Since the beginning of the 20th century, oil and gas supply has constantly increased to satisfy a growing demand worldwide [1
]. Along with the development of the oil industry, important efforts have been made to mitigate the associated environmental risks [3
]. Oil spills and leakages are of major concern in the onshore domain. They are likely to occur at every step of the production process (i.e., oil extraction, refining, and transportation) contaminating the soil and groundwater and remaining as mud pits after their cessation [6
]. The resulting soil contamination causes important ecological alterations (e.g., landscape fragmentation and habitat loss) [9
]. To avoid such consequences, fast and accurate detection and quantification of oil contamination are, therefore, necessary.
Total petroleum hydrocarbons (TPH) are good indicators of oil contamination in soils [13
]. They are the main constituent of oil and show high toxicity toward organisms. (In this study, the terms oil and TPH were used indifferently.) Among the methods proposed for detecting and quantifying TPH contamination, hyperspectral remote sensing has become a promising one [15
]. Recent advances in this field allow monitoring industrial facilities (e.g., pipelines and storage tanks) at a large scale using airborne or satellite imagery [17
], however, this approach is based on the detection of apparent oil at the surface, so its application in vegetated regions remains impossible. This causes a critical issue in regions with dense vegetation and intensive oil production activities. In this context, alternative approaches based on exploiting vegetation optical properties have emerged during the last decade [8
TPH induce strong alterations in leaf anatomy and biochemistry related to their optical properties in the reflective domain (400–2500 nm) [23
]. These alterations lead to modifications in the spectral signature of vegetation and suggest being able to detect oil in vegetated areas using optical remote sensing [28
]. On the basis of this assumption, a few studies have attempted to map oil spills and leakages using multi- and hyperspectral airborne or satellite imagery with four to 30 m spatial resolution (mainly Landsat, Hyperion, and Hymap systems) [8
]. Most of them have relied on comparisons of vegetation indices between healthy and contaminated sites, but only a few have proposed a method to detect oil contamination automatically, and even fewer have evaluated its performance. In addition, none of these studies attempted to quantify TPH. In most cases, the method of detection was applied to entire images [19
]. This gave rise to the apparition of false alarms, caused by differences in species’ sensitivity to oil, bare soil-mixed vegetation pixels, and the presence of other vegetation stressors (e.g., water deficit). False alarms have been more pronounced using multispectral satellite imagery. Thus, several conclusions emerged from these studies. First, very high spatial (1–2 m) and spectral (<10 nm) resolutions are needed to achieve accurate detection of oil. Then, prior application to entire images, i.e., it is necessary to calibrate the methods over restricted areas with known species and contamination. Then, the methods should be validated on spatially independent sites with the same species and, as a last step, applied to entire images, provided that the target species’ location is known.
Species established around industrial oil facilities are naturally tolerant to soil contamination and difficult to distinguish from healthy vegetation [33
]. It is, therefore, challenging to develop methods for detecting and quantifying TPH adapted to these species and intended to be applied to airborne and satellite hyperspectral images. Detecting and quantifying TPH is even more difficult in the presence of other environmental factors (e.g., water deficit). As suggested in previous work, this could be achieved by experiments carried out under controlled conditions [28
]. Several studies have aimed to characterize the effects of oil on vegetation reflectance under controlled conditions [29
]. Vegetation indices and spectrum transformations (first and second derivatives, continuum removal) have been frequently used for this purpose [20
]. The wavelengths linked to pigment and water contents were particularly suitable for distinguishing healthy and oil-exposed vegetation with no or minor confusion [21
]. More recently, Lassalle et al. [28
] succeeded in discriminating among various types of oil contamination at leaf and canopy scales using vegetation indices. The same approach performed well for detecting mud pits contaminated by TPH in the field, by exploiting the reflectance of an oil-tolerant species (Rubus fruticosus
L.). Radiative transfer models also sparked great interest for tracking alterations in leaf pigment contents caused by oil. For example, Arellano et al. [41
] inverted the PROSPECT model [42
] to detect changes in leaf chlorophyll content (LCC) resulting from an oil spill under tropical region. The same model was recently used for quantifying TPH in mud pit soils from leaf reflectance measurements performed in the field [43
]. As suggested, its coupling with the SAIL canopy model [44
] (forming the PROSAIL model [45
]) might be of great interest for quantifying oil contamination in vegetated areas, from hyperspectral images. This challenge remains, however, very difficult, as it implies being able to track subtle changes in LCC from airborne or satellite sensors.
