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are widely used for quantifying forest characteristics and for estimating biomass, but information may quickly become outdated in dynamic forest environments.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.
Highlights
Monitoring long-term forest productivity will be critical for determining the strength of forest ecosystems as carbon sinks to counter the anthropogenically-induced disruption of regional and global climate systems [1,2,3]
While evidence from previous studies proves that great potential exists for both Stand-based inventories (SBIs)-based and remote sensing-based approaches for estimating aboveground biomass, little research has been dedicated towards integrating these approaches
We suggest adequate input variables, either from remote sensing, SBI, or both combined for biomass estimates, depending on available input information
Summary
Monitoring long-term forest productivity will be critical for determining the strength of forest ecosystems as carbon sinks to counter the anthropogenically-induced disruption of regional and global climate systems [1,2,3]. Rising levels of atmospheric carbon dioxide and potentially complex interactions with other anthropogenic stressors [4,5] require rigorous analytical approaches for quantifying forest carbon sequestration and fluxes among multiple pools at scales ranging from individual stands to entire landscapes and bioregions [6]. Research on forest carbon modeling has advanced considerably over recent decades [2,7,8,9,10]. Accurate estimate of forest biomass is sometimes viewed as one of the most important parameters for carbon modeling [11]. Forest biomass is highly recognized for its large carbon sequestration potential [3]. Applications range from individual trees to whole regions to support the estimation of fixed carbon in forests [12,13]
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