IN011-05
Automated Contrail Detection on Terra MODIS Imagery
Automated Contrail Detection on Terra MODIS Imagery
Tuesday, 8 December 2020: 19:12
Virtual
Abstract:
Contrails are vapor condensation trails created by the engine exhausts of aircrafts which can persist in the troposphere from minutes to several hours. Recent estimates suggest that approximately 50% of global warming by aviation emissions is caused by these artificial clouds and if left unchecked, they may contribute up to 15% of all anthropogenic warming in the next three decades. Hence, detecting contrails in satellite imagery is required for large-scale climate-related analyses. However, manually identifying contrails is extremely time consuming and impractical. Therefore, automated contrail detection is of importance.
In this work, an algorithm for automatic detection of contrails in the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B data is presented. The algorithm, a form of unsupervised detection, implements a combination of thresholding, phase congruency, and Hough transforms to detect contrails in Brightness Temperature Difference (BTD) images created by subtracting Band 30 (11 um) from Band 31 (12 um) of the Terra satellite. Long, solid, and thin contrails are detected most easily with this algorithm. Due to the uneven nature of contrails however, some may be curved, diffused, or broken. Such contrails are not detected. This work presents a modular algorithm which can enable rapid automatic detection of most contrails on satellite imagery.
In this work, an algorithm for automatic detection of contrails in the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B data is presented. The algorithm, a form of unsupervised detection, implements a combination of thresholding, phase congruency, and Hough transforms to detect contrails in Brightness Temperature Difference (BTD) images created by subtracting Band 30 (11 um) from Band 31 (12 um) of the Terra satellite. Long, solid, and thin contrails are detected most easily with this algorithm. Due to the uneven nature of contrails however, some may be curved, diffused, or broken. Such contrails are not detected. This work presents a modular algorithm which can enable rapid automatic detection of most contrails on satellite imagery.