Remote Sens. 2011, 3(7), 1427-1446; doi:10.3390/rs3071427
Article

Evaluating the Remote Sensing and Inventory-Based Estimation of Biomass in the Western Carpathians

1 Geomatics Lab, Geography Department, Humboldt-Universit├Ąt zu Berlin, Unter den Linden 6, 10099 Berlin, Germany 2 Rocky Mountain Research Station, USFS, 507 25th Street, Ogden, UT 84401, USA 3 Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT 05405, USA
* Author to whom correspondence should be addressed.
Received: 14 May 2011; in revised form: 27 June 2011 / Accepted: 28 June 2011 / Published: 6 July 2011
PDF Full-text Download PDF Full-Text [1005 KB, uploaded 6 July 2011 17:08 CEST]
Abstract: Understanding the potential of forest ecosystems as global carbon sinks requires a thorough knowledge of forest carbon dynamics, including both sequestration and fluxes among multiple pools. The accurate quantification of biomass is important to better understand forest productivity and carbon cycling dynamics. Stand-based inventories (SBIs) are widely used for quantifying forest characteristics and for estimating biomass, but information may quickly become outdated in dynamic forest environments. Satellite remote sensing may provide a supplement or substitute. We tested the accuracy of aboveground biomass estimates modeled from a combination of Landsat Thematic Mapper (TM) imagery and topographic data, as well as SBI-derived variables in a Picea abies forest in the Western Carpathian Mountains. We employed Random Forests for non-parametric, regression tree-based modeling. Results indicated a difference in the importance of SBI-based and remote sensing-based predictors when estimating aboveground biomass. The most accurate models for biomass prediction ranged from a correlation coefficient of 0.52 for the TM- and topography-based model, to 0.98 for the inventory-based model. While Landsat-based biomass estimates were measurably less accurate than those derived from SBI, adding tree height or stand-volume as a field-based predictor to TM and topography-based models increased performance to 0.36 and 0.86, respectively. Our results illustrate the potential of spectral data to reveal spatial details in stand structure and ecological complexity.
Keywords: aboveground biomass; forest carbon; Random Forests; forest inventory; Picea abies; Carpathian Mountains; Landsat

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

MDPI and ACS Style

Main-Knorn, M.; Moisen, G.G.; Healey, S.P.; Keeton, W.S.; Freeman, E.A.; Hostert, P. Evaluating the Remote Sensing and Inventory-Based Estimation of Biomass in the Western Carpathians. Remote Sens. 2011, 3, 1427-1446.

AMA Style

Main-Knorn M, Moisen GG, Healey SP, Keeton WS, Freeman EA, Hostert P. Evaluating the Remote Sensing and Inventory-Based Estimation of Biomass in the Western Carpathians. Remote Sensing. 2011; 3(7):1427-1446.

Chicago/Turabian Style

Main-Knorn, Magdalena; Moisen, Gretchen G.; Healey, Sean P.; Keeton, William S.; Freeman, Elizabeth A.; Hostert, Patrick. 2011. "Evaluating the Remote Sensing and Inventory-Based Estimation of Biomass in the Western Carpathians." Remote Sens. 3, no. 7: 1427-1446.

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert