Project Title: Quantitative Correlations Between Remote Sensing and Surficial Geologic Data, Mojave Desert Region
Mendenhall Fellow: Sarah E. Robinson, (928) 556-7061, firstname.lastname@example.org
Duty Station: Flagstaff, AZ
Start Date: November 3, 2002
Education: Ph.D. (Geological Sciences), Arizona State University, 2002
Research Advisors: David Miller, (650) 329-4923, email@example.com; Pat Chavez, (928) 556-7221, firstname.lastname@example.org
Project Description: Understanding the distribution of biotic and abiotic material in the desert is the foundation for studying the influences of climate change on the landscape, identifying anthropogenic effects, predicting endangered species habitat and understanding geomorphic change in the landscape; without this basic material distribution as a baseline, evaluating these changes is impossible. The challenge of understanding this distribution is the varied spatial and temporal scales over which the relationships that control these patterns operate. This project focuses on quantifying the pattern of biotic and abiotic material in the desert at multiple spatial scales by combining detailed field studies with multiple remote sensing datasets. Remote sensing data from the millimeter to the tens of meters scale allows us to integrate point location observations into regional patterns and apply these derived relationships to places where we don’t have rich field datasets.
This project uses imagery at three spatial scales to tackle different patterns and processes in the landscape: vegetation relationships with geology, distribution of indicator soil layers, and mixing of eolian and fluvial sediments.
High resolution studies of vegetation and geology relationships: We have collected high resolution imagery in the visible and near infrared regions of the electromagnetic spectrum of field plots in the Mojave Desert where physical and biological processes have been intensely studied and mapped. The visible imagery was collected using a balloon platform that resulted in millimeter scale visible wavelength information of the surface; the color infrared imagery was collected from a helicopter platform resulting in centimeter scale information in the near infrared wavelength region. By exploiting the vegetation sensitivity in the near infrared imagery we can produce maps of vegetation cover that can be correlated with the geologic information collected from the same areas.
Medium scale resolution studies of indicator soil layers: As geomorphic surfaces age they undergo well documented soil development that alters the material properties of the deposit affecting infiltration, runoff, and nutrient content. A common feature of older soils is the accumulation of dust that forms a discrete layer in the soil horizon at or near the surface. This layer is often used as an indicator for age of geomorphic unit and has a significant effect on the material properties of the deposit. The ability to identify the location of such a layer and how its distribution varies with local climate, rock type and hydrology aides in understanding the local hydrology and vegetation patterns.
Large scale resolution mixing processes: Geologic maps provide crucial information about the distribution of material in the landscape, but often have difficulty representing materials in mixed zones. Of particular importance are the regions where varying amounts of wind-blown material mixes with fluvial material strongly influencing changes the soil properties and vegetation patterns. Remote sensing provides a tool for “unmixing” these mixed zones into their basic components and quantifying the amount of each endmember present. Because the eolian and fluvial materials often have different compositions, thermal wavelengths, which are sensitive to rock composition, are an ideal dataset for this project.
In addition to the varying spatial scales, remote sensing can also be used to address the temporal scale of piedmont deposition. Cosmogenic nuclides provide numerical ages on piedmont deposits that are difficult or impossible to date with other techniques. However, cosmogenic nuclide dating is time consuming and expensive, therefore prohibitive to do over a large region. However, by linking the quantitative cosmogenic information of specific locations to the signatures in the remote sensing image, a broader, regional pattern of surface ages may be feasible.
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