Abstract
Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA—Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)—in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R2) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA.
Highlights
Forest ecosystems store the majority (~80%) of terrestrial biomass and carbon [1], as well as contribute significantly to global carbon emissions (~33% over the past 150 years) through land use and land cover changes [2,3,4]
Same optimal set of predictors was used in both approaches
Within the context of remote sensing-assisted national greenhouse gas inventories (NGHGI), this study highlights the potential use of a generic model for estimation of forest aboveground tree biomass (AGB) across diverse forest types
Summary
Forest ecosystems store the majority (~80%) of terrestrial biomass and carbon [1], as well as contribute significantly to global carbon emissions (~33% over the past 150 years) through land use and land cover changes [2,3,4]. Sustainable land use and forestry practices focused on enhancing carbon storage in live woody biomass have been recognized as effective means to reduce the effects of emissions and mitigate the effects of climate change, because forests are estimated to absorb about 25%. The United Nations Framework Conventions on Climate Change (UNFCCC) has endorsed programs for reducing emissions from deforestation and forest degradation (REDD+), and mandated that member countries periodically report forest carbon estimates via national greenhouse gas inventories (NGHGI). The REDD+ initiatives and NGHGI will benefit from wall-to-wall (spatial) inventories of aboveground forest biomass (AGB) at regional and/or national scales. It can be hypothesized that a large-area spatial inventory of AGB can be implemented more efficiently with a generic model for diverse forest types, as site-specific data or models may not be available for individual forest types in any area of interest
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