title: Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images.
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.
authors.affiliation: University of Leicester, Department of Geography, Centre of Landscape and Climate Research, University Road, Leicester, LE1 7RH, UK; Yachay Tech University, School of Geological Sciences & Engineering, San Miguel de Urcuqui, Hacienda San Jose s/n, Imbabura, Ecuador. Electronic address: pa134@le.ac.uk.
authors.forename: Paul
authors.initials: P
authors.lastname: Arellano
authors.affiliation: University of Leicester, Department of Geography, Centre of Landscape and Climate Research, University Road, Leicester, LE1 7RH, UK.
authors.forename: Kevin
authors.initials: K
authors.lastname: Tansey
authors.affiliation: University of Leicester, Department of Geography, Centre of Landscape and Climate Research, University Road, Leicester, LE1 7RH, UK; National Centre for Earth Observation, University of Leicester, University Road, Leicester, LE1 7RH, UK.
authors.forename: Heiko
authors.initials: H
authors.lastname: Balzter
authors.affiliation: University of Nottingham, School of Geography, Nottingham, NG7 2RD, UK.
authors.forename: Doreen S
authors.initials: DS
authors.lastname: Boyd
mesh_terms: D002734:Chlorophyll / Q000737:chemistry / Q000378:metabolism; D004784:Environmental Monitoring / Q000379:methods; D004787:Environmental Pollution / Q000032:analysis; D065928:Forests; D006838:Hydrocarbons / Q000494:pharmacology*; D018515:Plant Leaves / Q000737:chemistry* / Q000378:metabolism; D063809:Satellite Imagery; D014197:Trees / Q000737:chemistry* / Q000378:metabolism
chemical_list: D006838:Hydrocarbons; D002734:Chlorophyll
keywords: Amazon forest; Hyperspectral remote sensing; Petroleum pollution; Vegetation indices; Yasuni National Park