 1999).
4.4. Quantifying above-ground vegetation biomass
stocks and changes in stocks
In the context of ARD (Article 3.3) and the CDM (Article 12), direct estimation of changes in biomass stocks using
remote sensing data is of prime interest. A wide range of approaches have been proposed for quantifying biomass using
optical, SAR and LIDAR systems, although no studies have
yet presented a technique that is consistent, reproducible andapplicable at regional or continental scales.
Using optical data, direct measurement of total above
ground biomass stocks, or changes in such, have not beenachieved. Indirect estimates have nevertheless been generated; with most relying on (either singly or in combination)

empirical relationships established with vegetation indices(e.g. the normalised difference vegetation index (NDVI)),
canopy cover measurements (e.g. FPC) and reflectance datain the red, NIR and/or SWIR regions. Photosynthetically active radiation (PAR) has also been estimated routinely frommulti-spectral sensors and has, in some studies (e.g. Asrar
et al., 1984; Tucker and Sellers, 1986; Prince and Goward,1995), been combined with environmental data and/or forest
growth models to predict net primary productivity (NPP).
More recently, the use of multi-angular measurements (e.g.
from the Terra-MISR instrument) has shown promise for indirectly quantifying biomass (Martonchik et al., 1998).
For some time, the potential of SAR data for quantifying
the above ground biomass of vegetation has been demonstrated and Kasischke et al. (1997) provide a comprehensive
review of the use of imaging SAR for biomass estimationand other ecological applications. The SAR response fromvegetation varies with wavelength and polarisation and isdependent largely upon the size, dielectric constant (which
relates to moisture content) and the geometry of leaves,branches and trunks and the roughness and moisture content of the underlying surface. At C-band, microwaves interact largely with the leaves and smaller branches and are
generally not well suited to biomass estimation, particularly as saturation of the signal occurs at approximately
20–30 Mg ha−1 . At L-band microwaves penetrate the leaves
and smaller branches and interact with larger woody components, particularly branches. Depending upon attenuation in
the crown layer and the openness of the vegetation, interaction with trunks may occur. Saturation of the L-band return
tends to occur at approximately 60–100 Mg ha−1 , depending upon the species type and biota (Dobson et al., 1992;
Imhoff, 1995; Luckman et al., 1998).
Space-borne SAR typically operate at C- and L-band. The
saturation of C- and L-band data at relatively low levels of
biomass limits the use of these data for routinely quantifying biomass, particularly as the majority of forests andwoodlands globally support an above ground biomass of>100 Mg ha−1 . Even so, these data may be useful for quantifying the biomass of vegetation <100 Mg ha−1 , particularly
those associated with A, R and revegetation activities after 1990. While the “biomass equivalent” forest/non-forest
threshold will vary within and between regions and biota,
the threshold nevertheless is generally set low and for a majority of areas can be expected to lie within the sensitivity
range of L-band SAR. The combination of C- and L-band
and also optical data may also be useful for discriminating
regenerating forests with high leaf/canopy cover and low
woody biomass (Lucas et al., 2003a). Improved C-band sensitivity to biomass has also been achieved using interferometric techniques (Askne et al., 1997; Santoro et al., 1999),
although the accuracy is largely dependent on factors suchas the orbit and surface conditions, and more research is required before the technique might become operational.
Time-series of SAR data, in particular at L-band, issuitable for detecting areas of change and establishingÅ. Rosenqvist et al. / Environmental Science & Policy 6 (2003) 441–455

