S46. Innovative land change science techniques and applications

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 to generate LULC and land change information (regarding changes in land use, cover, and condition) and assess the effect of those changes on environmental systems.  The LCMAP land surface change monitoring goals are to create a suite of annual land cover and land surface change products and statistics, provide a capability to detect and characterize historical land change at any point in the available satellite record (1985-present), and enable rapid detection of land surface change.

The initial approach for producing integrated land change and land cover data in LCMAP is based on the Continuous Change Detection and Classification (CCDC) algorithm developed by Zhu and Woodcock (2014), which is an automated method developed to detect land surface change in a wide variety of environments. Initial input data is the USGS Landsat Analysis Ready Data archive.  Current opportunities for LCMAP research and development include improving the consistency of change detection algorithms across space and time, testing and implementing additional change detection algorithms, improving the screening of clouds and other noise, increasing the timeliness of land change detection, and integrating additional satellite data sources (e.g. Sentinel-2) that provide higher temporal frequency for detecting land surface change.

Research under this opportunity is focused on further development and improvement of the CCDC 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 of the LCMAP change detection algorithms that are targets for improvement or enhancement. Examples include:

• 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. The development of change detection methods with the potential to handle data sources with differing bands is a goal of LCMAP.
• Another LCMAP goal is improving the detection and characterization of change in arid climates where weather events (e.g. droughts or irregular precipitation) can have a large and sustained effect on surface reflectance, producing detections of change that can be highly sensitive to the CCDC parameters used.
• In current LCMAP products, the likelihood of change detection is affected by the frequency at which time series data is available. Improved characterization of the factors that affect the likelihood of change detection is desired, as well as the development of algorithms with the potential to improve the consistency of LCMAP results between events of similar magnitude.
• In addition, a rapid (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 are computationally tractable for continuous calculation in the forward stream.

2. Applying results to Earth surface processes

Innovative applications of LCMAP data are sought with 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). The CCDC algorithm produces coefficients describing annual phenological signals for each Landsat wavelength band as well as a set of change detections, and the utilization of these coefficients and other intermediate LCMAP results is an area with much potential for exploration and research. Combining LCMAP results with other data (for example, climate, weather, soils, land ownership, conservation or economic data) is also encouraged.

Geographically, initial research should focus on the United States. Strong candidates are expected to have technical skills applicable to 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 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. https://doi.org/10.1016/j.rse.2014.01.011.

Brown, J. F., H. J. Tollerud, C. P. Barber, Q. Zhou, J. L. Dwyer, J. E. Vogelmann, and others. 2019. "Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach."  Remote Sensing of Environment:111356. https://doi.org/10.1016/j.rse.2019.111356

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 Geographer, Research 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: Audrey Tsujita, 916-278-9395, atsujita@usgs.gov