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Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories

Department of Forest Resources, University of Minnesota, Saint Paul, MN 55108, USA
Raspberry Ridge Analytics, 15111 Elmcrest Avenue North, Hugo, MN 55038, USA
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
Systèmes d’Information à Référence Spatiale, Parc de la Cimaise, 59650 Villeneuve d’Ascq, France
Department of Sustainable Resources Management, State University of New York, College of Environmental Science and Forestry, Syracuse, NY 13210, USA
Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, P.O. Box 11000, 00076 Aalto, Finland
Department of Forest Sciences, University of Helsinki, Latokartanonkaari 7, P.O. Box 27, FI-00014 Helsinki, Finland
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1891;
Received: 21 April 2020 / Revised: 6 June 2020 / Accepted: 8 June 2020 / Published: 11 June 2020
(This article belongs to the Special Issue Advances in Remote Sensing for Global Forest Monitoring)
For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed. View Full-Text
Keywords: statistical estimator; IPCC good practice guidelines; activity data; emissions factor; removals factor statistical estimator; IPCC good practice guidelines; activity data; emissions factor; removals factor
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MDPI and ACS Style

McRoberts, R.E.; Næsset, E.; Sannier, C.; Stehman, S.V.; Tomppo, E.O. Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories. Remote Sens. 2020, 12, 1891.

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