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. 2013;8(1):e54028.
doi: 10.1371/journal.pone.0054028. Epub 2013 Jan 16.

Soil TPH concentration estimation using vegetation indices in an oil polluted area of eastern China

Linhai Zhu  1 , Xuechun Zhao, Liming Lai, Jianjian Wang, Lianhe Jiang, Jinzhi Ding, Nanxi Liu, Yunjiang Yu, Junsheng Li, Nengwen Xiao, Yuanrun Zheng, Glyn M Rimmington
Affiliations

Soil TPH concentration estimation using vegetation indices in an oil polluted area of eastern China

Linhai Zhu et al. PLoS One. 2013.

Abstract

Assessing oil pollution using traditional field-based methods over large areas is difficult and expensive. Remote sensing technologies with good spatial and temporal coverage might provide an alternative for monitoring oil pollution by recording the spectral signals of plants growing in polluted soils. Total petroleum hydrocarbon concentrations of soils and the hyperspectral canopy reflectance were measured in wetlands dominated by reeds (Phragmites australis) around oil wells that have been producing oil for approximately 10 years in the Yellow River Delta, eastern China to evaluate the potential of vegetation indices and red edge parameters to estimate soil oil pollution. The detrimental effect of oil pollution on reed communities was confirmed by the evidence that the aboveground biomass decreased from 1076.5 g m(-2) to 5.3 g m(-2) with increasing total petroleum hydrocarbon concentrations ranging from 9.45 mg kg(-1) to 652 mg kg(-1). The modified chlorophyll absorption ratio index (MCARI) best estimated soil TPH concentration among 20 vegetation indices. The linear model involving MCARI had the highest coefficient of determination (R(2) = 0.73) and accuracy of prediction (RMSE = 104.2 mg kg(-1)). For other vegetation indices and red edge parameters, the R(2) and RMSE values ranged from 0.64 to 0.71 and from 120.2 mg kg(-1) to 106.8 mg kg(-1) respectively. The traditional broadband normalized difference vegetation index (NDVI), one of the broadband multispectral vegetation indices (BMVIs), produced a prediction (R(2) = 0.70 and RMSE = 110.1 mg kg(-1)) similar to that of MCARI. These results corroborated the potential of remote sensing for assessing soil oil pollution in large areas. Traditional BMVIs are still of great value in monitoring soil oil pollution when hyperspectral data are unavailable.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. The location of the study site in eastern China.
The spatial extent of the third panel is approximately 7 km by 8 km.
Figure 2
Figure 2. Reed communities with different oil pollution.
Figure 3
Figure 3. Reflectance of bare soil and reed communities with different soil TPH concentrations.
Horizontal lines denoted broad wavebands used to calculate BMVIs. From left to right, they were blue (450–515 nm), red (630–690 nm) and near-infrared (750–900 nm). Vertical lines indicated wavelengths used to calculate NMVIs. From left to right, they were blue (470 nm), red (680 nm) and near-infrared (800 nm). The reflectance of bare soil was the average of 16 quadrats of bare soil. Each reflectance curve of vegetation was the average of 3 quadrats of reed communities at the same distance to the oil well in the same plot. The numbers in the legend were soil TPH concentrations (mg kg−1).
Figure 4
Figure 4. Relationship between soil TPH concentrations and aboveground biomass of the reed communities.
Figure 5
Figure 5. The first derivative of reflectance of bare soil and reed communities with different soil TPH concentrations.
Numbers in the legend were soil TPH concentrations (mg kg−1). Vertical lines and numbers indicated the wavelengths of peaks (nm).
Figure 6
Figure 6. Performance of reflectance at specific wavelengths for estimating soil TPH concentrations. RMSE was the root mean square error. p<0.001.
Figure 7
Figure 7. Performance of the leading vegetation indices for estimating soil TPH concentrations.
NDVI, TSAVI, MCARI, deRES and sumREA denoted Normalized Difference Vegetation Index, Transformed Soil Adjusted Vegetation Index, Modified Chlorophyll Absorption Ratio Index, red edge slope calculated using maximum first derivative spectrum and red edge area calculated using the sum of the first derivative, respectively. RMSE was the root mean square error. p<0.001.
Figure 8
Figure 8. Comparison of observed and simulated TPH concentrations (mg kg−1).
The dashed line showed the 1:1 relationship, the solid line, the fitted regression equations. A, B, C, D, E, F denoted the validation for the regression equations derived from broadband NDVI, narrowband NDVI, narrowband TSAVI, MCARI, deRES and sumREA, and abbreviations were the same as Figure 7.
Figure 9
Figure 9. Residual plots for comparing prediction of TPH concentrations by different equations. Abbreviations were the same as Figure 7 .
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This work was supported by the National Key Technology R&D Program of China (2008BAC43B01). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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