%0 Conference Proceedings
%4 sid.inpe.br/marte2/2017/10.27.12.34.49
%2 sid.inpe.br/marte2/2017/10.27.12.34.50
%@isbn 978-85-17-00088-1
%F 59384
%T Hyperspectral remote sensing to detect petroleum pollution in the Amazon forest
%D 2017
%A Arellano, Paul,
%A Tansey, Kevin,
%A Balzter, Heiko,
%A Doreen, Boyd,
%@electronicmailaddress parellano@yachaytech.edu.ec
%E Gherardi, Douglas Francisco Marcolino,
%E Aragão, Luiz Eduardo Oliveira e Cruz de,
%B Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)
%C Santos
%8 28-31 maio 2017
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 2138-2145
%S Anais
%1 Instituto Nacional de Pesquisas Espaciais (INPE)
%X 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.
%9 Sensoriamento remoto hiperespectral
%@language en
%3 59384.pdf