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Remote Sens. 2014, 6(11), 10636-10655; doi:10.3390/rs61110636

Using Airborne LiDAR and QuickBird Data for Modelling Urban Tree Carbon Storage and Its Distribution—A Case Study of Berlin

1
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
2
Chair for Strategic Landscape Planning and Management, Technische Universität München, Emil-Ramann-Str. 6, 85354 Freising, Germany
3
Institute for Advanced Sustainable Studies e.v., IASS Potsdam, Berliner Straße 130, 14467 Potsdam, Germany
*
Author to whom correspondence should be addressed.
Received: 21 May 2014 / Revised: 16 October 2014 / Accepted: 21 October 2014 / Published: 3 November 2014
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Abstract

While CO2 emissions of cities are widely discussed, carbon storage in urban vegetation has been rarely empirically analyzed. Remotely sensed data offer considerable benefits for addressing this lack of information. The aim of this paper is to develop and apply an approach that combines airborne LiDAR and QuickBird to assess the carbon stored in urban trees of Berlin, Germany, and to identify differences between urban structure types. For a transect in the city, dendrometric parameters were first derived to estimate individual tree stem diameter and carbon storage with allometric equations. Field survey data were used for validation. Then, the individual tree carbon storage was aggregated at the level of urban structure types and the distribution of carbon storage was analysed. Finally, the results were extrapolated to the entire urban area. High accuracies of the detected tree locations were reached with 65.30% for all trees and 80.1% for dominant trees. The total carbon storage of the study area was 20,964.40 t (σ = 15,550.11 t). Its carbon density equaled 13.70 t/ha. A general center-to-periphery increase in carbon storage was identified along the transect. Our approach methods can be used by scientists and decision-makers to gain an empirical basis for the comparison of carbon storage capacities between cities and their subunits to develop adaption and mitigation strategies against climate change. View Full-Text
Keywords: LiDAR; QuickBird; urban vegetation; urban trees; carbon storage; sequestration; spatial patterns; climate change; mitigation LiDAR; QuickBird; urban vegetation; urban trees; carbon storage; sequestration; spatial patterns; climate change; mitigation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Schreyer, J.; Tigges, J.; Lakes, T.; Churkina, G. Using Airborne LiDAR and QuickBird Data for Modelling Urban Tree Carbon Storage and Its Distribution—A Case Study of Berlin. Remote Sens. 2014, 6, 10636-10655.

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