Remote sensing for detection and monitoring of vegetation affected by oil spills

Adamu, Bashir; Tansey, Kevin; Ogutu, Booker

doi:10.1080/01431161.2018.1448483

Cited by 46 publications

(33 citation statements)

References 39 publications

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“…This study was a first attempt in that direction, but further researches are needed to assess the reliability of our approach under natural conditions, using field measurements, and in the long term using multiand hyperspectral imagery. High to very high spatial resolution might help achieving this, because the contamination can occur on a few square meters and be therefore difficult to detect using medium to low spatial resolution imagery (Adamu et al, 2018;Arellano et al, 2015). As discussed in section 3.1, the case of persistent low contamination in brownfields and mud pits makes the estimation of TPH very challenging, because of the composition of the contamination and the tolerance of the species.…”

Section: Elastic Net Regressionsmentioning

confidence: 99%

“…More recently, a new approach has been proposed to extend its use to vegetated areas, where oil cannot be detected directly at the soil surface (Lassalle et al, 2018;Rosso et al, 2005;Sanches et al, 2013a). This approach shows great interest in tropical regions, where vegetation is particularly dense (Achard et al, 2018;Adamu et al, 2018;Arellano et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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

Lassalle

Crédoz

Hédacq

et al. 2019

Ecotoxicology and Environmental Safety

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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 to 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 β-carotene were the main pigments involved in the modifications of reflectance (R 2 > 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.

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Section: Elastic Net Regressionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lassalle

Crédoz

Hédacq

et al. 2019

Ecotoxicology and Environmental Safety

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“…To date, there was no remote sensing method for both detecting and quantifying oil contamination in vegetated areas based on hyperspectral imagery [19,31,32]. Our study is the first to achieve it using very high spatial resolution images acquired in a temperate region.…”

Section: Discussion and Perspectivesmentioning

confidence: 95%

“…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][29][30]. 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) [8,19,31,32]. 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.…”

Section: Introductionmentioning

confidence: 99%

Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery

et al. 2019

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Recent remote sensing studies have suggested exploiting vegetation optical properties for assessing oil contamination, especially total petroleum hydrocarbons (TPH) in vegetated areas. Methods based on the tracking of alterations in leaf biochemistry have been proposed for detecting and quantifying TPH under controlled and field conditions. In this study, we expand their use to airborne imagery, in order to monitor oil contamination at a larger scale. Airborne hyperspectral images with very high spatial and spectral resolutions were acquired over an industrial site with oil-contamination (mud pits) and control sites both colonized by Rubus fruticosus L. The method of oil detection exploiting 14 vegetation indices succeeded in classifying the sites in the case of high TPH contamination (overall accuracy ≥ 91.8%). Two methods, based on either the PROSAIL (PROSPECT + SAIL) radiative transfer model or elastic net multiple regression, were also developed for quantifying TPH. Both methods were tested on reflectance measurements in the field, at leaf and canopy scales, and on the image, and achieved accurate predictions of TPH concentrations (RMSE ≤ 3.28 g/kg −1 and RPD ≥ 1.90). The methods were validated on additional sites and open up promising perspectives of operational application for oil and gas companies, with the emergence of new hyperspectral satellite sensors.

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“…Nonetheless, mapping of post-spill affected vegetation areas using remote sensing is affected by overestimation due to similarities in spectral appearance of oil spill affected vegetation, wetland and other elements like burnt and dead vegetation [29,30]. To date, remote sensing multispectral images have been used in monitoring impacts of disasters like hurricanes [14,31] and oil spills [32][33][34] on vegetation. Previous studies such as [35][36][37][38] considered mainly terrestrial vegetation health indices which are only cable of assessing the impact of oil spills without giving the exact extent of the polluted and nonpolluted areas.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models

et al. 2020

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Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which offer limited area coverage and classification accuracy. Thus, this study utilizes multispectral Landsat 8-OLI remote sensing imagery and machine learning models to assess the impacts of oil spills on coastal vegetation and wetland and monitor the recovery pattern of polluted vegetation and wetland in a coastal city. The spatial extent of polluted areas was also precisely quantified for effective management of the coastal ecosystem. Using Johor, a coastal city in Malaysia as a case study, a total of 49 oil spill (ground truth) locations, 54 non-oil-spill locations and Landsat 8-OLI data were utilized for the study. The ground truth points were divided into 70% training and 30% validation parts for the classification of polluted vegetation and wetland. Sixteen different indices that have been used to monitor vegetation and wetland stress in literature were adopted for impact and recovery analysis. To eliminate similarities in spectral appearance of oil-spill-affected vegetation, wetland and other elements like burnt and dead vegetation, Support Vector Machine (SVM) and Random Forest (RF) machine learning models were used for the classification of polluted and nonpolluted vegetation and wetlands. Model optimization was performed using a random search method to improve the models’ performance, and accuracy assessments confirmed the effectiveness of the two machine learning models to identify, classify and quantify the area extent of oil pollution on coastal vegetation and wetland. Considering the harmonic mean (F1), overall accuracy (OA), User’s accuracy (UA), and producers’ accuracy (PA), both models have high accuracies. However, the RF outperformed the SVM with F1, OA, PA and UA values of 95.32%, 96.80%, 98.82% and 95.11%, respectively, while the SVM recorded accuracy values of F1 (80.83%), OA (92.87%), PA (95.18%) and UA (93.81%), respectively, highlighting 1205.98 hectares of polluted vegetation and 1205.98 hectares of polluted wetland. Analysis of the vegetation indices revealed that spilled oil had a significant impact on the vegetation and wetland, although steady recovery was observed between 2015-2018. This study concludes that Chlorophyll Vegetation Index, Modified Difference Water Index, Normalized Difference Vegetation Index and Green Chlorophyll Index vegetation indices are more sensitive for impact and recovery assessment of both vegetation and wetland, in addition to Modified Normalized Difference Vegetation Index for wetlands. Thus, remote sensing and Machine Learning models are essential tools capable of providing accurate information for coastal oil spill impact assessment and recovery analysis for appropriate remediation initiatives.

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Remote sensing for detection and monitoring of vegetation affected by oil spills

Cited by 46 publications

References 39 publications

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

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

Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery

Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models

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