relative estimates of incremental biomass change. The
integration of spatial information on land use change (derived from time-series comparison of remote sensing data),
species/community composition (derived from classification of reflectance data) and biomass (derived from SARdata) can also be used to better understand, quantify andpredict rates of biomass accumulation and turnover (Lucaset al., 2003b).
The ability to retrieve biomass is enhanced when fullypolarimetric SAR data are acquired. Such data are provided largely using sensors onboard airborne platforms (e.g.
NASA Jet Propulsion Laboratory’s (JPL) AIRSAR or the
ESA’s E-SAR). Several of these sensors also observe at
longer P-band (∼68 cm) wavelengths, which are able to penetrate further into the canopy and interact largely with trunks
and larger branches, and saturation occurs at higher levelsof biomass up to ∼150–200 Mg ha−1 (Ranson et al., 1997).
These polarimetric systems provide cross-polarised (HV)
data, which generally have a large dynamic range and exhibit greater sensitivity to canopy structures. Several studies(e.g. Dobson et al., 1995) have tried to extend the range ofbiomass that can be estimated by relating SAR backscatterat different wavelengths and polarisation’s to the biomass ofcomponents (i.e. leaves, branches and trunks).
Though using polarimetric data, alternative approaches
to biomass estimation have been advocated. Saatchi andMoghaddam (2000), for example, presented a semi-empiricalapproach to the estimation of biomass through evaluating
the contribution, through different scattering mechanisms,of different components of the vegetation (stem and canopy)
to the SAR return. More recently, Moghaddam and Lucas(2003) have demonstrated the potential of SAR inversion
modelling for quantifying vegetation biomass from polarimetric SAR data.
The use of polarimetric C- and L-band SAR data is anticipated to increase with the dual polarimetric ENVISATASAR and the forthcoming dual polarimetric/fully polarimetric ALOS PALSAR (L-band) and RADARSAT-2/3(C-band). The potential for mapping ARD activities andquantifying vegetation biomass, either through empirical orsemi-empirical (inversion) approaches, may be enhanced
considerably by the introduction of such data.
Although space-borne P-band SAR systems have been
proposed, questions over the ionospheric effects on these andother low frequency radars have not been resolved. These
effects relate to understanding the impact of total electron
content (TEC) in the ionosphere, which results in deformation and polarimetric rotation of the transmitted signal (Kimand van Zyl, 1999; Ishimaru et al., 1999). Such polarimetric rotation may be corrected using a fully polarimetric system (Siqueira et al., 2000), while other ionospheric artefacts
may be reduced to acceptable levels by accurate timing of
the data acquisition (dawn/dusk) when TEC is at minimum(Snoej et al., 2001).
Airborne SAR systems operating in VHF-band, with evenlonger wavelengths than P-band are potentially the most use-447

ful for directly measuring and quantifying biomass as tree
trunks act as the dominant scatterers and linear relationships
between the SAR return and trunk volume has been observed. Results from the airborne CARABAS (wavelength
3.3–15 m; Ulander, 1998), deployed in temperate and boreal forests in Europe, and the BioSAR system (2.5–3.75 m;
Imhoff et al., 2000), deployed in the neo-tropics, have shownthat biomass measures can be accurately derived within±10% of field measures for forests up to 500 Mg ha−1 . Saturation of the two systems has not yet been encounteredand remains to be determined. The sensitivity to surface topography is less than for shorter wavelength SAR and topographic effects can, to a large extent, be corrected (Smith
and Ulander, 2000). These systems show great promise for
local to sub-regional applications using aircraft. However,the deployment of space-based VHF sensors is not technically feasible due to ionospheric interference with the signal and its potential has yet to be explored (Ishimaru et al.,1999). Alternative platforms may also be considered, including long endurance stratospheric airships (UAV), which are
not subject to ionospheric distortions. Such platforms could
have an endurance of 6 months to 5 years using combinations of solar and fuel cell technologies.
LIDAR technology has only recently been explored forvegetation characterisation and mapping. The LIDAR transmits a vertical near infrared (NIR) laser pulse towards theland surface which then reflects from objects and returns
to the sensor. The delay in time between the transmission
and receipt of the pulse is related directly to distance andhence the height of objects (e.g. tree crowns) and the groundsurface can be retrieved. LIDAR footprint sizes may varyfrom 0.25 to 5 m with accuracies of <25 cm in elevationachievable. Using small footprint laser, strong relationships
with variables such as tree height, stem volume, biomass
and canopy closure have been obtained (Tickle et al., 2003).
Large footprint LIDAR are able to provide a complete waveform of reflected light allowing attributes such as stem diameter, basal area, stem volume and above ground biomass tobe retrieved (Means et al., 1999; Weishampel et al., 2000).
Such sensors are currently available on airborne platforms(Blair et al., 1999). A space-borne instrument, the vegetation canopy lidar (VCL), has been planned (Dubayah et al.,1997), although the launch has been postponed indefinitely.
The VCL is not an imaging instrument but has been designed to collect a series of point samples along the flightpath. Thematic products could only be foreseen if these data
are used in combination with other spatially extensive data
(e.g. optical or SAR).
Combined with allometric models, which approximate
component or total biomass from measurable parameterssuch as tree height, diameter and canopy closure, point datacollected by LIDAR systems relating to height and canopy
dimensions can also facilitate measurements of above
ground biomass to acceptable levels of accuracy. Interpolation of LIDAR-derived estimates of biomass may thenbe undertaken in conjunction with SAR and optical data

