Paul Arellano
Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images
Arellano, Paul; Tansey, Kevin; Balzter, Heiko; Boyd, Doreen S.
Authors
Abstract
The global demand for fossil energy is triggering oil exploration and production projects in remote areas of the world. During the last few decades hydrocarbon production has caused pollution in the Amazon forest inflicting considerable environmental impact. Until now it is not clear how hydrocarbon pollution affects the health of the tropical forest flora. During a field campaign in polluted and pristine forest, more than 1100 leaf samples were collected and analysed for biophysical and biochemical parameters. The results revealed that tropical forests exposed to hydrocarbon pollution show reduced levels of chlorophyll content, higher levels of foliar water content and leaf structural changes. In order to map this impact over wider geographical areas, vegetation indices were applied to hyperspectral Hyperion satellite imagery. Three vegetation indices (SR, NDVI and NDVI705) were found to be the most appropriate indices to detect the effects of petroleum pollution in the Amazon forest.
Citation
Arellano, P., Tansey, K., Balzter, H., & Boyd, D. S. (2015). Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. Environmental Pollution, 205, https://doi.org/10.1016/j.envpol.2015.05.041
Journal Article Type | Article |
---|---|
Acceptance Date | May 30, 2015 |
Online Publication Date | Jun 12, 2015 |
Publication Date | Oct 1, 2015 |
Deposit Date | Jun 25, 2015 |
Publicly Available Date | Jun 25, 2015 |
Journal | Environmental Pollution |
Print ISSN | 0269-7491 |
Electronic ISSN | 1873-6424 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 205 |
DOI | https://doi.org/10.1016/j.envpol.2015.05.041 |
Keywords | Petroleum pollution; Hyperspectral remote sensing; Amazon forest; Vegetation indices; Yasuni National Park |
Public URL | https://nottingham-repository.worktribe.com/output/981658 |
Publisher URL | http://www.sciencedirect.com/science/article/pii/S0269749115002754 |
Contract Date | Jun 25, 2015 |
Files
JofEP_accepted.pdf
(3.4 Mb)
PDF
Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0
You might also like
Size and frequency of natural forest disturbances and the Amazon forest carbon balance
(2014)
Journal Article
Using mixed objects in the training of object-based image classifications
(2017)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search