LAUSR

Abstract. Natural oil and gas are important sources of energy worldwide and their exploration and exploitation have significantly increased due to the global demand. The transportation of these valuable resources greatly depends on pipelines; however, pipeline leakages have huge economic and environmental impacts warranting an effective operational methodology for pipeline monitoring. We proposed a method for mapping soil contamination due to pipeline leakage in Dixonville, Alberta, Canada. In particular, very high-resolution unmanned aerial vehicle (UAV) imagery and electromagnetic induction (EM) surveying data were analyzed using a hierarchical object-based random forest (RF) algorithm. In level-1 classification, a land cover map was produced using UAV data. Next, all land cover classes, excluding contaminated soil, were masked out. In level-2 classification, the contaminated soil class was further partitioned into three subclasses representing varying degrees of contamination. Specifically, we proposed a 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 of about 77% was achieved for level-2 classification using the proposed method. The results demonstrate that the synergistic use of high spatial resolution UAV imagery and EM data is very promising for detecting soil contamination and examining ecosystem disturbance due to pipeline leakage.