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Å. Rosenqvist et al. / Environmental Science & Policy 6 (2003) 441–455(Lucas et al., 2003a). A recognised limitation of LIDAR,
however, is that tree species/genera cannot be discriminatedfrom the signal return and as wood density (and hence
biomass) varies between tree species of similar height andage, biomass estimates may be less accurate from LIDAR
alone. Even so, integration of LIDAR with hyperspectral
data (e.g. from the compact airborne spectrographic imager(CASI) instrument), from which tree species can be discriminated, may allow this information to be incorporated.
4.5. Mapping and monitoring of certain sources ofanthropogenic CH4
The mapping and monitoring of certain sources of anthropogenic CH4 is listed separately as it is not generally
related to forestry or to forest change. Apart from livestock
management—which is not considered feasible to monitor
by remote sensing—CH4 is emitted as a result of anaerobic
decomposition in open water bodies following extended periods of inundation. Typical anthropogenic sources of CH4
include irrigated rice paddies, aquaculture (e.g. fish andshrimp cultivation) and hydroelectric reservoirs. CH4 is also
emitted through burning of vegetation.
Detection and spatial quantification of open water bodies, such as those used for aquaculture and hydroelectricreservoirs, can be undertaken using single date fine spatial
resolution optical sensors (e.g. SPOT HRVIR). Rice paddies may also be identified in a single date image althoughrepetitive measurements during the growth season are recommended for allowing discrimination from other agricultural crops and to monitor changes in the water regime, a key
factor triggering the CH4 emissions. However, cloud covermay, in the latter case, constitute an obstacle to obtaining
relevant multi-temporal data. SAR data are also well suitedfor multi-temporal monitoring of irrigated rice, as regularacquisitions can be performed irrespective of weather conditions. Both C- and L-band SAR have been used to map
rice growth (Le Toan et al., 1997; Rosenqvist, 1999) and it
is now deemed possible to perform this in an operationalway, using current sensors (ERS-2 SAR, ENVISAT ASAR
and RADARSAT-1 SAR) as well as those planned in thenear future (ALOS PALSAR and RADARSAT-2 SAR). Estimates of CH4 emissions from fire may also be refined using
datasets on burning patterns and burnt area, as derived fromoptical and/or SAR data. The use of LIDAR for estimating
CH4 emissions has not, as yet, been considered.
5. The requirement for in situ data
For all applications discussed, up-to-date, quantifiable, insitu data are needed for development of algorithms for quantifying land cover, land cover change, biomass and sourcesof anthropogenic CH4 and validating derived products. A
thematic product derived from remote sensing data, be it aland cover classification, carbon stock estimate or a “simple”

ARD change map, has no value or credibility unless its accuracy can be reliably assessed and quantified. Althoughcollection of field data is generally a painstaking, time consuming and expensive endeavour, the relevance of in situ
data cannot be over-emphasised. In any operational monitoring effort using remote sensing technology performedin support to the Kyoto Protocol, systematic collection ofin situ data should be performed as an integral part of theundertaking.
An important component of estimating biomass forground truth purposes is the development of allometricequations that estimate, for example, biomass from tree
size (e.g. height) measurements. A key concept is that allometric equations may be similar for a number of genera
at any one site as growth and biomass allocation is dictatedlargely by prevailing environmental conditions. By harvesting species of ‘key’ genera along environmental gradients,
a generalised suite of allometric equations may be generated for local and global application and could be used tofurther the interpretation of remote sensing data and validation of data products. Cost effective and efficient methodsof quantifying component biomass in the field also need to
be developed further.

6. Accessibility and affordability
Over the last two decades there has been a revolution in
the way information about the environment is acquired, processed and stored, which has centred on the use of computer
technology for data collection and manipulation, including
the ability to spatially integrate, interrogate and analyse thenature of the relationships that exist with co-located data.
Remote sensing, together with the use of geographic information systems (GIS) and global positioning systems (GPS),has played a key role in this development and has had a significant impact on the way local, regional and global data
about the environment are acquired and analysed.
The major characteristics of ‘geographic information’are that the feature or object in question can be locatedaccurately, its dimensionality can be captured and it can bemeasured and described. Given that attributes about objects
are obtained through remote sensing, it is important thatthe processed information is stored systematically so that it
can be interrogated and translated from one measurement
framework to another and linked with other relational data
for mapping and modelling real world vegetation (including
forest) scenarios. GIS provides such a facility. The connectivity afforded by these systems is important because applications require not only an interdisciplinary approach, butalso the integration of information derived from a variety
of sources which span the physical and human sciences.
The development of such decision-support systems forvegetation assessment and analysis activities is jeopardised by difficulties arising out of operational constraints,
namely accessibility, affordability and timeliness and fromÅ. Rosenqvist et al. / Environmental Science & Policy 6 (2003) 441–455

