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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.


Related in: MedlinePlus

Scatter plots of biomass density at 250 m resolution from four national products versus CMS_RF product. From left to right are NBCD_NCE, Blackard, Wilson, and Saatchi, respectively. From top to down are NLCD2006 categorized total, forest, and non-forest, respectively. The y axis in each plot represents biomass values from national products, and the x axis represents the biomass values from CMS_RF product. Black dashed line is the fitted regression lines, gray solid line is the 1:1 line, and light blue to dark red represents sample kernal density. Forest and non-forest category are derived from aggregated NLCD2006 dataset at 250 m spatial resolution, with a threshold of 20 percentage for forest.
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Fig8: Scatter plots of biomass density at 250 m resolution from four national products versus CMS_RF product. From left to right are NBCD_NCE, Blackard, Wilson, and Saatchi, respectively. From top to down are NLCD2006 categorized total, forest, and non-forest, respectively. The y axis in each plot represents biomass values from national products, and the x axis represents the biomass values from CMS_RF product. Black dashed line is the fitted regression lines, gray solid line is the 1:1 line, and light blue to dark red represents sample kernal density. Forest and non-forest category are derived from aggregated NLCD2006 dataset at 250 m spatial resolution, with a threshold of 20 percentage for forest.

Mentions: Large disagreements were observed in the scatter plots and associated errors at the 250 m resolution (Fig. 8). Overall RMSD values ranged between 48.5 and 92.7 Mg ha−1. The RMSD values ranged between 55.0 and 90.0 Mg ha−1 over forested regions, and between 33.9 and 103.9 Mg ha−1 over non-forested regions. The Saatchi and NBCD maps agreed more closely with the CMS_RF map with fewer zero biomass values after spatial aggregation. The updated version of Saatchi map agreed closely with the NBCD and CMS_RF map, while the original version showed a large difference (Additional file 1: Figure S1 & Additional file 2: Figure S2). The Blackard map was the least correlated with the CMS_RF map while the Wilson map had a large scatter around the 1:1 line.Fig. 8


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)

Scatter plots of biomass density at 250 m resolution from four national products versus CMS_RF product. From left to right are NBCD_NCE, Blackard, Wilson, and Saatchi, respectively. From top to down are NLCD2006 categorized total, forest, and non-forest, respectively. The y axis in each plot represents biomass values from national products, and the x axis represents the biomass values from CMS_RF product. Black dashed line is the fitted regression lines, gray solid line is the 1:1 line, and light blue to dark red represents sample kernal density. Forest and non-forest category are derived from aggregated NLCD2006 dataset at 250 m spatial resolution, with a threshold of 20 percentage for forest.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig8: Scatter plots of biomass density at 250 m resolution from four national products versus CMS_RF product. From left to right are NBCD_NCE, Blackard, Wilson, and Saatchi, respectively. From top to down are NLCD2006 categorized total, forest, and non-forest, respectively. The y axis in each plot represents biomass values from national products, and the x axis represents the biomass values from CMS_RF product. Black dashed line is the fitted regression lines, gray solid line is the 1:1 line, and light blue to dark red represents sample kernal density. Forest and non-forest category are derived from aggregated NLCD2006 dataset at 250 m spatial resolution, with a threshold of 20 percentage for forest.
Mentions: Large disagreements were observed in the scatter plots and associated errors at the 250 m resolution (Fig. 8). Overall RMSD values ranged between 48.5 and 92.7 Mg ha−1. The RMSD values ranged between 55.0 and 90.0 Mg ha−1 over forested regions, and between 33.9 and 103.9 Mg ha−1 over non-forested regions. The Saatchi and NBCD maps agreed more closely with the CMS_RF map with fewer zero biomass values after spatial aggregation. The updated version of Saatchi map agreed closely with the NBCD and CMS_RF map, while the original version showed a large difference (Additional file 1: Figure S1 & Additional file 2: Figure S2). The Blackard map was the least correlated with the CMS_RF map while the Wilson map had a large scatter around the 1:1 line.Fig. 8

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.


Related in: MedlinePlus