Predicting Eucalyptus spp. stand volume in Zululand, South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets-Reference-Cited by-同舟云学术

Predicting Eucalyptus spp. stand volume in Zululand, South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets

Published:2015-07-18 Issue:14 Volume:36 Page:3751-3772
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Dube Timothy¹,Mutanga Onisimo¹,Abdel-Rahman Elfatih M.¹²,Ismail Riyad¹,Slotow Rob³

Affiliation:

1. Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, Pietermaritzburg 3209, South Africa

2. Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan

3. School of Life Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4000, South Africa

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2015.1070316

Reference89 articles.

1. Employing ground and satellite-based QuickBird data and random forest to discriminate five tree species in a Southern African Woodland

2. Forest biomass estimation over regional scales using multisource data

3. Estimativa de volume de madeira em plantios de Eucalyptus spp. utilizando dados hiperespectrais e dados topográficos

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