Skip to main content

Ph.D. or M.Sc. Assistantship in Hyperspectral Remote Sensing of Biodiversity Oklahoma State University





Ph.D. or M.Sc. Assistantship in Hyperspectral Remote Sensing of Biodiversity Oklahoma State University

Description: We invite applications for a Ph.D. or M.Sc. position in the field of remote sensing at Oklahoma State University. The successful candidate will use airborne and spaceborne hyperspectral data (from DLR's DESIS sensor), as well as in-situ measurements (functional traits and species diversity) to (1) map grassland diversity and (2) detect the spread of an invasive alien species in the Joseph H. Williams Tallgrass Prairie Preserve. The position will be housed in the Department of Geography at Oklahoma State University. The target start date is August 2021.

Qualifications: Applicants with a masters' degree or bachelor's degree (at the time of appointment) in physical sciences (remote sensing, ecology, environmental science, geography, plant biology), engineering (environmental, optical), or other related fields with relevant research or work experience (e.g., hyperspectral remote sensing, landscape ecology, spatial modeling) are encouraged to apply. Programming is expected to be the core for remote sensing data analysis. Therefore, having previous experience and knowledge on how to code (e.g., MATLAB, Python, R) is ideal. Familiarity with shell scripting, Linux command line tools, and high performance computing for image processing will be ideal (but not necessary). Ability to work independently and excellent written and oral communication skills will be desirable.

To Apply: If interested in the positon or for more information, please contact Dr. Hamed Gholizadeh (hamed.gholizadeh@okstate.edu) and include your CV and a brief cover letter explaining your research and career interests. Review of the applications will begin immediately and will continue until position is filled. A full application to the Graduate College will be required for an official offer to be made. More information can be found at https://geog.okstate.edu/programs/graduate-program/application-procedures

Oklahoma State University, as an equal opportunity employer, complies with all applicable federal and state laws regarding non-discrimination and affirmative action. Oklahoma State University is committed to a policy of equal opportunity for all individuals and does not discriminate based on race, religion, age, sex, color, national origin, marital status, sexual orientation, gender identity/expression, disability, or veteran status with regard to employment, educational programs and activities, and/or admissions. For more information, visit https://eeo.okstate.edu.





Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://www.facebook.com/Applied.Geography
http://geogisgeo.blogspot.com

Comments

Popular posts from this blog

GIS data continuous discrete ordinal interval ratio

In Geographic Information Systems (GIS) , data is categorized based on its nature (discrete or continuous) and its measurement scale (nominal, ordinal, interval, or ratio). These distinctions influence how the data is collected, analyzed, and visualized. Let's break down these categories with concepts, terminologies, and examples: 1. Discrete Data Discrete data is obtained by counting distinct items or entities. Values are finite and cannot be infinitely subdivided. Characteristics : Represent distinct objects or occurrences. Commonly represented as vector data (points, lines, polygons). Values within a range are whole numbers or categories. Examples : Number of People : Counting individuals on a train or in a hospital. Building Types : Categorizing buildings as residential, commercial, or industrial. Tree Count : Number of trees in a specific area. 2. Continuous Data Continuous data is obtained by measuring phenomena that can take any value within a range...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to  1832 , when  Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report  Rapport sur la marche et les effets du cholĂ©ra dans Paris et le dĂ©partement de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in  1854 , when  John Snow  inv...

Disaster Management

1. Disaster Risk Analysis → Disaster Risk Reduction → Disaster Management Cycle Disaster Risk Analysis is the first step in managing disasters. It involves assessing potential hazards, identifying vulnerable populations, and estimating possible impacts. Once risks are identified, Disaster Risk Reduction (DRR) strategies come into play. DRR aims to reduce risk and enhance resilience through planning, infrastructure development, and policy enforcement. The Disaster Management Cycle then ensures a structured approach by dividing actions into pre-disaster, during-disaster, and post-disaster phases . Example Connection: Imagine a coastal city prone to cyclones: Risk Analysis identifies low-lying areas and weak infrastructure. Risk Reduction includes building seawalls, enforcing strict building codes, and training residents for emergency situations. The Disaster Management Cycle ensures ongoing preparedness, immediate response during a cyclone, and long-term recovery afterw...

Representation of Spatial and Temporal Relationships

Geographical Information System (GIS) is a powerful tool for analyzing and visualizing spatial data. One of the key features of GIS is its ability to represent spatial and temporal relationships between different geographic features. Spatial relationships refer to the physical location of an object or feature in relation to other objects or features, while temporal relationships refer to the sequence or timing of events. Together, these relationships are essential for understanding and analyzing complex spatial and temporal data. Representation of Spatial Relationships in GIS: Spatial relationships in GIS can be represented using a variety of techniques such as distance, proximity, and topology. For example, distance-based relationships can be used to measure the distance between two points, while proximity-based relationships can be used to determine which objects or features are closest to one another. Topology-based relationships can be used to represent the connectivity between dif...

Data Generalization in GIS

Data generalization in GIS is the process of simplifying complex geographic data to make it suitable for visualization and analysis at specific map scales. It reduces unnecessary details while preserving the overall patterns and essential characteristics, ensuring that the map remains clear and interpretable at different zoom levels. Key Concepts and Terminologies Purpose of Data Generalization : To simplify spatial data for better visualization and usability at smaller scales. To prevent maps from becoming cluttered or unreadable due to excessive detail. To maintain the essence of geographic features while omitting minor details. Example : On a world map, a small island may be represented as a single point or omitted, while on a local map, it may appear with detailed boundaries. Key Data Generalization Techniques Simplification : Definition : Reduces the number of vertices or points in a line or polygon, removing minor details while retaining the general shap...