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    Author(s): Qi Chen; Ronald E. McRoberts; Changwei Wang; Philip J. Radtke
    Date: 2016
    Source: Remote Sensing of Environment
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: Download Publication  (3.0 MB)


    Remotely sensed data have been widely used in recent years for mapping and estimating biomass. However, the characterization of the uncertainty of mapped or estimated biomass in previous studies was either based on adhoc approaches (e.g., using model fitting statistics such root mean square errors derived frompurposive samples) or mostly limited to the analysis of mean biomass for the whole study area. This study proposed a novel uncertainty analysis method that can characterize biomass uncertainty across multiple spatial scales and multiple spatial resolutions. The uncertainty analysis method built on model-based inference and can propagate errors from trees to field plots, individual pixels, and small areas or large regions that consist of multiple pixels (up to all pixels within a study area). We developed and tested this method over northern Minnesota forest areas of approximately 69,508 km2 via a unique combination of several datasets for biomass mapping and estimation: wall-to-wall airborne lidar data, national forest inventory (NFI) plots, and destructive measurements of tree aboveground biomass (AGB). We found that the pixel-level AGB prediction error is dominated by lidar-based AGB model residual errors when the spatial resolution is near 380 m or finer and by model parameter estimate errorswhen the spatial resolution is coarser.We also found that the relative error of AGB predicted fromlidar can be reduced to approximately 11% (or mean 5.1 Mg/ha; max 43.6Mg/ha) at one-hectare scale (or at 100mspatial resolution) over our study area. Because our uncertainty analysis method uses model-based inference and does not require probability samples of field plots, our methodology has potential applications worldwide, especially over tropics and developing countries where NFI systems are not well-established.

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    Chen, Qi; McRoberts, Ronald E.; Wang, Changwei; Radtke, Philip J. 2016. Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference. Remote Sensing of Environment. 184: 350-360.


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    Biomass, Uncertainty, Lidar, Inventory plots, Destructive tree AGB measurements, Model-based inference

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