Code
Detrital Low-Temperature Thermochronology
Open-source Python code associated with the Bayesian modeling of detrital low-temperature thermochronology data, as described in
Avdeev, B., N. A. Niemi, and M. K. Clark, 2011, Doing more with less: Bayesian estimation of erosion models with detrital thermochronometric data, Earth and Planetary Science Letters, v. 305, p. 385-395. Link to article
is available at the Google Code Repository under the BSD license. It is no longer maintained.
This repository also contains an open-source implementation of the apatite fission-track annealing model presented in Ketcham et al. [1999], and a tool for converting Ray Donelick's AFTSolve LA-ICP-MS files into QTQt format for use with Kerry Gallagher's track-length modeling software.
Support for the development of this code was provided by NSF grant EAR-0810067.
Detrital Zircon Geochronology
Matlab code associated with the Bayesian comparison of detrital zircon geochronology data, as described in
Tye, A. R., A. S. Wolf, N. A. Niemi, 2019, Bayesian population correlation: A probabilistic approach to inferring and comparing population distributions for detrital zircon ages, Chemical Geology, v. 518, p. 67-78, doi:10.1016/j.chemgeo.2019.03.039. Link to article
is available at GitHub.
Support for the development of this code was provided by NSF grants EAR-1151247 and EAR-1524304.
Historial Aerial Photography Search
Historical aerial photography can be useful for studying environmental change, geomorphology, and natural hazards research. In particular, large quantities of overlapping historical aerial stereo photography can be used to construct relatively high resolution (2 - 5 m) digital surface models (DSMs) for quantitatively measuring surface change due to land-use, geomorphic events, or faulting. The USGS EROS Data Center provides a portal to ~60 years of historical single frame stereo aerial photography from the USGS, BLM, USDA, and other governmental agencies (https://earthexplorer.usgs.gov/).
The search functions on the USGS Earth Explorer are straightforward and simple to use, however, parsing the results of a single frame aerial photography search can be challenging, particularly when the results run in to the 100s or 1000s of photographs. This script, written in the Julia language for use in Jupyter Lab, parses the output of an aerial photography search in Earth Explorer and returns useful summary data including: the code of each aerial flight project covering the search area, the number of photographs in that project within the search area, the scale of the photographs, whether or not high resolution scans are available, the average photo quality, and the aveage cloud cover, along with the IDs of each individual photo frame within the search area. Useful for prioritizing aerial photography searches and downloads when similar scales and timing of acquisition is critical.