Tree species mapping around reclaimed oil and gas wells sites using hyperspectral and Light Detection and Ranging (LiDAR) remote sensing

Abstract

Oil and gas activities in Alberta require disturbing forested lands, among other ecosystems, in order to extract resources. Due to the number of oil and gas sites requiring reclamation, monitoring can be problematic. Remote sensing provides cost-effective, timely, and repeatable data of these areas in support of monitoring efforts. Support Vector Machine (SVM) and Multiple Endmember Spectral Mixture Analysis (MESMA) were tested in order to identify tree species around reclaimed and abandoned well sites near Cold Lake, Alberta using CHRIS satellite imagery with and without airborne LiDAR data. A hierarchical classification approach was employed, which achieved an accuracy of 83.4 % when using SVM together with CHRIS imagery and LiDAR. This positive result indicates the ability of remote sensing to support reclamation management and monitoring objectives within Alberta’s forested areas.