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The shelf-life of airborne laser scanning data for enhancing forest inventory inferencesAuthor(s): Ronald E. McRoberts; Qi Chen; Dale D. Gormanson; Brian F. Walters
Source: Remote Sensing of Environment
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionThe term shelf-life is used to characterize the elapsed time beyond which a commodity loses its usefulness. The term is most often used with reference to foods and medicines, but herein it is used to characterize the elapsed time beyond which airborne laser scanning (ALS) data are no longer useful for enhancing inferences for forest inventory population parameters. National forest inventories (NFI) have a long history of using remotely sensed auxiliary information to enhance inferences. Although the combination of model-assisted estimators and ALS auxiliary data has been demonstrated to be particularly useful for this purpose, the expense associated with the acquisition of the ALS data has been an argument against their operational use. However, the longer the shelf-life of ALS data, the less the continuing acquisition costs and the greater the utility of the data. The objective of the study was to assess the shelf-life of ALS data for enhancing inferences in the form of confidence intervals for mean aboveground, live tree, stem biomass per unit area. Confidence intervals were constructed using both model-assisted estimators and post-stratified estimators, four measurements of mostly the same forest inventory plots at 5-year intervals over a 17-year period, and a single set of ALS data acquired near the end of the 17-year period. The study area in north central Minnesota in the USA was characterized by naturally regenerated, uneven-aged, mixed species stands on both lowland and upland sites. The primary conclusions were twofold. First, the shelf-life of ALS data when used with model-assisted estimators exceeded 10 years, and second, even for 12 years elapsed time between plot measurement and ALS data acquisition, the variance of the model-assisted estimator of the mean was smaller by a factor of at least 1.75 than the variance of the stratified estimator used by the national forest inventory.
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CitationMcRoberts, Ronald E.; Chen, Qi; Gormanson, Dale D.; Walters, Brian F. 2018. The shelf-life of airborne laser scanning data for enhancing forest inventory inferences. Remote Sensing of Environment. 206: 254-259. https://doi.org/10.1016/j.rse.2017.12.017.
KeywordsModel-assisted estimator, Stratified estimator, Inference
- Multivariate inference for forest inventories using auxiliary airborne laser scanning data
- Post-classification approaches to estimating change in forest area using remotely sense auxiliary data.
- Using a remote sensing-based, percent tree cover map to enhance forest inventory estimation
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