Sample strategies for bias correction of regional LiDAR-assisted forest inventory Estimates on small woodlots

Author:

Hsu Yung-HanORCID,Chen Yingbing,Yang Ting-Ru,Kershaw John A.,Ducey Mark J.

Funder

Natural Sciences and Engineering Research Council of Canada

New Brunswick Innovation Foundation

Publisher

Springer Science and Business Media LLC

Subject

Ecology,Forestry

Reference50 articles.

1. Andersen H-E, Strunk JL, Temesgen H, Atwood D, Winterberger K (2011) Using multilevel remote sensing and ground data to estimate forest biomass resources in remote regions: a case study in the boreal forests of interior Alaska. Can J Remote Sens 37:596–611. https://doi.org/10.5589/m12-003

2. Asner GP, Hughes RF, Mascaro J et al (2011) High-resolution carbon mapping on the million-hectare island of Hawaii. Front Ecol Environ (e-View) 28. https://doi.org/10.1890/100179

3. Basu D (1969) Role of the sufficiency and likelihood principles in sample survey theory. Sankhyā: The Indian Journal of Statistics 31:441–454

4. Bell JF, Iles K, Marshall D (1983) Balancing the ratio of tree count-only sample points and VBAR measurements in variable plot sampling. In: Bell JF, Atterbury T (eds) Proceedings: renewable resource inventories for monitoring changes and trends. College of Forestry, Oregon State University, Corvallis, OR, pp 699–702

5. Brewer KRW (1963) Ratio estimation and finite populations: some results deducible from the assumption of an underlying stochastic process. Australian Journal of Statistics 5:93–105. https://doi.org/10.1111/j.1467-842X.1963.tb00288.x

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