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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.


Difference maps of biomass density. a CMS_RF-NBCD_NCE at 30 m spatial resolution; b CMS_RF-Blackard at 250 m spatial resolution; c CMS_RF-Wilson at 250 m spatial resolution; and d CMS_RF-Saatchi at 100 m spatial resolution. Areas in red have lower values and areas in blue have higher values than the CMS_RF map. Fuzzy Numerical Index (FNI) quantifies overall similarity between the national biomass maps and the CMS_RF map, ranging from 0 (fully distinct) to 1 (fully identical).
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Fig4: Difference maps of biomass density. a CMS_RF-NBCD_NCE at 30 m spatial resolution; b CMS_RF-Blackard at 250 m spatial resolution; c CMS_RF-Wilson at 250 m spatial resolution; and d CMS_RF-Saatchi at 100 m spatial resolution. Areas in red have lower values and areas in blue have higher values than the CMS_RF map. Fuzzy Numerical Index (FNI) quantifies overall similarity between the national biomass maps and the CMS_RF map, ranging from 0 (fully distinct) to 1 (fully identical).

Mentions: FNI provides a spatial representation of similarities and differences when calculated at a pixel-level. However, it does not capture the positive and negative deviations with respect to the CMS_RF map. We therefore calculated a mean FNI value for each map comparison with values ranging from 0 (perfect dissimilarity) to 1 (perfect similarity). A combination of map differences and FNI index values provided additional spatial and quantitative understanding of map discrepancies (Fig. 4; Table 2). Differences between maps were prominent in the Piedmont region, over counties in southern Maryland and along the Appalachians in the West. The Saatchi map was most similar to the CMS_RF map (FNI = 0.53) while the Blackard Map (FNI = 0.26) was the most dissimilar. The Wilson map had almost an equal proportion of similar and dissimilar pixels (FNI = 0.49) while the NBCD map was slightly lower with an FNI of 0.48.Fig. 4


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)

Difference maps of biomass density. a CMS_RF-NBCD_NCE at 30 m spatial resolution; b CMS_RF-Blackard at 250 m spatial resolution; c CMS_RF-Wilson at 250 m spatial resolution; and d CMS_RF-Saatchi at 100 m spatial resolution. Areas in red have lower values and areas in blue have higher values than the CMS_RF map. Fuzzy Numerical Index (FNI) quantifies overall similarity between the national biomass maps and the CMS_RF map, ranging from 0 (fully distinct) to 1 (fully identical).
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Difference maps of biomass density. a CMS_RF-NBCD_NCE at 30 m spatial resolution; b CMS_RF-Blackard at 250 m spatial resolution; c CMS_RF-Wilson at 250 m spatial resolution; and d CMS_RF-Saatchi at 100 m spatial resolution. Areas in red have lower values and areas in blue have higher values than the CMS_RF map. Fuzzy Numerical Index (FNI) quantifies overall similarity between the national biomass maps and the CMS_RF map, ranging from 0 (fully distinct) to 1 (fully identical).
Mentions: FNI provides a spatial representation of similarities and differences when calculated at a pixel-level. However, it does not capture the positive and negative deviations with respect to the CMS_RF map. We therefore calculated a mean FNI value for each map comparison with values ranging from 0 (perfect dissimilarity) to 1 (perfect similarity). A combination of map differences and FNI index values provided additional spatial and quantitative understanding of map discrepancies (Fig. 4; Table 2). Differences between maps were prominent in the Piedmont region, over counties in southern Maryland and along the Appalachians in the West. The Saatchi map was most similar to the CMS_RF map (FNI = 0.53) while the Blackard Map (FNI = 0.26) was the most dissimilar. The Wilson map had almost an equal proportion of similar and dissimilar pixels (FNI = 0.49) while the NBCD map was slightly lower with an FNI of 0.48.Fig. 4

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.