infrastructure problems related to poor management, inadequate staffing and lack of training opportunities withincountry agencies. Appropriate government policies and institutional frameworks therefore need to be introduced toaddress these limitations and to facilitate further applications research, training and education.
Accessibility and affordability of remote sensing andrelated technologies should be addressed through agreed
international protocols that ensure the open exchange of remotely sensed data between all countries and agency users.
Timeliness depends on the actual information requirements
and the frequency and type of data being sought. Information needs can most effectively be addressed by having access to both optical and SAR remote sensing for vegetation,
biomass and vegetation change analysis.
Strengthening the institutional infrastructure capabilities
of countries and organisations involved in implementing
the protocol, needs collaborative technology transfer programs in which the ideas, skills and operational procedures
necessary to utilise remote sensing and spatial information
technologies are shared. The success of any technology
transfer program depends on the level of provision includedin the program for training skilled personnel. Failure to
give due attention to the training and education needs that
accompany the implementation and use of remote sensing
will limit not only its adoption but also the quality of its
application. It is obvious that technically advanced nations,in which the expertise for using remote sensing for vegetation analysis and biomass estimation exists, need to be
become involved in sharing the necessary data, skills andoperational procedures with those wishing to upgrade theirnational capability for using this technology.
As always, the cost incurred in acquiring remotely sensed
data constitutes a major problem for many developing countries. This is likely to become a major constraint in the future as more and more governments opt out of the role ofdata provider, preferring instead to hand this over to private
sector groups. Equity of access to remotely sensed data continues to be an issue that has not been adequately addressedby the international community.

7. Research topics
While remote sensing technology stands alone in being able to provide regional-global scale data acquisition
schemes and comparable datasets, it can not yet be considered operational in more than a handful of applications
relevant to the Kyoto Protocol. Furthermore, it is importantto acknowledge that research should not be limited to fulfilling the requirements of the Kyoto Protocol but should
also address the larger context of global change and measures that reduce uncertainties in estimating the terrestrial
carbon budget. The Kyoto Protocol, in this respect, should
constitute a partial requirement. Key areas of research are
identified below.

449

7.1. Optical and SAR data fusion
While the advantages and disadvantages of using opticaland microwave technologies have been outlined, fusion of
the two technologies is expected to hold great potential forenhanced thematic mapping and biomass estimation. Bothtechnologies have co-existed for almost a decade but surprisingly little definitive work has occurred to take advantage ofthe potential of data fusion and data mining.
Major candidate instruments for the synergistic use ofdatasets have been described in Section 4.1. A high priority
is the need to be able to seamlessly translate and integrate
data acquired from newer or replacement sensors with thatobtained from older sensors in order to ensure the long term
continuity of data flow. The calibration of MODIS data with
AVHRR data is a case in point. In this calibration it is important that indices, such as the NDVI used over the past 20
years as a major indicator of vegetation status and condition,
can be continue to be derived into the future.
The ability to incorporate conventional aerial photography
and imagery from airborne sensors (e.g. CASI, AIRSAR
and CARABAS) enhances the prospect of data integrationand the likelihood of performing scalar studies of vegetation
cover and biomass. Clearly, major outcomes of data fusion
will be improved delineation of vegetation and land covertypes, with the facility to use very fine to fine resolution
hyperspectral data to classify within genus groups.
7.2. LIDAR and synergy with other sensors
With the indefinite postponement of the launch of the
VCL sensor, space-borne LIDAR systems are unlikely to
be available in the near future. Nevertheless, LIDAR holds
a specific potential for contributing to the Kyoto Protocol through estimation of above ground biomass, although
research needs to concentrate on how the LIDAR returns
can be translated to provide estimates of above ground andcomponent biomass and define the structure of forests andwoodlands. The integration of hyperspectral data for refining estimates of biomass should also be investigated further.
A second research topic related to the LIDAR is synergy
with data from other sensors. As LIDAR can only provide
data in a sampled manner, extrapolation between LIDAR
sample points should be attempted using optical or SAR
data, or a combination of both.
7.3. Interferometric, polarimetric and/or multi-frequency
SAR applications
SAR interferometry has recently been shown to have potential for quantifying biomass, even at C-band which, withtraditional processing techniques, saturates at very low levels of biomass (Askne et al., 1997; Santoro et al., 1999).
The use of interferometric C-band, as well as, L- and P-banddata for biomass retrieval requires further investigation.

