Searching for wavelength changes in satellite images of dryland vegetation patterns

Searching for wavelength changes in satellite images of dryland vegetation patterns

Mathematics

Rhea Nadar

2025

Patterns in dryland vegetation were first officially reported in the 1940s when aerial images revealed strips of vegetation separated by bare soil in East Africa. These are suspected to be due to water scarcity being a limiting factor influencing vegetation. These large-scale patterns are not easily recognized from the ground; moreover, studying them is challenging due to harsh climates and political instability. A key characteristic of these patterns is their wavelength—the distance between stripes of vegetation, which are typically seen on sloped terrain. Mathematical models predict that the wavelength of these stripes should change over time as environmental conditions, such as precipitation levels affected by climate change, fluctuate. However, this phenomenon has yet to be observed through aerial or satellite imagery. This may be due to a lack of comprehensive studies or potential inaccuracies in the mathematical models. To address this, I have created a python algorithm which finds the dominant wavelength in satellite images given to it. Further automation of collecting the images from Google Earth and feeding them into this program would allow for quick verification of mathematical models and their predictions.

Mathematics, coding, modelling, python, Fourier