Abstract
BackgroundSatellite-based aboveground forest biomass maps commonly form the basis of forest biomass and carbon stock mapping and monitoring, but biomass maps likely vary in performance by region and as a function of spatial scale of aggregation. Assessing such variability is not possible with spatially-sparse vegetation plot networks. In the current study, our objective was to determine whether high-resolution lidar-based and moderate-resolution Landsat-base aboveground live forest biomass maps converged on similar predictions at stand- to landscape-levels (10 s to 100 s ha) and whether such differences depended on biophysical setting. Specifically, we examined deviations between lidar- and Landsat-based biomass mapping methods across scales and ecoregions using a measure of error (normalized root mean square deviation), a measure of the unsystematic deviations, or noise (Pearson correlation coefficient), and two measures related to systematic deviations, or biases (intercept and slope of a regression between the two sets of predictions).ResultsCompared to forest inventory data (0.81-ha aggregate-level), lidar and Landsat-based mean biomass predictions exhibited similar performance, though lidar predictions exhibited less normalized root mean square deviation than Landsat when compared with the reference plot data. Across aggregate-levels, the intercepts and slopes of regression equations describing the relationships between lidar- and Landsat-based biomass predictions stabilized (i.e., little additional change with increasing area of aggregates) at aggregate-levels between 10 and 100 ha, suggesting a consistent relationship between the two maps at landscape-scales. Differences between lidar- and Landsat-based biomass maps varied as a function of forest canopy heterogeneity and composition, with systematic deviations (regression intercepts) increasing with mean canopy cover and hardwood proportion within forests and correlations decreasing with hardwood proportion.ConclusionsDeviations between lidar- and Landsat-based maps indicated that satellite-based approaches may represent general gradients in forest biomass. Ecoregion impacted deviations between lidar and Landsat biomass maps, highlighting the importance of biophysical setting in determining biomass map performance across aggregate scales. Therefore, regardless of the source of remote sensing (e.g., Landsat vs. lidar), factors affecting the measurement and prediction of forest biomass, such as species composition, need to be taken into account whether one is estimating biomass at the plot, stand, or landscape scale.
Highlights
Satellite-based aboveground forest biomass maps commonly form the basis of forest biomass and carbon stock mapping and monitoring, but biomass maps likely vary in performance by region and as a function of spatial scale of aggregation
We address the following questions: (1) at what aggregate-level do lidar and Landsat time series (LTS) based predictions of mean aboveground biomass become equivalent, if at all? (2) Do unsystematic and systematic deviations change as biophysical setting changes?
Stand- to landscape-level predictions of aboveground live forest biomass (AGB) based on lidar and LTS data differed in a consistent and predictable fashion across regions and aggregate-levels, important differences emerged as a function of biophysical setting, as represented by ecoregion-level mean vegetation characteristics
Summary
Satellite-based aboveground forest biomass maps commonly form the basis of forest biomass and carbon stock mapping and monitoring, but biomass maps likely vary in performance by region and as a function of spatial scale of aggregation Assessing such variability is not possible with spatially-sparse vegetation plot networks. 30-m spectral reflectance data from the Landsat satellite program provides the capacity for wall-to-wall vegetation mapping from 1984 to present [2, 3] Despite known limitations, such as the saturation in the relationship between Landsat time series (LTS) data and forest basal area or biomass [4], LTS data form the basis of multi-decadal landscape-, regional-, and continental-scale monitoring of land cover and vegetation attributes [5,6,7]. A key challenge for the mapping of carbon stocks, such as aboveground live forest biomass (AGB; Mg ha−1), is identifying the minimum appropriate area of aggregation, a task made challenging by the paucity of dense vegetation plot networks
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