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22-26. Advancing methods for the systematic regional surveillance of landslides using aerial and satellite imagery

Systematic surveillance of landslide movement is critical for hazard assessments, but methodological best practices and fundamental limitations of large spatial and temporal scale systems using remotely sensed data are poorly constrained. Designing and evaluating a versatile system supports reducing losses by improving monitoring capabilities and hazards assessments. 

Description of the Research Opportunity

The location, timing, and rate of deformation or failure velocity of landslides often play a key role in landslide hazard assessments (e.g., Guzzetti et al., 1999; Crozier and Glade, 2005; Hermmans et al., 2013). Despite its importance, systematic detection of landslide occurrence and surveillance of landslide movement using aerial or satellite imagery is not conducted over broad scales by USGS landslide scientists due to challenging and variable climatic and geographic conditions, varying data availability, and lack of a unifying processing framework. Furthermore, methodological best practices and fundamental limitations related to large spatial and temporal scale systematic landslide surveillance systems are poorly constrained. However, the need for routine surveillance is expected to continue to grow with changing climatic conditions that lead to permafrost degradation and glacial retreat, changes in the spatiotemporal patterns of precipitation, and the expected changes in frequency and location of extreme weather events such as hurricanes and atmospheric rivers, all of which influence landslide frequency and magnitude.  

We seek a Mendenhall Postdoctoral Fellow to assist with designing and evaluating a versatile surveillance system for landslide detection to support temporally dynamic landslide hazard assessments at regional scales using aerial or satellite remote sensing data. The successful candidate can leverage existing databases, such as landslide inventories, high-resolution satellite imagery, climate, vegetation, geology, rainfall, hydrology, and soils information to advance operational systems in a variety of climates and conditions. Primary data sources could include multispectral or hyperspectral imagery (e.g., Planet, Maxar, Landsat, MODIS, Sentinel-2, AVIRIS) and/or radar (e.g., Sentinel-1, ALOS-2, upcoming NISAR mission). Emphasis should also be placed on linking landslide occurrence information to satellite-derived and ground-calibrated measurements of the environmental conditions that contribute to landslide initiation, movement, and catastrophic failure. For example, quantifying the rate of temporal patterns of displacement of unstable slopes may allow for a better understanding of how slope stability conditions change over space and time due to climate variability and other transient environmental conditions. Finally, systematic analysis of measurement uncertainty, computational expenses, and person-hours must be considered in evaluating the applicability and sustainability of the approach for longer-term routine surveillance at regional scales. 

The fellowship research could, for example, work to advance previous methods for landslide detection and monitoring. Another possible area of research is improving processing pipelines that infer quantities required for hazard assessment from remote data streams (e.g., landslide magnitude or mobility from measurements of deformation). A final research area might evaluate the strengths and limitations of surveillance methodologies (e.g., what types of movement are reliably detected, what are the error characteristics of detected landslides). Some examples of questions motivating this research opportunity could include: 

  • How can regional-scale landslide change detection efficiency be improved? 

  • What local-scale processes and phenomena might not be captured in regional aerial and satellite analyses? 

  • What fraction of substantial mass movements have no precursory deformation? 

  • How can various landslide change detection or deformation techniques be integrated to produce more complete monitoring systems? 

A successful applicant should have background and interest in some of the following technical domains: open-source workflow and software development, aerial or satellite remote sensing, radar imaging (e.g., using synthetic aperture radar), change detection methods, landslide science. Experience with image processing software (e.g., GAMMA, ENVI) and open-source code development (e.g., Python) will benefit applicants. The fellow will perform research activities in collaboration with landslide researchers at the USGS Geologic Hazards Science Center (GHSC) or Alaska Volcano Observatory (AVO), who will provide the fellow with education and guidance on the physical science behind landslide processes.   

Interested applicants are strongly encouraged to contact the Research Advisors early in the application process to discuss project ideas.



Crozier, M.J. and Glade, T., 2005, Landslide hazard and risk: issues, concepts and approach. Landslide Hazard and Risk, Wiley, Chichester, p. 1-40. 

Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P., 1999, Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1-4), p. 181-216. 

Hermanns, R.L., Oppikofer, T., Anda, E., Blikra, L.H., Bohme, M., Bunkholt, H., Crosta, G.B., Dahle, H., Devoli, G., Fischer, L. and Jaboyedoff, M., 2013, Hazard and risk classification for large unstable rock slopes in Norway: Italian Journal of Engineering Geology and Environment, p.245-254. 


Proposed Duty Station(s)

Golden, Colorado

Anchorage, Alaska 


Areas of PhD

Geology, geophysics, computer science, engineering, or related fields (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered). 



Applicants must meet the qualifications for Research Geologist, Research Geophysicist, Research Civil Engineer, Research Computer Engineer, Research Computer Scientist, Research Geodesist 

(This type of research is performed by those who have backgrounds for the occupations stated above.  However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)