Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms-Reference-Cited by-同舟云学术

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Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms

Published:2012-05-29 Issue:19 Volume:33 Page:6254-6280
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Shataee Shaban¹,Kalbi Syavash²,Fallah Asghar²,Pelz Dieter³

Affiliation:

1. a Department of Forestry , Gorgan University of Agricultural Sciences and Natural Resources , Gorgan , Iran

2. b Department of Forestry , Sari University of Agricultural Sciences and Natural Resources , Sari , Iran

3. c Department of Forest Biometry , Freiburg University , Freiburg , Germany

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2012.682661

Reference65 articles.

1. Potentials and limits of vegetation indices for LAI and APAR assessment

2. Measuring the Color of Growing Turf with a Reflectance Spectrophotometer 1

3. Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data

4. Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, U.S.A.

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