Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

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 en...
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Autor*in:	Huang, Wenli [verfasserIn] Swatantran, Anu Johnson, Kristofer Duncanson, Laura Tang, Hao O’Neil Dunne, Jarlath Hurtt, George Dubayah, Ralph

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Temperate deciduous forest Lidar Aboveground biomass Carbon

Anmerkung:	© Huang et al. 2015

Übergeordnetes Werk:	Enthalten in: Carbon balance and management - London : Biomed Central, 2006, 10(2015), 1 vom: 16. Aug.
Übergeordnetes Werk:	volume:10 ; year:2015 ; number:1 ; day:16 ; month:08

Links:	Volltext

DOI / URN:	10.1186/s13021-015-0030-9

Katalog-ID:	SPR029478596

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520			\|a 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.
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allfields	10.1186/s13021-015-0030-9 doi (DE-627)SPR029478596 (SPR)s13021-015-0030-9-e DE-627 ger DE-627 rakwb eng Huang, Wenli verfasserin (orcid)0000-0001-9608-1690 aut Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Huang et al. 2015 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. Temperate deciduous forest (dpeaa)DE-He213 Lidar (dpeaa)DE-He213 Aboveground biomass (dpeaa)DE-He213 Carbon (dpeaa)DE-He213 Swatantran, Anu aut Johnson, Kristofer aut Duncanson, Laura aut Tang, Hao aut O’Neil Dunne, Jarlath aut Hurtt, George aut Dubayah, Ralph aut Enthalten in Carbon balance and management London : Biomed Central, 2006 10(2015), 1 vom: 16. Aug. (DE-627)515824364 (DE-600)2243512-8 1750-0680 nnns volume:10 year:2015 number:1 day:16 month:08 https://dx.doi.org/10.1186/s13021-015-0030-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2015 1 16 08
spelling	10.1186/s13021-015-0030-9 doi (DE-627)SPR029478596 (SPR)s13021-015-0030-9-e DE-627 ger DE-627 rakwb eng Huang, Wenli verfasserin (orcid)0000-0001-9608-1690 aut Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Huang et al. 2015 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. Temperate deciduous forest (dpeaa)DE-He213 Lidar (dpeaa)DE-He213 Aboveground biomass (dpeaa)DE-He213 Carbon (dpeaa)DE-He213 Swatantran, Anu aut Johnson, Kristofer aut Duncanson, Laura aut Tang, Hao aut O’Neil Dunne, Jarlath aut Hurtt, George aut Dubayah, Ralph aut Enthalten in Carbon balance and management London : Biomed Central, 2006 10(2015), 1 vom: 16. Aug. (DE-627)515824364 (DE-600)2243512-8 1750-0680 nnns volume:10 year:2015 number:1 day:16 month:08 https://dx.doi.org/10.1186/s13021-015-0030-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2015 1 16 08
allfields_unstemmed	10.1186/s13021-015-0030-9 doi (DE-627)SPR029478596 (SPR)s13021-015-0030-9-e DE-627 ger DE-627 rakwb eng Huang, Wenli verfasserin (orcid)0000-0001-9608-1690 aut Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Huang et al. 2015 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. Temperate deciduous forest (dpeaa)DE-He213 Lidar (dpeaa)DE-He213 Aboveground biomass (dpeaa)DE-He213 Carbon (dpeaa)DE-He213 Swatantran, Anu aut Johnson, Kristofer aut Duncanson, Laura aut Tang, Hao aut O’Neil Dunne, Jarlath aut Hurtt, George aut Dubayah, Ralph aut Enthalten in Carbon balance and management London : Biomed Central, 2006 10(2015), 1 vom: 16. Aug. (DE-627)515824364 (DE-600)2243512-8 1750-0680 nnns volume:10 year:2015 number:1 day:16 month:08 https://dx.doi.org/10.1186/s13021-015-0030-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2015 1 16 08
allfieldsGer	10.1186/s13021-015-0030-9 doi (DE-627)SPR029478596 (SPR)s13021-015-0030-9-e DE-627 ger DE-627 rakwb eng Huang, Wenli verfasserin (orcid)0000-0001-9608-1690 aut Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Huang et al. 2015 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. Temperate deciduous forest (dpeaa)DE-He213 Lidar (dpeaa)DE-He213 Aboveground biomass (dpeaa)DE-He213 Carbon (dpeaa)DE-He213 Swatantran, Anu aut Johnson, Kristofer aut Duncanson, Laura aut Tang, Hao aut O’Neil Dunne, Jarlath aut Hurtt, George aut Dubayah, Ralph aut Enthalten in Carbon balance and management London : Biomed Central, 2006 10(2015), 1 vom: 16. Aug. (DE-627)515824364 (DE-600)2243512-8 1750-0680 nnns volume:10 year:2015 number:1 day:16 month:08 https://dx.doi.org/10.1186/s13021-015-0030-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2015 1 16 08
allfieldsSound	10.1186/s13021-015-0030-9 doi (DE-627)SPR029478596 (SPR)s13021-015-0030-9-e DE-627 ger DE-627 rakwb eng Huang, Wenli verfasserin (orcid)0000-0001-9608-1690 aut Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Huang et al. 2015 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. Temperate deciduous forest (dpeaa)DE-He213 Lidar (dpeaa)DE-He213 Aboveground biomass (dpeaa)DE-He213 Carbon (dpeaa)DE-He213 Swatantran, Anu aut Johnson, Kristofer aut Duncanson, Laura aut Tang, Hao aut O’Neil Dunne, Jarlath aut Hurtt, George aut Dubayah, Ralph aut Enthalten in Carbon balance and management London : Biomed Central, 2006 10(2015), 1 vom: 16. Aug. (DE-627)515824364 (DE-600)2243512-8 1750-0680 nnns volume:10 year:2015 number:1 day:16 month:08 https://dx.doi.org/10.1186/s13021-015-0030-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2015 1 16 08
language	English
source	Enthalten in Carbon balance and management 10(2015), 1 vom: 16. Aug. volume:10 year:2015 number:1 day:16 month:08
sourceStr	Enthalten in Carbon balance and management 10(2015), 1 vom: 16. Aug. volume:10 year:2015 number:1 day:16 month:08
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topic_facet	Temperate deciduous forest Lidar Aboveground biomass Carbon
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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. 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author	Huang, Wenli
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topic_title	Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA Temperate deciduous forest (dpeaa)DE-He213 Lidar (dpeaa)DE-He213 Aboveground biomass (dpeaa)DE-He213 Carbon (dpeaa)DE-He213
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title	Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA
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title_full	Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA
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author_browse	Huang, Wenli Swatantran, Anu Johnson, Kristofer Duncanson, Laura Tang, Hao O’Neil Dunne, Jarlath Hurtt, George Dubayah, Ralph
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title_sort	local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in maryland, usa
title_auth	Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, 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. © Huang et al. 2015
abstractGer	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. © Huang et al. 2015
abstract_unstemmed	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. © Huang et al. 2015
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title_short	Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA
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author2	Swatantran, Anu Johnson, Kristofer Duncanson, Laura Tang, Hao O’Neil Dunne, Jarlath Hurtt, George Dubayah, Ralph
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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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Temperate deciduous forest</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Lidar</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Aboveground biomass</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Carbon</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Swatantran, Anu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Johnson, Kristofer</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Duncanson, Laura</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tang, Hao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">O’Neil Dunne, Jarlath</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hurtt, George</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dubayah, Ralph</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Carbon balance and management</subfield><subfield code="d">London : Biomed Central, 2006</subfield><subfield code="g">10(2015), 1 vom: 16. 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Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

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