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

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are widely used for quantifying forest characteristics and for estimating biomass, but information may quickly become outdated in dynamic forest environments.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, OPEN ACCESSRemote Sens. 2011, 3 1428 respectively. Our results illustrate the potential of spectral data to reveal spatial details in stand structure and ecological complexity.

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

“…Optical remote sensing data have proven to be a powerful means for biomass estimation [4,11]. However, the use of these data has some limitations such as model dependency on in situ data as well as low spectral saturation levels [9,12].…”

“…Information about forest stand structure and the quantification of AGB are of great importance to assess forest ecosystem productivity, determine carbon budget and support studies of the role of forests in the global carbon cycle [3][4][5][6][7][8]. The existing biomass estimation methods that rely on forest inventory data and allometric equations are accurate.…”

Remote Sensing of Woodland Structure and Composition in the Sudano-Sahelian zone : Application of WorldView-2 and Landsat 8

“…Many other methods have been suggested for calibrating models between RS predictor variables and reference data, including alternative statistical regression techniques, for example reduced major axis, Theil-Sen and Wald's method (Curran and Hay 1986, Ardo 1992; artificial neural networks (Foody, Boyd, and Cutler 2003); k-nearest neighbors (Fazakas, Nilsson, and Olsson 1999); support vector machines (Gleason and Im 2012); and Random Forest (RF; Breiman 2001, Powell et al 2010, Main-Knorn et al 2011. In recent years RF has been promoted as a well-suited and accurate technique for modeling ecological relationships, including the relationship between tree attributes and RS data (Prasad, Iverson, andLiaw 2006, Cutler et al 2007).…”

Carbon stock in forest aboveground biomass – comparison based on Landsat data

“…Vilém Pechanec,phone: +420 585 634 579 of tree allometry in connection with carbon measurement in biomass. Main-Knorn et al (2011) compared carbon stock assessed by forest inventory with figures derived on the base of satellite data analysis. Method of production tables, used in the frame of IPCC programme, is based on a link of individual classified categories and prepared values on carbon stock or production, derived from previous contact measurements and literary knowledge.…”