Integration of field sampling and LiDAR data in forest inventories: comparison of area-based approach and (lognormal) universal kriging-Reference-Cited by-同舟云学术

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Integration of field sampling and LiDAR data in forest inventories: comparison of area-based approach and (lognormal) universal kriging

Published:2021-04-09 Issue:2 Volume:78 Page:
ISSN:1286-4560
Container-title:Annals of Forest Science
language:en
Short-container-title:Annals of Forest Science

Author:

Aulló-Maestro Isabel,Gómez Cristina,Marino Eva,Cabrera Miguel,De La Cueva Antonio Vázquez,Montes Fernando

Funder

Ministerio de Ciencia, Innovación y Universidades

Secretaría de Estado de Investigación, Desarrollo e Innovación

Publisher

Springer Science and Business Media LLC

Subject

Ecology,Forestry

Link

https://link.springer.com/content/pdf/10.1007/s13595-021-01056-1.pdf

Reference64 articles.

1. Adams T, Brack C, Farrier T et al (2011) So you want to use LiDAR? A guide on how to use LiDAR in forestry. New Zeal J For 55:19–23

2. Ayrey E, Hayes DJ (2018) The use of three-dimensional convolutional neural networks to interpret LiDAR for forest inventory. Remote Sens 10:1–16. https://doi.org/10.3390/rs10040649

3. Bater CW, Wulder MA, Coops NC, Nelson RF, Hilker T, Nasset E (2011) Stability of sample-based scanning LiDAR-derived vegetation metrics for forest monitoring. IEEE Trans Geosci Remote Sens 49:2385–2392

4. Blomdahl EM, Thompson CM, Kane JR, Kane VR, Churchill D, Moskal LM, Lutz JA (2019) Forest structure predictive of fisher (Pekania pennanti) dens exists in recently burned forest in Yosemite, California, USA. For Ecol Manage 444:174–186. https://doi.org/10.1016/j.foreco.2019.04.024

5. Bouvier M, Durrieu S, Fournier RA, Renaud JP (2015) Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sens Environ 156:322–334. https://doi.org/10.1016/j.rse.2014.10.004

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