Monitoring Linear Disturbance Footprint Based on Dense Time Series Landsat Imagery

Abstract

Mapping linear disturbances, including pipelines, roads, and seismic lines created by resource exploration, traditionally relies on very high-resolution remote sensing data, which usually limits results to small operational areas. With increased availability of low-cost medium-resolution satellite data, complete information of linear disturbances may be monitored and reconstructed from processing time series images from more than 30 years archival data. In this study, we propose a novel approach to incorporate spectral, spatial, and temporal information for mapping and characterizing linear disturbances based on time series Landsat imagery. The mapping process involves 4 steps: line detection based on a multiscale directional template, line updating based on reappearance frequency, line connection using the Hough transform, and linear disturbance characterization. The proposed method was tested and evaluated over 4 sites in Alberta, Canada, with various linear densities for detecting and reconstructing linear disturbances from 1984–2013 using time series Landsat imagery. The results obtained by processing time series Landsat imagery have shown improved accuracy in detecting linear disturbances over that from single or multiple Landsat images. It is concluded that the strategy of integrating information from time series imagery has the potential to lead to improved operational mapping of linear disturbances.