mapping land-based oil spills using high spatial...

Mapping land-based oil spills usinghigh spatial resolution unmannedaerial vehicle imagery andelectromagnetic induction surveydata

Masoud MahdianpariBahram SalehiFariba MohammadimaneshGlen LarsenDerek R. Peddle

Masoud Mahdianpari, Bahram Salehi, Fariba Mohammadimanesh, Glen Larsen, Derek R. Peddle,“Mapping land-based oil spills using high spatial resolution unmanned aerial vehicle imagery andelectromagnetic induction survey data,” J. Appl. Remote Sens. 12(3), 036015 (2018),doi: 10.1117/1.JRS.12.036015.

Mapping land-based oil spills using high spatialresolution unmanned aerial vehicle imagery and

electromagnetic induction survey data

Masoud Mahdianpari,a,* Bahram Salehi,a

Fariba Mohammadimanesh,a Glen Larsen,b andDerek R. Peddlec

aMemorial University of Newfoundland, C-CORE and Department of Electrical Engineering,St. John’s, Newfoundland, Canada

bAKS Geoscience Inc., Alberta, Calgary, CanadacUniversity of Lethbridge, Alberta Terrestrial Imaging Centre (ATIC) and

Department of Geography, Lethbridge, Alberta, Canada

Abstract. Natural oil and gas are important sources of energy worldwide and their explorationand exploitation have significantly increased due to the global demand. The transportation ofthese valuable resources greatly depends on pipelines; however, pipeline leakages have hugeeconomic and environmental impacts warranting an effective operational methodology for pipe-line monitoring. We proposed a method for mapping soil contamination due to pipeline leakagein Dixonville, Alberta, Canada. In particular, very high-resolution unmanned aerial vehicle(UAV) imagery and electromagnetic induction (EM) surveying data were analyzed using a hier-archical object-based random forest (RF) algorithm. In level-1 classification, a land cover mapwas produced using UAV data. Next, all land cover classes, excluding contaminated soil, weremasked out. In level-2 classification, the contaminated soil class was further partitioned intothree subclasses representing varying degrees of contamination. Specifically, we proposeda salinity index, named the normalized salinity index, to detect areas of soil contamination.The salinity index proposed herein, as well as several other salinity indices and UAV bands,were used as input features for level-2 classification. An overall classification accuracy ofabout 77% was achieved for level-2 classification using the proposed method. The results dem-onstrate that the synergistic use of high spatial resolution UAV imagery and EM data is verypromising for detecting soil contamination and examining ecosystem disturbance due to pipelineleakage. © 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JRS.12.036015]

Keywords: oil and gas; pipeline leakage; unmanned aerial vehicle; electromagnetic induction;object-based; random forest; salinity index.

Paper 180228 received Mar. 29, 2018; accepted for publication Jul. 31, 2018; published onlineSep. 6, 2018.

1 Introduction

Natural oil and gas are important sources of energy worldwide and their transportation greatlydepends on pipelines.1 Pipelines are either under- or above-ground and may have a diameter ofup to 1 m.2 Pipelines are major and relatively safe tools for oil and gas transportation and, accord-ingly, their construction has rapidly increased due to anthropogenic activities, such as urbani-zation and industrial development.

Damages to pipelines result in leaks and, as such, the loss of large amounts of natural re-sources, causing huge economic burdens for oil and gas companies. Moreover, they may haveirreversible consequences for surrounding environments through pollution and contamination.

*Address all correspondence to: Masoud Mahdianpari, E-mail: [email protected]

1931-3195/2018/$25.00 © 2018 SPIE

Journal of Applied Remote Sensing 036015-1 Jul–Sep 2018 • Vol. 12(3)

https://doi.org/10.1117/1.JRS.12.036015

https://doi.org/10.1117/1.JRS.12.036015

https://doi.org/10.1117/1.JRS.12.036015

https://doi.org/10.1117/1.JRS.12.036015

https://doi.org/10.1117/1.JRS.12.036015

mailto:[email protected]



The main causes of pipeline leakage include corrosion and fatigue breakage of overage struc-tures, material failure, and external interference.3 Pipeline leakages have ecological effects onvarious ecosystem components, including vegetation, wildlife, fresh water systems, and theocean, and put human safety at risk.4,5 This highlights the importance of continuous surveillanceof pipeline networks for protecting the environment.6 Notably, an efficient monitoring systemcharacterizes the functional and structural requirements to prevent breakage and identifies poten-tial problems that will impact on pipeline safety.

Although several studies have been carried out for offshore oil spill detection using a widerange of remote sensing tools,7 fewer efforts have been devoted for monitoring onshore oil spills.This is unfortunate given the large numbers of oil spills that occur annually in terrestrial areasglobally and their devastating economic and environmental impacts. For example, the Michiganpipeline leakage, caused by corrosion and frazzle cracks in the pipes, was one of the most cata-strophic onshore spill events in recent history, wherein a large portion of heavy crude oil spilledinto the Kalamazoo River. Human health problems due to the presence of toxic material in theriver and huge cleanup costs were the major consequences of this event. Recurring pipelineleakages in Nigeria during the 2000s, and pipeline explosions in Belgium and along theTransCanada Pipeline in 2004 and 2014, respectively, are other examples of detrimental pipelinebreakages.2

