Development of an integrated forest carbon monitoring system
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*'''Funding:''' DFG | *'''Funding:''' DFG | ||
*'''Duration:''' 2012-2015 | *'''Duration:''' 2012-2015 | ||
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*'''Coordination:''' Prof. Dr. Christoph Kleinn and Prof. Dr. Florian Siegert | *'''Coordination:''' Prof. Dr. Christoph Kleinn and Prof. Dr. Florian Siegert | ||
*'''Project researcher:''' Dr. César Perez, [[User:Pmagdon|Paul Magdon]] and Yanti Sarodja (PhD) | *'''Project researcher:''' Dr. César Perez, [[User:Pmagdon|Paul Magdon]] and Yanti Sarodja (PhD) | ||
Revision as of 16:17, 21 March 2013
Project Title: Development of an integrated forest carbon monitoring system with field sampling and remote sensing
- Funding: DFG
- Duration: 2012-2015
- Coordination: Prof. Dr. Christoph Kleinn and Prof. Dr. Florian Siegert
- Project researcher: Dr. César Perez, Paul Magdon and Yanti Sarodja (PhD)
Background
Forests play a relevant role in mitigation of climate change. A major issue, however, is the scientifically well founded, transparent and verifiable monitoring of achievements in forest carbon sequestration through reduction of deforestation and forest degradation, and through fostering sustainable forest management. Monitoring is particularly difficult in diverse and inaccessible humid tropical forest areas. The project will contribute to the improvement of forest carbon monitoring under the challenging conditions of humid tropical forests. Sample based field observations and model based biomass predictions will be linked to area-wide satellite remote sensing imagery (RapidEye) and to strip samples of LiDAR imagery. Techniques of linking these data sources will be further developed and analysed with respect to (1) precision of carbon estimation and (2) accuracy of carbon regionalization. The project implies research on methodological improvements of both sample based forest inventories (resampling techniques for biomass, imputation of non-response) and remote sensing application to forest monitoring (regionalization, sample based application of LiDAR data). At the core of this research is the analysis of the error variance components that each data source brings into the system. Such error analysis will allow identifying optimal resource allocation for the efficient improvement of forest carbon monitoring systems.
