Using matrix models to estimate aboveground forest biomass dynamics in the eastern USA through various combinations of LiDAR, Landsat, and forest inventory data

Abstract

The ability to harmonize data sources with varying temporal, spatial, and ecosystem measurements (e.g. forest structure to soil organic carbon) for creation of terrestrial carbon baselines is paramount to refining the monitoring of terrestrial carbon stocks and stock changes. In this study, we developed and examined the short-(5 years) and long-term (30 years) performance of matrix models for incorporating light detection and ranging (LiDAR) strip samples and time-series Landsat surface reflectance high-level data products, with field inventory measurements to predict aboveground biomass (AGB) dynamics for study sites across the eastern USA-Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC). The rows and columns of the matrix were stand density (i.e. number of trees per unit area) sorted by inventory plot and by species group and diameter class. Through model comparisons in the short-term, we found that average stand basal area (B) predicted by three matrix models all fell within the 95% confidence interval of observed values. The three matrix models were based on (i) only field inventory variables (inventory), (ii) LiDAR and Landsat-derived metrics combined with field inventory variables (LiDAR+Landsat+inventory), and (iii) only Landsat-derived metrics combined with field inventory variables (Landsat+inventory), respectively. In the long term, predicted AGB using LiDAR+Landsat+inventory and Land-sat+inventory variables had similar AGB patterns (differences within 7.2 Mg ha −1 ) to those predicted by matrix models with only inventory variables from 2015-2045. When considering uncertainty derived from fuzzy sets all three matrix models had similar AGBs (differences within 7.6 Mg ha −1 ) by the year 2045. Therefore, the use of matrix models enabled various combinations of LiDAR, Landsat, and field data, especially Landsat data, to estimate large-scale AGB dynamics (i.e. central component of carbon stock monitoring) without loss of accuracy from only using variables from forest inventories. These findings suggest that the use of Landsat data alone incorporating elevation (E), plot slope (S) and aspect (A), and site productivity (C) could produce suitable estimation of AGB dynamics (ranging from 67.1-105.5 Mg ha −1 in 2045) to actual AGB dynamics using matrix models. Such a framework may afford refined monitoring and estimation of terrestrial carbon stocks and stock changes from spatially explicit to spatially explicit and spatially continuous estimates and also provide temporal flexibility and continuity with the Landsat time series.

Land Use Changes, Disturbances, and Their Interactions on Future Forest Aboveground Biomass Dynamics in the Northern US

“…The annual diameter growth of the tree of species group i and size class j from t and t + 1 is represented by the following model ( [27]; all notations defined in Table 2): (5) in which α i 's are parameters to be estimated with the generalized least squares (GLSs, [37]) for diameter growth of species group i and size class j. θ is an error term.…”

“…We adopted the same variables and functional form for the underlying diameter growth, mortality, recruitment, and AGB models as those established in previously published matrix models [27] to maintain accuracy and avoid overfitting and multicollinearity between predicted variables. A variety of stand level and Landsat variables selected for two species groups (deciduous and coniferous) were used in the matrix growth models (Table 3).…”

“…Physiographic variables, elevation (E) and slope (S), were used to control for site productivity [40]. Landsat variables, tasseled cap brightness (TCB), disturbance index (DI), enhanced vegetation index (EVI), shortwave infrared surface reflectance (SWIR), tasseled cap greenness (TCG), soil adjusted vegetation index (SAVI), and tasseled cap angle (TCA) were used to replace total stand basal area (B) as key predictors, due to the correlation between the Landsat predictors and forest stand characteristics [27]. For model calibration purposes, 62,766 permanent ground plots (80%) and plot-corresponded Landsat data were used to estimate parameters of the matrix growth model.…”

“…For model calibration purposes, 62,766 permanent ground plots (80%) and plot-corresponded Landsat data were used to estimate parameters of the matrix growth model. In addition, model validation (the rest: 15,692 permanent ground plots, 20%) followed Ma et al [27], where matrix models were used to enable various combinations of LiDAR, Landsat, and field data, especially Landsat data to estimate large-scale AGB dynamics (i.e., the central component of carbon stock monitoring) without loss of accuracy from only using variables from forest inventories ( Figure 4). Finally, the 15,692 permanent ground plots and plot-corresponded Landsat data were applied to make the future predictions.…”

“…Matrix growth models are useful for predicting forest population dynamics and associated forest C dynamics under varying natural disturbances, management, and climate scenarios through consideration of diameter growth, mortality, and recruitment [15,16,26,27,35,36,45]. Moreover, matrix growth models are highly accurate in characterizing forest C dynamics over large-scale forest areas based on empirical forest inventories and Landsat data [27]. Applying selected Landsat variables, we revalidated the strength of Landsat dependent matrix growth models in their applications for predicting large-scale forest AGB dynamics [27].…”