Notably, frequent small-scale oil leakages are more important from an environmental per-spective due to their substantial ecological consequences.8 For example, one common effect ofoil spills on vegetation health is a reduction in chlorophyll production, which causes either rapidor gradual vegetation death depending on the vegetation type and scale of the leakage.5

Accordingly, several studies have monitored vegetation under stress due to pipeline breakagesin different geographic locations, such as New Mexico,9 Louisiana,5 and the Amazon forest.10

Such minor failures and resulting leakage have been frequently reported in other places, includ-ing Europe, Russia, and Canada (Alberta), indicating the significance of an efficient approach foroil spill detection in terrestrial areas.2

Produced water, released from underground geological processes, is one of the main by-products of oil and gas production/exploitation.11 This water may be in direct contact with thegeologic strata for many years and, as such, may have elevated concentrations of hydrocarbons.The chemical composition of the produced water ranges widely from very fresh, containing<1000 mg∕L total dissolved solid (TDS) fluid, to brine, which contains >35;000 mg∕LTDS.11 Its main constituents are salt, oil and grease, organic and inorganic chemicals, and naturalradioactive components. The high concentration of hydrocarbons increases soil salinity anddecreases soil fertility, which is extremely detrimental to the environment.12–14 The salt contentof produced water also varies, and may reach up to 10 times higher than that of sea water.Notably, an increase in salinity due to produced water contamination is dangerous for soil, veg-etation, and water resources depending on the chemical components, of the produced waterand the characteristics of the local environment.11 This highlights the significance of a cost/time efficient and technically acceptable approach to delineate soil areas affected by sucha contamination.

Geophysical approaches, which determine the electrical conductivity (EC) of subsurfacematerial, are promising for detecting contaminated soil regions.13 This is because they can pro-vide an approximation of TDS released from produced water. Some of the most accurate mea-surements of soil properties can be obtained using electromagnetic induction (EM) sensors. Inparticular, EM instruments determine the spatial variability of soil properties, wherein changesin the apparent electrical conductivity (ECa) are measured. Thus, the characteristics of differentsoil layers are determined based on variation in EC. Previous studies have demonstrated thatEM sensors characterize a possible relationship between TDS and EC.13 Specifically, EC is pro-portional to the amount of inorganic ions (e.g., sodium, chloride, and sulfate) dissolved in thewater.11 EM sensors measure the amount of ionic concentration within soils, wherein an increasein ions (e.g., salt) increases the EC.13 Accordingly, these observations are useful for detecting soilcontaminated by produced water because it contains sodium and chloride, which are two highlyconductive salts. EM sensors have been widely used in a variety of soil studies, including thoseresearching soil salinity,15 agricultural applications,16 soil texture and the geomorphologicalprocess,17 and soil water content.18 The numerous applications of EM sensors in soil studies

Mahdianpari et al.: Mapping land-based oil spills using high spatial resolution unmanned aerial vehicle. . .


are likely due to the time- and cost-efficiency of the technique,19 wherein large amounts ofgeoreferenced data are easily obtained relative to conventional soil sampling methods.20

Figure 1 shows the location of studies that have used EM survey data for measuring soilsalinity.20

Visual inspections by on-site technicians and aerial light aircraft/helicopters are among tradi-tional yet effective approaches for oil spill detection.3 However, the main limitations of thesetechniques are delayed detection, high costs, and human involvement due to the need forrepeated on-site investigations. Both the length and remote locations of pipelines further com-plicate on-site surveillance. In such cases, aerial and remotely controlled systems offer greatpotential for pipeline monitoring. Despite their numerous military applications for manyyears, the potential of unmanned aerial systems and unmanned aerial vehicles (UAVs) for civil-ian purposes have only been recently recognized.21 For example, pipeline surveillance usingUAV imagery has gained interest in recent years due to its high capacity for monitoring remoteareas. The main advantages of the currently operating small UAVs are their low complexity andcost, high flexibility in terms of flight path, and high temporal and spatial resolution. Conversely,there are some limitations of UAVs concerning the size of platforms for civilian applications aswell as the weight of onboard sensors. Furthermore, the UAV flights are restricted to specific freeflight areas, which further hinder the applications of such techniques for pipeline monitoring.21,22

Nevertheless, a variety of UAVs, including fixed wing planes, multirotor copters, and unmannedhelicopters,23 have been used for different applications, such as forest inventory and speciesclassification,24 thus demonstrating their potential for pipeline monitoring.

The type of sensors onboard UAVs is the parameter that has the greatest effect in determiningtype of information collected during each mission.21 The sensors can be broadly categorized asactive or passive. These are similar to sensors commonly used in remote sensing, wherein pas-sive sensors depend on solar radiation, whereas active sensors operate independent of the sun’sillumination. The former sensors are less affected by haze, smoke, and clouds compared withoptical remote sensing satellites, as they operate at a low altitude. Furthermore, optical and ther-mal infrared passive sensors are better accommodated for UAVs. This is because active sensorsrequire a power source for pulse radiation, which adds extra weight to the system and, as such,causes difficulties for the implementation of such a configuration. Notably, passive sensors aremore affordable and provide data that can be easily processed (e.g., visual interpretation).2 Themost commonly used optical sensors are digital cameras due to their low cost, light weight, andease of use. A standard digital camera operates in the red, green, and blue (RGB) bands.However, they can be modified to collect data in the desired band. Several studies havebeen carried out using imagery obtained from modified digital cameras mounted on UAVsin a variety of applications, such as species classification.24,25

When using digital cameras as UAV sensors, it should be noted that they are not purelycalibrated sensors. Furthermore, the collected images are presented in digital numbers(DNs). This indicates the importance of examining the image quality to ensure that accurate

Fig. 1 World map illustrating the location of studies that have used EM survey data (i.e., EM-38)for measuring soil salinity.



spectral information is obtained. Quantitative image quality assessments include the vignettingeffect and signal-to-noise ratio (SNR) measurements.23 Although these image quality assess-ments have been performed in previous studies of species classification,25 they have notbeen carried out for oil spill detection using UAV imagery.