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Å. Rosenqvist et al. / Environmental Science & Policy 6 (2003) 441–455

Airborne SAR campaigns and the Shuttle Imaging Radar(SIR-C) missions have shown the potential of polarimetric
SAR applications for enhanced thematic sensitivity and vegetation structure. With the availability of ENVISAT ASAR
and the forthcoming launch of RADARSAT-2 and ALOS
(all with polarimetric capabilities) SAR, efforts should bemade to develop and enhance polarimetric techniques and toalign them with the requirements posed by the Kyoto Protocol and other international global change research issues.
Multi-frequency SAR applications also constitutes an area
which has been largely overlooked, despite the co-existenceof ERS-1 (C-band), RADARSAT-1 (C-band) and JERS-1(L-band) SAR for several years. This may be partly attributed to the lack of consistent data from the different sensors to allow fusion, and if synergies of using multi-band
SAR are to be explored further, proper co-ordination between space agencies to align the acquisition plans will be
required.
7.4. Space-borne P-band applications
Ionospheric interference with the radar signal at
VHF-band will generally prevent the operation of suchsensors from space. P-band (∼70 cm wavelength) is thelowest frequency possible to operate from an orbital platform in which the ionospheric effects can be corrected forby the use of a fully polarimetric system (Siqueira et al.,2000). Given the potential of polarimetric P-band datafor retrieving biomass, it is recommended that researchbe dedicated to further investigate the use of data from avariety of airborne platforms for biomass estimation andcharacterisation of vegetation structures across a range ofvegetation types. It is furthermore recommended that thenecessity of a space-borne P-band platform for terrestrial
carbon assessment be investigated not only in a scientificperspective, but also at political and administrative levels to overcome frequency allocation problems. Frequency
allocation requirements need to be put forward to the International Telecommunications Union (ITU) and the WorldRadio Conference (WCR) so that some part of the spectrum
can be reserved specifically for remote sensing purposes.
7.5. Low frequency SAR
Low-frequency (VHF band) radar holds a certain potential for biomass determination on a local to regional scale,particularly as saturation of the signal has not been observed(Imhoff et al., 1998, 2000; Ulander, 1998). The capabilities of low frequency radar systems for biomass retrieval
therefore need to be explored further within experiments
carried out across a greater range of vegetation types, particularly tropical rainforests and mixed species forests andwoodlands. The integration of VHF band radar data with
LIDAR and/or optical data also warrants further investigation, as well as the feasibility of developing platforms withlong endurance.

7.6. Field measurements and networking
Establishment of adequate, global scale, data bases ofground truth data is considered essential for the success ofusing remotely sensed data in support of the Kyoto Protocol.
Allometric models linking biophysical parameters and forest
biomass should also be developed. The distribution, geolocation accuracy, revision frequency, biophysical parameters
to be measured should be standardised and managed as apart of an international effort, such as that initiated within
the IGOS-P or the CEOS GOFC projects.

8. Legal issues
From the viewpoint of governing international laws andtreaties, a key consideration is the legal restrictions hat
might affect the utilisation of remote sensing technology tosupport treaty verification. Relevant in this context are the
UN Remote Sensing Principles (United Nations, 1986) andthe Outer Space Treaty (United Nations, 1967), as well as
the 1944 Chicago Convention governing international airlaw.
The UN Remote Sensing Principles—which do not constitute a binding treaty, but a set of statements from the UN towhich many countries subscribe—provide that space-borne
remote sensing activities can in principle be undertaken
without the specific permission of the state being imaged.
In contrast, airborne remote sensing activities fall within thejurisdiction of international aviation law which provide thatflights into and within the airspace of a country require theexplicit consent of the country in question. Neither air nor
space law make any distinction between passive and active
remote sensing techniques.
While the restrictions applying to space-borne remotesensing appear more liberal, the UN Remote Sensing Principles highlight the fact that remote sensing activities should(a) be carried out for the benefit and in the interests of all
countries, taking into particular consideration the needs ofthe developing countries (Principle II) and, (b) include international co-operation and technical assistance (Principles
V and VIII). Furthermore, when one country acquires dataover another, the sensed country should have access to thedata on a non-discriminatory basis and at reasonable cost(Principle XII).
In short, if monitoring is performed from outer space,states can legally collect data useful for the purposes statedin the Kyoto Protocol but should be willing to make thesedata available to the sensed state. It is not clear however, if
the data collected can be used to compel a state to comply
with the protocol when it has not expressly agreed to permit
verification. This issue has yet to be addressed.
Remote sensing technology should be viewed as a tool tosupport the Kyoto Protocol and its signatories and not as an
instrument for treaty policing. After all, the UN Principles
provide that remote sensing activities should be carried out

Å. Rosenqvist et al. / Environmental Science & Policy 6 (2003) 441–455

in the spirit of international co-operation and for the benefit
and in the interests of all countries.

