Estimating aboveground carbon density across forest landscapes of Hawaii: Combining FIA plot-derived estimates and airborne LiDAR

Estimating aboveground carbon density across forest landscapes of Hawaii: Combining FIA plot-derived estimates and airborne LiDAR

https://ift.tt/2KttpOD

Publication date: 15 September 2018
Source:Forest Ecology and Management, Volume 424
Author(s): R. Flint Hughes, Gregory P. Asner, James A. Baldwin, Joseph Mascaro, Lori K.K. Bufil, David E. Knapp

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.

Superforest

via ScienceDirect Publication: Forest Ecology and Management https://ift.tt/xxwarn

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