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Monitoring Soil Contamination by Remote Sensors

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 18604

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Special Issue Editors

Dr. Arnaud Elger

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Guest Editor

Laboratoire Écologie Fonctionnelle et Environnement, Université Paul Sabatier Toulouse III, Toulouse, France
Interests: ecology; integrative ecotoxicology; environmental impact assessment; plant sciences; remote sensing

Dr. Guillaume Lassalle

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Guest Editor

Institue of Geosciences (IGE), University of Campinas (UNICAMP), Campinas, SP, Brazil
Interests: environmental pollution; hyperspectral remote sensing; radar remote sensing; plant physiology

Dr. Sophie Fabre

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Guest Editor

ONERA (The French Aerospace Lab.), DOTA, F-31000 Toulouse, France
Interests: hyperspectral imagery; multitemporal change detection; species mapping; vegetation health; anthropogenic impact assessment

Special Issue Information

Dear Colleagues,

For about two centuries, the industrial revolution and associated demographic growth have led to increased environmental contamination by various chemical compounds related to human activities. Industrial and mining activities have notably been at the origin of legacy soil contamination by, e.g., metals, PCB or hydrocarbons.

Depending on their toxicity and persistence in the environment, chemical contaminants can alter the physical, chemical, and biological properties of soils and raise ecosystem and human health concerns. Over the last few decades, promising solutions based on remote sensing have emerged for monitoring contaminant release, fate and effects on soils and plants, from the field to higher scale applications. A wide variety of approaches coupling sensor-based data to plant and soil sciences have been proposed for detecting and assessing soil contamination directly or indirectly, opening the way to surveying contaminated areas and characterizing the impacts of anthropogenic activities on the environment.

This Special Issue aims to publish original research that specifically addresses various aspects of soil contamination monitoring over space and time using passive (multi- and hyperspectral, reflective or emissive spectral domains) and/or active (LiDAR, RADAR) remote sensing. We invite a wide range of contributions from methodological to applied and multidisciplinary research about the following (nonexclusive) topics:

Direct detection of chemical contamination via its impacts on soil properties;
Indirect detection of soil chemical contamination based on variation of vegetation health and associated traits (e.g., biochemical, structural, phenological, morphological);
Use of radiative transfer models, machine or deep learning algorithms, and other processing tools to discriminate chemical stress from other biotic and abiotic stresses;
Assessment of soil properties related to contaminant mobility, uptake, translocation, and accumulation in plant tissues;
Quantification of chemical contaminant concentrations in soils;
Integrative approaches for the upscaling of measures on leaf or soil samples to images at the landscape scale;
Monitoring of contaminated, remediated or restored areas (vegetated and non-vegetated), e.g., industrial or urban brownfields, phytomanaged sites;
Fusion of multimodal or multitemporal images giving access to new parameters to improve detection and/or quantification accuracy.

For this Special Issue, laboratory, greenhouse, and field approaches can be considered. Studies can focus on any kind of chemical contaminant (e.g., metals, hydrocarbons, pesticides, PCB, explosive derivatives). Reviews covering one or more topics are welcome.

Dr. Arnaud Elger
Dr. Guillaume Lassalle
Dr. Sophie Fabre
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Active and passive remote sensing
Field spectroscopy
Multiscale approach
Soil chemical contamination
Vegetation stress
Environmental monitoring
Risk assessment
Machine and deep learning
Radiative transfer model

Published Papers (5 papers)

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Research

31 pages, 6793 KiB

Open AccessArticle

Mapping Plant Species in a Former Industrial Site Using Airborne Hyperspectral and Time Series of Sentinel-2 Data Sets

by Rollin Gimenez, Guillaume Lassalle, Arnaud Elger, Dominique Dubucq, Anthony Credoz and Sophie Fabre

Remote Sens. 2022, 14(15), 3633; https://doi.org/10.3390/rs14153633 - 29 Jul 2022