Despite the numerous applications of UAV imagery for species classification24,25 and EMdata for characterizing soil properties,15,18 the efficiency of these data has not been investigatedfor terrestrial oil spill detection to the best of our knowledge. Given the lack of operationalmethodology for land-based oil spill monitoring using current remote sensing tools, this researchintroduces an innovative framework for terrestrial oil spill detection. In particular, the mainobjectives of this study were to: (1) produce a detailed land cover map using very high spatialresolution UAV imagery based on an object-based random forest (RF) algorithm (level-1 clas-sification), (2) detect traces of contaminated soil following soil stripping and vegetation mulch-ing (remediation work), (3) classify contaminated soil through the synergistic use of UAVimagery and EM data (level-2 classification), (4) identify the most important input featuresusing RF variable importance analysis for mapping contaminated soil, and (5) introduce an effi-cient method for mapping land-based oil spills using state-of-the-art remote sensing data andtechnologies.

2 Methods

2.1 Study Area and Pipeline Leakage

This study was conducted in Dixonville within the County of Northern Lights, located in westernAlberta, Canada. This region is characterized by a humid continental climate that experiencesrelatively warm summers and cold winters. The mean temperature in the area ranges from −14°C(December) to 18°C (July) and monthly precipitation ranges from 12 mm (November) to 80 mm(August). Figure 2 shows the geographic location of the study area.

A pipeline leakage event occurred on May 1, 2014, wherein produced water was the maincomponent of the spilled material. In this study, electromagnetic induction data of both EM-31and EM-38, as well UAV imagery, were collected after the oil spill. A detailed discussion of thesedata is presented in the following section.

2.2 Electromagnetic Induction and UAV Data

EM-31 was manufactured by Geonics Limited (Ontario, Canada) in 1977. It has an intercoilspacing of 3.66 m and a diploe transmitter and receiver. It operates at a frequency of9.8 kHz in either a horizontal or vertical dipole that measures soil properties at 3- and 6-m depthsof investigation (DOI).20 EM-38 is the most commonly used EM instrument for agriculturalapplications and was manufactured by Geonics Limited (Ontario, Canada) in 1980.26 It hasan intercoil spacing of 1 m and a diploe transmitter and receiver. It operates at a frequencyof 14.6 kHz in either a horizontal or vertical dipole that measures soil properties at DOIs of0.75 and 1.5 m, respectively.26

In situ EM data were collected after the oil spill on May 5, 2014, and also two years afterevent on June 26, 2016. In both experiments, EM-31 and EM-38 measurements were obtained ina vertical dipole at heights of 0.75 and 0.3 m above the ground surface, respectively. During datacollection, real-time ground positioning system (GPS) acquisition was employed; both conduc-tivity values and GPS locations were recorded every second. Both instruments were field cali-brated prior to data collection to minimize possible errors during data acquisition. Figure 2 showsthe traces of EM surveying for both dates.

In this study, the UAV platform was three-dimensional (3-D) Robotics X8-M with a weightof about 3.5 Kg (with battery) [Fig. 3(a)]. The flight control system was 3DR Pixhawk V2.4.5autopilot and it was also equipped with GPS and IMU to determine the location and flightposition. The imaging sensor used in this study was a modified X-Nite Canon SX280 camera(PowerShot) with an imaging resolution of 12.1 megapixels [Fig. 3(a)]. It originally had visiblebands but was modified by LDP LLC (MAXMAX) to obtain data in the full spectrum of UV,



visible, and near infrared (NIR) bands. Importantly, a standard digital camera has an NIR-light-blocking filter to omit NIR light for producing an RGB image. In contrast, the camera used inthis study had a UV/visible-light-blocking filter to omit wavelengths <780 nm (X-Nite780) toproduce an NIR image.27

Fig. 3 (a) The UAV platform and camera; sample photos in (b) NIR and (c) RGB compositions.

Fig. 2 UAV image (RGB), acquired on May 28, 2016, illustrating the geographic location of thestudy area with overlays of the EM surveying paths.



For UAV data collection, the flight was performed at an altitude of about 40 m above theground with a speed of 5 m∕s. Imagery was acquired with a spatial resolution of about 4 cm withthese flight settings. To produce a mosaicked image, a default overlap of 50% and sidelap of60% were used, respectively. Notably, a Magellan mobile mapper differential GPS was used toprovide positional data for all imagery. GPS data were projected onto UTM coordinates (zone11) using the NAD83 reference ellipsoid. Differential corrections were supplied by the wideangle augmentation system. This resulted in the submeter accuracy of the ground control points(GCPs) used for geometrical correction of the UAV imagery.

