19-39. Land Change Monitoring, Assessment, and Projection (LCMAP): Translating imagery and weather into predictions of surface reflectance

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, early warning systems, and disaster risk reduction efforts. 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. At the same time, climate and weather modulate the land surface, creating opportunities for the prediction of future land surface states and conditions.

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 also support many real-world decisions that help American farmers, ranchers, fire fighters, and water and land managers use natural resources effectively while protecting lives and livelihoods. This project will focus on predicting future LCMAP surface reflectance imagery, supporting useful and valuable projections of LCMAP-related products.

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.

To detect change, the CCDC algorithm compares a prediction of surface reflectance based on past satellite observations with new satellite observations. This approach can be used to predict surface reflectance in the future, but these predictions do not include the effect of weather conditions or anomalous land surface conditions. Research into the utilization of land surface state, weather observations, and seasonal forecasts to improve predictions of satellite surface reflectance observations has the potential to contribute to the development of a new outlook product.

We seek proposals that focus on developing statistical and/or machine learning-based outlooks for satellite observations, and evaluating the skill of these algorithms in a variety of geographic locations. Outlooks that produce a short-term future forecast can serve as the basis of important derived products (such as vegetation indices or fire potential indices) that support activities like rangeland management, ranching decisions, and fire danger prediction. While prior research has focused on the forecast of surface reflectance derivatives, such Normalized Difference Vegetation Index values (e.g. Funk and Brown, 2006), this project will pursue the exciting and novel idea of predicting the surface reflectance values themselves. This could lead to numerous opportunities for derived products.

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 as well as the LCMAP team at the USGS EROS. The Fellow will also be supported by resources and scientists at the University of California, Santa Barbara Climate Hazards Center and the EROS Early Warning team. 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.

Funk, Chris C., and Molly E. Brown. "Intra-seasonal NDVI change projections in semi-arid Africa." Remote Sensing of Environment 101.2 (2006): 249-256.

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 or Santa Barbara, CA

Areas of PhD: Meteorology, remote sensing, geography, 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

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