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    Author(s): Andrew T. Hudak; A. Tod Haren; Nicholas L. Crookston; Robert J. Liebermann; Janet L. Ohmann
    Date: 2014
    Source: Forest Science
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Northwest Research Station
    PDF: Download Publication  (1.82 MB)


    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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    Hudak, Andrew T.; Haren, A. Tod; Crookston, Nicholas L.; Liebermann, Robert J.; Ohmann, Janet L. 2014. Imputing forest structure attributes from stand inventory and remotely sensed data in western Oregon, USA. Forest Science. 60(2): 253-269.


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    gradient nearest neighbor (GNN), Landsat, LandTrendr, LiDAR, most similar neighbor (MSN), k nearest neighbor (k-NN), random forest (RF)

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