Monitoring oil contamination in vegetated areas with optical remote sensing: A comprehensive review

Unraveling the spectral and biochemical response of mangroves to oil spills and biotic stressors

Mangroves are prone to biotic and abiotic stressors of natural and anthropogenic origin, of which oil pollution is one of the most harmful. Yet the response of mangrove species to acute and chronic oil exposure, as well as to other stressors, remains barely documented. In this study, a non-destructive, non-invasive approach based on field spectroscopy is proposed to unravel these responses. The approach relies on tracking alterations in foliar traits (pigments, sugars, phenols, and specific leaf area) from reflectance data in the 400-2400 nm spectral range. Three mangrove species hit by two of the most notorious oil spills in Brazilian history (1983 and 2019) and various biotic stressors, including grazing, parasitism, and fungal disease, were investigated through field spectroscopy and machine learning. This study reveals strong intra- and interspecific variability of mangrove's spectral and biochemical responses to oil pollution. Trees undergoing acute exposure to oil showed stronger alterations of foliar traits than the chronically exposed ones. Alterations induced by biotic stressors such as parasitism, disease, and grazing were successfully discriminated from those of oil for all species based on Linear Discriminant Analysis (Overall Accuracy ≥76.40% and Kappa ≥0.70). Leaf chlorophyll, phenol, and starch contents were identified as the most relevant traits in stressor discrimination. The study highlights that oil spills affect mangroves uniquely, both acutely and chronically, threatening their global conservation.

Use of remote sensing to assess vegetative stress as a proxy for soil contamination

We report, for the first time, a multimodal investigation of current crude oil reprocessing and storage sites to assess their impact on the environment after 50 years of continuous operation. We have adopted a dual approach to investigate potential soil contamination. The first approach uses conventional analytical techniques i.e. energy dispersive X-ray fluorescence (ED-XRF) for metal analysis, and a complementary metabolomic investigation using hydrophilic liquid interaction chromatography hi-resolution mass spectrometry (HILIC-MS) for organic contaminants. Secondly, the deployment of an unmanned aerial vehicle (UAV) with a multispectral image (MSI) camera, for the remote sensing of vegetation stress, as a proxy for sub-surface soil contamination. The results identified high concentrations of barium (mean 21 017 ± 5950 μg g-1, n = 36) as well as metabolites derived from crude oil (polycyclic aromatic hydrocarbons), cleaning processes (surfactants) and other organic pollutants (e.g. pesticides, plasticizers and pharmaceuticals) in the reprocessing site. This data has then been correlated, with post-flight data analysis derived vegetation indices (NDVI, GNDVI, SAVI and Cl green VI), to assess the potential to identify soil contamination because of vegetation stress. It was found that strong correlations exist (an average R2 of >0.68) between the level of soil contamination and the ground cover vegetation. The potential to deploy aerial remote sensing techniques to provide an initial survey, to inform decision-making, on suspected contaminated land sites can have global implications.

Tracking the behavior of an accidental oil spill and its impacts on the marine environment in the Eastern Mediterranean

The eastern Mediterranean region is a vital hub for oil transportation and production because of its strategic location between Europe, Asia, and Africa. But its unique attributes, including narrow shipping routes, heavy marine traffic, and proximity to vulnerable ecosystems, render it particularly susceptible to accidental oil spills. In this research, an oil spill detection model, along with bathymetric and oceanographic parameters, was used to track oil spills that occurred at the Syrian Baniyas Station in the Eastern Mediterranean on August 23, 2021. Furthermore, the study employed a pairwise comparison matrix (PWCM) to assess the relative importance of wind speed and direction, water depth, and sea surface temperature (SST) in the dispersion of oil spills. Analysis of Sentinel-1 data obtained prior to, during, and after the incident revealed the accumulation of oil slicks along the Syrian coast from Baniyas to Latakia for up to twenty days. The spilled oil reached the coast of Cyprus six days after the incident. The study determined that wind speed and direction played a critical role in the dispersion of spilled oil, while water depth and SST were comparatively less significant factors in this process. The overall accuracy (OA) and Kappa coefficient (KC) for land, water, and oil slick classes derived from the random forest (RF) algorithm ranged from 90 % to 98 % and from 0.86 to 0.98, respectively. The spread of oil slicks at the incident location was revealed by the decorrelation stretch and band ratios of Sentinel-2 MultiSpectral Instrument (MSI) data. The accidental oil spill could have negative effects on the organic carbon cycle, chlorophyll production, and ecosystem productivity. It is essential to consider the vulnerability of specific regions in the Eastern Mediterranean to oil spills when developing adaptation strategies.

