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

Lassalle, Guillaume; Fabre, Sophie; Crédoz, Anthony; Hédacq, Rémy; Borderies, P.; Bertoni, Georges; Erudel, Thierry; Buffan‐Dubau, Evelyne; Dubucq, Dominique; Elger, Arnaud

doi:10.1016/j.scitotenv.2018.11.314

Cited by 31 publications

(72 citation statements)

References 103 publications

(128 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study focused on this species, as it is generally well-established in temperate industrial areas. Bramble served for developing the methods of oil detection and quantification under controlled and field conditions [28,34,43] and was suitable for application to hyperspectral imagery.…”

Section: Study Area and Soil Samplingmentioning

confidence: 99%

“…TPH induce strong alterations in leaf anatomy and biochemistry related to their optical properties in the reflective domain (400-2500 nm) [23][24][25][26][27]. 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].…”

Section: Introductionmentioning

confidence: 99%

“…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 [28,35]. Several studies have aimed to characterize the effects of oil on vegetation reflectance under controlled conditions [29,[36][37][38].…”

Section: Introductionmentioning

confidence: 99%

“…The wavelengths linked to pigment and water contents were particularly suitable for distinguishing healthy and oil-exposed vegetation with no or minor confusion [21,34]. 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.).…”

Section: Introductionmentioning

confidence: 99%

“…In the continuity of the abovementioned studies [28,43], 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 Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Study Area and Soil Samplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data

Lassalle¹,

Fabre²

2022

Advances in Remote Sensing for Forest Monitoring

View full text Add to dashboard Cite

Tracking canopy gaps in mangroves remotely using deep learning

Lassalle

Filho

2022

Remote Sens Ecol Conserv

Self Cite

View full text Add to dashboard Cite

Mangroves are among the most ecologically valuable ecosystems of the globe. Reliable remote sensing solutions are required to assist their management and conservation at broad scale. Canopy gaps are part of forests' turnover and rejuvenation, but yet no method has been proposed to map their occurrence and recovery in mangroves. Here, were propose an approach based on a deep learning framework called Mask R-CNN to achieve automatic detection and delineation of gaps using very-high-resolution satellite imagery (<1 m). The Mask R-CNN combines a series of neural network architectures to identify and delineate gaps, determine their recovery stage, and estimate their morphological attributes. The approach was tested on four mangroves from different regions of the globe with high concentration of gaps of various origins (lightning strikes, oil spills, cutting, pests). The Mask R-CNN performed well to detect gaps, and accurately delineated gap contours (F1-score of segmentation ≥0.89). The model also succeeded in distinguishing among five recovery stages of gaps, from their onset to closure (Overall Accuracy = 91.4, Kappa = 0.89). Accurate retrieval of gap area, eccentricity, and compactnessthree relevant morphological attributeswere obtained (R 2 ≥ 0.83, NRMSE ≤10%). Several sources of confusion and misdelineation were identified. Our approach shows promising transferability to other mangrove sites and optical sensors and could help monitor canopy recovery in mangroves. It also opens promising perspectives for identifying the origin of gaps (natural or human-induced). It is intended to assist environmental managers and field experts in the management and conservation of these fragile ecosystems.

show abstract

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

Cited by 31 publications

References 103 publications

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

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

Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data

Tracking canopy gaps in mangroves remotely using deep learning

Contact Info

Product

Resources

About