19-40. Land Change Monitoring, Assessment, and Projection (LCMAP): Improving the detection of land surface change using satellite time series data

Growing demands for temporally-specific land cover and land change information are fueling a new generation of maps and statistics that contribute to understanding geographic and temporal patterns across large regions, providing input into a wide range of environmental modeling studies, clarifying the drivers of change, and providing more timely information for water and 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 human activities and natural landscapes 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 recently released a suite of land change and land cover products and statistics from the Land Change Monitoring, Assessment, and Projection (LCMAP) program (Brown et al. 2020). The LCMAP products utilize the Landsat archive (beginning in 1982) to identify the date of land surface changes and are intended to be used for studies on topics such as 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). These products can support real-world decisions that help American farmers, ranchers, fire fighters, and water and land managers use natural resources effectively while protecting lives and livelihoods.

The initial techniques for producing integrated land change and land cover data used in LCMAP are based on the Continuous Change Detection and Classification (CCDC) algorithm developed by Zhu and Woodcock (2014). This algorithm 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. The usage of this large and well-developed data source provides many exciting new opportunities for LCMAP research and development.

We seek proposals that focus on improving methods for detection of land surface change. Current opportunities for research 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). Topics of special interest 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 use for change detection. More diverse data sources (such as radar) are available under different atmospheric conditions and provide information that is not available at Landsat wavelengths. The development of change detection methods with the potential to handle data sources with differing sources and bands is a goal of LCMAP.
• A more rapid (with a lag of weeks) 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 as new observations becomes available.

Geographically, initial research is ideally focused within 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 change detection development as well as the LCMAP team at the USGS EROS. Tools to investigate the extensive Landsat archive are being developed at EROS and will be available to support research. 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 Advisor(s) early in the application process to discuss project ideas.

References:

Brown, J. F., H. J. Tollerud, C. P. Barber, Q. Zhou, J. L. Dwyer, J. E. Vogelmann, and others. 2020. “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. doi: 10.1016/j.rse.2019.111356.

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, geoscience, 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).

Qualifications: Applicants must meet one of the following qualifications: Research Physical Scientist, Research Geographer

(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: Kimberly Sales, 703-648-7478, ksales@usgs.gov

Apply Here