Recent progress on emerging technologies for trace elements-contaminated soil remediation

Abiotic stresses from potentially toxic elements (PTEs) have devastating impacts on health and survival of all living organisms, including humans, animals, plants, and microorganisms. Moreover, because of the rapid growing industrial activities together with the natural processes, soil contamination with PTEs has pronounced, which required an emergent intervention. In fact, several chemical and physical techniques have been employed to overcome the negative impacts of PTEs. However, these techniques have numerous drawback and their acceptance are usually poor as they are high cost, usually ineffectiveness and take longer time. In this context, bioremediation has emerged as a promising approach for reclaiming PTEs-contaminated soils through biological process using bacteria, fungus and plants solely or in combination. Here, we comprehensively reviews and critically discusses the processes by which microorganisms and hyperaccumulator plants extract, volatilize, stabilize or detoxify PTEs in soils. We also established a multi-technology repair strategy through the combination of different strategies, such as the application of biochar, compost, animal minure and stabilized digestate for stimulation of PTE remediation by hyperaccumulators plants species. The possible use of remote sensing of soil in conjunction with geographic information system (GIS) integration for improving soil bio-remediation of PTEs was discussed. By synergistically combining these innovative strategies, the present review will open very novel way for cleaning up PTEs-contaminated soils.

Assessing potential of the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) for water quality monitoring across the coastal United States

The Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) will provide unique high temporal frequency observations of the United States coastal waters to quantify processes that vary on short temporal and spatial scales. The frequency and coverage of observations from geostationary orbit will improve quantification and reduce uncertainty in tracking water quality events such as harmful algal blooms and oil spills. This study looks at the potential for GLIMR to complement existing satellite platforms from its unique geostationary viewpoint for water quality and oil spill monitoring with a focus on temporal and spatial resolution aspects. Water quality measures derived from satellite imagery, such as harmful algal blooms, thick oil, and oil emulsions are observable with glint <0.005 sr-1, while oil films require glint >10-5 sr-1. Daily imaging hours range from 6 to 12 h for water quality measures, and 0 to 6 h for oil film applications throughout the year as defined by sun glint strength. Spatial pixel resolution is 300 m at nadir and median pixel resolution was 391 m across the entire field of regard, with higher spatial resolution across all spectral bands in the Gulf of Mexico than existing satellites, such as MODIS and VIIRS, used for oil spill surveillance reports. The potential for beneficial glint use in oil film detection and quality flagging for other water quality parameters was greatest at lower latitudes and changed location throughout the day from the West and East Coasts of the United States. GLIMR scan times can change from the planned ocean color default of 0.763 s depending on the signal-to-noise ratio application requirement and can match existing and future satellite mission regions of interest to leverage multi-mission observations.

Remote sensing reveals unprecedented sublethal impacts of a 40-year-old oil spill on mangroves

