Geospatial Data Analyst For Wildfire-Fuel Treatment Outcomes

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The School of Environmental and Forest Sciences is looking for a Research Scientist/Engineer 3 to serve as Geospatial Data Analyst for Wildfire-Fuel Treatment Outcomes.
Large-scale severe wildfires are expected to become increasingly common throughout much of North America, necessitating scientific data to inform postfire management treatment options and outcomes. Forest managers need site-specific quantitative monitoring data to develop long-term adaptative restoration plans based on science.
We are seeking a Geospatial Data Analyst with expertise in fire modeling and remotely sensed data to support innovative wildfire research. The incumbent will work on a project to improve our understanding of the conditions under which fuel treatments produce desired socioecological outcomes when encountered by wildfire. The incumbent will join a team of postdoctoral scholars, federal scientists, and university faculty who will develop and refine a core, scalable methodology that leverages existing spatial datasets on landscape, fire, and fuel treatment characteristics. The research team will apply this methodology to observed wildfire-treatment encounters in different landscapes across the United States to produce empirical case studies of wildfire-treatment outcomes (WTO’s). Fuel treatments are intended to change fire behavior (e.g., reduced intensity, spread rate, or spotting potential) in the event of a wildfire.
The selected candidate will collaborate on research to evaluate in the Pacific Northwest the reduction of fire severity, successful containment of fire spread, reduction of impacts to homes and critical infrastructure. These three outcomes are connected to the three pillars of the National Cohesive Wildland Fire Management Strategy (“Cohesive Strategy”) of fire resilient landscapes, safe and effective wildfire response, and fire-adapted communities, respectively. The research takes advantage of the tremendous variation in landscape, fire, and treatment characteristics to conduct a set of natural experiments, taking a “big data” and remote sensing approach to learning from past fuel treatment activities. Such empirical, data-driven assessments of WTO’s are critical to inform process-based models of future treatment outcomes under a changing climate, to improve future treatment layout and design, to guide decision-making on incidents, and to improve adaptive management and monitoring of fuel treatment investments. The proposed research involves both place-based empirical case studies and methodological development; therefore, the incumbent will work with a postdoctoral scholar and collaborators to build a foundation for improving monitoring systems, rapidly assessing drivers of treatment effectiveness on landscapes of interest, and fostering collaborative learning with agency partners.
RESPONSIBILITIES:
Research

Incumbent will assemble, process, and analyze large remote sensing datasets (e.g., Landsat, Sentinel-2, MODIS) and spatial datasets (e.g., FACTs, FTEM, field data) of wildfires and operational fuel treatments using R/Python and Google Earth Engine. Selected candidate will use cartographic principles and conventions sufficient to create maps for external and internal use that display natural resource and socio-economic information and highlight particular analysis results.

The incumbent will use multi-analysis and machine-learning based integration of high-resolution satellite imagery, model generated data, and field data of wildfire-treatment outcomes.

Selected candidate will collaborate with the larger national research teams to develop and refine a core, scalable methodology that leverages existing spatial datasets on landscape, fire, and fuel treatment characteristics for large wildfires. The team will develop a process to apply methodologies to observed wildfire-treatment encounters in different landscapes across the United States to produce empirical case studies of wildfire-treatment outcomes.

To provide research support with statistical and geospatial analysis, wildland fire modeling, fuel and vegetation modeling, risk analysis, R or Python programming to research team.

To co-lead a field crew on establishment plots and collection of forest biometric data related to postfire stand conditions on selected recent wildfires where fuels have been treated in the Pacific Northwest in collaboration with a wide variety of federal, tribal, and state partners.

To collaborate closely with other team members located in other USFS Research Stations in the country. The research will be conducted in the laboratory and will require field visits to wildfire sites.

Project Management

To support principal investigators on project management and interface with a group of research scientists, federal cooperators, and contractors.

To maintain and document projects as necessary.

To prepare and manage data, including collecting, cleaning, organizing, and maintaining datasets.

Outreach

To write internal/technical documentation as well as to contribute to external scientific documentation, including peer-reviewed research papers.

To provide technical assistance and support to other projects underway within the group.

To perform related duties as required.

