Two extreme rain events over the Atacama Desert and their consequences on the surface: Insights from Change Vector Analysis applied to Landsat 8 OLI imagery

In March of 2015 there was probably the most studied rain event that ever occurred in the Atacama Desert. Three days of heavy rain impacted the southern region, with peak amounts of 85 mm locally. Different approaches have been used to study this event, including field observations, isotopic analysis and examination of InSAR data. During February of 2019 there was another rain event in the northern Atacama Desert, during which over 160 mm of rain fell on the eastern part of the Atacama, and the influence on the surface is still unknown. This study examines both events. The two study areas have different relationships to the rain: the 2015 event is analyzed within the area in which it rained, whereas the 2019 study area is 60 km away from the heavy rain, connected by surface water drainage. Results of particular interest are the variable responses of the different types of surface materials (e.g., varying classes of terrain roughness and mineralogy) and the identification of locations of erosion and deposition.

We examine multispectral satellite imagery from the Landsat 8 satellite, an approach that has some advantages over other methods. Advantages include its free access, a longer historical record that may allow examination of more events, and the existence of observations at multiple wavelengths which allows evaluation of mineral phase changes due to the rain, vegetation increment and changes in the type of material.

In this work we apply Change Vector Analysis (CVA) (Bruzzone and Fernandez, 2000) to Landsat 8 OLI images to, first, validate the multispectral satellite CVA results using as ground truth the InSAR permanent coherence loss from the 2015 event. Then we apply the method to identify changes due to the 2019 rain event. We compared these results to our field observations.

Our results indicate that: 1) CVA applied to Landsat bracketing the 2015 rain event identifies the depositional and erosional areas, correlating well to permanent changes detected by InSAR coherence loss. 2) Surface materials react variably, and some categories of materials changed more due to a rain than others. 3) Spectral analysis and CVA do not detect mineralogic phase responses documented by surface data. 4) Wind driven changes were also detected in some areas. 5) Field observations reveal that erosion and deposition are always well identified by the algorithm as long as the extent of change is larger than the pixel size. 6) The distribution of changes is dependent on surface slope.

Reference

Lorenzo Bruzzone and Diego Fernández Prieto. Automatic Analysis of the Difference Image for Unsupervised Change Detection. Technical Report 3, 2000. DOI: 10.1109/36.843009