The UAV imagery was collected on May 28, 2016, 2 years after the 2014 event. A totalnumber of 86 RGB and 114 NIR images were collected. It is also worth noting that thestudy area had experienced remediation work (soil stripping and vegetation mulching) inMay to June 2016.

2.3 Proposed Method

The proposed method is shown in Fig. 4 and described in detail in the following sections. Briefly,the first step involves data preprocessing. In particular, the preprocessing of UAV imagery hadseveral steps, including image quality assessment, vignetting effects, image mosaicking, orthor-ectifying, and georeferencing. In contrast, the EM sensors were both field calibrated before datacollection and, thereby, these data were already prepared for analysis. We did, however, removeoutliers in these data to obtain conductivity values in a defined range. After the preprocessing

Fig. 4 The flowchart of the proposed method.



step, the land cover training samples were applied to an object-based RF algorithm in level-1classification. This classification map provided a detail distribution of all land cover classes inthis study. This step was followed by masking out all land cover classes, excluding the conta-minated soil class. The contaminated soil class was further divided into three subclasses to obtaina level-2 classification map representing different degrees of pollution. The training samples forthe level-2 classification were prepared using EM data. Different interpolation methods werealso used to produce EC maps using EM data. This is because EM sensors collect point-based observations of soil EC.

2.3.1 Image preprocessing

First, a visual evaluation of image quality, including identifying blurry effects and obliquescenes, was performed. These errors could be attributed to wind during data acquisition, resultingin vibration of the UAV platform. Imagery presenting such a defect was removed from the datastack. In particular, of 86 RGB and 114 NIR images, 5 RGB and 33 NIR images were removed,which resulted in a total number of 81 images that were subjected to further processing.

Atmospheric and vignetting effects also may cause radiometric distortion.26 As the UAVflown at a low altitude, the atmospheric effect was negligible. However, to examine the vignet-ting effects, the pixel values of the same polygon in different images were compared by meas-uring the distance between the plot and the image center. In particular, the same polygon couldappear in different locations, such as the center or edge of an image or any location between ina stack of imagery. In the case of a severe vignetting effect, an increase in the distance from theimage center causes a decrease in the pixel values of the polygon.26 The results of this analysisare shown in Fig. 5, using three polygons for three classes in five images. As seen, the pixelvalues are essentially unaffected by an increase in distance from the image center, illustrating

Fig. 5 Evaluating the vignetting effect by comparing the pixel values of the polygons in fiveimages.



a negligible vignetting effect in this study. Moreover, the images were mosaicked with an overlapof 50%, meaning that the pixel values of the same regions were averaged.26 This resulted ina further suppression of the vignetting effect25 and, as such, this effect did not influence theclassification results in this study.

SNR is a quantitative measurement for assessing image quality and has been recently per-formed in some related research.25 SNR was measured for homogeneous areas (i.e., purely uni-form areas with low standard deviations) selected in the UAV imagery. In particular, DN valueswithin homogeneous polygons in three different classes were averaged and divided by theirstandard deviation to examine the radiometric quality of the images at different spectralbands. Figure 6 shows the results of this analysis. As shown, the SNR values were>14.5 dB in all bands, which is in agreement with previous studies.25 In particular, SNR rangesbetween 14.5 and 20 dB in most cases; however, SNR values for the homogeneous water classare higher in the blue band and vary between 22 and 28 dB. This is due to the high reflectance ofwater in the blue band, whereas an increase in the wavelength (red and NIR) increases absorptionresulting in low SNR in this region. Notably, the tree class has a higher SNR value in the NIRband due to the high reflectance of green vegetation in the NIR region of the electromagneticspectrum.

The next step involved image mosaicking and georeferencing and was performed usingPix4Dmapper (Pix4D S.A., Lausanne, Switzerland). This is powerful software with the capabil-ity to process hundreds of UAV images and to produce georeferenced, orthomosaicked images.The general steps in Pix4Dmapper are: (1) calibrating the camera (alignment), (2) generatingpoint cloud and 3-D mesh, (3) generating the digital surface model (DSM), (4) rectifying theimage, and (5) producing the orthomosaicked image. This is basically an automatic procedure,wherein the calibration of the camera is the most challenging and important step. This is becauseany error due to misalignment of the images results in the poor quality of image mosaicking. Thepresence of some features, such as trees, intensifies this effect. This is because features mayappear differently in each image resulting in fewer points for image alignment. GPS informationfrom the camera and manual selection of points in the overlapped areas of the images can beuseful for addressing this problem. Particularly, for external and internal camera calibration,a number of matching points were required to perform a bundle adjustment. This producedthe absolute orientation of an entire block for a series of UAV images using several GCPs.The structure-from-motion algorithm was used to perform the bundle adjustment, whereinthe algorithm initiates with extracting matching points. These points were used to calibratethe camera and determine the sensor parameters for each image. Next, a DSM was constructedusing a dense point cloud. Each image was then orthorectified based on the sensor’s externalorientation and DSM. The orthorectified images were finally integrated into an orthomosaickedimage to produce the UAV orthophoto.

Fig. 6 SNR values calculated in homogeneous areas of the image in four spectral bands.