In the continuity of the abovementioned studies [28
], this work aims to detect and quantify oil contamination (i.e., soil TPH content) in vegetated areas using airborne hyperspectral images with very high spatial and spectral resolutions. To achieve this, a method of detection previously developed under controlled conditions, hereafter summarized, was first applied to the images. Then, two approaches, either PROSAIL or multiple regression, are proposed to quantify TPH. Both were assessed in the field and on airborne hyperspectral images, on selected vegetation patches with known species.
4. Discussion and Perspectives
To date, there was no remote sensing method for both detecting and quantifying oil contamination in vegetated areas based on hyperspectral imagery [19
]. Our study is the first to achieve it using very high spatial resolution images acquired in a temperate region. This opens up encouraging perspectives for application of the methods over oil industrial facilities. In a perspective of operational use, it is essential that remote sensing provides reliable detection and quantification of oil over large vegetated areas. Until now, the proposed methods remained poorly effective outside the calibration site [19
], making them unusable operationally. Conversely, those developed in this study were successfully validated, by detecting and quantifying oil on independent mud pits that did not serve for method calibration. Our methods were, however, applied under several assumptions. Since we focused on R. fruticosus
in the temperate context, the methods were only adapted to this species. Thus, they could be applied for identifying new contaminated mud pits and for quantifying TPH, provided the sites are colonized by dense R. fruticosus
covers with known location. This is of great interest for monitoring contamination, because this species is widespread on industrial sites under temperate regions [36
]. In an operational perspective, the methods should be applicable at a large scale (i.e., to entire images) in a wide range of contexts (in terms of species, contamination type and level, and environmental conditions). No extensive computational time was noticed in this study, but this should be considered when applying our methods to entire images. Thanks to a good revisit time and large spatial coverage [22
], satellite-embedded sensors show great interest for monitoring oil contamination continuously over industrial facilities, however, our methods were tested on a single date, so they should be first validated over a longer time scale [22
]. In addition, to date, none of the operating and planned hyperspectral satellite-embedded sensors covering the reflective domain offer a spatial resolution higher than 8 m with more than 250 spectral bands. It is highly probable that, when applying our methods to satellite imagery, changes in spatial and spectral resolutions will affect their performance. Thus, in order to support oil exploration and contamination monitoring, the methods should improve in two ways. First, by extending their scope, and then, by adapting them to satellite imagery. These two perspectives are discussed jointly in this section.
At this stage, the need to know the location of R. fruticosus
is one of the most important limits to the application of the methods at a large scale. The mapping of R. fruticosus
could be achieved quite easily for homogenous and dense covers, such as those studied here. Conversely, R. fruticosus
is more likely to be mixed with other species and bare soil in pixels using satellite imagery, affecting the values of the 14 VI used in the detection method and consequently reducing its accuracy. Likewise, PROSAIL inversions might experience difficulties in retrieving LCC accurately for sparse of plurispecific vegetation, because of the influence of bare soil and differences in optical properties, leaf orientation, and sensitivity to oil among species [45
]. In response to this limit, spectral unmixing [92
] seems a promising solution. It could be used for mapping R. fruticosus
, even mixed with bare soil or other species, before applying our methods of oil detection and quantification. A successful mapping of R. fruticosus
has been achieved using unmixing methods on HyMap images (spatial resolution < 10 m) in a previous study [95
]. Future studies should focus on assessing unmixing methods on known sites with this species, for example by degrading 1 m spatial resolution airborne images to 8 m and 30 m resolution. In that sense, it might be interesting not to limit to a single species in our case. This would be spatially too restrictive from an operational point of view, so an important effort remains to adapt our methods to other species. Other temperate species might serve for detecting and quantifying oil,- such as Q. pubescens
and P. canadensis
, as highlighted in our previous study [43
]. Thus, they could be used along with R. fruticosus
for assessing mud pit contamination at a large scale, after being identified by spectral unmixing.
