TY - JOUR
T1 - Estimating aboveground carbon density across forest landscapes of Hawaii
T2 - Combining FIA plot-derived estimates and airborne LiDAR
AU - Hughes, R. Flint
AU - Asner, Gregory P.
AU - Baldwin, James A.
AU - Mascaro, Joseph
AU - Bufil, Lori K.K.
AU - Knapp, David E.
N1 - Funding Information:
We sincerely thank K. Hiraoka, M. Murphy, and A. Cantan for their invaluable work collecting field data used in this study. We are also very grateful to S. Ayotte, D. Irvine, H. Hayden, M. Hansen, K.Thies, T. McGinely, R. Pattison, A. Lehman, C. Hubbard, R. Kolesar, G. Nicholas, J. Reid, T. Thompson, H. Anderson, A. Gray, and O. Kuegler for individual and collective efforts during all stages of Hawai’i Forest Inventory program funding, organization, training, data collection, data analysis, and data synthesis. Forest plot inventory fieldwork was supported by 2010 congressional appropriations dedicated to Forest Inventory and Analysis (FIA) intensification on USFS experimental forests; funds were administered by the Pacific Northwest Research Station – FIA program . We thank R. Ostertag for assistance in all aspects of local administration of funds as part of a cooperative agreement between USFS and the Research Corporation of the University of Hawai’i (RCUH); Ostertag also provided helpful comments on earlier versions of the manuscript. LiDAR data collection and processing were funded by the Avatar Alliance Foundation , Carnegie Institution for Science , and William R. Hearst IIII . LiDAR data analyses were supported by a Pacific Southwest Research Station , USDA-Forest Service internal grant award as well as the Carnegie Institution for Science. We thank the Hawai’i Island Branch of the Division of Forestry and Wildlife (DOFAW) and the Hawai’i Experimental Tropical Forest of the USDA-Forest Service (HETF) for permission to conduct field work on the Laupahoehoe and Laupahoehoe and Pu‘u Wa‘awa‘a landscapes. The Carnegie Airborne Observatory has been made possible by grants and donations to G.P. Asner from the Ava-tar Alliance Foundation , Margaret A. Cargill Foundation , David and Lucile Packard Foundation , Gordon and Betty Moore Foundation , Grantham Foundation for the Protection of the Environment , W. M. Keck Foundation , John D. and Catherine T. MacArthur Foundation , Andrew Mellon Foundation , Mary Anne Nyburg Baker and G. Leonard Baker Jr, and William R. Hearst III. We thank R. Ostertag and two anonymous reviewers for providing helpful comments that improved earlier versions of the manuscript.
Funding Information:
We sincerely thank K. Hiraoka, M. Murphy, and A. Cantan for their invaluable work collecting field data used in this study. We are also very grateful to S. Ayotte, D. Irvine, H. Hayden, M. Hansen, K.Thies, T. McGinely, R. Pattison, A. Lehman, C. Hubbard, R. Kolesar, G. Nicholas, J. Reid, T. Thompson, H. Anderson, A. Gray, and O. Kuegler for individual and collective efforts during all stages of Hawai'i Forest Inventory program funding, organization, training, data collection, data analysis, and data synthesis. Forest plot inventory fieldwork was supported by 2010 congressional appropriations dedicated to Forest Inventory and Analysis (FIA) intensification on USFS experimental forests; funds were administered by the Pacific Northwest Research Station – FIA program. We thank R. Ostertag for assistance in all aspects of local administration of funds as part of a cooperative agreement between USFS and the Research Corporation of the University of Hawai'i (RCUH); Ostertag also provided helpful comments on earlier versions of the manuscript. LiDAR data collection and processing were funded by the Avatar Alliance Foundation, Carnegie Institution for Science, and William R. Hearst IIII. LiDAR data analyses were supported by a Pacific Southwest Research Station, USDA-Forest Service internal grant award as well as the Carnegie Institution for Science. We thank the Hawai'i Island Branch of the Division of Forestry and Wildlife (DOFAW) and the Hawai'i Experimental Tropical Forest of the USDA-Forest Service (HETF) for permission to conduct field work on the Laupahoehoe and Laupahoehoe and Pu‘u Wa‘awa‘a landscapes. The Carnegie Airborne Observatory has been made possible by grants and donations to G.P. Asner from the Ava-tar Alliance Foundation, Margaret A. Cargill Foundation, David and Lucile Packard Foundation, Gordon and Betty Moore Foundation, Grantham Foundation for the Protection of the Environment, W. M. Keck Foundation, John D. and Catherine T. MacArthur Foundation, Andrew Mellon Foundation, Mary Anne Nyburg Baker and G. Leonard Baker Jr, and William R. Hearst III. We thank R. Ostertag and two anonymous reviewers for providing helpful comments that improved earlier versions of the manuscript.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/9/15
Y1 - 2018/9/15
