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Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California

Center for Spatial Technologies and Remote Sensing (CSTARS), University of California Davis, Davis, CA 95616, USA
The Climate Corporation, San Francisco, CA 94103, USA
Department of geology, geography and environment, University of Alcalá, Alcalá de Henares, 28801 Madrid, Spain
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1100;
Received: 18 March 2019 / Revised: 21 April 2019 / Accepted: 6 May 2019 / Published: 8 May 2019
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)
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Accurate information about ecosystem structure and biogeochemical properties is essential to providing better estimates ecosystem functioning. Airborne LiDAR (light detection and ranging) is the most accurate way to retrieve canopy structure. However, accurately obtaining both biogeochemical traits and structure parameters requires concurrent measurements from imaging spectrometers and LiDARs. Our main objective was to evaluate the use of imaging spectroscopy (IS) to provide vegetation structural information. We developed models to estimate structural variables (i.e., biomass, height, vegetation heterogeneity and clumping) using IS data with a random forests model from three forest ecosystems (i.e., an oak-pine low elevation savanna, a mixed conifer/broadleaf mid-elevation forest, and a high-elevation montane conifer forest) in the Sierra Nevada Mountains, California. We developed and tested general models to estimate the four structural variables with accuracies greater than 75%, for the structurally and ecologically different forest sites, demonstrating their applicability to a diverse range of forest ecosystems. The model R2 for each structural variable was least in the conifer/broadleaf forest than either the low elevation savanna or the montane conifer forest. We then used the structural variables we derived to discriminate site-specific, ecologically meaningful descriptions of canopy structural types (CST). Our CST results demonstrate how IS data can be used to create comprehensive and easily interpretable maps of forest structural types that capture their major structural features and trends across different vegetation types in the Sierra Nevada Mountains. The mixed conifer/broadleaf forest and montane conifer forest had the most complex structures, containing six and five CSTs respectively. The identification of CSTs within a site allowed us to better identify the main drivers of structural variability in each ecosystem. CSTs in open savanna were driven mainly by differences in vegetation cover; in the mid-elevation mixed forest, by the combination of biomass and canopy height; and in the montane conifer forest, by vegetation heterogeneity and clumping. View Full-Text
Keywords: vegetation structure; imaging spectroscopy data; random forests; LiDAR vegetation structure; imaging spectroscopy data; random forests; LiDAR

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Huesca, M.; Roth, K.L.; García, M.; Ustin, S.L. Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California. Remote Sens. 2019, 11, 1100.

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