Even if the methods can be applied to entire images, provided that the target species have been identified, it is important to note that their performance depends on the level of contamination. With respect to R. fruticosus, the method of detection was initially adapted to a high TPH concentration (≥17 g/kg−1). Satisfying accuracy was obtained for low-contaminated mud pit (3.15 g/kg−1 TPH), but accuracy fell below 1 g/kg−1 TPH. This helped determining a lower detection limit. Conversely, the method of quantification based on LCC retrieval was accurate within the range of TPH studied. Its exact range of effectiveness remains, however, unknown. Thus, further research should focus on determining the exact limits of detection and quantification of the methods. These limits may vary among species, depending on their sensitivity. All of them do not allow detecting and quantifying mud pit contamination in the same range. Species with different sensitivities could be complementary for quantifying TPH over a wide range of concentrations. Spatial resolution might be also crucial, especially if TPH concentrations vary locally. In the case of 8 m or 30 m satellite imagery, pixels may include vegetation exposed to varying levels of contamination, making it difficult to quantify accurately. An important effort still remains to determine which species are suitable for applying the methods and their respective range of effectiveness at different spatial resolutions.
Although the scope of the methods is restricted to assessing mud pit contamination (i.e., production residues and oil sludge) at this stage, it should extend to other scenarios. Crude oil or petroleum product leaks deriving from pipeline or storage tank failures are a priority. Along with wastewater, they represent the main sources of contaminant release from oil industry [6
]. Moreover, the detection of crude oil is of great interest in microseepage prospecting. This represents a major way of improvements toward operational applications. From the perspective of operational application, one possible limit to applying our methods may arise at the spatial resolution of satellite images for punctual oil patches. Microseepage and mud pits generally occupy large areas (>30 m), but pipeline and storage tank leaks can occur on a few square meters [17
]. Their detection might be very difficult without very high spatial resolution, because pixels will rarely include only oil-exposed vegetation. Thus, it is important to keep in mind that the needed spatial resolution also depends on the purpose of the detection and quantification of oil.
This study aimed to detect and quantify oil contamination (i.e., TPH) in soils in temperate vegetated areas using airborne imagery with very high spatial and spectral resolutions. A two-step approach was proposed. As a first step, a method of detection exploiting 14 vegetation indices performed well for discriminating between healthy and oil-exposed bramble plants (OA = 98% and kappa = 0.95). This method was tested on additional sites and succeeded in detecting oil contamination levels similar to those of the calibration site (17–39 g/kg−1). Then, the performance of the method decreased for lower levels of contaminations, especially below 1 g/kg−1. As a second step, two methods of TPH quantification were proposed and assessed on the same contaminated sites. The first one, which relied on LCC retrieval using PROSPECT and PROSAIL models, achieved accurate predictions of TPH concentrations both in the field, at leaf and canopy scales, and on the airborne image (RMSE ≤ 3.20 g/kg−1 and RPD ≥ 2), thanks to consistent LCC estimations. The second method combined spectrum transformations with ENET regression. This method provided slightly less accurate quantification of TPH (best RMSE ≤ 3.28 g/kg−1 and RPD ≥ 1.9) and required only few spectral bands in the VIS and the red edge regions. Both methods were validated on another contaminated site and their performances were compared.
In the continuity of previous work, this multiscale study highlighted the importance of controlled and field conditions for developing reliable methods that can be applied to hyperspectral imagery. Our methods focused on tracking oil-induced alterations in leaf biochemistry while preventing from undesired effects (plant architecture, bare soil, and illuminating and viewing geometry), which made them accurate regardless of the acquisition scale. High spatial resolution helped achieving this. As discussed above, further studies are needed to adapt our methods to various ecological contexts subject to oil contamination. In addition, promising perspectives of operational use will arise in the future with the emergence of new hyperspectral satellite sensors.