4.3 Article

Development of Height-Volume Relationships in Second Growth Abies grandis for Use with Aerial LiDAR

Journal

CANADIAN JOURNAL OF REMOTE SENSING
Volume 42, Issue 5, Pages 400-410

Publisher

CANADIAN AERONAUTICS & SPACE INST
DOI: 10.1080/07038992.2016.1232587

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Funding

  1. Department of Energy Big Sky Carbon Sequestration Partnership
  2. USFS Rocky Mountain Research Station [08-JV-11221633-159]
  3. RMRS
  4. University of Idaho
  5. National Aeronautics and Space Administration [NNX14AC26G]
  6. Spokane Tribe of Indians
  7. Inland Northwest Growth & Yield Cooperative
  8. Joint Fire Sciences Program [10-1-02-13, 13-1-04-53]

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Following typical forest inventory protocols, individual tree volume estimates are generally derived via diameter-at-breast-height (DBH)-based allometry. Although effective, measurement of DBH is time consuming and potentially a costly element in forest inventories. The capacity of airborne light detection and ranging (LiDAR) to provide individual tree-level information poses options for estimating tree-level attributes to enhance the information content of forest inventories. LiDAR provides excellent height measurements and, given the physiologic scaling connection of plant height and volume, using individual tree height-volume relationships could overcome errors associated with the intermediate step of inferring DBH from LiDAR. In this study, 60 Abies grandis (grand fir: 6cm-64cm DBH) were destructively sampled to assess stem volume across the Intermountain West in order to develop individual tree height-to-stem volume relationships. Results show DBH (r(2) > 0.98) and height (r(2) > 0.94) are significantly (p < 0.001) related to stem volume via power relationships. LiDAR-derived heights provided a 12% RMSE improvement in accuracy of individual tree volume over LiDAR-regressed DBH estimates. Comparing height-based estimates with an existing regional allometry by mapping stem volume in a grand fir-dominated stand yielded a 6.3% difference in total volume. This study demonstrates LiDAR's potential to estimate individual stem volume at forest management scales, utilizing height-volume relationships.

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