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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 county-level total biomass of four national products and CMS_RF against estimates from FIA_Jenkins. a NBCD_NCE at 30 m spatial resolution, b Blackard at 250 m spatial resolution, c Wilson at 250 m spatial resolution, d Saatchi at 250 m spatial resolution, and e CMS_RF at 30 m spatial resolution. The x axis represents the biomass totals from FIA_Jenkins, and the y axis represents corresponding national products in each plot. Red dashed line is the fitted regression line, and gray solid line is the 1:1 line. FIA_Jenkins represents biomass estimates using Jenkins allometrics and gap-filled for non-forest biomass.
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Fig12: Scatter plots of county-level total biomass of four national products and CMS_RF against estimates from FIA_Jenkins. a NBCD_NCE at 30 m spatial resolution, b Blackard at 250 m spatial resolution, c Wilson at 250 m spatial resolution, d Saatchi at 250 m spatial resolution, and e CMS_RF at 30 m spatial resolution. The x axis represents the biomass totals from FIA_Jenkins, and the y axis represents corresponding national products in each plot. Red dashed line is the fitted regression line, and gray solid line is the 1:1 line. FIA_Jenkins represents biomass estimates using Jenkins allometrics and gap-filled for non-forest biomass.

Mentions: County totals from the continental scale maps and the CMS_RF map were also compared with FIA totals (Fig. 12). For this comparison, we used the gap-filled Jenkins estimate from FIA data as it includes non-forested biomass [9]. Continental scale maps were strongly correlated with FIA at county level and had high coefficients of determination (0.63–0.80), but consistently underestimated biomass with a negative bias, ranging between −3.4 and −1.1 Tg for total biomass (Fig. 12a–d). The CMS_RF map showed good agreement too but had a positive bias and overestimated biomass, particularly in counties that had many low biomass areas such as in the Piedmont (Fig. 12e).Fig. 12


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 county-level total biomass of four national products and CMS_RF against estimates from FIA_Jenkins. a NBCD_NCE at 30 m spatial resolution, b Blackard at 250 m spatial resolution, c Wilson at 250 m spatial resolution, d Saatchi at 250 m spatial resolution, and e CMS_RF at 30 m spatial resolution. The x axis represents the biomass totals from FIA_Jenkins, and the y axis represents corresponding national products in each plot. Red dashed line is the fitted regression line, and gray solid line is the 1:1 line. FIA_Jenkins represents biomass estimates using Jenkins allometrics and gap-filled for non-forest biomass.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig12: Scatter plots of county-level total biomass of four national products and CMS_RF against estimates from FIA_Jenkins. a NBCD_NCE at 30 m spatial resolution, b Blackard at 250 m spatial resolution, c Wilson at 250 m spatial resolution, d Saatchi at 250 m spatial resolution, and e CMS_RF at 30 m spatial resolution. The x axis represents the biomass totals from FIA_Jenkins, and the y axis represents corresponding national products in each plot. Red dashed line is the fitted regression line, and gray solid line is the 1:1 line. FIA_Jenkins represents biomass estimates using Jenkins allometrics and gap-filled for non-forest biomass.
Mentions: County totals from the continental scale maps and the CMS_RF map were also compared with FIA totals (Fig. 12). For this comparison, we used the gap-filled Jenkins estimate from FIA data as it includes non-forested biomass [9]. Continental scale maps were strongly correlated with FIA at county level and had high coefficients of determination (0.63–0.80), but consistently underestimated biomass with a negative bias, ranging between −3.4 and −1.1 Tg for total biomass (Fig. 12a–d). The CMS_RF map showed good agreement too but had a positive bias and overestimated biomass, particularly in counties that had many low biomass areas such as in the Piedmont (Fig. 12e).Fig. 12

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