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.

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 eight-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 be 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.

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.

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

The monitoring of soil contamination deriving from oil and gas industry remains difficult in vegetated areas. Over the last decade, optical remote sensing has proved helpful for this purpose. By tracking alterations in vegetation biochemistry through its optical properties, multi- and hyperspectral remote sensing allow detecting and quantifying crude oil and petroleum products leaked following accidental leakages or bad cessation practices. Recent advances in this field have led to the development of various methods that can be applied either in the field using portable spectroradiometers or at large scale on airborne and satellite images. Experiments carried out under controlled conditions have largely contributed to identifying the most important factors influencing the detection of oil (plant species, mixture composition, etc.). In a perspective of operational use, an important effort is still required to make optical remote sensing a reliable tool for oil and gas companies. The current methods used on imagery should extend their scope to a wide range of contexts and their application to upcoming satellite-embedded hyperspectral sensors should be considered in future studies.

Estimating persistent oil contamination in tropical region using vegetation indices and random forest regression.

The persistence of soil contamination after cessation of oil activities remains a major environmental issue in tropical regions. The assessment of the contamination is particularly difficult on vegetated sites, but promising advances in reflectance spectroscopy have recently emerged for this purpose. This study aimed to exploit vegetation reflectance for estimating low concentrations of Total Petroleum Hydrocarbons (TPH) in soils. A greenhouse experiment was carried out for 42 days on Cenchrus alopecuroides (L.) under realistic tropical conditions. The species was grown on oil-contaminated mud pit soils from industrial sites, with various concentrations of TPH. After 42 days, a significant decrease in plant growth and leaf chlorophyll and carotenoid contents was observed for plants exposed to 5-19 g kg-1 TPH in comparison to the controls (p < 0.05). Conversely, pigment contents were higher for plants exposed to 1 g kg-1 TPH (hormesis phenomenon). These modifications proportionally affected the reflectance of C. alopecuroides at leaf and plant scales, especially in the visible region around 550 and 700 nm. 33 vegetation indices were used for linking the biochemical and spectral responses of the species to oil using elastic net regressions. The established models indicated that chlorophylls a and b and ß-carotene were the main pigments involved in the modifications of reflectance (R2 > 0.7). The same indices also succeeded in estimating the concentrations of TPH using random forest regression, at leaf and plant scales (RMSE = 1.46 and 1.63 g kg-1 and RPD = 5.09 and 4.44, respectively). Four out of the 33 indices contributed the most to the models (>75%). This study opens up encouraging perspectives for monitoring the cessation of oil activities in tropical regions. Further researches will focus on the application of our approach at larger scale, on airborne and satellite imagery.

Application of PROSPECT for estimating total petroleum hydrocarbons in contaminated soils from leaf optical properties.

Recent advances in hyperspectral spectroscopy suggest making use of leaf optical properties for monitoring soil contamination in oil production regions by detecting pigment alterations induced by Total Petroleum Hydrocarbons (TPH). However, this provides no quantitative information about the level of contamination. To achieve this, we propose an approach based on the inversion of the PROSPECT model. 1620 leaves from five species were collected on a site contaminated by 16 to 77 g.kg-1 of TPH over a 14-month period. Their spectral signature was measured and used in PROSPECT model inversions to retrieve leaf biochemistry. The model performed well for simulating the spectral signatures (RMSE < 2%) and for estimating leaf pigment contents (RMSE ≤ 2.95 µg.cm-2 for chlorophylls). Four out of the five species exhibited alterations in pigment contents when exposed to TPH. A strong correlation was established between leaf chlorophyll content and soil TPH concentrations (R2 ≥ 0.74) for three of them, allowing accurate predictions of TPH (RMSE =3.20 g.kg-1 and RPD = 5.17). The accuracy of predictions varied by season and improved after the growing period. This study demonstrates the capacity of PROSPECT to estimate oil contamination and opens up promising perspectives for larger-scale applications.

Detection and discrimination of various oil-contaminated soils using vegetation reflectance.

The use of hyperspectral spectroscopy for oil detection recently sparked a growing interest for risk assessment over vegetated areas. In a perspective of image applications, we conducted a greenhouse experiment on a brownfield-established species, Rubus fruticosus L. (bramble), to evaluate the potential of vegetation reflectance to detect and discriminate among various oil-contaminated soils. The species was grown for 32 days on four different soils with mixtures of petroleum hydrocarbons and heavy metals. Additional plants were grown on either uncontaminated control or water-deficient soils for comparison. Repeated reflectance measurements indicated modified spectral signatures under both oil and water-deficit exposure, from leaf to multi-plant scales. The amplitude of the response varied with mixture composition, exposure time, acquisition scale and spectrum region. Reflectance changes were linked to alterations in chlorophyll, carotenoid and water contents using vegetation indices. These indices were used to catch spectral similarities among acquisition scales and to discriminate among treatments using Kendall's coefficient of concordance (W) and regularized logistic regression. Of the 33 vegetation indices tested, 14 were concordant from leaf to multi-plant scales (W > 0.75, p < 0.05) and strongly related to leaf biochemistry (R2 > 0.7). The 14 indices allowed discriminating between each mixture and the control treatment with no or minor confusions (≤5%) at all acquisition scales, depending on exposure time. Some of the mixtures remained difficult to discriminate among them and from the water-deficit treatment. The approach was tested at the canopy scale under natural conditions and performed well for identifying bramble exposed to either one of the experimentally-tested mixtures (90% accuracy) or to uncontaminated soil (83% accuracy). This study provided better understanding of vegetation spectral response to oil mixtures and opens up promising perspectives for future applications.

Assessing Soil Contamination Due to Oil and Gas Production Using Vegetation Hyperspectral Reflectance.

The remote assessment of soil contamination remains difficult in vegetated areas. Recent advances in hyperspectral spectroscopy suggest making use of plant reflectance to monitor oil and gas leakage from industrial facilities. However, knowledge about plant response to oil contamination is still limited, so only very few imaging applications are possible at this stage. We therefore conducted a greenhouse experiment on three species long-term exposed to either oil-contaminated or water-deficient soils. Reflectance measurements were regularly performed at leaf and plant scale over 61 days of exposure. Results showed an increase of reflectance in the visible (VIS), the red-edge and the short-wave infrared (SWIR) under both oil and water-deficit stress exposure. A contrasted response in the near-infrared (NIR) was also observed among species. Spectra underwent transformations to discriminate species' responses to the different treatments using linear discriminant analysis (LDA) with a stepwise procedure. Original and transformed spectra enabled to discriminate the plants' responses to the different treatments without confusion after 61 days. The discriminating wavelengths were consistent with the spectral differences observed. These results suggest differential changes in plant pigments, structure and water content as a response to various stressors, and open up promising perspectives for airborne and satellite applications.