Stratified Light Detection and Ranging Double-Sample Forest Inventory

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Abstract:

An industrial application of a light detection and ranging (LiDAR) individual-tree, stratified double-sample forest inventory of approximately 18,000 ha of southeastern pine plantations was accomplished with an 9:1 ratio of 0.02-ha phase 1 LiDAR and phase 2 ground plots in ages 6 to 28 years. Phase 2 ground inventory data of tree dbh and sample tree heights for 2 trees per plot were used to obtain dbh-height relationships and volumes of standing trees. Phase 1 LiDAR data with 1.9 points per m2 were used to obtain ground–LiDAR height relationships for phase 2 matching LiDAR trees and phase 1 estimates of basal area and volume. A conventional ground inventory of 971 ground plots by private contractors applying standard company field specifications resulted in an overall sampling error of ±2.7% (α = 0.05) for a single-phase volume estimate and ±2.2% for the double-sample volume estimate. Sampling error was defined as one-half the 1-α confidence interval expressed as a percentage of the mean. Reducing the phase 2 ground sample to 15 plots per age class stratum achieved sampling errors of approximately ±15% for half the strata, with a combined error of ±3.9%. Adjusting the LiDAR-ground height bias of approximately 1.8 m resulted in more realistic volume estimates compared with the industry's continuing forest inventory volumes. The double-sample volume estimates were obtained at a cost of approximately $3.88/ha of timberland inventoried as compared with $1.67/ha for the conventional inventory.

Keywords: LiDAR; double-sample; inventory

Document Type: Research Article

Publication date: May 1, 2007

More about this publication?
  • Each regional journal of applied forestry focuses on research, practice, and techniques targeted to foresters and allied professionals in specific regions of the United States and Canada. The Southern Journal of Applied Forestry covers an area from Virginia and Kentucky south to as far west as Oklahoma and east Texas.
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