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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.


Biomass density maps at the state level. a Land cover at 30 m spatial resolution; b CMS_RF biomass product at 30 m spatial resolution; c NBCD_NCE biomass product at 30 m spatial resolution; d Blackard biomass product at 250 m spatial resolution; e Wilson biomass product at 250 m spatial resolution; and f Saatchi biomass product at 100 m spatial resolution.
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Fig2: Biomass density maps at the state level. a Land cover at 30 m spatial resolution; b CMS_RF biomass product at 30 m spatial resolution; c NBCD_NCE biomass product at 30 m spatial resolution; d Blackard biomass product at 250 m spatial resolution; e Wilson biomass product at 250 m spatial resolution; and f Saatchi biomass product at 100 m spatial resolution.

Mentions: Four national biomass products (Fig. 2; Table 1) were compared to the CMS_RF map. Each of these maps was derived using medium to coarse resolution satellite imagery. The NBCD2000 was the first 30 m national product developed using InSAR data from the 2000 Shutter Radar Topography Mission (STRM) and Landsat ETM+ data [13, 23]. NBCD2000 provided two versions of biomass: (A) NBCD_FIA map in which tree-level biomass estimates were obtained from tree tables in the FIA database (FIADB); and (B) NBCD_NCE or National Consistent allometric Equations in which biomass estimates were derived from equations developed by Jenkins et al. [17]. We used the NBCD_NCE version for consistency with our field biomass estimates, which were also derived from national allometric equations.Fig. 2


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)

Biomass density maps at the state level. a Land cover at 30 m spatial resolution; b CMS_RF biomass product at 30 m spatial resolution; c NBCD_NCE biomass product at 30 m spatial resolution; d Blackard biomass product at 250 m spatial resolution; e Wilson biomass product at 250 m spatial resolution; and f Saatchi biomass product at 100 m spatial resolution.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Biomass density maps at the state level. a Land cover at 30 m spatial resolution; b CMS_RF biomass product at 30 m spatial resolution; c NBCD_NCE biomass product at 30 m spatial resolution; d Blackard biomass product at 250 m spatial resolution; e Wilson biomass product at 250 m spatial resolution; and f Saatchi biomass product at 100 m spatial resolution.
Mentions: Four national biomass products (Fig. 2; Table 1) were compared to the CMS_RF map. Each of these maps was derived using medium to coarse resolution satellite imagery. The NBCD2000 was the first 30 m national product developed using InSAR data from the 2000 Shutter Radar Topography Mission (STRM) and Landsat ETM+ data [13, 23]. NBCD2000 provided two versions of biomass: (A) NBCD_FIA map in which tree-level biomass estimates were obtained from tree tables in the FIA database (FIADB); and (B) NBCD_NCE or National Consistent allometric Equations in which biomass estimates were derived from equations developed by Jenkins et al. [17]. We used the NBCD_NCE version for consistency with our field biomass estimates, which were also derived from national allometric equations.Fig. 2

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.