Spatially optimizing vegetation indices integrated with sparse partial least squares regression to detect and map the effects of Gonipterus scutellatus on the chlorophyll content of eucalyptus plantations-Reference-Cited by-同舟云学术

Spatially optimizing vegetation indices integrated with sparse partial least squares regression to detect and map the effects of Gonipterus scutellatus on the chlorophyll content of eucalyptus plantations

Published:2020-06-09 Issue:16 Volume:41 Page:6444-6459
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Lottering Romano¹^ORCID,Mutanga Onisimo¹,Peerbhay Kabir¹^ORCID,Lottering Shenelle¹

Affiliation:

1. Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa

Funder

National Research Foundation

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2020.1739350

Reference54 articles.

1. A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of Swiss chard grown under different irrigation water sources using hyperspectral data

2. Determining the susceptibility ofEucalyptus nitensforests toCoryphodema tristis(cossid moth) occurrence in Mpumalanga, South Africa

3. Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels

4. A review of remote sensing methods for biomass feedstock production

5. Transformed difference vegetation index (TDVI) for vegetation cover mapping

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