2.3.2 Level-1 classification: land cover mapping

Image classification was performed using an object-based image analysis (OBIA) framework.28

In this study, the object-based image classification was selected because it is superior to thepixel-based approach for classification of high and very-high spatial resolution images that con-tain a large amount of spatial and spectral information.28 Furthermore, several features, includingobject size and shape, and the hierarchical relation of neighboring pixels (e.g., geometrical, tex-tural, and contextual information) can be incorporated into the classification scheme using thisapproach.29 This is advantageous relative to the pixel-based approach, which only considers thespectral information of a single pixel.30 Accordingly, a great amount of information can beextracted for a given area, and the imagery is segmented into ecologically meaningful objects.

The OBIA was carried out using eCognition Developer 9 and has two main steps: imagesegmentation and classification. Multiresolution segmentation (MRS) is one of the most widelyused techniques for the analysis of high spatial resolution imagery.31,32 Scale, shape, and com-pactness were the three user-defined parameters used to execute MRS in eCognition software.There is no standard, widely accepted approach to specify the optimal segmentation parameters,although imagery with a high resolution and distinguishable ground objects are advantageous forthis purpose. In this study, the segmentation parameters were obtained based on (a) previoussimilar studies25,31,33 and (b) a trial-and-error procedure. Accordingly, the optimal values forshape and compactness were found to be 0.05 and 0.5, respectively. The shape parameter of0.05 emphasizes more spectrally homogeneous pixels rather than shape and the compactnessof 0.5 balances the compactness and smoothness of objects equally. Notably, the image segmen-tation was employed at two different scales: a coarse scale (100) to classify all presented landcover classes (i.e., level-1 classification) and a fine scale (10) to classify contaminated soil(i.e., level-2 classification). Scale values ranging from 5 to 200 were examined and valuesof 100 (level-1) and 10 (level-2) were found to be appropriate according to the visual analysisof the segmentation results and the level of detail required at each classification level. Otherparameters, including shape and compactness, remained constant for the two classificationlevels.

An ensemble of classifiers, namely random forest (RF), which is a nonparametric classifier,was selected for classification.34 RF is a sophisticated version of the decision tree algorithm,which employs a collection of classification and regression trees in the classificationscheme.35 Each tree is trained on a random subset of the input data, wherein the same samplemay be selected several times, although others may not be chosen at all. Approximately two-thirds (i.e., in-the-bag) of the samples are randomly selected to construct each tree, while theremaining one-third (i.e., the out-of-bag, OOB) is applied to assess the internal cross-validationaccuracy. The forest gradually grows to a user-defined number of trees that are produced withhigh variance and low bias.36 To classify an input vector in RF, a vector is assigned to each ofthe trees in the forest, followed by classification of each tree, called “giving a vote for eachclass.” Finally, a class with the maximum vote is assigned to the pixel of interest (inputvector).37,38

The main advantages of RF are that it is unaffected by noisy datasets and efficient whenmanipulating large numbers of input data.31 One of the great characteristics of RF is its capacityto measure the importance of each input variable based on both the Gini index and the meandecrease in accuracy.39 In the latter approach, which was selected in this study, RF removes thegiven variable while keeping the remaining variables constant, and it determines the decrease inthe accuracy using the OOB estimation.40 To normalize this index, the variable importance ofeach input variable (i.e., feature) is divided by its standard deviation.41 Finally, RF is easilyadjustable using only a few input parameters.31,32 In particular, the number of decision trees(Ntree) and the number of variables (Mtry) are two parameters that must be determined to employRF in eCognition software.42 In this study, the square root of the number of input variables (i.e.,default value) was selected for Mtry. However, the optimum number of trees was determined byexamining a wide range of values (Ntree: 1 to 400) and measuring the overall RF accuracies asa function of Ntree. As shown in Fig. 7, the overall accuracy significantly improves as the numberof trees increases to about 100, with accuracies remaining approximately constant beyond thispoint. Accordingly, a number of 100 trees was found to be optimal in this study.



A total of six distinct land cover classes were identified during in situ data collection. Theclasses are bare soil, contaminated soil, tree, herbaceous vegetation, water, and man-made path.Training and testing data for the purpose of supervised classification and accuracy assessment,respectively, were collected using visual interpretation of high-resolution UAVand Worldview2imagery, as well as notes recorded during field surveys. Thus, reference polygons representingsix land cover classes were produced. For each class, the reference polygons were sorted bysize and alternatively assigned to the training (∼50%) and testing samples (∼50%). This alter-native assignment resulted in both the training and testing samples having comparable pixelcounts for each class, and ensured a robust classification accuracy assessment. Figure 8shows the distribution of the training and the testing polygons for each land cover type acrossthe study region.

The training samples were used to train the RF classifier in level-1 classification, whereas thetesting samples were held back for validation purposes. Accordingly, a land cover map with sixdifferent classes was produced. All land cover classes were then masked out, excluding the

Fig. 7 Effect of the number of trees on the overall classification accuracy.

Fig. 8 Distribution of reference data (a) training and (b) testing polygons.



contaminated soil. This class was used for the final classification scheme to detect areas withdifferent degrees of contamination.

