Mapping and Scaling of In Situ Above Ground Biomass to Regional Extent With SAR in the Great Slave Region

Abstract Global forests are increasingly threatened by disturbance events such as wildfire. Spaceborne Synthetic Aperture Radar (SAR) missions at L‐ (or P‐) band, such as the upcoming NASA ISRO SAR (NISAR), have great potential to advance global mapping of above‐ground biomass (AGB). AGB mapping wit...

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Main Authors: S. Kraatz, L. Bourgeau‐Chavez, M. Battaglia, A. Poley, P. Siqueira
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2022-12-01
Series:Earth and Space Science
Subjects:
Online Access:https://doi.org/10.1029/2022EA002431
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author S. Kraatz
L. Bourgeau‐Chavez
M. Battaglia
A. Poley
P. Siqueira
author_facet S. Kraatz
L. Bourgeau‐Chavez
M. Battaglia
A. Poley
P. Siqueira
author_sort S. Kraatz
collection DOAJ
description Abstract Global forests are increasingly threatened by disturbance events such as wildfire. Spaceborne Synthetic Aperture Radar (SAR) missions at L‐ (or P‐) band, such as the upcoming NASA ISRO SAR (NISAR), have great potential to advance global mapping of above‐ground biomass (AGB). AGB mapping with SAR is challenging due to lack of available L‐ or P‐ band data, and because SAR data are sensitive to confounding factors such as hydrology and terrain. This study uses recently collected AGB validation site data (AGBV) to produce a 1 ha biomass map about the Great Slave Lake in Canada using SAR data, and reports on NISAR's anticipated performance. This study addresses errors inherent to the representativeness of AGBVs with coarser grid/landscape scale processes by evaluating model performance as data are aggregated over increasingly larger areas (AOAs). Air and spaceborne SAR data were found to be interoperable after processing them according to analysis ready data specifications, improving data availability. Owing to poor model performance at two AGBVs, root‐mean‐square errors (RMSEs) were ∼60 Mg/ha, irrespective of AOA. When instead using NISAR's more lenient assessment criteria, RMSEs decreased to 32, 15, and 21 Mg/ha for the small (∼0.1 ha), medium (∼3.5 ha), and large (∼14 ha) AOA. Thus, AGB mapping in this region appears to benefit significantly from coarser data aggregations than to be used by NISAR's. This approach is practical for identifying a suitable scale of correspondence between the AGBV and SAR data and the landscape‐scale processes and can substantially improve AGB mapping accuracy.
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spelling doaj.art-421348a508cc489d9edc8451f9b313fe2022-12-27T18:40:34ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842022-12-01912n/an/a10.1029/2022EA002431Mapping and Scaling of In Situ Above Ground Biomass to Regional Extent With SAR in the Great Slave RegionS. Kraatz0L. Bourgeau‐Chavez1M. Battaglia2A. Poley3P. Siqueira4Department of Electrical and Computer Engineering University of Massachusetts Amherst Amherst MA USAMichigan Tech Research Institute Michigan Technological University Ann Arbor MI USAMichigan Tech Research Institute Michigan Technological University Ann Arbor MI USAMichigan Tech Research Institute Michigan Technological University Ann Arbor MI USADepartment of Electrical and Computer Engineering University of Massachusetts Amherst Amherst MA USAAbstract Global forests are increasingly threatened by disturbance events such as wildfire. Spaceborne Synthetic Aperture Radar (SAR) missions at L‐ (or P‐) band, such as the upcoming NASA ISRO SAR (NISAR), have great potential to advance global mapping of above‐ground biomass (AGB). AGB mapping with SAR is challenging due to lack of available L‐ or P‐ band data, and because SAR data are sensitive to confounding factors such as hydrology and terrain. This study uses recently collected AGB validation site data (AGBV) to produce a 1 ha biomass map about the Great Slave Lake in Canada using SAR data, and reports on NISAR's anticipated performance. This study addresses errors inherent to the representativeness of AGBVs with coarser grid/landscape scale processes by evaluating model performance as data are aggregated over increasingly larger areas (AOAs). Air and spaceborne SAR data were found to be interoperable after processing them according to analysis ready data specifications, improving data availability. Owing to poor model performance at two AGBVs, root‐mean‐square errors (RMSEs) were ∼60 Mg/ha, irrespective of AOA. When instead using NISAR's more lenient assessment criteria, RMSEs decreased to 32, 15, and 21 Mg/ha for the small (∼0.1 ha), medium (∼3.5 ha), and large (∼14 ha) AOA. Thus, AGB mapping in this region appears to benefit significantly from coarser data aggregations than to be used by NISAR's. This approach is practical for identifying a suitable scale of correspondence between the AGBV and SAR data and the landscape‐scale processes and can substantially improve AGB mapping accuracy.https://doi.org/10.1029/2022EA002431aboveNISARbiomass
spellingShingle S. Kraatz
L. Bourgeau‐Chavez
M. Battaglia
A. Poley
P. Siqueira
Mapping and Scaling of In Situ Above Ground Biomass to Regional Extent With SAR in the Great Slave Region
Earth and Space Science
above
NISAR
biomass
title Mapping and Scaling of In Situ Above Ground Biomass to Regional Extent With SAR in the Great Slave Region
title_full Mapping and Scaling of In Situ Above Ground Biomass to Regional Extent With SAR in the Great Slave Region
title_fullStr Mapping and Scaling of In Situ Above Ground Biomass to Regional Extent With SAR in the Great Slave Region
title_full_unstemmed Mapping and Scaling of In Situ Above Ground Biomass to Regional Extent With SAR in the Great Slave Region
title_short Mapping and Scaling of In Situ Above Ground Biomass to Regional Extent With SAR in the Great Slave Region
title_sort mapping and scaling of in situ above ground biomass to regional extent with sar in the great slave region
topic above
NISAR
biomass
url https://doi.org/10.1029/2022EA002431
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AT mbattaglia mappingandscalingofinsituabovegroundbiomasstoregionalextentwithsarinthegreatslaveregion
AT apoley mappingandscalingofinsituabovegroundbiomasstoregionalextentwithsarinthegreatslaveregion
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