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The capability to monitor natural phenomena using mobile sensing is a benefit to the Earth science community, given the potentially large impact that humans have on naturally occurring processes. Such phenomena can be readily monitored using networks of mobile sensor nodes that are tasked to regions of interest by scientists. In our article, we hone in on a very specific domain, elevation changes in glacial surfaces, to demonstrate a concept applicable to any spatially distributed phenomena (e.g., temperature or humidity). Our article leverages the sensing of a vision-based odometry system and the design of robotic surveying navigation rules to reconstruct scientific areas of interest, with the goal of monitoring elevation changes in glacial regions. The reconstruction methodology presented makes use of Gaussian process (GP) regression to combine sparse visual landmarks extracted from the glacial scenery into a dense topographic map. Further, this method allows for the natural inclusion of a priori terrain knowledge, such as existing digital elevation models. Results from this system are presented from a three-dimensional (3-D) glacial simulation modeled after actual field trials on Alaskan glaciers. Additionally, we introduce a theory behind spatial coverage, in the context of sampling, as achieved by an intelligently navigating agent. Finally, we validate the output from our methodology and provide results and show that the reconstructed terrain error complies with acceptable mapping standards found in the scientific community.