9. Summary and recommendations
To support the requirements of the Kyoto Protocol, remotesensing can play an important role in providing systematic
observations of land cover, supporting establishment of a1990 carbon stock baseline, detecting and quantifying ratesof land cover change, quantifying above ground biomass
stocks and changes in these, and mapping and monitoringcertain sources of anthropogenic CH4 . The greenhouse gases
considered relevant in the context of remote sensing are
essentially CO2 and CH4 .
Systematic observations of the world’s forested regions are presently provided at a global level by coarse
and medium resolution sensors. For detecting hotspots ofchange, data from such sensors are most useful due totheir frequent temporal coverage, although for quantifying
change at the local to regional level, data provided by finer
resolution instruments, such as Landsat and SPOT sensors
are required. Space-borne SAR are suitable for detection
and quantification of land cover change, particularly inareas with persistent cloud cover.
For establishing a 1990 carbon stock baseline, historical
Landsat and sensors as well as JERS-1 SAR are most useful.
Within their range of biomass sensitivity (<100 Mg ha−1 ),SAR have the greatest potential for quantifying biomass (carbon stocks) at regional scales (Schmullius et al., 2001). At
more local levels and for a restricted range of environments,
airborne LIDAR, polarimetric SAR and VHF band radar
have shown significant potential for biomass estimation. For
detecting anthropogenic sources of CH4 , both space-borne
SAR and optical data are deemed important.
Key areas where further research activity should be directed to assist solving the specific needs of the protocol
include the fusion of optical and SAR data and developmentof procedures for data mining, increased synergy between
LIDAR and other sensors, development of applications
using SAR interferometry and polarimetry and low frequency radar, and the development of options for providing
space-borne P-band data. Better integration of field measurements and increased networking and communications
between scientists were also considered as priorities.
To ensure the uptake of remote sensing technology insupporting the Kyoto Protocol, access to and greater affordability of geographic information (including remote sensingdata) are considered essential. Internationally agreed protocols are therefore required to ensure the open exchange of
remote sensing data but also the provision of appropriate
training in the use of existing and new remote sensing technologies, particularly in applications relevant to the Kyoto
Protocol.
While the technological capacity of remote sensing to support the Kyoto Protocol exists, there seems a lack of commit-

451

ment from the international civilian space agencies to ensurethat adequate remote sensing data will be collected, even during the first commitment period. This lack of commitment
may in turn partly be linked to a lack of explicit demands
from policy makers in the environment arena. Nevertheless,the usefulness of remote sensing data on a regional scale
will depend largely upon whether a dedicated and systematic strategy for providing regional repetitive satellite sensor
data acquisition is implemented. If pursued by international
space agencies and other satellite operators, such dedicatedobservation plans would provide consistent archives useful for both ARD monitoring within the Kyoto context,
as well as for use in the broader global change scientificframework.
Arguments have been presented that much of the data required for the Kyoto Protocol could be centralised, thereby
ensuring consistency in data acquisition, interpretation,
product generation and emission estimation. Legally, no
obvious impediments to the acquisition of remote sensing
data over countries is evident. However, such data cannot
be used to compel states to fulfil certain obligations unless the states themselves expressly consent to monitoringby remote sensing. The issue of treaty verification is notaddressed in the protocol.

10. Conclusions
Although political in nature, the global impact of the Kyoto Protocol on technical and scientific issues of relevanceto the remote sensing community is considerable and unprecedented. Issues related to the protocol, in particular to
ARD activities, will affect the work of the scientific community for years to come. Consequently, it is recommended
that a considerable part of international remote sensing research activities be focused and aligned to fulfil the specificinformation needs posed by the Kyoto Protocol and, in a
broader context, the needs relating to full carbon accounting and an improved understanding of the terrestrial carbon
budget. Research topics of specific relevance not only relate
to remote sensing but also to the need for the availability ofin situ information.
Credibility and international acceptance of any method
proposed as a result of research into the terrestrial carbon budget are paramount. As such, the roles of the IPCC
and international science programmes and entities, suchas IGBP, IHDP, WCRP, IUFRO and IIASA, in providing
scientific guidance and dialogue to the Kyoto Protocol are
duly recognised. Dialogue with other national and international entities, such as the World Bank, GEF and national
development agencies will also be essential for capacity
building and technology transfer.
It is acknowledged that harmonising international efforts
is essential. Therefore, the activities recommended here
could best be pursued within the context of the terrestrial
carbon initiative and through IGOS Partnership and the452

