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Literature DB >> 26074164

Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images.

Paul Arellano¹, Kevin Tansey², Heiko Balzter³, Doreen S Boyd⁴.

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.

Entities: Chemical

Keywords: Amazon forest; Hyperspectral remote sensing; Petroleum pollution; Vegetation indices; Yasuni National Park

Mesh：

Substances：
Hydrocarbons
Chlorophyll

Year: 2015 PMID： 26074164 DOI： 10.1016/j.envpol.2015.05.041

Source DB: PubMed Journal: Environ Pollut ISSN： 0269-7491 Impact factor: 8.071

Keyword Cloud
Cited

7 in total

1. Plant Family-Specific Impacts of Petroleum Pollution on Biodiversity and Leaf Chlorophyll Content in the Amazon Rainforest of Ecuador.

Authors: Paul Arellano; Kevin Tansey; Heiko Balzter; Markus Tellkamp
Journal: PLoS One Date: 2017-01-19 Impact factor: 3.240

2. Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches.

Authors: Arpah Abu; Lee Kien Leow; Rosli Ramli; Hasmahzaiti Omar
Journal: BMC Bioinformatics Date: 2016-12-22 Impact factor: 3.169

3. Identification and monitoring of coal dust pollution in Wucaiwan mining area, Xinjiang (China) using Landsat derived enhanced coal dust index.

Authors: Nan Xia; Wenyue Hai; Gimei Song; Mengying Tang
Journal: PLoS One Date: 2022-04-08 Impact factor: 3.240

Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images.

1. Plant Family-Specific Impacts of Petroleum Pollution on Biodiversity and Leaf Chlorophyll Content in the Amazon Rainforest of Ecuador.

2. Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches.

3. Identification and monitoring of coal dust pollution in Wucaiwan mining area, Xinjiang (China) using Landsat derived enhanced coal dust index.

4. Hyperspectral Face Recognition with Adaptive and Parallel SVMs in Partially Hidden Face Scenarios.

5. Ultrafast laser filament-induced fluorescence for detecting uranium stress in Chlamydomonas reinhardtii.

6. Large expansion of oil industry in the Ecuadorian Amazon: biodiversity vulnerability and conservation alternatives.

Review 7. A State-of-the-Art Review of Indigenous Peoples and Environmental Pollution.