Classifying forest type in the national forest inventory context with airborne hyperspectral and lidar data

Caileigh Shoot, Hans Erik Andersen, L. Monika Moskal, Chad Babcock, Bruce D. Cook, Douglas C. Morton

Research output: Contribution to journalArticlepeer-review

Abstract

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

Original languageEnglish (US)
Article number1863
JournalRemote Sensing
Volume13
Issue number10
DOIs
StatePublished - May 2 2021

Bibliographical note

Funding Information:
Funding: This study was generously supported by: The USDA Forest Service Pacific Northwest Research Station, The University of Washington Precision Forestry Cooperative, NASA Goddard Space Flight Center, NASA Arctic Boreal Vulnerability Experiment (ABoVE), The University of Washington School of Environmental and Forest Sciences David Briggs Memorial Fellowship.

Publisher Copyright:
© 2021 by the authors.

Keywords

  • Classification
  • Forest type
  • Forest type
  • Hyperspectral
  • Land cover classification
  • LiDAR
  • Machine learning
  • Random forest

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