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    Author(s): Brian J. Clough; Edwin J. Green; Richard B. Lathrop
    Date: 2012
    Source: In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 185-191.
    Publication Series: Paper (invited, offered, keynote)
    Station: Northern Research Station
    PDF: View PDF  (434.54 KB)

    Description

    Managing forest soil organic matter (SOM) stocks is a vital strategy for reducing the impact of anthropogenic carbon dioxide emissions. However, the SOM pool is highly variable, and developing accurate estimates to guide management decisions has remained a difficult task. We present the results of a spatial model designed to map soil organic matter for all forested land in the Coastal Plain physiographic province of New Jersey. SOM stocks from 60 sampling locations, distributed across the region in a stratified random design based on vegetation type and drainage class, were used in a kriging model that incorporated several indices derived from Landsat Thematic Mapper data as predictor variables. This model reduced mean squared error at validation plots (n=26) by 10 to 23 percent when compared to kriging models that did not use a predictor variable. Our results suggest that this approach, combining SOM inventory and remote sensing data in a geostatistical framework, is a useful method for reducing uncertainty in forest SOM estimates.

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    Citation

    Clough, Brian J.; Green, Edwin J.; Lathrop, Richard B. 2012. Mapping forest soil organic matter on New Jersey's coastal plain. In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 185-191.

    Keywords

    statistics, estimation, sampling, modeling, remote sensing, forest health, data integrity, environmental monitoring, cover estimation, international forest monitoring

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