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

Huang W, Swatantran A, Johnson K, Duncanson L, Tang H, O'Neil Dunne J, Hurtt G, Dubayah R - Carbon Balance Manag (2015)

Bottom Line: A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making.Discrepancies reduce with aggregation and the agreement among products improves at the county level.There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels.

View Article: PubMed Central - PubMed

Affiliation: Department of Geographical Sciences, University of Maryland, College Park, USA.

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.

No MeSH data available.


Discrepancies in spatial distribution of biomass density at fine-scale. a Google Earth image in 2012; b high resolution [1 m] land cover map; c NLCD2006; d CMS_RF biomass product at 30 m spatial resolution; e NBCD_NCE biomass product at 30 m spatial resolution; f Saatchi biomass product at 100 m spatial resolution; g Wilson biomass product at 250 m spatial resolution; and h Blackard biomass product at 250 m spatial resolution. Zoom-in figures are for Frederick County.
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Fig3: Discrepancies in spatial distribution of biomass density at fine-scale. a Google Earth image in 2012; b high resolution [1 m] land cover map; c NLCD2006; d CMS_RF biomass product at 30 m spatial resolution; e NBCD_NCE biomass product at 30 m spatial resolution; f Saatchi biomass product at 100 m spatial resolution; g Wilson biomass product at 250 m spatial resolution; and h Blackard biomass product at 250 m spatial resolution. Zoom-in figures are for Frederick County.

Mentions: Although spatial patterns were similar, biomass densities and levels of detail varied considerably (Fig. 2). The CMS_RF biomass map provided greater detail over urban/suburban landscapes (Fig. 3, e.g. trees along roadsides, hedges and backyards) when compared visually with high-resolution [1 m] land cover map and high-resolution imagery (Google Earth). The other maps predicted little or no biomass in non-forested areas. Differences over heterogeneous areas were particularly large (Fig. 3). Results ranged between 36,600 and 119,679 Mg, showing wide local-scale differences.Fig. 3


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

Huang W, Swatantran A, Johnson K, Duncanson L, Tang H, O'Neil Dunne J, Hurtt G, Dubayah R - Carbon Balance Manag (2015)

Discrepancies in spatial distribution of biomass density at fine-scale. a Google Earth image in 2012; b high resolution [1 m] land cover map; c NLCD2006; d CMS_RF biomass product at 30 m spatial resolution; e NBCD_NCE biomass product at 30 m spatial resolution; f Saatchi biomass product at 100 m spatial resolution; g Wilson biomass product at 250 m spatial resolution; and h Blackard biomass product at 250 m spatial resolution. Zoom-in figures are for Frederick County.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4537504&req=5

Fig3: Discrepancies in spatial distribution of biomass density at fine-scale. a Google Earth image in 2012; b high resolution [1 m] land cover map; c NLCD2006; d CMS_RF biomass product at 30 m spatial resolution; e NBCD_NCE biomass product at 30 m spatial resolution; f Saatchi biomass product at 100 m spatial resolution; g Wilson biomass product at 250 m spatial resolution; and h Blackard biomass product at 250 m spatial resolution. Zoom-in figures are for Frederick County.
Mentions: Although spatial patterns were similar, biomass densities and levels of detail varied considerably (Fig. 2). The CMS_RF biomass map provided greater detail over urban/suburban landscapes (Fig. 3, e.g. trees along roadsides, hedges and backyards) when compared visually with high-resolution [1 m] land cover map and high-resolution imagery (Google Earth). The other maps predicted little or no biomass in non-forested areas. Differences over heterogeneous areas were particularly large (Fig. 3). Results ranged between 36,600 and 119,679 Mg, showing wide local-scale differences.Fig. 3

Bottom Line: A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making.Discrepancies reduce with aggregation and the agreement among products improves at the county level.There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels.

View Article: PubMed Central - PubMed

Affiliation: Department of Geographical Sciences, University of Maryland, College Park, USA.

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.

No MeSH data available.