Cited by 8 | Viewed by 2125

Abstract

Industrial activities induce various impacts on ecosystems that influence species richness and distribution. An effective way to assess the resulting impacts on biodiversity lies in vegetation mapping. Species classification achieved through supervised machine learning algorithms at the pixel level has shown promising results using hyperspectral images and multispectral, multitemporal images. This study aims to determine whether airborne hyperspectral images with a high spatial resolution or phenological information obtained by spaceborne multispectral time series (Sentinel-2) are suitable to discriminate species and assess biodiversity in a complex impacted context. The industrial heritage of the study site has indeed induced high spatial heterogeneity in terms of stressors and species over a reduced scale. First, vegetation indices, derivative spectra, continuum removed spectra, and components provided by three feature extraction techniques, namely, Principal Component Analysis, Minimal Noise Fraction, and Independent Component Analysis, were calculated from reflectance spectra. These features were then analyzed through Sequential Floating Feature Selection. Supervised classification was finally performed using various machine learning algorithms (Random Forest, Support Vector Machines, and Regularized Logistic Regression) considering a probability-based rejection approach. Biodiversity metrics were derived from resulted maps and analyzed considering the impacts. Average Overall Accuracy (AOA) reached up to 94% using the hyperspectral image and Regularized Logistic Regression algorithm, whereas the time series of multispectral images never exceeded 72% AOA. From all tested spectral transformations, only vegetation indices applied to the time series of multispectral images increased the performance. The results obtained with the hyperspectral image degraded to the specifications of Sentinel-2 emphasize the importance of fine spatial and spectral resolutions to achieve accurate mapping in this complex context. While no significant difference was found between impacted and reference sites through biodiversity metrics, vegetation mapping highlighted some differences in species distribution. Full article

(This article belongs to the Special Issue Monitoring Soil Contamination by Remote Sensors)

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25 pages, 11615 KiB

Open AccessArticle

Leaf Spectra Changes of Plants Grown in Soils Pre- and Post-Contaminated with Petroleum Hydrocarbons

by Salete Gürtler, Carlos R. Souza Filho, Ieda D. Sanches, Lucíola A. Magalhães, Marcos N. Alves, Wilson J. Oliveira and Giuliana C. M. Quitério

Remote Sens. 2022, 14(14), 3475; https://doi.org/10.3390/rs14143475 - 20 Jul 2022

Cited by 4 | Viewed by 1738

Abstract

Leaks from accidents or damage to pipelines that transport liquid petroleum hydrocarbons (PHC) such as gasoline and diesel are harmful to the environment as well as to human health, and may be hard to detect by inspection mechanisms alone when they occur in small volumes or persistently. In the present study, we aim to identify spectral anomalies in two plant species (Brachiaria brizantha and Neonotonia wightii) linked to contamination effects at different developmental phases of these plants. To do so, we used spectroscopy and remote sensing approaches to detect small gasoline and diesel leaks by observing the damage caused to the vegetation that covers simulated pipelines. We performed a contamination test before and after planting using gasoline and diesel volumes that varied between 2 and 16 L/m³ soil, in two experimental designs: (i) single contamination before planting, and (ii) periodic contaminations after planting and during plant growth. We collected the reflectance spectra from 35 to approximately 100 days after planting. We then compared the absorption features positioned from the visible spectral range to the shortwave infrared and the spectral parameters in the red edge range of the contaminated plants to the healthy plants, thus confirming the visual and biochemical changes verified in the contaminated plants. Despite the complexity in the indirect identification of soil contamination by PHCs, since it involves different stages of plant development, the results were promising and can be used as a reference for methods of indirect detection from UAVs (Unmanned Aerial Vehicles), airplanes, and satellites equipped with hyperspectral sensors. Full article

(This article belongs to the Special Issue Monitoring Soil Contamination by Remote Sensors)

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25 pages, 7318 KiB

Open AccessArticle

Spectral and Growth Characteristics of Willows and Maize in Soil Contaminated with a Layer of Crude or Refined Oil

by Raquel Serrano-Calvo, Mark E. J. Cutler and Anthony Glyn Bengough

Remote Sens. 2021, 13(17), 3376; https://doi.org/10.3390/rs13173376 - 25 Aug 2021

Cited by 6 | Viewed by 2727

Abstract

Remote sensing holds great potential for detecting stress in vegetation caused by hydrocarbons, but we need to better understand the effects of hydrocarbons on plant growth and specific spectral expression. Willow (Salix viminalis var. Tora) cuttings and maize (Zea mays var. Lapriora) seedlings were grown in pots of loam soil containing a hydrocarbon-contaminated layer at the base of the pot (crude or refined oil) at concentrations of 0.5, 5, or 50 g·kg⁻¹. Chlorophyll concentration, biomass, and growth of plants were determined through destructive and nondestructive sampling, whilst reflectance measurements were made using portable hyperspectral spectrometers. All biophysical (chlorophyll concentration and growth) variables decreased in the presence of high concentrations of hydrocarbons, but at lower concentrations an increase in growth and chlorophyll were often observed with respect to nonpolluted plants, suggesting a biphasic response to hydrocarbon presence. Absorption features were identified that related strongly to pigment concentration and biomass. Variations in absorption feature characteristics (band depth, band area, and band width) were dependent upon the hydrocarbon concentration and type, and showed the same biphasic pattern noted in the biophysical measurements. This study demonstrates that the response of plants to hydrocarbon pollution varies according to hydrocarbon concentration and that remote sensing has the potential to both detect and monitor the variable impacts of pollution in the landscape. Full article