2.3.3 Level-2 classification: oil spill detection

The training data used for the classification of contaminated soil were identified using the EMsurvey measurements. The first EM surveying data, collected a few days after the event, rep-resented high ECa values for contaminated soil. In particular, contaminated soil presenteda higher ECa, varying between 10 and 70 dSm−1, whereas other classes (the masked area) unaf-fected by pollution presented lower ECa values (<10 dSm−1). Notably, the second EM surveywas carried out 2 years after the event and, importantly, following remediation in May andJune 2016.

Since our UAV data were collected 2 years after the event, we used the second EM surveyingdata (2016) for preparing training data for oil spill classification. This resulted in a minimumtime gap (<1 month) between the two observations and reduced the degree of uncertainty in thefinal classified map. Table 1 shows the training and testing pixels for different classes. In level-2classification, the thresholds for different classes were determined based on the plot of land,illustrating the center of spill and field notes, which were collected during the field survey.In particular, the center of the oil spill and its vicinity presented high EC values; however,the EC values decreased with increasing distance from the center of the spill.

For detecting contaminated soils, salinity indices were extracted and used in the level-2 clas-sification. This is because soils contaminated by produced water as a result of pipeline leakageexhibit high levels of salinity. Several soil salinity indices have been developed based on differ-ent bands of multispectral data. Particularly, these indices determine the amount of mineral salt insoils using the different responses of saline soils to multispectral bands. We also proposed a newsalinity index, the normalized salinity index (NSI), using blue and NIR bands (see Table 2, the15th feature). In particular, this index was developed based on the lowest and highest correlationof the NIR and blue bands, respectively, with the soil conductivity map. Table 2 shows all inputfeatures (original bands and salinity indices) used in the level-2 classification.

To evaluate the efficiency of salinity indices as an indicator of the salinity level, the corre-lation between different salinity indices and EM-31 and EM-38 surveying data was determined(Fig. 9). As mentioned earlier, an increase in the salt content within soil increases the EC of thelatter. As shown in Fig. 9, some indices, such as SI5 and NSI, produced a correlation of >0.6,confirming the suitability of these indices as indicators of the salinity levels.

Notably, both EM-31 and EM-38 data were used to generate the conductivity map. As shownin Fig. 2, EM data are point-based observations. To produce the conductivity map using thesepoint-based observations, different interpolation methods, including ordinary kriging, empiricalBayesian kriging (EBK), inverse distance weighting (IDW), radial basis function (RBF), andlocal polynomial interpolation (LPI), were employed in this study.

Table 1 Testing and training pixel counts for oil spill classification (level-2).

Class EM surveying Class description #Training pixels #Testing pixels

Low EM-31 Points with EC less than 55mS∕m 88 87

EM-38 Points with EC less than 20 mS∕m 25 21

Moderate EM-31 Points with EC between 55 and 65 mS∕m 234 264

EM-38 Points with EC between 20 and 40 mS∕m 188 198

High EM-31 Points with EC greater than 65 mS∕m 171 160

EM-38 Points with EC greater than 40 mS∕m 237 228

Total 943 958



Ordinary kriging is one of the most well-known kriging interpolation methods for spatialdata, wherein the error variance is minimized. It uses a semivariogram to estimate values fora region based on the observation of single points. In particular, the mean in a given neighbor-hood is evaluated using the local second-order stationarity.48 Empirical Bayesian kriging is aninterpolation approach that considers errors to determine the semivariogram through repeatedsimulation. This contrasts with other kriging approaches, which produce a single semivariogramfrom known points to make a prediction. In particular, these latter approaches do not consider theuncertainty within semivariograms.

IDW interpolator is based on the assumption that objects close together are more similar toeach other compared with those that are farther away. For interpolation, IDW uses known values

Fig. 9 The correlation between various salinity indices and EM-31 and EM-38 survey data.

Table 2 An overview of extracted features for level-2 classification.

Salinity index Formula References # feature

Blue (UAV band) B N/A 1

Green (UAV band) G N/A 2

Red (UAV band) R N/A 3

NIR (UAV band) NIR N/A 4

Normalized difference salinity index NDSI ¼ R−NIRRþNIR 43 5

Salinity index-1 SI1 ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðG × RÞp44 6

Salinity index-2 SI2 ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðB × RÞp44 7

Salinity index-3 SI3 ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðG2 þ R2Þ

p45 8

Salinity index-4 SI4 ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðG2 þ R2 þ NIR2Þ

p45 9

Salinity index-5 SI5 ¼ BR 46 10

Salinity index-6 SI6 ¼ B−RBþR 46 11

Salinity index-7 SI7 ¼ G×RB 46 12

Salinity index-8 SI8 ¼ B×RG 47 13

Salinity index-9 SI9 ¼ NIR×RG 47 14

Normalized salinity index NSI ¼ B−NIRBþNIR This study 15



to predict the values in a given neighborhood. The weights are proportional to the inverse of thedistance, wherein higher weights are assigned to the nearest objects. Accordingly, the weightsdecrease as the distance increases from a given point.48

RBF is an exact interpolator, wherein the surface should pass through each individual sam-ple. This is a less flexible approach relative to kriging methods and does not consider the auto-correlation of the data. There are five functions that determine the shape of the final interpolationsurface. An RBF is a function that changes with distance from a location. Finally, LPI is a quick/inexact interpolator, wherein the surface is a best fit to the data. Particularly, it fits severalspecified order polynomials (e.g., first and second) using points within a given overlappingneighborhood. The searching neighborhoods are defined using several parameters, such assize and shape, as well as the number of neighbors. This interpolator is useful for data withlittle variation.49

