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Imputing Forest Structure Attributes from Stand Inventory and Remotely Sensed Data in Western Oregon, USA

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Imputation is commonly used to assign reference stand observations to target stands based on covariate relationships to remotely sensed data to assign inventory attributes across the entire landscape. However, most remotely sensed data are collected at higher resolution than the stand inventory data often used by operational foresters. Our primary goal was to compare various aggregation strategies for modeling and mapping forest attributes summarized from stand inventory data, using predictor variables derived from either light detection and ranging (LiDAR) or Landsat and a US Geological Survey (USGS) digital terrain model (DTM). We found that LiDAR metrics produced more accurate models than models using Landsat/USGS-DTM predictors. Calculating stand-level means of all predictors or all responses proved most accurate for developing imputation models or validating imputed maps, respectively. Developing models or validating maps at the unaggregated scale of individual stand subplots proved to be very inaccurate, presumably due to poor geolocation accuracies. However, using a sample of pixels within stands proved only slightly less accurate than using all available pixels. Furthermore, bootstrap tests of similarity between imputations and observations showed no evidence of bias regardless of aggregation strategy. We conclude that pixels sampled from within stands provide sufficient information for modeling or validating stand attributes of interest to foresters.
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Keywords: LandTrendr; Landsat; LiDAR; gradient nearest neighbor (GNN); k nearest neighbor (k-NN); most similar neighbor (MSN); random forest (RF)

Document Type: Research Article

Publication date: 2014-04-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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