Lead responsibilities:
Incumbent will be responsible of gathering data, processing, and analyzing large remote sensing datasets and spatial datasets for wild-treatment outcome research. It includes analysis of spatial and related data (vector, raster, imagery) sufficient to support multi-scale and/or multi-resource planning, assessments, and monitoring.
Incumbent will lead a field crew on the second year on establishment and collection of wildfire-fuel treatment outcomes on large wildfires in the Pacific Northwest. Selected candidate will be responsible of ensuring that research protocols are followed during the execution, data entry and analysis of studies on fire and smoke hazards for federal, state, and tribal lands.
Supervisory responsibilities:
Works effectively as a member of a team under technical guidance of seasoned staff and collaborators; Incumbent will lead a seasonal field crew during the summer of the second year composed of students and technicians as necessary to achieve specific assignments.
MINIMUM REQUIREMENTS:

A master’s degree in a science-related field in forestry, fire ecology, forestry, forest ecology, botany, natural resources, or geospatial science

2 years of experience or equivalent combination of education and experience processing and analyzing large datasets and geospatial datasets with R/Python and via Google Earth Engine for wildfire and fuel treatment outcome research.

Equivalent education/experience will substitute for all minimum qualifications except when there are legal requirements, such as a license/certification/registration.
ADDITIONAL REQUIREMENTS:

Experience integrating, processing, and analyzing large remote sensing datasets (e.g., Landsat, Sentinel-2, MODIS) and spatial datasets (e.g., FACTs, FTEM, field data) of wildfires and operational fuel treatments using R/Python and Google Earth Engine

2 years of experience using use multi-analysis and machine-learning based integration of high-resolution satellite imagery, model generated data, and field data.

Knowledge of fire science and geospatial analysis or expertise experience using a variety of GIS, both proprietary (e.g., ArcGIS Pro, Google Earth Engine R geospatial packages, python - geopandas, arcpy) and open source (e.g., QGIS, Python, R, Julia, PostGIS, GRASS) to maintain data repositories and complete geospatial analyses.

Field experience working with forest and fuels inventory data and forest growth and yield modelling.

Programming skills either in Python or R and experience writing SQL queries or using code-driven analytical tools (e.g., R, Python, Julia, and others.) to manipulate and analyze both spatial and tabular data.

Experience using GIS and remote sensing to map and characterize distribution of plant species, vegetation communities, fire regimes and wildland fuels with a familiarity with FACTS, LANDFIRE, GNN, and CWHR spatial datasets.

DESIRED:

Skills on using and calibrating inventory tools that include hypsometer, clinometer, laser, stereoscope, loggers/diameter tape, chaining tape, increment borers, and others related to repeatable and accurate measurements.

Experience on fire behavior and fire effects computer models.

Experience with videoconference software.

Experience with web services and applications.

Experience working collaboratively with research scientists.

Demonstrated ability to create clear and concise visual displays of quantitative data.

Strong communication and interpersonal skills.

Demonstrated ability to work collaboratively in a team environment.

Sincere interest in learning about natural resource management applications and fire science.

WORKING CONDITIONS:
The appointment is full time, 40 hours per week. Most of the work will be conducted in the laboratory. On the second year of the project, we will conduct a field data collection and it will be required to hike carrying field packs that may weight approximately 60 pounds. Crews work at elevations ranging from 3,500 to 14,000 feet when accessing permanent inventory plots and expected to hike long distances to reach plot locations both on- and off-trail, and work on steep and or rough terrain. Crews will be working in sun, rain, and snow, across the full range of temperatures which can change rapidly throughout the day.
Application Process:
The application process for UW positions may include completion of a variety of online assessments to obtain additional information that will be used in the evaluation process. These assessments may include Work Authorization, Cover Letter and/or others. Any assessments that you need to complete will appear on your screen as soon as you select “Apply to this position”. Once you begin an assessment, it must be completed at that time; if you do not complete the assessment you will be prompted to do so the next time you access your “My Jobs” page. If you select to take it later, it will appear on your "My Jobs" page to take when you are ready. Please note that your application will not be reviewed, and you will not be considered for this position until all required assessments have been completed.