Literature DB >> 29101574

Health condition assessment for vegetation exposed to heavy metal pollution through airborne hyperspectral data.

Bikram Pratap Banerjee1,2, Simit Raval3,4, Hao Zhai2, Patrick Joseph Cullen5.   

Abstract

Recent advancements in hyperspectral remote sensing technology now provide improved diagnostic capabilities to assess vegetation health conditions. This paper uses a set of 13 vegetation health indices related to chlorophyll, xanthophyll, blue/green/red ratio and structure from airborne hyperspectral reflectance data collected around a derelict mining area in Yerranderie, New South Wales, Australia. The studied area has ten historic mine shafts with a legacy of heavy metals and acidic contamination in a pristine ecosystem now recognised as Great Blue Mountain World Heritage Area. The forest is predominantly comprised of different species of Eucalyptus trees. In addition to the airborne survey, ground-based spectra of the tree leaves were collected along the two accessible heavy metal contaminated pathways. The stream networks in the area were classified and the geospatial patterns of vegetation health were analysed along the Tonalli River, a major water tributary flowing through the National Park. Despite the inflow of contaminated water from the near-mine streams, the measured vegetation health indices along Tonalli River were found to remain unchanged. The responses of the vegetation health indices between the near-mine and away-mine streams were found similar. Based on the along-stream and inter-stream analysis of the spectral indices of vegetation health, no significant impact of the heavy metal pollution could be noticed. The results indicate the possibility of the vegetation having developed immunity towards the high levels of heavy metal pollution over a century of exposure.

Entities:  

Keywords:  Abandoned mines; Heavy metal pollution; Hyperspectral remote sensing; Vegetation stress

Mesh:

Substances:

Year:  2017        PMID: 29101574     DOI: 10.1007/s10661-017-6333-4

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  3 in total

1.  Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination.

Authors:  Pablo H Rosso; James C Pushnik; Mui Lay; Susan L Ustin
Journal:  Environ Pollut       Date:  2005-09       Impact factor: 8.071

2.  Legacy soil contamination at abandoned mine sites: making a case for guidance on soil protection.

Authors:  Konstantinos Kostarelos; Ifigenia Gavriel; Marinos Stylianou; Andreas M Zissimos; Eleni Morisseau; Dimitris Dermatas
Journal:  Bull Environ Contam Toxicol       Date:  2015-01-20       Impact factor: 2.151

3.  The role of balanced training and testing data sets for binary classifiers in bioinformatics.

Authors:  Qiong Wei; Roland L Dunbrack
Journal:  PLoS One       Date:  2013-07-09       Impact factor: 3.240

  3 in total
  3 in total

1.  Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis.

Authors:  Mohsen Mirzaei; Jochem Verrelst; Safar Marofi; Mozhgan Abbasi; Hossein Azadi
Journal:  Remote Sens (Basel)       Date:  2019-11-20       Impact factor: 5.349

2.  Developing a New Spectral Index for Detecting Cadmium-Induced Stress in Rice on a Regional Scale.

Authors:  Chuanyu Wu; Meiling Liu; Xiangnan Liu; Tiejun Wang; Lingyue Wang
Journal:  Int J Environ Res Public Health       Date:  2019-11-29       Impact factor: 3.390

3.  Temporal Characteristics of Stress Signals Using GRU Algorithm for Heavy Metal Detection in Rice Based on Sentinel-2 Images.

Authors:  Yu Zhang; Meiling Liu; Li Kong; Tao Peng; Dong Xie; Li Zhang; Lingwen Tian; Xinyu Zou
Journal:  Int J Environ Res Public Health       Date:  2022-02-23       Impact factor: 3.390

  3 in total

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