(This article belongs to the Special Issue Monitoring Soil Contamination by Remote Sensors)

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23 pages, 16551 KiB

Open AccessArticle

Spatio-Temporal Analysis of Heavy Metals in Arid Soils at the Catchment Scale Using Digital Soil Assessment and a Random Forest Model

by Ruhollah Taghizadeh-Mehrjardi, Hassan Fathizad, Mohammad Ali Hakimzadeh Ardakani, Hamid Sodaiezadeh, Ruth Kerry, Brandon Heung and Thomas Scholten

Remote Sens. 2021, 13(9), 1698; https://doi.org/10.3390/rs13091698 - 27 Apr 2021

Cited by 28 | Viewed by 3725

Abstract

Predicting the spatio-temporal distribution of absorbable heavy metals in soil is needed to identify the potential contaminant sources and develop appropriate management plans to control these hazardous pollutants. Therefore, our aim was to develop a model to predict soil adsorbable heavy metals in arid regions of Iran from 1986 to 2016. Soil adsorbable heavy metals were measured in 201 samples from locations selected using the Latin hypercube sampling method in 2016. A random forest (RF) model was used to determine the relationship between a suite of geospatial predictors derived from remote sensing and digital elevation model data with georeferenced measurements of soil absorbable heavy metals. The trained RF model from 2016 was used to reconstruct the spatial distribution of soil absorbable heavy metals at three historical timesteps (1986, 1999, and 2010). Results indicated that the RF model was effective at predicting the distribution of heavy metals with coefficients of determination of 0.53, 0.59, 0.41, 0.45, and 0.60 for Fe, Mn, Ni, Pb, and Zn, respectively. The predicted maps showed high spatio-temporal variability; for example, there were substantial increases in Pb (the 1.5–2 mg/kg⁻¹ class) where its distribution increased by ~25% from 1988 to 2016—similar trends were observed for the other heavy metals. This study provides insights into the spatio-temporal trends and the potential causes of soil heavy metal contamination to facilitate appropriate planning and management strategies to prevent, control, and reduce the impact of heavy metal contamination in soils. Full article

(This article belongs to the Special Issue Monitoring Soil Contamination by Remote Sensors)

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28 pages, 86897 KiB

Open AccessEditor’s ChoiceArticle

Hydrocarbon Pollution Detection and Mapping Based on the Combination of Various Hyperspectral Imaging Processing Tools

by Véronique Achard, Pierre-Yves Foucher and Dominique Dubucq

Remote Sens. 2021, 13(5), 1020; https://doi.org/10.3390/rs13051020 - 8 Mar 2021

Cited by 12 | Viewed by 4006

Abstract

Oil extraction and transportation may lead to small or large scale accidental spills, whether at sea or on land. Detecting these spills is a major problem that can be addressed by means of hyperspectral images and specific processing methods. In this work, several cases of onshore oil spills are studied. First, a controlled experiment was carried out: four boxes containing soil or sand mixed with crude oil or gasoil were deployed on the ONERA site near Fauga, France, and were overflown by HySpex hyperspectral cameras. Owing to this controlled experiment, different detection strategies were developed and tested, with a particular focus on the most automated methods requiring the least supervision. The methods developed were then applied to two very different cases: mapping of the shoreline contaminated due to the explosion of the Deepwater Horizon (DWH) platform based on AVIRIS images (AVIRIS: Airborne Visible/InfraRed Imaging Spectrometer), and detection of a tar pit on a former oil exploration site. The detection strategy depends on the type of oil, light or heavy, recently or formerly spilled, and on the substrate. In the first case (controlled experiment), the proposed methods included spectral index calculations, anomaly detection and spectral unmixing. In the case of DWH, spectral indices were computed and the unmixing method was tested. Finally, to detect the tar pit, a strategy based on anomaly detection and spectral indices was applied. In all the cases studied, the proposed methods were successful in detecting and mapping the oil pollution. Full article

(This article belongs to the Special Issue Monitoring Soil Contamination by Remote Sensors)

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