Oil spills cause long-lasting mangrove loss, threatening their conservation and ecosystem services worldwide. Oil spills impact mangrove forests at various spatial and temporal scales. Yet, their long-term sublethal effects on trees remain poorly documented. Here, we explore these effects based on one of the largest oil spills ever recorded, the Baixada Santista pipeline leak, which hit the mangroves of the Brazilian southeastern coast in 1983. Historical, Landsat-derived normalized difference vegetation index (NDVI) maps over the spilled mangrove reveal a large dieback of trees within a year following the oil spill, followed by a heigh-year recolonization period and a stabilization of the canopy cover, however 20-30% lower than initially observed. We explain this permanent loss by an unexpected persistence of oil pollution in the sediments based on visual and geochemical evidence. Using field spectroscopy and cutting-edge drone hyperspectral imaging, we demonstrate how the continuous exposure of mangrove trees to high levels of pollution affects their health and productivity in the long term, by imposing permanent stressful conditions. Our study also reveals that tree species differ in their sensitivity to oil, giving the most tolerant ones a competitive advantage to recolonize spilled mangroves. By leveraging drone laser scanning, we estimate the loss of forest biomass caused by the oil spill to 9.8-91.2 t ha^-1, corresponding to 4.3-40.1 t C ha^-1. Based on our findings, we encourage environmental agencies and lawmakers to consider the sublethal effects of oil spills on mangroves in the environmental cost of these accidents. We also encourage petroleum companies to use drone remote sensing in monitoring routines and oil spill response planning to improve mangrove preservation and impact assessment.

Evaluation of machine learning techniques to select marine oil spill response methods under small-sized dataset conditions

Oil spill incidents can significantly impact marine ecosystems in Arctic/subarctic areas. Low biodegradation rate, harsh environments, remoteness, and lack of sufficient response infrastructure make those cold waters more susceptible to the impacts of oil spills. A major challenge in Arctic/subarctic areas is to timely select suitable oil spill response methods (OSRMs), concerning the process complexity and insufficient data for decision analysis. In this study, we used various regression-based machine learning techniques, including artificial neural networks (ANNs), Gaussian process regression (GPR), and support vector regression, to develop decision-support models for OSRM selection. Using a small hypothetical oil spill dataset, the modelling performance was thoroughly compared to find techniques working well under data constraints. The regression-based machine learning models were also compared with integrated and optimized fuzzy decision trees models (OFDTs) previously developed by the authors. OFDTs and GPR outperformed other techniques considering prediction power (> 30 % accuracy enhancement). Also, the use of the Bayesian regularization algorithm enhanced the performance of ANNs by reducing their sensitivity to the size of the training dataset (e.g., 29 % accuracy enhancement compared to an unregularized ANN).

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

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.

Development of a soil heavy metal estimation method based on a spectral index: Combining fractional-order derivative pretreatment and the absorption mechanism

Visible and near-infrared (Vis-NIR) reflectance is an effective way to estimate soil heavy metal content. In this study, in order to magnify the spectral information of the soil heavy metals and solve the collinearity and redundancy of hyperspectral datasets, we aimed to explore the potential of the fractional-order derivative (FOD) spectral pretreatment method and the band combination algorithm in soil heavy metal estimation. A total of 120 soil samples were collected in Xuzhou city, Jiangsu province, China, and their heavy metal contents and spectra were measured. The FOD (intervals of 0.25, range of 0-2) and a new three-band spectral index which take into account the electronic transition of metal ions in the visible region and organic matter and clay minerals in the near-infrared region were utilized for the spectral pretreatment and the selection of characteristic bands, respectively. FOD with an order of 0.75 exhibited the best model performance for estimating Cr and Zn, yielding R_P² values of 0.74 and 0.81, respectively. As regards Pb, the highest estimation accuracy was achieved with the 0.5-order reflectance, yielding R_P² values of 0.56. The three-band spectral indices with the best performance were then combined for a better estimation. To improve the estimation accuracy and generalization, partial least squares (PLS), support vector machine (SVM), random forest (RF), ridge regression (RR), XGBoost and extreme learning machine (ELM) were used to estimate the heavy metals by incorporating multiple spectral indices, and it was found that ELM outperformed other counterparts (the highest R_P² = 0.77 for Cr, the highest R_P² = 0.86 for Zn, the highest R_P² = 0.63 for Pb). The main spectral absorption mechanisms and modes of heavy metals were also analyzed. This estimation method combining FOD and a three-band index will provide a reference to estimate soil heavy metals using Vis-NIR spectra over a large scale.

