Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy

Sanches, I. D.; Filho, Carlos Roberto de Souza; Magalhães, Lucíola Alves; Quitério, G.C.M.; Alves, Marcos Nopper; Oliveira, Wilson Jose ́ de

doi:10.1016/j.isprsjprs.2013.01.007

Cited by 52 publications

(67 citation statements)

References 34 publications

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“…These transformations are usually employed for reducing the high variability of spectral signatures while enhancing the information they contain [34,66]. In our case, reflectance varied little along the gradient of TPH (≤10% at 550 nm and in the red edge), because of the strong tolerance of bramble to oil exposure, in comparison to other species [20,30]. SNV, Log(1/R), and AUCN transformations were unable to conserve this information in the transformed spectra, which introduced inaccuracies in TPH predictions.…”

Section: Elastic Net Based Methodsmentioning

confidence: 91%

“…As for the PROSPECT and PROSAIL based method, the accuracy of predictions was reduced while increasing acquisition scale (best RPD = 2.16, 2.07 and 1.9 at leaf and canopy scales and on the image, respectively, Figure 4). Spectrum transformations have been extensively used for detecting vegetation stress in various contexts, including oil contamination [30,34,83,84]. The first derivative sparked particular interest, as it allows tracking shifts in the red edge position (REP) caused by chlorophyll alteration [20,29,85].…”

Section: Elastic Net Based Methodsmentioning

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%

“…Several studies have aimed to characterize the effects of oil on vegetation reflectance under controlled conditions [29,[36][37][38]. Vegetation indices and spectrum transformations (first and second derivatives, continuum removal) have been frequently used for this purpose [20,30,[38][39][40]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Elastic Net Based Methodsmentioning

confidence: 91%

Section: Elastic Net Based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Curran et al (2001) and Sanches et al (2013) successfully applied VIs in photogrammetry and remote sensing for plant-related research, especially for identifying stressed plants.…”

Section: Plant Stress Detectionmentioning

confidence: 99%

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Ahmed¹,

Yasmin²,

Mo³

et al. 2016

Journal of Biosystems Engineering

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Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.

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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

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Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy

Cited by 52 publications

References 34 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

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

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

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