A review of remote sensing technology in support of the Kyoto Protocol

doi:10.1016/S1462-9011(03)00070-4

Environmental Science & Policy

Volume 6, Issue 5, October 2003, Pages 441-455

https://doi.org/10.1016/S1462-9011(03)00070-4 Get rights and content

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, and the 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 COP 7) meetings in Bonn and Marrakesh, respectively in 2001 are also discussed. Past, current and near-future remote sensing instruments with 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.

Introduction

The Kyoto Protocol to the United Nations Framework Convention on Climate Change (UNFCCC) contains quantified and legally binding commitments to limit or reduce greenhouse gas emissions to 1990 levels. The protocol allows sinks associated with vegetation growth and expansion to be included to offset carbon emissions, which in turn raises debate about the adequacy of existing methods for establishing reliable estimates of 1990 carbon stocks/sinks levels and for measuring and monitoring current and future carbon 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 for the provision of data archives extending back over several decades. These issues also underscore a need for the exchange of information between remote sensing scientists and organisations (e.g. national and international policy makers, government agencies and both legal and scientific bodies) involved in the development and implementation of the protocol.

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, international organisations and academic institutions, set out to review the Kyoto Protocol and to identify areas where remote sensing technology could provide support; to review current and near future remote sensing technologies that could support the information requirements identified; and to highlight shortcomings 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.

Section snippets

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, ensure that their aggregate anthropogenic carbon dioxide equivalent 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)

Implications of the Bonn Agreements and Marrakesh Accords

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.

The contribution of remote sensing

Over the past half century, a range of airborne and space-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 from visible to microwave, at spatial resolutions ranging from sub-metre to kilometres and temporal frequencies ranging from 30 min to weeks or months. In addition, archives of remotely sensed data are increasing and provide a

The requirement for in situ data

For all applications discussed, up-to-date, quantifiable, in situ data are needed for development of algorithms for quantifying land cover, land cover change, biomass and sources of anthropogenic CH₄ and validating derived products. A thematic product derived from remote sensing data, be it a land 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. Although collection of field data is

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 the nature 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

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 important to 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

Legal issues

From the viewpoint of governing international laws and treaties, a key consideration is the legal restrictions hat might affect the utilisation of remote sensing technology to support treaty verification. Relevant in this context are the UN Remote Sensing Principles (United Nations, 1986) and the Outer Space Treaty (United Nations, 1967), as well as the 1944 Chicago Convention governing international air law.

The UN Remote Sensing Principles—which do not constitute a binding treaty, but a set of

Summary and recommendations

To support the requirements of the Kyoto Protocol, remote sensing can play an important role in providing systematic observations of land cover, supporting establishment of a 1990 carbon stock baseline, detecting and quantifying rates of land cover change, quantifying above ground biomass stocks and changes in these, and mapping and monitoring certain sources of anthropogenic CH₄. The greenhouse gases considered relevant in the context of remote sensing are essentially CO₂ and CH₄.

Systematic

Conclusions

Although political in nature, the global impact of the Kyoto Protocol on technical and scientific issues of relevance to 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 specific information needs posed by

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, and on 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

Åke Rosenqvist is a visiting scientist with the Japanese Space Agency (NASA), where he currently is leading the Kyoto and carbon 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 Research Centre and before that with the Swedish Space Corporation, his research interests include development of remote sensing applications for monitoring

References (57)

P.M Barbosa et al.
Compositing criteria for burned area assessment using multitemporal low resolution satellite data
Remote Sens. Environ.
(1998)
P.M Barbosa et al.
An algorithm for extracting burned areas from time series of AVHRR GAC data applied at a continental scale
Remote Sens. Environ.
(1999)
J.B Blair et al.
The laser vegetation imaging sensor (LVIS): a medium-altitude, digitization only, airborne laser altimeter for mapping
ISPRS
(1999)
E.S Kasischke et al.
The use of imaging radars for ecological applications—a review
Remote Sens. Environ.
(1997)
A Luckman et al.
Tropical forest biomass density estimation using JERS-1 SAR: seasonal variation, confidence limits and application to image mosaics
Remote Sens. Environ.
(1998)
J.E Means et al.
Use of large footprint scanning airborne LIDAR to estimate forest stand characteristics in the western Cascades of Oregon
Remote Sens. Environ.
(1999)
K.J Ranson et al.
Northern forest classification using temporal multifrequency and multipolarimetric SAR images
Remote Sens. Environ.
(1994)
K.J Ranson et al.
Forest biomass from combined ecosystem and radar backscatter
Remote Sens. Environ.
(1997)
Achard, F., Eva, H., Glinni, A., Mayaux, P., Richards, T., Stibig, H.J., 1997. Identification of deforestation hot spot...
Ahern, F.J., Belward, A., Churchill, P., Davis, R., Janetos, A., Justice, C., Loveland, T., Malingreau, J.-P., Maiden,...

Antikidis, E., Arino, O., Laur, H., Arnaud, A. 1997. Deforestation evaluation by synergetic use of ERS SAR coherence...

J Askne et al.