Salinity Modulates Juncus acutus L. Tolerance to Diesel Fuel Pollution

Soil contamination with petroleum-derived substances such as diesel fuel has become a major environmental threat. Phytoremediation is one of the most studied ecofriendly low-cost solutions nowadays and halophytes species has been proved to have potential as bio-tools for this purpose. The extent to which salinity influences diesel tolerance in halophytes requires investigation. A greenhouse experiment was designed to assess the effect of NaCl supply (0 and 85 mM NaCl) on the growth and photosynthetic physiology of Juncus acutus plants exposed to 0, 1 and 2.5% diesel fuel. Relative growth rate, water content and chlorophyll a derived parameters were measured in plants exposed to the different NaCl and diesel fuel combinations. Our results indicated that NaCl supplementation worsened the effects of diesel toxicity on growth, as diesel fuel at 2.5% reduced relative growth rate by 25% in the absence of NaCl but 80% in plants treated with NaCl. Nevertheless, this species grown at 0 mM NaCl showed a high tolerance to diesel fuel soil presence in RGR but also in chlorophyll fluorescence parameters that did not significantly decrease at 1% diesel fuel concentration in absence of NaCl. Therefore, this study remarked on the importance of knowing the tolerance threshold to abiotic factors in order to determine the bioremediation capacity of a species for a specific soil or area. In addition, it showed that NaCl presence even in halophytes does not always have a positive effect on plant physiology and it depends on the pollutant nature.

History of a disaster: A baseline assessment of the Wakashio oil spill on the coast of Mauritius, Indian Ocean

Oil spills from tanker ships provide adverse and irreversible impacts of a pollutant over coastal and marine environments. Using Sentinel-1 and 2 satellite images, this baseline paper presents the detection, assessment, and monitoring of the aground and further oil spill from the Wakashio ship of August 06, 2020, on the Mauritius coast. The oil spill started on August 06, after cracks developed on the hull, and continued until the total breakup of the ship on August 15, 2020. Data shows displacements in ship position of about 100 m, and a maximum change of 80° in orientation (from NS to NE). The remote sensing results were validated using met-ocean observations and reanalysis, which showed winds, waves, and tides of high magnitude at the accident site during the incident period. Analysis of the results of this event using REAS and CMEMS data indicate their usefulness to study similar future oil spills events.

Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy

Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.

Mapping annual land disturbance and reclamation in rare-earth mining disturbance region using temporal trajectory segmentation

Rare-earth mining has caused extensive damage to soil, vegetation, and water, significantly threatening ecosystems. Monitoring environmental disturbance caused by rare-earth mining is necessary to protect the ecological environment. A spatiotemporal remote sensing monitoring method for mining to reclamation processes in a rare-earth mining area using multisource time-series satellite images is described. In this study, the normalized difference vegetation index (NDVI) is used to evaluate the mining impact. Regression analysis is conducted to relate the HJ-1B CCD and Landsat 5/8 data to reduce the NDVI error related to sensor differences between different datasets. The analysis method of NDVI trajectory data of ground objects is proposed, and areas of environmental disturbance caused by rare-earth mining are identified. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and a temporal trajectory segmentation method is established based on the vegetation changes in different disturbance stages. The temporal trajectory of the rare-earth disturbance points is segmented to extract features in each stage to obtain the disturbance year, recovery year, and recovery cycle and evaluate the vegetation recovery after rare-earth mining disturbance. We applied the method to a stack of 20 multitemporal images from 2000 to 2019 to analyze vegetation disturbance due to rare-earth mining and vegetation recovery in the upper reaches of the Guangdong-Hong Kong-Macao Greater Bay Area, China. The results show the following. (1) Mining industry in the study area experienced rapid expansion before 2008, but growth slowed since the policies implemented by the government since 2009 to restrict rare-earth mining. (2) The continuous influence to the land caused by rare-earth mining can last for decades; however, the reclamation activities shorten the recovery cycle of mining land from 5 to 3 years.

