Understanding how plants mitigate water-stress through landscape growth pattern has implications for predicting complex landscape response to climate change. This is particularly true in water-limited regions of the US and other countries. My research will use airborne sensors and computer simulation to explore this topic.

This research will comprise my thesis for my M.Sc. in Environmental Informatics.

The ordered growth patterns of woody vegetation in water-limited landscapes is theorized to be the result of plant-to-plant mechanisms, and is demonstrated to be more rain-efficient than random distribution. I plan to create fine-scale maps of real plant distributions using hyper-spectral and photographic airborne imaging of semi-arid study sites in New Mexico and Arizona. I will encode existing theories of individual-scale plant growth mechanisms into individual-based computer simulation models. I will test and refine those theories by comparing their abilities to reproduce the aerially-observed plant distribution patterns.

I will think of a more concise and coherent title for my project.

Close coupling of remote sensing and modeling to explore individual-scale mechanism using real-world data is rare, and to my knowledge has not been attempted in these climate-sensitive and highly dynamic systems. Integrating modern data-gathering tools with emerging theories and methodologies in ecology and systems dynamics offers new ways understanding critical ecosystem functions.