Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm-Reference-Cited by-同舟云学术

Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm

Published:2015-03 Issue: Volume:101 Page:89-101
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Ahmed Oumer S.,Franklin Steven E.,Wulder Michael A.,White Joanne C.

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference80 articles.

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2. Integration of LiDAR and Landsat to estimate forest canopy cover in coastal British Columbia;Ahmed;Photogramm. Eng. Remote Sens.,2014

3. Object based image analysis for remote sensing;Blaschke;ISPRS J. Photogramm. Rem. Sens.,2010

4. Random forests;Breiman;Mach. Learn.,2001

5. Breiman, L., Cutler, A., Liaw, A., Wiener, M., 2006. Breiman and Cutler’s Random Forests for Classification and Regression. (R package version 4.5 – 16).

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