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Open AccessArticle

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

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Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
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Norwegian Forest and Landscape Institute, P.O. Box 115, NO-1431 Ås, Norway
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Author to whom correspondence should be addressed.
Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail
Remote Sens. 2015, 7(8), 9865-9885; https://doi.org/10.3390/rs70809865
Received: 22 April 2015 / Revised: 30 June 2015 / Accepted: 27 July 2015 / Published: 4 August 2015
Forest inventories based on field sample surveys, supported by auxiliary remotely sensed data, have the potential to provide transparent and confident estimates of forest carbon stocks required in climate change mitigation schemes such as the REDD+ mechanism. The field plot size is of importance for the precision of carbon stock estimates, and better information of the relationship between plot size and precision can be useful in designing future inventories. Precision estimates of forest biomass estimates developed from 30 concentric field plots with sizes of 700, 900, …, 1900 m2, sampled in a Tanzanian rainforest, were assessed in a model-based inference framework. Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radio detection and ranging (InSAR) were used as auxiliary information. The findings indicate that larger field plots are relatively more efficient for inventories supported by remotely sensed ALS and InSAR data. A simulation showed that a pure field-based inventory would have to comprise 3.5–6.0 times as many observations for plot sizes of 700–1900 m2 to achieve the same precision as an inventory supported by ALS data. View Full-Text
Keywords: ALS; airborne laser scanning; LiDAR; relative efficiency; tropical rainforest ALS; airborne laser scanning; LiDAR; relative efficiency; tropical rainforest
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Hansen, E.H.; Gobakken, T.; Solberg, S.; Kangas, A.; Ene, L.; Mauya, E.; Næsset, E. Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest. Remote Sens. 2015, 7, 9865-9885.

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