Terrestrial oil spill mapping using satellite earth observation and machine learning: A case study in South Sudan

Terrestrial oil spills are a major threat to environmental and human well-being. Rapid, accurate, and remote spatial assessment of oil contamination is critical to implementing countermeasures that prevent potentially lasting ecological damage and irreversible harm to local communities. Satellite remote sensing has been used to support such assessments in inaccessible regions, although mapping small terrestrial oil spills is challenging - partly due to the pixel size of remote sensing systems, but also due to the distinguishability of small oil spill areas from other land cover types. We assessed the usability of freely available Sentinel satellite images to map terrestrial oil spills with machine learning algorithms. Using two test sites in South Sudan, we demonstrated that information from the Sentinel-1 and -2 instruments can be used to map oil spills with more than 90 % classification accuracy. Classification accuracy was significantly increased (>95 %) with the addition of multi-temporal information and spatial predictor variables that quantify proximity to oil production infrastructure such as pipelines and oil pads. The mapping of terrestrial oil spills with freely available Sentinel satellite images may thus represent an accurate and efficient means for the regular monitoring of oil-impacted areas.

A novel approach for long-term spectral monitoring of desert shrubs affected by an oil spill

Crude oil pollution is a global environmental concern since it persists in the environment longer than most conventional carbon sources. In December 2014, the hyper-arid Evrona Nature Reserve, Israel, experienced large-scale contamination when crude oil spilled. The overarching goal of the study was to investigate the possible changes, caused by an accidental crude oil spill, in the leaf reflectance and biochemical composition of four natural habitat desert shrubs. The specific objectives were (1) to monitor the biochemical properties of dominant shrub species in the polluted and control areas; (2) to study the long-term consequences of the contamination; (3) to provide information that will assist in planning rehabilitation actions; and (4) to explore the feasibility of vegetation indices (VIs), along with the machine learning (ML) technique, for detecting stressed shrubs based on the full spectral range. Four measurement campaigns were conducted in 2018 and 2019. Along with the various stress indicators, field spectral measurements were performed in the range of 350-2500 nm. A regression analysis to examine the relation of leaf reflectance to biochemical contents was carried out, to reveal the relevant wavelengths in which polluted and control plants differ. Vegetation indices applied in previous studies were found to be less sensitive for indirect detection of long-term oil contamination. A novel spectral index, based on indicative spectral bands, named the "normalized blue-green stress index" (NBGSI), was established. The NBGSI distinguished significantly between shrubs located in the polluted and in the control areas. The NBGSI showed a strong linear correlation with pheophytin a. Machine learning classification algorithms obtained high overall prediction accuracy in distinguishing between shrubs located in the oil-polluted and the control sites, indicating internal component differences. The findings of this study demonstrate the efficacy of indirect and non-destructive spectral tools for detecting and monitoring oil pollution stress in shrubs.

A Preliminary Study on Lupinus albus and Raphanus sativus Grown in Soil Affected by Oil Spillage

Oil spills from pipelines are a hazardous contamination source for agricultural soils. We investigated the effects of petroleum hydrocarbon (PHC) soil contamination, resulting from a real diesel oil pipeline spill, on the growth of Lupinus albus and Raphanus sativus plants. These species are widely cultivated for food purposes and have not been previously tested in soils affected by oil spills. Mesocosm-scale experiments were conducted in a greenhouse, and the potential transfer of hydrocarbons from soil to plant was evaluated. The results indicated that hydrocarbons in soil altered the soil nutrient balance and adversely affected plant growth. The C > 12 content in the aerial part was lower in plants grown in the contaminated soil than in plants grown in the control soil. The reduction in plant growth was not related to the accumulation of hydrocarbons in plant tissue, but rather to the deterioration in soil quality due to the oil spill.

