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Dissertation zugänglich unter
URN: urn:nbn:de:gbv:18-62018
URL: http://ediss.sub.uni-hamburg.de/volltexte/2013/6201/

Monitoring Reduced Emissions from Deforestation and Forest Degradation (REDD+) : Capabilities of High- Resolution Active Remote Sensing

Monitoring reduzierter Emissionen aus Entwaldung und Walddegradierung (REDD+) : Potenziale hochaufgelöster aktiver Fernerkundung

Baldauf, Thomas

 Dokument 1.pdf (7.766 KB) 
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 All_extracted_trees_AGB-CrownArea-SocPositionDom.xls.zip (177 KB) 

SWD-Schlagwörter: Entwaldung , Degradierung , Fernerkundung , Radar , Tropischer Wald , Monitoring
Freie Schlagwörter (Deutsch): REDD, REDD+, vermiedene Entwaldung
Freie Schlagwörter (Englisch): REDD, REDD+, avoided deforestation
Basisklassifikation: 30.00 , 48.40 , 38.82 , 38.99
Institut: Biologie
DDC-Sachgruppe: Naturwissenschaften
Dokumentart: Dissertation
Hauptberichter: Köhl, Michael (Prof. Dr.)
Sprache: Englisch
Tag der mündlichen Prüfung: 22.04.2013
Erstellungsjahr: 2013
Publikationsdatum: 26.06.2013
Kurzfassung auf Englisch: REDD+ is a climate change mitigation mechanism for tropical forests presently being negotiated under the UNFCCC. It aims to attribute economic value to the carbon stored in forests, and thereby integrates forest protection into economic and political decision making processes. REDD+ embraces five activities that show a mitigating effect on climate change. One of these activities is reducing emissions from forest degradation.
Although forest degradation is an intrinsic part of REDD+, only rough estimates are available for the total of emissions from forest degradation. Nevertheless, these estimates show the importance of grappling with forest degradation in REDD+, if significant emission reductions are envisaged. Currently, however, REDD+ lacks access to scientifically sound, applicable and cost-efficient methods for reporting on forest degradation on a large scale.
The present case study analyzed high-resolution active remote sensing data to determine its suitability for reporting on forest degradation within the scope of REDD+. In the process it developed a method involving TerraSAR-X data to detect patterns of selective logging. Then, based on an accuracy assessment, it identified and quantified the influences of three stand characteristics, i.e. aboveground tree biomass, tree crown area, and social position and dominance, on the reliability of the developed method. Finally, the study demonstrates how the developed method could be implemented into the setup of an operational, robust, and transparent MRV-system.
The study proved that space-born RADAR can be used for monitoring patterns of forest degradation in tropical moist forests. Combined with appropriate methods, it enables the collection of unbiased activity data and thereby serves as a suitable tool for reporting on forest degradation within the scope of REDD+.


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