Å. Rosenqvist et al. / Environmental Science & Policy 6 (2003) 441–455

GOFC Project, both which are considered to be of particularimportance and relevance in this context.

Acknowledgements
The views and conclusions expressed in this review
paper are to the greatest extent based on the discussions
held at the Michigan workshop in October 1999, andon the workshop report that followed (Rosenqvist et al.,2000a). The authors would hereby like to acknowledge
the joint nature of this work and convey our gratitude
to all workshop speakers and participants: Frank Ahern(previously Canada Centre for Remote Sensing), Alan Belward (European Commission, DG JRC), Ralph Dubayah(University of Maryland), Alfred de Gier (ITC), Steven
Mirmina (NASA Headquarters), Jiaguo Qi (Michigan
State University), Dan Reifsnyder (US State Department),
Paul Siqueira (Jet Propulsion Laboratory), David Skole(Michigan State University), John Townshend (Universityof Maryland), Philip Tickle (Australian Bureau of Rural
Sciences), Robert Treuhaft (Jet Propulsion Laboratory),
Compton Tucker (University of Maryland), Lars Ulander(Swedish Defense Research Establishment), Thomas Wagner (University of Michigan) and Diane Wickland (NASAHeadquarters).

Appendix A. Overview of remote sensing
instruments
Optical systems (panchromatic/multi-spectral)—fine resolution
Spatial resolution: 1–250 m.
Temporal re-visit time: ∼14–45 days (depending on the
resolution/swath width).
Landsat TM, ETM+ and MSS, USA, 1972–present.
SPOT HRV, HRVIR, France/Sweden/Belgium, 1986–
present.
JERS-1 OPS, Japan, 1992–1998.
IRS PAN, LISS and WiFS, India, 1995–present.
ADEOS AVNIR, Japan, 1996–1997.
CBERS CCD and IR-MSS, Brazil/China, 1999–present.
IKONOS, USA, 1999–present.
Terra MODIS, ASTER, MISR, USA/Japan, 1999–present.
Aqua ALI and Hyperion, USA, 2000–present.
ALOS AVNIR-2 and PRISM, Japan, planned launch
2004.
Optical systems (multi-spectral)—medium and coarse
resolution
Spatial resolution: 250 m–1 km.
Temporal re-visit time: daily–weekly.
NOAA AVHRR, USA, 1970s–present.
ERS ATSR, ATSR-2, Europe, 1991–present.

SPOT VEGETATION, France/EU/Sweden/Belgium,
1998–present.
ADEOS OCTS, Japan, 1996–1997.
CBERS WFI, Brazil/China, 1999–present.
Terra ASTER, MISR and MODIS, USA, 1999–present.
ADEOS-II GLI, Japan, 2002–present.
ENVISAT MERIS, AATSR, Europe, 2002–present.
AQUA MODIS, USA, 2002–present.
Active microwave systems (SAR)
Spatial resolution: 3–100 m.
Temporal re-visit time: ∼14–45 days.
SEASAT (L-HH), USA, 1976.
Shuttle Imaging Radar (SIR-A, SIR-B) (L-HH) USA,1981, 1984.
SIR-C/X-SAR (L- and C-band polarimetric, X-VV)
USA/Germany/Italy, 1994.
Shuttle Topography Radar Mission (SRTM/X-SAR)(C-HH, C-VV, X-VV) USA/Germany/Italy, February
2000.
Almaz (S-HH), Russia, 1992–1993.
ERS AMI (C-VV), Europe, 1991–present.
JERS SAR (L-HH), Japan, 1992–1998.
RADARSAT-1 (C-HH), Canada, 1995–present.
ENVISAT ASAR (C-band dual polarisation), Europe,
2002–present.
RADARSAT-2 (C-band polarimetric), Canada, planned
launch 2004.
ALOS PALSAR (L-band polarimetric), Japan, planned
launch 2004.
Active optical systems (LIDAR)
Spatial resolution: [VCL] 25 m (point measurements:
non-spatial extensive)
Temporal re-visit time: [VCL] 2 weeks.
Height accuracy: [VCL] <1 m.
Vegetation canopy LIDAR—VCL, USA, launch postponed indefinitely.