Intelligent computational techniques in marine oil spill management: A critical review

Effective marine oil spill management (MOSM) is crucial to minimize the catastrophic impacts of oil spills. MOSM is a complex system affected by various factors, such as characteristics of spilled oil and environmental conditions. Oil spill detection, characterization, and monitoring; risk evaluation; response selection and process optimization; and waste management are the key components of MOSM demanding timely decision-making. Applying robust computational techniques based on real-time data (e.g., satellite and aerial observations) and historical records of oil spill incidents may considerably facilitate decision-making processes. Various soft-computing and artificial intelligence-based models and mathematical techniques have been used for the implementation of MOSM's components. This study presents a review of literature published since 2010 on the application of computational techniques in MOSM. A statistical evaluation is performed concerning the temporal distribution of papers, publishers' engagement, research subfields, countries of studies, and selected case studies. Key findings reported in the literature are summarized for two main practices in MOSM: spill detection, characterization, and monitoring; and spill management and response optimization. Potential gaps in applying computational techniques in MOSM have been identified, and a holistic computational-based framework has been suggested for effective MOSM.

Remote Laser Induced Fluorescence of Soils and Rocks

The laser induced fluorescence spectroscopy was systematically utilized for remote sensing of different soils and rocks for the first time, to the best of our knowledge. Laser induced fluorescence spectroscopy measurements were carried out by the developed nanosecond LIDAR instrument with variable excitation wavelength (355, 532 and 1064 nm). LIDAR sensing of different Brazil soil samples have been carried out in order to construct a spectral database. The laser induced fluorescence spectra interpretation for different samples has been discussed in detail. The perspectives of LIDAR sensing of organic samples deposited at soils and rock have been discussed including future space exploration missions in the search for extraterrestrial life.

Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review

This review outlines the advances achieved in monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing over the last 50 years. A broad diversity of methods based on field and imaging spectroscopy were developed in that field for precision farming and environmental monitoring purposes. From the 466 articles reviewed, we identified the main factors to consider to achieve accurate monitoring of plant stress, namely: The plant species and the stressor to monitor, the goal (detection or quantification), and scale (field or broad-scale) of monitoring, and the need for controlled experiments. Based on these factors, we then provide recommendations and guidelines for the development of reliable methods to monitor 11 major biotic and abiotic plant stressors. For each stressor, the effects on plant health and reflectance are described and the most suited spectral regions, scale, spatial resolution, and processing approaches to achieve accurate monitoring are presented. As a perspective, we discuss two major components that should be implemented in future methods to improve stress monitoring: The discrimination of plant stressors with similar effects on plants and the transferability of the methods across scales.

Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices

Monitoring plant metal uptake is essential for assessing the ecological risks of contaminated sites. While traditional techniques used to achieve this are destructive, Visible Near-Infrared (VNIR) reflectance spectroscopy represents a good alternative to monitor pollution remotely. Based on previous work, this study proposes a methodology for mapping the content of several metals in leaves (Cr, Cu, Ni and Zn) under realistic field conditions and from airborne imaging. For this purpose, the reflectance of Rubus fruticosus L., a pioneer species of industrial brownfields, was linked to leaf metal contents using optimized normalized vegetation indices. High correlations were found between the vegetation indices exploiting pigment-related wavelengths and leaf metal contents (r ≤ - 0.76 for Cr, Cu and Ni, and r ≥ 0.87 for Zn). This allowed predicting the metal contents with good accuracy in the field and on the image, especially Cu and Zn (r ≥ 0.84 and RPD ≥ 2.06). The same indices were applied over the entire study site to map the metal contents at very high spatial resolution. This study demonstrates the potential of remote sensing for assessing metal uptake by plants, opening perspectives of application in risk assessment and phytoextraction monitoring in the context of trace metal pollution.

Preparation and Characterization of New Electrically Conductive Composites Based on Expanded Graphite with Potential Use as Remote Environmental Detectors

The presented paper is focused on studying electrically conductive composites based on an elastomeric matrix and expanded graphite as the filler. A potential application as an environmental remote detector was studied. The influence of filler particle size, film thickness, detector length, temperature, and the amount of oil on the detector response rate were explored. Peel tests were performed in order to investigate the adhesion of prepared detector films to different materials. Expanded graphite with average particle size 5 µm was chosen for the experiments due to its fastest response. Decreasing the detector film thickness has caused an increase in the response rate but also a decrease in the signal measured. The response rate of the detector system was in a practical range even for lower temperatures. From the obtained data, the proposed detector seems to be suitable for a practical application.