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Article

Spatially Distributed Overstory and Understory Leaf Area Index Estimated from Forest Inventory Data

1
Forest Inventory and Analysis Program, Rocky Mountain Research Station, USDA Forest Service, Ogden, UT 84321, USA
2
Utah Water Research Laboratory, Utah State University, Logan, UT 84322, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Zheng Hong Tan and Ge Sun
Water 2022, 14(15), 2414; https://doi.org/10.3390/w14152414
Received: 13 June 2022 / Revised: 29 July 2022 / Accepted: 30 July 2022 / Published: 4 August 2022
Forest change affects the relative magnitudes of hydrologic fluxes such as evapotranspiration (ET) and streamflow. However, much is unknown about the sensitivity of streamflow response to forest disturbance and recovery. Several physically based models recognize the different influences that overstory versus understory canopies exert on hydrologic processes, yet most input datasets consist of total leaf area index (LAI) rather than individual canopy strata. Here, we developed stratum-specific LAI datasets with the intent of improving the representation of vegetation for ecohydrologic modeling. We applied three pre-existing methods for estimating overstory LAI, and one new method for estimating both overstory and understory LAI, to measurements collected from a probability-based plot network established by the US Forest Service’s Forest Inventory and Analysis (FIA) program, for a modeling domain in Montana, MT, USA. We then combined plot-level LAI estimates with spatial datasets (i.e., biophysical and remote sensing predictors) in a machine learning algorithm (random forests) to produce annual gridded LAI datasets. Methods that estimate only overstory LAI tended to underestimate LAI relative to Landsat-based LAI (mean bias error ≥ 0.83), while the method that estimated both overstory and understory layers was most strongly correlated with Landsat-based LAI (r2 = 0.80 for total LAI, with mean bias error of -0.99). During 1984-2019, interannual variability of understory LAI exceeded that for overstory LAI; this variability may affect partitioning of precipitation to ET vs. runoff at annual timescales. We anticipate that distinguishing overstory and understory components of LAI will improve the ability of LAI-based models to simulate how forest change influences hydrologic processes. View Full-Text
Keywords: forest inventory; leaf area index; hydrologic modeling; overstory; understory forest inventory; leaf area index; hydrologic modeling; overstory; understory
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MDPI and ACS Style

Goeking, S.A.; Tarboton, D.G. Spatially Distributed Overstory and Understory Leaf Area Index Estimated from Forest Inventory Data. Water 2022, 14, 2414. https://doi.org/10.3390/w14152414

AMA Style

Goeking SA, Tarboton DG. Spatially Distributed Overstory and Understory Leaf Area Index Estimated from Forest Inventory Data. Water. 2022; 14(15):2414. https://doi.org/10.3390/w14152414

Chicago/Turabian Style

Goeking, Sara A., and David G. Tarboton. 2022. "Spatially Distributed Overstory and Understory Leaf Area Index Estimated from Forest Inventory Data" Water 14, no. 15: 2414. https://doi.org/10.3390/w14152414

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