Cargando…

Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing

Ore processing is a source of soil heavy metal pollution. Vegetation traits (structural characteristics such as spatial cover and repartition; biochemical parameters—pigment and water contents, growth rate, phenological cycle…) and plant species identity are indirect and powerful indicators of resid...

Descripción completa

Detalles Bibliográficos
Autores principales: Fabre, Sophie, Gimenez, Rollin, Elger, Arnaud, Rivière, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506696/
https://www.ncbi.nlm.nih.gov/pubmed/32854456
http://dx.doi.org/10.3390/s20174800
_version_ 1783585073218453504
author Fabre, Sophie
Gimenez, Rollin
Elger, Arnaud
Rivière, Thomas
author_facet Fabre, Sophie
Gimenez, Rollin
Elger, Arnaud
Rivière, Thomas
author_sort Fabre, Sophie
collection PubMed
description Ore processing is a source of soil heavy metal pollution. Vegetation traits (structural characteristics such as spatial cover and repartition; biochemical parameters—pigment and water contents, growth rate, phenological cycle…) and plant species identity are indirect and powerful indicators of residual contamination detection in soil. Multi-temporal multispectral satellite imagery, such as the Sentinel-2 time series, is an operational environment monitoring system widely used to access vegetation traits and ensure vegetation surveillance across large areas. For this purpose, methodology based on a multi-temporal fusion method at the feature level is applied to vegetation monitoring for several years from the closure and revegetation of an ore processing site. Features are defined by 26 spectral indices from the literature and seasonal and annual change detection maps are inferred. Three indices—CI(red-edge) (CIREDEDGE), IRECI (Inverted Red-Edge Chlorophyll Index) and PSRI (Plant Senescence Reflectance Index)—are particularly suitable for detecting changes spatially and temporally across the study area. The analysis is conducted separately for phyto-stabilized vegetation zones and natural vegetation zones. Global and specific changes are emphasized and explained by information provided by the site operator or meteorological conditions.
format Online
Article
Text
id pubmed-7506696
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-75066962020-09-26 Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing Fabre, Sophie Gimenez, Rollin Elger, Arnaud Rivière, Thomas Sensors (Basel) Article Ore processing is a source of soil heavy metal pollution. Vegetation traits (structural characteristics such as spatial cover and repartition; biochemical parameters—pigment and water contents, growth rate, phenological cycle…) and plant species identity are indirect and powerful indicators of residual contamination detection in soil. Multi-temporal multispectral satellite imagery, such as the Sentinel-2 time series, is an operational environment monitoring system widely used to access vegetation traits and ensure vegetation surveillance across large areas. For this purpose, methodology based on a multi-temporal fusion method at the feature level is applied to vegetation monitoring for several years from the closure and revegetation of an ore processing site. Features are defined by 26 spectral indices from the literature and seasonal and annual change detection maps are inferred. Three indices—CI(red-edge) (CIREDEDGE), IRECI (Inverted Red-Edge Chlorophyll Index) and PSRI (Plant Senescence Reflectance Index)—are particularly suitable for detecting changes spatially and temporally across the study area. The analysis is conducted separately for phyto-stabilized vegetation zones and natural vegetation zones. Global and specific changes are emphasized and explained by information provided by the site operator or meteorological conditions. MDPI 2020-08-25 /pmc/articles/PMC7506696/ /pubmed/32854456 http://dx.doi.org/10.3390/s20174800 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fabre, Sophie
Gimenez, Rollin
Elger, Arnaud
Rivière, Thomas
Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing
title Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing
title_full Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing
title_fullStr Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing
title_full_unstemmed Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing
title_short Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing
title_sort unsupervised monitoring vegetation after the closure of an ore processing site with multi-temporal optical remote sensing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506696/
https://www.ncbi.nlm.nih.gov/pubmed/32854456
http://dx.doi.org/10.3390/s20174800
work_keys_str_mv AT fabresophie unsupervisedmonitoringvegetationaftertheclosureofanoreprocessingsitewithmultitemporalopticalremotesensing
AT gimenezrollin unsupervisedmonitoringvegetationaftertheclosureofanoreprocessingsitewithmultitemporalopticalremotesensing
AT elgerarnaud unsupervisedmonitoringvegetationaftertheclosureofanoreprocessingsitewithmultitemporalopticalremotesensing
AT rivierethomas unsupervisedmonitoringvegetationaftertheclosureofanoreprocessingsitewithmultitemporalopticalremotesensing