3 Results and Discussion

3.1 Land Cover Classification

Figure 10 shows the level-1 classification map covering the whole study region. As seen, theclassified map illustrates a detailed spatial distribution of all land cover classes. The classifiedmap is a noiseless and accurate representation of ground targets. For example, the presence ofsparse herbaceous vegetation within soil and water is well represented. Figure 11 shows theproducer’s and user’s accuracies for the level-1 classification. As shown in Fig. 11, waterand herbaceous vegetations have the highest and lowest accuracies, respectively. Precisely,all land cover classes, excluding herbaceous vegetation, have producer’s and user’s accuracies>75%. Notably, the contaminated soil class has accuracies >80%, representing a large numberof pixels that were correctly identified in this category.

Fig. 10 (a) original UAV image and (b) the classified map produced by object-based RF classi-fication. An overall accuracy and Kappa coefficient of 91.27% and 0.86, respectively, wereobtained.



Fig. 11 The producer’s and user’s accuracies for different land cover classes in this study (i.e., thelevel-1 classification).

Table 3 Comparing different interpolation methods for EM-31 and EM-38 data for both dates.

EM surveying # samples Methods Mean error RMSE

EM-31(2014) 5816 OK 0.0028 4.4217

EBK 0.0098 4.2799

IDW 0.0439 5.3367

RBF 0.0519 5.8759

LPI 0. 173 9.3868

EM-38(2014) 5079 OK 0.0146 6.0371

EBK 0.0103 6.2968

IDW 0.0229 10.5434

RBF 0.0234 10.6560

LPI 0.1257 9.8928

EM-31(2016) 2848 OK 0.0057 1.0578

EBK 0.0019 1.1041

IDW 0.0370 1.3040

RBF 0.0151 1.5345

LPI 0.0286 1.2638

EM-38(2016) 2110 OK 0.0099 1.9415

EBK 0.0318 2.9029

IDW 0.1513 3.1332

RBF 0.1144 3.9341

LPI 0.1063 3.0803

Note: OK, ordinary kriging; EBK, empirical Bayesian kriging; IDW, inverse distance weighting; RBF, radialbasis function; LPI, local polynomial interpolation



Figure 10 shows a large portion of the study area is covered by either herbaceous vegetationor trees. These classes are at risk of contamination, which indicates the significance of oil spillmonitoring after field remediation to ensure complete removal of pollution. Using this land coverclassification map, all land cover classes, excluding contaminated soil, were masked out.Next, this unmasked class was further partitioned into subclasses with varying degrees ofcontamination.

3.2 Oil Spill Detection

Table 3 shows the mean errors and root mean squared errors (RMSE) obtained from differentinterpolation techniques when producing conductivity maps. As shown in Table 3, ordinarykriging and empirical Bayesian kriging performed better than other approaches in interpolationin all cases. However, the difference between the interpolation methods was higher for 2014 EMdata. Given the better results obtained from the kriging methods, the conductivity maps obtainedusing this method were used for visualization purposes and for defining the proposed salinityindex. Figure 12 shows the conductivity maps obtained from ordinary kriging for both dates.

As shown in Fig. 12, the 2014 conductivity maps are considerably more conductive than the2016 maps, suggesting a higher concentration of toxic materials within the study area in theearlier year. The 2014 maps illustrate the possible source of leakage, which is representedby a red polygon in the centers of the maps. Despite the remediation work, the 2016 conductivitymaps show that pollution remains present in this region.

Next, we applied different input features, including the original bands of UAV imagery andsalinity indices (see Table 2), to the RF classifier to produce a contaminated soil map (seeFig. 13). The contaminated soil map has three classes illustrating varying degrees of pollutionin the study area. This map is also in agreement with the conductivity maps of 2016. As seen, alarge portion of study area remains affected by moderate pollution. The confusion matrix for thelevel-2 classified map is shown in Table 4. The confusion matrix illustrates a large number ofpixels that were accurately classified with some degree of confusion between successionalclasses. For example, there are confusions between low and moderate and between moderateand high contaminated classes in some cases.

One advantage of RF is its capability to rank the importance of input features in the clas-sification scheme. However, previous studies showed that the RF variable ranking may change indifferent runs even when employing the same input feature and number of trees.39 In particular,Behnamian and colleagues (2017) suggested that to obtain the most stable variable ranking eithera very large number of trees (e.g., 10,000) or an average variable importance calculated frommultiple runs of RF should be employed. In this study, the latter strategy was carried out due toits time efficiency to obtain the most stable RF variable ranking. Figure 14 shows the averagevariable importance obtained from 30 model runs using the same input variables and Ntree. Asshown in Fig. 14, SI5

46 and NSI (proposed in this study) indices were ranked as the two mostimportant features for level-2 classification.

Fig. 12 The conductivity maps obtained from ordinary kriging interpolation for EM-31 and EM-38survey data for 2014 and 2016.



4 Conclusion

The main purposes of pipeline monitoring are to detect and control leakage, to reduce itsdestructive consequences on surrounding environments, and to maintain pipeline function.An efficient approach for prompt leakage detection in emergency conditions should be accu-rate and easy to operate. EM sensors measure the spatial variability of soil properties and they

Table 4 Confusion matrix for the level-2 classification. An overall accuracy of 77.04% and Kappacoefficient of 0.61 were obtained.