N2 - Remote sensing data have increasingly been employed in combination with field plot data to estimate aboveground carbon (C) stocks across heterogeneous forested landscapes around the world. The Forest Inventory and Analysis (FIA) program of the US Forest Service offers a gridded network of field plots which potentially can be linked to airborne Light Detection and Ranging (LiDAR) data to estimate forest aboveground carbon density (ACD; units of Mg C ha−1). Here we utilized FIA plot and airborne LiDAR data sets collected across two contrasting landscapes known as Laupahoehoe and Pu‘u Wa‘awa‘a on Hawai'i Island to explore strengths and weaknesses of linking those two data sets to estimate ACD. We varied FIA plot sample designs with respect to sampling density (i.e., the number of plots across landscape) and intensity (i.e., the structural detail within inventory plots) to test the capability of the mapping approach. Results indicated that Laupahoehoe and Pu‘u Wa‘awa‘a landscapes supported an estimated 545 Gg C and 157 Gg C aboveground, respectively, and mean ACD values of the wet windward Laupahoehoe landscape (109 Mg ha−1) were an order of magnitude greater than those of the leeward dry Pu‘u Wa‘awa‘a landscape (9.7 Mg ha−1). Patterns of ACD were largely determined by combined factors of precipitation, lava substrate, prior land use, and presence of non-native, often invasive, species. Results demonstrated the relative importance of sample plot density over sample plot intensity, and showed that FIA inventory plots, even at their lowest sample intensity design, can be linked with LiDAR data to accurately estimate ACD across spatially heterogeneous landscapes. We also developed and applied a straightforward, statistically-robust approach to provide error estimates for the 100 million pixels that characterize the Laupahoehoe and Pu‘u Wa‘awa‘a landscapes as well as for any sub-units of those landscapes. We contend that augmenting existing FIA forest plot data with airborne LiDAR coverage, even if that requires an increase in plot density somewhat above the FIA standard 1X or 2X approaches, is a feasible, cost-effective, scientifically sound approach from which to obtain accurate landscape- to regional-scale ACD measures across the extensive and heterogeneous forests of the United States.
AB - Remote sensing data have increasingly been employed in combination with field plot data to estimate aboveground carbon (C) stocks across heterogeneous forested landscapes around the world. The Forest Inventory and Analysis (FIA) program of the US Forest Service offers a gridded network of field plots which potentially can be linked to airborne Light Detection and Ranging (LiDAR) data to estimate forest aboveground carbon density (ACD; units of Mg C ha−1). Here we utilized FIA plot and airborne LiDAR data sets collected across two contrasting landscapes known as Laupahoehoe and Pu‘u Wa‘awa‘a on Hawai'i Island to explore strengths and weaknesses of linking those two data sets to estimate ACD. We varied FIA plot sample designs with respect to sampling density (i.e., the number of plots across landscape) and intensity (i.e., the structural detail within inventory plots) to test the capability of the mapping approach. Results indicated that Laupahoehoe and Pu‘u Wa‘awa‘a landscapes supported an estimated 545 Gg C and 157 Gg C aboveground, respectively, and mean ACD values of the wet windward Laupahoehoe landscape (109 Mg ha−1) were an order of magnitude greater than those of the leeward dry Pu‘u Wa‘awa‘a landscape (9.7 Mg ha−1). Patterns of ACD were largely determined by combined factors of precipitation, lava substrate, prior land use, and presence of non-native, often invasive, species. Results demonstrated the relative importance of sample plot density over sample plot intensity, and showed that FIA inventory plots, even at their lowest sample intensity design, can be linked with LiDAR data to accurately estimate ACD across spatially heterogeneous landscapes. We also developed and applied a straightforward, statistically-robust approach to provide error estimates for the 100 million pixels that characterize the Laupahoehoe and Pu‘u Wa‘awa‘a landscapes as well as for any sub-units of those landscapes. We contend that augmenting existing FIA forest plot data with airborne LiDAR coverage, even if that requires an increase in plot density somewhat above the FIA standard 1X or 2X approaches, is a feasible, cost-effective, scientifically sound approach from which to obtain accurate landscape- to regional-scale ACD measures across the extensive and heterogeneous forests of the United States.
KW - Carnegie Airborne Observatory
KW - Forest carbon
KW - Hawai'i
KW - Hawai'i Experimental Tropical Forest
KW - Invasive species
KW - Inventories
KW - LiDAR
UR - http://www.scopus.com/inward/record.url?scp=85047259841&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047259841&partnerID=8YFLogxK
U2 - 10.1016/j.foreco.2018.04.053
DO - 10.1016/j.foreco.2018.04.053
M3 - Article
AN - SCOPUS:85047259841
SN - 0378-1127
VL - 424
SP - 323
EP - 337
JO - Forest Ecology and Management
JF - Forest Ecology and Management
ER -