C-band repeat-pass interferometric SAR observations of the forests

IEEE Trans. Geosci. Remote Sens.

(1997)

G Asrar et al.

Estimating absorbed photosynthetically active radiation and leaf area index from spectral reflectance in wheat

Agron. J.

(1984)

A.S Belward et al.

The IGBP-DIS global 1 km land cover dataset DISCover: a project overview

Photogr. Eng. Remote Sens.

(1999)

M.C Dobson

Dependence of radar backscatter on coniferous forest biomass

IEEE Trans. Geosci. Rem. Sens.

(1992)

C Dobson et al.

Estimation of forest biomass characteristics in northern Michigan with SIR-C/X-SAR data

IEEE Trans. Geosci. Remote Sens.

(1995)

Dubayah, R., Blair, B., Bufton, J., Clark, D., Jálá, J., Knox, R., Luthcke, S., Prince, S., Weishampl, J. The...

E Dwyer et al.

A global analysis of vegetation fires: spatial and temporal dynamics

AMBIO

(1998)

Grégoire, J.-M., Pinnock, S., Dwyer, E., Janodet, E., 1998. Satellite monitoring of vegetation fires for EXPRESSO:...

M Imhoff

Radar backscatter and biomass saturation: ramifications for global biomass inventory

IEEE Trans. Geosci. Remote Sens.

(1995)

M Imhoff et al.

A low frequency radar experiment for measuring vegetation biomass

IEEE Trans. Geosci. Remote Sens.

(1998)

M Imhoff et al.

BioSAR: an inexpensive airborne VHF multi-band SAR system for vegetation biomass measurement

IEEE Trans. Geosci. Remote Sens.

(2000)

A Ishimaru et al.

Ionospheric effects on synthetic aperture radar imaging at 100 MHz to 2 GHz

Radio Sci.

(1999)

Kim, Y., van Zyl, J., 1999. Ionosperic effects on polarimetric and interferometric space-borne SAR. In: Proceedings of...

Kuhnell, C.B., Goulevitch, B., Danaher, T., Harris, D., 1998. Mapping woody vegetation cover over the State of...

T Le Toan et al.

Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results

IEEE Trans. Geosci. Remote Sens.

(1997)

Lee, P.F., James, K., 2001. The RADARSAT-2/3 topographic mission. In: Proceedings of the International Geoscience and...

Lucas, R.M., Milne, A.K., Lee., A., Cronin, N., 2003a. Remote Sensing of woodland biomass, structure and community...

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Anthony Milne is professor of geography in the School of Biological, Earth and Environmental Sciences at the University of New South Wales and 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-Pacific region 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 and LIDAR data for characterising the biomass (carbon), structure and species/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 Directorate at NASA’s Goddard Space Flight Center. He specializes in the use of remote sensing and computer modeling to study human impacts to the biosphere and climate through various bio-geochemical cycles. He has worked extensively in the area of radar applications in terrain mapping and developed the first civilian VHF SAR sensor for vegetation biomass measurement. He has a PhD from Stanford University in biological sciences and is currently serving as NASA’s Earth System Science Pathfinder Project Scientist.

Craig Dobson is a research scientist at the Radiation Laboratory in the Electrical Engineering and Computer Science Department at the University of Michigan. His research interests are radar remote sensing of terrain 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.

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ReviewA review of remote sensing technology in support of the Kyoto Protocol

Abstract

Introduction

Section snippets

Remote sensing relevance to the Kyoto Protocol

Implications of the Bonn Agreements and Marrakesh Accords

The contribution of remote sensing

The requirement for in situ data

Accessibility and affordability

Research topics

Legal issues

Summary and recommendations

Conclusions

Acknowledgements

Remote Sens. Environ.

Remote Sens. Environ.

ISPRS

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sens. Environ.

C-band repeat-pass interferometric SAR observations of the forests

IEEE Trans. Geosci. Remote Sens.

Estimating absorbed photosynthetically active radiation and leaf area index from spectral reflectance in wheat

Agron. J.

The IGBP-DIS global 1 km land cover dataset DISCover: a project overview

Photogr. Eng. Remote Sens.

Dependence of radar backscatter on coniferous forest biomass

IEEE Trans. Geosci. Rem. Sens.

Estimation of forest biomass characteristics in northern Michigan with SIR-C/X-SAR data

IEEE Trans. Geosci. Remote Sens.

A global analysis of vegetation fires: spatial and temporal dynamics

AMBIO

Radar backscatter and biomass saturation: ramifications for global biomass inventory

IEEE Trans. Geosci. Remote Sens.

A low frequency radar experiment for measuring vegetation biomass

IEEE Trans. Geosci. Remote Sens.

BioSAR: an inexpensive airborne VHF multi-band SAR system for vegetation biomass measurement

IEEE Trans. Geosci. Remote Sens.

Ionospheric effects on synthetic aperture radar imaging at 100 MHz to 2 GHz

Radio Sci.

Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results

IEEE Trans. Geosci. Remote Sens.

Review
A review of remote sensing technology in support of the Kyoto Protocol