Appendix B. Acronyms used
APAR
ARD
ATSR
AVHRR
CASI
CDM
COP
CPC
ETM+
FAO
FPC
GEF
GLI

absorbed photosynthetically active radiation
afforestation, reforestation and deforestation
along track scanning radiometer
advanced very high resolution radiometercompact airborne spectrographic imager
clean development mechanism
conference of parties
canopy projected cover
Enhanced Thematic Mapper Plus
Food and Agriculture Organization of theUnited Nations
foliage projected coverglobal environment facility
Global Land Imager

Å. Rosenqvist et al. / Environmental Science & Policy 6 (2003) 441–455

GOFC
GTOS
HRVIR
IGBP
IGBP-DIS
IGOS-P
IHDP
IIASA
IPCC
ISPRS
ITU
IUFRO
LAI
LIDAR
LULUCF
NDVI
NIR
NOAA
NPP
PAR
SAR
SWIR
TEC
UAV
UNFCCC
WCRP

global observations of forest coverglobal terrestrial observing systemhigh resolution visible infrared
International Geosphere Biosphere
Programme
IGBP-data and information system
integrated global observation strategy
partnership
International Human Dimensions ProgrammeInternational Institute for Applied Systems
AnalysisIntergovernmental Panel on Climate ChangeInternational Society for Photogrammetry
and Remote Sensing
International Telecommunications Union
International Union of Forest Research
Organisations
leaf area index
laser infrared detection and ranging
land use, land use change and forestry
normalised difference vegetation index
near infrared
National Oceanic and Atmospheric
Administration
net primary production
photosynthetically active radiation
synthetic aperture radar
short-wave infrared
total electron content
unmanned aerial vehicle
United Nations Framework Convention
on Climate Change
World Climate Research Programme

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Åke Rosenqvist is a visiting scientist with the Japanese Space Agency(NASDA),where
wherehehecurrently
is currently
leadingthe
theKyoto
Kyotoand& Carbon(NASA),is leadingcarbon Initiative
initiative—
an international collaborative project set out to support environmental
conventions and carbon cycle science information needs with Earth Observation data. Previously, with the European Commission Joint ResearchCentre and before that with the Swedish Space Corporation, his researchinterests include development of remote sensing applications for monitoring and conservation of forests and wetlands. He was awarded the ISPRS
President’s Honorary Citation in 2000, for promotion of Earth Observation applications to the UNFCCC.
Anthony Milne is professor of geography in the School of Biological,
Earth and Environmental Sciences at the University of New South Walesand director of the University’s Centre for Remote Sensing and GIS. His
current research interests lie in radar remote sensing, vegetation assessment and the mapping of wetlands. He was chairman of the IGARSS
Symposium held in Sydney in 2001 and co-chairman of the NASA sponsored Pacific Rim (PACRIM) deployments of AIRSAR to the Asia-Pacificregion in 1996 and 2000.
Richard Lucas is a senior lecturer within the Institute of Geography and
Earth Sciences at the University of Wales at Aberystwyth, Aberystwyth.
His research interests include the integration of optical/hyperspectral, radarÅ. Rosenqvist et al. / Environmental Science & Policy 6 (2003) 441–455
and LIDAR data for characterising the biomass (carbon), structure andspecies/community composition of tropical and subtropical ecosystems
and understanding the impacts of natural and anthropogenic change.
Marc Imhoff is a research scientist in the Earth Sciences Directorateat NASA’s Goddard Space Flight Center. He specializes in the use of
remote sensing and computer modeling to study human impacts to thebiosphere and climate through various bio-geochemical cycles. He has
worked extensively in the area of radar applications in terrain mappingand developed the first civilian VHF SAR sensor for vegetation biomass
measurement. He has a PhD from Stanford University in biological sci-

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ences and is currently serving as NASA’s Earth System Science Pathfinder
Project Scientist.
Craig Dobson is a research scientist at the Radiation Laboratory in theElectrical Engineering and Computer Science Department at the University of Michigan. His research interests are radar remote sensing ofterrain and the characterisation of soils and vegetation in particular. He
has developed and validated algorithms for estimation of near-surface soil
moisture and vegetation structural parameters such as height and biomass
using airborne and space-borne SAR. Since 2001, he has been serving as
program manager for Earth observing SAR at NASA Headquarters.