Limits...
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.


Study area showing physiographic regions and field plot locations. Physiographic provinces (Appalachian, Piedmont, and Eastern Shore) are divided based on species-composition and environmental gradients. Land cover classes (Evergreen, Deciduous, Mixed, Wetlands, and Non-forest) are taken from the NLCD2006 database.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4537504&req=5

Fig1: Study area showing physiographic regions and field plot locations. Physiographic provinces (Appalachian, Piedmont, and Eastern Shore) are divided based on species-composition and environmental gradients. Land cover classes (Evergreen, Deciduous, Mixed, Wetlands, and Non-forest) are taken from the NLCD2006 database.

Mentions: Maryland has a land area of ~25,600 km2 (Fig. 1) and can be divided into 3 major physiographic provinces (or ecoregions) based on species-composition and environmental gradients. These are the Eastern Coastal Plain (hereafter, “Eastern Shore”), the combined Western Coastal Plain and Piedmont (hereafter, “Piedmont”) and the combined Blue Ridge, Valley and Central Appalachians (hereafter, “Appalachian”). The wide variability in topography, forest types, and environmental gradients makes it a suitable test-bed for national map comparisons.Fig. 1


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)

Study area showing physiographic regions and field plot locations. Physiographic provinces (Appalachian, Piedmont, and Eastern Shore) are divided based on species-composition and environmental gradients. Land cover classes (Evergreen, Deciduous, Mixed, Wetlands, and Non-forest) are taken from the NLCD2006 database.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Study area showing physiographic regions and field plot locations. Physiographic provinces (Appalachian, Piedmont, and Eastern Shore) are divided based on species-composition and environmental gradients. Land cover classes (Evergreen, Deciduous, Mixed, Wetlands, and Non-forest) are taken from the NLCD2006 database.
Mentions: Maryland has a land area of ~25,600 km2 (Fig. 1) and can be divided into 3 major physiographic provinces (or ecoregions) based on species-composition and environmental gradients. These are the Eastern Coastal Plain (hereafter, “Eastern Shore”), the combined Western Coastal Plain and Piedmont (hereafter, “Piedmont”) and the combined Blue Ridge, Valley and Central Appalachians (hereafter, “Appalachian”). The wide variability in topography, forest types, and environmental gradients makes it a suitable test-bed for national map comparisons.Fig. 1

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.