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      Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

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          Abstract

          Background

          Continental-scale aboveground biomass maps are increasingly available, but their estimates vary widely, particularly at high resolution. A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making. To this end, we compare four continental-scale maps with a recent high-resolution lidar-derived biomass map over Maryland, USA. We conduct detailed comparisons at pixel-, county-, and state-level.

          Results

          Spatial patterns of biomass are broadly consistent in all maps, but there are large differences at fine scales (RMSD 48.5–92.7 Mg ha −1). Discrepancies reduce with aggregation and the agreement among products improves at the county level. However, continental scale maps exhibit residual negative biases in mean (33.0–54.6 Mg ha −1) and total biomass (3.5–5.8 Tg) when compared to the high-resolution lidar biomass map. Three of the four continental scale maps reach near-perfect agreement at ~4 km and onward but do not converge with the high-resolution biomass map even at county scale. At the State level, these maps underestimate biomass by 30–80 Tg in forested and 40–50 Tg in non-forested areas.

          Conclusions

          Local discrepancies in continental scale biomass maps are caused by factors including data inputs, modeling approaches, forest/non-forest definitions and time lags. There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels. Local, high-resolution lidar-derived biomass maps provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale maps produced in carbon monitoring systems.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13021-015-0030-9) contains supplementary material, which is available to authorized users.

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          Most cited references15

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          Random forests for classification in ecology.

          Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.
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            The potential and challenge of remote sensing‐based biomass estimation

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              Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

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                Author and article information

                Contributors
                wlhuang@umd.edu
                aswatan@umd.edu
                kristoferdjohnson@fs.fed.us
                lduncans@umd.edu
                htang@umd.edu
                Jarlath.ONeil-Dunne@uvm.edu
                gchurtt@umd.edu
                dubayah@umd.edu
                Journal
                Carbon Balance Manag
                Carbon Balance Manag
                Carbon Balance and Management
                Springer International Publishing (Cham )
                1750-0680
                16 August 2015
                16 August 2015
                December 2015
                : 10
                : 19
                Affiliations
                [ ]Department of Geographical Sciences, University of Maryland, College Park, USA
                [ ]USDA Forest Service, Northern Research Station, Newtown Square, PA USA
                [ ]Rubenstein School of the Environment and Natural Resources, University of Vermont, Burlington, USA
                Author information
                http://orcid.org/0000-0001-9608-1690
                Article
                30
                10.1186/s13021-015-0030-9
                4537504
                a4196253-b11d-4718-bab9-3e87f8d396db
                © Huang et al. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 15 June 2015
                : 31 July 2015
                Categories
                Research
                Custom metadata
                © The Author(s) 2015

                Environmental change
                temperate deciduous forest,lidar,aboveground biomass,carbon
                Environmental change
                temperate deciduous forest, lidar, aboveground biomass, carbon

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