Classified data

Low Moderate High Tot. Prod. acc.

Reference data Low 73 28 7 108 67.59

Moderate 31 369 62 462 79.87

High 8 84 296 388 76.29

Tot. 112 481 365 958

User. Acc. 65.18 76.72 81.10

Fig. 13 Contaminated soil map with three classes illustrating different degrees of pollutions. Anoverall accuracy of 77.04% and Kappa coefficient of 0.61 were obtained.



are sensitive to various soil characteristics (e.g., water content and salinity). Despite their accu-rate measurement of soil properties, these types of observations are hindered by lengthy sam-pling rates, making them inefficient for mapping large areas. A practical approach to improvethe efficiency of algorithms for pipeline leakage can be obtained from enhancing the type ofinformation. In this study, we proposed an effective approach for improving the quality andquantity of data by integrating UAV and EM surveying observations to produce a soil con-tamination map. In particular, we proposed a hierarchical object-based random forest algo-rithm for classification at two levels.

Several recent studies have reported the importance of preprocessing steps, such as thevignetting effect and signal-to-noise ratio, for species classification using UAV imagery. Inthis study, we evaluated these effects to ensure the accuracy of information content obtainedfrom UAV data. Next, a land cover classification map, with an overall accuracy of about91%, was produced using very high-resolution UAV imagery. This classified map provideda detailed spatial distribution of different species, which is of great significance for con-servation plans in this region. In level-2 classification, all land cover classes distinguishedin the level-1 classification were masked out, excluding the contaminated soil class. Thisclass was further partitioned into three subclasses representing varying degrees of pollution.An overall accuracy of about 77% was achieved for the contaminated soil map using theproposed method. The variable importance of RF found that the SI5 index and the NSIindex proposed in this study are the most important features for mapping contaminated soil.

The results of this study demonstrate that, despite field remediation work, pollution frompipeline leakage remains present in the study area. Much effort is needed to remove toxic mate-rials, which affect species distribution and development in areas of contaminated soil. The resultsof this study represent the first investigation toward a site-specific tool using state-of-the-art datafor pipeline leakage that builds upon the synergistic use of high-resolution UAV imagery and EMsurveying data.

Acknowledgments

This project was undertaken with the financial support of Natural Sciences and EngineeringResearch Council of Canada (NSERC) under a Discovery Grant to B. Salehi (NSERCRGPIN-2015-05027) and the Research & Development Corporation of Newfoundland andLabrador Grant to M. Mahdianpari (RDC 5404-2108-101). The data used in this study wereprovided by AKS Geoscience Inc. The authors thank these organizations for the generous finan-cial support and providing such valuable datasets. Also, the authors would like to thank theanonymous reviewers for their helpful comments and suggestions.

Fig. 14 Variable importance ranking obtained from 30 model runs.



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Masoud Mahdianpari is a PhD candidate in electrical engineering at Memorial University ofNewfoundland and a research assistant at C-CORE. He won the Research and DevelopmentCorporation Ocean Industries Student Awards in 2016. He also won The Com-Adv DevicesInc. Scholarship for innovation, creativity, and entrepreneurship in 2017. He received the arti-ficial intelligence for Earth grant organized by Microsoft in 2018. His research interests includeremote sensing and environmental hazard monitoring, machine learning, geo big data, and deeplearning.

Bahram Salehi received his BSc degree in geomatics engineering from the University ofTehran, Iran, in 2001, and his MSc degree in photogrammetry and remote sensing fromK. N. Toosi University of Technology, Tehran, Iran, in 2005. He received his PhD in geomaticsengineering–remote sensing from the University of New Brunswick in 2012. Currently, he isa senior remote sensing engineer with C-CORE and a cross-appointed professor in Faculty ofEngineering and Applied Science at Memorial University of Newfoundland.

Fariba Mohammadimanesh received her BS and MSc degrees in remote sensing from theSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran,Iran, in 2010 and 2014, respectively. Currently, she is working toward a PhD in electrical engi-neering at Memorial University of Newfoundland, Canada. She received the Emera GraduateScholarship in 2017 and 2018 at Memorial University. Her research interests include radarremote sensing, including SAR, InSAR, and PolSAR image processing for environmentalapplications.

Glen Larsen is the director of remote sensing at AKS Geoscience, Calgary, Canada. He spe-cializes in earth surface evaluation through the use of multispectral imagery as well as theapplication of shallow ground imaging techniques, such as ground penetrating radar, electricalresistivity tomography, refraction, and electromagnetics.

Derek R. Peddle is professor of geography and ATIC director at University of Lethbridge,Canada. His remote sensing research involves software development applications in environ-mental change. He received Fulbright Awards from the University California Santa Barbaraand NASA GSFC. He is a past-president in Canadian Remote Sensing Society; associate editorfor the Canadian Journal of Remote Sensing; and CSRS Conference Chair. He received his PhD(Waterloo), MSc (Calgary), and B.Sc.H (Memorial) degrees in geography/computer science, anda NASA graduate diploma.



https://doi.org/10.1080/00103620802432717

https://doi.org/10.1371/journal.pone.0054660

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