Mendenhall Research Fellowship Program

S34. Research and development of innovative land change science techniques and applications

 

Closing Date: May 28, 2019

This Research Opportunity will be filled depending on the availability of funds. All application materials must be submitted through USAJobs by 11:59 pm, US Eastern Standard Time, on the closing date.

How to Apply

Apply Here

Growing demands for temporally-specific land cover and land change are fueling a new generation of maps and statistics that contribute to understanding geographic and temporal patterns across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely information for land managers. Land change science has emerged as the foundation for understanding the global environment and enabling sustainable resource management for the benefit of all. Land change science seeks to understand the interactions between people and nature that lead to changes in the type, magnitude, condition, and location of land use and land cover (LULC). Furthermore, it is well established that LULC forcing and feedbacks play an important role in the planet’s climate system. 

To support our core mission, understanding a changing Earth, the USGS Earth Resources Observation and Science (EROS) Center has initiated the Land Change Monitoring, Assessment, and Projection (LCMAP) project that is generating annual LULC and land change information (regarding changes in land use, cover, and condition) and the assessment of those changes on environmental systems.  The LCMAP land cover mapping and land change monitoring goals are to create a suite of annual land cover and land change maps and statistics, provide a capability to detect and characterize historical land change at any point from the 1985-present Landsat record, and enable the detection of land change in near real time.

The initial techniques for producing integrated land change and land cover data in LCMAP are based on the Continuous Change Detection and Classification (CCDC) algorithm developed by Zhu and Woodcock (2014), and a time series of satellite imagery consisting of all available cloud- and shadow-free pixels in the USGS Landsat Analysis Ready Data archive. While the current techniques for detecting land change and producing integrated change and cover geospatial monitoring products are fairly robust over most of the variability of the U.S. land surface, there are opportunities to test and implement additional change detection algorithms, improve screening of clouds and other noise, improve timeliness of land change detection, integrate other satellite data sources into source data streams (e.g. Sentinel-2), and improve the consistency of change detection algorithms across space and time.

Research under this opportunity is focused on further development and improvement of the change detection algorithm and novel applications for results. To that end, we seek proposals focusing on one or both of the following areas:

1. Improving change detection methods

There are several areas to research improvement in change detection algorithms. While the Landsat wavelength bands used in CCDC are relatively consistent across Landsat 4-8, other possible data sources (including Sentinel-2 and Landsat 1-3) have different bands available, complicating their incorporation into CCDC. Methods for improving dealing with inconsistent data sources is one area for research, another is improving change detection in arid climates where weather events (e.g. droughts or irregular precipitation) affect surface reflectance and can under some circumstances produce detections of change.  The degree of consistency of change detection between different events is not clear and needs a more robust approach. In addition, a near-real-time (with a lag of weeks to months) change detection capability is desired, requiring development and improvement of the CCDC algorithm or investigation of different algorithms that is computationally tractable for continuous calculation in the forward stream. These topical areas are all potential targets for research for improving change detection.

2. Applying results to Earth surface processes

The CCDC algorithm produces coefficients describing annual phenological signals for each Landsat wavelength band as well as a set of change detections.  Thematic land cover can be classified at multiple times throughout the temporal record. Innovative applications of these data are sought with particular emphasis on phenological cycles, conditional land change related to disturbances and/or hazards (for example, fires, floods, forest disease, etc.), or gradual change (for example, woody encroachment).  Combining CCDC output with other data (for example, climate, weather, soils, land ownership, conservation or economic data) as appropriate would be useful to demonstrate applicability of the CCDC data for a variety of applications.

Geographically, initial research should focus on the United States. Strong candidates are expected to have technical skills in remote sensing and data analysis. The postdoctoral fellow will have opportunities to collaborate with university partners focused on CCDC development as well as the LCMAP team at EROS. Tools to investigate the extensive Landsat archive are being developed at EROS to support various studies. This Mendenhall Research Fellowship is an excellent opportunity to participate with a multidisciplinary team in a large scale remote sensing project.

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

References

Zhu, Z., and C. E. Woodcock. 2014. "Continuous change detection and classification of land cover using all available Landsat data."  Remote Sensing of Environment 144:152-71. doi: 10.1016/j.rse.2014.01.011.

Proposed Duty Station: Sioux Falls, SD

Areas of PhD: Remote sensing, geography, ecology (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered).

Qualifications: Applicants must meet one of the following qualifications: Research GeographerResearch Physical Scientist, or Research Ecologist

(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.)

Human Resources Office Contact: Kellie Sierra, 303-236-9335, ksierra@usgs.gov

Apply Here

Contacts

Jesslyn Brown

Scientist
USGS
Phone: 605-594-6003

Kristi Sayler

Physical Scientist
EROS Center
Phone: 605-594-6058